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Professor Milovan Stanišić, Singidunum University mstanisic@singidunum.ac.rs Emeritus Slobodan Unković, Singidunum University unkovic@singidunum.ac.rs Professor Francesco Frangialli, UNWTO frangialli@gmail.com Professor Gunther Friedl, Technische Universität München, München gunther.friedl@wi.tu-muenchen.de Professor Karl Ennsfellner, IMC University of Applied Sciences, Krems karl.ennsfellner@fh-krems.ac.at Professor Gyorgy Komaromi, International Business School, Budapest gyorgy@komaromi.net Professor Vasile Dinu, University of Economic Studies, Bucharest dinu_cbz@yahoo.com Professor Ada Mirela Tomescu, University of Oradea, Oradea ada.mirela.tomescu@gmail.com Professor Radojko Lukić, University of Belgrade rlukic@ekof.bg.ac.rs Professor Alexandar Angelus, Lincoln University angelus@lincolnuca.edu Professor Nemanja Stanišić, Singidunum University nstanisic@singidunum.ac.rs Professor Verka Jovanović, Singidunum University vjovanovic@singidunum.ac.rs Professor Milan Milosavljević, Singidunum University mmilosavljevic@singidunum.ac.rs Professor Olivera Nikolić, Singidunum University onikolic@singidunum.ac.rs Professor Goranka Knežević, Singidunum University gknezevic@singidunum.ac.rs Professor Mladen Veinović, Singidunum University mveinovic@singidunum.ac.rs Professor Jovan Popesku, Singidunum University jpopesku@singidunum.ac.rs Professor Zoran Jeremić, Singidunum University zjeremic@singidunum.ac.rs Professor Vesselin Blagoev, Varna University of Management blagoev@vum.bg Professor Michael Minkov, Varna University of Management minkov@iuc.bg Associate Professor Christine Juen, Austrian Agency for International Mobility and Cooperation in Education, Science and Research, Wien chrisine.juen@oead.at Associate Professor Anders Steene, Södertörn University, Stockholm/Hudinge anders.steene@sh.se Associate Professor Ing. Miriam Jankalová, University of Zilina, Prague miriam.jankalova@fpedas.uniza.sk Associate Professor Bálint Molnár,Corvinus University of Budapest, Budapest molnarba@inf.elte.hu Associate Professor Vesna Spasić, Singidunum University vspasic@singidunum.ac.rs Associate Professor Michael Bukohwo Esiefarienrhe, University of Agriculture, Dept. of Maths/Statistics, Markurdi esiefabukohwo@gmail.com Associate Professor Goh Yen Nee, Graduate School of Business, Universiti Sains Malaysia yngoh@usm.my Research Associate Professor Aleksandar Lebl, Research and Development Institute for Telecommunications and Electronics, Belgrade lebl@iritel.com Assistant Professor Patrick Ulrich, University of Bamberg patrick.ulrich@uni-bamberg.de Assistant Professor Konstadinos Kutsikos, University of the Aegean, Chios kutsikos@aegean.gr Assistant Professor Theodoros Stavrinoudis, University of Aegean, Chios tsta@aegean.gr Assistant Professor Marcin Staniewski, University of Finance and Management, Warsaw staniewski@vizja.pl Assistant Professor Gresi Sanje, İstanbul Bilgi Üniversitesi, Istanbul gresi.sanje@bilgi.edu.tr Assistant Professor Michał Biernacki, Wrocław University of Economics, Poland michal.biernacki@ue.wroc.pl Assistant Professor Piotr Luty, Wrocław University of Economics, Poland piotr.luty@ue.wroc.pl Assistant Professor Blazenka Hadrovic Zekic, Faculty of Economics in Osijek, Croatia hadrovic@efos.hr E d it o r ia l O f f ice
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Prepress: Jelena Petrović Design: Aleksandar Mihajlović Technical Assistant: Aleksandra Stojanović ISSN: 2406-2588 The European Journal of Applied Economics is published twice a year. Contact us: The European Journal of Applied Economics 32 Danijelova Street, 11010 Belgrade, Serbia Phone No. +381 11 3094046, +381 11 3093284 Fax. +381 11 3093294 E-mail: journal@singidunum.ac.rs Web: www.journal.singidunum.ac.rs Printed by: Caligraph, Belgrade
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CONTENTS
1 - 19 20 - 37
38 - 45
46 - 82 83 - 93 94 - 109 110 - 122
The indicator of employment protection legislation for Canada Samir Amine, Véra-Line Montreuil
The impact of certain psychological factors of investors and managers on the capital structure Mirko Babanić
Test of a quadratic relationship between aggregate output and government debt in Hungary Yu Hsing
A simultaneous equation model of globalization, corruption, democracy, human development and social progress
Sudhanshu K. Mishra
Improving business performance with ISO 9001: a review of literature and business practice Mihalj Bakator, Dragan Ćoćkalo
Income-health nexus in Sub-Saharan Africa: evidence from heterogeneous panel models Ibrahim Abidemi Odusanya, Akinwande A. Atanda
Behavioral economics: how well do investors in Serbia predict the stock prices? Dora Petronijević
III
EJAE 2018, 15(1): 1-19 ISSN 2406-2588 UDK: 331.53:34(71) DOI: 10.5937/EJAE15-15824 Original paper/Originalni naučni rad
THE INDICATOR OF EMPLOYMENT PROTECTION LEGISLATION FOR CANADA Samir Amine*, Véra-Line Montreuil Université du Québec en Outaouais, Canada
Abstract: Since the early 1990s, employment protection legislation (EPL) has become a concern for public policy makers in particular with respect to its impact on unemployment and productivity. This topic arose pronounced attention of international organizations. Indeed, these institutions have built analytical models to assess the strictness of EPL in different countries, but we find that these models are sometimes incomplete despite the abundance of information they provide. The purpose of this article is to propose the introduction of new elements to the Organization for Economic Co-operation and Development (OECD) analysis model based on the Canadian experience. We, thus, propose to adjust the synthetic indicator of EPL for Canada taking into account local specificities not taken into consideration by the OECD.
Article info: Received: November 25, 2017 Correction: December 12, 2017 Accepted: December 12, 2017
Keywords: Employment Protection Legislation, Synthetic Indicator, OECD, Voluntary Departure, Work-Sharing.
INTRODUCTION The employment protection legislation (EPL) is at the heart of a debate in the field of industrial relations and labour law related to the flexibility-security dilemma. According to the Organization for Economic Co-operation and Development (OECD): “employment protection refers both to regulations concerning hiring (e.g. rules favouring disadvantaged groups, conditions for using temporary or fixed-term contracts, training requirements) and firing (e.g. redundancy procedures, mandated prenotification periods and severance payments, special requirements for collective dismissals and short-time work schemes). Various institutional arrangements can provide employment protection: the private market, labour legislation, collective bargaining agreements and, not the least, court interpretation of legislative and contractual provisions” (p. 50: OECD, 1990). In the light of this definition we can therefore assume that EPL refers to the rules surrounding employment adjustment.
* E-mail: samir.amine@uqo.ca
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Since the last financial crisis, many countries have implemented reforms to modify the strictness of EPL in order to enhance flexibility in difficult circumstances. In 2010, Greece, Portugal, and Spain reduced severance pay in the case of dismissal for regular contracts and also reduced the strictness of regulation regarding collective dismissals (OECD, 2012; Martin, 2013). In Italy, the regulations on temporary contracts have been reduced (Martin, 2013). Similarly, in 2008, France implemented a system to end a job contract through mutual consent between the employer and the worker (OECD, 2012). Other measures to make the labour market more flexible were adopted in 2016 under the “El KhomriLabour Law”. In the same context, other proposals have been made, such as the introduction of taxation on redundancy named as a “bonus-malus” system, currently in force in the United States and built around the principle that firms must contribute to the unemployment insurance according to their history of layoffs (Amine et al., 2007; Blanchard & Tirole, 2003). In the literature, several studies examined the effects and the analysis models of EPL (see OECD, 1993; International Labour Organization, 2013). However, only a few of them seem to emphasize the need for improvements in these models to reflect precisely the context of each country. The model that has retained our attention is the OECD model, built since 1993.This model covers the G20 countries and has 21 weighted analysis criteria to allow international comparisons in terms of employment protection legislation. OECD model has received much attention from international researchers, labour lawyers and policy makers who have been trying to better understand the level of EPL strictness in different countries and its impact on the labour market. However, despite the abundance of information it provides, we believe that this model needs improvement in order to provide a full and accurate portrait of employment protection legislation for each country. Therefore, based on the Canadian experience, this article suggests adding two essential sub-components to the OECD model so as to expand its scope: the voluntary departure plan and the work- sharing. We also suggest reviewing the weighting of the components in order to properly reflect the dynamics of labour market. Our results indicate that the consideration of new sub-components and the review of the weighting door slightly upward Canadian synthetic indicator. This observation thus leads us to believe that the mere evaluation of the constraints imposed on the employer in terms of redundancies and the use of temporary contracts, may underestimate the degree of strictness of EPL in Canada, when we do not take into account other employment adjustment mechanisms such as voluntary departure plan and work-sharing. We believe that the addition of these elements will bring more details and nuances in terms of global comparisons between Canada and the United States, that are often perceived as being similar despite the differences in the configuration of their social and legal protection systems. Besides, Block (2007) highlights the differences between the rigour of Canadian and US labour standards by creating indicators, including standards that could force employers in their adjustment to the workforce. Moreover, since OECD model is an international framework whose main purpose is to help and guide policy makers and labour lawyers from various governments to introduce new reforms or apply changes to public policies and laws in the field of labour, we believe that the adjustments proposed in this article will help this model to reflect more adequately the reality of the EPL strictness in countries. Although we have chosen Canada as a study case, voluntary departure plans can be found in different countries (e.g. France and the United States). We can also find work-sharing in France, Italy, Germany, Belgium, Portugal and Australia. Therefore, Canada is an interesting case since our proposals can lead to revising the EPL indicators of several countries that have those two workforce adjustment mechanisms. In fact, voluntary departure is often seen as an alternative or complementary mechanism to economic redundancies. The voluntary departure is usually a cheaper and less restrictive option for employers 2
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than proceeding directly to layoffs despite sometimes severance pay can be high. Indeed, the risk of litigation in the courts decreases when the employer initiates such a measure and, generally, it imposes less of legal obligations. However, the legal obligations associated to voluntary departure may be different depending on the country (Bourguignon & Garaudel, 2015; Niel & Hautefort, 2015). In addition, since this practice seems to become more and more common, the integration of this component in the OECD model becomes essential. As for work-sharing, it is a practice that is generally implemented with the aim to avoid layoffs or redundancies when a firm is facing economic downturns. Essentially, the employer asks several of its employees to temporarily reduce their working hours for a given period or, in some cases, rather than just dismiss the employees, the employer may temporarily suspend its activities. This mechanism is also called “partial/technical unemployment” or “partial activity” (Bisignano, 2014; Werner & Marx, 2009). In brief, we support that these two mechanisms should be included in the OECD model since they allow employers to adjust their workforce in the same manner as firing and hiring. Furthermore, they protect workers from job losses or at least mitigate its probability to occur. As we will see, these mechanisms have a different level of strictness (constraints), depending on the country. This article is organized as follows. The second section will present the literature review related to the EPL. Afterwards, the methodology will be the subject of the third section by addressing the limitations of existing work and presenting our proposals for the introduction of specific variables. The results of our analysis and concluding remarks will be discussed respectively in the fourth and fifth sections.
LITERATURE REVIEW Different analysis models of Employment Legislation since 1990s In the early 1990s, scientific community realized that there was a clear variation among countries regarding their employment protection legislation. Indeed, the literature found that Anglo-Saxon countries seemed to have a much less rigid regulation than that observed in Europe as a whole and these differences could have a different impact on the labour market: presence or absence of market rigidification through a high unemployment rate and stagnation in job creation, also known as institutional sclerosis (Deslauriers, Dostie & Gagné, 2009; OECD, 1999). Thus, countries with a strict EPL seemed to have a labour market suffering from institutional sclerosis whereas those with very low EPL did not appear to exhibit symptoms related to this phenomenon. The need to measure these laws in order to make international comparisons was thus born, as was the development of several research studies to clarify the effects of this legislation on labour market dynamics. Before presenting the OECD model, which is at the heart of this article, it is important to briefly mention that two other international organizations had developed their own employment protection legislation model. First, the ILO had focused its analysis of the EPL in seven specific categories of provisions in 90 countries: type of employment contract, substantive requirements for dismissals, procedural requirements for individual dismissals, procedural requirements for collective dismissals for economic reasons, severance pay and redundancy payment and avenues for redress which are presented in table 1. However, it should be noted that only workers regulated by federal legislation were considered in the analysis conducted in 2012 for Canada by the ILO. This analysis excludes all workers covered by provincial labour laws (about 90% of Canadians workers) (ILO, 2013). Thus, while the ILO database 3
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is rich in information for the lawyers and policy makers, it should be borne in mind that for Canada, this database does not fully reflect the full reality of employment protection legislation. Category Type of employment contract
Provision ◆◆ Maximum probationary period
◆◆ Use of fixed-term contract
Substantive requirements for dismissals
◆◆ Obligation to provide reasons to the employee ◆◆ Valid grounds
◆◆ Prohibited grounds ◆◆ Workers enjoying special protection
Procedural requirements for individual dismissals
◆◆ ◆◆ ◆◆ ◆◆
Notification to the worker to be dismissed Notice period Pay in lieu of notice Notification to the public administration
◆◆ Notification to workers’ representatives ◆◆ Approval by public administration or judicial bodies ◆◆ Approval by workers’ representatives
Procedural requirements for collective dismissals for economic reasons
◆◆ ◆◆ ◆◆ ◆◆ ◆◆
Definition of collective dismissal Prior consultations with trade unions Notification to the public administration Notification to workers’ representatives Approval by public administration or judicial bodies
◆◆ Approval by workers’ representatives ◆◆ Priority rules for collective dismissals ◆◆ Employer’s obligation to consider alternatives to dismissal ◆◆ Priority rules for reemployment
Severance pay and redundancy payment Avenues for redress
◆◆ Severance pay
◆◆ Redundancy payment
◆◆ Compensation for unfair dismissal - free determination by court ◆◆ Compensation for unfair dismissal – legal limits ◆◆ Reinstatement
◆◆ Preliminary mandatory conciliation ◆◆ Competent court(s) or tribunal(s) ◆◆ Existing arbitration
Table 1. Employment protection legislation (EPL) Source: Data from International Labour Organization, 2013
Second, the World Bank Group (Doing Business) also addressed the issue of strict regulation of the labour market by developing its own model of analysis. Thus, Doing Business evaluates the flexibility of a country’s regulation around three areas: the difficulty of recruiting, the rigidity of working hours and the difficulty of dismissing. It also assesses the cost of redundancy and, in 2015, new variables related to the social protection system and the existence of labor dispute tribunals was added. However, the analysis of Doing Business is based on certain assumptions that cannot fully represent the reality of all the regulations of a country affecting employment protection. Indeed, in the analysis model, it is postulated that the worker is a full-time cashier in a supermarket and is not a member of a union unless it is an obligation. In addition, it is assumed that the company under study runs a supermarket or food store in the largest business metropolis of the economy and holds 60 employees (World Bank Group, 2016). As a result, these limitations on the analytical framework greatly limit its ability to generalize its results to a national economy, especially to certain countries, such as Canada, where the majority of the labour law is under the jurisdiction of the provinces and territories. That 4
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said, the Doing Business model becomes particularly useful and relevant when we want to look at the issue of employment legislation in the food sector specifically, but more difficult when we want to get a portrait of a national situation as a whole. Flexibility Difficulty of hiring ◆◆ Fixed-term contracts prohibited for permanent tasks ◆◆ Maximum length of fixed-term contracts ◆◆ Minimum wage for a full-time worker ◆◆ Ratio of minimum wage to value added per worker
Rigidity of hours
Difficulty of redundancy
50 hours workweek allowed Maximum working days per week Premium for night work Premium for work on weekly rest day ◆◆ Major restrictions on night work ◆◆ Major restrictions on weekly holiday work ◆◆ Paid annual leave
◆◆ Maximum length of probationary period ◆◆ Dismissal due to redundancy allowed by law ◆◆ Third-party notification if 1 worker is dismissed ◆◆ Third-party approval if 1 worker is dismissed ◆◆ Third-party notification if 9 workers are dismissed ◆◆ Third-party approval if 9 workers are dismissed ◆◆ Retraining or reassignment ◆◆ Priority rules for redundancies ◆◆ Priority rules for reemployment.
◆◆ ◆◆ ◆◆ ◆◆
Redundancy cost ◆◆ Notice period for redundancy dismissal ◆◆ Severance pay for redundancy dismissal New variables ◆◆ Unemployment protection scheme ◆◆ Health insurance for permanent employees ◆◆ Courts or court sections specializing in labour disputes Table 2. Labour Market Regulation – Doing Business Source: Data from World Bank Group, 2015 (p.232)
Finally, the OECD, on its side, has built an analytical framework for measuring the degree of EPL strictness through an analysis of the rules governing the dismissal and recruitment of temporary employees. It has developed its model with a particular focus on the cost of adjusting the workforce to employers that can sometimes be excessive (OECD, 2013). Moreover, OECD warns the researcher who wants to use its model for other purposes that the model aims to study, stressing that “by contrast, the effectiveness of legislation in protecting workers might not be well captured by these indicators. Therefore, care must be exerted when not using these indicators as measures of legislation-induced costs for employers making staffing changes.” (p.74: OCDE, 2013). Certainly, this model has its limits. The weighting of the criteria in the OECD grid in the EPL synthetic indicators seems to be established on a universal basis and, therefore, seems not to be modulated according to the composition of the labour market in each country. Indeed, the weight of the component of regular contracts is the same as temporary contracts (5/12) and collective dismissals represent 2/12 of the EPL indicator of all countries. These non-indexed weights on the labour market reality 5
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can lead to a biased assessment of the strictness of EPL in any country analysed (Kirat, 2006; Martin et al., 2013; Niel, 2009; Niel & Hautefort, 2015). Furthermore, OECD mentions that “a re-assessment of the scoring grid for each component, as well as of the weights used to aggregate them, is probably warranted, but it is left for future work” (p. 108). Moreover, Martin, Du Marais and Dehaze (2013) note that the current synthetic EPL indicators do not include new employment adjustment modes such as voluntary departure plan. Along with these authors we also believe, based on Canadian experience, that it is necessary to incorporatethe voluntary departure plan and work-sharing into the evaluation of EPL in order to provide a more inclusive and complete portrait of the employment protection legislation. It should be noted, however, that over time, the OECD has made adjustments to its model following proposals made within the scientific community. Specifically, it included the content of collective agreements and jurisprudence in the evaluation of the indicator (OECD, 2013; Venn, 2009). Moreover, in its detailed analysis, for example for Canada, disparities in employment protection legislation within provinces are now taken into account (OECD, 2014). Therefore, the purpose of this paper is to address some of these limitations, especially those relating to the distribution of weights within the model and the integration of some mechanisms that allow employers to adjust their labour force when they faced a decline in productivity, an economic shock, etc. Consequently, the originality of this article lies in the fact that it does not merely provide a criticism of the OECD model but, through the analysis of the case of Canada, suggests adjustments in order to provide a more precise and representative portrait of the legislative and administrative realities of the countries. Whereas the OECD provides a quantitative evaluation of EPL, it is important to note that some authors (Deslauriers et al., 2009) have stressed the limits of quantifying employment protection legislation for international comparisons. These authors argue, in particular, that it is difficult to compare EPL indicators across countries, as this would hardly reflect the social context, culture and legal procedures unique to each country. These procedures may, in fact, be more or less long and tedious depending on the country in which we find ourselves. However, Adams et al. (2017) point out: “A somewhat different argument about the limits of quantitative approaches to legal research turns on the view that mathematical models assume a rigidly deterministic relationship between variables which is at odds with the open, dynamic nature of social systems in general, and a fortiori with the opentextured and indeterminate nature of legal rules, not least those of labour law. This is a more weighty objection to quantitative legal research, but we think that would be going much too far to take it to the point of rejecting all uses of statistical and mathematical approaches to empirical legal analysis. The issue, rather, is to understand what quantitative methods can achieve, and not to push them beyond their inherent limits” (p.63). While some papers criticize such a model for focusing primarily on the dismissal (individual and collective) and for using temporary contracts, thus restricting the scope of its definition of employment protection (Deslauriers et al., 2009), the OECD model remains highly developed and covers all G20 countries. Indeed, the OECD studies three types of dispositions. First, there are the rules governing the protection of regular workers in cases of individual dismissal. This component is divided into sub-components as shown in table 1: procedural inconveniences, notice and severance pay for no-fault individual dismissal, difficulty of dismissal. Thus, in order to assess the strictness of this EPL component, OECD examines the clauses relating to notification procedures, the delay to start a notice, the definition of unfair dismissal, etc. The second EPL component is the regulation of temporary contracts which is divided into two sub-components as also shown in table 1: fixed-term contracts and temporary work 6
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agency employment (interim). For this component, the OECD assesses, for example, the maximum number of successive contracts that an organization can undertake, the maximum cumulated duration of temporary work, etc. Finally, the third component represents the obligations associated with collective dismissal. In assessing this component, OECD relies on the definition of collective dismissal, additional notification obligations, other special costs to employers, etc. (OECD, 2014). In addition, each of the 21 criteria used to analyze the rigour of EPL is weighted to try to reflect as closely as possible the weight of each component in regulating the behavior of actors in society (see table 3). Synthetic Indicator
Regular contract
Weighting
Procedural inconveniences
Notification procedures Delay to start a notice
1/2 1/2
Notice and severance pay for individual dismissal without fault
Notice period after 9 months Notice period after 4 years Notice period after 20 years Severance pay after 9 months Severance pay after 4 years Severance pay after 20 years
1/7 1/7 1/7 4/21 4/21 4/21
Difficulty of dismissal
Definition of unfair dismissal Duration of the trial period Compensation Reinstatement Maximum time for claim
1/5 1/5 1/5 1/5 1/5
Valid cases for use of FTCs Fixed-term contracts (FTC) Maximum number of successive FTCs Maximum cumulated duration of successive FTCs Temporary contract Temporary work agency employment
Collective dismissals
1/2 1/4 1/4
Types of work for which is legal Restrictions on number of renewals Maximum cumulated duration Information requirements and authorization Fair treatment
1/3 1/6 1/6 1/6 1/6
Definition of collective dismissal Additional notification obligations Additional deadlines Other specific costs for employers
1/4 1/4 1/4 1/4
Table 3. OECD model of EPL Source: OECD (2014)
In summary, we have decided to present more fully the models that have been developed by international organizations, which are consulted by many international policy makers, scientists and lawyers in the field of labour. However, it cannot be overlooked that other researchers have also developed models measuring employment protection, as did Deakin et al. (2009) of the Center for Business Research (CBR), which built their own model of analysis of employment protection legislation through 7
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the evaluation of 10-12 variables on a sample of 25 countries between 1995 and 2005. Block et al. (2003) also constructed an indicator based on 10 variables: minimum wage, overtime, paid time-off, unemployment insurance, workers’ compensation, collective bargaining, discrimination in employment, unjust dismissal, occupational health and safety, and notices for large-scale layoffs or plant closures in order to assess the strictness of labour standards in the United States and the European Union (EU).
Some theoretical analysis Researchers have attempted to measure the impact of EPL on various economic indicators such as unemployment, employment rate, labour mobility, productivity, innovation to assess the level of accountability of this institution (employment protection legislation) in the efficiency of the labour market. Among them, Boeri and Garibaldi (2007) demonstrated, by using a dynamic model of labour demand, that the transition from a system where the EPL is rigid to “two-tier” regime increased employment growth. These “two-tier” reforms, also referred to as “at the margin”, were characterized by liberalization of temporary contracts (specific contracts) while maintaining unchanged protection on permanent contracts (open-end contracts). Kugler and St. Paul (2004) also provide theoretical results related to the impact of strict EPL on the selection of employees when hiring. The authors point out that the higher the redundancy costs are, the more firms tend to discriminate in hiring unemployed jobseekers. Other researchers (Postel-Vinay & Saint-Martin, 2004) also found that the EPL can create a feeling of insecurity among all employees. Indeed, when EPL is strict, it limits the reallocation of labour (market rigidity), which implies that employees cannot expect to easily find a new job when they are dismissed, and thus maintain the unemployed status for a long period of time (Blanchard & Wolfers, 2000; L’Haridon & Malherbet, 2003). However, Belot et al. (2007) indicate that when severance pay is high, workers tend to invest more efforts in their work since they hold a job security (Suedekum & Ruehmann, 2003). Finally, some research has also been conducted on the effects of EPL on productivity levels. In this sense, Poschke (2009) finds that the firing costs not only create poor reallocation of labour, but also discourage the exit of less productive firms. Specifically, the author argues that when firms leaving the market are under the rules governing the costs of layoffs, these costs are perceived as an exit tax.
Empirical results validate the theoretical analysis In the literature, we find that the results of the empirical studies point in the same direction in general as the results of the theoretical studies cited above. Among others, Lazear (1990) argues, using data from 22 developed countries between 1956 and 1984, that greater rigour on redundancy compensation has the effect of reducing the employment-population ratio. Specifically, the passage of any requirement for severance pay to the obligation of compensation, representing three months’ salary for an employee with seniority of 10 years, would reduce the employment-population ratio of about 1%. More recently, Di Tella and McCulloch (2005) have shown that if France would have a similar level of market flexibility to the United States, the employment rate would increase by 1.5% in the short term and 3.6% in the long term.
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With respect to the mobility of the workforce, Millán et al. (2013), based on data ranging from 1994 to 2001 from the European Community Household Panel, have shown that EPL has a negative impact on the probability that an individual goes from self-employed to employer (with employees under his responsibility) because of the high costs of hiring in the context of strict EPL. Specifically, they have found that a decrease of 0.43 in the EPL indicator increases the probability of hiring new employees by 2.9% (Boeri & Jimeno, 2005). Haltiwanger et al. (2014) have also reach the result that a strict regulation of layoffs and hiring would reduce the efficiency of job reallocation, and particularly in industries where job turnover is high. In terms of productivity, Van Schaik and Van Klunbert (2013) point out, by using data between 1960 and 2005 from 21 OECD countries, that a highly protective EPL now may not be as relevant and adapted to the market as it once was, since it impedes the productivity largely driven by innovation. The study of Auer et al. (2005) provides a different light on the link between EPL and productivity based on the age of the workers. The authors show a clear positive and significant correlation between EPL strictness and the proportion of workers with seniority of 10 years (Muller & Berger, 2013).
METHODOLOGY As explained in the previous section, the OECD analysis model focuses primarily on layoffs and the use of temporary contracts (see table 3). However, although the guidance associated with layoffs and temporary contracts may be particularly restrictive in some countries, we believe it is important to include other mechanisms related to employment adjustments whose contours are also provided by the regulatory and administrative framework. Consequently, we believe that the evaluation of the strictness of voluntary departure plan and work-sharing (other employment adjustment modes) will lead to a model which is more comprehensive and representative of the constraints imposed on employers in terms of the obligations under the employment protection. We followed the same methodology used by the OECD in the construction of measurement scales and the choice of subcomponents in order to maintain continuity in the model (OECD, 2014). The measurement scales that will be presented will retain their amplitudes from zero to six and the assertions of each of the scales are graduated according to the level of constraint and costs imposed on the employer for each condition association with job protection, which respect the analytical framework adopted by the OECD.
Introduction of a new component We have identified four dimensions that constitute the “voluntary departure”: the types of eligible contracts, compensation, notification requirements, and employment support obligation. It is important to mention that the choice of these four variables is not random and was done according to the following three criteria: the variables had to be obligations regulated by the legal or administrative framework, they should also have the capacity to impose costs on the employer and, finally, they had to allow a comparative analysis among the countries under study in order to avoid assessment of discriminatory data. For each dimension, we established a grid that shows the various scenarios. Table 4 summarizes all the cases expected for the “voluntary departure”.
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Dimension
Grid
Score
Types of eligible The voluntary departure may apply to all types of contracts contracts The voluntary departure may apply only to regular contracts and temporary contracts. (Scale 0-2 x 3) The voluntary departure is only for regular contracts.
0 1 2
Compensation (Scale 0-2 x3)
No obligation to pay compensation. The compensation must meet a minimum. The compensation may not be less than the legal allowance provided for economic redundancy.
0 1
0
Notification requirements (Scale 0-3 x 2)
When oral notification is sufficient to inform the employee of the voluntary departure opportunities. When the employer must inform the employee in writing of the voluntary departure opportunities. When a third party must be informed. When the employer cannot proceed with the implementation of the voluntary departure plan without the consent of a third party.
Employment Support Obligation (Scale 0-2 x 3)
When there is no obligation for the employer to offer employment support for employees leaving voluntarily. When outplacement assistance and/or rehiring priority must be offered to employees leaving voluntarily When the voluntary departure must follow, in certain circumstances, a job preservation plan
2
1 2 3 0 1 2
Table 4. The dimensions of the subcomponent “Voluntary Departure”
Regarding the first dimension “types of eligible contracts”, we gave a score of “0” to the statement “the voluntary departure may apply to all types of contracts” because, in these circumstances, the employer has the flexibility and freedom to identify eligible employees for voluntary dismissal plan without any regulation constraints. In contrast, we have allocated the score “2” to the statement “the voluntary departure is only for regular contracts”. Finally, to be consistent with the OECD’s methodology and to assess these dimensions on a scale from 0 to 6, we assigned a scale of 0-2 multiplied by 3 or, in some cases, a scale of 0-3 multiplied by 2. As you will see, we have applied the same methodology with the dimensions for work-sharing. As to compensation, the Canadian legislation imposes no obligation on the employer to pay compensation to workers who voluntarily leave (Government of Canada, 2016). Our analysis has led us to include three levels in the dimension of compensation. Thus, we assigned the lowest score, “0”, to the statement “no obligation to pay compensation”. In addition, when the compensation established by the legislation, for an employee leaving voluntarily, cannot be less than the legal allowance provided for redundancy, the statement gets the score “2”. In general, the voluntary departure also requires notification obligations. Indeed, the employer has to inform employees of the opportunities to voluntarily leave through amicable separation. However, the legislative framework of this notification may vary with the country. In Canada, employers must not only inform the employee in writing of the possibility of a voluntary departure through, for example, a letter, a notice or a memo, but they must also inform a third party, such as labour authority, in order to ensure that the employee can receive employment-insurance benefits (Government of Canada, 2016; Niel, 2013). 10
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Finally, with respect to the fourth dimension (obligations relating to employment support) in Canada, the employer is not required by law or administrative authorities to offer employment support to an employee who has voluntarily left through a voluntary plan (Government of Canada, 2016). However, in other cases, it can be required from the employer to offer outplacement assistance and/or rehiring priority to the employee who has voluntarily left. We have also identified five dimensions constituting this subcomponent: the maximum initial period for work-sharing granted by public authorities, obligations recovery, training obligations, the obligation of fair treatment in the reduced hours and finally, compensation. As for the “voluntary departure”, we selected variables based on the same criteria. Table 5 summarizes all the cases expected for the “work-sharing”. Dimension
Grid
Score
Maximum initial period (Scale 0-2 x 3)
Maximum initial period of over 12 months Maximum initial period of six to 12 months Maximum initial period of six months or less
0 1 2
Obligations recovery (Scale 0-2 x3)
Non-mandatoryrecovery plan Mandatory recovery plan in certain circumstances Mandatoryrecovery plan
0 1 2
Training obligations (Scale 0-2 x 3)
No training required Training obligations in certain circumstances Training obligations
0 1 2
Fair treatment (Scale 0-6)
No obligation for the equitable sharing of work Obligation of fair work-sharing
2 4
Compensation (Scale 0-6)
The nonworking hours are supported by a public program The nonworking hours are paid by the employer and the government
2 4
Table 5. The dimensions of the subcomponent “Work-Sharing”
First, the maximum initial period is the maximum time initially granted to the firm to recover. This varies somewhat depending on the country. In Canada, that period is capped at 26 weeks. In most cases, this period may be extended following a request for an extension made by the employer to the public authorities (Government of Canada, 2016). As for the obligations of recovery, we assigned the lowest score to the statement “not mandatory recovery plan” since in this case, the employer has no obligation to prepare a recovery plan for the firm and present it to the administrative authority. Regarding training obligations, Portugal requires from the employer to provide mandatory training for workers in short-time working (Hijzen & Venn, 2011), whereas, in other countries, such as France, training is mandatory only in special circumstances. In Canada, there is no obligation for the employer to offer training which is rather optional, and therefore left to the discretion of the employer (Government of Canada, 2016). The fourth dimension is “fair treatment”. Indeed, in Canada, the employer must ensure not only reduced work hours fairly, but also make sure to spread the available work hours equitably, regardless 11
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of seniority clauses in collective agreements. In addition, if during the period of the work-sharing agreement the level of labour increases, overtime should be divided equally between employees (Government of Canada, 2016). However, we do not always find that fair treatment in all countries. For example, in France and Belgium, the administrative or regulatory authority does not state explicitly that requirement. In Canada, nonworking hours are paid through the employment-insurance plan and the employer is not constrained to pay compensation. However, the employer is subjected to a rigorous audit of its recovery efforts and should, in particular, demonstrate that he operates its business for at least two years (Government of Canada, 2016). In other cases, as in France, the employer is responsible for paying compensation for work-sharing workers and the state offers a partial refund. As discussed above, we believe that assign weighting 5/12 to “regular contracts” and “temporary contract” and 2/12 to “collective dismissals” for all countries without modulation, as does the OECD, does not provide indicators that do not exactly represent the reality and dynamics of the labour market. Regular and Temporary Contracts First, following our analysis of trends in temporary and permanent employment in Canada (Statistics Canada, 2016b), we find that, from 2005 to 2015, the share of permanent and temporary employment represents 86.65% and 13.35% respectively of the total employment. Although the evolution of temporary and permanent employment is cyclical, the wide gap observed in their share in the Canadian labour market leads us to reasonably believe that attributing the same weighting to the components’ “regular contracts” and “temporary contracts” could result in a biased assessment of the EPL indicator. As a result, we propose to give the weighting of 3/12 to “temporary contract” and of 6/12 to “regular contracts” which would make the synthetic indicator more suited to the composition of the labour market. Collective dismissals To assess the extent of redundancies, we relied on the Canadian study produced by Statistics Canada (Morissette et al., 2007) using data from the Longitudinal Worker File. Data from this study allowed us to measure the proportion of workers affected by economic collective redundancy (0.25% in 2002) during an economically stable period (Statistics Canada, 2004; 2011). Note that collective dismissals represent a small share of permanent layoffs in Canada (8.77% in 2002) (layoffs occur for any reason, including temporary employment cessation), hence the need to give it a much lower weighting to that attributed to the components’ “regular contracts” and “temporary contracts”. Work-sharing and Voluntary departure To properly integrate this new component (which includes work-sharing and voluntary departure) of the analysis model, we will assign a weighting of its weight on the labour market. Using the Statistics Canada database (2016a), we have calculated the rate of work-sharing benefits as presented in Figure 1. The latter allows us to see that the evolution of benefits is counter-cyclical; an increase in a recession and a decline in a growth period. Indeed, the lowest benefit rate was recorded 12
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in 2006 (0.13%) and the highest was in 2009 (3.47%) at the heart of the latest economic crisis. However, between 2002 and 2015, the average benefit rate for work-sharing amounted to 0.32%. Therefore, this finding suggests that this mechanism, in general, is not particularly used when we compare the data with those concerning individual dismissals. As for the “voluntary departure”, it was particularly difficult to obtain data. However, we assume that it would follow a similar pattern to “work-sharing” since it is generally used to reduce the number of layoffs. We then assign a weight (1.5/12) to the components “collective dismissals” and “work-sharing and voluntary departure” (Venn, 2009).
Figure 1: Benefit rate (%) for work-sharing in Canada from 2002 to 2015 Source:Statistics Canada data. Table 282-0002 - Labour Force Survey (LFS) estimates by sex and detailed age group, annual (persons unless otherwise noted), CANSIM (database). (Accessed at: June 30, 2016)
RESULTS AND DISCUSSION We will now measure the strictness of “voluntary departure” and “work-sharing” for Canada. To do this, first, we begin by assessing their dimensions. Regarding the “voluntary departure” and when we analyze the law and administrative obligations under the legislation, it is not indicated that this mechanism is only open to employees of a specific category (Government of Canada, 2016). So that is why we assign the score “0” for this dimension. With respect to the “compensation” dimension, in Canada, the legislative and administrative authorities do not mention obligations for the employer to pay compensation as a result of a voluntary departure agreement. Therefore, we place the score “0” to this dimension also. When we examine the dimension “notification obligations”, the given rating is different from the first two dimensions. Indeed, in Canada, the employer must not only inform in writing the employees of voluntary departure opportunities but also notify a third party, so that employees who left voluntarily can benefit from employment-insurance benefits (Government of Canada, 2016). It is for these reasons that we allocate the score “2” to this dimension. As for the employment support obligation, the employer is not required by law or administrative authorities to provide assistance to employment to employees who use “voluntary departure” (Government of Canada, 2016). It is especially through the employment-insurance program that the worker may receive this type of aid. We attribute the score “0” for this dimension. 13
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Furthermore, we rebuilt the OECD formula which allows calculating the synthetic indicator of each country (OECD, 2014). We will then use the following expression:
i = ( ( c1 ∗ e1) ∗ p1) + ( ( c 2 ∗ e2 ) ∗ p 2 ) + ( ( c3 ∗ e3) ∗ p3) + ( ( c 4 ∗ e4 ) ∗ p 4 ) i: Indicator obtained for the sub-component c1: Score obtained for the first dimension
c2: Score obtained for the second dimension c3: Score obtained for the third dimension
c4: Score obtained for the fourth dimension
e1: Graduation of the measurement scale for the first dimension
e2: Graduation of the measurement scale for the second dimension e3: Graduation of the measurement scale for the third dimension
e4: Graduation of the measurement scale for the fourth dimension p1: Weighting for the first dimension
p2: Weighting for the second dimension p3: Weighting for the third dimension
p4: Weighting for the fourth dimension Applying this formula to the “voluntary departure”, we get the indicator ipdv:
i pdv =
( ( 0 ∗ 3) ∗ ¼ ) + ( ( 0 ∗ 3) ∗ ¼ ) + ( ( 2 ∗ 2 ) ∗ ¼ ) + ( ( 0 ∗ 3) ∗ ¼ ) = 1
Canada secured the result of 1 on a scale of 6 for its rigour on “voluntary departure”. This result is also registered in the objective pursued by the legislature to give more flexibility to the employer when faced with restructuring (Government of Canada, 2015). Regarding “work-sharing”, when we look at the study of the maximum initial period, we note that in Canada, this term is limited to 26 weeks, which represents a little more than six months (Government of Canada, 2016). This brings us to assign the score “1” on the measurement scale constructed for this dimension. As for the obligations of recovery, the plan must be submitted to the competent administrative authorities to register the work-sharing program (Government of Canada, 2016). This strict framework may be explained by the fact that the employment-insurance program completely covers the allowances for short-time working. This requirement of the recovery plan, therefore, ensures that the firm will implement concrete actions within a reasonable time to remedy the situation. Our analysis thus leads us to give score “2” to this dimension. With respect to training obligations, it is not mandatory for the employer to provide this type of assistance for employees on work-sharing. This is quite optional and the decision rests in the hands of the employer and the worker (Government of Canada, 2016). That is why we allocate the score “0” for this dimension. Regarding the “fair treatment” dimension, the employer must actually ensure equitable sharing of work hours among workers (Government of Canada, 2016), hence the allocation of the score “4”. Finally, for the last dimension “compensation”, we find through our analysis that the nonworking hours are supported by a state program (employment insurance) and the employer is not required to pay compensation to workers affected by partial unemployment, which brings us to give score “2” for that dimension. 14
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We can now calculate the indicator, iws, for “work-sharing” according to the OECD formula:
iws 1 = ( ∗ 3) ∗1/ 5 + ( 2 ∗ 3) ∗1/ 5 + ( 0 ∗ 3) ∗1/ 5 + ( 4 ∗1/ 5 ) + ( 2 ∗1/ 5 ) = 3 We can also calculate the synthetic indicator, is, revised for Canada, following indicators obtained for “voluntary departure” and “work-sharing” and using the new weights given to “regular contracts”, “temporary contract” and to “collective dismissals”. Using the same formula, we obtain:
is =( 6 /12 ∗ 0.92 ) + ( 3 /12 ∗ 0.21) + (1.5 /12 ∗ 2.97 ) + (1.5 /12 ∗ 2 ) = 1 .13
Level 4
Level 3
Level 2
Level 1 Scale (0 to 6) Notification procedures Delay opening of the notice period
1/2 1/2
Notice and severance pay for individual dismissal without fault
Notice period after 9 months Period of notice after 4 years Period of notice after 20 years Severance pay after 9 months Severance pay after 4 years Severance pay after 20 years
1/7 1/7 1/7 4/21 4/21 4/21
Difficulty of dismissal
Definition of unfair dismissal Duration of the trial period Compensation Reinstatement Maximum time for claim
1/5 1/5 1/5 1/5 1/5
Fixed-term contracts (FTC)
Should the use of FTC is justified Maximum number of successive FTC Maximum cumulated duration of successive FTC
1/2 1/4 1/4
Types of jobs for which is legal Restrictions on the number of renewals Maximum cumulated duration Information requirements and authorization Fair treatment
1/3 1/6 1/6 1/6 1/6
Definition of collective dismissal Additional notification obligations Additional deadlines Other specific costs for employers
1/4 1/4 1/4 1/4
Types of eligible contracts Compensation Notification requirements Employment Support Obligation
1/4 1/4 1/4 1/4
Maximum initial period Obligations recovery Training obligations Fair treatment Compensation
1/5 1/5 1/5 1/5 1/5
Constraints procedures
Synthetic Indicator is= 1.13
Regular contracts (6/12)
Temporary contract (3/12)
Temporary work agency employment
Collective dismissals (1.5/12)
Other employment adjustment modes (1.5/12)
Weighting
Table 6 presents a detailed compilation of the OECD model taking into account our adjustments. Note that the OECD base model (without adjustments) reaches a synthetic indicator of 0.97.
Voluntary departure (1/2) ipdv = 1 Work-sharing (1/2) iws = 3
Table 6.Revised OECD Synthetic indicator of EPL in Canada 15
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It is, therefore, clear from these results that the integration of other employment adjustment modes and the revision of weights are rising the Canadian synthetic indicator. Indeed, our synthetic indicator amounted to 1.13 while that of the OECD is 0.97. The particularly high indicator for “work-sharing” can be explained by the fact that the government plays an important role in the support offered to employees in partial unemployment. Indeed, firms have no obligation for compensation and training with regard to work-sharing workers since the employment-insurance program fully covers the severance pay and guarantees help in the search for jobs to recipients of employment-insurance benefits. Therefore, as the employer has little responsibility in these matters, the legislature imposes in return strict monitoring in regard to the recovery of the firm and fair treatment of employees affected by this mechanism. In addition, although having scored below the “work-sharing”, the “voluntary departure” also has a slightly higher synthetic indicator (1.00) than “regular contracts” (0.92) and “temporary contracts” (0.21). This can be explained primarily by the “notification obligations” dimension which has received a score of 2. In short, the consideration of new items and the review of the weights with slightly rising the Canadian synthetic indicator show that the assessment of the constraints imposed on employers during layoffs and use of temporary contracts may understate the degree of EPL strictness for Canada. The addition of two new items to the OECD analysis grid and the revision of weights, increasing the Canadian synthetic indicator, may also slightly change the overall ranking of countries made by the OECD. Our findings could also have implications in terms of public policy. Indeed, poor assessment of the EPL strictness skews the meaning and direction of reforms to stimulate job creation. Furthermore, and although our indicator is revised upward, it remains important to consider other alternatives to better protect workers against the economic uncertainty. In this perspective, lifelong training is an option that should be seriously considered in the formulation of Canadian public policy so that workers can find a job efficiently following a layoff and have a human capital which is not only associated with the firm but a capital that is transportable and maintaining their employability. France has also begun the step in this direction by adopting in 2009 the individual right to training (Pereira, 2010).
CONCLUSIONS The proliferation of EPL analysis models was caused by a significant and justified concern about the rigidity of the labour market, particularly in a recession, and the presence of institutional sclerosis in some countries. Thus, it is important to consider all the employment protection measures that impose constraints on employers in adjusting their workforce. We have shown that the inclusion of two mechanisms (voluntary departure and work-sharing) in evaluating the EPL strictness has increased the composite indicator for Canada. Certainly, our approach is not perfect. Besides, our measurement scales associated with the “voluntary departure” and “work-sharing” deserve to be deepened in their international coverage. In other words, when we made them, we focused on a few countries, but to make the scales even more reliable and valid, we should analysetheir terms in all OECD countries. In addition, in our analysis, we relied on the legislative and administrative data issued by public authorities. It would then be appropriate to broaden our thinking to incorporate jurisprudence aspects to capture all legal information related to mechanisms that caught our attention in this article.
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Statistics Canada. (2016b). Permanent layoff rates from the CEEDD and SLID, employees aged 18 to 64, by province, 2003 to 2011. Retrieved Jun 6, 2017, from http://www.statcan.gc.ca/pub/11-633-x/2016001/t/tbl10-eng.htm Suedekum, J., & Ruehmann, P. (2003). Severance payments and firm-specific human capital. Labour, 17(1), 4762. DOI: 10.1111/1467-9914.00221 Van Schaik, T., & Van de Klundert, T. (2013). Employment Protection Legislation and Catching-up. Applied Economics, 45(8), 973-981. DOI: 10.1080/00036846.2011.613784 Venn, D. (2009). Legislation, collective bargaining and enforcement: Updating the OECD employment protection indicators. Retrieved Jun 6, 2017, from http://www.oecd.org/employment/emp/43116624.pdf Werner, E., & Marx, P. (2009). Le chômage partiel, amortisseur social de la crise? Regards surl’économie allemande, 90, 23-30.
PREGLED OECD INDIKATORA ZAKONA O ZAŠTITI ZAPOŠLJAVANJA ZA PODRUČJE KANADE Rezime: Počev od ranih devedesetih, Zakon o zaštiti zapošljavanja predstavlja važnu stavku za političke činioce, naročito kada je reč o uticaju ove regulative na zapošljavanje i produktivnost rada. Ova tema privukla je veliku pažnju međunarodnih organizacija koje su kreirale brojne analitičke modele kako bi procenile koliko je ovaj zakon strog u različitim zemljama. Smatramo da su svi do sada razvijeni modeli nepotpuni, uprkos činjenici da pružaju mnoštvo informacija. Svrha ovog rada jeste da predloži uvođenje novih elemenata u model analize koji primenjuje Organizacija za ekonomsku saradnju i razvoj (OECD), a na osnovu iskustva Kanade. Stoga predlažemo da se sintetički (zbirni) pokazatelj Zakona o zaštiti zapošljavanja izmeni, uzimajući u obzir lokalne specifičnosti koje OECD prethodno nije razmatrala.
Ključne reči: Zakon o zaštiti zapošljavanja, sintetički indikator, Organizacija za ekonomsku saradnju i razvoj (OECD) , dobrovoljni odlazak, podela rada
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EJAE 2018, 15(1): 20-37 ISSN 2406-2588 UDK: 330.14 005.332:336 DOI: 10.5937/EJAE15-15647 Original paper/Originalni nauÄ?ni rad
THE IMPACT OF CERTAIN PSYCHOLOGICAL FACTORS OF INVESTORS AND MANAGERS ON THE CAPITAL STRUCTURE Mirko Babanić PhD candidate, Singidunum University, Belgrade, Serbia
Abstract: Psychology represents the basic requirement for the emergence of disciplines such as behavioral finance and behavioral economics. It has contributed to a better understanding of the behavior of economic actors under conditions of risk arising from the imperfections of cognitive abilities of human beings. Consequently, it is necessary to change the economic models based on mathematical laws in favor of descriptive models that consider the cognitive abilities of the human mind. The most common decisions that are being studied in the field of behavioral finance are the decisions regarding capital structure in companies. The methodology in this paper is based on the net operating income approach. This approach is analyzing the financial section of income statement that refers to the financial expenses of the companies. Financial expenses are assumed to be fixed, determining financial break even, as a consequence of use of financial leverage. The main task of this paper is to determine the impact of some psychological factors in the terms of capital structure and financial leverage, through two case studies of comparative analysis of income statements of the companies Puma and Adidas that will consequently affect the achievement of financial breakevens well as the profitability of the companies. Therefore it is possible to conclude that some analyzed psychological aspects in the process of financial decision making of investors and managers can influence the capital structure decisions, which can be a subject of further researches.
Article info: Received: November 9, 2017 Correction: January 22, 2018 Accepted: January 22, 2018
Keywords: Biases, personal beliefs, capital structure, financial leverage, beta coefficient
INTRODUCTION According to Baker and Ricciardi (2013) scientific areas such as behavioral finance and behavioral economics did not appear until the early 1990s of the 20th century in various scientific journals, and were primarily used by the professors of finance, and later on by investors and the broad reading audience. These authors further state that the foundations of behavioral finance and similar sub-subjects, such as the behavior of investors, can be traced back to events that were defined as speculative behavior during the so-called tulip mania crisis of the 17th century. Books that were written in the 19th and 20th century about investing psychology signed an initiation of theoretical foundations in modern theory of investor behavior. 20
* E-mail: mirko.rp.b@gmail.com
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American psychologist Daniel Kahneman and economist Vernon Lomax Smith, who were awarded Nobel Prize in Economics in 2002, confirmed the inevitability of psychology in the prediction and explanation of human behavior. Psychology thereby contributed to the creation of more realistic economic models and behavioral economy was then created, which mainly dealt with the fact that emotions and the structure of the mindset of people affected the process of economic decision-making. Namely, the economic models based on mathematical laws, from which inferences were made about the economic impact in making economic decisions, had to be replaced by descriptive models that would consider human imperfections due to the effect of incomplete information, emotions, context and the form in which the problem was defined. Also, an important aspect are categories such as experience, attitudes and beliefs of individuals, economic decision makers, not only of investors and company managers but also of consumers in general. In the 1970s of the 20th century a cognitive psychology in the study of decision-making was established, which is a common approach in psychology, oriented towards the understanding and explanation of psychological processes. The dominant interest of cognitive psychology, as a new branch of psychology, are phenomena such as perceptions and the level of attention as well as the definition of concepts, information processing, memory, and problem solving. By developing prospect theory which undoubtedly started the analysis of decision making under the risk as a consequence of the aforementioned psychological phenomena, Kahneman and Twersky (1979) established the basis of behavioral economics. Hackbarth (2008) proves that economists are focused on models in which market participants behave rationally and have homogeneous expectations. Nevertheless, a great number of researches in psychology show that people are inclined to excessive optimism and overconfidence. This is actually the consequence of the fact that they believe they have more reliable knowledge of the future events than they actually do. The explanation of these attitudes is that there is a divergence in the beliefs between the managers and the market in relation to the value of the company. Namely, we can conclude that personal beliefs of managers make a heterogeneity between anyhow identical companies. Besley and Brigham (2015) explain this attitude by examples of pharmaceutical and biotechnology companies that do not use high amounts of debt, because their production is unpredictable, and therefore the large use of debt instruments is considered to be extremely inconsequential. Contrary to them, public utility companies use high amounts of debt, especially long-term debts, because their fixed assets and stable sales represent high-quality insurance for mortgage-backed securities. Besley and Brigham (2015) also cite examples from 2010 of companies like the Genetech, which had about 25% of debt in capital structure, which was also the industry average, and GlaxoSmithKline, which had almost 80% of debt in its capital structure (see examples 1 and 2 in Chapter on Methodology), which leads to the conclusion that the managers’ attitudes play a basic part in determining an aim of capital structure. This paper will show that the following hypothesis applies: H1: the company’s capital structure change by increasing the level of its debt, caused by the influence of some psychological factors inherent of investors and managers, will always as a result have an increase of its systemic risk exposure measured by the beta coefficient. This paper is organized in two global parts. The first part consists of a literature review, which covers the fields of the rationality of human behavior, investor behavior, investor psychology and the influence of some psychological constructs on decision-making process on capital structure. The second part introduces the methodology based on the approach of net operating income, which deals with the areas of derivation of the equations and independency of net operating income from capital structure, while at the end, the last topic under the title of account examples, shows the testing of the net profits over income statements (see Appendix) of companies such as Puma and Adidas (for 2014 and 2015 years). 21
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LITERATURE REVIEW Economics as Mankiw (2008) argues that it is the science that deals with the study of human behavior, but it also deals with choices which people make in life. The basic premise of traditional economics is that people always behave rationally, similar to the attitude that company managers always make the maximum profit, or the consumers always get the maximum utility, or select a position on the highest indifference curve. Therefore, with all the restrictions, consumers measure all the incurred costs and benefits, and on this basis, select the best relation. However, real people are complex beings, and they are not always calculating individuals, which is the premise of the traditional economic theory. Herbert Simon, one of the first sociologists who were engaged in marginal issues of economics and psychology, suggested that people are not viewed as rational beings that maximize, but as beings who satisfy their needs (Mankiw, 2008). Thus, the research needs to go in the direction that people make decisions that are not always the best option, but are good enough. Some economists believe that people are just “approximately rational“ and that attempts should be made in studying the human behavior and decision-making in order to identify systemic errors that they make. The most common errors include: excessive self-confidence, which manifests itself as an overestimation of their own opportunities; attributing extreme importance to a small number of striking observations, which occurs due to excessive confidence in someone’s judgment; reluctance to change opinions or interpretation of evidence in a manner that it confirms existing beliefs (Mankiw, 2008). The question which arises is why the economy is based on the assumption of rationality when it comes to everyday behavior. The answer may be that the assumption of rationality, if not fully, is at least approximately accurate. Namely, economic models are not imagined as a copy of reality, but should show the essence of specific problems, and help in its understanding (Mankiw, 2008). According to this, economists can be satisfied with the theory which is not perfect but is good enough. The further insight of human behavior can be illustrated by an experiment, which is known under the name of the ultimatum game. By conducting such experiments psychologists have come to the conclusion that people rely on their innate sense of justice, which can be particularly noticeable when analyzing the behavior of employees in connection with their wages, when for example, the company that they work for has made above average results, they will justifiably expect to be paid a fair share of award, even in cases that the equilibrium in the labor market does not support it. The subsequent insight in human behavior can be analyzed in connection with decisions about consumption and saving, which are an important example in which people eventually show inconsistency. Namely, most people have a desire for instant gratification than denial, thereby initiating the decision-maker to abandon his previous plans. Thus, the decision about saving requires sacrifice in the present in order to receive an award in the future. The general conclusion of the previously described may be that there is a ubiquitous imperfection, given that, the information and political system and people are not perfect, economists have to understand all these imperfections as precisely as possible if they want to explain or develop the world that surrounds them (Mankiw, 2008). Namely, the study of human behavior conducted in psychology and economics shows that the way people decide is more complex than assumed in traditional economic theory, where a conclusion was reached that people are not always rational. Ackert (2014) argues that the traditional approach in the field of finance, which has been the dominant paradigm for a few decades, is characterized by the fact that investors make rational decisions, taking care of maximizing the expected utility. The main areas of traditional finance are portfolio theory, (see Markowitz, 1959), capital asset pricing model (see Sharpe, Alexander & Bailey, 1995), the efficient market hypothesis, (see Fama, 1970). Ackert (2014) further says that the evidence shows that many 22
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of the assumptions and conclusions associated with traditional finance are invalid. On the contrary, researchers of behavioral finance turned towards observing behavior, in order to develop models that describe how investors really make decisions. Behavioral finance uses understanding of the social sciences, especially psychology, for a better understanding of investor behavior of both individuals and groups and the entire market. Altman (2014) argues that the behavioral economy approach in making decisions is based on the bounded rationality process of investors. This approach is compared to the errors and biases approach, for a better understanding of the decision-making process and obtaining results. The emphasis is placed on the value of cognitive illusions and biases considering the limited capacity of information processing which a human brain has. These two approaches try to create meaning and try to explain why the results of decision-making, as a rule, are inconsistent in relation to the predictions of traditional economics and, in particular, from the standpoint of the efficient market hypothesis, see (Fama, 1970). Bogan (2014) argues that, when it comes to behavior in making decisions related to household investments, they are based on traditional financial models which are often not precise. Farrell (2014) argues that, when it comes to demographic and socio-economic factors in the behavior of investors, it is crucial to understand how they are choosing their portfolio. Existing working papers describe that men are more aggressive in investing than women, as well as white people compared to black investors. Mansour and Ilassi (2014) write about the impact of religion on financial and investment decisions. Religion actually has an enormous influence on people’s lives and is closely linked to their economic conditions. Xiao (2014) writes about the issue of the relation of money and happiness. Money in this case represents factors associated with income, while happiness is usually measured as life satisfaction or life fulfillment. Researchers suggest that personal income is responsible for the increase of happiness. What’s more, studies show that people who live in wealthy countries are happier and that there is an inverse causal relation that happier people create a higher income (Xiao, 2014). Fisher (2014) argues that in accordance with the behavioral finance theory, investors are not rational participants, as described by the economic theory. A lot of evidence from studies of investment behavior indicates that they perform contrary to their best interests (Fisher, 2014). However, there exists, fortunately for the investors, an adviser institute which can help them to remain rational, regardless of how the market would behave in improving their odds to stay focused on long-term investment strategy and therefore realize their own benefit. Kramer (2014) says that some aspects of people psychology induced by external factors such as the changes of the seasons play a very important role in making financial decisions of individuals with significant economic consequences, which are perceived even at the level of the entire market. Consequently, psychological explanations are necessary to clear up the real decision-making process, especially in cases where there are rumors that affect the mood of market participants and their behavior at the level of total financial market. All these aspects are often excluded from the scientific finance literature. Ricciardi and Rice (2014) write that, when it comes to risk, there is a lot of scientific literature, which identify, describe and analyze risk. Behavioral finance includes subjective and objective risk factors, both in the field of risk observation and risk tolerance. The perception of risk is, according to these authors, a subjective process regarding assess of risk and the level of risk tolerance (Ricciardi & Rice, 2014). Considering the emotions and financial markets, Fairchild (2014) argues that the latest research in the field of behavioral finance recognizes, in contrast to traditional finance which are based on a model of rational choice and on the assumption that market participants are fully rational which maximize the expected utility, that in reality investors and managers are not fully rational, for the reason that they are affected by psychological biases and influenced by emotions when making decisions. 23
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Capital structure is the most significant issue in the corporate finance theory (Bilgehan, 2014). In accordance with the references in the field of finance, there are many approaches that define the theory of capital structure, of which the most important are, as Besley and Brigham (2015) argue, the trade off theory, created in 1958 by professors Modigliani and Miller, and the pecking order theory, which is a consequence of asymmetries in information. Modigliani and Miller assumed that investors have the same information about the business operations of companies as their managers, which is called the symmetry of information in the literature. However, managers who manage companies have much better information about their companies than external players, like investors, which creates a relationship known as information asymmetries. This relationship has a significant impact on decisions on the capital structure of companies. Namely, such relationship significantly influences whether debt or equity will be used to finance capital projects. For this purpose, we will consider the different perceptions of managers regarding the business of their companies. As Besley and Brigham (2015) write, if managers perceive the growth in sales by introducing a new product for which new installations need to be built, the question arises as to how the new capital should be raised? The considerations of managers can be explained in the following way: if a company is issuing shares, when the profit from the new product begins to arrive, the price of the shares will grow very fast, and the buyers of new shares will make a fortune. Old shareholders, including managers, will also achieve good results, but not as good as if the company did not issue new shares before the rise of stock prices, as in that case old shareholders would not have to share benefits with new shareholders. Therefore, in the case described above, regarding the perception of managers regarding the favorable outlook for business operations of the company, one should expect the decision that the necessary additional capital is to be raised by debt issuance, which would change the capital structure in terms of the higher level of debt (see examples 1 and 2 in the Chapter on Methodology). However, since managers of a company keep projects on introducing new products a secret, in order to put off the entry of competitors on the market, the same is true for the managers of competing companies, which creates an impression for both sides, which Bilgehan (2014) and Hackbarth (2008) call biases, overconfidence and optimism, in achieving better results than the competition. Namely, the projection of future events can be based on incomplete or even incorrect information, which by themselves represent a risk and can lead to wrong decisions, which can further cause the bankruptcy of the company. Žigić and Hadžić (2012) say that risk assessment is a comprehensive analysis process in analyzing risk, because risk evaluation is very important in the decision-making process regarding capital structure of companies, where experts in companies determine potential losses with the likelihood of their occurrence. It can be considered in the following way: what will happen if the market does not accept the new product of a company at all, which is not a rare case, or if the competition enters the market with its own new product before it is presumed. In this case, companies can endure serious losses, which are caused by the irrational behavior of managers, which, in the literature of behavioral finance, as referred to above, is called overconfidence and optimism. The previously described presents a mechanism by which personal beliefs of managers can influence decision-making on the capital structure as well as the business operations of companies in general. As discussed further by Besley and Brigham (2015), company managers behave completely the opposite, due to cancellation of orders they have to modernize their own capacities in order to maintain the level of production, which requires high costs of investment in fixed assets. Namely, such companies must invest in new production capacities in order to avoid the possibility of initiating bankruptcy. The question is: how will they finance the necessary resources? In this case, companies with unfavorable prospects in terms of future business operations, will want to sell shares, because potential new losses 24
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could be shared with new investors, which actually shows a sort of (sui generis) hypocrisy of managers of such companies who, when issuing new shares, can meet legal requirements regarding the issue, but do not have to disclose unfavorable prospects in terms of the company’s future business operations .A conclusion to the previous statement is that companies whose managers perceive favorable aspects, will not finance their business operations by issuing new shares, while companies whose managers have a pessimistic view of the future business operations want to be financed because of their own equity. In this way, the mechanism for the performance of personal managers’ beliefs based on the experiences or emotions, regarding decisions on the capital structure and the future operations of companies, can be explained. Of course, the main assumption in a traditional finance literature is that market participants, investors and managers make the rational decisions. However, in contrast to this behavioral finance which is based on human psychology, there is an alternative approach which calls into question definitions of the classical finance on economic rationality. In particular, psychological studies have found that people have unreasonably high confidence in their own decision making, which essentially means that most people have exaggerated confidence in their abilities (Bilgehan, 2014). It is particularly important that behavioral finance uses psychology and similar disciplines to explore why individual solutions, often deviate from the strictly rational choices. As Bilgehan (2014) further states, most people are aware that emotions affect financial decisions, thus analysis of behavioral finance expands the role of biased insights when making decisions. The greatest number of studies in behavioral finance refers to the behavior of investors, not only them, but also to the behavior of managers in financial decisionmaking in companies. The latest behavioral finance theories give an explanation how biases such as overconfidence and optimism can affect corporate decisions (Bilgehan, 2014). According to the definition of Hersh Shefrin, which Bilgehan (2014) cites, bias is nothing else than a predisposition towards errors if there is influence of some basic belief. By citing other authors Bilgehan (2014) also writes that biases and their presence in decision-making, provides an insight in connection with irrationality of individuals and thereby expands the general attitude of irrational behavior. There are also arguments in relation to financial decisions which are subject to psychological factors that affect decisions about capital structure, which deals with the area of the so-called behavioral finances. In line with this, behavioral finance thus uses the theory based on psychology that would explain certain market anomalies (Bilgehan, 2014).Managers must choose between debt and proprietary capital when making financial decisions says Bilgehan (2014), therefore psychological biases that affect financial decision-making by the managers are responsible for inconsistency in relation to what is expected or preferred by investors. Hackbarth (2008) argues that optimism and overconfidence are described as reasoning in cases of uncertainty. He further argues that managers who perceive growth have tendency to achieve higher debt levels and more frequently recourse to issue new instruments of debt compared with managers who do not have such perceptions. As a result of this, the behavior of managers can cause considerable variations in the capital structure no matter the characteristics of the industry which companies belong to unchanged (Hackbarth, 2008). Hackbarth further says that managerial features, in relation to the growth and risk perceptions, are very important for decision-making on the capital structure, such as the issue of debt issuance. Namely, managers with higher expectations perceive the excessive increase in the earnings of their companies in the future and for this reason they use excessive external debt financing, thus when they approach the capital markets they already have debt preferences. In this model, a rational market reaction to a higher ratio between debt and equity will tend to erode the value of the firm. Stanišić et al. (2012) say that debt to equity ratio (see examples 1 and 2 in the Chapter 25
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on Methodology) is an indicator that is often used in models for predicting bankruptcy and assessing solvency of companies. Hackbarth (2008) also argues that decisions regarding capital structure represent tradeoff between not just corporate taxes and bankruptcy costs, but also a conflict of interest among claimholders. Therefore, there is a disadvantage in terms of any qualitative or quantitative guide which would explain the interaction between agency conflicts and managerial traits. In fact, there is general concern about the personal interests of managers who tend to create personal benefits (Jensen & Meckling, 1976). Static models of capital structure, aforetime point out the prospect that debt, if the available funds are restrictive, forces managers to be less wasteful in terms of spending money. A dynamic model of capital structure requires a debt issuance discipline due to the inability to return to the initial level of leverage ratio. Given that the financial decisions in the companies are in the hands of their managers, one of the unresolved tasks in terms of capital structure decisions is which type of manager will choose discipline in regarding the level of debt, and the opposite which type of them will likely continue with the policy of increased borrowing in a dynamic environment. Due to the fact that bond holders anticipate investment behavior, the cost of debt is included in the value of the debt on the day of issuance as an agency cost of debt, in which case the underinvestment process creates a higher cost of risky debt (Hackbart, 2008). In this paper an attempt will be made to explain the impact of some of the aforementioned factors on the shift of the financial break even and the level of net profit, using the net operating income approach. Primarily, we are interested in the influence of factors such as overconfidence and optimism on increasing the level of debt in the capital structure of companies, which will result in not only an increased level of systemic risk, measured by the coefficient of beta, but also in a lower level of net profit. Namely, if we imagine that there are two companies, which do not at all differ apart from the structure of capital, where in one there is a certain share of debt in total capital, while in the other there is solely fixed capital, a company that has no debt in the capital structure will achieve a higher level of net profit, for the simple reason that in the financial section of the income statement there will be no payments of interest due. This work will show that the financial leverage can lead (not necessarily) to the fall in the level of net profit, which will be shown in the case of Puma Company in the net profit account examples.
METHODOLOGY The influence of psychological factors on the capital structure change and consequently on net profit can be illustrated with the net operating income approach: We will set the following equality of Van Horne’s (1993), kd =F/B where, F is the annual interest expense, B is the market value of existing debt, kd is required return to debt (cost of debt). ke =E/S where, E is earnings attributable to shareholders, S is the market value of equity, ke is required return on levered equity (cost of levered equity). 26
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ku=EBIT/V where, EBIT (earning before interest and taxes)=NOI is net operating income, V is the total market value of the company (value of shares in the unlevered company), ku is required return to unlevered equity (cost of unlevered equity), and also this equation applies: V=B+S
Derivation From the previous equations, follows: EBIT=F+E ku=(F+E)/(B+S) ku=F/(B+S)+E/(B+S)
k u =k d •B/ ( B+S) +k e •S/ ( B+S)
(1)
Using net operating income approach with the copied examples (Van Horne, 1993), for the reasons of simplifications, we obtain the following results. If a company with $1000 debt and with an interest rate of 10%, and the expected value of NOI is $1000, the total capitalization ku is 15%. We will express the value of the company in the following manner: NOI
1000
ku
0,15
V=B+S
6667
B
1000
S
5667
Example 1.Influence of debt on capital structure Source: Van Horne (1997, p. 278)
Interest (I)=B•kd =1000$ • 0,1=100$ E=NOI – I=1000$ – 100$=900$ ke=E/S=900$/5667$=0,1588=15,88% Using now the equations of Fernandez (2003), we can calculate beta-coefficients as: βl=(ke – Rf)/Pm βu=(ku – Rf)/Pm βd=(kd – Rf)/Pm with the necessary prerequisites that Rf=2%, Pm=E(Rm – Rf)=5% provided that the expected return of the market portfolio is E(Rm)=7%, where Rf is risk free rate, Pm is market risk premium, followed by beta coefficients, see (Sharpe, Alexander & Bailey, 1995): 27
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βl=(15,88 – 2)/5=2,776 βu=(15 – 2)/5=2,6 βd=(10 – 2)/5=1,6 Net operating income approach means that the capitalization rate ku and the level of cost of debt kd remains the same regardless of the degree of use of financial leverage. In this case the required rate on levered equity ke grows linearly depending on the degree of utilization of financial leverage. Increasing the amount of debt on $3000 with the use of income from issuing debt by which the shares are bought and the same interest rate on debt kd=10%, the value of the company will be as follows: EBIT=NOI
1000
ku
0,15
V
6667
B
3000
S
3667
Example 2. Influence of increased debt on capital structure Source: Van Horne (1997, p. 278)
Interest (I)= B•kd =3000$ • 0,1=300$ E=NOI – interest=1000$ – 300$=700$ ke=E/S=700$/3667$=0,1909=19.09% Then the corresponding beta, with unchanged preconditions for the values of Rf and Pm, will be: βl=(19,09 – 2)/5=3,42 βu=(15 – 2)/5=2,6 βd=(10 – 2)/5=1,6 Thus, with increasing the levels of debt, in regard to higher debt / equity ratio, the systemic risk of the company, measured with coefficient beta, grows steadily, which in fact is the evidence that this hypothesis H1 is true, that the level of debt affects the growth of systemic risks measured by the beta. If the ratio of debt and permanent capital is fixed, the relevant equation will be as follows, based on equation (1), which reads: ku=kd•B/(B+S)+ke•S/(B+S) which implies the following,
k e =k u + ( B/S)( k u – k d )
(2)
If EBIT>0 and if as Besley & Brigham (2015) write, EBIT=I where I represents long term interest costs, then we get a financial break even, which may present itself in an equation: 28
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EBIT – I =0
(3)
Namely, at this point the operating profit EBIT is sufficient to cover all fixed financial costs that appear as consequences of the companies' debts. In this case, EBT (earnings before taxes) is equal to zero, which implies that net profit is equal to zero. Financing break even thus is the point at which the company will neutralize all costs as argued by Besley & Brigham (2015) and operational ones which are calculated in the operational section of the income statement and financial ones which are calculated in the financial section of the income statement.
Independence of EBIT from capital structure The crucial question is whether capital structure means anything at all? Namely, whether the companies can influence the cost of capital and request return by changing the combinations of funding. In order to answer this question, we will perform an analysis of what happens to the value of the company and the costs of capital if the relation of debt and constant equity is changed, that is if the level of use of finance leverage is changing. In order to depict the analysis as simple as possible, certain previous assumptions should be made, as following: Debt to equity ratio in the total capital changes by bond issuance, which is used to redeem shares accordingly by emitting equity that would settle the debt. This means that changes in capital structure occur instantly. We will also assume that there are no transaction costs. We also assume that the entire net profit, available to shareholders, is paid in the form of dividends and that the future operating income is not expected to grow. The following three rates will be used: kd=F/B=(annual interest expense/market value of existing debt) ke=E/S=(earnings attributable to shareholders/market value of issued shares ) ku=O/V=(net operating income/total market value of the company) given that EBIT=NOI (Net operating income) and we apply the equations: EBIT(NOI) – F=EBT EBT(1 – T)=E if the tax rate T=0, then EBT=E accordingly, it follows that EBIT=F+E where EBIT(NOI)= O = net operating income, then: ku=(F+E)/(B+S) or by equation (1) ku=kd•B/(B+S)+ke•S/(B+S) or by alternating by equation (2) it follows that: ke=ku+(B/S)(ku – kd) 29
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Let’s examine now what will happen with rates, kd, ke and ku, if the level of use of financial leverage is increased which is measured by the increasing ratio of B/S. Using the net operating income approach, with the assumed examples 1 and 2, which have been previous explained, we realize that the required return on levered equity ke increases with the degree of utilization of financial leverage (Van Horne, 1997). Implication of this approach is that the capital structure does not affect the total value of the company. A key prerequisite for such a model, according to Van Horne (1997) is that the overall capitalization rate ku is constant, regardless of the degree of use of financial leverage. The market therefore performs capitalization of value of the company as a whole and, as a consequence the partition of capital structure on a long debt and equity is irrelevant. The increasing use of debt in this case is equal to the growth of required return on levered equity ke. Thus, with the increase in the level of use of financial leverage every company becomes riskier (Van Horne, 1997). Investors in this case punish the shares of these companies in a way that they require more returns, by directly following the growth of the ratio of debt and equity. As long as the level of kd is constant, ke is a linear function of debt to equity ratio which is clearly evident from equation (2). Given that financial leverage cannot change total cost of capital of the company ku, net operating income approach assumes that there is no optimal capital structure (Van Horne, 1997). Namely, we obtained the result that the net operating income (EBIT) is fully independent of the structure of capital, apropos capital structure does not affect EBIT=NOI. We will now consider the behavioral model Modigliani – Miller in support of the independence of the total value of the company and the cost of capital from the capital structure (Van Horne, 1997). Modigliani and Miller offer a behavioral justification for the constant cost of capital ku and through all the extents of use of financial leverage while claiming that net operating income approach explains the relationship between the cost of capital and the use of financial leverage (Van Horne, 1997). Their attitude is based on the fact that no matter how we divide the capital structure between debt and equity, the result is a constant value, and the preservation of value for investors exists. Thus, the total investment value of a company depends on its profitability and risk and remains unchanged in terms of the relative change in the financial capitalization (Van Horne, 1997), or the changes in the ratio of debt and equity. The attitude of (MM) is actually based on the fact that investors are able to substitute the use of "personal" financial leverage with "company" financial leverage, thereby forming a response to any capital structure that the company can form (Van Horne, 1997). A previous attitude rests on the fact that the company is unable to do anything for its shareholders what they themselves have not been able to do, in terms of creating financial leverage, so the changes in capital structure do not have valuable sense under conditions of perfect capital market, which (MM) implies (Van Horne, 1997). Thus, the two companies that are "identical in all respects" except in the structure of capital, must be equal in the total value. If this is not fulfilled, there is the possibility of arbitration which will lead to these two companies being sold on the market with equal total values (Van Horne, 1997). Thus, from what has previously been written, it is possible to conclude that company managers can shape the capital structure on the basis of their own preferences, and that it will not affect the operating income of the company, however it will definitely affect the net profit which depends on the level of interest that the company must periodically pay to its creditors, indirectly confirming hypothesis H1. The further methodological approach is applied to two case studies on companies such as Puma and Adidas as two of the world's leading manufacturers of sports equipment, where on the basis of available data from the statements of financial position and the income statements, the net profits of these companies are analyzed, respectively. Then a comparative analysis will be made of the results obtained for the net profits of the two companies that will provide an insight in their financial performance that will be presented in the account examples topic of this paper. 30
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Net profit account examples over companies such as Puma and Adidas Given that Puma company's EBIT in 2015 was equal to €96.3 million and that Adidas company's EBIT in 2015 was equal to €1059 million, and that the financial results based on the interest collected and paid were equal to €11.2 million and €21million respectively, the results obtained for EBT, from the income statements of these two companies were €85 million and €1039 million. So, for Puma Company in 2015 it was enough to keep its EBIT at €11.2 million to reach the financial break even and neutralize not only the operational, but financial costs as well. Similarly, in 2015 Adidas Company would reach the financial break even in the case where its EBIT is €21 million. Summarized comparative financial data of the companies in 2015 are presented in table at the end of this paper. Given that both companies achieved substantially higher values of EBIT, they could quite simply serve their due obligations because of financial arrangements. Given that both companies are levered and pay interest on the basis of financial agreements, from the previously analyzed facts, the influence on systemic risk measured with coefficient beta is clear. Given the fact that the company's EBIT in 2014 was equal to €128.0 million (Puma) and that the company's EBIT in 2014 was equal to €883 million (Adidas) and that the financial results based on the interest collected and paid were equal to €6.2 million and €48million respectively, the results obtained for EBT from income statements of these two companies were €121.8 million and €835 million. Summarized comparative financial data of the companies in 2014 are presented in table 2 at the end of the paper. So, it is clear that Puma Company had better financial results in 2014 than in 2015. In contrast, Adidas improved its financial position in 2015 in comparison to 2014. Net profit of Puma Company in 2015 (from income statement) was €61.7 million while net profit of Adidas Company was €640 million. Comparative analysis of the net profits of these two companies may be shown as a percentage of net profit in relation to total sales, which was 1.82% of Puma Company, and 3.78% of Adidas Company, which shows that Adidas made two times more net profit compared to total sales of Puma Company. The ratio of EBIT in accordance with the total sales of Puma Company was 2.84%, while this amount was 6.26% of Adidas Company, which practically means that Adidas Company realizes significantly lower operating costs of Puma Company. The ratio of EBIT and financial expenses (interest) of Puma Company was 8.6, while of Adidas company was 50.43, (see table 1.) and therefore the interest coverage with operating income of Adidas Company is much higher than the indicator of interest coverage of Puma. Net profit of Puma Company in 2014 (from income statement) was €84.8 million while the net profit of Adidas Company was €496 million. Comparative analysis of the net profits of these two companies may be shown as a percentage of net profit in relation to total sales which was 2.85% of Puma Company and 3.41% of Adidas, which shows that Adidas implemented slightly more net profit compared to total sales of Puma Company. The ratio of EBIT in accordance with the total sales of Puma company was 4.31%, while this amount of Adidas company was 6.08%, which practically means that Adidas did not realize significantly lower operating costs in comparison to Puma in 2014. The ratio of EBIT and financial expenses (interest) of Puma Company was 20.65, while of Adidas Company it was 18.4, (see table 2.) and therefore the interest coverage with operating income of Puma is higher than the indicator of interest coverage of Adidas in 2014. It is obvious from the income statement that Puma has a significantly lower indicator of TIE (times interest earned) in 2015 when it was 8.6, compared to 2014 when it was 20.65, which is due not only to increased financial expenses, but also to a lower level of EBIT (earnings before interest and taxes), 31
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therefore, the net profit level was lower in 2015 when it amounted to €61.7 million compared to 2014 when it amounted to €84.8 million. The opposite happened in the Adidas Company, where the TIE indicator in 2015 was 50.4 and it was significantly higher compared to 2014 when it was 18.4, which is due not only to the higher level of EBIT, but also to the more favorable level of financial result achieved by a higher level of financial income in 2015 (see appendix).
CONCLUSION This paper discusses the impact of behavioral finance on the decisions of managers and investors. It specifically explains the influence of certain psychological factors on the manager's capital structure decision-making process. These psychological factors can affect the financial part of income statement, as confirmed in this paper. Namely, the biases and personal beliefs of the managers and investors which are defined in literature as overconfidence and optimism lead to the fact that, managers of the companies in particular, form the capital structure of the companies on the basis of their own affinities, thereby directly affecting not only the level of risk of shares of their companies, but also the profit of those companies, along with the influence on the moving of financial breakeven which is discussed in the financial section of the income statement. Thus, some aspects of psychology of managers significantly affect not only the capital structure, but also the company's profitability and level of risk, thus confirming result that decisions about the capital structure change can affect the changing of the net profit. Therefore, financial leverage definitely makes sense when a company invests borrowed funds at a higher rate of return than the interest rate on debt, in which case managers have correctly estimated the level of net sales and cost of sales that generate a higher level of EBIT and net profit (as shown in the case of Adidas where the TIE indicator is growing) when the company actually has "healthy" business operations. In this case, the company generates a higher ROE (return on equity), which, along with the tax advantage of financial leverage, where the interest on the debt is not taxed, brings increased benefits to the company and shareholders. However, financial leverage can also cause serious problems in the event of a company facing poor business conditions such as a lower volume of net sales and a higher level of costs than expected, when the EBIT level can drop significantly. Since the cost of debt is usually contractually fixed and repayable, such interest payments can impose a large financial burden on the company that leads to a loss of net profit (as shown in Puma's case where the TIE indicator decreases). Then, the financial leverage works to the detriment of the company and its shareholders. In general, it can be concluded that companies with higher levels of debt have higher expected returns in the case when the business operations are habitually good, but are also exposed to a higher risk of loss when the business operations are worse than expected. Contrary to this, companies with a low level of debt are less risky, but they in some ways disclaim the possibility of financial leverage in order to increase their ROE (return on equity).
REFERENCES Ackert, L. F. (2014). Traditional and Behavioral Finance: Chapter 2. In H. Kent Baker & Victor Ricciardi (Ed.) Investor Behavior: The Psychology of Financial Planning and Investing (pp. 25-41). Hoboken, NJ: John Wiley & Sons. DOI: 10.1002/9781118813454.ch2 Altman, M. (2014). Behavioral Economics, Thinking Processes, Decision Making, and Investment Behavior: Chapter 3. In H. Kent Baker & V. Ricciardi (Ed.). Investor Behavior Investor Behavior: The Psychology of Financial Planning and Investing. DOI: 10.1002/9781118813454.ch3 32
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Baker, H. K., & Ricciardi, V. (2014). Investor Behavior: An Overview: Chapter 1. In H. K. Baker & V. Ricciardi (Ed.) Investor Behavior: The Psychology of Financial Planning and Investing (pp. 3-24). Hoboken, NJ: John Wiley & Sons. DOI: 10.1002/9781118813454.ch1 Besley, S., & Brigham, E. F. (2015). Poslovne finansije: CFIN. Beograd: Data status. In Serbian. Bilgehan, T. (2014). Psychological biases and the capital structure decisions: a literature review. Theoretical and Applied Economics, XXI (12), 123-142. Bogan,V. L. (2014). Household Investment Decisions: Chapter 5. In H. K. Baker & V. Ricciardi (Ed.) Investor Behavior: The Psychology of Financial Planning and Investing (pp. 83-98). Hoboken, NJ: John Wiley & Sons. DOI: 10.1002/9781118813454.ch5 Fairchild, R. (2014). Emotions in the Financial Markets: Chapter 19. In H. K. Baker & V. Ricciardi (Ed.) Investor Behavior: The Psychology of Financial Planning and Investing (pp. 347-364). Hoboken, NJ: John Wiley & Sons. DOI: 10.1002/9781118813454.ch19 Fama, E. (1970). Efficient capital markets: a review of theory and empirical work. Journal of Finance, 25(2), 383417. DOI: 10.2307/2325486 Farrel, J. (2014). Demographic and Socioeconomic factors of Investors: Chapter 7. In H. K. Baker & V. Ricciardi (Ed.) Investor Behavior: The Psychology of Financial Planning and Investing (pp. 117-134). Hoboken, NJ: John Wiley & Sons. DOI: 10.1002/9781118813454.ch7 Fernández, P. (2003). Levered and Unlevered Beta. Retrieved November 1, 2016, from http://www.iese.edu/ research/pdfs/DI-0488-E.pdf Fisher, G. S. (2014). Advising the Behavioral Investor-Lessons from the Real World: Chapter 15. In H. K. Baker & V. Ricciardi (Ed.) Investor Behavior: The Psychology of Financial Planning and Investing (pp. 265-283). Hoboken, NJ: John Wiley & Sons. DOI: 10.1002/9781118813454.ch15 Hackbarth, D. (2008). Managerial Traits and Capital Structure Decisions. Journal of Financial and Quantitative Analysis, 43(4), 843-881. DOI: 10.2139/ssrn.362740 Jensen, M., & Meckling, W. (1976). The Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure. Journal of Financial Economics, 3 (1976), 305-360. Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291. DOI: 10.2307/1914185 Kramer, L. A. (2014). Human Psychology and Market Seasonality: Chapter 20. In H. K. Baker & V. Ricciardi (Ed.) Investor Behavior: The Psychology of Financial Planning and Investing (pp. 365-383). Hoboken, NJ: John Wiley & Sons. DOI: 10.1002/9781118813454.ch20 Mankiw, G. N. (2008). Principi ekonomije. Beograd: Ekonomski fakultet. In Serbian. Mansour, W., & Ilassi, M. (2014). The Effect of religion on Financial and Investing Decision: Chapter 8. In H. K. Baker & V. Ricciardi (Ed.) Investor Behavior: The Psychology of Financial Planning and Investing. Hoboken, NJ: John Wiley & Sons. DOI: 10.1002/9781118813454.ch8 Markowitz, H. (1959). Portfolio Selection: Efficient Diversification of Investments. Yale University Press. Retrieved from http://www.jstor.org/stable/j.ctt1bh4c8h Ricciardi, V., & Rice, D. (2014). Risk Perception and risk Tolerance: Chapter 18. In H. K. Baker & V. Ricciardi (Ed.) Investor Behavior: The Psychology of Financial Planning and Investing (pp. 327-345). Hoboken, NJ: John Wiley & Sons. DOI: 10.1002/9781118813454.ch18 Ritter,J. R. (1988). The Buying and Selling behaviour of Individual Investors at the Turn of the Year. The Journal of Finance 43(3), 701-717. DOI: 10.2307/2328193 Sharpe, W. F., Alexander, G. J., & Bailey, J. V. (1995). Investments. Englewood Cliffs, New Jersey: Prentice Hall. Stanišić, N., Radojević, T., Mizdraković, V., Stanić, N. (2012). Capital efficiency analysis of Serbian companies. Singidunum Journal of Applied Sciences, 9(2), 41-49. Van Horne, J. C. (1997). Financial management and policy. Upper Saddle River, NJ: Prentice Hall. Žigić, D., Hadžić, M. (2012). The process of risk management in financial business. Singidunum Journal of Applied Sciences, 9(2), 33-40.
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Xiao,J. J. (2014). Money and Happiness - Implications for Investor Behavior: Chapter 9. In H. K. Baker & V. Ricciardi (Ed.) Investor Behavior: The Psychology of Financial Planning and Investing (pp. 153-169). Hoboken, NJ: John Wiley & Sons. DOI: 10.1002/9781118813454.ch9
APPENDIX
Source:http://about.puma.com/damfiles/default/investor-relations/financial-reports/en/2015/GB_2015_ENG_Final_links_low-res-8932dbc11383cd85124e1ba63d86b5cc.pdf
34
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35
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Source: http://www.adidas-group.com/media/filer_public/e9/73/e973acf3-f889-43e5-b3c0-bc870d53b964/2015_gb_en.pdf
Puma (2015)
Adidas (2015)
EBIT (millions €)
96.3
1059
interest (millions €)
11.2
21
85
1039
net profit (millions €)
61.7
640
net profit/total sale (%)
1.82
3.78
EBIT/total sale (%)
2.84
6.26
EBIT/interest
8.6
50.43
EBT (millions €)
Table 1. Comparative financial data of the companies in 2015 Source: Author
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Puma (2014)
Adidas (2014)
128.0
883
6.2
48
EBT(millions €)
121.8
835
net profit (millions €)
84.8
496
net profit/total sale (%)
2.85
3.41
EBIT/total sale (%)
4.31
6.08
EBIT/interest
20.65
18.4
EBIT(millions €) interest(millions €)
Table 2. Comparative financial data of the companies in 2014 Source: Author
UTICAJ ODREĐENIH PSIHOLOŠKIH FAKTORA INVESTITORA I MENADŽERA NA STRUKTURU KAPITALA Rezime: Psihologija predstavlja osnovni uslov nastanka disciplina kao što su bihejvioralne finansije i bihejvioralna ekonomija. Ona je doprinela boljem razumevanju ponašanja ekonomskih aktera pod uslovima rizika koji proističu iz nesavršenosti kognitivnih sposobnosti ljudskih bića. Samim tim došlo se do zaključka da je potrebno menjati ekonomske modele zasnovane na matematičkim zakonitostima u korist deskriptivnih modela koji uvažavaju ograničene kognitivne sposobnosti ljudskog uma. Najčešće odluke koje se izučavaju u oblasti bihejvioralnih finansija jesu odluke o strukturi kapitala kompanija. Metodologija u ovom radu se zasniva na pristupu neto operativnog prihoda. Ovim pristupom se analizir afinansijska sekcija bilansa uspeha, koja se odnosi na finansijske troškove kompanija. Za finansijske troškove se pretpostavlja da su fiksni, čime se determiniše tačka indiferencije finansiranja, koja je posledica korišćenja finansijskog leveridža. Osnovni zadatak ovog rada jeste da se kroz dve studije slučaja komparativne analize bilansa uspeha kompanija Puma i Adidas determiniše uticaj psiholoških faktora na strukturu kapitala i nivo zaduženosti, koji će posledično da deluju na dostizanje tačke indiferencije finansiranja kao i na profitabilnost kompanija. Stoga, moguće je izvesti zaključak da psihologija donosilaca finansijskih odluka može da utiče na odluke o strukturi kapitala, što može da bude predmet daljih istraživanja.
Ključne reči: pristrasnosti, lična uverenja, struktura kapitala, finansijski leveridž, beta koeficijent
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EJAE 2018, 15(1): 38-45 ISSN 2406-2588 UDK: 336.27(439) 336.277:336.143 DOI: 10.5937/EJAE15-15684 Original paper/Originalni nauÄ?ni rad
TEST OF A QUADRATIC RELATIONSHIP BETWEEN AGGREGATE OUTPUT AND GOVERNMENT DEBT IN HUNGARY Yu Hsing College of Business, Management and Business Administration, Southeastern Louisiana University, USA
Abstract: The study which applies extended model of aggregate demand and aggregate supply (AD/AS) and uses a quarterly sample during the period from 2001 (Q1) till the last quarter of 2015, has shown that real GDP in Hungary exhibits a bell-shaped quadratic relationship with government debt as a percent of GDP (DY), direct relationship with the real effective exchange rate (ER), stock market price (SP) and the real crude oil price (OP), and an inverse relationship with the real government bond yield (IR) and the expected inflation rate (EI). The critical value of government debt as a percent of GDP is estimated to be 69.22%, which is higher than the EU criterion of 60.00% but less than the threshold of 90.00% proposed by Reinhart and Rogoff (2010).
Article info: Received: November 14, 2017 Correction: January 24, 2018 Accepted: January 24, 2018 Keywords: government debt as a percent of GDP, real exchange rates, i nterest rates, stock prices, oil price.
INTRODUCTION The economy of Hungary shows both - signs of encouragement and concern. Real GDP declined by 6.56% in 2009 due to the global financial crisis, but showed positive growth of 2.00% or more since 2013. Due to the adoption of inflation targeting in 2001, its inflation rate has continued to improve and has been below 3.00% since 2013. The unemployment rate reached the peak of 11.25% in 2010 because of the lingering impact of the global financial crisis, and has displayed improvements as evidenced by the declining unemployment rate of less than 5.00% since 2016. Relatively prudent fiscal policy was demonstrated by the declining government borrowing-to-GDP ratio of less than 3.00% since 2007. The current account balance has exhibited positive values since 2010 and it has been greater than 3.00% since 2015. A matter of major concern is a relatively high government debt as a percent of GDP changing from a low value of 51.70% in 2001 to a high value of 80.667% in 2011. Although it has declined to below 75.00% since 2015, it is still higher than the EU threshold of 60.00%. 38
* E-mail: yhsing@selu.edu
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HSING, Y. TEST OF A QUADRATIC RELATIONSHIP BETWEEN AGGREGATE OUTPUT AND GOVERNMENT DEBT IN HUNGARY
This study examines the effects of the government debt-to-GDP ratio and other related economic variables on real GDP in Hungary. The study applies an extended AD/AS model and estimates a reduced-form equation. An increase in debt-financed government spending tends to shift AD rightward. However, the crowding-out effect of a higher real interest rate, triggered by higher government debt, tends to reduce consumption and investment expenditures and shift AD leftward. Rising government debt might be beneficial to aggregate output, but this needs to be tested empirically. A quadratic form is employed to detect whether a nonlinear relationship between aggregate output and government debt as a percent of GDP may exist. The critical value is the government debt as a percent of GDP corresponding to the maximum value of real output. To the left of the critical value, real output and government debt as a percent of GDP has a direct relationship whereas to the right of the critical value, real output and government debt as a percent of GDP have an inverse relationship. Potential impacts of real currency depreciation and supply shocks are incorporated in the model as well.
LITERATURE REVIEW Whether more government debt may affect aggregate output depends on its potential effect on the real interest rate, which would affect private spending. More government debt-financed spending tends to shift aggregate demand to the right and increase aggregate output in the short run. The positive effect on real output may be cancelled out partially or completely by the negative impact on consumption and investment spending due to a higher real interest rate in the long run. There have been several recent studies examining the effect of expansionary fiscal policy on the real interest rate or aggregate output for Hungary and other related countries. Based on a sample of 31 emerging and advanced countries during 1980-2008, Baldacci and Kumar (2010) show that higher government/public debt and deficit cause the long-term increase of the interest rate, including the sovereign bond yield. The specific magnitude depends on initial structural, fiscal and institutional conditions. Using a sample of 54 countries including Hungary during 1990-2009, López, Riquelme and Muñoz (2011) reveal that a 1.00% rise in the government deficit causes the long-term interest rate to rise by 10.00 to 12.00 basis points, and that approximately 40.00% of the change in the long-term interest rate in G7 countries can be explained by fiscal policy. Furthermore, fiscal rules reduce the impact of the deficit on long-term rate, and more credibility of a central bank leads to a lower interest rate due to lower inflation expectations. Based on a sample of G7 countries during 1960-2005, Hauner and Kumar (2011) show that higher government deficits raise the long-term interest rate, but the effect is relatively small and that there is no structural break in interest rate determination during the sample period. Gruber and Kamin (2012) study the effect of expansionary fiscal policy on the long-term rate for OECD countries during 1988-2007. They find that the deterioration of fiscal position raises the U.S. bond yield by 60.00 base points, and the bond yield by a smaller magnitude in other G7 countries. Claeys, Moreno and Suriñach (2012) examine the effect of fiscal policy on the interest rate based on a sample of 46 countries including Hungary during 1990-2005. They indicate that the crowding-out effect is relatively small because1.00% increase in the government debt ratio leads to an increase in the domestic interest rate by 2.00 percentage points at the most. The effect is smaller among OECD or EU countries due to global financial integration and larger in emerging countries due to less financial integration. 39
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HSING, Y. TEST OF A QUADRATIC RELATIONSHIP BETWEEN AGGREGATE OUTPUT AND GOVERNMENT DEBT IN HUNGARY
Ağca and Celasun (2012) reveal that a rising external public debt leads to a higher corporate borrowing cost whereas a higher domestic public debt does not affect corporate borrowing cost in 15 emerging countries including Hungary, and that countries with past default incidents or frail creditor rights tend to have a higher correlation. Aisen and Hauner (2013) investigate the effect of government budget deficits on the interest rate using a sample of 60 advanced and emerging countries including Hungary during 1970-2006. They show that more budget deficits raise the interest rate if a country has high deficits, high domestic government debt, low financial openness, low financial depth, liberalized interest rate, or high domestic financing.
THE MODEL We can express extended AD/AS model (Hsing, 2016, 2017a, 2017b) as follows:
AD = w( IF , GS , GT , IR, SP, ER)
(1)
AS = z ( IF , ER, OP,.EI )
(2)
where AD, IF, GS, GT, IR, SP, ER, AS, OP and EI stand for aggregate demand or aggregate output demanded, the inflation rate, real government spending, real government tax revenue, the real interest rate, the stock market index, the real effective exchange rate, short-run aggregate supply or aggregate output supplied, real oil price per barrel, and the expected inflation rate. By solving aggregate output and IF simultaneously, we can find equilibrium aggregate output (Y) as:
= Y*
f (GS − GT , ER, IR, SP, EOP, EI )
(3)
As investors are more concerned about potential default of government debt, which is an accumulation of the government deficit, we replace GS – GT with the government debt-to-GDP ratio (DY):
Y * = h( DY , ER, IR, SP, OP, EI ) ?
?
–
+
?
(4)
–
The sign beneath each of the independent variables is the hypothesis to be tested. Real depreciation tends to stimulate exports, impair imports, increase import prices and domestic inflation, and reduce capital inflows. Conversely, currency appreciation is expected to impair exports, increase imports, reduce import prices and domestic inflation, and increase capital inflows. The net effect needs to be tested and determined empirically. Previous studies including Hungary and other related countries show different findings. According to Bahmani-Oskooee and Kutan (2008), currency depreciation has a negative effect on real GDP in the short run; Miteza (2006), Kalyoncu, Artan, Tezekici and Ozturk (2008) state its negative impact on real GDP in the long run whereas Nusair (2014) argues that currency depreciation has positive effect on real GDP in the long run. A neutral effect of currency depreciation is determined in the studies run by Bahmani-Oskooee and Kutan ( 2008) 40
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HSING, Y. TEST OF A QUADRATIC RELATIONSHIP BETWEEN AGGREGATE OUTPUT AND GOVERNMENT DEBT IN HUNGARY
A higher real oil price is expected to reduce short-run AS and aggregate output. Nonetheless, a rising real oil price triggered by strong AD tends to increase AD. Therefore, the net impact is unclear (Hamilton, 1996; Kilian, 2014a, 2014b). Figure 1 shows that a nonlinear relationship between aggregate output and the government debtto-GDP ratio may exist. The nonlinear relationship may be described by a quadratic form:
Y * = v( DY , DY 2 , ER, IR, SP, OP, EI ) +
–
?
–
+
?
(5)
–
Figure 1 - Scatter diagram between real GDP and government debt as a percent of GDP (DY) Notes: RGDP is actual real GDP. RGDPF1 and RGDPF2 are the fitted real GDP at the 95.00% interval.
Taking the partial derivative of the dependent variable with respect to DY and setting the equation equal to zero, we find the critical value of government debt as a percent of GDP corresponding to the maximum aggregate output as:
DY * = θ1 / 2θ 2
(6)
where θ1 and θ2 are the estimated coefficients for DY and DY2.
EMPIRICAL RESULTS The sources of the data came from the Eurostat, the Bank of Hungary, and IMF’s International Financial Statistics. Aggregate output in real terms is measured in million forints. This study uses the amount of real GDP instead of the growth rate of real GDP as the dependent variable as equilibrium real GDP instead of the growth rate of real GDP is derived from the AD/AS model. General government debt is measured as a percent of GDP. A higher real effective exchange rate suggests real appreciation of the forint. The real interest rate is represented by the difference between the government bond yield and 41
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the expected inflation rate. The equity index is selected to represent the stock market index. The real oil price per barrel is calculated as the nominal oil price per barrel adjusted by the CPI and is measured in the forint. The lagged real oil price is used in order to consider potential impact lag of the real oil price on aggregate output and to reduce the problems caused by a high degree of multicollinearity among independent variables. These problems include the change in the signs and insignificance of the coefficients. The mean value of the past four inflation rates is used to represent the expected inflation rate. The sample has a total of 60.00 observations ranging from 2001.Q1 to 2015.Q4. The data for the government bond yield before 2001.Q1 are unavailable. Descriptive statistics are presented in the Appendix. The augmented Dickey-Fuller test reveals that each time series variable has a unit root in level and is stationary in first difference. To test whether these time series variables have a long-term stable relationship, the augmented Dickey-Fuller unit root test of the regression residual reveals that the critical value of -3.55 is less than the test statistics of -3.88 in absolute values. As a result, these variables are cointegrated. Table 1 presents empirical results. The exponential GARCH model is used in empirical work because it can detect and correct potential autoregressive conditional heteroscedasticity and because it has less restrictions in the variance equation. Approximately 87.84% change in the dependent variable can be explained by theexogenous variables. All the estimated coefficients are found to be significant at the 1.00% or 2.50% level. Aggregate output has a bell-shaped quadratic relationship with DY, a positive relationship with ER, SP and the lagged OP, and a negative relationship with IR and EI. The critical value based on equation (5) is estimated to be 69.22%, suggesting that aggregate output and DY have a direct relationship when the government debt-to-GDP ratio is less than 69.22% and an inverse relationship when the ratio is higher than 69.22%. The critical value of 69.22% is less than the 90.00% threshold proposed by Reinhart and Rogoff (2010) but higher than the EU criterion of 60.00%. Variable
Coefficient
z-Statistic
-5523.01
-20.34
859.48
1935.51
DY^2
-6.21
-437.91
ER
12.16
2.90
IR
-172.96
-6.37
SP
22.28
26.84
OPt-1
0.01
2.47
-365.83
-16.26
C DY
EI R-squared
0.88
Adjusted R-squared
0.86
Akaike info criterion
15.14
Schwarz criterion
15.49
Methodology
EGARCH
Sample period
2001.Q1 – 2015.Q4
Number of observations
60.00
Mean absolute percent error
1.60%
Table 1- Estimated regression of aggregate output for Hungary 42
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Notes: As the real interest rate, the expected inflation rate and the binary variables have zero or negative values, these 3 variables cannot be transformed into the log scale. The choice of the linear form is mainly to make the interpretation of the results easier, because ,except for the intercept binary variable, all the estimated coefficients are the slopes. The variables are defined as follows: Y = aggregate output, DY = the government debt-to-GDP ratio, ER = the real effective exchange rate, IR = the real interest rate, SP = the stock price, OP = the real oil price, and EI = the expected inflation rate. The positive and significant coefficient of ER implies that real appreciation raises aggregate output and that the positive impacts of real appreciation such as potential lower inflation and capital inflows dominate the negative effect such as less export. The positive and significant coefficient of SP implies that rising stock prices increase household wealth, consumption spending, and aggregate demand. Several different regressions were estimated. When the log scale is used except for the real government bond yield, the expected inflation rate and the binary variable with zero or negative values, the value of R-squared is 90.41%, but the positive coefficients of ER and OP become insignificant at the 10.00% level. The critical value corresponding to the maximum aggregate output is estimated to be 68.62%, which is slightly less than the 69.22% when the linear form is chosen in Table 1. The interactive and intercept binary variables are applied to analyze the relationship between aggregate output and DY. A binary variable having a value of 0 during 2001.Q1 – 2008.Q4 and 1 during 2009.Q1 – 2015.Q4 is created. The value of R-squared is calculated to be 0.88. The positive coefficient during 2001.Q1 – 2008.Q4 and the negative coefficient during 2009.Q1 – 2015.Q4 are both significant at the 1.00% level. On the other hand, the estimated positive coefficient of SP becomes insignificant at the 10.00% level.
CONCLUSIONS This paper has studied the impact of government debt and other variables on aggregate output. When government debt as a percent of GDP is less than 69.22%, the relationship is positive whereas when government debt as a percent of GDP is greater than 69.22%, the relationship is negative. In addition, a higher ER, a lower IR, a higher SP, a higher OP or a lower EI increases aggregate output. In comparison, the 69.22% critical value for Hungary is slightly higher than the 60% threshold for the EU countries but lower than the 90% threshold proposed by Reinhart and Rogoff (2010). In comparison, the negative relationship between government debt as a percent of GDP and real GDP in this article are consistent with previous findings by Baldacci and Kumar (2010), Hauner and Kumar (2011), López, Riquelme and Muñoz (2011), Gruber and Kamin (2012), Ağca and Celasun (2012),Claeys, Moreno and Suriñach (2012) and Aisen and Hauner (2013) when government debt as a percent of GDP is greater than the critical value of 69.22%. But the results cannot be compared with previous findings when the debt-to-GDP ratio is less than the critical value of 69.22%, because they do not estimate a critical value of government debt as a percent of GDP. The finding of the critical value of government debt as a percent of GDP for Hungary may provide some insights into the subject for 43
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HSING, Y. TEST OF A QUADRATIC RELATIONSHIP BETWEEN AGGREGATE OUTPUT AND GOVERNMENT DEBT IN HUNGARY
some EU countries such as Greece, Italy, Portugal and Spain with relatively high government debt as a percent of GDP. A case study of each of these countries may find the critical value and provide policymakers guidelines in reviewing and improving fiscal policy. The Hungarian government may need to exercise caution in order to reduce potential negative impacts of a higher Debt-to-GDP ratio on aggregate output. Fortunately, its debt-to-GDP ratio declined from the peak value of 83.70% in 2010.Q2 to 73.50% in 2015.Q4. The declining real effective exchange rate from the peak value of 110.00 in 2008.Q3 to 88.00 in 2015.Q4 would impede aggregate output. In addition, to continue pursuing economic growth, the Hungarian government needs to maintain a lower real cost of borrowing, a stable and strong stock market, and a lower expected inflation rate so that households and firms would have a lower borrowing cost and more wealth to increase consumption and investment expenditures, and short-run aggregate supply would shift rightward, leading to a higher real GDP.
REFERENCES REFERENCES Ağca, Ş., & Celasun, O. (2012). Sovereign debt and corporate borrowing costs in emerging markets. Journal of International Economics, 88(1), 198-208. DOI: 10.1016/j.jinteco.2012.02.009 Aisen, A., & Hauner, D. (2013). Budget deficits and interest rates: a fresh perspective. Applied Economics, 45(17), 2501-2510. DOI: 10.1080/00036846.2012.667557 Bahmani-Oskooee, M., & Kutan, A. M. (2008). Are devaluations contractionary in emerging economies of Eastern Europe? Economic Change and Restructuring, 41(1), 61-74. DOI: 10.1007/s10644-008-9041-9 Baldacci, E., & Kumar, M. S. (2010). Fiscal deficits, public debt, and sovereign bond yields. Washington, DC: International Monetary Fund. Claeys, P., Moreno, R., & Suriñach, J. (2012). Debt, interest rates, and integration of financial markets. Economic Modelling, 29(1), 48-59. DOI: 10.1016/j.econmod.2011.05.009 Gruber, J. W., & Kamin, S. B. (2012). Fiscal positions and government bond yields in OECD. countries. Journal of Money, Credit and Banking, 44(8), 1563-1587. DOI: 10.1111/j.1538-4616.2012.00544.x Hamilton, J. D. (1996). This is What Happened to the Oil Price-Macroeconomy Relationship. Journal of Monetary Economics, 38(2), 215-220. DOI: 10.1016/S0304-3932(96)01282-2 Hauner, D., & Kumar, M. S. (2011). Interest rates and budget deficits revisited-evidence from the G7 countries. Applied Economics, 43(12), 1463-1475. DOI: 10.1080/00036840802600574 Hsing, Y. (2016). Is real depreciation contractionary? The case of South Korea. Economics Bulletin, 36(4), 19511958. DOI: 10.22606/jaef.2016.11002 Hsing, Y. (2017a). Is real depreciation or more government deficit expansionary? The case of Slovenia. South East European Journal of Economics and Business, 12(1), 50-56. DOI: 10.1515/jeb-2017-0005 Hsing, Y. (2017b). Is more government debt or currency depreciation expansionary? The case of Poland. Theoretical and Applied Economics, 3(612), 63-70. Kalyoncu, H., Artan, S., Tezekici, S., & Ozturk, I. (2008). Currency devaluation and output growth: an empirical evidence from OECD. countries. International Research Journal of Finance and Economics, 14(2), 232-238. Kilian, L. (2008). The economic effects of energy price shocks. Journal of Economic Literature, 46(4), 871-909. DOI: 10.1257/jel.46.4.871 Kilian, L. (2009). Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market. American Economic Review, 99(3): 1053-69. DOI: 10.1257/aer.99.3.1053 44
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López, E., Riquelme, V., & Muñoz, E. (2011). Long-term interest rate and fiscal policy. Documentos de Trabajo, 633(1). Santiago: Banco Central de Chile. Miteza, I. (2006). Devaluation and output in five transition economies: a panel cointegration approach of Poland, Hungary, Czech Republic, Slovakia and Romania, 1993-2000. Applied Econometrics and International Development, 6(1), 77-86. Nusair, S. A. (2014). Are devaluations expansionary or contractionary in transition economies? Applied Economics Quarterly, 60(3), 215-251. DOI: 10.3790/aeq.60.3.215 Reinhart, C. M., & Rogoff, K. S. (2010). Debt and growth revisited. MPRA Paper 24376. Retrieved September 16, 2017, from https://mpra.ub.uni-muenchen.de/24376/1/MPRA_paper_24376.pdf
APPENDIX - DESCRIPTIVE STATISTICS Y
DY
ER
IR
SP
OPt-1
EI
Mean
24668.92
69.24
93.44
2.09
76.17
22810.85
4.90
Median
24861.95
66.9
94.11
2.52
82.02
22488.21
4.93
Maximum
27051.60
83.70
110.39
5.15
124.50
36181.17
10.26
Minimum
20752.10
51.70
72.93
-2.50
28.37
10742.01
-0.49
Std. Dev.
1620.36
10.38
7.59
1.97
26.60
7428.73
2.56
Skewness
-0.85
-0.23
-0.45
-0.67
-0.44
0.07
-0.15
Kurtosis
2.91
1.59
3.13
2.64
2.02
1.69
3.20
Jarque-Bera
7.22
5.47
2.05
4.87
4.37
4.33
0.25
Probability
0.03
0.07
0.36
0.9
0.11
0.11
0.88
1480135.00
4154.20
5606.41
125.12
4570.38
1368651.00
294.20
Sum Sq. Dev.
1.55E+08
6359.70
3400.99
229.69
41732.73
3.26E+09
386.57
Observations
60.00
60.00
60.00
60.00
60.00
60.00
60.00
Sum
ISPITIVANJE KVADRATNE RELACIJE AGREGATNOG AUTPUTA I DRŽAVNOG DUGA U MAĐARSKOJ Rezime: Primenom proširenog modela agregatne tražnje i agregatne ponude (AD / AS) i korišćenjem kvartalnog uzorka za period 2001.K1-2015.K4, ova studija pokazuje da stvarni BDP u Mađarskoj ispoljava kvadratnu vezu zvonastog oblika gledano prema državnom dugu koji se iskazuje kao procenat BDP-a, zatim odnos direktne zavisnosti od stvarnog efektivnog deviznog kursa, berzanske cene i stvarne cene nafte; dok je relacija u odnosu na stvarni prinos od državnih obveznica i očekivane stope inflacije inverzna. Procenjuje se da je kritična vrednost državnog duga iskazanog u vidu procenta BDP-a 69.22%, što predstavlja veću vrednost u odnosu na kriterijum EU od 60%, ali znatno manju u odnosu na prag od 90% koji predlažu autori Reinhart i Rogoff (2010).
Ključne reči: javni dug kao procenat BDP-a, realni devizni kurs, kamatna stopa, vrednost akcija, cena nafte. 45
EJAE 2018, 15(1): 46-82 ISSN 2406-2588 UDK: 316.32 321.7 339.97 DOI: 10.5937/EJAE15-16414 Original paper/Originalni nauÄ?ni rad
A SIMULTANEOUS EQUATION MODEL OF GLOBALIZATION, CORRUPTION, DEMOCRACY, HUMAN DEVELOPMENT AND SOCIAL PROGRESS Sudhanshu K. Mishra North-Eastern Hill University, India
Abstract: This study builds a simultaneous equation model that establishes interconnections among the measures of globalization, measures of democracy, human development, corruption perception index and per capita income, which in turn jointly influence social progress. The model has eleven equations in which the response variables and the predictor variables are log-linearly related. The empirical data used for estimation of the model pertain to the period 2006-2016 for 116 countries distributed over all the continents. The model has been estimated by the conventional Two-Stage Least Squares (2-SLS) and alternatively by a modified 2-SLS in which, at the second stage, Shapley value regression has been used to ameliorate the detrimental effects of collinearity among the predictor variables. The modified 2-SLS outperforms the conventional 2-SLS. The study finds that globalization positively influences and is influenced by democracy, human development and social capital. Globalization reduces corrupt practices and integrity promotes globalization. Democracy, social capital, human development and globalization affect social progress positively. It has also been found that trans-border personal connection, cultural proximity, democracy and social capital are elastic with respect to their predictors.
Article info: Received: February 1, 2018 Correction: February 7, 2018 Accepted: February 7, 2018
Keywords: Globalization, democracy, social progress, simultaneous equation model, Shapley value regression.
INTRODUCTION This study investigates into the debated inter-relationships among globalization, political regimes, corruption, human development and social progress in a simultaneous model framework. It recognizes that a school of scholars holds that globalization and democracy uphold each other and they jointly hold back corruption, endorse human development and finally promote social progress. Globalization also positively responds to democratic practices, human development and strong social capital. This article has been prepublished online. The prepublished version of the article can be accessed through the following link: https://mpra.ub.uni-muenchen.de/84213/
46
* E-mail: mishrasknehu@hotmail.com
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MISHRA, S. K. A SIMULTANEOUS EQUATION MODEL OF GLOBALIZATION, CORRUPTION, DEMOCRACY,HUMAN DEVELOPMENT AND SOCIAL PROGRESS
Nevertheless, it is acknowledged that the opponent school of scholars relate globalization to limiting the scope of democracy, promoting corruption, misaligning human and non-human capital with globalization sponsored development and consequently thwarting social progress. In what follows, an attempt has been made to put together the views and most important empirical findings of various scholars and drawing upon the same build as well as estimate a simultaneous equation model that may reveal the structural relationships among the said variables.
A LITERATURE SURVEY ON RELATIONSHIPS AMONG GLOBALIZATION WITH OTHER SOCIO-ECONOMIC VARIABLES In this section we put together the views and empirical findings of various scholars on the relationship between globalization, political regime, human capital, social capital and social progress as visualized by Stiglitz et al. (2009) and Social Progress Imperative. Human capital is summarily measured by the human development index and corruption perception index has been used as a prototype measure of social capital.
Relationship between Globalization and Political Regime Numerous studies have been carried out to investigate into the relationship between regime type (democracy to authoritarian) and globalization with the causal arrow indicating towards either direction. A good number of studies investigate into the relationship between regime type and development (Przeworski and Limongi, 1993) that cluster around the Lee thesis and in view of globalization being considered as a means to development have a discernible bearing on the relationship between regime type and globalization. Among such studies, Huntington and Jorge (1975), Marsh (1979), Weede (1983), Landau (1986), Kohli (1986) and Helliwell (1992) provide empirical evidences that indicate negative to inconsequential impact of democracy (or positive to insignificant impact of authoritarianism) on development. On the contrary, Dick (1974), Kormendi and Meguire (1985), Pourgerami (1988, 1991), Scully (1988; 1992), Barro (1989), Remmer (1990), Leblang (1997), Halperin et al. (2005) and Knutsen (2008a; 2008b; 2010) provide empirical evidences of a favourable impact of democracy (or unfavourable impact of authoritarianism) on development. A number of studies assert that there is no direct relationship between regime type and development. There are intermediate factors such as the (already) attained development level (Przeworski, 1966; Adelman and Morris, 1967), type (whether bureaucratic or traditional) of authoritarian regime (Sloan and Tedin, 1987), attributes and inclination of the authoritarian ruler (Barro, 1997), regional factors with the historical, institutional, cultural and geographic specificities that vary over the continents (Grier and Tullock, 1989), degree of entrenchment of the political elite class and political competition that they face (Acemoglu and Robinson, 2006a), etc that modify the relationship between regime type and development and, therefore, one cannot relate them unconditionally. A number of empirical studies establish connection between the regime type and the factors determining development. Boix (2003) and Knutsen (2007) found a positive impact of democracy on rule of law and consequentially the protection of property rights. Knutsen (2008b) and Hegre and Fjelde (2008) found that democratic governments perform better on control of corruption. Rodrik (1998) found that democracy helps increase real wages of workers leading to increase in consumption, which may have efficiency-promoting effects leading to development (Myrdal ,1972: p. 54). Sen (1999) stresses on freedom and social progress, rather than economic development, and favours democracy for that reason. 47
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A number of studies assess the impact of trade and development on the regime type. Schumpeter (1950), Lipset (1959) and Hayek (1960) hold that free trade and capital flows foster demands for democracy via (and also in favour of) enhancement of the efficiency of resource allocation and consequent economic development. Eichengreen and Lebang (2006) find a bi-directional causality that mutually re-enforce democracy and globalization. Kollias and Paleologou (2016) find a positive impact of globalization on democracy, although it is not true for the countries of all income groups. Globalization hardly promotes democracy in poor economies. Acemoglu and Robinson (2006b) shows that key democratizing forces associated with trade openness depend on country’s relative factor endowment. Rudra (2005) observes that economic globalization leads to improvements in democracy only if safety nets are used simultaneously as a strategy for providing stability and building political support. Milner and Mukherjee (2009) find that democracy fosters trade and capital account liberalization, but not all the aspects of globalization. Li and Reuveny (2003) find that different constituents of globalization affect democracy in different manner not conformal to each other. Haffoudhi and Bellakhal (2016) find that the efforts of globalization in poor countries suffering from famines, chronic under-nutrition, poor state of human development, low efficiency and poor state of resource allocation would not promote democracy. There are a number of studies that point out undesirable effects of globalization on the political sphere of less developed countries. Schwartzman (1998) observes that globalization and democracy reinforce each other to facilitate the fulfilment of the interest of the dominant world economic system. Sobhan (2003) observes that the countries with weak democratic institutions and undiversified or externally dependent economies are often exploited. Turyahikayo (2014) observes that globalization has been used as a tool by the established democracies/economies for exploitation of cheap labour and dumping the industrial waste in poor countries. Steiner (2015) observes that globalization may have a negative effect on public participation in the political domain. Stein (2016) opines that a sovereign state system, democratic governments, and an integrated global marketplace cannot coexist. It is most likely therefore that globalization will affect the sovereignty of less developed countries adversely.
Relationship between Globalization and Non-Material Capital Scholars are divided on the relationship of globalization with human development. Sirageldin (2002) recognises the complex character of human development which is an outcome of the historical process of symbolic cultural evolution. Globalization may interfere with the social process. The Human Development Report 1999 took note of the adverse consequences of unregulated globalization on human development and recommended stronger global governance (Naqvi, 2002). Rabbanee et al. (2010) observe that while globalisation has often gone along with privatization and reduction of government help to the poor, it affects human development adversely. Huynen et al. (2005) analyse various pathways in which globalization may affect public health and highlights the need to regulate the impacts of globalization. Ball (2005) observes that globalization romanticizes ‘the private’ and demonizes the public welfare provision for the poor. Yang (2006) laments the pervasive ill effects of privatization of education in China. Globalization has affected the education sector to turn against the poor. As Lake and Baum (2001) point out, democracy is often instrumental in looking into the interest of the weaker section through public provisioning. Globalization may affect government aided public provisioning and affect social welfare, especially of the deprived class, adversely. Diametrically opposite to this, Tsai 48
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MISHRA, S. K.  A SIMULTANEOUS EQUATION MODEL OF GLOBALIZATION, CORRUPTION, DEMOCRACY,HUMAN DEVELOPMENT AND SOCIAL PROGRESS
(2006) finds that globalization affects human development/welfare positively. Sapkota (2011) studies a large number of countries and finds that all components of globalization (economic, social and political) have positive and statistically significant effect on human development. There are many research studies that observe the impact of globalization on human development conditional or partial. For Sabi (2007) impact of globalization on human development is not appreciable in developing countries at low or low-middle income groups. Figueroa (2014) finds that in Central and South American countries overall globalization as well as social and political components of it has positive effect, but economic globalization has a negative effect on human development. Asongu (2012) studies African countries and finds that while trade globalization improves human development, financial globalization has the opposite effect. Lee and Vivarelli (2006) hold that levels of economic and human development are crucially important to determine the direction and the scope to globalization forces. Along with the human capital, the social capital (Durkheim, 1997; Hanifan, 1916) is crucially important for development. Social capital not only generates internal economies, it also attracts material capital from abroad and helps in globalization. It is well acknowledged that corruption and malpractices erode away social capital and discourage inflow of foreign capital while a strong legal framework to check corruption enhances the inflow of foreign capital (Bayer and Alakbarov, 2016). Knutsen (2008b) and Hegre and Fjelde (2008) found that democratic governments perform better on control of corruption. This control may support globalization. Lalountas et al. (2011) observe that globalization is a powerful weapon against corruption only for middle and high income countries, while for low income countries globalization has no significant impact on corruption. Das and DiRienzo (2009) find a nonlinear relationship between globalization and corruption. The effect of globalization on corruption is dependent on the level of globalization. The highest corruption levels are realized at moderate or transitioning levels of globalization. Globalization has brought government officials and international businesses and trade agents into a close relationship and consequentially increased the opportunities for rent-seeking. Eisner (1995), Gould (1991) and Jreisat (1997) argue, therefore, that globalization has increased the opportunity of the use of official position for personal gain. Globalization has also made the detection of corrupt practices more difficult (Leiken, 1997; Elliott, 1997). Ewoh et al. (2013) find that while globalization of assets and capital markets has promoted corruption worldwide, it affects developing nations negatively more than it impacts advanced countries. On the contrary, Ades and Di Tella (1997; 1999), Brunetti and Weder (2003), Treisman (2000) and Herzfeld and Weiss (2003) find that globalization leads to reduction in corruption mainly due to openness. Badinger and Nin (2014) find that globalisation (trade and financial openness) has a negative effect on corruption, which is more pronounced in developing countries, while inequalities increase corruption. Golden (2002) found that in Italy globalization led to decrease in corruption levels.
Relationship between Globalization and Social Progress Globalization necessarily favours a market-based economy because it means economic integration of economies through markets. However, market that caters to the private interest may go against the public interest (Keynes, 1926; Hirsch, 1977; Naqvi, 2002). Singer (1950), Streeten (1998) and Naqvi (2002) argue that globalization may distort structural transformation, induce social tension, aggravate inequalities and erode the social-support systems as well as the established identities and values. Stiglitz et al. (2009) have pointed out that globalization is market-based and only poorly integrated with 49
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MISHRA, S. K. A SIMULTANEOUS EQUATION MODEL OF GLOBALIZATION, CORRUPTION, DEMOCRACY, HUMAN DEVELOPMENT AND SOCIAL PROGRESS
the non-market based social processes, consequently contributing to the weakening of a sense of community. On the contrary, empirically, it has been found that the social progress index responds positively to globalization index (Mishra, 2017). From the literature cited above, it is understandable that there is no direct relationship among globalization, political regimes, corruption, human development and social progress; they are related with each other through a complex network of institutions, historical precedents, resource endowments, socio-economic class structure and a host of other country-specific attributes. However, when such relationships are investigated for a large number of countries together, the country-specific attributes may be cancelled out to a large extents and some clear pattern might be discernible. The present investigation begins with such an optimistic presupposition.
A SIMULTANEOUS EQUATION MODEL OF GLOBALIZATION, NON-MATERIAL CAPITAL, REGIME TYPE AND SOCIAL PROGRESS In the light of the literature cited above as well as the reasoning that guides an empirical research in economics, the present study hypothesizes a bi-directional causal relationship between the two sets of variables; the first set incorporating the measures of economic, social and political globalization and the second set consisting of the measures of political regime type and the measures of non-material capital (human development as a measure of human capital and corruption perception as a measure of social capital). Additionally, the measures of globalization and the measures of non-material capital are directly or indirectly influenced by the economic prosperity of a country (represented by per capita income). Finally, it is visualized that social progress is influenced by globalization, non-material capital, political regime type as well as economic development.
Chart-1. Schematic Flow Diagram of the Model
The schematic flow diagram of the model (which extends the abridged model in Mishra, 2018) is presented in Chart-1. It is a system of eleven structural equations (Chart-2) of which the first ten make three stimulator and/or moderator blocks while the last equation makes the fourth or final impact or response block. The first three blocks formulate how the different aspects of globalization are self-concordant and how they are influenced by non-material capital, political organization and per capita income of a nation. Per capita income is a stimulant to globalization. Globalization and the measures in the third block are mediator or moderators. They conceptualize how different aspects of 50
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MISHRA, S. K. A SIMULTANEOUS EQUATION MODEL OF GLOBALIZATION, CORRUPTION, DEMOCRACY,HUMAN DEVELOPMENT AND SOCIAL PROGRESS
globalization influence as well as are influenced by non-material capital and political organization. The fourth block formulates how globalization, non-material capital, political regime and economic development influence the overall social welfare or social progress of a nation. The eleven structural equations of the model are presented below. Functional form-wise,m it is visu= ( y ) log(α 0 ) + ∑α j log ( x j ) alized that the relationships among the variables are linear in logarithm or log j =1 , where y is a response variable, xj is a stimulus, predictor or explanatory variable, α0 is a constant and αj is the coefficient (which may also be interpreted as a measure of elasticity of y with respect to xj).
E1t = f ( E 2t , S1t , PCL06 , CP06 , HD06 , PCY06 ) eq. ( 01) E 2t = f ( S 2t , S 3t , Pt , EPP06 , PPN 06 , CP06 , HD06 ) eq. ( 02 )
S1t = f ( E1t , S 3t , FOG06 , PCL06 , CVL06,CP06 , HD06 ) eq. ( 03) S 2t = f ( E 2t , PPN 06 , PCL06 , CVL06 , HD06 , PCY06 ) eq. ( 04 ) S 3t = f ( Pt , PCL06 , CP06 , HD06 ) eq. ( 05 ) Pt = f ( E1t , E 2t , S1t , S 2t , S 3t , PCY06 ) eq. ( 06 ) DI16 = f ( E 2t , S1t , S 2t , S 3t , Pt ) eq. ( 07 ) CP16 = f ( E1t , E 2t , S1t , S 2t , S 3t , Pt ) eq. ( 08 ) HD15 = f ( E1t , S 2t , S 3t ) eq. ( 09 )
GI10 = f ( CP06 , HD06 , PCY06 , DI 06 )
eq. (10 )
SP16 = f ( DI16,CP16 , HD15 , GI10 , PCY06 )
eq. (11)
Chart-2. Functional Structure of the Model
The lists of endogenous and predetermined/exogenous variables of the model are presented in Chart 3 and Chart-4. Socio-Economic and political Aspects
Sl. No.
Symbol
Description
1
E1
Economic Globalization Actual economic flows such as trans-border trade, direct Max or Min (2006-14) investment and portfolio investment.
2
E2
Economic Globalization Relaxation of restrictions on trans-border trade as well as Max or Min (2006-14) capital movement by means of taxation, tariff, etc.
3
S1
Social Globalization Max or Min (2006-14)
Trans-border personal contacts such as degree of tourism, telecom traffic, postal interactions, etc.
4
S2
Social Globalization Max or Min (2006-14)
Flow of information. 51
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5
S3
Social Globalization Max or Min (2006-14)
Cultural proximity.
6
P
Political Globalization Max or Min (2006-14)
Trans-national political set up.
7
DI16
Political Regime
8
CP16
Social Capital
Corruption Perception index for 2016.
9
HD15
Human Development
Human Development Index for 2015.
10
GI10
Overall Globalization
Max (2006-2014) or Min (2006-2014)
11
SP16
Social Progress
Democracy Index for 2016.
Social Progress Index for 2016.
Chart-3. List of Endogenous Variables Sl. No.
Symbol
Socio-Economic and political Aspects
Description
1
EPP06
Measure of Democratic Practices
Electoral Process and Pluralism for 2006.
2
FOG06
Measure of Democratic Practices
Functioning of Government for 2006.
3
PPN06
Measure of Democratic Practices
Political Participation for 2006.
4
PCL06
Measure of Democratic Practices
Political Culture for 2006.
5
CVL06
Measure of Democratic Practices
Civil Liberties for 2006.
6
CP06
Social Capital
Corruption Perception index for 2006.
7
HD06
Human Development
Human Development Index for 2005.
8
PCY06
Per Capita Income
Per capita Income (in Int$1000) for 2006
9
DI06
Overall Measure of Democracy
Overall Democracy Index for 2006
Chart-4. List of Exogenous/Predetermined Variables
DATA OR THE MEASURES USED IN THIS STUDY This study covers 116 countries drawn from all the continents including Africa (38 countries), the Americas (23 countries), Asia (26 countries), Europe (26 countries) and Oceania (3 countries). These countries together represent all types of political regime (full democracy to authoritarian), all levels of globalization (very low, to very high) and all levels of economic development, social progress, human capital and social capital. The data used by us are presented in the appendix. Table-A-1 present five measures for democracy (EPPi06, FOGi06, PPNi06, PCLi06, CVLi06 and DI06; i=1 through 116) for the year 2006 as well as the overall measure of democracy DI16 for 2016. Table-A.2 presents corruption perception Index, human development Index and also the overall democracy index for 2006 and 2016. Table-A-2 also contains Social Progress Index (2016), Per Capita Income (2015 – in Int$1000) and overall Globalization Indices scenario-wise (GImax and GImin, explained below). Table-A-3 and Table-A-4 present aspect-wise sub-indices as well as overall globalization indices for the two alternative (optimistic and pessimistic) scenarios explained below.
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Measures of Different Aspects of Globalization with Two Scenarios
As it has been pointed out earlier, KOF(2017) visualizes and constructs three complementary aspects of globalization, economic, social and political, which are merged together by using the Principal Component Analysis to provide the overall index of globalization (Dreher, 2006; Dreher et al., 2008). This study uses those KOF sub-indices for the period 2006-2014 (KOF, 2017), but not the KOF measure of overall globalization. Instead, it uses AEMC overall index (Mishra, 2016b) of globalization based on the principle of ‘almost equi-marginal contribution’ that derives weights differently. Yet, the KOF and AEMC indices of overall globalization are highly correlated (r=0.98). The AEMC index, denote by G, is for 9 years (2006-2014) and 116 countries, i.e. Gi,j ;i =1,2,…,116; j=2006, 2007,…, 2014. The indices of globalization of different countries fluctuate over the years on account of interactions among domestic (country-specific) and international politico-economic forces. Yet, during the study years the fluctuations are within a range. The country-specific ranges are wide or narrow depending on a particular country’s domestic socio-political conditions and the overall acceptance of the globalization policy. Since the objective of this study is to gauge into the overall incidence and effects of globalization (and not into the temporal fluctuations) it is visualized that the range limits would provide better measures than the temporal variations. These limits are given by the maximum and the minimum values taken on by the globalization measures. Correspondingly, two scenarios have been visualized; the one that relates to the lower value (pessimistic scenario) and the other that relates to the upper value (optimistic scenario) of the AEMC globalization index of the country concerned. For every Gij there are the associated sub-indices [E1ij , E2ij , S1ij , S2ij , S3ij and Pij]; j=2006 through 2014 and i=1,2,..., 116. A pessimistic scenario vector is formed by:
E1imin , E 2imin , S1imin , S 2imin , S 3imin , Pi min
(1)
which is associated with
(
)
min min G ij ; j∈ [2006, 2014] , where i 1, 2, …, 1 16 G= = i j
that gives the set of values associated with the lowest extent of globalization experienced by any country during 2006-2014. Similarly, the optimistic scenario vector is:
E1imax , E 2imax , S1imax , S 2imax , S 3imax , Pi max
(2)
associated with
(
)
max G= max G ij ; j∈ [2006, 2014] , where i = 1, 2, …, 1 16 i j
that gives the set of values associated with the highest extent of globalization experienced by any country during 2006-2014. 53
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We have these two scenario vectors of globalization as our endogenous variables (along with other endogenous variables) for estimation of our model. These scenarios influence and are also influenced by other variables such as the measures of democracy, human and social capital, the social progress, etc.
The Measures of Non-Material Capital, democracy, economic development and social Progress This study uses the human development index as a comprehensive measure of human capital. The corruption perception index is uses as a prototype measure of social capital. The Economist Intelligence Unit (EIU) of the Economist Group has published the Democracy Index for 2006 onwards for several years, including 2016. The index is based on 60 indicators grouped in five different categories namely, Electoral process and pluralism (EPP), Functioning of government (FOG), Political participation (PPN), Political culture (PCL) and Civil liberties (CVL), and a linear aggregation of indicators under each category provides a sub-index of democracy in that category or aspect. Subsequently, these five sub-indices of different aspects of democracy are linearly aggregated to yield an overall index (DI or the Index of Democracy). On the basis of the overall score value of DI the political systems of different countries may be classified into full democracies, flawed democracies, hybrid regimes and authoritarian regimes. The present study uses the aspect-wise sub-indices for 2006 and the overall indices of democracy (DI) for 2006 and 2016. Per capita income is a standard measure of potentialities to save and invest, productivity, the level of economic activities as well as the purchasing power of a country and, therefore, by implication, the proclivities to globalization. This study uses per capita income as a promoter of globalization. Yet, per capita income may not be a good measure social welfare. In view of this, the social progress index constructed by Social Progress Imperative may be a better measure. In the present study per capita income has been considered as an input variable while the social progress index has been considered as an output variable.
METHODOLOGICAL ASPECTS OF ESTIMATION OF THE MODEL To estimate the parameters (of the structural equations) of the model this study uses Two-Stage Least Squares (2-SLS) method, which may be considered as instrumental variable method of estimation (Reiersøl, 1945). The 2-SLS uses the Least Squares methods to estimate reduced form as well as structural parameters. However, the very procedure adopted by the 2-SLS - that at the second stage it uses the linear function of all exogenous variables together with some exogenous variables (explicitly) as predictors - renders it susceptible to collinearity, which may have deleterious effects on the standard errors of the estimated parameters, including sign reversal (Smith and Brainard, 1976). To ameliorate the obnoxious effects of collinearity, this study uses the Shapley value regression (Lipovetsky, 2006; Mishra, 2016a) at the second stage of the 2-SLS. Optimization has been done by the Differential Evolution method of global optimization (Storn and Price, 1997).
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FINDINGS
MISHRA, S. K. A SIMULTANEOUS EQUATION MODEL OF GLOBALIZATION, CORRUPTION, DEMOCRACY,HUMAN DEVELOPMENT AND SOCIAL PROGRESS
In what follows, the main findings of estimation of the model using the conventional as well as Shapley value based 2-SLS are presented for both alternative scenarios of globalization. As mentioned earlier, the use of Shapley value regression to estimate the parameters of the model is motivated by the presence of strong collinearity among the predictor variables that may not only render the coefficients estimated by conventional 2-SLS statistically insignificant, they also may bear incorrect sign. The findings presented in the next section corroborate to this concern. The relative performance of Shapley-value based 2-SLS vis-à-vis the conventional 2-SLS in explaining different endogenous (response) variables also is important. To this end, the correlation matrices, presented in appendix Table-A-7 (pessimistic globalization scenario) and Table-A-8 (optimistic globalization scenario), are helpful. The correlation coefficients are: ri j = r ( yi , y j ) , where yi is the ith observed endogenous variable and y j is the jth endogenous variable estimated by conventional 2-SLS. 2 2 It may be noted that ri , i = r ( yi , yi ) is the usual R2 or the coefficient of determination that one reports in the regression results. Similarly, ri j = r ( yi , y j ) , where yi is the ith observed endogenous variable and y j is the jth endogenous variable estimated by Shapley value regression based 2-SLS. The coefficient of correlation between conventional 2-SLS estimated endogenous variable and Shapley value regres sion based 2-SLS is ri j = r ( yi , y j ) . A large value of r ( yi , yi ) indicates that the correlation between the conventional 2-SLS predicted and Shapley value regression based 2-SLS predicted vectors (of the same endogenous variable) is large or, in other words, the conventional 2-SLS and Shapley value re gression based 2-SLS are highly conformal. Throughout it may be seen that rii = r ( yi , yi ) is large for all endogenous variables (Panel-3). Further, r ( yi , yi ) and r ( yi , yi ) are very close to each other for all endogenous variables, although the latter is somewhat smaller than the former. This is the cost that one must pay to circumvent the deleterious effects of collinearity. These results confirm that Shapley value regression based 2-SLS will not mislead us.
Estimated Structural Equations for the Pessimistic Scenario of Globalization The reduced form coefficients for the pessimistic scenario of globalization are presented in appendix Table-A-5. Therefore, only the estimated structural equations are presented here. Figures in the 2nd row are standard error of estimates.
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It is observed that in explaining E1 (Actual economic flows such as trans-border trade, direct investment and portfolio investment) most of the predictor variables bear statistically insignificant coefficients. The coefficients that are not statistically different from zero even at 10% level of significance have been underlined. Only HD06 (human development index) has the coefficient significantly different from zero at 10% significance. Relaxation of restrictions on trans-border trade as well as capital movement by means of taxation, tariff, etc (E2) has a negative coefficient (significant at 5%) associated with S2 (flow of information) which is not expected. Similarly, effects of trans-border trade, flow of finance etc (E1) and functioning of the government (FOG) affect S1 (trans-border personal contacts) adversely, which is contrary to expectation. Flow of information (S2) is adversely affected by relaxation of restrictions on trans-border trade and capital movement (E2), cultural proximity (S3) is adversely affected by political culture (PCL), political aspect of globalization (P) is adversely influenced by trans-border flow of goods, services and capital (E1) and so on which are contrary to expectation. In short, the conventional 2-SLS gives the results that are unexpected or contrary to expectation. However, the structural coefficients associated with all predictor variables estimated by the Shapley value based 2-SLS (presented below) are positive as expected and except for a few (viz. FOG in predicting S1 and PPN in predicting S2) all others are significant at 5% or less (1% or even 0.1%). None of the coefficients is statistically insignificant (beyond 10% level of significance). It may be noted that there is no straightforward method to obtain standard error of estimates of the structural coefficients estimated by the Shapley value regression, and hence the Student’s t values as well, which may be used for 56
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testing the maintained hypothesis. This study, therefore, obtains the standard error of estimates of the Shapley value based structural parameters by jackknife resampling (presented in row 2 for every equation) and the associated t values (row 3 for every equation) are based on those standard error of estimates.
Estimated Structural Equations for the Optimistic Scenario of Globalization The reduced form coefficients for the optimistic scenario of globalization are presented in appendix Table-A-6. Here the estimated structural equation coefficients only are presented.
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The highlights of the findings based on the structural coefficients estimated by the conventional 2-SLS are: (i) political culture (PCL) affects E1 (trans-border trade and flow of capital) adversely; (ii) flow of information (s2) affects relaxation of restriction on flow of trans-border trade, capital, etc adversely; (iii) functioning of the government (FOG) affects trans-border personal contacts (S1) adversely; (iv) political culture (PCL) affects cultural proximity (S3) adversely; (v) trans-border flow of information (S2) affects democracy adversely (DI) and (vi) trans-border flow of information (S2) and cultural proximity (S3) affect corruption perception (CP) adversely. These findings are contrary to what one may expect and hence misguiding. However, as in the case of the pessimistic scenario noted earlier, the structural coefficients associated with all predictor variables estimated by the Shapley value based 2-SLS (presented below) are positive as expected and except one (EPP in predicting E2); all others are statistically significant at 5% (or less) level of significance. None of the structural coefficients is statistically insignificant (beyond 10% level of significance). As mentioned before, the standard error of estimates for the estimated structural parameters obtained by Shapley value regression have been worked out by jackknife resampling and the associated t values are based on those standard error of estimates.
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The Sum of Elasticities m
The structural equations of model in this study are all log-linear (or y = α 0 ∏x j in the natural form) j =1 and,mtherefore, αj may be interpreted as the elasticity of y with respect to xj. The sum total of elasticities ( s = ∑a j ) determines the degree of homogeneity of a function. If every xj is multiplies by a constant j =1 (say, λ) then y will be multiplies by λs. The Table-1 below presents the sum of elasticities for different endogenous variables under the alternative procedures of estimation. The sum of elasticities for E1, E2, S2, P, HD15, GI10 and SP16 are all below unity. A 10% increase (λ=1.1) in the present values of their predictors would give rise to less than 10% (or λs;0<s<1) increase in the quantity of those response variables. The elasticity in case of P and GI are only slightly more than 0.5. However, the value of s for S3, DI and CP is greater than unity and, therefore, 10% increase in the present values of their predictors would give rise to greater than 10% (or λs;s>1) increase in the quantity of those endogenous (response) variables. It suggests that CP is elastic and S3 is hyper-elastic (s>5). As to S1 the conventional 2-SLS and αj
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Shapley value based 2-SLS give quite different results. However, in view of better performance of the latter, one may conclude that S1 is elastic (since s for both the scenarios is greater than unity). These results clearly suggest that even if the pace of globalization would be tapering off over time, its impacts on trans-border personal connections (S1), acculturation or cultural proximity, democratization (DI) and social capital (corruption perception, CP) will continue increasing with acceleration. It may suggest that globalization will have more impact on socio-cultural and political spheres than economic sphere. Scenario
Estimator
E1
E2
S1
S2
S3
P
DI16
CP16
HD15
GI10
SP16
Pessimistic
Conventional
0.7063
0.8458
0.6519
0.7609
5.9947
0.0686
2.6742
1.1974
0.9151
0.5790
0.8421
Shapley
0.7757
0.8099
1.2750
0.8226
5.2738
0.2692
1.3885
1.0830
0.6364
0.6310
0.6748
Conventional
0.3063
0.3800
0.8177
0.5881
7.1938
0.2804
1.9922
1.5267
0.8033
0.5126
0.8271
Shapley
0.4720
0.8216
1.3630
0.7564
5.7191
0.2566
1.4415
1.3831
0.7673
0.5377
0.7097
Scenario
Optimistic Scenario
Table-1. Degree of homogeneity or Sum of Elasticities (the Structural Coefficients) for Endogenous Variable
CONCLUDING REMARKS The present study purported to investigate into the relationship among globalization, political regime type, human capital, social capital and social progress. A literature survey suggested differing views of the scholars, supported by arguments as well as empirical findings. Suggestions abound that hinted at bi-directional causality among the variables of interest. The study formulated a simultaneous equation model connecting globalization, political regime type, human capital, corruption, per capita income and the social progress index. The specification of the model depended partly on the literature review and partly on reasoning. As to the structural equations, the endogenous variables were conceived to be connected to the predictor variables in a log-linear form. The model was estimated by the conventional 2-SLS method. It was found that the structural coefficients of the model were poorly estimated by the conventional 2-SLS owing to collinearity among the predictor variables. When the collinearity problem was treated by using the Shapley value regression (at the second stage of 2-SLS) much better and unambiguous results were obtained. All the estimated structural parameters bore the expected sign. Additionally, only a few of them were significant at 10% or 5% while most of them were significant at 1% level of significance. On the ground of the findings, it may be asserted that in predicting globalization FOG, PPN and EPP have been relatively weaker than other two (PCL and CVL) measures of regime type. On the other side, globalization affects democracy, social capital, human capital and social progress positively and in a statistically significant manner. The findings confirm that globalization measures are consistent and conformal among themselves. Globalization positively influences and is influenced by democracy, human development and social capital. Globalization reduces corrupt practices and integrity promotes globalization. Finally, democracy, social capital (integrity) human development and globalization affect social progress positively. It has also been found that trans-border personal connection (S1), cultural proximity (S3) democracy (DI) and social capital (CP) are elastic (with the degree of homogeneity larger than unity) with respect to their predictors. 60
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As a policy consideration, the findings suggest that economic globalization requires more political support, especially in matters of the functioning of the government, political participation by the people or people’s representatives on their behalf and pluralistic political climate. It will curb the practices discouraging economic globalization. Social globalization is likely to follow the suit automatically. In spite of notable and statistically significant findings, the present study has several limitations. First of all, the model does not include many variables (such as physical and financial capital, freedom index, innovation index, income inequalities, etc.) explicitly since it assumes that per capita income and the level of human development incorporate them indirectly. Incorporation of such relevant variables explicitly may shed more light on the relationships studied here. Similarly, institutions are indirectly represented by political regime and corruption perception index. However, many potent measures of social institutions can be included. Corruption is only a minor measure of social capital. The scope of social capital is vast and it requires many more indicators to represent it. As to estimation of the model, it has been accomplished by a method that ignores correlation among the residuals of the endogenous variables across the equations. It directly speaks on the efficiency of estimation. System methods of estimation together with more concern shown to specification of the model in every equation may be the next step to refine the present work.
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Steiner, N.D. (2015). Essays on Globalization and Democracy. D. Phil dissertation, vorgelegt dem Fachbereich 02 – Sozialwissenschaften, Medien und Sport der Johannes Gutenberg Univ., Mainz. https://nilssteiner.com/ wp-content/uploads/2017/01/Dissertation_Steiner_Introduction-only.pdf Stiglitz, J.E., Sen, A. and Fitoussi, J. (2009). The measurement of economic performance and social progress revisited. OFCE - Centre de recherche en économie de Sciences Po, France. Storn, R. and Price, K. (1997). Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359. Streeten, P. (1998). Globalization: threat or opportunity. Pakistan Development Review, 37(4), 51-85. Treisman, D. (2000) The causes of corruption: A cross-national study. Journal of Public Economics 76(3), 399-457. Tsai, M.C. (2006). Does Globalization Affect Human Well-being? Paper presented in XVI ISA World Congress of Sociology (WG06 Social Indicators Session), July 23-29, 2006 in Durban, South Africa. https://pdfs.semanticscholar.org/1720/acc411e5437f6ecf0b6319fb79c50dde08dd.pdf Turyahikayo, E. (2014). The Impact of Globalization on Domestic Political Structures in Established, Transitional and Non-Democracies. Journal of Good Governance and Sustainable Development in Africa, 2(2), 94-109. Weede, E. (1983). The Impact of Democracy on Economic Growth: Some Evidence from Cross-National Analysis. Kyklos, 36(1), 21-39. Yang, R. (2006). The Commodification of Education and Its Effects on Developing Countries: A Focus on China. Journal für Entwicklungpolitik, 22(4), 52-69.
APPENDIX [DATA USED IN THE PRESENT STUDY]
SL
Democracy Democracy Index Index 2006 2016
Dimensions of Democracy – 2006
Country EPP
FOG
PPN
PCL
CVL
DI06
DI16
1
Albania
7.33
5.07
4.44
5.63
7.06
5.91
5.91
2
Argentina
8.75
5.00
5.56
5.63
8.24
6.63
6.96
3
Australia
10.00
8.93
7.78
8.75
10.00
9.09
9.01
4
Austria
9.58
8.21
7.78
8.75
9.12
8.69
8.41
5
Azerbaijan
3.08
0.79
3.33
3.75
5.59
3.31
2.65
6
Burundi
4.42
3.29
3.89
6.25
4.71
4.51
2.40
7
Belgium
9.58
8.21
6.67
6.88
9.41
8.15
7.77
8
Benin
6.83
6.43
3.89
6.88
6.76
6.16
5.67
9
Burkina_Faso
4.00
1.79
2.78
5.63
4.41
3.72
4.70
10
Bulgaria
9.58
5.71
6.67
5.00
8.53
7.10
7.01
11
Bolivia
8.33
5.71
4.44
3.75
7.65
5.98
5.63
12
Brazil
9.58
7.86
4.44
5.63
9.41
7.38
6.90
13
Bhutan
0.08
4.64
1.11
3.75
3.53
2.62
4.93
14
Botswana
9.17
7.86
5.00
6.88
9.12
7.60
7.87
15
C._Afr_Rep
0.42
1.43
1.67
1.88
2.65
1.61
1.61
16
Canada
9.17
9.64
7.78
8.75
10.00
9.07
9.15
17
Switzerland
9.58
9.29
7.78
8.75
9.71
9.02
9.09 65
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66
18
Chile
9.58
8.93
5.00
6.25
9.71
7.89
7.78
19
China
0.00
4.64
2.78
6.25
1.18
2.97
3.14
20
Cote_d'Ivoire
1.25
2.86
3.33
5.63
3.82
3.38
3.81
21
Cameroon
0.92
3.21
2.78
5.63
3.82
3.27
3.46
22
Congo_Rep.
4.58
0.36
2.78
3.75
2.35
2.76
2.91
23
Colombia
9.17
4.36
5.00
4.38
9.12
6.40
6.67
24
Costa_Rica
9.58
8.21
6.11
6.88
9.41
8.04
7.88
25
Cyprus
9.17
6.79
6.67
6.25
9.12
7.60
7.65
26
Germany
9.58
8.57
7.78
8.75
9.41
8.82
8.63
27
Denmark
10.00
9.64
8.89
9.38
9.71
9.52
9.20
28
Domin_Rep
9.17
4.29
3.33
5.63
8.24
6.13
6.67
29
Algeria
2.25
2.21
2.22
5.63
3.53
3.17
3.56
30
Ecuador
7.83
4.29
5.00
3.13
7.94
5.64
5.81
31
Egypt
2.67
3.64
2.78
6.88
3.53
3.90
3.31
32
Spain
9.58
7.86
6.11
8.75
9.41
8.34
8.30
33
Ethiopia
4.00
3.93
5.00
6.25
4.41
4.72
3.60
34
Finland
10.00
10.00
7.78
8.75
9.71
9.25
9.03
35
Fiji
6.50
5.21
3.33
5.00
8.24
5.66
5.64
36
France
9.58
7.50
6.67
7.50
9.12
8.07
7.92
37
Gabon
0.50
3.21
2.22
5.63
2.06
2.72
3.74
38
U.K.
9.58
8.57
5.00
8.13
9.12
8.08
8.36
39
Ghana
7.42
4.64
4.44
4.38
5.88
5.35
6.75
40
Guinea
1.00
0.79
2.22
3.75
2.35
2.02
3.14
41
Gambia
4.00
4.64
4.44
5.63
3.24
4.39
2.91
42
Greece
9.58
7.50
6.67
7.50
9.41
8.13
7.23
43
Guatemala
8.75
6.79
2.78
4.38
7.65
6.07
5.92
44
Guyana
8.33
5.36
4.44
4.38
8.24
6.15
6.25
45
Honduras
8.33
6.43
4.44
5.00
7.06
6.25
5.92
46
Haiti
5.58
3.64
2.78
2.50
6.47
4.19
4.02
47
Hungary
9.58
6.79
5.00
6.88
9.41
7.53
6.72
48
Indonesia
6.92
7.14
5.00
6.25
6.76
6.41
6.97
49
India
9.58
8.21
5.56
5.63
9.41
7.68
7.81
50
Ireland
9.58
8.93
7.78
8.75
10.00
9.01
9.15
51
Iceland
10.00
9.64
8.89
10.00
10.00
9.71
9.50
52
Israel
9.17
6.64
7.78
7.50
5.29
7.28
7.85
53
Italy
9.17
6.43
6.11
8.13
8.82
7.73
7.98
54
Jamaica
9.17
7.14
5.00
6.25
9.12
7.34
7.39
55
Jordan
3.08
3.79
3.89
5.00
3.82
3.92
3.96
56
Japan
9.17
7.86
5.56
8.75
9.41
8.15
7.99
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MISHRA, S. K. A SIMULTANEOUS EQUATION MODEL OF GLOBALIZATION, CORRUPTION, DEMOCRACY,HUMAN DEVELOPMENT AND SOCIAL PROGRESS
57
Kenya
4.33
4.29
5.56
6.25
5.00
5.08
5.33
58
Cambodia
5.58
6.07
2.78
5.00
4.41
4.77
4.27
59
South_Korea
9.58
7.14
7.22
7.50
7.94
7.88
7.92
60
Kuwait
1.33
4.14
1.11
5.63
3.24
3.09
3.85
61
Lebanon
7.92
2.36
6.11
6.25
6.47
5.82
4.86
62
Lesotho
7.92
6.43
4.44
6.25
7.35
6.48
6.59
63
Luxembourg
10.00
9.29
7.78
8.75
9.71
9.10
8.81
64
Morocco
3.50
3.79
2.78
5.63
3.82
3.90
4.77
65
Moldova
9.17
4.29
6.11
5.00
7.94
6.50
6.01
66
Madagascar
5.67
5.71
5.56
6.88
5.29
5.82
5.07
67
Mexico
8.75
6.07
5.00
5.00
8.53
6.67
6.47
68
Mali
8.25
5.71
3.89
5.63
6.47
5.99
5.70
69
Malta
9.17
8.21
6.11
8.75
9.71
8.39
8.39
70
Myanmar
0.00
1.79
0.56
5.63
0.88
1.77
4.20
71
Montenegro
9.17
5.71
5.00
5.63
7.35
6.57
5.72
72
Mongolia
9.17
6.07
3.89
5.63
8.24
6.60
6.62
73
Mauritania
1.83
4.29
2.22
3.13
4.12
3.12
3.96
74
Mauritius
9.17
8.21
5.00
8.13
9.71
8.04
8.28
75
Malawi
6.00
5.00
3.89
4.38
5.59
4.97
5.55
76
Malaysia
6.08
5.71
4.44
7.50
6.18
5.98
6.54
77
Niger
5.25
1.14
1.67
3.75
5.88
3.54
3.96
78
Nigeria
3.08
1.86
4.44
4.38
3.82
3.52
4.50
79
Nicaragua
8.25
5.71
3.33
3.75
7.35
5.68
4.81
80
Netherlands
9.58
9.29
9.44
10.00
10.00
9.66
8.80
81
Norway
10.00
9.64
10.00
8.13
10.00
9.55
9.93
82
Nepal
0.08
3.57
2.22
5.63
5.59
3.42
4.86
83
New_Zealand
10.00
8.57
8.33
8.13
10.00
9.01
9.26
84
Pakistan
4.33
5.36
0.56
4.38
5.00
3.92
4.33
85
Panama
9.58
7.14
5.56
5.63
8.82
7.35
7.13
86
Peru
8.75
3.29
5.56
5.00
7.94
6.11
6.65
87
Philippines
9.17
5.36
5.00
3.75
9.12
6.48
6.94
88
Poland
9.58
6.07
6.11
5.63
9.12
7.30
6.83
89
Portugal
9.58
8.21
6.11
7.50
9.41
8.16
7.86
90
Paraguay
7.92
5.00
5.00
4.38
8.53
6.16
6.27
91
Romania
9.58
6.07
6.11
5.00
8.53
7.06
6.62
92
Rwanda
3.00
3.57
2.22
5.00
5.29
3.82
3.07
93
Saudi_Arabia
0.00
2.36
1.11
4.38
1.76
1.92
1.93
94
Senegal
7.00
5.00
3.33
5.63
5.88
5.37
6.21
95
Singapore
4.33
7.50
2.78
7.50
7.35
5.89
6.38 67
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96
Sierra_Leone
5.25
2.21
2.22
3.75
4.41
3.57
4.55
97
El_Salvador
9.17
5.43
3.89
4.38
8.24
6.22
6.64
98
Sweden
10.00
10.00
10.00
9.38
10.00
9.88
9.39
99
Swaziland
1.75
2.86
2.22
3.13
4.71
2.93
3.03
100 Syr_Arab_Rep
0.00
1.79
1.67
6.88
1.47
2.36
1.43
101 Chad
0.00
0.00
0.00
5.00
3.24
1.65
1.50
102 Togo
0.00
0.79
0.56
5.63
1.76
1.75
3.32
103 Thailand
4.83
6.43
5.00
5.63
6.47
5.67
4.92
104 Trinid & Tobago
9.17
6.79
6.11
5.63
8.24
7.18
7.10
105 Tunisia
0.00
2.36
2.22
6.88
3.82
3.06
6.40
106 Turkey
7.92
6.79
4.44
3.75
5.59
5.70
5.04
107 Tanzania
6.00
3.93
5.06
5.63
5.29
5.18
5.76
108 Uganda
4.33
3.93
4.44
6.25
6.76
5.14
5.26
109 Uruguay
10.00
8.21
5.00
6.88
9.71
7.96
8.17
110 U.S.A.
8.75
7.86
7.22
8.75
8.53
8.22
7.98
111 Venezuela_RB
7.00
3.64
5.56
5.00
5.88
5.42
4.68
112 Vietnam
0.83
4.29
2.78
4.38
1.47
2.75
3.38
113 Yemen_Rep.
2.67
2.71
2.78
4.38
2.35
2.98
2.07
114 South_Africa
8.75
7.86
7.22
6.88
8.82
7.91
7.41
115 Congo_D_Rep.
4.58
0.36
2.78
3.75
2.35
2.76
1.93
116 Zambia
5.25
4.64
3.33
6.25
6.76
5.25
5.99
Table-A-1. Scores Obtained by Countries on the Measures in Different Dimensions of Democracy [Source: https://en.wikipedia.org/wiki/Democracy_Index]
SL#
68
Country
Corruption Perception
Human Development
PC Income
Social Progress
Democracy Index
Overall Globalization Index (AEMC)
CP06
CP16
HD06
HD15
PCY06
SP16
DI06
DI16
GI (Min)
GI (Max)
1
Albania
26
39
7.03
7.64
4.90
69.79
5.91
59.10
50.86
61.61
2
Argentina
29
36
7.88
8.27
13.70
75.20
6.63
69.60
57.09
59.19
3
Australia
87
79
9.18
9.39
32.00
89.13
9.09
90.10
82.24
84.03
4
Austria
86
75
8.60
8.93
32.90
86.60
8.69
84.10
91.36
93.95
5
Azerbaijan
24
30
7.08
7.59
4.70
63.76
3.31
26.50
52.78
54.69
6
Burundi
24
20
3.09
4.04
0.60
37.33
4.51
24.00
26.92
34.79
7
Belgium
73
77
8.71
8.96
31.90
86.19
8.15
77.70
92.32
93.75
8
Benin
25
36
4.38
4.85
1.10
50.03
6.16
56.70
41.61
48.99
9
Burkina_Faso
32
42
3.34
4.02
1.20
49.34
3.72
47.00
41.27
49.12
10
Bulgaria
40
41
7.55
7.94
9.00
72.14
7.10
70.10
69.36
76.34
11
Bolivia
27
33
6.26
6.74
2.70
64.74
5.98
56.30
53.62
56.38
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12
Brazil
33
40
7.00
7.54
8.40
71.70
7.38
69.00
55.59
58.16
13
Bhutan
60
65
5.50
6.07
1.40
65.65
2.62
49.30
35.44
47.07
14
Botswana
56
60
6.30
6.98
10.00
67.04
7.60
78.70
49.05
60.64
15
C._Afr_Rep
24
20
3.30
3.52
1.10
30.03
1.61
16.10
34.45
37.27
16
Canada
85
82
8.94
9.20
32.90
89.50
9.07
91.50
86.39
87.51
17
Switzerland
91
86
9.11
9.39
35.30
88.87
9.02
90.90
91.37
93.18
18
Chile
73
66
7.97
8.47
11.30
82.12
7.89
77.80
69.54
72.77
19
China
33
40
6.59
7.38
6.30
62.11
2.97
31.40
55.12
56.85
20
Cote_d'Ivoire
21
34
4.18
4.74
1.50
48.97
3.38
38.10
48.82
53.08
21
Cameroon
23
26
4.56
5.18
1.90
47.22
3.27
34.60
40.16
42.75
22
Congo_Rep.
22
20
5.17
5.92
0.70
49.74
2.76
29.10
47.78
57.31
23
Colombia
39
37
6.75
7.27
7.10
70.84
6.40
66.70
54.44
58.23
24
Costa_Rica
41
58
7.34
7.76
10.10
80.12
8.04
78.80
61.03
63.45
25
Cyprus
56
55
8.36
8.56
20.30
80.75
7.60
76.50
78.44
89.36
26
Germany
80
81
8.98
9.26
29.80
86.42
8.82
86.30
85.16
87.44
27
Denmark
95
90
9.04
9.25
33.40
89.40
9.52
92.00
88.85
91.90
28
Domin_Rep
28
31
6.85
7.22
6.60
65.66
6.13
66.70
55.44
67.20
29
Algeria
31
34
6.90
7.45
7.20
61.19
3.17
35.60
42.36
53.32
30
Ecuador
23
31
6.96
7.39
3.90
69.57
5.64
58.10
51.64
56.77
31
Egypt
33
34
6.44
6.91
4.40
60.75
3.90
33.10
53.67
59.62
32
Spain
68
58
8.49
8.84
25.20
85.88
8.34
83.00
84.60
86.71
33
Ethiopia
24
34
3.62
4.48
0.80
43.50
4.72
36.00
37.47
39.87
34
Finland
96
89
8.73
8.95
30.60
90.10
9.25
90.30
85.04
87.36
35
Fiji
40
40
6.98
7.36
6.10
66.50
5.66
56.40
57.81
61.30
36
France
74
69
8.73
8.97
30.00
84.79
8.07
79.20
87.32
89.36
37
Gabon
30
35
6.45
6.97
5.80
60.22
2.72
37.40
51.79
59.46
38
U.K.
86
81
8.89
9.10
30.90
88.58
8.08
83.60
88.15
89.91
39
Ghana
33
43
5.19
5.79
2.40
60.38
5.35
67.50
50.64
55.67
40
Guinea
19
27
3.64
4.14
2.20
41.66
2.02
31.40
40.45
46.82
41
Gambia
25
26
4.20
4.52
1.80
50.30
4.39
29.10
51.12
54.92
42
Greece
44
44
8.55
8.66
22.80
78.27
8.13
72.30
80.21
83.44
43
Guatemala
26
28
5.78
6.40
5.20
61.69
6.07
59.20
56.59
57.71
44
Guyana
25
34
6.20
6.38
3.80
60.00
6.15
62.50
49.78
59.99
45
Honduras
25
30
5.90
6.25
2.80
60.65
6.25
59.20
57.05
60.57
46
Haiti
18
20
4.58
4.93
1.60
43.15
4.19
40.20
34.53
38.47
47
Hungary
52
48
8.09
8.36
16.10
76.88
7.53
67.20
86.30
87.02
48
Indonesia
24
37
6.38
6.89
3.70
62.28
6.41
69.70
54.53
57.96
49
India
33
40
5.46
6.24
3.40
53.92
7.68
78.10
47.98
50.87
50
Ireland
74
73
9.02
9.23
34.10
87.94
9.01
91.50
89.89
95.20 69
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70
51
Iceland
96
78
8.87
9.21
34.90
88.45
9.71
95.00
71.77
81.39
52
Israel
59
64
8.72
8.99
22.30
75.32
7.28
78.50
75.13
80.79
53
Italy
49
47
8.62
8.87
28.40
82.49
7.73
79.80
81.77
83.57
54
Jamaica
37
39
7.14
7.30
4.20
71.94
7.34
73.90
62.05
66.57
55
Jordan
53
48
7.36
7.42
4.80
65.44
3.92
39.60
69.18
73.94
56
Japan
76
72
8.77
9.03
30.70
86.54
8.15
79.90
65.61
68.81
57
Kenya
22
26
4.94
5.55
1.20
53.72
5.08
53.30
42.55
45.80
58
Cambodia
21
21
4.95
5.63
2.20
54.29
4.77
42.70
49.02
54.22
59
South_Korea
51
53
8.67
9.01
20.40
80.92
7.88
79.20
61.36
66.05
60
Kuwait
48
41
7.87
8.00
22.80
71.84
3.09
38.50
67.03
72.18
61
Lebanon
36
28
7.31
7.63
5.30
64.43
5.82
48.60
67.36
74.20
62
Lesotho
32
39
4.40
4.97
3.00
52.39
6.48
65.90
36.96
48.77
63
Luxembourg
86
81
8.77
8.98
55.60
91.00
9.10
88.10
83.89
89.59
64
Morocco
32
37
5.81
6.47
4.30
61.93
3.90
47.70
56.51
64.33
65
Moldova
32
30
6.56
6.99
2.10
64.74
6.50
60.10
58.36
61.70
66
Madagascar
31
26
4.83
5.12
0.90
45.91
5.82
50.70
39.25
42.98
67
Mexico
33
30
7.31
7.62
10.10
70.03
6.67
64.70
57.99
61.61
68
Mali
28
32
3.63
4.42
1.00
46.24
5.99
57.00
44.06
46.72
69
Malta
64
55
8.08
8.56
19.00
84.60
8.39
83.90
76.39
78.24
70
Myanmar
19
28
4.84
5.56
1.60
49.84
1.77
42.00
32.04
38.40
71
Montenegro
28
45
7.62
8.07
2.70
68.17
6.57
57.20
56.97
66.92
72
Mongolia
28
38
6.61
7.35
2.20
62.81
6.60
66.20
46.41
55.63
73
Mauritania
31
27
4.75
5.13
2.00
46.08
3.12
39.60
43.65
52.55
74
Mauritius
51
54
7.20
7.81
13.20
73.24
8.04
82.80
60.47
66.81
75
Malawi
27
31
3.87
4.76
0.60
53.44
4.97
55.50
40.16
46.09
76
Malaysia
50
49
7.36
7.89
10.40
70.08
5.98
65.40
79.14
81.07
77
Niger
23
35
2.93
3.53
0.80
41.63
3.54
39.60
41.05
50.86
78
Nigeria
22
28
4.77
5.27
1.00
46.49
3.52
45.00
48.17
52.53
79
Nicaragua
26
26
6.01
6.45
2.40
63.04
5.68
48.10
51.57
53.56
80
Netherlands
87
83
8.99
9.24
30.60
88.66
9.66
88.00
93.78
95.24
81
Norway
88
85
9.34
9.49
42.40
88.70
9.55
99.30
85.24
86.83
82
Nepal
25
29
4.86
5.58
1.50
57.41
3.42
48.60
34.44
36.70
83
New_Zealand
96
90
8.91
9.15
24.20
88.46
9.01
92.60
78.48
80.12
84
Pakistan
22
32
5.05
5.50
2.40
49.13
3.92
43.30
48.64
51.16
85
Panama
31
38
7.43
7.88
7.10
73.02
7.35
71.30
65.63
67.56
86
Peru
33
35
6.96
7.40
6.10
70.10
6.11
66.50
62.50
65.24
87
Philippines
25
35
6.48
6.82
5.10
65.93
6.48
69.40
55.98
59.19
88
Poland
37
62
8.08
8.55
12.70
79.76
7.30
68.30
76.61
79.32
89
Portugal
66
62
7.97
8.43
18.60
83.88
8.16
78.60
83.54
88.21
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90
Paraguay
26
30
6.49
6.93
4.90
67.45
6.16
62.70
56.32
59.39
91
Romania
31
48
7.66
8.02
8.40
72.24
7.06
66.20
64.99
73.36
92
Rwanda
25
54
4.24
4.98
1.30
51.91
3.82
30.70
34.22
43.83
93
Saudi_Arabia
33
46
7.73
8.47
12.90
66.31
1.92
19.30
66.57
69.75
94
Senegal
33
45
4.25
4.94
1.70
55.65
5.37
62.10
51.75
54.59
95
Singapore
94
84
8.73
9.25
29.90
82.19
5.89
63.80
87.04
91.52
96
Sierra_Leone
22
30
3.57
4.20
0.90
44.22
3.57
45.50
36.81
48.29
97
El_Salvador
40
36
6.57
6.80
5.10
66.37
6.22
66.40
59.25
64.02
98
Sweden
92
88
8.95
9.13
29.80
88.80
9.88
93.90
89.13
91.73
99
Swaziland
25
43
5.08
5.41
5.50
51.76
2.93
30.30
47.23
51.92
100 Syr_Arab_Rep
29
13
6.44
5.36
3.40
52.10
2.36
14.30
45.17
50.02
101 Chad
20
20
3.06
3.96
1.80
36.38
1.65
15.00
39.14
41.70
102 Togo
24
32
4.43
4.87
1.70
49.03
1.75
33.20
47.25
54.25
103 Thailand
36
35
6.87
7.40
8.30
67.44
5.67
49.20
62.95
71.71
104 Trinid & Tobago
32
35
7.60
7.80
12.90
69.00
7.18
71.00
59.84
65.62
105 Tunisia
46
41
6.95
7.25
7.60
68.01
3.06
64.00
58.22
60.63
106 Turkey
38
41
6.97
7.67
7.90
67.83
5.70
50.40
65.92
69.88
107 Tanzania
29
32
4.57
5.31
0.70
49.99
5.18
57.60
34.91
37.42
108 Uganda
27
25
4.42
4.93
1.70
50.69
5.14
52.60
42.80
45.69
109 Uruguay
64
71
7.60
7.95
16.00
80.12
7.96
81.70
66.74
68.14
110 U.S.A.
73
74
9.01
9.20
42.00
84.62
8.22
79.80
78.47
81.15
111 Venezuela_RB
23
17
7.28
7.67
6.50
63.46
5.42
46.80
48.92
55.45
112 Vietnam
26
33
6.25
6.83
3.00
63.47
2.75
33.80
42.59
54.98
113 Yemen_Rep.
26
14
4.77
4.82
0.80
41.76
2.98
20.70
42.64
46.66
114 South_Africa
46
45
6.12
6.66
12.10
67.61
7.91
74.10
64.93
67.54
115 Congo_D_Rep.
20
21
3.70
4.35
0.80
46.23
2.76
19.30
24.95
42.31
116 Zambia
26
38
4.92
5.79
0.90
50.00
5.25
59.90
46.41
54.04
Table-A-2. Corruption Perception Index, Human Development Index, Per Capita Income, Social Progress Index, Democracy Index and Overall Globalization Index in the Countries under Study Sources: Wikipedia for Corruption Perception, Human Development, Per-capita Income (in Int$1000), Social Progress and Democracy Indices. For Overall Globalization Index, GI(Max) and GI(Min) based on AEMC principle, see Tables 3 and 4 below.
SL
Country
Year-H
E1
E2
S1
S2
S3
P
KOF
AEMC
1
Albania
2009
56.57
73.00
52.55
73.90
2.42
80.69
61.60
61.61
2
Argentina
2008
45.92
39.11
43.30
71.50
41.47
92.07
59.95
59.19
3
Australia
2007
74.79
81.24
73.40
87.55
94.03
89.71
83.80
84.03
4
Austria
2007
89.34
86.56
87.06
92.06
95.54
96.86
91.87
93.95
5
Azerbaijan
2007
67.38
63.70
37.92
77.61
34.96
54.01
57.02
54.69
6
Burundi
2014
23.53
33.37
21.02
37.22
3.10
62.17
35.04
34.79 71
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72
7
Belgium
2007
96.71
82.81
81.94
96.39
91.22
97.67
92.41
93.75
8
Benin
2014
53.79
42.92
28.55
39.46
2.48
75.17
46.67
48.99
9
Burkina_Faso
2014
59.67
46.84
19.43
44.62
2.17
76.88
48.69
49.12
10
Bulgaria
2013
80.04
72.93
51.55
77.71
85.30
84.96
76.98
76.34
11
Bolivia
2006
62.03
59.79
39.52
51.01
3.78
75.69
54.42
56.38
12
Brazil
2014
51.77
52.82
24.46
70.50
39.58
94.30
61.40
58.16
13
Bhutan
2014
60.64
56.77
46.83
45.54
6.87
38.85
43.58
47.07
14
Botswana
2008
77.58
59.64
59.54
57.17
5.88
59.28
55.50
60.64
15
C._Afr_Rep
2014
49.56
28.29
13.44
40.71
2.24
58.39
36.34
37.27
16
Canada
2007
76.20
82.03
80.78
94.74
96.09
92.91
87.15
87.51
17
Switzerland
2014
95.02
70.51
91.77
87.57
94.47
93.40
88.79
93.18
18
Chile
2007
82.68
87.08
41.25
77.69
41.18
87.67
74.31
72.77
19
China
2014
43.49
62.19
18.71
65.65
78.37
84.26
62.02
56.85
20
Cote_d'Ivoire
2007
63.35
40.17
41.85
52.15
2.85
70.72
49.83
53.08
21
Cameroon
2014
44.96
38.31
16.91
52.02
2.24
73.16
44.20
42.75
22
Congo_Rep.
2014
96.24
41.58
35.45
43.93
1.25
63.67
51.83
57.31
23
Colombia
2013
58.32
57.38
33.46
69.69
38.12
79.65
60.15
58.23
24
Costa_Rica
2007
64.79
73.30
60.37
78.75
45.65
58.63
63.66
63.45
25
Cyprus
2008
93.50
84.06
88.10
95.69
93.84
78.36
87.32
89.36
26
Germany
2007
81.36
84.49
76.35
87.52
92.57
92.43
86.48
87.44
27
Denmark
2007
87.80
89.09
83.64
89.59
93.06
93.75
90.01
91.90
28
Domin_Rep
2014
64.15
59.56
53.70
64.97
79.14
73.31
66.45
67.20
29
Algeria
2006
55.36
52.55
32.39
64.92
1.93
80.65
54.00
53.32
30
Ecuador
2006
55.97
46.00
36.82
65.37
38.22
79.01
57.39
56.77
31
Egypt
2013
42.96
48.68
27.64
66.78
77.77
93.01
63.10
59.62
32
Spain
2007
78.33
81.36
74.93
87.72
90.22
95.93
85.92
86.71
33
Ethiopia
2014
24.93
28.39
19.32
33.17
2.85
82.51
39.33
39.87
34
Finland
2007
85.16
87.39
72.07
90.60
91.67
91.64
87.22
87.36
35
Fiji
2014
74.43
25.70
56.98
57.20
43.56
69.68
57.56
61.30
36
France
2007
76.99
87.19
80.56
88.36
91.79
97.96
88.23
89.36
37
Gabon
2014
75.55
42.75
52.22
63.44
2.36
72.30
55.96
59.46
38
U.K.
2006
81.91
89.75
79.57
90.54
93.30
94.90
89.06
89.91
39
Ghana
2014
62.30
54.48
27.85
45.77
3.96
85.72
54.17
55.67
40
Guinea
2014
57.21
31.29
21.72
41.38
2.73
76.19
44.40
46.82
41
Gambia
2006
70.76
49.68
45.63
57.79
6.31
61.86
51.78
54.92
42
Greece
2007
68.15
83.53
76.51
83.41
85.44
92.38
82.59
83.44
43
Guatemala
2014
48.00
74.96
26.23
57.23
42.95
83.01
60.42
57.71
44
Guyana
2006
80.52
62.07
56.43
55.51
44.10
43.34
56.44
59.99
45
Honduras
2014
74.61
71.19
28.45
58.46
39.51
71.84
61.42
60.57
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46
Haiti
2010
34.21
62.93
28.71
50.84
1.00
45.88
39.36
38.47
47
Hungary
2009
92.14
85.86
65.93
89.31
89.62
91.47
86.99
87.02
48
Indonesia
2014
56.25
71.79
20.40
49.92
33.89
86.83
59.65
57.96
49
India
2014
43.78
44.93
14.10
45.12
32.98
91.23
52.38
50.87
50
Ireland
2014
99.52
89.78
89.37
91.72
91.88
90.47
92.15
95.20
51
Iceland
2008
89.32
64.89
81.47
80.36
91.88
70.11
77.86
81.39
52
Israel
2010
71.59
83.51
75.06
67.25
90.37
80.29
78.15
80.79
53
Italy
2007
68.17
83.24
70.46
78.72
86.52
97.92
82.85
83.57
54
Jamaica
2007
80.64
70.00
63.13
69.52
7.11
68.56
62.72
66.57
55
Jordan
2006
79.36
59.47
67.97
71.54
41.11
84.27
70.31
73.94
56
Japan
2014
50.41
76.54
43.39
75.59
87.91
88.10
72.26
68.81
57
Kenya
2007
27.19
46.79
29.61
46.02
3.72
82.92
46.46
45.80
58
Cambodia
2014
85.86
50.76
29.52
48.48
1.31
62.36
50.69
54.22
59
South_Korea
2014
62.52
63.76
43.81
73.55
42.42
89.58
67.03
66.05
60
Kuwait
2008
61.31
75.01
78.96
76.28
90.41
59.54
70.76
72.18
61
Lebanon
2006
86.92
62.30
70.38
81.04
43.26
74.55
70.50
74.20
62
Lesotho
2014
80.48
41.22
25.58
48.74
6.87
54.09
45.94
48.77
63
Luxembourg
2007
100.00
88.46
96.09
97.51
48.25
80.06
85.62
89.59
64
Morocco
2014
60.71
53.68
45.87
83.86
37.71
89.50
65.95
64.33
65
Moldova
2007
67.96
69.67
44.90
84.17
39.27
67.22
64.04
61.70
66
Madagascar
2014
62.47
36.71
11.21
48.02
2.73
65.10
42.90
42.98
67
Mexico
2014
63.45
68.45
44.30
68.92
40.12
71.72
62.29
61.61
68
Mali
2014
50.97
41.67
22.46
44.10
1.12
75.98
46.07
46.72
69
Malta
2009
99.76
87.06
83.18
96.04
49.74
52.58
76.16
78.24
70
Myanmar
2014
56.93
56.33
11.89
42.07
1.00
44.74
39.03
38.40
71
Montenegro
2010
81.65
79.55
72.69
94.41
5.08
56.33
65.48
66.92
72
Mongolia
2014
84.88
65.73
16.76
59.40
1.43
71.89
56.91
55.63
73
Mauritania
2014
79.30
58.16
19.77
51.82
1.37
66.99
51.45
52.55
74
Mauritius
2014
91.12
84.89
58.78
82.06
42.61
45.32
66.61
66.81
75
Malawi
2013
49.90
52.47
26.25
41.95
6.99
64.35
45.40
46.09
76
Malaysia
2010
89.03
69.62
64.71
75.92
87.52
83.17
79.12
81.07
77
Niger
2014
54.67
50.44
32.41
35.30
1.74
74.33
47.92
50.86
78
Nigeria
2009
65.10
47.51
12.39
52.93
3.47
89.37
54.36
52.53
79
Nicaragua
2012
61.15
61.69
34.97
56.57
40.24
57.38
53.99
53.56
80
Netherlands
2014
97.64
88.48
85.98
93.26
92.75
95.41
92.84
95.24
81
Norway
2013
80.32
72.93
81.74
85.52
91.68
92.27
84.48
86.83
82
Nepal
2013
13.26
39.95
24.97
44.85
2.79
70.69
38.18
36.70
83
New_Zealand
2008
76.62
90.04
79.32
91.46
50.44
80.05
79.17
80.12
84
Pakistan
2007
40.85
43.25
23.40
44.12
32.38
87.55
51.83
51.16 73
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85
Panama
2009
89.59
71.32
50.84
81.17
47.74
60.74
67.70
67.56
86
Peru
2011
69.02
82.53
32.33
58.27
36.87
84.74
66.14
65.24
87
Philippines
2006
65.22
52.73
30.26
49.70
39.96
81.96
58.39
59.19
88
Poland
2014
77.73
76.38
57.40
92.23
89.22
88.82
81.32
79.32
89
Portugal
2007
82.71
87.10
76.48
91.10
88.73
93.85
87.61
88.21
90
Paraguay
2012
62.44
56.59
36.33
65.09
39.86
77.61
60.13
59.39
91
Romania
2014
60.67
83.22
48.07
82.02
82.39
89.82
76.51
73.36
92
Rwanda
2014
34.81
63.91
17.27
39.87
7.05
71.53
45.56
43.83
93
Saudi_Arabia
2009
62.95
76.19
69.00
71.18
83.25
60.43
68.43
69.75
94
Senegal
2012
57.58
47.32
29.33
58.91
3.53
87.90
54.64
54.59
95
Singapore
2009
99.01
95.35
92.18
88.25
96.12
71.77
88.27
91.52
96
Sierra_Leone
2011
69.70
46.89
19.84
38.92
3.16
65.10
45.90
48.29
97
El_Salvador
2007
61.06
72.79
49.35
64.68
40.80
75.40
63.79
64.02
98
Sweden
2007
88.33
86.26
80.84
84.38
94.73
96.03
89.41
91.73
99
Swaziland
2014
77.83
43.61
59.31
60.20
6.37
36.55
47.48
51.92
100 Syr_Arab_Rep
2011
53.48
55.43
51.94
65.49
1.00
52.73
48.93
50.02
101 Chad
2006
55.49
27.21
23.94
32.35
2.91
60.04
38.37
41.70
102 Togo
2014
78.62
46.54
25.04
57.99
3.72
73.38
53.70
54.25
103 Thailand
2012
83.87
59.54
42.90
72.93
80.93
81.22
72.06
71.71
104 Trinid & Tobago
2012
86.13
68.86
58.65
67.24
41.73
53.54
63.09
65.62
105 Tunisia
2008
70.83
48.71
41.68
76.78
2.67
86.29
60.45
60.63
106 Turkey
2014
51.09
66.13
50.76
72.49
81.59
91.88
71.33
69.88
107 Tanzania
2007
35.61
53.20
16.78
31.93
3.04
55.74
37.71
37.42
108 Uganda
2013
44.01
58.02
21.59
37.01
4.52
70.23
45.48
45.69
109 Uruguay
2008
65.66
68.87
51.35
65.92
42.10
85.45
67.23
68.14
110 U.S.A.
2007
65.17
85.34
67.13
82.45
91.90
92.10
81.80
81.15
111 Venezuela_RB
2006
62.32
47.83
38.48
68.43
41.65
65.68
56.17
55.45
112 Vietnam
2014
80.26
49.28
16.43
63.78
31.92
71.13
56.69
54.98
113 Yemen_Rep.
2008
53.37
63.83
23.57
41.91
1.68
62.24
46.51
46.66
114 South_Africa
2014
72.64
65.18
41.53
61.39
41.93
88.04
66.72
67.54
115 Congo_D_Rep.
2013
69.13
37.26
6.23
43.38
1.00
62.03
41.67
42.31
116 Zambia
2007
64.24
63.96
27.92
45.69
4.09
73.93
52.96
54.04
E1, E2, S1, S2, S3, P and KOF are for the Year-H when the overall index AEMC attained maximum (Gmax) during 2006-2014. AEMC Indices are computed by the author. Table-A-3. Economic, Social and Political Dimensions and Overall Indices of Globalization in Different Countries [Source: http://globalization.kof.ethz.ch]
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SL
Country
Year-L
E1
E2
S1
S2
S3
P
KOF
AEMC
1
Albania
2006
35.89
58.68
52.56
69.39
2.24
67.63
51.18
50.86
2
Argentina
2012
41.13
30.68
43.54
72.69
40.54
92.83
57.89
57.09
3
Australia
2013
68.41
78.01
73.79
85.80
92.90
90.42
81.97
82.24
4
Austria
2013
85.52
76.50
86.51
91.31
95.46
96.36
89.09
91.36
5
Azerbaijan
2009
59.96
57.99
38.90
78.95
34.51
55.51
55.35
52.78
6
Burundi
2006
24.06
35.17
16.96
35.39
4.15
36.97
27.89
26.92
7
Belgium
2013
95.51
73.19
84.04
96.99
91.01
96.51
90.70
92.32
8
Benin
2006
28.32
40.26
28.88
35.40
2.54
71.83
40.22
41.61
9
Burkina_Faso
2006
16.39
50.78
32.95
36.90
3.90
71.57
40.68
41.27
10
Bulgaria
2010
71.76
74.41
50.21
82.83
40.81
83.13
70.59
69.36
11
Bolivia
2011
56.44
50.56
37.79
58.44
2.91
76.81
52.76
53.62
12
Brazil
2008
48.27
53.34
20.26
68.50
38.23
92.27
59.38
55.59
13
Bhutan
2007
34.97
56.40
46.37
41.28
5.32
21.18
33.12
35.44
14
Botswana
2012
60.07
53.50
56.45
55.16
4.95
39.77
45.21
49.05
15
C._Afr_Rep
2007
40.14
22.02
15.27
32.43
2.24
57.98
32.80
34.45
16
Canada
2013
74.03
77.68
81.23
92.24
94.97
92.94
85.60
86.39
17
Switzerland
2011
94.70
60.22
91.35
89.06
94.96
92.44
86.84
91.37
18
Chile
2013
77.71
75.92
38.21
76.16
40.69
88.74
71.11
69.54
19
China
2012
41.21
56.27
16.75
65.54
78.02
84.80
60.42
55.12
20
Cote_d'Ivoire
2013
56.86
36.44
29.24
53.69
2.61
74.19
47.92
48.82
21
Cameroon
2010
35.79
41.44
16.83
51.95
2.73
70.25
42.67
40.16
22
Congo_Rep.
2008
91.35
37.23
31.94
40.90
1.74
39.88
42.91
47.78
23
Colombia
2008
54.98
42.87
30.73
70.80
38.22
78.48
56.48
54.44
24
Costa_Rica
2013
62.90
66.25
55.31
81.31
45.89
59.43
62.05
61.03
25
Cyprus
2006
91.53
84.62
86.55
95.34
47.57
59.05
76.11
78.44
26
Germany
2013
75.94
73.34
79.32
85.40
92.01
91.93
83.41
85.16
27
Denmark
2013
84.52
80.70
81.47
88.35
93.53
91.65
86.99
88.85
28
Domin_Rep
2009
54.07
57.06
53.37
67.39
36.62
56.88
55.00
55.44
29
Algeria
2007
49.62
47.76
33.94
64.81
2.05
48.49
43.47
42.36
30
Ecuador
2014
40.55
36.53
34.14
62.25
38.21
80.97
52.78
51.64
31
Egypt
2012
41.62
46.07
22.45
66.66
35.94
93.45
56.99
53.67
32
Spain
2013
75.24
74.68
73.88
86.21
89.60
95.51
83.68
84.60
33
Ethiopia
2011
28.98
21.94
10.54
29.29
2.17
81.88
36.82
37.47
34
Finland
2009
77.81
86.19
72.26
88.86
91.36
90.25
85.08
85.04
35
Fiji
2009
64.73
25.64
56.01
50.18
43.87
66.56
53.75
57.81
36
France
2013
73.58
78.12
81.13
89.14
92.48
97.29
86.09
87.32
37
Gabon
2011
75.77
31.78
51.97
61.25
2.36
51.11
47.92
51.79
38
U.K.
2014
80.71
85.27
76.35
87.66
93.64
94.67
87.26
88.15 75
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76
39
Ghana
2008
36.37
51.83
35.82
43.80
4.52
83.98
49.19
50.64
40
Guinea
2010
35.70
31.29
21.36
39.92
4.15
71.90
39.38
40.45
41
Gambia
2009
50.86
50.47
45.99
61.95
5.38
64.80
50.18
51.12
42
Greece
2012
61.28
77.37
75.14
84.24
84.42
91.33
79.82
80.21
43
Guatemala
2010
46.46
68.40
27.08
56.03
43.98
82.47
58.89
56.59
44
Guyana
2013
61.74
58.98
48.79
58.06
5.76
44.66
47.60
49.78
45
Honduras
2010
63.36
65.10
30.16
60.23
39.72
70.29
58.38
57.05
46
Haiti
2014
35.21
68.47
6.41
51.82
1.00
48.28
38.81
34.53
47
Hungary
2011
91.22
81.45
66.67
89.18
90.33
90.93
86.05
86.30
48
Indonesia
2008
49.64
69.02
17.85
47.95
33.79
84.05
56.64
54.53
49
India
2006
35.28
43.76
13.64
46.46
32.53
89.37
50.22
47.98
50
Ireland
2008
97.80
88.49
91.12
92.11
48.10
87.41
85.93
89.89
51
Iceland
2013
89.48
59.80
80.56
78.37
50.11
54.09
67.32
71.77
52
Israel
2011
69.88
76.98
75.38
66.87
90.37
65.01
72.46
75.13
53
Italy
2013
64.98
75.44
70.42
78.44
88.21
97.52
80.94
81.77
54
Jamaica
2014
73.94
51.72
57.00
67.13
6.93
72.58
58.43
62.05
55
Jordan
2013
72.22
61.91
52.07
69.51
42.37
86.09
67.93
69.18
56
Japan
2011
43.92
65.57
42.19
76.22
87.85
88.66
69.25
65.61
57
Kenya
2012
25.69
44.87
19.21
48.47
3.59
82.94
45.16
42.55
58
Cambodia
2011
70.40
50.86
26.14
44.44
2.17
59.93
46.83
49.02
59
South_Korea
2006
54.55
65.58
39.06
76.10
41.38
83.59
63.92
61.36
60
Kuwait
2013
53.45
65.47
70.68
73.63
89.69
60.31
66.44
67.03
61
Lebanon
2011
77.07
56.80
70.26
90.02
45.95
60.76
65.70
67.36
62
Lesotho
2006
59.43
37.57
24.70
45.45
6.68
33.39
35.69
36.96
63
Luxembourg
2006
99.72
87.43
96.37
96.87
48.06
60.97
80.05
83.89
64
Morocco
2006
49.22
40.66
35.46
67.40
37.20
87.73
57.63
56.51
65
Moldova
2014
60.52
63.40
40.67
84.06
37.77
69.00
61.39
58.36
66
Madagascar
2011
56.71
28.24
8.15
49.42
2.67
63.64
39.71
39.25
67
Mexico
2008
55.23
60.32
42.67
70.30
41.09
70.95
59.27
57.99
68
Mali
2007
44.08
41.64
20.96
36.32
2.17
73.60
43.06
44.06
69
Malta
2006
97.19
87.13
83.62
96.07
50.17
47.77
74.50
76.39
70
Myanmar
2009
47.20
49.84
9.82
27.94
1.00
36.00
31.86
32.04
71
Montenegro
2006
52.52
76.75
73.23
94.86
6.25
46.57
57.31
56.97
72
Mongolia
2006
54.54
60.02
19.54
57.15
2.05
65.31
48.72
46.41
73
Mauritania
2006
72.75
40.60
25.64
43.51
1.37
45.02
40.79
43.65
74
Mauritius
2006
57.62
70.87
59.49
85.06
40.57
57.79
61.85
60.47
75
Malawi
2009
32.32
44.30
27.07
39.17
6.74
61.73
39.76
40.16
76
Malaysia
2014
88.91
66.95
57.96
77.28
87.65
83.69
78.14
79.14
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77
Niger
2007
24.17
37.19
32.59
30.52
1.68
71.94
38.88
41.05
78
Nigeria
2014
46.48
52.49
9.46
46.64
1.43
90.79
50.24
48.17
79
Nicaragua
2008
53.72
63.14
35.68
56.50
39.11
55.74
52.42
51.57
80
Netherlands
2009
95.28
88.51
84.91
90.53
92.90
93.23
91.35
93.78
81
Norway
2006
81.16
70.67
79.65
83.91
91.99
88.88
82.87
85.24
82
Nepal
2008
11.40
31.69
25.16
37.96
3.35
68.10
34.85
34.44
83
New_Zealand
2013
72.83
85.72
78.84
89.57
50.42
80.03
77.41
78.48
84
Pakistan
2014
33.87
45.27
19.22
48.01
32.32
87.30
51.02
48.64
85
Panama
2006
91.07
65.78
50.23
73.96
47.74
56.13
64.69
65.63
86
Peru
2006
66.78
67.15
32.70
54.46
37.01
84.09
62.39
62.50
87
Philippines
2014
58.47
49.32
24.22
54.23
41.28
82.83
56.84
55.98
88
Poland
2011
72.22
68.03
56.29
91.86
87.36
89.58
78.67
76.61
89
Portugal
2013
79.89
82.09
68.63
91.19
89.70
88.98
84.05
83.54
90
Paraguay
2008
53.18
57.92
36.26
60.83
37.09
75.13
57.14
56.32
91
Romania
2006
60.44
60.73
44.18
78.72
38.69
89.91
66.50
64.99
92
Rwanda
2006
19.54
34.11
23.81
38.03
4.27
60.31
34.49
34.22
93
Saudi_Arabia
2006
52.82
76.19
70.24
69.12
82.06
57.24
65.22
66.57
94
Senegal
2006
40.99
38.14
40.60
58.22
4.09
86.13
50.65
51.75
95
Singapore
2014
99.01
96.53
93.20
85.75
96.53
54.77
83.64
87.04
96
Sierra_Leone
2009
30.15
41.28
19.63
33.56
3.22
61.16
36.20
36.81
97
El_Salvador
2011
57.17
63.11
35.53
66.64
41.19
78.63
60.89
59.25
98
Sweden
2013
85.48
75.35
81.30
81.02
93.46
94.65
86.05
89.13
99
Swaziland
2007
63.20
36.36
61.97
54.71
6.37
33.68
42.40
47.23
100 Syr_Arab_Rep
2007
49.06
38.95
43.38
63.66
1.00
54.93
44.26
45.17
101 Chad
2011
50.22
28.12
19.94
36.74
2.91
58.55
37.11
39.14
102 Togo
2008
53.50
37.49
28.74
54.91
3.53
71.19
46.93
47.25
103 Thailand
2008
74.06
55.41
39.67
68.67
37.94
78.48
62.87
62.95
104 Trinid & Tobago
2007
79.71
71.95
61.64
66.92
5.76
47.01
56.82
59.84
105 Tunisia
2011
68.94
42.49
40.06
78.34
2.48
83.92
58.35
58.22
106 Turkey
2006
46.77
69.54
40.93
72.69
78.12
89.96
69.07
65.92
107 Tanzania
2006
27.06
50.59
17.16
33.54
2.61
55.17
35.78
34.91
108 Uganda
2006
35.99
52.16
24.19
35.24
3.53
67.77
42.31
42.80
109 Uruguay
2012
60.28
67.75
52.98
69.97
42.11
84.09
66.43
66.74
110 U.S.A.
2009
59.05
78.48
66.91
81.46
91.77
91.43
79.14
78.47
111 Venezuela_RB
2010
40.82
37.04
38.46
70.34
40.30
66.51
50.75
48.92
112 Vietnam
2006
70.58
39.35
17.13
59.33
3.04
50.33
43.21
42.59
113 Yemen_Rep.
2014
35.99
54.18
26.38
44.10
1.12
65.01
42.99
42.64
114 South_Africa
2011
67.26
63.98
39.51
61.09
40.86
86.20
64.64
64.93
77
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115 Congo_D_Rep.
2006
19.87
28.69
8.76
34.02
1.00
44.96
26.11
24.95
116 Zambia
2012
50.36
55.83
16.51
43.66
3.78
73.04
47.36
46.41
E1, E2, S1, S2, S3, P and KOF are for the Year-L when the overall index AEMC attained minimum (Gmin) during 2006-2014. AEMC Indices are computed by the author. Table-A-4. Economic, Social and Political Dimensions and Overall Indices of Globalization in Different Countries [Source: http://globalization.kof.ethz.ch]
Exogenous / Predetermine Variables (Predictors at 2-SLS Stage-1)
Endogenous
EPP06
FOG06 PPN06
CVL06
CP06
HD06
PCY06
E1
0.0353
-0.0880 -0.0779 -0.2510 -0.3210
0.2066
0.7242
(SEE)
0.0351
0.0590
0.0706
0.1642
0.1192
E2
0.0093
0.0414
-0.1017 -0.1346 -0.1563
SEE
0.0263
0.0443
0.0529
0.1335
S1
0.0538
-0.0133
0.0376
(SEE)
0.0440
0.0742
S2
0.0039
(SEE)
DI06
Constant
R2
0.0561
0.4427
1.0808
0.5649
0.2361
0.0645
0.3166
0.8970
0.1731
0.5338
-0.0120
0.4174
0.9137
0.1231
0.0894
0.1771
0.0484
0.2374
0.6728
0.2887
0.5016
0.3852
0.6971
0.1551
-0.9627 -0.9281
0.0887
0.2236
0.2063
0.1498
0.2967
0.0810
0.3978
1.1271
-0.0384
0.0397
0.0642
0.0830
-0.0389
0.7784
0.0656
-0.1078
0.5895
0.0171
0.0287
0.0343
0.0866
0.0799
0.0580
0.1149
0.0314
0.1540
0.4363
S3
0.0452
0.0456
-0.3037 -0.8856 -0.2577
0.0937
0.8872
0.7382
1.3123
-4.0018
(SEE)
0.1046
0.1761
0.2107
0.5311
0.4899
0.3558
0.7047
0.1925
0.9447
2.6769
-0.0140 -0.0413
0.0590
0.1266
0.0154
-0.1374 -0.1605
0.0990
0.2297
3.5050
(SEE)
0.0317
0.0533
0.0638
0.1607
0.1483
0.1077
0.2133
0.0582
0.2859
0.8101
DI16
-0.0383
0.1010
-0.0229
0.0314
0.2141
0.0861
0.0778
-0.0198
0.5551
0.0727
(SEE)
0.0220
0.0370
0.0443
0.1117
0.1030
0.0748
0.1482
0.0405
0.1987
0.5630
CP16
0.0026
0.0985
-0.0403
0.0812
0.1666
0.6498
-0.1923
0.0657
-0.1688
1.3076
(SEE)
0.0244
0.0410
0.0491
0.1237
0.1141
0.0829
0.1641
0.0448
0.2200
0.6233
HD15
0.0025
-0.0054 -0.0162
0.0176
-0.0085
0.0002
0.8315
-0.0015
0.0510
-1.6935
(SEE)
0.0044
0.0075
0.0226
0.0208
0.0151
0.0300
0.0082
0.0402
0.1138
GI10
0.0138
-0.0258 -0.0116 -0.0043 -0.0235
0.1206
0.3406
0.0936
0.0985
1.6204
(SEE)
0.0149
0.0251
0.0300
0.0756
0.0697
0.0507
0.1003
0.0274
0.1345
0.3810
SP16
-0.0103
0.0007
-0.0031 -0.0005
0.0125
0.0715
0.4939
0.0169
0.1301
1.2664
(SEE)
0.0075
0.0127
0.0151
0.0352
0.0256
0.0507
0.0138
0.0679
0.1924
P
0.0090
PCL06 0.1780
0.0382
0.6268
0.6954
0.8359
0.7214
0.2545
0.8427
0.8135
0.9822
0.8527
0.9420
Table-A-5. Coefficients of the Reduced Form Equation with their Standard Error of Estimate (SEE): Pessimistic Scenario 78
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Exogenous / Predetermine Variables (Predictors at 2-SLS Stage-1)
Endogenous
EPP06
FOG06 PPN06
CVL06
CP06
HD06
PCY06
E1
0.0720
-0.1249 -0.0406 -0.2353 -0.3101
0.2985
0.4259
(SEE)
0.0317
0.0534
0.0639
0.1486
0.1079
E2
0.0055
0.0438
-0.0611 -0.1000 -0.0985
SEE
0.0231
0.0389
0.0466
0.1174
S1
0.0331
0.0119
-0.0114
(SEE)
0.0407
0.0686
S2
0.0051
(SEE)
DI06
Constant
R2
0.0228
0.2710
2.7392
0.4213
0.2138
0.0584
0.2866
0.8120
0.1624
0.5962
-0.0295
0.2559
0.9869
0.1083
0.0786
0.1557
0.0425
0.2088
0.5916
0.2058
0.4477
0.2694
0.8125
0.1278
-0.7073 -1.1285
0.0821
0.2069
0.1908
0.1386
0.2745
0.0750
0.3680
1.0427
-0.0192
0.0634
0.0511
0.0611
0.0081
0.6361
0.0736
-0.1898
1.3097
0.0154
0.0260
0.0311
0.0783
0.0723
0.0525
0.1040
0.0284
0.1394
0.3949
S3
0.0601
0.0847
-0.3366 -1.0896 -0.5415 -0.1104
1.3049
0.7577
1.5880
-4.1574
(SEE)
0.1062
0.1789
0.2139
0.5394
0.4975
0.7157
0.1955
0.9594
2.7185
P
0.0164
-0.0399
0.0444
0.1394
-0.0656 -0.0102 -0.1109
0.0651
0.1193
3.7019
(SEE)
0.0231
0.0389
0.0465
0.1173
0.1082
0.0786
0.1556
0.0425
0.2086
0.5912
DI16
-0.0383
0.1010
-0.0229
0.0314
0.2141
0.0861
0.0778
-0.0198
0.5551
0.0727
(SEE)
0.0220
0.0370
0.0443
0.1117
0.1030
0.0748
0.1482
0.0405
0.1987
0.5630
CP16
0.0026
0.0985
-0.0403
0.0812
0.1666
0.6498
-0.1923
0.0657
-0.1688
1.3076
(SEE)
0.0244
0.0410
0.0491
0.1237
0.1141
0.0829
0.1641
0.0448
0.2200
0.6233
HD15
0.0025
-0.0054 -0.0162
0.0176
-0.0085
0.0002
0.8315
-0.0015
0.0510
-1.6935
(SEE)
0.0044
0.0075
0.0090
0.0226
0.0208
0.0151
0.0300
0.0082
0.0402
0.1138
GI10
0.0332
-0.0379 -0.0074
0.0012
-0.0758
0.1441
0.3184
0.0739
0.0590
2.1082
(SEE)
0.0118
0.0199
0.0238
0.0600
0.0553
0.0402
0.0796
0.0217
0.1066
0.3022
SP16
-0.0103
0.0007
-0.0031 -0.0005
0.0125
0.0715
0.4939
0.0169
0.1301
1.2664
(SEE)
0.0075
0.0127
0.0151
0.0352
0.0256
0.0507
0.0138
0.0679
0.1924
PCL06 0.1611
0.0382
0.3614
0.6512
0.7051
0.8366
0.7269
0.2994
0.8427
0.8135
0.9822
0.8716
0.9420
Table-A-6. Coefficients of the Reduced Form Equation with their Standard Error of Estimate (SEE): Optimistic Scenario
79
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MISHRA, S. K. A SIMULTANEOUS EQUATION MODEL OF GLOBALIZATION, CORRUPTION, DEMOCRACY, HUMAN DEVELOPMENT AND SOCIAL PROGRESS
Variable
E1
E2
S1
S2
S3
P
DI16
CP16
HD15
GI10
SP16
Panel-1: Observed Response Variable and Expected Response Variable (Conventional 2-SLS or C-2-SLS) or r ( y, y ) E1
0.731
0.760
0.790
0.896
0.821
0.376
0.678
0.759
0.970
0.904
0.951
E2
0.701
0.792
0.754
0.853
0.811
0.403
0.790
0.788
0.944
0.889
0.952
S1
0.700
0.732
0.824
0.863
0.806
0.388
0.690
0.829
0.915
0.904
0.930
S2
0.712
0.740
0.782
0.913
0.807
0.396
0.676
0.708
0.982
0.893
0.950
S3
0.703
0.760
0.783
0.883
0.831
0.430
0.771
0.770
0.944
0.905
0.953
P
0.590
0.707
0.706
0.805
0.791
0.451
0.807
0.724
0.859
0.830
0.887
DI16
0.551
0.708
0.628
0.693
0.712
0.463
0.884
0.759
0.761
0.780
0.836
CP16
0.601
0.698
0.769
0.723
0.729
0.377
0.756
0.893
0.771
0.831
0.843
HD15
0.724
0.737
0.782
0.914
0.801
0.389
0.653
0.700
0.983
0.893
0.946
GI10
0.713
0.764
0.809
0.886
0.831
0.422
0.740
0.814
0.948
0.921
0.958
SP16
0.709
0.778
0.787
0.894
0.820
0.417
0.767
0.780
0.973
0.908
0.970
Panel-2: Observed Response Variable and Expected Response Variable (Shapley Value 2-SLS or SV-2-SLS) or r ( y, y ) E1
0.709
0.762
0.813
0.875
0.812
0.411
0.730
0.832
0.941
0.916
0.953
E2
0.695
0.766
0.783
0.879
0.822
0.446
0.794
0.786
0.943
0.908
0.957
S1
0.701
0.778
0.796
0.867
0.820
0.427
0.787
0.832
0.938
0.914
0.959
S2
0.695
0.774
0.783
0.882
0.819
0.441
0.795
0.793
0.954
0.909
0.965
S3
0.671
0.748
0.778
0.850
0.788
0.453
0.784
0.819
0.911
0.897
0.941
P
0.733
0.763
0.805
0.895
0.837
0.404
0.703
0.779
0.960
0.919
0.953
DI16
0.692
0.765
0.780
0.876
0.831
0.456
0.798
0.784
0.938
0.909
0.954
CP16
0.721
0.766
0.809
0.894
0.835
0.420
0.739
0.794
0.957
0.922
0.959
HD15
0.738
0.757
0.792
0.902
0.832
0.397
0.682
0.740
0.970
0.911
0.949
GI10
0.702
0.771
0.802
0.871
0.824
0.431
0.779
0.832
0.935
0.917
0.958
SP16
0.698
0.771
0.803
0.872
0.828
0.431
0.784
0.830
0.936
0.916
0.958
Panel-3: C-2-SLS based Expected Response Variable and SV-2-SLS, Expected Response Variable or r ( y , y ) E1
0.971
0.961
0.965
0.965
0.925
0.990
0.954
0.982
0.990
0.968
0.968
E2
0.963
0.968
0.983
0.978
0.945
0.964
0.966
0.968
0.956
0.974
0.974
S1
0.982
0.944
0.966
0.949
0.946
0.973
0.947
0.976
0.958
0.974
0.974
S2
0.957
0.960
0.948
0.966
0.929
0.977
0.957
0.976
0.984
0.953
0.955
S3
0.961
0.991
0.975
0.982
0.948
0.979
0.984
0.985
0.976
0.981
0.980
P
0.889
0.925
0.911
0.933
0.904
0.895
0.944
0.909
0.881
0.916
0.929
DI16
0.846
0.891
0.896
0.896
0.909
0.812
0.903
0.843
0.788
0.887
0.886
CP16
0.922
0.880
0.927
0.885
0.907
0.870
0.878
0.890
0.829
0.923
0.923
HD15
0.955
0.956
0.944
0.960
0.925
0.977
0.951
0.974
0.987
0.948
0.947
GI10
0.994
0.984
0.992
0.988
0.975
0.993
0.986
0.996
0.982
0.996
0.996
SP16
0.982
0.986
0.989
0.994
0.967
0.983
0.983
0.990
0.979
0.987
0.988
Note: y = Observed response variable; y = Expected response variable (C-2-SLS); y = Expected response variable (SV-2-SLS)
Table-A-7. Correlation between Observed, Expected (C-2-SLS) and (SV-2-SLS) for Pessimistic Globalization Scenario 80
EJAE 2018 15 (1) 46-82
MISHRA, S. K. A SIMULTANEOUS EQUATION MODEL OF GLOBALIZATION, CORRUPTION, DEMOCRACY,HUMAN DEVELOPMENT AND SOCIAL PROGRESS
Variable
E1
E2
S1
S2
S3
P
DI16
CP16
HD15
GI10
SP16
Panel-1: Observed Response Variable and Expected Response Variable (Conventional 2-SLS or C-2-SLS) or r ( y, y ) E1
0.579
0.748
0.797
0.867
0.784
0.392
0.613
0.783
0.913
0.888
0.907
E2
0.540
0.806
0.781
0.861
0.807
0.436
0.776
0.775
0.958
0.888
0.958
S1
0.555
0.767
0.831
0.878
0.813
0.440
0.716
0.812
0.942
0.912
0.949
S2
0.541
0.761
0.799
0.913
0.815
0.431
0.654
0.709
0.977
0.895
0.944
S3
0.575
0.776
0.794
0.894
0.827
0.452
0.716
0.732
0.962
0.910
0.947
P
0.552
0.750
0.782
0.837
0.797
0.469
0.691
0.827
0.889
0.902
0.913
DI16
0.469
0.725
0.674
0.686
0.715
0.480
0.862
0.791
0.777
0.801
0.845
CP16
0.496
0.717
0.779
0.755
0.717
0.461
0.785
0.873
0.813
0.850
0.873
HD15
0.548
0.765
0.810
0.913
0.820
0.433
0.665
0.727
0.976
0.902
0.948
GI10
0.555
0.777
0.825
0.887
0.821
0.468
0.733
0.821
0.946
0.923
0.957
SP16
0.536
0.796
0.810
0.889
0.821
0.455
0.767
0.780
0.973
0.906
0.970
Panel-2: Observed Response Variable and Expected Response Variable (Shapley Value 2-SLS or SV-2-SLS) or r ( y, y ) E1
0.547
0.776
0.822
0.876
0.800
0.469
0.732
0.836
0.938
0.917
0.952
E2
0.554
0.782
0.800
0.880
0.815
0.489
0.774
0.792
0.943
0.917
0.955
S1
0.556
0.790
0.809
0.864
0.814
0.482
0.787
0.828
0.935
0.919
0.956
S2
0.528
0.789
0.803
0.882
0.815
0.481
0.788
0.791
0.956
0.907
0.965
S3
0.538
0.762
0.793
0.856
0.774
0.504
0.756
0.822
0.916
0.908
0.940
P
0.580
0.775
0.823
0.896
0.835
0.450
0.696
0.781
0.958
0.926
0.951
DI16
0.556
0.784
0.810
0.885
0.826
0.485
0.765
0.794
0.953
0.923
0.961
CP16
0.575
0.779
0.821
0.891
0.828
0.474
0.733
0.795
0.955
0.930
0.957
HD15
0.600
0.764
0.806
0.894
0.830
0.441
0.657
0.739
0.957
0.921
0.936
GI10
0.547
0.782
0.816
0.871
0.817
0.482
0.777
0.833
0.935
0.919
0.958
SP16
0.543
0.783
0.819
0.871
0.821
0.478
0.781
0.831
0.937
0.918
0.958
Panel-3: C-2-SLS based Expected Response Variable and SV-2-SLS, based Expected Response Variable or r ( y , y ) E1
0.945
0.929
0.933
0.916
0.900
0.964
0.930
0.949
0.960
0.940
0.936
E2
0.961
0.970
0.979
0.978
0.944
0.961
0.972
0.967
0.949
0.970
0.971
S1
0.985
0.960
0.974
0.966
0.953
0.985
0.973
0.982
0.966
0.983
0.983
S2
0.954
0.961
0.943
0.966
0.933
0.978
0.969
0.972
0.975
0.951
0.951
S3
0.949
0.984
0.967
0.973
0.937
0.983
0.982
0.984
0.987
0.965
0.964
P
0.972
0.945
0.962
0.944
0.960
0.960
0.959
0.959
0.940
0.969
0.966
DI16
0.868
0.888
0.919
0.891
0.893
0.832
0.887
0.865
0.803
0.901
0.900
CP16
0.940
0.902
0.942
0.908
0.932
0.888
0.909
0.910
0.846
0.940
0.941
HD15
0.961
0.967
0.952
0.970
0.937
0.985
0.974
0.979
0.981
0.959
0.960
GI10
0.995
0.983
0.989
0.985
0.977
0.992
0.992
0.994
0.973
0.996
0.995
SP16
0.981
0.983
0.986
0.994
0.966
0.981
0.990
0.987
0.965
0.986
0.988
Note: y = Observed response variable; y = Expected response variable (C-2-SLS); y = Expected response variable (SV-2-SLS)
Table-A-8. Correlation between Original, Expected (C-2-SLS) and (SV-2-SLS) for Optimistic Globalization Scenario 81
EJAE 2018 15 (1) 46-82
MISHRA, S. K. A SIMULTANEOUS EQUATION MODEL OF GLOBALIZATION, CORRUPTION, DEMOCRACY, HUMAN DEVELOPMENT AND SOCIAL PROGRESS
MODEL SIMULTANIH JEDNAČINA GLOBALIZACIJE, KORUPCIJE, DEMOKRATIJE, LJUDSKOG RAZVOJA I DRUŠTVENOG NAPRETKA Rezime: Ova studija gradi model simultane jednačine koji uspostavlja međusobne veze između mera globalizacije, mera demokratije, ljudskog razvoja, indeksa percepcije korupcije i dohotka po glavi stanovnika, što zajednički utiče na društveni napredak. Model ima jedanaest jednačina u kojima su varijable odgovora i varijable prediktora logično-linearno povezane. Empirijski podaci korišćeni za procenu modela odnose se na period 2006-2016. godine za 116 zemalja raspoređenih na svim kontinentima. Model je procenjen na osnovu konvencionalnih dvostepenih kvadrata (2-SLS) i alternativno modifikovanih 2-SLS u kojem je u drugoj fazi korišćena Shapley-eva vrednost regresijea za poboljšanje štetnih efekata kolinearnosti između varijabli prediktora. Modifikovani 2-SLS nadmašuje konvencionalni 2-SLS. Studija utvrđuje da globalizacija pozitivno utiče na demokratiju, ljudski razvoj i društveni kapital. Globalizacija smanjuje korupciju dok integritet promoviše globalizaciju. Demokratija, društveni kapital, ljudski razvoj i globalizacija pozitivno utiču na društveni napredak. Takođe je utvrđeno da su prekogranična lična veza, kulturna blizina, demokratija i društveni kapital elastični u odnosu na svoje prediktore.
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Ključne reči: globalizacija, demokratija, društveni napredak, model simultanih jednačina, Shapley-eva vrednost regresije.
EJAE 2018, 15(1): 83-93 ISSN 2406-2588 UDK: 005.21:005.336.1 006.83 DOI: 10.5937/EJAE15-16145 Review paper/Pregledni naučni rad
IMPROVING BUSINESS PERFORMANCE WITH ISO 9001: A REVIEW OF LITERATURE AND BUSINESS PRACTICE Mihalj Bakator*, Dragan Ćoćkalo Technical Faculty ”Mihajlo Pupin”, University of Novi Sad, Serbia
Abstract: This paper studies the impact of the ISO 9001 on business performance. In addition, the impact on business performance metrics is also investigated. These metrics are product, and service quality, customer satisfaction, financial performance, and operational performance. In the process of selection, 25 out of 110 papers were chosen for further analysis. The sum of samples is 6605. Therefore, the paper has moderate significance and moderate contribution in the domain of ISO 9001 certification. The key findings of this paper indicated that ISO 9001 certification can improve operational performance, customer satisfaction, financial performance, and overall business performance. However, negative impact of ISO 9001 on the mentioned constructs is also noted. For practical implication, companies may use this systematic review as a tool which may help in decision making regarding ISO 9001 standards certification or overall business performance improvement.
Article info: Received: December 26, 2017 Correction: February 7, 2018 Accepted: February 12, 2018
Keywords: ISO 9001, impact, benefits, certification, performance.
INTRODUCTION In this paper a systematic literature review is conducted in the domain of ISO 9001 standard benefits, and its overall impact on business performance. Now, Sitki IIkay, & Aslan, (2012) described that there is no statistical difference between ISO 9001 certified, and non-certified companies regarding business performance. The researched metrics in their study included: profitability, turnover, inventory turnover, waste re-processing costs, capacity utilization rate, defective product ratio, manufacturing lead time, employee satisfaction levels, number of complaints from customers, customer satisfaction levels, number of customers returning products, on-time delivery, response speed of technical services, competitive position, regular training for employees, and cost savings. * E-mail: mihalj.bakator@uns.ac.rs
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BAKATOR, M., ĆOĆKALO, D. IMPROVING BUSINESS PERFORMANCE WITH ISO 9001: A REVIEW OF LITERATURE AND BUSINESS PRACTICE
In contrast to the research of Sitki IIlkay, and Aslan (2012), Dick, Heras, and Casadesus (2008), argued that ISO 9001 certification positively influences business performance. The measured metrics in their research were product quality, waste control, cost reduction, competitiveness improvement, sale volume, and profitability. However, it is also noted that good performing companies are more likely to start the certification process. Therefore, these companies have an advantage over companies with low business performance. Further, Aba, Badar, and Hayden (2016) concluded that ISO 9001 certified companies had better operating performance (consistent volume deliveries, productivity, cost savings, defect product reduction) opposite to non-certified companies. In the same study, it is also noted that significant improvement of operating performance was evident only a year prior to the certification year. Early research of Simmons, and White (1999) argued that there is no significant difference in operating performance (profit, foreign sales, employee communication, productivity) between ISO 9001 certified and non-certified companies. In contrast, Kafetzopoulos, Psomas, and Gotzamani (2015) noted that ISO 9001 positively affects product quality, and operational performance (productivity, cost savings, waste reduction). There are many more contradictory research results in the domain of ISO 9001 certification. Therefore, the main objective of this review is to analyse various scientific articles regarding the benefits, and impact of the ISO 9001 standard on business performance. The data is collected in the form of full/ complete scientific papers. The main research questions that this paper addresses are: 1. What are the ISO 9001 standards certification benefits? 2. Is there a substantial difference in business performance between ISO 9001 certified companies and non-certified companies? In the next section the review methodology will be described. Further, the results are presented. The results include tables of cross-analysed research articles in order to provide a concise preview of the whole review process.
METHODOLOGY Literature sources There are seven (7) major sources/journals from where the articles are obtained. The papers from these seven journals make more than 60% of the overall collected literature that was considered for the systematic review. In Table 1, the seven major journals are presented in more detail. Journal name
Publisher
1.
International Journal of Quality & Reliability Management
0265-671X
Emerald
2.
International Journal of Operations & Production Management
0144-3577
Emerald
3.
Journal of Manufacturing Technology Management
1741-038X
Emerald
4.
Total Quality Management & Business Excellence
1478-3363
Taylor & Francis
5.
The TQM Journal
1754-2731
Emerald
6.
The TQM Magazine
0954-478X
Emerald
7.
International Journal of Productivity and Performance Management
1741-0401
Emerald
Table 1 – Seven major sources of literature Source: Developed for this research
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ISSN
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BAKATOR, M., ĆOĆKALO, D. IMPROVING BUSINESS PERFORMANCE WITH ISO 9001: A REVIEW OF LITERATURE AND BUSINESS PRACTICE
Literature eligibility criteria The earliest published article used in this review, dates from 2000, and the newest article is from 2017. This way, article age bias is avoided. All articles are published in journals with moderate to strong significance. The subjects of the articles addressed one or more of the following constructs: ◆◆ ISO 9001 certification ◆◆ ISO 9001 certification impact on business performance ◆◆ Quality management systems ◆◆ Case studies addressing ISO 9001 ◆◆ ISO 9001 product, and service quality ◆◆ ISO 9000 standards Furthermore, articles in a review process for a certain journal, and articles that are accepted but are not published yet, are not taken into consideration for this review. Predatory journals and predatory conferences are avoided.
Protocol diagram The review was conducted according to a structured protocol diagram. The majority of the scientific papers were searched through the Google Scholar service, and KoBSON service. Next, the articles were downloaded, and the duplicates were removed. Afterwards, a screening process was conducted to determine which articles are eligible for the systematic review. The eligible articles were thoroughly analysed. All irrelevant sources were excluded. Figure 1 depicts the protocol diagram used for the literature review process.
Figure 1 - Protocol diagram (made in accordance with the PRISMA protocol: Moher, Liberati, Tetzlaff, Altman, & Group, 2010). 85
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Sample independence Few research articles are published by the same authors, with different years of publication, and similar sample sources. For this, each article is analysed, and it is evident that the samples are independent. This independence is ensured through the difference of countries that the research took place in, difference in the industries of the companies, and randomized selection of samples from independent databases (ex. ICAP database used by Psomas, and Kafetzopoulos). There may be a chance that randomized database sampling can result in double counting. However, in this case the consequences are not severe, as the randomization process ensures a low percentage of sample overlapping. In the next section the research results are presented. Afterwards, the paper discusses the findings, and conclusions are drawn.
RESULTS Results of individual studies After a thorough systematic literature analysis, the main findings of each reviewed article were noted. Author details, sample details, and findings are presented in Table 2.
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#
Authors
Sample
Findings
R1
(Evangelos L. Psomas & Kafetzopoulos, 2014)
140 manufacturing companies in Greece
ISO 9001 certified companies significantly outperform non-certified companies in the domain of product quality, customer satisfaction, operational performance, and financial performance.
R2
(Evangelos L. Psomas, Fotopoulos, & Kafetzopoulos, 2011)
196 manufacturing companies in Greece
ISO 9001 certified companies achieved significant improvements regarding product quality.
ISO 9001 certification contributed to business perfor1500 companies mance. However, this is the case only when the manthat have been ISO agers were focused on performance, and not only on 9001 certified certification.
R3
(Terziovski, 2003)
R4
(Withers & Ebrahimpour, 2000)
11 ISO 9001 certified companies from Europe
R5
(Allur, Heras-Saizarbitoria, & Casadesús, 2014)
110 companies in Early ISO 9001 certification that is driven by internal Spain, surveyed in motives doesn’t bring benefits to the company. 1999 and 2011
R6
(Zhang, 2000)
10 manufacturing companies in the Netherlands
TQM has a much better effect on business performance in opposite to ISO 9001.
R7
(Zeng, Tian, & Tam, 2007)
156 companies from China
The negative aspects of ISO 9001 certification are shortsighted goals, lack of commitment from certifying bodies, and excessive competition between certifying bodies.
From 11 European companies, only 2 reported very positive impact on quality. Top management involvement, implementation time, and standard interpretation were reported as main obstacles.
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BAKATOR, M., ĆOĆKALO, D. IMPROVING BUSINESS PERFORMANCE WITH ISO 9001: A REVIEW OF LITERATURE AND BUSINESS PRACTICE
R8
(Feng, Terziovski, & Samson, 2007)
A strong positive relationship was found between ISO 613 ISO 9001 9001 certification and operational performance. In certified compacontrast, there was a positive, but insignificant relationnies from Australia ship between business performance and ISO 9001 cerand New Zealand tification.
R9
(Tzelepis, Tsekouras, Skuras, & Dimara, 2006)
1572 manufacturing companies from Greece
ISO 9001 certification is superior to non-certified companies regarding production inefficiency.
R10
(Paulo Sampaio, Saraiva, & Monteiro, 2012)
6 companies (not specified)
The findings argued that it is not unanimous that ISO 9001 certified companies would have lower financial performance if they had not implemented the ISO 9001 certificate.
R11
Data from across countries was collected including the top ten coun(Paulo Sampaio, tries in ISO 9001 Saraiva, & Guimarães certificate growth: Rodrigues, 2009) China, Italy, Japan, Spain, UK, USA, Germany, India, France, Australia
Companies achieve benefits if the certification is driven by internal motives. There is a positive impact on business performance. Lack of top management involvement in the certification process is considered as a big obstacle.
(Quazi & Jacobs, 2004)
33 ISO 9001 certified companies from Singapore
ISO 9001 certified companies reported higher business performance.
R13
(Quazi, Hong, & Meng, 2010)
93 service, and construction companies from Singapore, crossreferenced with 346 companies from Mexico, China, India, and USA
The findings suggest that ISO 9001 certification doesn’t have an impact on quality results.
R14
(Honore, Yaya, Marimon, & Casadesus, 2013)
R15
(Evangelos L. Psomas, Pantouvakis, & Kafetzopoulos, 2013)
100 ISO certified companies from Greece
The results indicate that product, and service quality, and operational performance were significantly influenced by ISO 9001.
R16
(Poksinska, Eklund, & Dahlgaard, 2006)
3 case studies of small organizations
After certification, minimum change was experienced, as the standard was perceived as a tool for handling documentation. There were external benefits due to the certificate. However, no internal benefits were achieved.
R17
(Ochieng, Muturi, & Njihia, 2015)
20 organizations from East Africa
Findings argued that ISO 9001 certified companies had higher performance regarding net assets in opposite to non-certified companies. However, there was no significant difference in profit and revenue.
R12
Results indicate that ISO 9001 didn’t have an impact on 123 online banking customer satisfaction. However, the customer loyalty customers and customer satisfaction relationship showed a staggering 47 percent of improvement.
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R18
(Morris, Crawford, Carter, & Mazotta, 2000)
15 organizations
The findings indicate that the ISO 9001 certificate doesn’t necessarily provide effective performance improvement.
R19
(Martínez‐Costa, Martinez, & Martínez‐Lorente, 2007)
713 companies
The paper’s findings suggest that ISO 9001 can reduce benefits, and profitability.
R20
(Kim, Kumar, & Kumar, 2011)
Systematic review
It is argued that the various internal and external motivators for certification play an important role in achieving higher business performance. Therefore, merely adopting the ISO 9001 certificate, won’t bring benefits, if other organizational factors are not included.
R21
(Jang & Lin, 2008)
441 ISO 9001 certified companies
ISO 9001 certification improved operational performance, indirectly improved market performance, and business performance.
R22
(K. D. Gotzamani, Tsiotras, Nicolaou, Nicolaides, & Hadjiadamou, 2007)
352 ISO 9001 certified companies from Cyprus
The main conclusion is that ISO 9001 certified companies improved process management performance.
R23
(K. Gotzamani, 2010)
87 SMEs from Greece
ISO 9001 standard contributed to higher product quality, and improvement of processes.
R24
(Chatzoglou, Chatzoudes, & Kipraios, 2015)
168 companies from Greece
The study noted that ISO 9001 implementation improved financial performance, quality awareness, operation execution, market share, customer satisfaction, and sales revenue.
(Cândido, Coelho, & Peixinho, 2016)
143 Portuguese companies that lost their ISO 9001 certification and companies that maintained the certificate
Findings indicated that there is no significant difference regarding financial performance, between companies who lost their ISO 9001 certification, and certified companies.
R25
Table 2 - Authors, sample details, and findings of reviewed articles Source: Developed for this research
Synthesis of results For this systematic review, twenty-four (24) research articles, and one (1) review article were investigated. The total number of companies of 6605 is significant enough for a systematic review. The sample details are presented in Table 3.
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#
Country
Sample size from positive research findings
Sample size where negative or no benefits of ISO 9001 were reported
1.
Greece
2163
100
2.
Global (not specified)
441
2360
3.
Europe (not specified)
4
7
4.
Spain
0
110
5.
Netherlands
0
10
6.
China
0
156
7.
Australia, New Zealand
613
0
8.
Singapore
33
93
9.
East-Africa
0
20
10.
Cyprus
352
0
11.
Portugal
0
143
3606
2999
TOTAL
Table 3 - Samples categorized by country, and findings Source: Developed for this research
According to Table 3, the sum of samples, where ISO 9001 certification brought benefits to the company, is 3606. The number of samples where the research showed that there are no benefits of ISO 9001 certification is 2999. Next, on Figure 2, the percentage of positive and negative/no impact research results are presented. In addition, the five factors of improvement are shown, along with the research articles and the sample size details.
Figure 2 - Percentage of positive, and negative/no benefits of ISO 9001 certification with detailed factors, and article details (Source: Developed for this research) 89
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Figure 2 shows that 55 percent of the total sample size, experienced benefits from ISO 9001 certification. Product/service quality was recorded in six reviewed papers, operational performance in seven, customer satisfaction in three, business performance in four, and financial performance increase in two reviewed papers. In the next section the results are discussed, and conclusions are drawn.
DISCUSSION AND CONCLUSION In their meta-analysis Psomas, and Fotopoulos (2009) noted that the ISO 9001:2000 certificate generally contributed to business performance, and operational performance. Complementary to their meta-analysis, this systematic review obtained similar results. Next, Casadesús, and Karapetrovic (2005) investigated the possible long-term benefits, and the negative impact of ISO 9001 certification. They noted that the ISO 9001 standard is limited regarding implementation time, and overall benefits. In addition, Ataseven, Prajogo, and Nair (2014) discussed that ISO 9001 certification improves business performance through improved processes. The ISO 9001:2015 standard brought improvements over the previous ISO 9001:2008 standard. These improvements include greater leadership engagement, simplified language and terms, structured organizational risks and opportunities, user friendly documentation, and the supply chain management is more effectively defined (International Organization for Standardization, 2015). The newer ISO 9001:2015 standard is more risk oriented in opposite to previous editions. It holds accountable the top management for risk management (Rybski, Jochem, & Homma, 2017). Similarly, Sari, Wibisono, Wahyudi, and Lio (2017) noted that ISO 9001:2015 has a risk-based approach. Therefore, organizations can formulate their implementation strategies in such a manner that the negative impact of ISO 9001:2015 certification is minimal. Furthermore, the findings answered the previously defined research questions. These are the following: 1. What are the ISO 9001 standards certification benefits? The benefits may include higher product, and service quality, strong improvement in operational performance, moderate improvement in financial performance, moderate improvement in customer satisfaction, improved overall business performance. 2. Is there a substantial difference in business performance between ISO 9001 certified companies and non-certified companies? The results of this review paper suggested that in some findings there is no difference between certified and non-certified companies regarding business performance. In addition, some articles noted that there are no benefits before, and after ISO 9001 certification. Other research findings indicated that there were mild external benefits regarding the certificate itself, while minimal internal improvement was reported. There are many factors that influence the results of ISO 9001 certification. These factors may include type of organization, size, industry, market, customers, and organizational culture. Furthermore, the contributions of this paper are moderate. Qualitative insight is obtained in the domain of ISO 9001 certification. The reviewed articles provided a large sum of samples, and the size of the samples further strengthened the contribution of the final results. The main limitation of this paper is the non-analytical approach regarding the quantitative results of individual research papers. However, the qualitative data obtained from the analysed literature gives a insight when it comes to the impact of ISO 9001 certification. 90
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Furthermore, this review paper concisely shows the findings of 25 scientific articles. According to the synthesis of the results, there is a chance that ISO 9001 certification brings benefits in 50% of all cases. It is important to note, that this represents only the sample size of individual research articles. The exact number of companies that reported positive or negative impact of ISO 9001 certification is not presented. In the majority, companies reported that they experienced improved product/service quality, and operational performance. Moderate improvement was noted in the domain of customer satisfaction, financial performance, and overall business performance. As mentioned before, the significance of these percentages must be taken with appropriate reservation. The samples are viewed as a whole. This excludes the number of companies that reported benefits, when the majority responded negatively, and vice-versa. Practical implications of this systematic review may include improved decision making of managers regarding ISO 9001 certification. For future research, other factors should be included in the review. It is recommended to address a larger body of literature, and define the development and certification of ISO 9001 within defined time frames including a minimum time span of 30 years, where various versions of ISO 9001 standards are analysed.
REFERENCES Aba, E. K., Badar, M. A., & Hayden, M. A. (2016). Impact of ISO 9001 certification on firms financial operating performance. International Journal of Quality & Reliability Management, 33(1), 78-89. DOI: 10.1108/ ijqrm-02-2014-0021 Allur, E., Heras-Saizarbitoria, I., & Casadesus, M. (2014). Internalization of ISO 9001: a longitudinal survey. Industrial Management & Data Systems, 114(6), 872-885. DOI: 10.1108/IMDS-01-2014-0013 Ataseven, C., Prajogo, D. I., & Nair, A. (2014). ISO 9000 internalization and organizational commitmentimplications for process improvement and operational performance. IEEE Transactions on Engineering Management, 61(1), 5-17. Candido, C. J. F., Coelho, L. M. S., & Peixinho, R. M. T. (2016). The financial impact of a withdrawn ISO 9001 certificate. International Journal of Operations & Production Management, 36(1), 23-41. DOI: 10.1108/ IJOPM-11-2014-0540 Casadesus, M., & Karapetrovic, S. (2005). Has ISO 9000 lost some of its lustre? A longitudinal impact study. International Journal of Operations & Production Management, 25(6), 580-596. DOI: 10.1108/01443570510599737 Chatzoglou, P., Chatzoudes, D., & Kipraios, N. (2015). The impact of ISO 9000 certification on firms’ financial performance. International Journal of Operations & Production Management Decision, 35(1), 145-174. DOI: 10.1108/IJOPM-07-2012-0387 Dick, G. P., Heras, I., & Casadesús, M. (2008). Shedding light on causation between ISO 9001 and improved business performance. International Journal of Operations & Production Management, 28(7), 687-708. DOI: 10.1108/01443570810881811 Feng, M., Terziovski, M., & Samson, D. (2007). Relationship of ISO 9001: 2000 quality system certification with operational and business performance: A survey in Australia and New Zealand-based manufacturing and service companies. Journal of Manufacturing Technology Management, 19(1), 22-37. DOI: 10.1108/01409170410784365 Gotzamani, K. (2010). Results of an empirical investigation on the anticipated improvement areas of the ISO 9001:2000 standard. Total Quality Management & Business Excellence, 21(6), 687-704. DOI:10.1080/14783 363.2010.483101 Gotzamani, K. D., Tsiotras, G. D., Nicolaou, M., Nicolaides, A., & Hadjiadamou, V. (2007). The contribution to excellence of ISO 9001: the case of certified organisations in Cyprus. The TQM Magazine, 19(5), 388-402. DOI: 10.1108/09544780710817838 91
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Honore, L., Yaya, P., Marimon, F., & Casadesus, M. (2013). Can ISO 9001 improve service recovery? Industrial Management & Data Systems, 113(8), 1206-1221. DOI: 10.1108/IMDS-03-2013-0150 International Organization for Standardization (2015). ISO 9001: Moving from ISO 9001:2008 to ISO 9001:2015. Geneva, Switzerland: ISO Central Secretariat Chemin de Blandonnet. Jang, W.-Y., & Lin, C.-I. (2008). An integrated framework for ISO 9000 motivation, depth of ISO implementation and firm performance. Journal of Manufacturing Technology Management, 19(2), 194-216. DOI: 10.1108/17410380810847918 Kafetzopoulos, D. P., Psomas, E. L., & Gotzamani, K. D. (2015). The impact of quality management systems on the performance of manufacturing firms. International Journal of Quality & Reliability Management, 32(4), 381-399. DOI: 10.1108/IJQRM-11-2013-0186 Kim, D. Y., Kumar, V., & Kumar, U. (2011). A performance realization framework for implementing ISO 9000. International Journal of Quality & Reliability Management, 28(4), 383-404. DOI: 10.1108/02656711111121807 Martinez‐Costa, M., Martinez, V., & Martínez‐Lorente, Á. R. (2007). A triple analysis of ISO 9000 effects on company performance. International Journal of Productivity and Performance Management, 56(5/6), 484-499. DOI: 10.1108/17410400710757150 Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & Group, P. (2010). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. International Journal of Surgery, 8(5), 336-341. Morris, A., Crawford, J., Carter, D., & Mazotta, F. (2000). Management decisions for effective ISO 9000 accreditation. Management Decision, 38(3), 182-193. DOI: 10.1108/EUM0000000005346 Ochieng, J., Muturi, D., & Njihia, S. N. (2015). The impact of ISO 9001 implementation on organizational performance in Kenya. The TQM Journal, 27(6), 761-771. DOI: 10.1108/TQM-06-2015-0071 Poksinska, B., Eklund, J. A. E., & Dahlgaard, J. J. (2006). ISO 9001:2000 in small organisations. International Journal of Quality & Reliability Management, 23(5), 490-512. DOI: 10.1108/02656710610664578 Psomas, E. L., & Fotopoulos, C. V. (2009). A meta analysis of ISO 9001: 2000 research–findings and future research proposals. International Journal of Quality and Service Sciences, 1(2), 128-144. DOI: 10.1108/17566690910971418 Psomas, E. L., Fotopoulos, C. V., & Kafetzopoulos, D. P. (2011). Core process management practices, quality tools and quality improvement in ISO 9001 certified manufacturing companies. Business Process Management Journal, 17(3), 437-460. DOI: 10.1108/14637151111136360 Psomas, E. L., & Kafetzopoulos, D. P. (2014). Performance measures of ISO 9001 certified and noncertified manufacturing companies. Benchmarking: An International Journal, 21(5), 756-774. DOI: 10.1108/BIJ-04-2012-0028 Psomas, E. L., Pantouvakis, A., & Kafetzopoulos, D. P. (2013). The impact of ISO 9001 effectiveness on the performance of service companies. Managing Service Quality: An International Journal of Advertising, 23(2), 149-164. DOI: 10.1108/09604521311303426 Rybski, C., Jochem, R., & Homma, L. (2017). Empirical study on status of preparation for ISO 9001:2015. Total Quality Management & Business Excellence, 1076-1089. Sari, Y., Wibisono, E., Wahyudi, R., & Lio, Y. (2017). From ISO 9001: 2008 to ISO 9001: 2015: Significant changes and their impacts to aspiring organizations. Paper presented at the IOP Conference Series: Materials Science and Engineering, 273(1), 12-21 Quazi, H. A., Hong, C. W., & Meng, C. T. (2010). Impact of ISO 9000 certification on quality management practices: A comparative study. Total Quality Management & Business Excellence, 13(1), 53-67. DOI: 10.1080/09544120120098564 Quazi, H. A., & Jacobs, R. L. (2004). Impact of ISO 9000 certification on training and development activities. International Journal of Quality & Reliability Management, 21(5), 497-517. DOI: 10.1108/02656710410536545 Sampaio, P., Saraiva, P., & Guimarães Rodrigues, A. (2009). ISO 9001 certification research: questions, answers and approaches. International Journal of Quality & Reliability Management, 26(1), 38-58. DOI: 10.1108/02656710910924161 92
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Sampaio, P., Saraiva, P., & Monteiro, A. (2012). ISO 9001 certification pay-off: myth versus reality. International Journal of Quality & Reliability Management, 29(8), 891-914. DOI: 10.1108/02656711211270351 Simmons, B. L., & White, M. A. (1999). The Relationship Between ISO 9000 and Business Performance: Does Registration Really Matter? Journal of Managerial Issues, 11(3), 330-343. Sıtkı İlkay, M., & Aslan, E. (2012). The effect of the ISO 9001 quality management system on the performance of SMEs. International Journal of Quality & Reliability Management, 29(7), 753-778. DOI:10.1108/02656711211258517 Terziovski, M., Damien Power, & Sohal, A. S. (2003). The longitudinal effects of the ISO 9000 certification process on business performance. European Journal of Operational Research, 146(3), 580-595. Tzelepis, D., Tsekouras, K., Skuras, D., & Dimara, E. (2006). The effects of ISO 9001 on firms’ productive efficiency. International Journal of Operations & Production Management, 26(10), 1146-1165. DOI: 10.1108/01443570610691111 Withers, B., & Ebrahimpour, M. (2000). Does ISO 9000 certification affect the dimensions of quality used for competitive advantage? European Management Journal, 18(4), 431-443. Zeng, S. X., Tian, P., & Tam, C. M. (2007). Overcoming barriers to sustainable implementation of the ISO 9001 system. Managerial Auditing Journal, 22(3), 244-254. DOI: 10.1108/02686900710733125 Zhang, Z. (2000). Developing a model of quality management methods and evaluating their effects on business performance. Total Quality Management & Business Excellence 11(1), 129-137. DOI: 10.1080/0954412007071
UNAPREĐENJE POSLOVNIH PERFORMANSI UVOĐENJEM STANDARDA ISO 9001: PREGLED LITERATURE I POSLOVNE PRAKSE Rezime: Ovaj rad proučava uticaj standarda ISO 9001 na poslovne performanse. Pored toga, istražuje se i uticaj na merenje performansi poslovanja. Kao merne jedinice uzeti su proizvod, kvalitet usluga, zadovoljstvo kupaca, finansijski učinak i operativni učinak. U procesu selekcije, 25 od 110 radova je izabrano za dalju analizu. Ukupno je analizirano 6605 uzoraka. Stoga rad ima umereni značaj i umereni doprinos u domenu sertifikacije ISO 9001. Ključni nalazi ovog rada pokazali su da sertifikacija ISO 9001 može poboljšati operativne performanse, zadovoljstvo kupaca, finansijske performanse i ukupne poslovne performanse. Međutim, primećen je i negativan uticaj ISO 9001 na pomenute konstrukcije. Zarad praktične implikacije, kompanije mogu koristiti ovaj sistematski pregled kao alat koji može pomoći pri donošenju odluka u vezi sa ISO 9001 sertifikacijom ili poboljšanjem ukupnog poslovanja.
Ključne reči: ISO 9001, uticaj, benfiti, sertifikacija, performanse
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EJAE 2018, 15(1): 94-109 ISSN 2406-2588 UDK: 330.564:614(6) DOI: 10.5937/EJAE15-16293 Original paper/Originalni nauÄ?ni rad
INCOME-HEALTH NEXUS IN SUB-SAHARAN AFRICA: EVIDENCE FROM HETEROGENEOUS PANEL MODELS Ibrahim Abidemi Odusanya1*, Akinwande A. Atanda2 1 Department of Economics, Olabisi Onabanjo University, Ago-Iwoye, Nigeria 2 Department of Economics and Finance, University of Canterbury, New Zealand
Abstract: An attempt is made in this research to examine the relationship between income and health by testing the Absolute Income-Health Hypothesis (AIH). The study primarily focuses on 34 Sub-Saharan Africa (SSA) countries for the period of 2001-2016. The data for the study were mainly sourced from World Development Indicators (WDI) and the World Health Organization (WHO) Global Health Observatory Data Repository. Using heterogeneous slopes modelling set-up that incorporates series of non-stationarity, cross-section dependence, and group-specific trends, we failed to find evidence in support of the AIH. Our empirical outcome cast doubts on the robustness of previous studies that ignored such modelling attributes, while we deduced that methodology matters in analysing income-health nexus and testing the validity of the AIH for cross-section of countries. By contrast, we find income to be an insignificant determinant of health in SSA compared to health spending and improved sanitation.
Article info: Received: January 17, 2018 Correction: March 18, 2018 Accepted: March 20, 2018
Keywords: Health, Income, Sub-Saharan Africa, Heterogeneous slope, Cross-section Dependence
INTRODUCTION Average householdsâ&#x20AC;&#x2122; health status is one of the barometers of population well-being, and is largely dependent on income level among other determinants. The relationship between income and health has generated some hypotheses and empirical conclusions. One of the leading predictions, Absolute Income-Health Hypothesis (henceforth, AIH) can be traced to Grossman (1972) model, and later on to Preston, (1975) both of whom state that wage increase can enhance health. The hypothesis suggests that health improves with average income but at a decreasing rate. This implies a curvilinear association between income and health. 94
* E-mail: ibrahim.odusanya@oouagoiwoye.edu.ng
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There is a plethora of empirical evidence on the validity of AIH, mostly documented for developed and high-middle income countries. The studies that confirm the validity are Qi (2012) for 19 OECD countries, Kuehnle (2014) for United Kingdom, and Ram (2005) for 51 US states. To the best of our knowledge, there is a dearth of research validating AIH for Africa, only few selected African countries are pooled with other developed and less developing countries. Studies in this regard include Qi (2012) that considered 34 non-OECD countries and Ram (2006) that pooled 26 OECD and 82 less-developed countries. On this basis, the precise relationship between income and health in Africa is unclear, especially in Sub-Saharan Africa (henceforth SSA) characterized by increasing absolute per capita income but coupled with deteriorating health status (see estimates from WHO, 2014). It is very imperative to understand the relationship between income and health for two cogent reasons. Studying the relationship between income and health would offer better understanding on whether social differences in health status are attributable to poverty (or low income), or whether there is a socio-economic gradient in health status (implying that health status rises with each level of socio-economic or income status). Additionally, the investigation of the relationship between income and health will contribute to the debate on the diminishing returns of health with respect to income (Jusot 2006). Recent developments in panel data econometrics have revealed that standard panel data estimators such as pooled ordinary least square (POLS), fixed effect (FE) and random effect (RE) estimators can be misleading ignoring heterogeneity (of observed and unobserved factors), cross-section dependence and non-stationarity (Ando and Bai, 2016; Eberhardt and Presbitero, 2015). Neglecting those attributes has severe implications on the standard estimators’ biasness and consistency properties. To the best of our knowledge, our study is the first attempt to acknowledge the attributes and apply heterogeneous panel data estimators to test the validity of AIH for SSA. The remaining part of this paper is divided into four sections. Section 2 deals with literature review while section 3 focuses on the data and the adopted methodology. Section 4 discusses the results from estimation while the last paragraphs provide conclusions of the study.
LITERATURE REVIEW Earlier studies on the relationship between average income and health submitted that their association is concave. This implies that as average income increases, health status also increases. Later on, the increases will be at a decreasing rate after the point of inflexion, which is due to epidemiological transition (Omran, 2005). Epidemiological transition is the period when the patterns of disease change from infectious or contagious diseases (like cholera, chicken pox, tuberculosis, HIV/AIDS) to degenerative or non-communicable diseases (like cancer and cardiovascular diseases). This period has peculiarity of a burden of mortality comprising of the old rather than the very young people, while overall life expectancy will increase (Omran, 2005). During this period, the marginal effect of increases in absolute income falls to the extent that more variations in health are due to differences in income inequality. Therefore, as the level of per capita income increases, the effect of income inequality on population health becomes greater (Gravelle, Wildman, and Sutton, 2002). Rajan, Kennedy, and King (2013) also surmised that the positive relationship between average income and health discontinues once epidemiological transition sets in.
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An inextricable relationship exists between income and health in the health economics literature over a long period. The relationship between income per capita and life expectancy (a measure of health outcome) across countries for years 1900, 1930 and 1960 was found to be positive, with life expectancy varying more with income in poor countries than in rich countries (Preston, 1975). The correlation between income per head and life expectancy was 0.89. This implies that 1 percent variation in income results in 89 percent variation in life expectancy. The study culminated into the famous ‘Preston Curve’ in which the regression line was fitted to data (having life expectancy on the y-axis and income on the x-axis). In the same vein, the association between income and measures of health in the UK was found to be linear (except for very high and low income), both before and after controlling for selected socio-economic variables (Ecob and Smith, 1999). A doubling of income caused health to change by the same magnitude. It thus implies that increasing income is associated with improvement in health, but there are diminishing returns at higher levels of income. Using data set involving self-perceived health and chronic illness as dependent variables with the sample made up of 28,023 individuals across the 17 autonomous regions in Spain, personal income exerted significantly and positively on health, with these effects diminishing at higher level of income (Karlsdotter, Martin and Gonzales, 2012). One percentage point in personal income caused 87.3 percent reduction in the probability of suffering from chronic illness. They used multilevel logistic regression models that allowed for the determination of the direct effect of the individual and group explanatory variables and the interactions between levels. The outcome of their study supports the absolute income hypothesis for both perceived health and chronic illness, and conforms to the findings emanating from most international and Spanish studies. It therefore suggests that richer populations do not improve their health with additional income as the poorer people do. In the study of the association of self-rated health with income dynamics among male employees in Japan, it was deduced that an individual’s self-rated health was generally associated with short-term changes in income (Oshio,Umeda, and Fujii, 2013). Specifically, the survey focused on four annual income variables (i.e. current income, income in the previous year, average income and maximum income) and five income dynamic variables (i.e. increase and reduction in income from the previous year, increase and reduction in average income, and reduction in maximum income). They revealed that an individual’s average and maximum income levels were irrelevant vis-à-vis self-rated health while no clear association was observed between longerterm changes in income levels and self-rated health. They reiterated that this finding could be attributed to the extremely homogeneous nature of their sample (consisting of only males, 74.6% of whom are graduates of college and 76.7% of them are regularly employed) while they also excluded people who had any interruption in their wage history (Oshio et al., 2013). Thus, income variables used in the study sample did not reflect substantial differences in educational attainment and occupational status, which are likely to have a close relationship with self-rated health independent of income. Importantly, they found a reduction in income, compared to the previous year, to be negatively associated with self-rated health. In a regression of under-five mortality rates on district level average income, income inequality, poverty gap and literary rate in India, infant mortality rates were found to be negatively associated with poor average income levels and positively associated with poverty at both state and district level (Rajan et al., 2013). A unit increase in average income resulted into 0.0028 reduction in mortality. In addition, linear regression models controlling average income and variation in some state-level variables showed that income inequality was not a statistically significant predictor of infant or under-five mortality rates. Similarly, the coefficient of average income was statistically insignificant after controlling poverty gap and literacy rates. 96
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Bloom and Bowser (2008) examined the relationship between income and life expectancy using data from 3,049 counties in 50 states in the US for the years 1970 and 2000 (including the District of Columbia). It involved the classification of counties to the Mississippi River Delta Region (where population health indicators are significantly lower than the rest of the US) and non-Delta region (where population health indicators are far better). The difference in health between the Delta and non-Delta regions in 1970 was due to 64% variation in income while the remaining 36% was attributed to the non-income factors. However, for 2000, non-income factors explained 77% disparities in health between the Delta and non-Delta counties while 23% of the variation was due to the non-income factors. Likewise, one percent increase in income per capita is associated with a 0.035-year increase in life expectancy for Delta counties in 1970, a 0.032-year increase in life expectancy for Delta counties in 2000, a 0.035-year increase in life expectancy for non-Delta counties in 1970 and 0.038-year increase in life expectancy for non-Delta counties in 2000. Over time, non-income factors drove health particularly when epidemiological transition sets in. These non-income factors included dietary habits, smoking, occupational status, school enrolment and school curriculum (Bloom and Bowser, 2008). Meanwhile, evidence supporting the absolute income hypothesis holds when probit regression was applied to micro data on the Chinese economy. The results showed a concave relationship between self-reported health status and per capita income, implying that additional income brought about greater improvement in the health of the poor than the rich (Li and Zhu, 2006). A significant and Ushaped relationship existed between self-reported health status and community-level income inequality. Thus, rising inequality tends to improve health when inequality is low and tends to harm health when inequality is above a certain level. In the same vein, individual income level has a considerably greater influence on health than neighborhood affluence in the US economy when Hierarchical Ordinal Logit models was applied to variables driving self-rated health (Wen, Browning, and Cagney 2003). This suggests that individual level poverty has a persistent noxious effect on health even with the potential benefits attached to residence in affluent neighborhood. To the contrary, income was associated with health in Canadian provinces whereas income inequality was significant with chronic health conditions (Safaei, 2007). Therefore, whatever the magnitude of the estimated health inequalities, ill health is mostly concentrated in the poor and low-income groups. However, income, income inequality and other socio-economic measures accounted for seven-fold differences in the self-reported health of the elderly between the worst and the best provinces in China (Feng, Wang, and Li, 2012). They also deduced that for the poorest three quarters of their sample of 14,744 elderly individuals from 23 Chinese provinces, income brought about greater improvement in the health of the poor other than the rich, reflecting the concavity effect i.e. a curvilinear relationship between self-rated health and total family income. The study found odds of poor health to be three times greater for the elderly with the lowest income compared to those at the upper quartile. Meanwhile a strong correlation exists between the risk of death (measured via mortality rate) and income in France in a study involving classification of income into different quintiles. This strong relationship holds for all levels of income, despite controlling for occupational status. Every increase in equivalent taxable income was associated with significant reduction in risk of mortality (Jusot, 2006). A major dispersion of his findings to others is that a positive association exists between income and health even at higher levels of income. This is indeed contrary to the hypothesis of diminishing returns of income on health as reported by many of the authors that have tested the absolute income hypothesis. Pritchett and Summer (1996) used an instrumental variable estimator to analyze cross-country data spanning over 97
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twenty six years (1960-1985) to test the relationship between income and health, it was also deduced that increased income leads (causally) to improvements in health. Growth in mean income by 71 percent in the sampled countries culminated into 50 percent fall in infant mortality rate. A positive but diminishing relationship between income and health was found for a cross-section of 75 developed and developing countries, thereby confirming the concavity (or non-linearity) of the health-income curve (Gravelle, Wildman, and Sutton, 2002). Therefore, they corroborated the proposition of the absolute income hypothesis. Contoyannis, Jones and Rice (2004) examined health dynamics, health and income using the British Household Panel Survey (BHPS) data. They disaggregated income into current and permanent income. They observed that current income only affected health status of man significantly, while it exerts no significant impact on health status of women. Jones and Wildman (2008) also investigated the relationship between income and health based on the British Household Panel Survey (BHPS) for men and women. Health status was measured using the self-assessed health (SAH), absolute income was measured by annual household income, while a measure of relative deprivation was also introduced as an independent variable. The pooled OLS, fixed effects and random effects models revealed a positive and significant relationship between income and health for both men and women, without the measure of relative deprivation in the model (Jones and Wildman, 2008). When the measure of relative deprivation is included in the model, the relationship was still significant and positive for men and women, but was not significant using the random effects estimator. It is however apt to note that the magnitudes of the coefficients are generally small, with large changes in income resulting in small changes in health. Based on pooled 1990 and 2000 data for 51 states in the US with death rate (death per 100,000 populations) specified as the dependent variable, a significant negative relationship was found between income and death rate. An increase in personal income generally resulted in a more proportionate diminution in death rate for all the specified models. This implies that increases in income are associated with improvement in health status (Ram, 2005). A very strong positive correlation existed between income inequality and death rate, with poverty exerting negatively on health. On a study of six European countries and with data on individuals aged between 50 and 65, absolute income had a positive but quite modest effect on health after controlling for endogeneity (Theodossiou and Zangelidis, 2009). In another study to examine cross-country association between income and health for 108 countries (comprising 18 OECD countries and 90 LDCs), a very strong correlation exists between income and health (measured by infant mortality rate). Income exerted a significant negative effect on mortality for all the countries. When the less developed countries (LDCs) were isolated, poverty index1 had a positive relationship with infant mortality rate while income had a negative relationship with infant mortality (Ram, 2006). Meanwhile income inequality also had a positive relationship with the mortality rate. He inferred that income inequality other than poverty contributed more to infant mortality rate in the LDCs. Moreover, both income and GINI (a measure of income inequality) have larger effects in the LDCs, but the relative effect of GINI is stronger in the full sample (and thus in the DCs) while the marginal effect of income is stronger in the LDCs. This view was corroborated by Qi (2012), who found that the effect of absolute income is stronger in the LDCs. The detrimental effect of income inequality is largely obvious among the developed countries. By applying multilevel logistic regressions to 19 OECD and 31 non-OECD countries, the effect of GDP per capita on self- rated health was statistically significant for non-OECD countries (Qi, 2012). A unit increase in income caused 0.063 unit increase in self-rated 1 Poverty index is the percentage of population whose income is below the international poverty line of one PPP dollar per day (per person).
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health based on the entire sample. This reflects that absolute national wealth is more important for countries at the lower level of economic development (i.e. the non-OECD countries). The results from the study generally supported the absolute income hypothesis, and no significant detrimental effect of income inequality on individual health has been found. The findings also imply that when a country’s distribution of income is more unequal, the gap in self-rated health widens between people at different deciles of household income and the poor suffered more deprivation in terms of health. French (2012) investigated causation between income and health for 13 OECD countries with the aid of Panel Analysis of Non-Stationarity in Idiosyncratic and Common Components (PANIC) model. One percent increase in income per capita caused 0.02 percent increase in life expectancy. Obviously, income improved health while health also exerted significantly on income. Rehnberg and Fritzell (2016) examined the longitudinal relationship between midlife income and mortality as well as late-life income for an ageing Swedish. For midlife ages (50-60 years), the association between income and mortality was curvilinear i.e. returns to health diminishes as income rises. As income increases, the hazard ratio of mortality decreases, especially for households with equivalised income of between 100,000 and 300,000 Swedish crowns (SEK). This group constitutes the largest proportion of the population. In the same vein, the relationship was curvilinear for those in the age bracket of 65-75 years. The results generally reveal the prolongation of the non-linear relationship between income and mortality into old age. A number of studies have also examined the relationship between family income and child health for different countries in which they reported a positive association between family income and parentreported child health in the US, Australia, Canada and Germany. Aponey and Geoffard (2013) examined the income gradient in health across UK. The study found the emergence of the income gradient for children around age 2, while it was constant for children between ages 2 to 17. However, no correlation existed between income and child’s health at ages 0-1. Using instrumental variable to examine the causal effect of family income on child health in the UK, income was found to have a significant but small causal effect on child health. Specifically, a positive association existed between family income and parent reported health (Kuehnle, 2014). Furthermore, various transmission mechanisms (housing, nutrition, medications, and behavioural problem of the child) through which family income affects child health were all found to be influential on the health of the children. Goode, Mavromanas and Zhu (2014) examined the effect of family income on child health in China. Family income exerted positively on child health via household sanitation conditions, parental health consciousness and nutrition intake. Family income exerted positively on height for age-z-score (HAZ) of children in ages 4-8, 9-12 and 13-17 at 1 percent level of significance except for ages 0-3. However, children from poorer homes were susceptible to chronic illnesses. The study confirmed the validity of the AIH. Nakaruma (2014) studied the relationship between parental income and child health in Japan. A significant existed between parental income and incidences of dental problems, asthma and hearing problems. In the same vein, income exerted a significant positive effect on health as 10% increase in income generally translated to 0.02 reductions in number of illness in Tanzania (Fichera and Savage, 2015). Daley (2017) investigated the effect of income transfer on health of families with young children. Income transfer improved mental health and life satisfaction irrespective of the structure of the family. A synthesis of the reviewed empirical studies in Table 1 shows that larger number of these studies support the AIH while findings from others are to the contrary. It is quite apt to note that there is a relative dearth of studies investigating income-health relationship for SSA countries while the literature is replete with such studies for the developed economies, particularly the OECD-member countries. Thus, whether this theory actually holds in general, for all categories of economies, is a matter of continuing debate. 99
100 Self-Rated Health
Pre-tax income Household equivalised income Personal income per capita
Per capita household income
City of Chicago, US
British Household Panel Survey (BPHS) 8 waves (1991-1998)
51 US states for 1990 & 2000
China Health and Nutrition Survey on 8 Chinese provinces
Wen et al. (2003)
Contoyannis et al. (2004)
Ram (2005)
Li & Zhu (2006)
Jusot (2006)
3
4
5
6
7
8
French 1988 Wealth at Death Taxable income Survey & 1990 Taxable Income Survey
Risk of death (mortality rate)
SRH
Crude death rate
Self-Rated Health
Height, waist-hip ratio, respiratory function, malaise
Health and LifeHousehold style (First Wave) equivalised for England, Wales income and Scotland
Logit model
Risk of death strongly correlated with income, but no diminishing returns of health to income.
SRH increases with per capita but at a decreasing rate. Strongly supports the AIH.
Income inequality has stronger effect on health than state-level income in the US; Validates Income Inequality Hypothesis (IIH) other than the Absolute Income Hypothesis (AIH). Ordinary Least-Squares (OLS)
Probit regression
Permanent income has greater impact on health than transitory income, with the effect larger for men than women; Weak support for AIH.
Individual-level income has greater effect on health of individuals than neighbourhood affluence. Weakly supports the AIH.
Hierarchical Ordinal Logit model Ordered probit model
Increasing income associated with better health but there are diminishing returns at higher levels of income. Strongly supports the AIH.
Results showed evidence of a causal relationship running from income to health. Increases in income tends to raise health status
Income is an important determinant of health
Findings
Logistic model
Instrumental variable
Infant mortality rate and life expectancy
Ecob & Smith (1999)
Income per capita
58 countries
Pritchett & Summer (1996)
Simple Correlation
Methodology
Life expectancy
Measure of Health Outcome
2
National income per head
Measure of Income
60 countries (1900, 1930, 1960)
Sample/Data Structure
Preston (1975)
Author/Year
1
S/N
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Life expectancy
Self-Assessed Health
3049 US countiesDelta & Non-Delta Per capita regions income (1970-2000) Annual household income
Absolute or own income
GDP per capita
GDP per capita
Family income
British Household Panel Survey (BPHS)
UK, France, Finland, Netherlands, Greece & Denmark (individuals aged 50-65 for 3853 individuals)
19 OECD & 31 non-OECD countries from World Values Survey (WVS)
13 OECD countries (1960-2007)
China Longitudinal Healthy Longevity Survey (2008) for 23 provinces
Ram (2006)
Bloom & Bowser (2008)
Jones & Wildman (2008)
Theodossiou & Zangelidis (2009)
Qi (2012)
French (2012)
Feng et al. (2012)
9
10
11
12
13
14
15
Self-Rated Health
Life expectancy
Self-Rated Health
Physical & metal health; Self-Assessed Health
Death rate
Income per capita
108 countries (27 OECD and 81 non-OECD) 1990-2000
Measure of Health Outcome
Measure of Income
Author/Year
Sample/Data Structure
S/N
Health of the elderly affected by family income; Curvilinear relationship between income and health; Supports AIH strongly
Income affects health mildly while health also improves health.
Panel Analysis of Non-stationary Idiosyncratic & Common Components (PANIC) Multilevel logistic model
Individual’s health increases with rising income but at a decreasing rate; Supports AIH strongly.
Income has positive but modest effect on health while those from families that are deprived have poorer physical and mental health.
Income is a core determinant of health.
Income is not the core determinant of health within US. Do not support AIH.
Income has stronger effect on health in the less developed countries (LDCs) than in the developed countries (DCs). Health has no diminishing returns to income.
Findings
Multilevel logistic model
Instrumental variable estimator
OLS, Fixed effects, Random Effects
OLS regression
OLS
Methodology
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101
102 Height for age-z-score
Weekly income of the family
2001-2008 Family and Children Study (FACS) for UK (ages 0-17)
Millennium Cohort Study Permanent (MCS) for children household born in UK income (2000 & 2001)
China Health & Nutrition Survey for ages 0-17 (1991-2009)
Rajan et al. (2013)
Aponey & Geoffard (2013)
Kuehnle (2014)
Goode et al. (2014)
17
18
19
20
Annual household income
Parent reported child health
Average income
2001 Indian Census & the Indian National Statistical Survey (60th round)
Oshio et al. (2013)
16
Fixed effects model; Multilevel Logistic model
Ordered Logit model
Multilevel Logistic model
Methodology
OLS & unconditional quartile regression
OLS; Two-Stage Least Square
Child good general Linear probability health/ specific models health problem
Under-five mortality rate; infant mortality rate
Self-Reported Health (SRH)
Annual income variables
1004 males extracted from 2001 Japanese Longitudinal Survey on Employment and Fertility
Self-Rated Health & Chronic health
Measure of Health Outcome
Personal Income
Measure of Income
Karlsdotter et al. (2012)
Sample/Data Structure
16
Author/Year
2007 Spanish Life Conditions Survey covering 28,023 individuals from 17 autonomous regions
S/N
Existence of significant child health/family income gradient for the overall sample of the Chinese children; Supports AIH
Income has small but significant effect on subjective child health and no significant effect on child chronic health.
No correlation between income and general health for infants; significant correlation exists around age 2 and it remains stable from age 2-7.
State and district level mortality have negative association with average income and positively related with poverty
No close or clear association between income levels and self-rated health
Higher level of personal income associated with lower probability of negative health outcomes.
Findings
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Mental health; Stress
26,886 mothers covered by the Universal Child Care Benefit in Canada
Rehnberg & Fritzell (2016)
Daley (2017)
23
24
Table 1. Synthesis of Reviewed Studies
Real equivalised income
Ordered Probit regression
Cox proportional hazard models
All-cause mortality
Swedish Population Cohort between ages 50-60 years After-tax income in 1999 (801,017 individuals)
Fichera & Savage (2015)
22
Income transfer brought about improvement in mental health irrespective of family structure
A curvilinear relationship exists between income and mortality, with returns diminishing as income increases; Supports AIH strongly
Increases in income reduced number of illness while it increased vaccination of children under six.
Two-Stage Least Square (2SLS)
Body mass index; height for age; weight for height; number of vaccination
Tanzania Living Standard Measurement Survey (LSMS) Real equivalised household income
Logit probit model There is significant and positive income gradient and binary probit in child health, but the gradient is weaker for the model Japanese adults.
Subjective general health
Nakaruma (2015)
21
Findings
Comprehensive Survey of Living Pre-tax Parental Conditions (CSLC) income on 47 Japanese Prefectures
Methodology
Measure of Health Outcome
Sample/Data Structure
Measure of Income
Author/Year
S/N
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DATA AND METHODOLOGY The Data Data on under-five mortality rate and real per capita income are taken from World Development Indicators (WDI) online database of the World Bank while data on proportion of the population having access to improved sanitation were sourced from United Nations Millennium Development Goals (UN-MDGs) Indicators database. Data on real government health expenditure per capita are from World Health Organization (WHO) Global Health Observatory Data Repository. The extracted data covered the period of 2000-2012 for 34 Sub-Sahara African countries (see Table 1). Sub-Region
Countries
West Africa
Benin, Burkina Faso, Cote d’Ivoire, Gambia, Ghana, Guinea, Guinea-Bissau, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone and Togo
Eastern Africa
Burundi, Ethiopia, Kenya, Madagascar, Malawi, Mauritius, Mozambique, Uganda, United Republic of Tanzania and Zambia
Central Africa
Angola, Cameroon, Central African Republic, Chad, Congo and Gabon
Southern Africa
Botswana, Lesotho, Namibia and South Africa
Table 2. List of Sampled Sub-Sahara African Countries Source: Authors’ compilation
Empirical Model In the context of this study, we specify a heterogeneous model set-up based on the variables considered by previous studies (Feng, Wang, Jones, and Li, 2012; Qi, 2012). This is expressed as:
UM it = δ i′PYit + λi′PY 2it + θi′SN it + ϕi HYit + wit
(1)
where i 1,= = 2,..., N ; t 1, 2,..., T ; UM is under-five mortality rate; PY is real per capita income;
PY2 is squared per capita income as a proxy for concavity relationship between health and income; and the other considered control variables are proportion of population having access to improved sanitation (SN), and real government health expenditure per capita (HY). The control variables- per capita health care expenditure and the proportion of population using improved sanitation are core determinants of health outcomes, especially in low-income regions (Lee et al, 2013; Odusanya, and Akinlo, 2015). Access to sanitation and health care expenditure are important complement to income. The composite unobservable error structure (wit ) is modelled as combination of individual specific effects (α i ), unobservable common factors ( f t ) with heterogeneous factor loadings (ηi ), and finite error term (vit ) as:
wit =α i + ηi' ft + vit
(2)
Eq. (2) accounts for unobservable risk factors that explains the variation in health status (UM) such as regional concentration of diseases (such as malaria and polio) induced by proximity of boarders in Africa. Similar factor model was adopted by Baltagi and Moscone (2010) within the context of explaining 104
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unobservable risk factors for health spending variation in OECD. The incorporated parameters for the observable ( δ i , λi , θi , φi ) and unobservable (ηi ) follow a random coefficient structure that combines a common slope vector ( k ) and country-specific random term (ε i).
ki= k + ε i
(3)
The key underlying properties of the specified heterogeneous model set-up (1-3) as extensively discussed in Pesaran (2006), Bond and Eberhardt (2009) and Eberhardt & Teal (2012) are parameters heterogeneity, non-stationarity potentials of the observable and unobservable series, cross-section dependence, and endogeneity. Hypothetically, all the explanatory variables excluding PY2 are expected to have negative effect on under-five mortality rate. AIH holds if PY is negative and significant as a necessary condition. Sufficiently, PY2 must be positive and significant to reflect the concavity of the nexus.
Methods Pesaran and Smith (1995) Mean Group (MG) estimator, Common Correlated Effects Mean Group (CCEMG) estimator by Pesaran (2006), and the Augmented Mean Group (AMG) estimator by Eberhardt and Teal (2012) and Bond and Eberhardt (2009) are considered for estimating Eq.(1). Discussions on additional diagnostics test are in the next section.
RESULTS AND DISCUSSION The preliminary analyses conducted using the standard panel data estimators (FE and RE) confirms the validity of AIH for SSA countries. After rejecting the Hausman test in favour of FE model, additional robustness tests revealed the sensitivity of the model to heterogeneity, non-stationarity and cross-sectional dependence. The validity of AIH collapses for SSA when the FE model is estimated with observable variables at logarithm level and robust standard error-(to control for serial correlation and heterokedasticity). The under-five mortality rate (UM) variable rejects the null hypothesis of non-stationarity at level. But, the other observable variables (PY, PY2, SN and HY) are found to be non-stationary at level and cross-sectionally correlated as shown on Table 2. The evidence of non-stationarity and cross-section dependence of the observable and unobservable series challenges the standard panel data estimators’ biasness and consistency attributes for validating the AIH for SSA. Summary Statistics
Pesaran (2004) CD
PURT at level
PURT at FD
Obs
Mean
Std. Dev.
CD-Test
| ρˆ |
MW
CIPS
MW
CIPS
UM
544
98.9
39.1
92.1*
0.972
700.7*
-2.80*
133.7*
-2.35*
PY
544
1814.3
2358.8
58.0*
0.704
56.0
0.92
189.5*
-1.57*
PY
544
8845259
2.05E+07
58.1*
0.703
42.3
1.34
168.4*
-6.62*
2
SN
544
29.6
18.8
70.7*
0.918
29.3
-7.24*
338.5*
-3.94*
HY
544
203.8
239.7
54.1*
0.638
73.9
-0.34
214.3*
-2.67* 105
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Note: * denotes significance at 1% critical level; CD = Cross-section Dependence; | ρˆ | = average absolute value of the off-diagonal elements of the cross-sectional correlation matrix of the series; PURT = Panel Unit Root Test with the null hypothesis series is I(1). PURT was estimated at lag 1 and first order serial correlation was considered for the CIPS test; FD = First Difference; MW = Maddala & Wu (1999) Fisher’s PURT chi-square value; CIPS = Cross-sectionally augmented Im, Pesaran, and Shin (IPS) PURT Z(t-bar). Table 3. Data Summary and Properties Source: Authors’ Computation from Stata 12
Dependent Variable Method
Without Trend
With Trend
MG
CCEMG
AMG
MG
CCEMG
AMG
1
2
3
4
5
6
-0.4705*
-0.0648
-0.0078
-0.3053*
-0.0529
-0.0029
(-3.17)
(-1.00)
(-0.14)
(-3.03)
(-0.86)
(-0.08)
0.0002*
0.00003
0.00003
0.0002**
0.00005
0.00001
(2.63)
(0.89)
(0.85)
(2.35)
(1.26)
(-0.20)
-6.2499*
0.1379
-0.3099***
-0.7789**
-0.0362
-0.2816
(-5.61)
(0.71)
(-1.76)
(-2.15)
(-0.23)
(-1.64)
-0.0346***
0.0066
-0.0021
0.0039
0.0068
0.0025
(-1.73)
(1.04)
(-0.21)
(0.22)
(1.08)
(0.68)
RMSE
3.3990
0.6014
1.0614
1.4074
0.4875
0.6796
CD test
13.27*
-1.73***
-0.89
2.64*
-0.85
0.19
| ρˆ |
0.140
-0.018
-0.009
0.028
-0.009
0.002
-7.547*
-5.082*
-3.972*
-7.911*
-7.433*
-5.071*
Observation
544
544
544
544
544
544
N
34
34
34
34
34
34
Model PY PY2 SN HY
CIPS test
*, ** and *** denote significance at 1%, 5% and 10% critical level respectively; Model 1-6 estimated with a constant term; z-statistic shown in parentheses; RMSE = Root Mean Square Error; Pesaran’s CD test p-values; Constant deterministic model with three lags and first-order serial correlation estimated for the CIPS test; the values reported for the CIPS test are Z (t-bar); N = Number of SSA countries; and the standard errors are robust to outliers. Table 4: Heterogeneous Slopes Models Source: Authors’ Computation from Stata 12
The results of the heterogeneous slopes estimators with and without group-specific trends are shown on Table 3. Out of all the heterogeneous models, it is only MG estimates of real per capita income and its squared have the expected signs and statistically significant (Table 3 column 1 and 4). The evidence from MG model supports the validity of AIH for SSA. However, the significance disappears when the other robust heterogeneous estimators (CCEMG and AMG) accounted for cross-sectional dependence. Comparing the post-estimation diagnostic test results (such as RMSE, CD test, and absolute crosssection correlation [ | ρˆ | ]) show that the MG estimator performs poorly. Based on the inconsistent results, we have no sufficient evidence to support the AIH for SSA (in the presence of cross-sectional dependence). Our evidence from the cross-sectional correlation cor106
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rected heterogeneous and non-stationary estimators (CCEMG and AMG) contradicts2 previous studies such as Qi (2012) and Ram (2006) that pooled few selected Africa countries with other developing and developed nations. The difference in our empirical outcomes with previous studies emanate from the adopted methods of estimation and their underlying attributes relating to slopes heterogeneity, observable and non-observable non-stationarity and cross-section dependence. Lastly, we are able to establish a long-run relationship between income and health based on the CIPS test results on Table 3.
CONCLUSION This study has clearly shown that modelling country-specific observable and unobservable heterogeneity and cross-section correlation, AIH validity disappears for SSA. This questions the empirical outcomes of previous studies that employed homogenous slopes model set-up. The analyses further revealed that income is not a significant determinant of reducing under-five mortality as a measure of health status improvement in SSA. This could be due to high poverty prevalence rate, wide income inequality gap and high deprivation rate of health facilities that heterogeneously engulfed SSA countries compared to other developed and developing countries where the AIH is valid. Consequently, it implies that the wealthier seems to be the healthier in SSA as increases in income tend to be associated with improvement in health. Therefore, the AIH cannot be confirmed for the SSA region based on the data and the methodology adopted. However, health spending per capita and improved sanitation that are purely non-income determinants are found to be core drivers of health outcome in SSA countries. Since access to sanitation and health care expenditure (aside from income) have been established to be contributing significantly to the diminution in mortality, it becomes imperative for the government to set out policies that will improve its spending on health and access to improved sanitation. This is achievable via quality health infrastructure and specific health intervention programmes among inhabitants of the region. This will indeed complement the effect of higher income on healthy living in the region.
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ODUSANYA, I. A., ATANDA, A. A. INCOME-HEALTH NEXUS IN SUB-SAHARAN AFRICA: EVIDENCE FROM HETEROGENEOUS PANEL MODELS
Preston, S. H. (1975). The changing relation between mortality and level of economic development. Population studies, 29(2), 231-248 Qi, Y. (2012). The impact of income inequality on self-rated general health: Evidence from a cross-national study. Research in Social Stratification and Mobility, 30(4), 451-471. DOI: 10.1016/j.jce.2006.08.005 Rajan, K., Kennedy, J., & King, L. (2013). Is wealthier always healthier in poor countries? The health implications of income, inequality, poverty, and literacy in India. Social Science & Medicine, 88, 98-107. DOI: 10.1016/j. socscimed.2013.04.004 Ram, R. (2005). Income inequality, poverty, and population health: Evidence from recent data for the United States. Social Science & Medicine, 61(12), 2568-2576. DOI: 10.1016/j.socscimed.2005.04.083 Ram, R. (2006). Further examination of the cross-country association between income inequality and population health. Social Science & Medicine, 62(3), 779-791. DOI: 10.1016//j.socscimed.2005.06.034 Reinberg, J., & Fritzell, J. (2016). The shape of association between income and mortality in old age: A longitudinal Swedish national register study. SSM-Population Health, 2, 750-756. DOI: 10.1016/j.ssmph.2016.10.005 Safaei, J. (2007). Income and health inequality across Canadian provinces. Health and Place 13(3), 629-638. DOI: 10.1016/j.healtplace.2006.09.003 Theodossiou, I., & Zangalidis, A. (2009). The social gradient in health: The effect of absolute income and subjective social status assessment on the individual’s health in Europe. Economics and Human Biology, 7(2), 229-237. DOI: 10.1016/j.ehb.2009.05.001 Wen, M. Browning, C. R., & Cagney, K. A. (2003). Poverty, affluence, and income inequality: Neighbourhood economic structure and its implications for health. Social Science and Medicine 57(5), 843-860 WHO. (2014). Global Health Observatory Data Repository. Retrieved April 23, 2017, from World Health Organisation http://apps.who.int/gho/data World Bank. (2015). World Development Indicators. Retrieved April 23, 2017, from World Bank http://data. worldbank.org/data-catalog/world-development-indicators
POVEZANOST DOHOTKA I ZDRAVLJA POJEDINCA U PODSAHARSKOJ AFRICI: DOKAZI NA OSNOVU MODELA HETEROGENOG PANELA Rezime: Istraživanje predočeno u ovom radu predstavlja pokušaj da se ispita odnos između dohotka i zdravlja pojedinca primenom AIH hipoteze (Absolute Income-Health Hypothesis, hipozete o odnosu apsolutnog dohotka i zdravlja). Ispitivanje je prvenstveno usredsređeno na 34 podsaharske zemlje u periodu od 2001-2016. Podaci za studiju su uglavnom preuzeti iz baze Indikatora svetskog razvoja (WDI) i repozitorijuma podataka o globalnom javnom zdravlju Svetske zdravstvene organizacije (ZSO). Korišćenjem postavke heretogenih slopova koja uključuje seriju nestacionarnih, unakrsno zavisnih i grupno specifičnih trendova, nismo uspeli da pronađemo dokaze koji bi potkrepili AIH hipotezu. Naš empirijski ishod podstakao je sumnju na robustnost prethodnih studija koje su ignorisale takve atribute modeliranja, a dovodi nas do zaključka da je metodologija zaista važna u analizi povezanosti zarade pojedinca i njegovog zdravlja, kao i u testiranju validnosti AIH hipoteze za presek zemalja. Nasuprot tome, smatramo da prihodi predstavljaju beznačajnu determinantu zdravlja u podsaharskoj Africi u poređenju sa potrošnjom u zdravstvu i poboljšanom sanitacijom.
Ključne reči: zdravlje, dohodak, podsaharska Afrika, heterogeni nagib, unakrsna zavisnost
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EJAE 2018, 15(1): 110-122 ISSN 2406-2588 UDK: 338.57:336.763.2 330.112.1:316.6 DOI: 10.5937/EJAE15-16665 Original paper/Originalni naučni rad
BEHAVIORAL ECONOMICS: HOW WELL DO INVESTORS IN SERBIA PREDICT THE STOCK PRICES? Dora Petronijević PhD candidate, Singidunum University, Belgrade, Serbia
Abstract: The main goal of this paper is to investigate whether investors in Serbia can predict future stock prices. This research has been inspired by a number of similar researches conducted in other countries. The research has been conducted on two Belgrade Stock Exchange's indexes: BELEX 15 (represents the market movement of stock prices) and BELEX Sentiment (represents the aggregate investors’ predictions of future stock prices behavior). In methodological sense, this analysis was made by using Vector Auto-regression (VAR) model and Granger causality model. The results show that investors in Serbia are not very successful in prediction of future prices. The research also shows that investors have based their expectations on historical prices, which resulted in bad predictions. The results of this research are in cohesion with the previous researches conducted in other developing, transitional countries.
Article info: Received: February 25, 2018 Correction: March 21, 2018 Accepted: March 28, 2018
Keywords: behavioral finance, irrational investor, prediction of stock prices, investor sentiment, market noise
INTRODUCTION Most economic models are based on efficient market theory (Fama, 1970), which assumes that all investors are rational. Behavioral studies are pointing out that human irrationalities may cause mistakes and fallacies in economic conclusions and decision making.1 The main aim of this paper is to examine the extent to which investors in Serbia can predict the stock prices on Belgrade Stock Exchange. This topic belongs to the field of behavioral economics, as a relatively new scientific research field. In order to analyze investors’ expectations, we will use BELEX Sentiment. BELEX Sentiment represents the aggregate expectations of the investors in Belgrade Stock Exchange regarding future behavior of stock market. It is primarily based on institutional investors’ expectations (90%) (Belgrade Stock Exchange, 2017). Assuming their predictions (expectations) turn out to be generally correct, BELEX Sentiment can be a useful indicator for smaller investors in their decision making. 1 The founders of behavioral economics are Daniel Kahneman and Amos Tversky. They have devoted their carrier on analyzing cognitive biases in humans and its implications on economics. (See more: Kahneman, Slovic, Tversky, 1982; Kahneman, Tversky, 1996; Kahneman, 2011) Some of the other distinguished authors in this field are Richard Thaler and Dan Ariely. They have both, among other things, discussed the quazi rationality of humans. (See: Thaler, 1991; Ariely, 2012; Ariely, 2010)
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* E-mail: doracapa@yahoo.com
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The main hypothesis of this paper is that investors are not successfully predicting the stock prices. This hypothesis will be tested using quantitative models, namely Vector Auto-Regression Model and Granger Causality Test.
Contribution Most studies conducted in this field are focusing on developed markets. Very few researchers have analyzed undeveloped markets and this research, as the first one conducted on Serbian market, contributes both to theory and practice. Majority of other studies have focused on consumer confidence indices (noise traders). This research, on the other hand, analyzes the index primarily based on institutional investors’ opinions, which should be more rational as based on economic models. Kling and Gao (2008) have conducted similar research on Chinese market (transitional market), and they have come to conclusion that the institutional investors in China are not predicting the future prices well. The results of this paper could be of interest to all investors, whether institutional (fund managers, etc) or individual. The paper consists of introduction, three sections and the conclusion. The first section is dedicated to literature review, presenting the most interesting studies and articles in this research field. The second section is devoted to description of methodology used in the research and the third section presents and discusses the results of the research.
LITERATURE REVIEW When analyzing the relationship between returns and Investor Sentiment, researchers have come to different conclusions. Some researchers have found that investor sentiment predicts the future behavior of stock prices (some find positive and some negative relation), while others have come to the opposite conclusion. Brown and Cliff (2004) have found that investor sentiment predicts market returns over the next one to three years. Schmeling (2009) has conducted the research on 18 industrialized countries and has found that on average investor sentiment negatively forecasts the aggregate stock returns (value stocks, growth stocks, small stocks). In other words, when sentiment is high, the future stock returns tend to be lower and vice versa. Fisher and Statman (2003) have come to the same conclusion in their research, where they have analyzed the relationship between investor sentiment (for which they have used consumer confidence) and the level of returns of S&P 500, NASDAQ and small companies’ stocks. Charoenrook (2005) has used University of Michigan Consumer Sentiment Index as a proxy for sentiment of individual investors (High education market). He has found that sentiment is positively related to present excess market returns, and negatively to future market returns. He has analyzed future returns with monthly and yearly horizon. On the other hand, Lemmon and Portniaguina (2006) have analyzed the relationship between investor sentiment (consumer confidence) and small stock premium, and their research concludes that sentiment doesn’t seem to predict future momentum premiums and their value. 111
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Wang, Keswani and Taylor (2006) have included the lagged returns in their research, and have found that sentiment does not influence the stock returns. Instead, they have discovered that the opposite is true: stock returns influence sentiment. Kling and Gao (2008) have conducted a research on Chinese stock market, and have not found the long-term relationship between investor sentiment and stock prices. Oprea (2014) has conducted a research on 8 transitional countries. The research has shown that out of 6 analyzed countries sentiment does not Granger cause the future behavior of stock prices, but returns Granger cause sentiment. The exceptions were Lithuania and Hungary, where he has found mutual Granger causality between these two factors. The results of this research will analyze mutual causality between sentiment and stock returns on Belgrade Stock Exchange. This research will use the same methodology as Oprea (2014), because it is developed for the similar stock market conditions.
RESEARCH SAMPLE AND METHODOLOGY In the field of behavioral economics, given the nature of the subject, qualitative analyses are predominant. The space for quantitative research is narrow and it was challenging to find series that allows this kind of analysis. Investor sentiment as instrument is useful because it quantifies aggregate expectations of investors in stock market. This allowed me to make a quantitative research that will analyze the extent to which investors in Serbia can predict the movements of stock prices. The stock market in Serbia is relatively new and not very developed. After 2000, the trading on secondary market was stimulated because of the privatization of many public companies. Through time some companies got listed on the market and their stocks are continually traded. Until the crisis in 2008, the liquidity of the market and the number of companies continually increased. The idea of mutual regional stock market appeared in this period. Unfortunately, after the crisis the liquidity of the market has dramatically decreased. Even though the market has partially recovered, the liquidity still remains the challenge ‘till the present day.
ANALYZED TIME SERIES BELEX Sentiment: Its structure and the methodology of its calculation BELEX sentiment is calculated as weighted average of three types of market participants. The first group includes the most active members of Stock Exchange that have traded on the market and had an important share of market turnover in the period that preceded the voting. The second group is derived from the portfolio managers in investment and pension funds. The third group is general public (the individuals can vote on the Belgrade Stock Exchange web site). The first and second groups are having equal weighted averages of 45%, and the expectations of general public have the lowest weighted average of 10%. It can be noticed that institutional investors have the biggest influence (90%) in creation of this sentiment, and they have better knowledge of economics as well as more trading experience than average investor. For that reason one can expect that part of the expectations should be based on economic indicators and market data (the rest is highly influenced by the factors of irrationality).
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The base value of BELEX Sentiment is 100 and it can move in the range between 0 and 200. The values under 100 are representing expectation of prices’ decrease and above 100 the expectation of increase. The voters are offered seven potential answers for their expectations: strong, moderate and mild decrease/increase, or stagnation (Belgrade Stock Exchange, 2017).
BELEX 15 The other series that will be used in the research is BELEX 15. It is used as an indicator of behavior of Serbian financial market. BELEX 15 is a price index of 15 most liquid stocks on Belgrade Stock Exchange. This index was initiated in October 2005 and its base value is 1000 index points (Belgrade Stock Exchange, 2012).
SAMPLE AND METHODOLOGY OF THE RESEARCH This research analyzes the mutual causality between BELEX sentiment and BELEX 15. The data used for this research is issued by Belgrade Stock Exchange, and the analyzed period is between October 2005 and December 2014. BELEX 15 is issued daily, while BELEX sentiment is issued monthly. For that reason, BELEX 15 had to be converted into monthly time series. This transformation is performed by averaging. The final sample has 111 observations. In order to find a trend, both time series have been logarithmed. Neither of the two series has seasonal component, which means, that there is not seasonal regularity in yearly movements of series. The quantitative research, presented in this paper, will include lagged (past) values of both series and will analyze their mutual causality. Therefore, an adequate Vector Autoregressive (VAR) model will be created and through the estimated VAR model we will analyze mutual causality of time series through Granger causality test. Granger causality test analyzes if the past values of one time series can improve the prediction of the behavior of the other series. In order to crate adequate VAR model it is necessary to perform numerous tests such as: a) Unit root tests: Augmented Dickey Fuller test and Kwiatkowsky-Phillips-Schmidt-Shin (KPSS) test. b) Sequential testing and information criteria will suggest the optimal number of lags for VAR model. c) Box – Ljung autocorrelation test. d) Dornik- Hansen normality test.
RESULTS AND DISCUSSION The transformation of time series (they have both been logarithmed), is followed by unit root test of time series. The following graphs are presenting the analyzed series and their first differentials.
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Figure 1. BELEX 15 & BELEX Sentiment and their first differentials Source: Graphs are created by author in E-views program
Stationarity of time series - Unit root testing Many series in economic researches are non-stationary. The non-stationary time series is a series that has stochastic trend, which means that its behavior cannot be predicted based on historical data (Gujarati & Porter, 2009). Variance of non-stationary time series is not constant, but time variant. Constant variance is the basic assumption of standard linear regression (Mladenović & Petrović, 2010). If this assumption is not met, the results gained by model become unreliable. For that reason, if the series turns out to be non-stationary, the researcher has two options: a) if all analyzed time series are non-stationary, but are co-integrated (their behavior is highly synchronized, which makes their linear combination stationary), they do not have to be altered in further analyses. B) if that is not the case, non-stationary series have to be differentiated. (Mills & Markellos, 2008) The stationarity of time series is tested in this paper, using the Augmented Dickey Fuller (ADF) and Kwiatkowsky-Phillips-Schmidt-Shin (KPSS) unit root tests. The results of ADF test are presented in the following table:
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The name of time series
H0: I(1), H1:I(0)
H0: I(2), H1:I(1) ADF
Critical value (5% level)
N of lags
Deterministic components
3
-3.52
-1.94
2
None
0
-
-
-
Constant
ADF
Critical value (5% level)
N of lags
BELEX 15
-0.35
-1.94
BELEX sentiment
-5.39
-2.89
o
o
Table 1. The results of ADF test Source: Calculations are performed by author in E-views program
Null hypothesis (H0) of ADF test states that the series is not stationary (has one unit root), the alternative hypothesis (H1) is that the time series is stationary (Dickey & Fuller, 1979). The critical values given in the table have been calculated at the significance level of 5% for the sample size of 111 observations. The levels of critical values vary depending on the deterministic components included in the test. Stock Watson test has been performed in order to determine which deterministic components should be included. BELEX 15 has higher ADF value than the critical value, which leads to the conclusion that series has at least one unit root. After testing for the presence of two unit roots in the series, it has been concluded that series has only one unit root (ADF value is lower than critical value). BELEX Sentiment on the other hand has lower ADF value than the critical value. This has led to the rejection of the H0, and the acceptance of H1, that states that this series has no unit roots. These results will be additionally tested using KPSS unit root test. This test is based on different methodology than ADF test.
KPSS test Contrary to ADF test whose H0 assumes that the series is not stationary (has one unit root), the H0 of KPSS test assumes that the series is stationary (Kwiatkowsky et al., 1992). Therefore the H1 of KPSS test is that the series has unit root. Critical value at the significance level of 5% in this model equals 0.463 for both variables. KPSS statistic for BELEX sentiment is 0.191, which leads to the conclusion that the series is stationary. KPSS statistic for BELEX 15 equals 0.74; leading to the conclusion that BELEX 15 is not stationary time series. The results of KPSS test have confirmed the results gained by ADF test. BELEX sentiment is stationary series and BELEX 15 has one unit root. The combination of stationary and non-stationary series always produces a non-stationary process (process with unit root). Therefore, we will use the first differential of BELEX 15 in the following research.
Vector Auto-regression model – VAR model The time series in this model are represented in matrices – for that reason it has vector in its name. For example, vector time series Xt with dimensions m x 1 can be presented as follows:
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x1t x X t = 2t , t = 1,2,.... ... xmt The use of vector time series allows us to analyze the dynamic relationship between variables in the model. Each variable is modeled both by its previous values – lags (this explains why the name of the model includes the term auto-regression), as well as the previous values of other variables in the model. The dimension of VAR model equals to the number of time series included in the model. Therefore the dimension of VAR model in this research is 2, since it includes two time series BELEX Sentiment and differentiated BELEX 15 (d (BELEX15)). Constant is the only deterministic component included in the model (Tsay, 2010). In order to create adequate VAR model, we have to determine its order (the optimal number of lags included in the model) (Brooks, 2008: 293-295). So, if it is determined that the optimal number of lags in the model equals 2, than we say that that the model is VAR(2) model. It is important that the model is both congruent and economical. Congruent model is in accordance with all sources of information and time sustainable. In other words, it cannot have autocorrelation. Economical model includes only the necessary number of parameters. The use of higher lag order than optimal in VAR model will dramatically increase the number of parameters in the model. The optimal number of lags in this model was searched using sequential testing and information criteria (Akaike information criterion – AIC (1974), Schwarz information criterion - SC (1978) and Hannan-Quinn information criterion - HQ (1979)). The results are presented in the table below: Lag
LogL
LR
FPE
AIC
SC
HQ
0
128.2693
NA
0.000316
-2.38244
-2.33219
-2.362072
1
177.5828
95.83559
0.000135
-3.23741
-3.08665
-3.176307
2
179.7577
4.144642
0.000139
-3.20298
-2.95171
-3.101135
3
189.5877
18.36174
0.000125
-3.31298
-2.96120
-3.170399
4
190.1939
1.109354
0.000133
-3.24894
-2.79666
-3.065628
Table 2. Identification of the optimal VAR model lag Source: Calculations are performed by author in E-views program
Sequential testing and AIC suggest that the optimal lag order for the VAR model is 3 while SC and HQ are suggesting that the lag order of the model should be 1. VAR (1) model has autocorrelation, and is therefore not adequate. If residuals of suggested VAR (3) model prove not to have autocorrelation and if they are normally distributed, this model will be accepted.
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Autocorrelation test (Auto- and Cross- correlation) In this research, the presence of autocorrelation in residuals is tested by Box-Ljung autocorrelation test. The null hypothesis of this test is that there is no auto- or cross- correlation in the residual series, the alternative hypothesis is that the residuals are auto- and/or cross-correlated. Lags
4
5
6
7
8
9
10
11
12
Adj Q-Stat
7.36
8.01
8.46
11.95
28.70
31.71
35.85
40.01
41.51
p value
0.39
0.71
0.90
0.89
0.19
0.24
0.25
0.26
0.36
Table 3. Results of multidimensional Box-Ljung autocorrelation test Source: Calculations are performed by author in E-views program
The results show that in the residual series of estimated VAR (3) model has no auto- or cross- correlation.
Dornik-Hansen normality test The null hypothesis is that the residuals of estimated VAR (3) model are multivariate normal and the alternative hypothesis is that they are not normally distributed. Component
Jarque-Bera
Degrees of freedom
p – value
1
19.0198
2
0.0001
2
4.5119
2
0.1048
Joint
23.5317
4
0.0001
Table 4. Dornik-Hansen normality test Source: Calculations are performed by author in E-views program
The above data shows that residuals in estimated VAR (3) model are normally distributed. Since we have found that residuals are both normally distributed and have no auto- or cross- correlation, it can be concluded that the optimal lag order for this VAR model is 3. VAR (3) model of the time series analyzed in this research can be presented by following set of equations: 3
3
j =1
j =1
d ( LN _ BELEX 15) t = α 0 + ∑ α j d ( LN _ BELEX 15) t − j + ∑ β j LN _Sentimentt − j + u1t 3
3
j =1
j =1
LN _ Sentimentt = α o + ∑ α j d ( LN _ BELEX 15)t − j + ∑ β j LN _ Sentimentt − j + u2t
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Where α0 represents constant, j represents the number of lags, αj and βj are parameters for analyzed time series (d(BELEX15) and Sentiment), and u represents error term.
Granger causality test Through the estimated VAR (3) model the mutual interdependence of variables will be tested. The analysis will be made by using Granger causality test (Granger, 1969). This test is based on the assumption that if one variable (xt) influences the movement of the other one (yt), then its lags should statistically significantly influence the behavior of yt. If the causality is proven, than one can say that time series xt Granger causes yt. If it turns out that xt causes yt, but not vice versa (yt doesn’t Granger causes xt), this implies that xt is exogenous time series in reference to yt. If none of the series Granger causes the other, than we refer to these series as independent. Finally, if both series Granger cause one another, this implies mutual interdependence of the variables (Brooks, 2008: 298; Mladenović & Nojković, 2011: 113). It is important to stress that Granger causality shows correlation between historical values of one variable with present value of the other variable. Granger causality does not necessarily imply that changes in movement of one variable cause the movement of the other (Brooks, 2008: 298). Granger causality test is conducted on analyzed time series and on the following tables one can see the results Dependent variable: DBELEX15 χ2
Degrees of freedom
p value
BELEX SENTIMENT
3.561405
3
0.3129
All
3.561405
3
0.3129
Dependent variable: BELEX_SENTIMENT χ2
Degrees of freedom
p value
BELEX 15
34.39383
3
0.0000
All
34.39383
3
0.0000
Table 5. Granger causality test Source: Calculations are performed by author in E-views program
The results of the test show that in short term BELEX sentiment doesn’t Granger cause BELEX 15, on the other hand, BELEX 15 Granger causes BELEX sentiment. These results indicate that investors in Serbia are not very successful in predicting the future behavior of stock prices. On the other hand, the fact that BELEX 15 Granger causes BELEX Sentiment implies that investors in Serbia are basing their expectations on historical movements of BELEX 15, which, based on this data, is obviously not the best predictor of future price movements. This result is in accordance with Wang, Keswani and Taylor (2006) that have also included the lagged returns in their research, as well as with the results of comparative analysis conducted on the sample of 8 post-socialist countries (Bulgaria, Czech Republic, Estonia, Hungary, Lithuania, Poland, Romania 118
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and Slovenia). However, as both of the studies above have analyzed consumer confidence indices, one would expect that BELEX Sentiment, which is primarily derived from institutional investors’ opinions, would be able to predict better BELEX 15 behavior. This result can be explained by the fact that securities market in Serbia is relatively new (its true development has started in 2000) and undeveloped. Therefore, investors didn’t have the benefit of prolonged empirical exposure in order to further their knowledge and skills. The same problem was observed in China (their market started developing after 1992) (Kling & Gao, 2008). The development of the market and the increase of investors’ experience should through time lead toward improvement in the quality of market predictions. Investors should also be aware of the cognitive biases (irrationalities) that are characteristic for all humans, and that can influence the wrong decision making. In decision making, human brain uses heuristics that allow us to efficiently process the data and make adequate decisions (Kahneman, Slovic & Tversky, 1982). This useful characteristic of human brain has inherent risk of ignoring potentially important data, which can sometimes lead to lower quality, less reliable and even flawed decisions. Some of the typical human mistakes that influence the decision making in investing are: Humans don’t pay enough attention to size and representativeness of sample. Number of authors is pointing out that individuals often overestimate their capabilities and are overly confident in their beliefs and forecasts (Bodie, Kane, Marcus, 2009: 260). Ariely (2010) has also pointed out that individuals are often overly confident in the investment strategies that have worked well in the past and keep using them even when the market circumstances dramatically change. Researchers have further found that humans are often unwilling to change their original decision, even when they realize that they have made a mistake (Ariely, 2010: 200-201). They react too slowly on new information on market (conservativism, inertion), which can lead to bad investment decisions, especially in modern, increasingly dynamic world (mistake of keeping status quo). Kahneman and Tversky (1996) have conducted numerous experiments that have analyzed the problem of unreliability of memory. The experiments have shown that individuals tend to give more importance to the events that happened recently then to the ones that happened further in the past. For example, if a company has recently had good business results, investors are prone to expect the same or even better future profitability. The awareness that investors (like all humans) are prone to these cognitive biases, should also help them improve their decision making process.
CONCLUSIONS Contemporary behavioral economics stresses that human irrationality often has an important influence on decision making, therefore causing significant economic consequences. Consequently, it is essential to take into consideration the impact of human psychology when creating effective risk management strategies. The scientists are trying to develop behavioral risk models and their creation may soon become a reality thanks to big data, development of behavioral and social sciences and human resources management (Lo, 2016). This research has analyzed how well do investors in Serbia predict the future stock prices. The initial hypothesis that investors on Belgrade Stock Exchange are not good in predicting the future stock returns has been confirmed. The research has further shown that investors are basing their expectations on historical data. 119
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PETRONIJEVIĆ, D. BEHAVIORAL ECONOMICS: HOW WELL DO INVESTORS IN SERBIA PREDICT THE STOCK PRICES?
This result can be explained by the fact that securities market in Serbia is relatively new and undeveloped. Through time and development of the market, investors should keep furthering their knowledge and skills in order to improve the quality of their investments. They should also be mindful of cognitive biases that can influence their decision making. Further development of securities market in Serbia, together with inclusion of derivatives, will create new space for this kind of research. Consequently, it would be interesting to conduct a similar research that would include spot and futures prices (which naturally incorporate human expectations). Considering that behavioral economics is predominantly qualitative analytic field, there is also broad scope for qualitative researches, which would complement and enrich previous results. Qualitative research could include various exogenous factors (political system, regulations, customs, culture), as well as endogenous factors which affect decision making process in investment. It is very important to continue with the research relating to cognitive biases (irrational fear of new; unjustified adhering to practices that are no longer profitable in changed circumstances etc.). Further development of both quantitative and qualitative researches in combination with big data could lead to development of new behavioral risk models.
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PETRONIJEVIĆ, D. BEHAVIORAL ECONOMICS: HOW WELL DO INVESTORS IN SERBIA PREDICT THE STOCK PRICES?
BIHEVIORALNA EKONOMIJA: KOLIKO DOBRO INVESTITORI U SRBIJI PREDVIĐAJU CENE AKCIJA? Rezime: Većina ekonomskih modela je bazirana na teoriji efikasnih tržišta koja između ostalog pretpostavlja da su svi investitori racionalni. Bihevioralne studije, sa druge strane, istražuju iracionalne aspekte ljudskog ponašanja i procesa donošenja odluka. Ove studije ukazuju da date iracionalnosti mogu izazvati greške i slabosti u ekonomskom zaključivanju. Cilj ovog rada je da istraži koliko dobro investitori u Srbiji predviđaju buduće cene akcija. Ovo istraživanje je inspirisano sličnim istraživanjima sprovedenim u drugim zemljama. Istraživanje analizira uzajamni odnos dva indeksa Beogradske berze: BELEX 15 (predstavlja tržišno kretanje cena) i BELEX Sentiment (predstavlja agregatno predviđanje investitora o kretanju budućih cena). Sa metodološkog stanovišta, ova analiza je sprovedena korišćenjem Vektorsko Autoregresionog modela (VAR) i Grandžerovog modela kauzalnosti. Rezultati istraživanja ukazuju da investitori u Srbiji nisu veoma uspešni u predviđanju budućih cena. U idealnoj situaciji BELEX Sentiment bi trebalo da može da predvidi kretanje BELEXa 15, međutim ovo istraživanje pokazuje da BELEX15 utiče na kretanje Sentimenta, a ne obrnuto. Iz toga možemo zaključiti da investitori u Srbiji baziraju svoja očekivanja na istorijskom kretanju cena. Rezultati ovog istraživanja su u skladu sa rezultatima sličnih istraživanja sprovedenih u drugim zemljama u razvoju.
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Ključne reči: bihevioralne finansije, iracionalni investitor, predviđanje cena akcija, investicioni sentiment, tržišni šum
CIP - Каталогизација у публикацији Народна библиотека Србије, Београд 33 The EUROPEAN Journal of Applied Economics / editor-in-chief Nemanja Stanišić. Vol. 12, No. 1 (2015)- . - Belgrade : Singidunum University, 2015- (Loznica : Mobid). - 28 cm Dva puta godišnje. - Је наставак: Singidunum Journal of Applied Sciences = ISSN 2217-8090 ISSN 2406-2588 = The European Journal of Applied Economics COBISS.SR-ID 214758924 123