JUNE 2016
DEVELOPING A MEASURE FOR QUANTIFYING ECONOMIC IMPACTS: THE BIOECONOMY CONTRIBUTION INDEX
Disclaimer
Please note that the information contained in the Bioeconomy Malaysia Report is intended to be used for guidance and knowledge only and is generally based on information made available or rendered to Malaysian Biotechnology Corporation (BiotechCorp). Whilst every effort has been taken to ensure the accuracy and completeness of the contents at the time this Report is issued, inaccuracies may exist due to several reasons including constant changes and advancement in the bio-based industry and/or changes in circumstances. BiotechCorp does not accept any responsibility for the accuracy or completeness of the information in the aforesaid Report. BiotechCorp, its subsidiary, related companies, directors, employees, and agents and consultants, are neither liable nor responsible for any loss whatsoever and/or howsoever occasioned arising from any reliance made on the information rendered therein. For this reason, the reader is advised to undertake necessary due diligence on the information before relying on the same for any purpose whatsoever.
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Abstract: The development of biotechnology as part of bioeconomy in Malaysia has led to an increased need to measure the industry’s performance on a frequent basis. As such, the study proposes that a specific methodology be developed to measure Bioeconomy contribution to the country, encompassing a multitude of measurable aspects, which can be packaged and communicated in a meaningful form to enable analysis and interpretation. Specifically, the study proposed to create an index which includes quantifiable parameters namely Value-Added, Employment, Exports, Productivity, and Investment to track the bioeconomy development. The parameters’ performance is then compared with the results from a dynamic computable general equilibrium model to measure its contributions over time. The result from the study indicates that Malaysian Bioeconomy has grown progressively although the performance is not equal across the parameters. The analysis of the parameters’ performance allows policy makers to take the necessary action to address the lacking parameters and strategise towards the national bioeconomy vision set earlier. The findings would be useful in the case study in Malaysia and elsewhere.
Keywords: Bioeconomy, Biotechnology, Malaysia, Modelling, Index, Economic Impact, Policy, Dynamic Stochastic General Equilibrium (DCGE)
Prepared by, Frontier Private Advisors Sdn. Bhd. SUNIL BHALLA ZAIN BAHARUDDIN DR. ABUL QUASEM AL-AMIN Malaysian Biotechnology Corporation (BiotechCorp) DATO’ DR NAZLEE KAMAL ZURINA CHE DIR Level 23, Menara Atlan 161B Jalan Ampang, 50450 Kuala Lumpur Malaysia T: +6 03 2116 5588 F: +6 03 2116 5411 For further enquiries, please contact: Nazmi Idrus at nazmi.idrus@biotechcorp.com.my
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LIST OF ABBREVIATIONS 10MP 1MBAS ASEAN BCI BiotechCorp BS BTP BCDP CES CET DCGE DOS DP E&I EPPs EPU ETP FGS GAMS GDP GNI HIES I-O KeTTHA LFS MIDA MOA MOSTI MPIC NBC NBP NBS NGO NGP NRE R&D ROW SAM SEDA TP
Tenth Malaysia Plan 1Malaysia Biomass Alternative Strategy Association of Southeast Asian Nations Bioeconomy Contribution Index Malaysian Biotechnology Corporation Bioeconomy Shares Bioeconomy Transformation Programme Bioeconomy Community Development Programme Constant Elasticity of Substitution Constant Elasticity of Transformation Dynamic Computable General Equilibrium Department of Statistics Domestic Production Export and Import Entry Point Projects Economic Planning Unit Economic Transformation Programme Final Goods and Services General Algebraic Modelling System Gross Domestic Product Gross National Income Household Income and Expenditure Survey Input-Output Table Ministry of Energy, Green Technology and Water, Malaysia Labour Force Survey Malaysian Investment Development Authority Ministry of Agriculture & Agro-Based Industry Ministry of Science, Technology & Innovation Ministry of Plantation Industries and Commodities National Bioeconomy Council National Biotechnology Policy National Biomass Strategy Non-government Organization National Greentech Policy Ministry of Natural Resources and Environment Research & Development Rest of the World Social Accounting Matrix Sustainable Energy Development Authority Malaysia Trigger Projects
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INTRODUCTION The contribution of Bioeconomy1 is increasing worldwide and so is the concern about global climate change and other sustainability issues. This results in demand and investments in biobased goods and services related to Bioeconomy. Industries such as medical, food, feed, wellness, chemicals and alternative fuel are some of the key fundamentals of the global Bioeconomy and it is recently a subject of focused attention from decision makers, researchers and public policymakers alike (Wesseler, J., Spielman, D.J., & Demont, M. 2010). This global development has resulted in the expansion of biotechnology capabilities and subsequently the biotechnology sector as a whole, and towards developing a Bioeconomy. The Bioeconomy cuts across all other sectors, such as agriculture, health and manufacturing sectors. Around the world, nations have put into place comprehensive frameworks and policies to nurture the growth of their respective bioeconomies. Among others, the United States developed its National Bioeconomy Blueprint in 2012 (House, 2012), the South African government announced The Bioeconomy Strategy in 2013 (Thomson, 2013) and the European Commission continues to spearhead global Bioeconomy growth via the Horizon 2020 EU Framework Programme for Research and Innovation (R&I, 2014). Bioeconomy is defined in different ways around the world. According to the Global Bioeconomy Council 2 the definition that is shared by many can be coined as “the knowledge-based production and utilisation of biological resources, innovative biological processes and principles to sustainably provide goods and services across all economic sectors”. There are at least forty countries globally that have some elements of bioeconomy in their policy formulation and strategies, and these includes countries such as the G7 and the BRICS. While the main purpose is to foster the advancement of bioeconomy, some country strategies have emphasised linkages between bioeconomy and health (e.g. biopharmaceuticals; health, nutrition & wellness) or sustainable biomass production and utilisation. These bioeconomy policies are each tailored to the specific economic circumstances, technological advancement level, and native biological resource available in their respective economies. In addition, each strategy also builds upon the strong foundation laid by ground-breaking initiatives driving development of technologies and associated products and services, focused on commercialisation of technologies that are close to the market, representing a quick return on investment (House, 2012; Thomson, 2013). The global evolution towards a bio-based economy is on the way, facilitated by increased commercialisation of biotech research (R&I, 2014). This study aims to describe the current environment of the Malaysian Bioeconomy through benchmarking ‘Bioeconomy Contribution Indicators’ (BCI) for the National Bioeconomy strategy. Specifically, the idea on Bioeconomy Contribution Index is to be used as to (i) assess real current achievement against expected achievements (based on real economic circumstances); (ii) relate performance in a specific year in contrast with performance in preceding years and as part of an overall trend; (iii) compare sentiment and economic activity in Bioeconomy sector in contrast with other national industries; and (iv) compare sentiment and economic activity in Malaysian Bioeconomy in contrast with Bioeconomy of other nations. The study proposes that a specific methodology be developed to benchmark ‘Bioeconomy Contribution’, encompassing a multitude of measurable aspects, which can be packaged and communicated in a meaningful form to enable analysis and interpretation. In order to develop this Bioeconomy Contribution Index, this study has developed Bioeconomy related indicators 3 which is the first in Malaysia and in the region, and which can measure Bioeconomy and its success. To capture a snapshot of the full Bioeconomy4 in this study, we selected five subindices as (i) Value-Added, (ii) Employment, (iii) Exports, (iv) Productivity, and (v) Investment to track the bioeconomy development. 1 Refers
to all economic activity that is derived from the continued commercial application of biotechnology and encompassed the production of renewable biological resources and their conversion into food, feed, chemicals, energy and healthcare wellness products via innovative and efficient technologies. 2 From the Communique of the 2015 Global Bioeconomy Summit: http://gbs2015.com/fileadmin/gbs2015/Downloads/Communique_final_neu.pdf 3 The broad framework of indicators assessed as by Input and Output indicators and those can be considered parameters. 4 Thus, here the measurement of BCI classifies the key drivers of Bioeconomy to observe the progress and innovations to assess the Bioeconomy drivers.
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BIOECONOMY INDICATORS: WHY MEASURE BIOECONOMY? Malaysia has recently taken priority in creating a sustainable resource-based method for the country’s future economic development. The Malaysian government acknowledged biotechnology and, more recently, Bioeconomy as one of the key strategic drivers to lift the nation’s development by the adoption of sustainable industrial processes, discoveries in healthcare, and improved agricultural productivity. The government has also provided support to bio-based industries through pro-business policies, improvements of human resource development and infrastructure building. Further providing attractive incentives, the government has also invested extensively in logistic supports, building infrastructure, and technology to bring the industry on par with the advanced economies. The National Biotechnology Policy (NBP) was launched in 2005 to strategise and to achieve quantified economic targets (BiotechCorp, 2014) which consists of three phases: Phase I: national capacity building (2005-2010), Phase II: science to business (2011-2015) and Phase III: going global (2016-2020)5. The Malaysian Biotechnology Corporation (BiotechCorp), a government agency, was created in 2005 to spearhead the development of bioeconomy in Malaysia. BiotechCorp is currently stimulating the transition of local companies into the global market under the third phase by providing a suitable environment to further catalyse the nation’s Bioeconomy. This transition is driven by the Bioeconomy Transformation Programme (BTP), launched in October 2012 (BiotechCorp, 2014). The BTP is projected to result in an increase of Gross National Income (GNI) by nearly RM48 billion in 2020, including 170,000 job opportunities and a cumulative attraction of RM50 billion domestic and foreign investments (BiotechCorp, 2014). This estimation is made based on the current condition of the Bioeconomy involvement, sectoral bio-share to the other sectors and on-going projects added with engagements with private sector involvement over the period from 2013 to 2020 (BiotechCorp, 2014). Despite this, there are only four years to go to reach 2020 and there should be good and sustained progress to achieve the quantified targets. In terms of concrete outcomes, as of 2014, BioNexus Status 6 companies alone have achieved around RM9 billion investments and more than RM1 billion in revenue per year (BiotechCorp, 2014). For comparison, the NBP target for revenue generation by 2020 is RM100 billion. This gap suggests that an acceleration programme is necessary to boost revenue generation and drive the industry to reach its 2020 target. In general, Research & Development (R&D) spending, GNI, capital raised, income generation, and commercialisation are also still somehow lacking in Malaysia 7 . Furthermore, Malaysian companies are still behind due to current inability to effectively commercialise biotech products. Henceforth, the fundamental question is: how to effectively close the gap between actual achievements and target benchmarks within the stipulated timeframe? At the root of it, the lack of alignment of numerous initiatives, strategies and policies makes it a challenge for Bioeconomy to progress further as expected. There are however a good number of key developments and action plans in the national level surrounding the Bioeconomy sector namely; (a) Strategies from BiotechCorp, (b) National Greentech Policy, (c) National Biomass Strategy, (d) Vision for biotechnology in the Malaysian Plans, (e) 1Malaysia Biomass Alternative Strategy (1MBAS), (f) Economic Transformation Programme, (g) National Biotechnology Policy, (h) Renewable Energy Act (i) Bioeconomy Transformation Programme (BTP), (j) Malaysian Industry-Government Group for High Technology and (h) Sustainable Energy Authority Malaysia (SEDA). Nevertheless, due to the overlapping responsibilities among agencies, lack of cooperation, and a strong sense of competition among various ministries, the Malaysian Bioeconomy could not get the outcomes that were expected. To overcome the lacking however is not easy, and to create sustainable market potential of Bioeconomy in the long-run is not straightforward. Consequently, precise allocation of investments and a focused 5
Malaysian Biotechnology Corporation (BiotechCorp) was set up as an agency to drive its growth, expansion and to assist the national three phase’s targets. It successfully provides a suitable platform through NBP for the continuous development of biotechnology, Bioeconomy and bio-based industries to improve the industry’s competitiveness to contribute more towards nationwide development. 6 BioNexus is a special status awarded to qualified international and Malaysian biotechnology companies. The status bestows fiscal incentives, grants and other guarantees to assist growth. 7 Recent figures show that the Research & Development spending in United States is USD73.2 (US $1= RM 3.6) billion whereas in Malaysia USD 0.03 billion (Battelle, 2012).
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direction is required together with the acceleration programme in the correct sectors for effective value addition (Figure 1) both in the short and long-term. The improving productive efficiency to R&D is also important by both science and social science related efforts. In addition, to reach the targeted goal, Bioeconomy must bring an applicable master plan and policy, good governance in the implementation process, and regulation to the Bioeconomy with immediate interest, including: i) best way to characterise Bioeconomy’s scope with national structure, thrust, agenda, and interrelationships to the rest of the national economy; ii) best action to find the long-term aggregated impacts of Bioeconomy in the society, iii) best roadmap for implementation; and (iv) best set of alignment, execution, performance, monitoring and regular strategic reviews namely by a bunch of ‘indicators’ that are able to measure success and constraints. Factor income
Domestic private saving Factors of production Taxes
Firms/ Productions
Consumptions/ households
Institution/ Government
Savings/ Investments
Commodities & Bioeconomy Exports
Government spending
Imports
International transections/Rest of the world
Remittance s
Demands for Investments
Foreign loans, grants
Capital inflow
Figure 1. Circular flow map of Malaysian economy Malaysia has to set a ‘strategic master-plan’ to improve the industry’s competitiveness to contribute toward sustained development. However, to ensure appropriate allocation of resources and strategic direction, it is necessary to have a clear view of the immediate economic status of the sector. At the international level, there is already an effort to collaborate and gather economic data from the various active Bioeconomies around the world. This initiative, the Bioeconomy Observatory8, is spearheaded by the European Commission and aims to collate information on economic achievements within bio-based and biotechnology industries from international Bioeconomies in order to provide a basis for collaborative development of the sector (R&I, 2014). With the assistance of increasingly powerful processing and computing power, a centralised repository of such data can provide insight into trends and economic patterns and subsequently open doors to strategic initiatives and policies that can be built on synergies between different Bioeconomies. In order to take full advantage of this emerging resource, this study has proposed that a clear and standardised methodology be developed to capture the overall sentiment of a nation’s Bioeconomy and eventually to be used
8
The Bioeconomy Observatory https://biobs.jrc.ec.europa.eu/
was
launched
in
October
2014
by
the
European
Commission:
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as a comparative tool to identify trends, patterns, and synergies between national Bioeconomies9.
BIOECONOMY INDICATORS MEASURING PROCEDURE In guiding the strategy and policy for Bioeconomy, the measurement of indicators can assess, quantify and classify the key drivers of Bioeconomy progress and innovation. These can be used to estimate the success through measurable scale and become a criteria in developing strategic reference. The measurement of indicators is largely for policy-orientated outcomes and input, and it often provides an empirical reference point for assessing the drivers of Bioeconomy whether the status of Bioeconomy is on the right track. To be effectively implemented, all programs, actions, allocation of resources and plans or public policy require the establishment of a solid target, with clear progress of indicators and periodic review for adjustments. Furthermore, the fulfilment of effective strategy and the sustainable development of Bioeconomy largely depends on the alignment and level of integration by the various stakeholders, agencies and bodies that are involved in the Bioeconomy at the different levels of national economy e.g. local, state and federal levels. The right directions are measurable through the use of indicators that assess and classify the key drivers of Bioeconomy progress particularly over time (e.g. from the short run to long-run). The indicators would consolidate competencies, provisions, regulatory requirements, action plans, and transformations to be achieved. Importantly, the Bioeconomy indicators assess various aspects of development, success, productivity, output, efficiency, trend and uncertainty in the Bioeconomy. It aims to put into the context of three general policy questions on how to (a) evaluate the impacts of the Bioeconomy, (b) monitor the evolution of the Bioeconomy, and (c) assess the future prospects for a sustainable Bioeconomy (Figure 2).
Incentives
Figure 2. Techniques and estimation procedure for Bioeconomy indicators and index Currently, there is no comprehensive and convenient method to assess the three policy questions raised. Through increasing efforts by agencies like the Department of Statistics Malaysia, the quantity of data of relevant indicators is growing, which should provide a clearer 9
This is similar conceptually to stock indices around the world e.g. the Dow Jones Industrial Index and the FTSE Kuala Lumpur Composite Index, which provide a snapshot of overall sentiment of equity investment in their respective countries.
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picture of the status of the Bioeconomy. However, without a methodology to easily and quickly consolidate the data into a meaningful and clear picture, it is difficult to extract trends and interpret the data. Thus, it is essential to find a system that is comprehensive and sufficient to accurately monitor the development of Bioeconomy in Malaysia. If the evaluation, monitoring and assessment system is insufficiently accurate and lacking in detail then the Bioeconomy success cannot be fulfilled and the development of the Bioeconomy cannot be outlined efficiently. At the same time, it is crucial to narrow down the selection of indicators that are the sub-set of the full bioeconomy. In narrowing down these indicators, we need to focus on those that are readily quantifiable and are able to demonstrate sufficiently comprehensive impact to the Bioeconomy. This will expedite analysis and enable quick and direct policy actions and strategies. Within the broad framework of indicators in the context of policy questions, there is a need to classify the fundamental indicators according to (i) output indicators, and (ii) input indicators. The output indicators are the indictors as we have identified which are (a) Value-Added, (b) Employment, (c) Exports, (d) Productivity, and (e) Investment10. In contrast, the input indicators are a result of (a) number of collaborative product development, number of projects granted, number of multidisciplinary research and development programmes supported, and number of technology-transfer to the new bioeconomy product, (b) number of bioeconomy innovation workforce as percentage of science and technology workforce, and (c) trades of bioeconomy products, bioeconomy processes and services, (d) new balance of innovation outputs, new joint ventures, and number of bioeconomy innovative firms, and (e) the R&D spending on new innovation11. For this specific study, we have focused on utilising the five output indicators (a) Value-Added, (b) Employment, (c) Exports, (d) Productivity, and (e) Investment as parameters for the overall Bioeconomy Contribution Index. MODEL AND RESEARCH METHOD To measure the multiple quantified impacts of the Malaysian Bioeconomy in an integrated way, this study has considered the development of ‘Bioeconomy Contribution Index’ (BCI) 12. BCI is a standardised way of providing a useful measure (e.g. placed on based/ selected year) of overall bioeconomy market and its sectoral contribution and performance over time. It achieves this by creating convenient estimation methodologies as a sub-set of total bioeconomy and consolidating the five selected indicators: (a) Value-Added, (b) Employment, (c) Exports, (d) Productivity, and (e) Investment. Crucially, it also computes a yearly adjusted expected baseline of impact for each of these parameters that is used as a yearly comparison to assess the relative actual performance for specific parameters or the Index as a whole. This baseline uses a dynamic computable general equilibrium (DCGE)13 model which is established following on applied general equilibrium framework (Robinson, S., Yunez-Naude, A., Hinojosa-Ojeda, R., Lewis, D. J. & Devarjan, S. 1999; Relnert, K. A. & Roland-Holst, D. W., 1997; Robinson, S. 1990; Robinson, S. 1989 and Sadoulet & Janvry. 1995). BCI considers non-linear quantitative analysis that uses secondary data from different institutions of Malaysia, mainly from BiotechCorp, Department of Statistics (DOS), Economic Planning Unit (EPU), Household Income and Expenditure Survey (HIES) and Labour Force Survey (LFS). Basic structure of the model: This study assumed that as a (relatively) small open economy Malaysia is a price taker country. Thus import price is considered as exogenously taken in the model. Bioeconomy is contributing to the national development with a certain share and estimated from the Social Accounting Matrix (SAM). The export demand function is considered as downward sloping following the scope of DCGE. The domestic prices of imports and exports are determined by world prices, exchange rate and import tariff or export subsidy. The price 10
The output indicators monitor (e.g. for assessment) the real expansion and the development of the Bioeconomy over time (e.g. the time lag should be yearly). 11 The input indicators fuel the development of Bioeconomy quantitatively within the economy as a value chain through (a) new innovative sub-sectors (e.g. from the existing industries) and (b) new bio-based markets by bio-innovation. 12 BCI provides a useful scale of achievements over time against 100 points in the base year. 13 The general equilibrium framework has been chosen for this study because it has the capability to represent in a comprehensive way to see the bioeconomy: vision 2020 and beyond by sectoral scope of policy changes and responses.
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system of the model is ironic, primarily because of the assumed quality differences among commodities of different origins and destinations (exports, imports, and domestic outputs used domestically). The original I-O tables regrouped into 15 groups of sectors to meet the Bioeconomy objectives. This study consists of four institutional agents, two primary factor productions, and the rest of the world (ROW). The 15 sectors were aggregated from the 2005 Malaysian Input-Output Table and later updated to 2010 with the details inputs of Bioeconomy contributions. The full details of Dynamic Computable General Equilibrium (DCGE) syntax and notation direction is given as supplementary materials in the appendix. Data sources: This study uses cross-section data for all Bioeconomy sectors from the national economy which gathered from recent Input Output (I-O) table, SAM, BiotechCorp, EPU, HIES, LFS and industrial classification prepared by DOS Malaysia. Among the data that used primarily are Bioeconomy Shares (BS) to the national economy, Intermediate Inputs (II), Final Goods and Services (FGS), Domestic Production (DP), Total National Demand (TND), Total Supply (TS), Export and Import (E&I), labour and capital and indirect taxes (DOS, 2005, 2010; DOS, 2013a & b; MDP, 2006 & 2010). SAM requires additional data following on the Bioeconomy mission, vision, government expenditure and investment for Bioeconomy, sectoral shares, total factor payments and shares, total household income (e.g. by BCI category), total government receipts (including intergovernmental transactions), institutional income distribution, and transfer payments both to households and to production sectors. It is also combined with the national accounts and Malaysian Household Income and Expenditure Survey (HIES) data within a consistent framework for expenditures and savings patterns. The details of bioeconomy related data collection and sources are utilised for BCI with Social Accounting Matrix (SAM) are placed in the appendix. Instrument for Data Analysis: This study utilises several instrumental techniques for the data analysis. In order to develop a benchmark database by Input Output (I-O) table with SAM framework, this study uses the cross-entropy method to update and balance SAM of year 2005 until 2010 prepared by DOS and Economic Planning Unit (EPU) Malaysia. The main instrument for analysis to achieve the target is the General Algebraic Modelling System (GAMS) and Syntax Programming (SP). The GAMS and SP is used to solve non-nonlinear and mixedinteger problems and make Malaysian economy-wide mathematical models to construct. The instrument for data analysis proceeds through 8 steps which are: 1. 2. 3. 4. 5. 6. 7. 8.
Delineate agents (producers, consumers, state) and markets, Organise the data for a computer program, Market form development, Set an arbitrary benchmark price, Set up the functional forms of supply and demand, Calibration of the model, Conduct procedure with the analysis of dynamic effects, and Compute the index effects.
This study considers the circular flow map of Malaysia which captures all Bioeconomy transfers and transactions between sectors and institutions. Productive activities including Bioeconomy involvement and capital inputs from the factor markets, and intermediate inputs from commodity markets, and use these to produce goods and services. These are supplemented by imports and commodity markets to households, the government, and investors. The household and government purchases of commodities provide the incomes producers need to continue the production process. Additional inter-institutional transfers, such as taxes and savings, ensure that the circular flow of incomes is considered closed. Importantly, all income and expenditure flows are accounted for, and there are no leakages from the system. This study has chosen 15 types of different sectors, and activities and commodities following on Bioeconomy target set by national government and BiotechCorp (Malaysian Biotechnology Corporation, 2005-2014). In the study modelling, government receives transfer payments from the rest of the world (e.g. foreign grants and development assistance). This is added to all of the different tax incomes to determine total government revenues. The government uses revenues to pay for recurrent consumption spending and transfers to households. The difference between total revenues and expenditures to the national economic with Bioeconomy is the fiscal inputs. Information on the government accounts is drawn from public-sector budgets published by EPU. According to the 10 | P a g e
ex-post accounting identity, investment or gross capital formation considered changes in stocks or inventories. The difference between total domestic savings and total investment demand is total capital inflows from abroad in the current account balance. Information on the current account (or rest of world) is drawn from the balance of payments, which is published by Department of Statistics Malaysia (National Accounts; retrieved from www.statistics.gov.my). Finally, all Bioeconomy related information has taken from BiotechCorp (Malaysian Biotechnology Corporation, 2005-2014).
RESULTS AND FINDINGS Bioeconomy Malaysia and its impacts are computed using the dynamic modelling. The results are summarised in Table 1 through 4 and discussed in terms of Bioeconomy context and related national impacts following on Bioeconomy’s actual development and contribution over time. The Bioeconomy Contribution Index (BCI) is constructed using the starting year 2005 at 100 points. The BCI indicates the current contribution of the overall Bioeconomy over base contribution in yearly structure. Thus, the current contribution and achievement of bio-based goods and services produced in the national economy that determines the BCI growth is equal over base contribution and achievement of bio-based goods and services produced 14. The outcome of Bioeconomy Contribution Index (BCI) is divided into the 5 sub-indices to observe the sub-indicator achievements, patterns and trends from the base year 2005 to 2014. Table 1 shows full details of the yearly BCI findings used from 2005 to 2014 including both the BCI as overall outcomes as well as the 5 sub-indices to show the achievements of the parameters. The middle and final breakdown of BCI values indicate that in 2010 and 2014, the achievements increased by 17.89% and 19.28% (per cent) respectively from the base year 15. It is obvious from the findings that the BCI values particularly in 2009, 2013 and 2014 are not impressive compared to the efforts done by the related agencies. The BCI show decreasing trends in those particular years due to recessionary downtowns and national stagnation effects. This study thus provides useful estimations of potential ability to achieve 2020 targets on job creation, investment, value-added, exports and productivity from technological development chain and overall to oversee the development of Malaysia’s bio-based industry to be the pioneer in the region. Table 1: Bioindex 2005-2014 (e.g. used as actual values) Index/Period Value-Added Employment Export Productivity Investment BCI
2005 100 100 100 100 100 100
2006 105.59 95.68 110.15 98.04 98.98 101.69
2007 124.76 90.84 132.62 96.04 104.01 109.65
2008 137.69 84.25 159.07 98.96 118.6 119.71
2009 121.28 79.35 118.4 99.03 101.69 103.95
2010 139.64 76.84 154.77 94.39 123.8 117.89
2011 163.38 72.95 190.1 104.96 143.94 135.07
2012 148.96 76.67 165.22 91.87 147.13 125.97
2013 144.83 75.96 143.19 85.62 145.97 119.11
2014 149.74 72.15 135.73 85.31 153.44 119.28
Additionally, the BCI and the 5 sub-indices are extended from 2015 until 2020 using BiotechCorp internal projections and linked to likely growth rates 16 applied to prevailing growth trends for other sub-indicators which are shown to Table 2, Table 3 and Table 4. Table 2 demonstrates the indices and the postulated growth rate of 8% from 2015 to 2020. The BCI here shows as overall achievements and showing increasing trends however, the two subindices, namely employment and productivity show declining trends in the entire time periods. Thus, the findings indicate that additional stimulus conditions needs to be considered further to oversee the expected outcomes from the bioeconomy related industries. The additional 14
The Bioeconomy Contribution Index (BCI) is estimated by two parts. The first part is considered from 2005 to 2014 based on actual position and the second part is considered from 2015 to 2020 based on simulation and forecast. 15 However, the BCI other than base line reference can be read as point basis from year to year (e.g. comparison between 2006 and 2007) instead of percentage point. For example, the BCI values indicate that in year 2013 to 2014 the achievements increased by 119.11 points to 119.28 points respectively. 16 Follows the long-run expected growth of each key parameters, the growth rates of roughly 8%, 10% and 12% (slightly different for each parameters) are applied to represent the lower bound, middle bound and upper bound of the BCI respectively. Result of the forecasts are highlighted in Table 2, 3 and 4.
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stimulus conditions must be more than 8% growth for the index to improve considerably throughout the forecast period from 2015-2020. Thus, the two sub-indices, employment and productivity can be improved from the business as usual growth rates and even above 8% rates or higher and it would be possible if effective and proper actions, strategies and combined efforts by agencies like BiotechCorp, Malaysian Investment Development Authority (MIDA) and others related agencies working together in nurturing further domestic and foreign investment in the Bioeconomy. Table 2: BCI forecast for 2015-2020 (8% growth rate) Index/Period Value-Added Employment Export Productivity Investment BCI
2015 154.14 74.48 142.29 88.38 156.49 123.16
2016 158.67 76.66 149.19 84.24 159.62 125.68
2017 163.33 78.9 156.41 96.35 162.78 131.55
2018 168.12 81.17 163.98 91.83 166.00 134.22
2019 173.06 83.45 171.92 87.53 169.32 137.06
2020 178.14 85.91 180.26 97.33 172.69 142.86
To oversee the achievements and gaps from 2005 to 2014 and going forward, the 5 sub-indices are considered as actual values and details of findings are shown in Table 1. For the VALUEADDED, the sub-indicator indicates that in 2010 and 2014 the achievements increased by 39.64% and 49.74% respectively compared to the base year. This can be attributed to the fact that the research & development-centric nature of biotechnology ventures create a value addition steadily towards the national bioeconomy, except in 2009. The downward trend in 2009 was due to national recession together with the international stagnations. We find further evidence on this in Table 1 which shows projections of value-added in 2011 is increased by 63.38% which is higher rate than other following consecutive years. This was due to the combined efforts by BiotechCorp in nurturing domestic bio-initiatives. For the EMPLOYMENT, the sub-indicator indicates that in 2010 and 2014 the achievements decreased by 23.16% and 27.85% respectively. This sub-indicator shows the overall findings are decreasing in trends. Furthermore, Table 3 demonstrates that under 10% growth conditions, income index value from employment will improve by 2020, however it will be showing decreasing in trends still against base year (since it is below 100). Only over 12% growth rate as shown in Table 4, the value expected to exceed base contribution as expected for the year in 2020, i.e. 6.87% increase versus base year. Several factors need to be considered in this circumstance. Particularly, it is important to note that in terms of raw number of employment, the Bioeconomy has shown a generally increasing trend of jobs created over the historical time period, as represented by employment data collected by BiotechCorp from BioNexus status companies. However, in measuring the income created by the jobs, it is found that it does not match the base contribution expected for the years throughout the time period. It can be hypothesized that although jobs are increasing in number, the actual income per job is not increasing at sufficient growth rate particularly in relation to factors like inflation and cost of living. This analysis demonstrates the potential insight that is provided using the proposed methodology in which actual performance is indexed against base values that are real in nature (taking into account multiple impacting economic factors) as opposed to base values that are purely nominal in nature. Policy makers can then identify and focus on specific areas that require special attention. For the EXPORTS sub-indicator, over the entire time period of 2005-2014, the index tends to increase with the base contribution from 2005. The overall index values shows increasing against the base year 2005 and those are due to the strategies and combined efforts by agencies like BiotechCorp, Malaysian Investment Development Authority (MIDA) and others have been successful in stimulus the exports for the Bioeconomy. However, the trends of increase show decreasing in 2009, 2013 and 2014 suggesting that the downtown is due to recession and Malaysian overall business cycle stagnation to the national economy. However, according to further stimulus expected scenario conditions Table 3 and Table 4 demonstrate that by 10% and 12% likely growth rates, the export sub-indicator index values are expected to increase significantly going forward, with values in 2015 and 2020 showing increase by 44.87% and 100.83% and 47.46% and 123.33% respectively compared against base year. This clear 12 | P a g e
increase would be attributed to the development of industry infrastructure with sector growth, supportive government action as well as greater investment capabilities in the future time periods.
Table 3: BCI forecast for 2015-2020 (10% growth rate) Index/Period Value-Added Employment Export Productivity Investment BCI
2015 156.99 75.67 144.87 88.38 160.89 125.36
2016 164.6 79.38 154.65 101.09 168.69 133.68
2017 172.57 83.22 165.11 96.35 176.91 138.83
2018 180.92 87.35 176.23 107.14 185.42 147.41
2019 189.69 91.51 188.13 102.11 194.4 153.17
2020 198.88 96.01 200.83 111.23 203.85 162.16
For the PRODUCTIVITY sub-indicator, over the entire time period, the index tends to show declining in trends except 2011. This is due to the ineffectiveness of productive effort and inefficiency, especially in the bioindustry overall as estimated considering the rate of output per unit of input. The sub-indicator values indicate that in 2010 and 2014 the achievements decreased by 5.61% and 14.69% respectively in the business as usual scenarios from 2005. Thus, efficient deliberate direction and sectoral investment targets based on lackings should be directed effectively in minimising the gaps. Table 3 and Table 4 demonstrate that by 10% and 12% growth conditions are important for the productivity sub-indicator index value and then are expected to increase significantly going forward. Specifically, by a 12% growth conditions are showing better achievements over 10% rate and scenario findings show an increase by 12.41% in 2017, 22.44% in 2018, 31.29% in 2019 and 39.04% in 2020 compared against base year. This findings show that the downtown can be overcome and therefore action should be attributed to intensify the development of the components related to productivity and efficiency of the bio-based sectors with supportive government action as well as greater investment from 2016 to 2020. Table 4: BCI forecast for 2015-2020 (12% growth rate) Index/Period Value-Added Employment Export Productivity Investment BCI
2015 159.85 77.09 147.46 88.38 165.28 127.61
2016 170.64 82.32 160.25 101.09 178.03 138.46
2017 182.16 87.98 174.1 112.41 191.71 149.67
2018 194.45 93.74 189.17 122.44 206.5 161.26
2019 207.57 100.15 205.55 131.29 222.42 173.39
2020 221.58 106.87 223.33 139.04 239.56 186.07
For the INVESTMENT sub-indicator, over the entire time period, the index tends to match with the base contribution expected for the year, suggesting that the strategies and combined efforts by agencies like BiotechCorp, Malaysian Investment Development Authority (MIDA) and others have been successful in fostering investment in the Bioeconomy. The sub-indicator indicates that in 2010 and 2014 the achievements are increased 23.67% and 53.44% respectively, except the downtown in 2006 and 2009. Importantly, the downward trend in 2009 was due to national and international recessions. Table 3 and Table 4 demonstrate that by 10% and 12% growth conditions, the investment index value is expected to increase significantly, with values in 2015 and 2020 showing increase trends respectively compared against base year. This clear increase would be attributed to the development of industry infrastructure in tandem with sector growth, supportive BiotechCorp and government actions as well as greater investment drawn based on sectoral needs and following by demonstrated revenue generation capabilities in the future periods. Thus, if the stimulated progression and rate is possible (e.g. over 10-12% growth over time) then investment sub-indicator would be increased significantly and that would lead to increase overall BCI over time as shown in Table 4.
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DISCUSSION: CHALLENGES, ROADMAP & WAY FORWARD The actual BCI contribution and achievement from 2005 to 2014 and scenario forecasts extrapolated from year 2015 to 2020 in this dynamic study would provide insights to make effective policy and investment choices to make Malaysia’s Bioeconomy’s vision a reality. Importantly, this study has been able to quantify the actual and likely overall index and subindices which may be able to identify potential competence in the industry to be explored further, and what needs to be done. It is important to know the business-as-usual as well as the stimulated targets as the latter, assumes that problems are able to be overcome over time. Therefore, overall BCI and the sub-indices would help in developing regulatory frameworks, create future strategic thrust, and enhance public commitment on issues that needs to be corrected. It may also create support and assistance between technology developers, business organisations and policy makers to reach the stimulated targets for the sustainable Bioeconomy in the future. A proper and efficient deliberate direction and sectoral investment targets based on contributions and achievements can allow directed focus on gaps rather than non-specific policies that influence on the broader aspect. The selection of efficient deliberate direction and sectoral investment targets based on necessities is however always a crucial issue to the policy makers. The focus from now and onward is therefore would be on technological and advancement in all the relative production technologies by focusing on the sub-indices that are unable to reach the greater achievements as expected until year 2020 by the “business-asusual” growth rate (at 8% lower bound forecast). It is evident that Malaysia has recognised the enormous potential from Bioeconomy in strengthening national aspirations and is committed to providing favourable environment for Bioeconomy to prosper. Thus, there are a number of policies, strategies and initiatives that have been launched since 2005 including the Biotechnology Transformation Programme (BTP), for a solid foundation and layout for the biobased economy. In line with the policies and initiatives, the three key focus areas such as agriculture, industrial biotechnology and healthcare are identified to become the high income contribution towards Bioeconomy in the country. However, there is a question mark as to why then some of the sub-indices did not progress as expected despite the initiatives taken through BTP and others. It could be because of the lacking of alignment to the numerous strategies along with international recession which hampers the success of Malaysian Bioeconomy to become a highly growing sector. The actual findings on the two sub-indices namely employment (income) and productivity from 2005 to 2014 show the validity of the lacking. Moreover, the scenario results also show that “business-as-usual” trends cannot even lead to an improvement in some of the subcomponents which may lead to the inability of Bioeconomy to become a highly growing sector if things are left as is. Going forward, it is thus a must to define what has been achieved until 2014 and what needs to be done by 2020. The regular strategic reviews, alignment of actions, performance, execution, and monitoring evaluation are all necessary for success 17. Thus, the key priorities should be to (a) develop a clear roadmap using Bioeconomy indicators to measure Bioeconomy progress and milestones, (b) develop bio-based innovation clusters, and (c) anticipate correct future trends, commercialise, attract, and develop talents based on the assessed indicators. In addition, there is a need to set targets in all sub-indices based on the BCI. Here, BiotechCorp as the agency tasked with overseeing the development of the Bioeconomy will be involved to monitor progress of the awarded investments and sort the wrong selections of investments. The robust selection will be based on the key priorities as measured by the stimulated BCI index and sub-indices. Thus, the determinants must be based on input and output indicators as mentioned in the earlier section for a further consideration and the selection criteria would be established for a good practice based on also the guidance on the national and regional profile of the Bioeconomy. The creation of this Bioeconomy Contribution Index methodology gives rise 17
There is a need to extract maximum value from the natural resources further downstream by making sustainable investments in the value chain to the new areas such as on bioplastics, bio-based pharmaceuticals and bio-based chemicals. Thus, the bio-based industry requires a solid supporting ecosystem toward effective collaboration, service, research of interconnected companies, research institutions, suppliers and other related organisations. There is no doubt that more successful Bioeconomy required a strong governance structures, solid policy strategies, and necessary national environments.
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to a tool that will aid in identifying priority sectors and provide a basis for formulation of economic strategies going forward. Appendix II provides specific instructions on how to utilise the methodology to generate index output for analysis, particularly to identify industry performance (index value) for any given year. Meanwhile in the BCI index itself, despite all our efforts to create most comprehensive index, there is still more room for improvements. The BCI index measures primarily the capital economy where revenue is concerned. However there is increasing movement around the world to create a more inclusive statistics which takes into account a broader range of socioeconomic or environmental spectrum. For instance, the index could incorporate measures of poverty reductions among the rural areas, measures of income inequality in the Bioeconomy industry, amount of carbon released and absorbed, or level of local biodiversity to name a few. This could be designated as further improvements to the BCI index which can be explored further.
CONCLUSION This study developed BCI to provide a strategic plan for the Bioeconomy in Malaysia going forward. Within the formulation of the BCI estimation, this study considers the measurement and consolidation of five indicators to assess overall and sectoral BCI as key drivers: ValueAdded, Employment, Exports, Productivity, and Investment. The measurement of BCI also involves a number of Bioeconomy structures and assessment by a measureable norm and criteria by DCGE approach to generate a yearly adjusted baseline as a tool to compare actual achievements in the future. The BCI outlines the progress towards the national set targets measurement from 2005 to 2014 and then used the scenarios until 2020 to understand the gaps and prospects, and enable comprehensive evaluation on a yearly basis to guide the future national Bioeconomy development agendas. The BCI provides a reference point for assessing the drivers of Bioeconomy whether it is in the right pathway and correct manner running alongside national policies. Although the Ministry of Science, Technology & Innovation (MOSTI) is a key stakeholder, with the primary support of BiotechCorp, and secondary support of National Biotechnology Policy (NBP), Bioeconomy Transformation Programme (BTP), National Green-tech Policy (NGP), the Malaysian Plans, Economic Transformation Programme, Pemandu NKEA Labs, National Biomass Strategy (NBS), 1Malaysia Biomass Alternative Strategy (1MBAS), Sustainable Energy Authority Malaysia (SEDA) and other related agencies; there must be a proper coordination with all related agencies and institutions. Hence, the BCI guides the future pathways with involvement of all the relevant sectors, agencies, industries and stakeholders needing to commit and support together to achieve the Bioeconomy targets. It is hoped that with the development of the Bioeconomy Contribution Index, policy makers now can take advantage of the increasing scope and quantity of data available as well as the growing computing capabilities afforded by up-to-date technology. This is in line with existing global efforts to achieve similar objectives, most notably the Bioeconomy Observatory in the European Union. The BCI is envisioned as a tool to assess the status of the Bioeconomy in a quick, consistent and comprehensive manner to enable targeted and timely policy and strategy. Going forward, we hope to see the methodology further utilised to compare Bioeconomy impacts across nations, and even used in other sectors or industries, as this will provide even greater insights and foundations for national and international economic policy.
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REFERENCES AR. 2014. Annual Report: Bioeconomy Malaysia, Malaysian Biotechnology Corporation, Kuala Lumpur, Malaysia. Battelle. 2012. E&Y-Beyond Borders 2011, BiotechCorp, FPA analysis. BiotechCorp, 2014. BIOECONOMY MALAYSIA: Economics Findings Report-The Vision for 2020 and Beyond. Malaysian Biotechnology Corporation, Kuala Lumpur, Malaysia. BioTech 2005-2014 (2014). BiotechCrop report for various years, Malaysian Biotechnology Corporation, Kuala Lumpur, Malaysia. Doing Business. 2014. Understanding Regulations for Small and Medium-Size Enterprises. International Finance Corporation, World Bank 1818 H Street NW, Washington, DC 20433. DOS. 2005 Input-Output Table of Malaysia 2000. Ministry of Finance, Department of Statistics, Malaysia. DOS. 2010. Input-Output Table of Malaysia 2005. Ministry of Finance, Department of Statistics, Malaysia. DOS. 2013a. Malaysia Economic Statistics (MES)-Time series. Department of Statistics, Malaysia. DOS. 2013b. Economic Report, Various Issues. Ministry of Finance, Department of Statistics, Malaysia. House, T.W. 2012. National Bioeconomy Blueprint, April 2012. Industrial Biotechnology, 8(3), 97-102. MDP. 2006. Ninth Malaysia Plan. Economic Planning Unit, Prime Minister’s Department, Putrajaya, Malaysia. MDP. 2010. Tenth Malaysia Plan. Economic Planning Unit, Prime Minister’s Department, Putrajaya, Malaysia. Robinson, S., Yunez-Naude, A., Hinojosa-Ojeda, R., Lewis, D. J. & Devarjan, S. 1999. From Stylized to applied models: Building multisector CGE models for policy analysis. North American Journal of Economics and Finance 10: 5-38. R&I, D.G. 2014. Horizon 2020 Work Programme 2014-2015 on Food security, sustainable agriculture and forestry, marine and maritime and inland water research and the bioeconomy. Brussels: DG Research & Innovation, European Commission. Robinson, S. 1990. Pollution, Market Failure, and Optimal Policy in an Economy-wide Framework. Department of Agricultural and Resource Economics. Berkeley: University of California. Working Paper no. 559. Robinson, S. 1989. Multisectoral models. In Holis Chenery and T. N. Srinivas (Eds.), Handbook of Development Economics. North Holland: Amsterdam. Relnert, K. A. & Roland-Holst, D. W. 1997. Social accounting matrices. Applied methods for trade policy analysis: a handbook, 94. Sadoulet & Janvry. 1995. Quantitative Development Policy. London: The Johns Hopkings University press. Thomson J.A. 2013. South Africa’s Bio-economy Strategy. Seminar proceeding, Dept Molecular and Cell Biology, University of Cape Town, South Africa. Wesseler, J., Spielman, D.J., & Demont, M. 2010. The future of governance in the global Bioeconomy: Policy, regulation, and investment challenges for the biotechnology and bioenergy sectors. AgBioForum, 13(4), 288-290.
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APPENDIX APPENDIX I: DCGE Method and Syntax orientation DCGE method and calibrating the model: The dynamic method is extended in this study based on primarily considering the standard CGE model developed by Robinson et al (1999). The standard CGE model consists of a set of nonlinear simultaneous equations where the number of equations is equal to the number of endogenous variables in its mathematical forms (e.g. equations 6-33). The mathematical forms consists of model equations, divided into ‘blocks’ for prices, production and commodities, institutions, and system constraints with different sets of equations. In this study, this standard model extended using bioeconomy component (e.g. bioeconomy block: equations 1-5) to capture additional component of the Malaysian bioeconomy related contribution. The Social Accounting Matrix (SAM) is a fundamental framework used that assigns monetary values to the expenditures and the incomes in the circular flow of economy. It is a matrix representation of the circular flow with real monetary figure in which each column and row is denoted as monetary account (Table 1A). The Malaysian SAM with the components of bioeconomy related data carefully assessed for years 2005 and later updated to 2010 have been used as a data for model calibration. The model equation is written in General Algebraic Modelling System (GAMS) language to estimate the solving parameters with a non-liner programming for all equations from 1-33. DCGE method and Bioeconomy Contribution Index (BCI) The dynamic calibration technique is performed in this study to estimate the related coefficient parameters to find the desired outcomes. All procedures exactly followed by input and output indications to represent the full Malaysian bioeconomy. The parameters of the Bioeconomy Contribution Index (BCI) are then measured such as current contribution and achievement over base contribution and achievement to quantify the related contractions. The base contribution is adjusted over time to capture the real achievements following on the national real growth rate essentially by interest rates, consumer confidence, asset prices, real wages, exchange rate, commodity prices, levels of infrastructure, human capital, development of technology and political instability as proxy in the dynamic (DCGE) modelling system. The parameter and elasticity values (i.e. CES, CET) that are employed in the study model are also considered carefully as it is vital to assess the impact of various BCI effects. The standard calibration of CGE model from blocks B to E is available in online publication (e.g. Robinson et al (1999)) and the extension used in this study as by DCGE that quantifies additionally the quantity of bioeconomy by equation no 1, price of bioeconomy by equation no 2, value added by equation no 3, intermediate demand of bioeconomy by equation no 4 and finally bioeconomy index (more commonly known as BCI in the paper) by equation no 5. The full mathematical forms under different five blocks that consists calibrated model equations are used in this study as given below: A. Bioeconomy block: Quantity of Bioeconomy
QBt c biotot t c PBt c (1 tqct )
(1)
Price of Bioeconomy
PBt c sbiot c PQt c
(2)
Value added price for Bioeconomy
PBVAt a PB t ca PQB t c ica t ca acC
(3)
Intermediate demand of Bioeconomy
QBINT t ca icat a QBt a
(4)
Bioeconomy index (more commonly known as BCI in the paper) 17 | P a g e
nt QBct int BI t n 100 t 1 i i QBc t
(5)
t
B. The price block: Domestic absorption
PQc QQc PDc QDc ( PM c QM c ) (1 tqc )
(6)
Domestic output value
PX c QX c PDcQDc PEcQEc
(7)
Activity price
PAa PX c ac
(8)
cC
Value added price
PVAa PAa PQc icaca
(9)
cC
Import and export price
PM c pwmc (1 tmc ) EXR PEc pwec (1 tec ) EXR
(10) (11)
C. Production and commodity block: Activity production function
QAa ad a QFfa fa
(12)
f F
Factor demand
WFf WFDIST fa
a fa PVAa QAa
(13)
QFfa
Intermediate demand
QINTca icaaQAa
(14)
Output function
Qxc acQAa
(15)
aA
Composite supply (Armington) functions
QQc aqc QM q c
cq c
cq c
(1 )QD q c
1
cq
(16)
Import-domestic demand ratio
QM c PDc q QDc PM c (1 c ) q c
1 1 cq
(17)
Composite supply for non-imported commodities
QQc QDc
(18)
Output transformation function
ct
ct
QX c atc QEc (1 )QDc t c
t c
1
ct
(19)
Export-domestic demand ratio
QEc PEc 1 QDc PDc ct t c
1
cq 1
(20) 18 | P a g e
Output transformation for non-exported commodities
QX c QDc
(21)
D. Institution block: Factor income
YFhf shryhf WFf WFDIST faQFfa
(22)
aA
Non-government domestic institution
YH h YFhf trh, gov EXR trh ,row
(23)
f F
Household consumption demand
QH ch
ch (1 mpsh )(1 tyh )YH h
(24)
PQc
Investment demand
QINVc qinvc IADJ
(25)
Government Revenue
YG tyh YH h EXR trgov ,row hH
tmc EXR pwmc QM c cCM
Government Expenditures
EG trh , gov
PQ qg
hH
c
cC
tq ( PD QD cC
c
c
c
te EXR pwe QE
cCE
c
c
c
PM cQM c ) + ygi
(26)
(27)
c
E. System constraint block: Factor markets
QF a A
QFS f
fa
(28)
Composite commodity markets
QQc QINTca aA
QH
hH
ch
qgc QINVc
Current account balance for ROW
pwe QE tr c
cCE
c
iI
i .row
FSAV
cCM
(29)
pwmc QM c + irepat + yfrepatf (30)
Savings-Investment balance
mps
h
hH
(1 tyh )YH h (YG EG ) EXR FSAV
ygi EXR irepat PQc QINVc WALRAS
(31)
cC
Price normalisation
PQ cwts cC
c
c
cpi
(32)
Bioeconomy absorption
QBct sbiostotct .PB(1 tqc )
(33)
Algebraic notations: a∈A c∈C
A is activities. C is commodities. 19 | P a g e
c ∈ CM c ∈ CNM C ∈ CE c ∈ CNE f∈F h∈H i∈I ada aqc atc cpi cwtsc icaca mpsh pwec pwmc qgc qinvc shryhf tec tmc tqc trii’ tyh αfa βch δcq δct θac ρcq ρct σcq σct ygi irepat yfrepatf QB
t
quantity of Bioeconomy
c
PB
CM is imported commodities and is subset of C. CNM is non-imported commodities and is subset of C. CE is exported commodities and is subset of C. CNE is nonexported commodities and is subset of C. F is factors with f being labor or capital. non-government domestic institutions with h. institutions with i being household, enterprise, government, or rest of world. production function efficiency parameter. shift parameter for composite supply (Armington) function. shift parameter for output transformation (CET) function. consumer price index. commodity weight in CPI. quantity of c as intermediate input per unit of activity a. share of disposable income to savings. export price (foreign currency). import price (foreign currency). government commodity demand. base-year investment demand. share of the income from factor f in h. export tax rate. import tariff rate. sales tax rate. transfer from institution i' to institution i. rate of income tax for h. value-added share for factor f in activity a. share of commodity c in the consumption of h share parameter for composite supply (Armington) function. share parameter for output transformation (CET) function. yield of commodity c per unit of activity a. exponent for composite supply (Armington) function, (-1 < ρcq < ∞). exponent for output transformation (CET) function, (-1 < ρct < ∞). elasticity of substitution for composite supply (Armington) function. elasticity of transformation for output transformation (CET) function. government investment income investment surplus to ROW factor income to ROW
t
price of Bioeconomy
c
PBVA
t
value added price for Bioeconomy
a
QBINT
t
ca
sbiostot t
t
intermediate demand of Bioeconomy total Bioeconomy in the national economy time periods for dynamic option conditional time period
EG government expenditure EXR FSAV foreign savings IADJ PAa activity price PDc PEc export price (domestic currency) PMc PQc composite commodity price PVAc PXc producer price QAa QDc qty of domestic output sold domestically QFfa quantity demanded of factor f by activity a QHch qty of consumption of commodity c by h QINVc quantity of investment demand QQc qty supplied to domesticmarket WALRAS dummy variable (zero at equilibrium) WFDISTfa wage distortion factor for f in a YFhf YG government revenue t
PQ c
price of quantity production
foreign exchange rate investment adjustment factor domestic price of domestic output import price (domestic currency) value-added price activity level QEc quantity of exports QFSf supply of factor f QINTc quantity of cused in activity a QMc quantity of imports QXc quantity of domestic output WFf average wage of factor f transfer of income to h from f YHh income of h
icat ca
intermediate input share
20 | P a g e
sbioct
i
sectoral Bioeconomy share
total Bioeconomy parameter
i
t
t
biotot c total Bioeconomy shares
Bioeconomy parameter weight
SAM: Table 1A SAM components in the Malaysian Economy: 2005 (RM thousands): ACT
COM
LAB
CAP
HOH
COM
GOV
S-I
Ytax
Stax
TAR
ROW
Total
ACT
0
1238856
0
0
0
0
0
0
0
0
0
0
1238856
COM
729584
0
0
0
192404
0
57068
48309
0
0
0
LAB
145723
0
0
0
0
0
0
0
0
0
0
0
145723
CAP
363549
0
0
0
0
0
0
0
0
0
0
0
363549
576542 1603907
HOH
0
0
127316
101794
0
35415
13387
0
0
0
0
0
277911
COM
0
0
0
193149
0
0
0
0
0
0
0
0
193149
GOV
0
0
0
0
0
0
0
58229
53543
8620
3540
0
123932
S-I
0
0
0
0
72911
116787
35324
0
0
0
0
0
225022
Ytax
0
0
0
0
12596
40947
0
0
0
0
0
0
53543
Stax
0
8620
0
0
0
0
0
0
0
0
0
0
8620
TAR
0
3540
0
0
0
0
0
0
0
0
0
0
3540
ROW
0
352891
18407
68606
0
0
18153
118484
0
0
0
0
576542
1238856 1603907 145723
363549
277911
193149
123932
225022
53543
8620
3540
576542
Total
ACT COM LAB CAP HOH COM GOV S-I YTAX STAX TAR ROW
Activity Commodity Labour Capital Label for all private consumption Label for enterprise Government Savings-investments Tax income Sale tax Tariff Rest of the world
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APPENDIX II: BCI Utilisation Method The Bioeconomy Contribution Index methodology was developed to ensure convenient utilisation by BiotechCorp. Below are instructions to use the BCI methodology to generate index values for the sub-parameters VALUE-ADDED, PRODUCTIVITY, EXPORTS, INVESTMENT and EMPLOYMENT as well as the overall index value for the Bioeconomy with the objective of assessing the economic status of the Bioeconomy sector in any given year. As described in previous sections, this will enable yearly review of the industry’s performance as a whole, and comparison with trends for specific sub-parameters. Additionally, this methodology can be used to compare performance against other economic sectors in Malaysia as well as against other Bioeconomies globally. This however, will require data inputs from the desired sectors or countries but the methodological framework can be the same. Conceptually, the sub-index values generated for each specific parameter for a selected year are representative of the (1) actual quantified achievement in the selected year, in relation to (2) the “adjusted” quantity expected for the selected year. “Adjusted” here is defined as the quantity expected based on economic circumstances, having applied modifications to simulate real (as opposed to nominal economic situation). This means accounting for changes in variables within the economic block such as inflation rates, import-export values, exchange rates, and many others (computed utilising Dynamic Computable General Equilibrium in the General Algebraic Modeling System software), e.g.:
The computable nature of the base performance expected for each year means that it is possible to generate the denominator value for several years into the future, as has been done with this particular study. This allows the user to know beforehand the quantities expected for each year within each parameter. As example below, the user can see the adjusted base quantities in the following cells, marked in red: E.g.: Investment (RM billion): Cells D15-T15
For the figures above, the cells marked in yellow represent the historical and projected quantities for each specified parameter. These values are compared individually against the “adjusted base” value, or expected quantity for the respective year (marked in red). This generates the sub-index value for the specific parameters and specific years, marked as the green cells.
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The adjusted base value is calculated from the Dynamic Stochastic General Equilibrium model (using the software General Algebraic Modelling Systems, GAMS) developed in this paper (Appendix I) which would provide a value of what the parameters would be like given the movements of all other demand and supply side factors that would influence the outcome of the economy under study. As an example, for the parameter INVESTMENT, in year 2009, “adjusted base” or expected quantity of INVESTMENT was RM8.36 billion (marked red, cell M15). The actual quantity achieved was RM9.23 billion (marked yellow, cell M14). By exceeding the adjusted base value, the index value was calculated at 110.08 (marked green, cell M16) which implies improvement in the index compared to the base year 2005. Going forward, the user can input actual achievements in the cells marked yellow as data becomes available. For instance, in the year 2015, the user can obtain from Department of Statistics data for VALUE ADDED to substitute the projected value for 2015 with actual achievement as it becomes available: # 1
Parameter Value-added
2
Productivity
3 4 5
Exports Investment Employment
Specific Input Required GDP value or biobased-related production from Department of Statistics Estimated productivity of biobased sectors released by Malaysian Productivity Corporation Export value of biobased products from UN Comtrade Gross Fixed Capital Formation of all biobased-related sectors Estimated biobased sectors’ employment data from Labour Force Survey
IMPORTANT: It is critical to note that the accuracy of the model degrades over time. As such, it is recommended that key values are recalculated after a suitable time period i.e. 5 years. Specifically, the “adjusted base” values computed using GAMS, ratios used to estimate Bioeconomy from BioNexus data, percentage contribution of Bioeconomy to national GDP, and income created per job are some of the values that may need to be reassessed in coming years due to changes in national economic circumstances and other factors.
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APPENDIX III: BCI data collection process
Data for the 5 indicators are collected from Department of Statistics Malaysia, Malaysian Productivity Corporation and United Nations Comtrade. Description of the data sources are described below:
From the sources above, the raw input for the model is derived as per table below: Value added Productivity Exports Investment Employment
Unit RM mil Index USD RM mil Number
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 76010.8 84206.1 104386.1 120882.8 111713.6 134951.6 165662.0 158472.3 161647.7 175358.8 35009 35983 36996 40083 41787 41795 49194 44906 44145 46311 2.11E+10 2.43E+10 3.08E+10 3.87E+10 3.02E+10 4.15E+10 5.34E+10 4.87E+10 4.43E+10 4.41E+10 6,763 7,023 7,737 9,263 8,332 10,637 12,981 13,925 14,486 15,984 1761.336 1761.336 1752.116 1704.134 1683.354 1709.184 1707.163 1883.37 1956.34 1951.061
-End-