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EJAE 2021, 18(1): 106 - 125 ISSN 2406-2588 UDK: 159.9.072-057.875(497.11) 005.342-057.875(497.11) DOI: 10.5937/EJAE18-27707

Original paper/Originalni naučni rad

THE ENTERPRISE POTENTIAL, INDIVIDUAL ENTREPRENEURIAL ORIENTATION AND ENTREPRENEURIAL INTENTIONS OF STUDENTS IN SERBIA

Jelena Rajković*, Jasmina Poštin, Marko Konjikušić, Aleksandra Jagodić Rusić, Hadži Strahinja Stojković, Milan Nikolić

University ’’Union Nikola Tesla’’, Faculty of Engineering, Belgrade, Serbia

Abstract:

In this paper are given the findings of the research of the effects of five variables on the dimensions of the enterprise potential, individual entrepreneurial orientation, the Theory of Planned Behaviour (TPB) and dimension entrepreneurial intention, by students. The effects of the following variables are observed: Gender, One of my parents has a private business, The year in which the student is studying, Student success in learning and studying and The financial opportunities to start a new business. The respondents are studying at seven faculties in Serbia. The sample included 488 respondents. The most influential variable on the dimension entrepreneurial intentions is the financial opportunities to start a new business. Also, men have more pronounced entrepreneurial intentions. Success in studies positively influences entrepreneurial potentials and proactivity, while the possession of finance positively influences entrepreneurial intentions and risk readiness. In cases of successful studies and the possession of finance, women are more motivated, more determined, and have more pronounced entrepreneurial intentions (especially in the case of having financial resources). Although men may show a more preference to become entrepreneurs, women approach entrepreneurship more realistically and decisively.

Article info:

Received: July 29, 2020 Correction: September 7, 2020 Accepted: October 19, 2020

Keywords:

Enterprise potential, Individual entrepreneurial orientation, Entrepreneurial intentions, Students, Serbia.

JEL Classification:

L26, D23

INTRODUCTION

Entrepreneurship represents one of the leading aspects of socio-economic development (Coulibaly, Erbao, & Mekongcho, 2018), and plays a key role in economic growth (Mahfud, Triyonoa, Sudiraa, & Mulyani, 2020). More precisely, entrepreneurship contributes to job creation, increases productivity and competitiveness, encourages companies to innovate and act effectively (Mortan, Ripoll, Carvalho, & Bernal, 2014), creates wealth and reduces unemployment (Paul, & Shrivatava, 2016).

Because of all this, it is not surprising that state institutions in many countries, at the highest levels, increasingly view entrepreneurship as an instrument for achieving economic growth and technological progress (Fellnhofer, & Kraus, 2015). However, entrepreneurship is popular, not only from the perspective of state-level institutions, but also from the perspective of students as potential entrepreneurs, as well as academicians, as researchers in this field (Mwasalwiba, 2010).

Much of the research in the field of entrepreneurship is related to entrepreneurial intentions, their recognition, understanding and possibility to manage. Entrepreneurial intentions are important and deserve attention because, as proven, they confirm entrepreneurial behaviour (Koe, 2016; Sahinidis, Stavroulakis, Kossieri, & Varelas, 2019). Delanoë‐Gueguen and Fayolle (2018) point to the importance of the dynamics that connect entrepreneurial intentions and entrepreneurial actions. According to (Delanoë‐Gueguen, & Liñán, 2018), the greatest and lasting impact in this process (transition of entrepreneurial intentions in entrepreneurial actions) is the pursuit of providing the job security.

Entrepreneurial intentions represent a theoretical construct, which is very often used in order to explain the transition of an individual from ordinary thinking to concrete entrepreneurial action. Extensive literature in this area indicates that the importance of developing and nurturing initial entrepreneurial desires has been recognized, in order to enable the translation of these desires into immediate economic and social values (Donaldson, 2019). Similarly, Nowiński and Haddoud (2019) note that discovering everything that makes people engage in entrepreneurial activities occupies the special attention of researchers in the field of entrepreneurship and management, for over the last thirty years. Understanding these mechanisms is of great importance in forming appropriate programs to encourage and support entrepreneurial ventures. For example, research among students in Pakistan (Samo, & Huda, 2019) has shown that support from governing structures and academic community has a strong, positive impact on entrepreneurial intentions, while industry impact is positive but not significant.

There are numerous influences on entrepreneurial intentions, as well as research examining these influences. What is important for this paper is that there are numerous research papers that deal with the influences of the gender of respondents on entrepreneurial intentions, for example (Santos et al., 2016; Huezo-Ponce, & Saiz-Álvarez, 2020; Vamvaka, Stoforos, Palaskas, & Botsaris, 2020; Nguyen, 2018), furthermore there are research papers that deal with influences of family background on entrepreneurial intentions (Jena, 2020; Herman, 2019; Palmer, Fasbender, Kraus, Birkner, & Kailer, 2019; Sahinidis, Stavroulakis, Kossieri, & Varelas, 2019; Hatak, Harms, & Fink, 2015); research papers that deal with the influences of the age of the respondents on entrepreneurial intentions (Minola et al., 2016; Tsai et al., 2016; Nguyen, 2018) and research papers that deal with the influences of having the finances to start their own business on entrepreneurial intentions (Kim, Longest, & Aldrich, 2013; Rodriguez, Tuggle, & Hackett, 2009; Shinnar, & Young, 2008; Sieger, & Minola, 2017). The general impression is that similar research in Serbia has not yet been done to a sufficient extent.

The aim of this paper was to explore the impact of different variables on enterprise potential dimensions, individual entrepreneurial orientation dimensions, the Theory of Planned Behaviour (TPB) dimensions and entrepreneurial intention dimension (entrepreneurial dimensions). The research was carried out on students in Serbia. The effect of the following five variables is observed: Gender, One of my parents has a private business, The year in which the student is studying, Student success in learning and studying and The financial opportunities to start a new business. It should be noted that the students are similar in age, and therefore, in this part the focus is the year in which the student is studying, in order to see any changes in the initial and final years of study. It should be noted here that, in previous research, the year of study has not been sufficiently considered as an influential factor on entrepreneurial intentions.

The possession of finance for opening one’s own company variable is very important for the transition conditions in Serbia, where young people face serious financial constraints for starting their own business. Finally, the success in studies variable is, also, particularly interesting because it has not been widely considered so far in the context of entrepreneurial intentions.

The paper has a pronounced theoretical and practical contribution. The theoretical contribution and importance of the paper stem from the fact that, in many researches of various influences on entrepreneurial intentions, the influences of two variables have not been sufficiently observed so far: The year in which the student is studying and, especially, Student success in learning and studying. The practical contribution and importance of the paper is to consider the impact of the given five control variables on the entrepreneurial intentions of students in Serbia. Based on this acknowledgment, it will be possible to define certain, specific proposals, which may be useful to the relevant state institutions, which deal with the promotion of entrepreneurship in Serbia.

THEORY

Enterprise Potential

Enterprise potential presents the latent inclination of an individual to engage in entrepreneurship. The presence of such potentials does not imply the obligatory presence of a complete determination for a person to engage in entrepreneurship, or that this will happen, but only that such an individual possesses the characteristics and attitudes that favour the launch of an entrepreneurial venture. According to Athayde (2009) enterprise potential determination is a significant and frequently explored topic in the field of entrepreneurship. In this paper, enterprise potential is monitored and measured using the model established by Athayde (2009). This model contains the following constructs: leadership, creativity, achievement and personal control. A large number of references point to the importance of these dimensions for entrepreneurship.

Individual Entrepreneurial Orientation

According to Mueller and Thomas (2000), entrepreneurial orientation represents a group of individual characteristics, which have a connection with entrepreneurship. According to these authors, entrepreneurial orientation includes locus of control and innovativeness. This concept of was originally developed by Miller (1983) and basically has three constructs: risk taking, innovativeness and proactiveness. Observed at the individual level, entrepreneurial orientation is analogously transformed into individual entrepreneurial orientation (Robinson, & Stubberud, 2014), which is important in studying the entrepreneurial intentions of individuals. A positive link between individual entrepreneurial orientation and entrepreneurial intentions is generally confirmed (Bolton, & Lane, 2012; Koe, 2016; Muñoz-Bullón, Sánchez-Bueno, & Vos-Saz, 2015; Reijonen, Hirvonen, Nagy, Laukkanen, & Gabrielsson, 2015).

Risk taking is a feature that is more present in entrepreneurs than other people (Brandstätter, 2011; Caliendo, Fossen, & Kritikos, 2014; Karabulut, 2016). Innovativeness is also more prominent in entrepreneurs than other people (Çolakoğlu & Gözükara, 2016), and also has an impact on entrepreneurial intentions (Mueller, & Thomas, 2000; Padilla-Meléndez et al., 2014). Similarly, proactivity, as a personal trait, has a positive link with entrepreneurial intentions (Usaci, 2015; Paul, & Shrivatava, 2016).

The Theory of Planned Behavior and Entrepreneurial Intention

According to Ajzen (1991) and Liñán and Chen (2009) the Theory of Planned Behavior (TPB) is based on three motivation factors that affect behaviour:

a) Attitude towards entrepreneurship (Personal Attitude) represents the personal opinion about how nice, good and useful it is to have your own company. b) Subjective norm represents an assessment of the extent to which there is or is not social pressure from the environment to undertake a business venture. c) Perceived behavioural control represents assessment of the degree of ability to start an entrepreneurial business, and later to run it successfully.

Within the extended Theory of Planned Behaviour (TPB), entrepreneurial intentions are seen as the effort that an individual will invest in performing an entrepreneurial activity (Paul, & Shrivatava, 2016). In doing so, such attitudes towards entrepreneurship predict entrepreneurial intentions, and in turn entrepreneurial intentions predict planned entrepreneurial behaviour (Ajzen, 1991). TPB dimensions have a significant impact on entrepreneurial intentions (Roy, Akhtar, & Das, 2017; Zhang, Wang, & Owen, 2015; Moriano, Gorgievski, Laguna, Stephan, & Zarafshani, 2012).

According to Fayolle and Liñán (2014) entrepreneurial intention is a characteristic that leads a person to open up his own company, start an entrepreneurial career and become self-employed. The study of entrepreneurial intentions provides a significant opportunity to understand and predict the ability for entrepreneurship (Rauch, & Hulsink, 2015; Karimi, Biemans, Lans, Chizari, & Mulder, 2016).

Variables which Influence Entrepreneurial Intentions

There are numerous research studies about the effects of various variables on the emergence of entrepreneurial intentions and entrepreneurship in general. Among other things, the effects of variables such as gender, the existence of an entrepreneurial tradition in the family, age and the possession of finance for starting a business are examined.

Gender

One of the most frequently studied topics in entrepreneurship is the impact of gender on the startup of an entrepreneurial venture and its success. According to the Global Entrepreneurship Monitor 2013, in 2012 more than 187 million out of 400 million entrepreneurs were women (Poggesi et al., 2016). The authors conclude that female entrepreneurship is a key component of the business sector worldwide. One group of studies deals with the comparison of women and men who are already entrepreneurs. According to (Jayawarna et al., 2015), there are many similarities between male and female entrepreneurs, which disperse the assumptions and prejudices about the shortcomings and weaknesses of women in the role of entrepreneurs.

The second group of studies deals with the examination of the entrepreneurial characteristics and intentions in both women and men. According to Santos et al. (2016), men and women share a similarity in entrepreneurial intentions, although they are somewhat more pronounced in men. In society, men are more encouraged to undertake entrepreneurial ventures, so women feel that entrepreneurship is not an acceptable career choice for them. Similarly, male students in Norway and Turkey show a much higher degree of entrepreneurial intentions than female students (Shneor et al., 2013). According to Diaz-Garcia and Jiménez-Moreno (2010) men put more thought into creating a company than determination to establish one.

In doing so, there are no significant differences in entrepreneurial intentions for women and men. Recent research from different parts of the world also show similar tendencies: in Mexico men and women have similar levels of entrepreneurial intentions (Huezo-Poncen, & Saiz-Álvarez, 2020), while in Greece (Vamvaka, Stoforos, Palaskas, & Botsaris, 2020) and Vietnam (Nguyen, 2018), men still have slightly higher entrepreneurial intentions.

Family Background

The existence of a family background is also a significant topic in research into the impact on entrepreneurial intentions and entrepreneurship in general. These studies mostly confirm the positive relationship between these variables, for example (Jena, 2020; Herman, 2019; Palmer, Fasbender, Kraus, Birkner, & Kailer, 2019; Sahinidis, Stavroulakis, Kossieri, & Varelas, 2019). According to Kotlar and De Massis (2013) in family firms there are various goals, which is a consequence of the overlapping and interaction between family, ownership and business aspects. If a young person grows up in such an environment, one can assume that entrepreneurship and work in their own company is a natural phenomenon, and the entrepreneurial environment is thus a natural environment in which it will be easier to develop entrepreneurial intentions.

Similarly, a supportive family background increases the likelihood of someone starting an entrepreneurial venture (Jayawarna et al., 2014). Jain and Wajid Ali (2012) point out that those entrepreneurs who have a family business background show significantly more prominent features such as: achievement, innovativeness, locus of control and risk-taking. Also, according to Altinay et al. (2012) an existence of an entrepreneur in the family has an impact on the intentions to start a business venture.

Age

A somewhat smaller number of studies are directed on examining the impact of age on entrepreneurial intentions. According to Minola et al. (2016), self-employment motivation depends on age. In terms of both desirability and feasibility beliefs, motivation to open your own business reaches its peak in young years, experiencing a pronounced reduction towards late years. Similarly, Tsai et al. (2016) found that years of age have a negative impact on assessment of their own capabilities and entrepreneurial intention. A somewhat different result was obtained in the research (Jain, & Wajid Ali, 2012), which states that younger entrepreneurs are more proactive than others, while entrepreneurs in their middle age have a stronger locus of control and high willingness to take risks than others. According to Nguyen (2018), age does not affect entrepreneurial intentions.

Financial Opportunities

The financial opportunities to start a new business can often affect entrepreneurial intentions. However, not much research has been dedicated to this topic. The reason for this may be that these surveys are mainly carried out in developed countries where the possession of money is not such a limiting factor due to the higher standard of living of potential entrepreneurs, and the better regulated financial incentives of the society. Perceived capability positively affects entrepreneurship intention (Tsai et al., 2016). Perceived capability Estimated capability may be partly understood as the possession of finance for launching entrepreneurial ventures, so it can be indirectly concluded that having finances affect entrepreneurial intentions. Pfeifer et al. (2016) showed that family wealth and family business exposure have a positive impact on the entrepreneurial intentions of Croatian students.

The disposal of financial capital, in the general case, has a positive relationship with self-employment (Kim, Longest, & Aldrich, 2013; Rodriguez, Tuggle, & Hackett, 2009; Shinnar, & Young, 2008). However, according to Sieger and Minola (2017), family financial support for an entrepreneurial venture can create some pressure on a potential entrepreneur and diminish his or her actual entrepreneurial intentions.

On the basis of the introductory and theoretical considerations, the six research questions were asked:

RQ1: Is there a statistically significant influence of the GEN – gender variable on the observed entrepreneurial dimensions?

RQ2: Is there a statistically significant impact of PAR – One of my parents has a private business variable on the observed entrepreneurial dimensions?

RQ3: Is there a statistically significant impact of YEA – The year in which the student is studying variable on the observed entrepreneurial dimensions?

RQ4: Is there a statistically significant impact of SUC – Student success in learning and studying variable on the observed entrepreneurial dimensions?

RQ5: Is there a statistically significant impact of FIN – The financial opportunities to start a new business variable on the observed entrepreneurial dimensions?

RQ6: Is there a moderating impact of GEN – gender variable in the relation between the influence of the variables of YEA – The year in which the student is studying, SUC – Student success in learning and studying and FIN – The financial opportunities to start a new business on the observed entrepreneurial dimensions?

METHODOLOGY

Survey Instruments (Measures)

Enterprise potential. The measurement of entrepreneurial potentials was carried out using a questionnaire based on the Attitude Toward Enterprise (ATE) test (Athayde, 2009). The questionnaire comprises 18 items (4 dimensions). The respondents evaluate the items on a seven-point Likert scale.

Individual entrepreneurial orientation. The measurement of individual entrepreneurial orientation was carried out using the Individual Entrepreneurial Orientation (IEO) questionnaire (Bolton, & Lane 2012). The questionnaire comprises 10 items (3 dimensions). The respondents evaluate the items on a seven-point Likert scale.

The Theory of Planned Behaviour and entrepreneurial intention. The measurement of Theory of Planned Behaviour and entrepreneurial intentions dimensions was realized using the Entrepreneurial Intention Questionnaire (EIQ) (Liñán, & Chen, 2009). The questionnaire comprises 20 items (4 dimensions). The respondents evaluate the items on a seven-point Likert scale.

Participants

The respondents were students in Serbia. This survey includes students from seven faculties of technical and economic orientation (University of Belgrade and University of Novi Sad). The sample consists of undergraduate and master's students, from the first to the fifth year of study, whereas this distribution is even. Students included in the research have no previous entrepreneurial experience.

The survey was conducted through an anonymous questionnaire. The questionnaires were completed either during or after classes. A total of 700 questionnaires were distributed to students, 532 of which were returned.

A number of returned questionnaires were not completely filled in, so these questionnaires were removed. There were 44 such questionnaires. Thus, 488 questionnaires remained for statistical analysis and this number is the final scope of the research sample. The percentage of successfully returned and completed questionnaires by respondents is 69,7%. The sample of 488 questionnaires obtained this way can be considered representative, both in qualitative and quantitative terms.

According to the observed variables, the sample has these characteristics: ◆ There were 337 (69.1%) female students and 151 (30.9%) male students in the sample. ◆ The age of the respondents ranges from 18 to 32, with the average age of the respondents being 21.38 (standard deviation 1,962). ◆ The research included undergraduate and master’s students, from the first to the fifth year of study. There were 157 first-year students (32.17%), 88 second-year students (18.03%), 122 thirdyear students (25.00%), 70 fourth-year students (14.34%) and 51 fifth-year students (10.45%). ◆ When considering the question (statement): I am an excellent and very successful student, 5 (1.02%) students said they did not agree at all, 25 (5.12%) students said they did not agree, 155 (31, 76%) of the students expressed a neutral attitude, 227 (46.52%) of the students said to agree and 76 (15.57%) of the students stated that they completely agreed. ◆ When considering the question (claim): One of my parents has their own private business, 106 (21.7%) students said yes, while 382 (78.3%) students said that their parents do not have a private business. ◆ When considering the question (statement): I have the necessary finances to open my own company, 213 (43.65%) students said they did not agree at all, 102 (20.90%) students said they did not agree, 81 (16, 60%) student expressed a neutral attitude, 76 (15.57%) students agreed and 16 (3.28%) students completely agreed with the statement.

RESEARCH RESULTS

For statistical data processing were used: t-tests, correlation and regression analysis and hierarchical regression analysis. The impact of the variables GEN - the respondents’ gender and PAR - one of my parents has a private business on the observed entrepreneurial dimensions was assessed by means of t-tests because these are categorical variables. The impact of the variables YEA - the year in which the student is studying, SUC - student success in learning and studying and FIN - the financial opportunities to start a new business on the observed entrepreneurial dimensions was tested by correlation and regression analysis because these are continual variables.

Descriptive Statistics

The descriptive statistics is shown in Table 1.

Table 1: Descriptive statistics. Dimensions and items Abbr. N Min Max Mean Deviation Std. α

Leadership

Creativity

Achievement LEA 488 1 7 3.88 1.265 .837

CRE 488 1 7 5.54 1.158 .792

ACH 488 1 7 4.25 1.259 .809

Personal control PC 488 1 7 4.11 1.205 .702

Risk taking

Innovativeness RT 488 1 7 4.16 1.335 .777

IN 488 1 7 4.86 1.142 .799

Proactiveness

Personal attitude PR 488 1 7 4.98 1.217 .786

PA 488 1 7 4.80 1.231 .860

Subjective norm SN 488 1 7 5.60 1.248 .846

Perceived behavioral control PBC 488 1 7 4.14 1.189 .894

Entrepreneurial intention EI 488 1 7 3.78 1.486 .937 Student success in learning and studying SUC 488 1.00 5.00 3.70 .828 Financial opportunities to start a new business FIN 488 1.00 5.00 2.13 1.073

T-test analysis (examining the impact of variables GEN – Gender and PAR – One of my parents has a private business)

Table 2 shows the t-test analysis. This analysis shows whether the respondents of different gender, as well as those whose parents have a private business and those who do not, differ in relation to particular entrepreneurial dimensions. The bold values in Table 2 indicate the dimensions where there is a statistically significant difference in the average grades, relative to a particular categorical variable.

Table 2: T-test of the average scores of the observed entrepreneurial dimensions for two categorical variables (GEN - Gender; PAR - One of my parents has a private business).

Abbr. Mean Total GEN - The respondents’ gender PAR - One of my parents has a private business

Male Female t-test Sig. (2-tailed) No Yes t-test Sig. (2-tailed)

LEA 3.88 4.10 3.79 2.709 .007 3.85 3.89 -.307 .759

CRE 5.54 5.28 5.65 -3.309 .001 5.53 5.54 -.093 .926

ACH 4.25 4.26 4.25 .134 .893 4.31 4.24 .507 .07

PC 4.11 4.30 4.02 2.617 .009 4.09 4.11 -.252 .801

RT 4.16 4.27 4.12 1.248 .158 4.20 4.15 .349 .727

IN 4.86 4.82 4.88 -.582 .562 4.86 4.86 .043 .966

PR 4.98 4.83 5.05 -1.950 .005 5.09 4.96 1.015 .311

PA 4.80 4.89 4.76 1.106 .269 4.77 4.81 -.252 .801

SN 5.60 5.38 5.69 -2.466 .014 5.70 5.57 .986 .325

PBC 4.14 4.30 4.07 1.913 .005 4.29 4.10 1.433 .153

EI 3.78 4.09 3.65 3.012 .003 3.75 3.86 -.654 .513

Correlation and regression analysis (examining the impact of variables YEA – The year in which the student is studying, SUC – Student success in learning and studying and FIN – The financial opportunities to start a new business)

The correlation coefficients between the continual variables YEA - the year in which the student is studying, SUC - student success in learning and studying, FIN - the financial opportunities to start a new business and the observed entrepreneurial dimensions are presented in Table 3. Pearson’s correlation was used.

Table 3: Correlation coefficients.

YEA - The year in which the student is studying

SUC - Student success in learning and studying

FIN - Financial opportunities to start a new bus. LEA CRE ACH PC RT IN PR PA SN PBC EI

.022 -.094* -.019 .047 -.046 -.038 -.048 .024 -.018 -.035 -.077

.142** .137** .199** .098* .015 .072 .184** -.038 .100* .051 -.033

.086 -.055 .060 .012 .141** .069 .058 .166** .062 .274** .302**

*p<0.05; **p<0.01.

The predictive effects of the variables YEA - the year in which the student is studying, SUC - student success in learning and studying, FIN - the financial opportunities to start a new business (independents) on the observed entrepreneurial dimensions (dependents) were determined by Multiple Regression Analysis. This results are given in Table 4. Statistically significant values are given in bold font.

Table 4: Regression analysis.

Independent variables

Dependent variables

YEA - The year in which the student is studying

SUC Student success in learning and studying FIN - Financial opportunities to start a new bus.

R2 F Sig.

β LEA .004 .140* .083 .027 4.486 .004

CRE -.114* .153** -.059 .035 5.817 .001

ACH -.046 .203** .056 .045 7.542 .000

PC .035 .093* .010 .011 1.789 .148

RT -.048 .018 .141** .022 3.686 .012

IN -.048 .077 .067 .012 1.956 .120

PR -.073 .192** .054 .042 7.073 .000

PA .031 -.046 .167** .030 5.040 .002

SN -.031 .103* .060 .015 2.378 .069

PBC -.041 .050 .273** .079 13.769 .000

EI -.073 -.031 .302** .098 17.098 .000

Gender as a Moderator

The correlation analysis between the variables YEA - the year in which the student is studying, SUC - student success in learning and studying, FIN - the financial opportunities to start a new business and the observed entrepreneurial dimensions, for male and female students is presented in Table 5. In this table, the results confirming the moderating effect of gender are highlighted by bold font. Table 6 gives additional statistics only for couples in which the gender moderating effect was confirmed.

Table 5: Correlation coefficients. GEN - Gender of the respondents

LEA CRE ACH PC RT IN PR PA SN PBC EI

YEA - Year -.048 -.104 -.033 -.053 -.058 -.046 -.094 .081 -.128 .014 .038 Male SUC - Success .113* .103* .183* .069 .086 .209* .150* .025 .138 .106 .027

FIN - Finance -.005 -.066 .048 -.024 -.001 -.010 -.042 -.029 -.011 .162* .119

YEA - Year .048 -.095 -.014 .082 -.044 -.037 -.030 .000 .025 -.048 -.114*

Fem. SUC - Success .199** .110* .216** .150** .003 .002 .178** -.053 .044 .056 -.017

FIN - Finance .110* -.029 .066 .012 .201** .113* .115* .259** .125* .310** .365**

*p<0.05; **p<0.01

Table 6: Hierarchical regression analysis with GEN - gender as a moderator. Independent

SUC - Student success in learning and studying Dependent R square change F-change

IN .009 .263

PA .018 .689

FIN - Financial opportunities to start a new business PBC

EI

RT .008

.017

.009 5.448

5.791

.227

DISCUSSION

Discussion of the Results of the Descriptive Statistics

The results of the descriptive statistics (Table 1) show that from the observed entrepreneurial dimensions, the largest average scores are achieved for the dimensions SN - subjective norm and CRE - creativity. The high average score for SN - subjective norm is is in line with the national culture in Serbia (Vukonjanski et al., 2012), which is pronounced collectivist: promotes unity and solidarity, which, in this case, is manifested through increased human support in their surroundings.

The lowest average values were assigned to the EI - entrepreneurial intentions and LEA - leadership dimensions. Most of the other dimensions related to entrepreneurship have values around or above average. However, it is one thing to have certain characteristics, such as innovation and pro-activity and a positive attitude towards entrepreneurship, but it is quite another when it comes to firm entrepreneurial intentions. Simply, it is easier to sympathize with the business of an entrepreneur and have the desire to become an entrepreneur, rather than really having the strong desire and the will to achieve such a venture. Due to the low standard of living in Serbia, especially that of students, the very low average value for item FIN – the financial opportunities to start a new business comes as no surprise.

Discussion of the Results of the t-test (Answering RQ1 and RQ2)

The results of the t-test (Table 2) show that the variables GEN - the respondents’ gender have a statistically significant influence on most of the observed entrepreneurial constructs. For men the more powerful dimensions are: LEA - leadership, PC - personal control, PBC - perceived behavioural control and EI - entrepreneurial intentions. The more pronounced dimensions for women are: CRE - creativity, PR - proactiveness and SN - subjective norm. There are no statistically significant differences in the dimensions: ACH - achievement, RT - risk taking, IN - innovativeness and PA - personal attitude. In this section, it can be concluded that men have more pronounced leadership qualities, as well as a greater desire and need for control, all of which leads to more pronounced entrepreneurial intentions. The result indicating that entrepreneurial intentions are more pronounced among male students. This is in agreement with most of the existing research, for example (Shneor et al., 2013; Antoncic et al., 2015; Leppel, 2016; Santos et al., 2016; Vamvaka, Stoforos, Palaskas, & Botsaris, 2020; Nguyen, 2018). On the other hand, women are more creative and proactive, and they also have more support from their environment. Higher proactivity can be explained by being female students more conscientious.

Greater support from the environment is probably not the result of more incentives for women to enter into entrepreneurship, but a higher level of confidence in girls in that period of life, and this is reflected in the result of greater proactivity, which indicates a higher degree of responsibility and conscientiousness. Knorr, Garzón and Martinez (2011) obtained a similar result regarding the need for achievement, according to which men and women have equal motivation to start their own business.

According to Table 2, the variable PAR - one of my parents has a private business does not show a statistically significant influence on any of the observed entrepreneurial constructs. This result is significantly different from most of the existing ones, indicating the positive impact of the existence of an entrepreneur in the family on entrepreneurial intentions and preferences for entrepreneurship (Jayawarna et al., 2014; Jain et al., 2012; Altinay et al., 2012; Jena, 2020; Herman, 2019; Palmer, Fasbender, Kraus, Birkner, & Kailer, 2019; Sahinidis, Stavroulakis, Kossieri, & Varelas, 2019). There is a similarity with research (Hatak, Harms, & Fink, 2015; Nguyen, 2018), where it was shown that the entrepreneurial intentions of an individual are not influenced by having or not parents who are entrepreneurs. Such a uniform situation, in this paper, is apparently created because students whose parents do not have a private business also have similar entrepreneurial intentions. A possible explanation is that regardless of whether their parents have a private business, students see entrepreneurship as an opportunity and consider self-employment to be a great way to gain employment and a decent standard of living. It should be borne in mind that unfavourable circumstances (a relatively high unemployment rate and low standard of living) are strongly expressed in Serbia. The second question is to what extent entrepreneurial intentions are present. As shown by the descriptive statistics, they are still below average.

The answer to RQ1: the variable of GEN – gender has a statistically significant impact on the most of the observed entrepreneurial dimensions (LEA - leadership, CRE - creativity, PC - personal control, PR - proactiveness, SN - subjective norm, PBC - perceived behavioural control and EI - entrepreneurial intentions).

The answer to RQ2: the variable of PAR – One of my parents has a private business has virtually no statistically significant influence on the observed entrepreneurial dimensions.

Discussion of the Correlation and Regression Analysis Results (Answering RQ3, RQ4 and RQ5)

According to the correlation analysis (Table 3), the variable YEA - the year in which the student is studying has no statistically significant correlation, except with the CRE - creativity dimension. This correlation is negative. Younger students may overestimate their creativity and the importance of creativity for education, while older students view creativity-related issues more objectively. It should be noted that the correlation between the variable YEA - the year in which the student is studying and the dimension EI - entrepreneurial Intentions is negative, but not statistically significant. This points to the slightly negative impact of the year in which the student is studying on entrepreneurial intentions, where there is a slight drop in entrepreneurial intentions at the end of studies. At the end of their studies, students find it easier to find work within an existing company with their diploma. This result has a similarity with the results obtained in the research by Minola et al. (2016) and Tsai et al. (2016).

The variable SUC - student success in learning and studying produces statistically significant and positive correlations with all the dimensions of enterprise potential (LEA - leadership, CRE - creativity, ACH - achievement and PC - personal control), as well as with the dimensions PR - proactiveness and SN - subjective norm (Table 3). Therefore, student success in learning and studying develops and positively affects individual personality traits, which in this case are seen as entrepreneurial potentials and other dimensions related to entrepreneurship.

However, the very existence of these potentials and traits contributes significantly to successful studying. Here, there seems to be a cyclical effect, where certain personality traits contribute to success in studies, and in turn this success serves to further enhance these same qualities.

Finally, support from the environment, and especially from family members becomes stronger thanks to success in studies. It is also interesting that SUC – student success in learning and studying has negative correlations with the constructs of PA - personal attitude and EI - entrepreneurial intentions. Although these correlations are not statistically significant, this result points to the direction of the influence of the variable SUC – student success in learning and studying. Successful students find good jobs more easily and will be able to live more effectively with their acquired professional knowledge and developed skills, so they are less likely to think about the risks and uncertainties of entrepreneurial ventures.

The variable FIN - the financial opportunities to start a new business realizes statistically significant and positive correlations with the following dimensions: RT - risk taking, PA - personal attitude, PBC - perceived behavioural control and EI - entrepreneurial intentions (Table 3). In doing so, the strongest correlation is achieved with the EI - entrepreneurial intentions dimension. This result is consistent with some existing research, for example (Kim, Longest, & Aldrich, 2013; Rodriguez, Tuggle, & Hackett, 2009; Shinnar, & Young, 2008). It may thus be concluded that the possession of finance is the most influential variable for the existence of entrepreneurial intentions. The other correlations of this variable show that this is due to the fact that individuals who (relative/perceived) have significant financial resources also have an increased perception of their entrepreneurial abilities, a better opinion on entrepreneurship and are more willing to take risks (it is easier for them to take risks or risk is not perceived as strongly as it is by someone who does not have access to money). Practically, money helps in lifting of selfconfidence in entrepreneurial abilities and risk-taking possibilities. In addition, this result is largely the consequence of the current conditions in Serbia, which primarily refers to the low standard of living and the poor financial capabilities of most citizens. García-Rodríguez, Gil-Soto, Ruiz-Rosa and Mamour Sene (2015), also showed that desire has the greatest impact on entrepreneurial intentions in Spain (a representative of a highly developed country), while feasibility has the greatest impact on entrepreneurial intentions in Senegal (a representative of a less developed country). Serbia can be classified into the category of less developed countries, and the feasibility of engaging in entrepreneurial ventures is certainly linked to the possession of finance.

According to the regression analysis (Table 4), the corrected determination indexes R2 have quite low, but predominantly statistically significant values (0.011 to 0.098). The dimension EI - entrepreneurial intentions, followed by the dimension PBC - perceived behavioural control, are under the strongest predictive impact of the observed independent variables. The variable FIN - the possession of finance has the decisive influence on this result.

The results of regression analysis are consistent with the results of the correlation analysis. For example, according to both correlation and regression analysis, variables SUC - student success in learning and studying and FIN - the financial opportunities to start a new business have statistically significant impacts on completely different dimensions. Success in studies may develop entrepreneurial potentials dimensions (leadership, creativity, achievement, personal control) and proactivity, while the possession of finance develops the Theory of Planned Behaviour dimensions (personal attitude, perceived behavioural control), entrepreneurial intention and risk taking.

The answer to RQ3: the variable of YEA – the year in which the student is studying has a statistically significant and negative impact on only one of the observed entrepreneurial dimensions (CRE - creativity).

The answer to RQ4: the variable of SUC – student success in learning and studying has a statistically significant and positive impact on the most of the observed entrepreneurial dimensions (LEA - leadership, CRE - creativity, ACH - achievement, PC - personal control, PR - proactiveness and SN - subjective norm).

The answer to RQ5: the variable of FIN - the financial opportunities to start a new business has a statistically significant and positive impact on some of the observed entrepreneurial dimensions (RT - risk taking, PA - personal attitude, PBC - perceived behavioural control and EI - entrepreneurial intentions).

Discussion of the Moderating Effects of GEN - Gender (Answering RQ6)

Tables 5 and 6 show relatively weak moderating influence of the variable GEN - gender on the relation between the variables YEA – the year in which the student is studying, SUC - student success in learning and studying and FIN - the financial opportunities to start a new business and the observed entrepreneurial dimensions. The statistically significant moderating impact occurs only in a small number of cases, which are concentrated around the variable FIN - the financial opportunities to start a new business. For women this variable has much more powerful and positive effects in several dimensions: RT - risk taking, PA - personal attitude, PBC - perceived behavioural control and EI - entrepreneurial intentions. Also, the variable SUC - student success in learning and studying has a more positive impact on certain dimensions for women: LEA - leadership, ACH - achievement, PC - personal control and PR - proactiveness. In these cases, the moderating effect of SUC - student success in learning and studying is not statistically significant, but the correlations in Table 5 indicate certain tendencies in this direction. Practically, the results of the correlation analysis increase for women. It is interesting that men showed a statistically significant increase in PBC - perceived behavioural control and EI - entrepreneurial Intentions (T-test, Table 2).

The explanation for such results may lie in the fact that in cases of success in studying and the good financial opportunities, women are significantly more motivated and more determined, they know exactly what they want and how to use their professional and financial potentials. All this in turn leads to more positive attitudes towards entrepreneurship, greater self-confidence in assessing their own abilities, greater willingness to take risks, and, finally, stronger entrepreneurial intentions (especially in the case of having access to finance). Observed in the other direction, when women do not have the needed resources to start their own firm, they think much less about entrepreneurship. At the same time, men in such conditions still do not give up, and they continue to imagine their possible engagement in entrepreneurial work. It can be concluded that in terms of entrepreneurship women are more realistic and decisive, while men may prefer to be entrepreneurs, but they do not know how to go about achieving such a goal. This conclusion bears similarities with the result obtained by Diaz-Garcia and Jiménez-Moreno (2010), which suggests that, although men think more about setting up a company, they show less determination to do so.

The answer to RQ6: the moderating effect of the variable GEN - gender on the observed relations is not so strongly expressed, and acts in the way that for women the variables FIN - the financial opportunities to start a new business and SUC - student success in learning and studying are much more powerful and positively affect certain dimensions.

CONCLUSION

The variable GEN - gender has a statistically significant effect on most of the observed dimensions. For men the most powerful dimensions are: LEA - leadership, PC - personal control, PBC - perceived behavioural control and EI - entrepreneurial intentions. The more pronounced dimensions for women are: CRE - creativity, PR - proactiveness and SN - subjective norm. The variable PAR - one of my parents has a private business does not show any statistically significant influence on any of the observed dimensions. The same holds true for the YEA - the year in which the student is studying variable, which has a statistically significant and negative impact only with the CRE - creativity dimension.

The variable SUC - student success in learning and studying generates statistically significant and positive impacts with all the entrepreneurial potential dimensions (LEA - leadership, CRE - creativity, ACH - achievement and PC - personal control), as well as with the dimensions of PR - proactiveness and SN - subjective norm. The variable FIN - the financial opportunities to start a new business realizes statistically significant and positive impacts predominantly with the dimensions of the Theory of Planned Behaviour dimensions (PA - personal attitude, PBC - perceived behavioural control) and with the dimensions of EI - entrepreneurial intentions and RT - risk taking. It should be noted that the variables SUC - student success in learning and studying and FIN - the financial opportunities to start a new business have statistically significant impacts on quite another entrepreneurial dimensions.

The influences of the observed variables on dimension of EI are of special importance – entrepreneurial intentions. The most important variable for entrepreneurial intentions is FIN - the financial opportunities to start a new business. Also, men have more pronounced entrepreneurial intentions. The other variables do not have a statistically significant impact on entrepreneurial intentions, but the tendency is for entrepreneurial intentions to decline in cases where parents do not have a private business, then in the final years of study and in situations where there is significant success in studies. Under such conditions, students are more inclined to focus on their careers in existing companies.

The moderating effect of the variable GEN - gender is as follows: in the case of successful studying and the possession of finance, women are more motivated and more determined, have more positive attitudes towards entrepreneurship, highly assess their own abilities, are more ready to take risks and have stronger entrepreneurial intentions (especially in the case of the possession of finance). When women do not have the money to start their own businesses, then they think much less about entrepreneurship. Overall, men may prefer more to be entrepreneurs, but women approach entrepreneurship more realistically and decisively.

The findings in this paper are based on a sample of students in Serbia. This statement is a basic limitation of the study. However, the findings may be particularly suitable for understanding similar relationships in other transitional and less developed countries. In practical terms, the research has shown that the intensification of entrepreneurial intentions among young people in Serbia may be achieved through education and assistance from state institutions. The help of state institutions should be logistical and advisory, but it is particularly important to provide appropriate financial support to potential entrepreneurs who have good ideas, but not the funds to start an entrepreneurial venture. In addition, stronger support and more attention should be paid to groups with reduced entrepreneurial intentions, namely female students and successful students in the final years of study.

A special contribution of this paper in the theoretical sense is that the effects of The year in which the student is studying and Student success in learning and studying on the observed entrepreneurial dimensions are examined, as these are two variables that have not been sufficiently researched in this context so far, despite numerous published papers in the field of entrepreneurial intentions. The contribution of this paper in the practical sense is visible in the consideration of the way all five observed variables influence on entrepreneurial dimensions, which resulted in appropriate proposals for encouraging entrepreneurial intentions among students in Serbia.

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PREDUZETNIČKI POTENCIJAL, INDIVIDUALNA PREDUZETNIČKA ORIJENTACIJA I PREDUZETNIČKE NAMERE STUDENATA U SRBIJI

Rezime:

U ovom radu dati su rezultati istraživanja učenika o uticajima pet varijabli na dimenzije potencijala preduzeća, individualnu preduzetničku orijentaciju, teoriju planiranog ponašanja (TPB) i dimenziju preduzetničke namere studenata. Uočeni su uticaji sledećih varijabli: Pol, Jedan od mojih roditelja ima privatno preduzeće, Godina u kojoj student studira, Uspeh studenata u učenju i Finansijske mogućnosti za započinjanje novog posla. Ispitanici studiraju na sedam fakulteta u Srbiji. Uzorak je obuhvatio 488 ispitanika. Najuticajnija varijabla na dimenziju preduzetničke namere su finansijske mogućnosti za započinjanje novog posla. Takođe, muškarci imaju izraženije preduzetničke namere. Uspeh u studijama pozitivno utiče na preduzetničke potencijale i proaktivnost, dok posedovanje finansija pozitivno utiče na preduzetničke namere i spremnost na rizik. U slučajevima uspešnih studija i posedovanja finansija, žene su više motivisane, odlučnije i imaju izraženije preduzetničke namere (posebno u slučaju da imaju finansijska sredstva). Iako muškarci možda više žele da postanu preduzetnici, žene preduzetništvu pristupaju realnije i odlučnije.

Ključne reči:

preduzetnički potencijal, individualna preduzetnička orijentacija, preduzetničke namere, studenti, Srbija.

Klasifikacija jela:

L26, D23

EJAE 2021, 18(1): 126 - 136 ISSN 2406-2588 UDK: 336.27(439:437.6:437.1:438) DOI: 10.5937/EJAE18-29568

Original paper/Originalni naučni rad

IS THERE ANY GOVERNMENT DEBT THRESHOLD IN FOUR SELECTED CENTRAL EUROPEAN COUNTRIES?

Yu Hsing*

Southeastern Louisiana University, College of Business, Department of Management & Business Administration, Hammond, Louisiana, USA

Abstract:

This article shows that there is no government debt threshold in Hungary and Slovakia and that the respective government debt thresholds in Czechia and Poland are estimated to be 27.51% and 46.86%, which are far smaller than the 90% threshold suggested by Reinhart and Rogoff. Hence, the Reinhart-Rogoff threshold is not applicable to Czechia, Hungary, Poland and Slovakia.

Article info:

Received: December 14, 2020 Correction: January 12, 2021 Accepted: February 12, 2021

Keywords:

fiscal expansion, government debt, Reinhart-Rogoff threshold, production function.

JEL Classification:

E62

INTRODUCTION

During the 2008-2009 global financial crisis, many countries pursued fiscal expansion in order to rescue their economies. For example, during 2008-2009, Czechia increased the government debt ratio from 28.25% to 33.56%. Hungary’s government debt ratio rose from 71.24% to 77.47%. Poland’s debt ratio went up from 46.30% to 49.43%. Slovakia raised the debt ratio from 28.46% to 36.29%. These debt ratios continued to rise up until 2011 in Hungary and 2013 in Czechia, Poland and Slovakia. Whether an increase in the government debt ratio raises or haram the growth rate of real GDP has been studied extensively. Reinhart and Rogoff (2010a, 2010b) show that the turning point or threshold of the government debt ratio is 90%. Above 90%, a higher debt ratio tends to reduce the growth rate.

The objective of this paper is to determine whether the Reinhart-Rogoff proposal may apply to four selected Central European countries, namely, Czechia, Hungary, Poland and Slovakia.

Is it possible to have a threshold or turning point of 90% for these four countries with debt ratios less than 90%? This paper differs from most previous studies in several aspects. First, a theoretical model based on an extended production function is presented. Second, a quadratic function is applied to test whether an inverted U-shape relationship is achievable. Third, the GARCH model is used in order to detect possible conditional heteroskedasticity in time series data.

LITERATURE REVIEW

Based on the data of forty-four countries covering about 200 years, Reinhart and Rogoff (2010a, 2010b) and Reinhart, Reinhart and Rogoff (2012) find that the growth rate and the debt ratio have a weak relationship when the government debt ratio is smaller than 90% whereas a government debt ratio above 90% causes the growth rate to decline. This threshold for the government debt ratio is similar in advanced and emerging countries. However, using the same sample constructed by Reinhart and Rogoff and other specifications, Minea and Parent (2012) find that the turning point for the government debt ratio is 115%. Herndon, Ash and Pollin (2014) use the same data and find that for 20 advanced countries, the negative impact of the debt ratio above 90% on the growth rate cannot be confirmed. According to Herndon, Ash and Pollin, during 1946–2009, countries with the debt ratios above 90% recorded a growth rate of 2.2% instead of −0.1% as claimed by Reinhart and Rogoff. The impact of the debt ratio on the growth rate varies substantially across countries and periods. Égert (2015a, 2015b) could not confirm the 90% debt threshold, indicating that the threshold was in the range of 20% - 60%. A nonlinear relationship is sensitive to the specifications of the model. Parameter estimates differ by country. Lee, Park, Seo and Shin (2017) find no support for the 90% debt threshold. Using the post-WWII data developed by Reinhart and Rogoff, they indicate that the debt threshold is around 30%. Above 30%, the growth rate will decline by 1 percentage point.

Based on the data for eighteen OECD countries during 1980-2008, Cecchetti, Mohanty and Zampolli (2011) find that the debt threshold is 85%, indicating that a higher government debt ratio above 85% tends to have a negative effect on the growth rate. They also show that when the corporate debt ratio is greater than 90% of GDP, an adverse impact on the growth rate is found.

Using a sample of 12 countries in the euro area, Checherita-Westphal and Rother (2012) find a debt threshold between 90% and 100%, indicating that a higher government debt ratio tends to reduce the growth rate if the debt ratio is greater than 90% - 100%. The threshold starts in the range of 70% - 80% based on the confidence interval.

Based on the data for twelve countries in the euro area, Baum, Checherita-Westphal and Rother (2013) find that a higher debt ratio increases the growth rate but has no impact when the debt ratio reaches about 67%. When the debt ratio is greater than 95%, a higher debt ratio reduces the growth rate.

Afonso and Jalles (2013) examine the relationship between government debt and economic growth for 155 advanced and developing countries during 1970 – 2008. The debt threshold is estimated to be 59% for the euro zone and 79% for emerging economies. If the debt ratio increases 10%, the growth rate would decrease by 0.2% if the debt ratio is over 90% and increase by 0.1% if the debt ratio is below 30%.

Chirwa (2020) studies the relationship among government debt, growth and other related variables for ten countries in the euro area. The threshold is estimated to be 70% in the long run whereas more government debt has a negative effect on the growth rate in the short run.

Woo and Kumar (2015) reveal that if the initial debt ratio rises by 10 percentage points, the growth rate of real per capita GDP tends to reduce by 0.2 percentage points. A higher initial government debt ratio tends to cause a larger negative effect. The negative effect is the result of decrease in labor productivity growth.

Lechtenberg (2017) analyzes the subject using a sample of 10 countries. Australia, Canada, Chile, Germany and New Zealand have had low and declining debt ratios, and a higher debt ratio has not caused a decline in economic growth of these countries. On the other hand, debt thresholds are detected for several advanced countries such as the USA , the UKU, France, Greece, and Italy. A higher debt ratio beyond the threshold tends to reduce economic growth in Greece, Italy, the UK and the USA but increase economic growth rate for France.

Yared (2019) examines the trend of growing government debt, analyzes causes, and provides answers to the issue concerned. He indicates that fiscal rules are needed for policymakers not to spend beyond means and provide flexible measures to react to economic crises.

Based on the data consisting of many developing and advanced countries, Swamy (2020) reveals that if the debt ratio rises by 10 percentage points, the average growth rate will decline by 23 basis points and that government debt and economic growth have a nonlinear relationship. The effect of government debt on the growth rate varies by country, depending upon several major macroeconomic factors and debt regimes.

Jacobs, Ogawa, Sterken and Tokutsu (2020) explore the impact of government debt on the economic growth rate for 27 European Union members and 4 OECD countries during 1995-2013. They show that economic growth Granger causes government debt, but government debt does not cause Granger economic growth. Slow economic growth causes more government debt. In high-debt economies, slow economic growth increases government debt, which causes a higher interest rate in the long run, dampens interest-rate sensitive private spending, and increases public debt. In addition, they indicate that high-debt economies show greater impacts of the growth rate on the debt ratio and that low-debt countries exhibit greater effects of the debt ratio on the growth rate.

THEORETICAL MODEL

Based on the studies of Ram (1986, 1989), Goel, Payne and Ram (2008) and others, we can express the growth rate of real GDP as:

Where Y = real GDP, L = labor employment, K = capital, = the growth rate of Y, = the growth rate of L, = the growth rate of K, and D = the ratio of government debt to GDP. (1)

Because data for capital is not available, the growth rate of capital can be substituted by the ratio of investment spending to Y (IY) (Ram, 1986, 1989).

(2)

In a linear form, the coefficient of measures the percent change in output over the percent change in labor, and the coefficient of IY represents the partial derivative of Y with respect to K. The coefficients of the first two explanatory are expected to be positive, and the coefficient of the debt ratio is unclear. A lower and rising government debt ratio for infrastructural improvements may be conducive to economic growth whereas a higher and rising debt ratio may cause the interest rate to rise, crowd out consumption and investment spending, cause the local currency to appreciate, and hurt exports. and the debt ratio may exhibit an inverted U-shaped relationship. In that case, we can consider the following equation:

(3)

If there is an inverted U-shaped relationship between and the debt ratio, the coefficient of D should be positive, and the coefficient of should be negative.

The turning point of the debt ratio is given by:

where is the coefficient of D and is the coefficient of . (4)

EMPIRICAL RESULTS

The sources of data came from the International Financial Statistics, the Eurostat, and the World Economic Outlook. The growth rate of real gross domestic product (GDP) or labor employment is expressed as a percent. Investment spending as a percent of GDP is used as data for capital is not available. Government debt as a percent of GDP is used. This study covers the sample period from 1995 or 1996 to 2019. Data for the government debt ratio or the growth rate before 1995 or 1996 is not available.

Figure 1 exhibits scatter diagrams between the economic growth rate and the government debt ratio for these four countries during the sample period. It seems that the government debt ratio and the economic growth rate in Czechia or Poland have a nonlinear relationship whereas the government debt ratio and the economic growth rate in Hungary and Slovakia shows a negative or an unclear relationship. Hypothesis testing is required as to determine whether the above observations can be verified statistically.

Table 1 presents the estimated regressions and related statistics. Two versions with and without the quadratic term are estimated for each country. For Czechia, in version A, the quadratic term is negative and statistically significant at 1% level. The threshold or turning point is estimated to be 27.51%. A further increase in the debt ratio beyond the threshold reduces the growth rate. In version B without the quadratic term, the debt ratio has a positive and significant coefficient, which makes us draw a misleading conclusion that the government can continue increasing the debt ratio and generate a positive impact on the economic growth rate.

Figure 1: Scatter Diagrams between the Growth Rate and the Debt Ratio

Slovakia B

5.0770 (0.0007) 0.7713 (0.0000) 0.1304 (0.0002) -0.1203 (0.0000) 0.5413 4.2086 4.5499 1995-2019

Slovakia A

20.5162 (0.0000) 0.7349 (0.0000) 0.1890 (0.0000) -0.8985 (0.0000) 0.0088 (0.0000) 0.3678 4.4114 4.8041 1995-2019 None

Poland B

0.2538 (0.9390) 0.4112 (0.0030) 0.2650 (0.0214) -0.0389 (0.3413) 0.4450 3.5363 3.8776 1995-2019

Poland A

-64.4444 (0.0000) 0.1099 (0.0000) 0.5508 (0.0000) 2.4553 (0.0000) -0.0262 (0.0000) 0.5463 3.1393 3.5294 1995-2019 46.8577%

Hungary B

-10.0552 (0.0558) 0.4717 (0.0001 0.5212 (0.0000 -0.0021 (0.9549) 0.5744 3.8470 4.1883 1995-2018

Estimated Regressions of the Growth Rate of Real GDP

Table 1: Hungary A

17.3740 (0.0000) 0.5617 (0.0000) 0.6907 (0.0000) -0.9881 (0.0000) 0.0074 (0.0000) 0.6406 3.9369 4.3269 1995-2019 None

Czechia B

-11.7630 (0.0000) 0.5543 (0.0000) 0.4644 (0.0000) 0.0506 (0.0000) 0.2768 4.3464 4.6900 1996-2019

Czechia A

Variable

-17.4477 (0.0000) 0.6332 (0.0000) 0.5265 (0.0000) 0.4483 (0.0000) -0.0082 (0.0000) 0.3298 4.2813

Constant Growth rate of employment Investment/GDP ratio Debt ratio Debt ratio squared R squared Akaike info criterion

4.6740 1996-2019 27.5144%

Schwarz criterion Sample period Estimated debt threshold

Notes : Figures in the parenthesis are probabilities.

For Hungary, in version A with the quadratic term, all coefficients are significant. However, a positive significant sign of the quadratic term suggests that the government debt ratio and the growth rate exhibit a U-shape correlation, which is contrary to economic theory. In version B without the quadratic term, a coefficient of the government debt ratio is negative and insignificant at 10% level. In comparison, the findings by Hsing (2018) cannot be compared with the result in this paper because real GDP instead of the percent change in real GDP, is chosen as the dependent variable.

For Poland, in version A, a coefficient of the quadratic term is negative and significant at 1% level. The threshold of the debt ratio is calculated to be 46.86%. A higher government debt ratio than this threshold tends to reduce the growth rate. In version B without the quadratic term, a coefficient of the government debt ratio is negative and insignificant at 10% level. If version B is chosen, we may draw a misleading conclusion that a higher government debt ratio will not be harmful to the growth rate.

For Slovakia, in version A with the quadratic term, a negative significant sign of the debt ratio and a positive significant sign of the quadratic term indicate a U-shape relationship, which is inconsistent with economic theory. In version B without the quadratic term, a negative significant sign of the government debt ratio suggests that a higher government debt ratio causes the growth rate to decline regardless of the level of debt ratio.

In comparison, the findings in this paper are different from and similar to some of previous studies. Reinhart and Rogoff (2010a, 2010b) show that the threshold for the debt ratio is 90% whereas the respective thresholds of debt ratio for Czechia and Poland are estimated to be 27.51% and 46.86%. The results for Hungary (Version A) and Slovakia (Versions A and B) are consistent with Kumar and Woo (2015) and Swamy (2020), who indicate that the debt ratio has a negative impact on the growth rate. The thresholds reported by Cecchetti, Mohanty and Zampolli (2011), Checherita-Westphal and Rother (2012), Minea and Parent (2012), Afonso and Jalles (2013), Baum, Checherita-Westphal, and Rother (2013), and Chirwa (2020) are higher than the respective thresholds of 27.51% and 45.26% for Czechia and Poland. The estimated thresholds for Czechia and Poland are close to the 30% debt threshold found by Lee, Park, Seo and Shin (2017), the threshold between 20% and 60% estimated by Égert (2015a, 2015b), and the threshold of 47.45% for Italy reported by Lechtenberg (2017).

SUMMARY AND CONCLUSIONS

This article has studied the impact of the government debt ratio on the growth rate of real GDP for Czechia, Hungary, Poland and Slovakia using an extended production function. The growth rate is specified as function of the percent change in labor employment, the ratio of investment spending to nominal gross domestic product, and the government debt ratio. A quadratic form is used for the debt ratio to test whether a nonlinear inverted U-shape relationship exists.

The results have confirmed that Czechia or Poland exhibits a threshold or turning point whereas Hungary and Slovakia do not show an inverted U-shape relationship. The threshold for Czechia is estimated to be 27.51%. The growth rate and the government debt ratio have a positive relationship when the debt ratio is up to 27.51% whereas they have a negative relationship when the government debt ratio is greater than 27.51%. The estimated threshold for Poland is 46.86%, suggesting that a higher government debt ratio beyond 46.86% tends to reduce economic growth.

These results have some policy implications. An analysis of data indicates that debt ratios in these four countries show declining trends. For example, the debt ratio in Czechia declined from 44.91% in 2013 to 31.62% in 2019. The debt ratio in Hungary declined from 80.48% to 67.52%. The debt ratio in Poland dropped from 54.23% to 47.77% in 2019. The debt ratio in Slovakia decreased from 54.74% in 2013 to 48.35% in 2019. Debt ratios in Czechia and Poland declined and moved toward the respective thresholds of 27.51% and 46.86%, suggesting that the direction of changes in debt ratio is justified. Lower debt ratios in Hungary and Slovakia in recent years have also helped to raise the growth rate as the debt ratio has an adverse effect on the economic growth rate as shown in the estimated regression with the quadratic term in Hungary and the regression with or without the quadratic term in Slovakia.

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POSTOJI LI PRAG JAVNOG DUGA U ČETIRI IZABRANE CENTRALNOEVROPSKE ZEMLJE?

Rezime:

Ovaj rad pokazuje da u Mađarskoj i Slovačkoj ne postoji prag javnog duga i da se procenjuje da su pragovi javnog duga u Češkoj i Poljskoj 27,51% i 46,86%, što je daleko manje od praga od 90% koji su predložili Rajnhart i Rogof. Stoga, ovaj prag nije primenljiv na Češku, Mađarsku, Poljsku i Slovačku.

Ključne reči:

fiskalna ekspanzija, javni dug, prag Rajnharta i Rogofa, proizvodna funkcija.

Klasifikacija jela:

E62