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FEBRUARY 2017

THE INFLUENCES OF BIOECONOMY AND ITS STRUCTURAL CHANGES

IN MALAYSIA: 2005-2015


BIOECONOMY MALAYSIA REPORT

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 Bioeconomy Development Corporation (Bioeconomy Corporation). 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 biobased industry and/or changes in circumstances. Bioeconomy Corporation does not accept any responsibility for the accuracy or completeness of the information in the aforesaid Report. Bioeconomy Corporation, its subsidiary, related companies, directors, employees and agents, 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 purpose of this study is to analyse the influences of bioeconomy and its structural changes to the Malaysian economy from the year 2005 to 2015. Structural Changes (SC) method with Constrained Multivariate Regression (CMR) as well as Likelihood Ratio Test (LRT) was carried out to analyse and assess the changes in market demand of a specific sector from the other related inter-industries both through the backward and forward linkages in the production process. This comparative study focuses on changes in the economic structure with different levels of bioeconomy development to analyse changes in factors of production utilised at a given period of time. To understand the nature of the influences of the bioeconomy, Malaysian economic sectors are reclassified into 23 industries for the model construction, and the sectors under study are aggregated and rearranged as three sub-sectors: (a) AgBiotech, (b) BioIndustrial and (c) BioMedical, to conform with the bioeconomy estimation. This study notably conducted three different analyses for the Structural Changes for each sector and primarily oversees the standard deviation within the historical and estimated Input-Output (IO) coefficients for three explanatory variables. This explores the magnitude of the dynamic changes between the IO coefficients for the year 2005, 2010 and 2015. The results show that, contrary to AgBiotech and BioIndustrial, the BioMedical influences and contributions by LRT estimation (χ2) in 2015 and 2010 had been found insignificant. Moreover, the SC analysis also show incoherent result as there is insufficient indication that the bioeconomy is improving as expected. This study thus consequently discusses on what needs to be done to those sectors that are lagging behind to support the Malaysian bioeconomy target. Keywords: Bioeconomy, contribution, structural change, impact, Malaysia, Bioeconomy Development Corporation

REPORT PREPARED BY: MALAYSIAN BIOECONOMY DEVELOPMENT CORPORATION (Bioeconomy Corporation) Level 23, Menara Atlan, 161B Jalan Ampang, 50450 Kuala Lumpur Malaysia; T: +603 2116 5588 F: +603 2116 5411 In collaboration with: PROF. DR. ABUL QUASEM AL-AMIN Institute of Energy Policy and Research (IEPRe), Universiti Tenaga Nasional, 43650, Bandar Baru Bangi, Malaysia; T: +603 8921 2020 ext. 3400

For further enquiries, please contact: Nazmi Idrus at nazmi.idrus@bioeconomycorporation.my

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LIST OF ABBREVIATIONS 11MP BCI BiotechCorp BCDP BTP CMR DOS EPPs EPU ETP FGS GAMS HIES IO LRT NBC NBP NBS SC

Eleventh Malaysia Plan Bioeconomy Contribution Index Malaysian Biotechnology Corporation Bioeconomy Community Development Programme Bioeconomy Transformation Programme Constrained Multivariate Regression Department of Statistics Entry Point Projects Economic Planning Unit Economic Transformation Programme Final Goods and Services General Algebraic Modelling System Household Income and Expenditure Survey Input-Output Table Likelihood Ratio Test National Bioeconomy Council National Biotechnology Policy National Biomass Strategy Structural Changes

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1. INTRODUCTION Malaysia’s economic growth performance had been impressive in the last five decades, with a sustained average annual GDP growth of above 6.0% per annum since the 1970s (MDP, 2006; 2010). The economy moved from being a predominantly agriculture-based in the 1970s, into manufacturing in the 1980s, and subsequently concentrated on the services sector in the 1990s. The changes are due to the structure of the economy that underwent major transformations over these three decades, resulting in substantial improvements in income especially between 1980s through the mid-1990s (Al-Amin, 2016). Performance peaked in the early 1980s through mid-1990s and moved Malaysia from a low-income economy to a high-middle income where it remains today. Despite its achievements, the country aspires to gear-up to a high-income economy by the year 2020. Malaysia is thus looking for a sustained conducive environment onwards, and a focus towards key development sectors, to reach the 2020 targets (Al-Amin, 2016). Therefore, in developing a strategy to ensure a sustained and vigorous economic growth, structural changes within the economy needs to be considered. Specialisation into selected economic sectors especially into the ones that caters well with the existing specific economic circumstances, technological advancement level, and availability of native biological resources would be the most sensible thing to do (Al-Amin, 2016). In this context, a move into bio-based economy is a step in the right direction. In fact, developed nations are already working into bio-based economy as it simultaneously creates income, investment, and revenue while addressing key issues relating to sustainable developments. For this objective, the Malaysian Government launched the National Biotechnology Policy (NBP) in year 2005 to further develop three economic sectors namely agriculture, healthcare and industrial manufacturing, as well as to support the growth of an enabling ecosystem throughout the scientific, academic and business communities in the country. Moreover, in 2012, the Bioeconomy Transformation Programme (BTP) was launched to further develop the bio-based industry in Malaysia (BiotechCorp, 2015). Broadly, a ‘Bio-based economy’ or ‘Bioeconomy’ is the sustainable production of renewable biological resources and their conversion into food, feed, chemicals, energy, and healthcare wellness products via innovative and efficient technologies (Bioeconomy Corporation). While it encompasses biotechnology, the bioeconomy also include all industries and economic sectors that produce, manage and utilise biological resources. Latest studies and findings recently shows that the bio-based economy can be a new engine for the future economy that will, in turn, provide basis for future growth (Pasculea, M., 2015). The global evolution towards bio-based economy is on the way, facilitated by increased commercialisation of biotech research in the areas of agriculture, industrial and services sectors that are further driven through special intensification in innovation and technology by bio-based researchers (Pasculea, M., 2015a). Malaysia is making progress toward the bioeconomy development and it is exemplified by the various national policies and initiatives. The National Biotechnology Policy (NBP), launched in 2005, serves as the major policy agenda for bioeconomy development in Malaysia. Within the Malaysian Bioeconomy Development Corporation - an agency under the Ministry of Science, Technology and Innovation (MOSTI) tasked to drive the Malaysian bioeconomy agenda, few initiatives are already in place such as the BioNexus Status Programme, the Bioeconomy Transformation Programme (BTP), the Bioeconomy Community Development Programme, and other Bioeconomy Programmes (Bioeconomy Corporation, 2015). However, the developments of the bio-based industry are far from complete nor far-reaching. There are still gaps in the developmental progress against the targets specified during the planning phase of the NBP which we will discuss further in this paper. With only 3 to 4 years left to the year 2020 deadline, reaching the goal of creating a self-sustaining and welldeveloped bioeconomy ecosystem would be a challenge. These challenges may suggest that either revisits and re-reviews in the policies or targets are needed to match the current progress, or the current 5|P a g e


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pace of growth needs to be elevated much further to at least reaching the original target goal. Whichever the root of the problem, it is clear that there is a mismatch between current progress and the intended target. Towards this end, there are some studies done on the progress and contribution of Malaysian bioeconomy such as the Economic Findings Report – Vision for 2020 and Beyond, Beyond Borders – Global Biotechnology Report 2011, RMK-9 Industry Scorecard Report, Bioeconomy Corporation Annual Reports and other reports by the relevant agencies (Bioeconomy Corporation, 2015; Al-Amin, 2015). There are also studies conducted to recognise the contribution of Malaysian bioeconomy on valueadded, investment, employment, patent approval, GNI, and exports. Unfortunately, there are no studies to identify why Malaysia is showing gaps in terms of the development of a whole bioeconomy valuechain through backward and forward linkages, as well as no study done on inter-industry contribution over time. While the issue has been touched briefly in other studies, it would be of interest to see how much inter-industry linkages are presently persisting from 2005 to 2015 in the bioeconomy value-chain. This can only be perceived with proper analyses using relevant modelling exercises such as the Structural Changes (SC) analysis. The Structural Changes (SC)1 analysis shows the shift in market demand of a specific sector from the other related inter-industries both through the backward and forward linkages in the production process (using the Input-Output table) and determines how the changes being passed-through in the economic structure. The focus will be on the growth process to bioeconomy from 2005 to 2015, the period of study analysed. In simple terms, it assesses the patterns of changes in the Malaysian bioeconomy sectors, then examine the effects of structural changes on the economy. Rapid structural change in the bio-based economy that the European Union and North America pursued could be great examples of countries catching up to reach their targeted level of bioeconomy’s expansion (Kircher, 2012; Kolker et al, 2012; McCormick and Kautto, 2013; Staffas et al., 2013; Albrecht and Ettling, 2014; McCormick, 2014; Philp, 2014; Jiménez‐Sánchez and Philp, 2015). Similarly, in the context of Malaysia, estimating the catching up and relating it to the structural changes will be explored and answered in this study. In addition, this study will not only look at the level of structural changes (SC) in Malaysia and the influences of economy wide bioeconomy factors but also comparative transformation of factors to quantify the future structural changes for bioeconomy. With that background, this study focuses on Malaysia catching up strategy target following the influences of the bioeconomy and its structural changes (SC) from 2005 to 2015, while highlighting macroeconomic bio-based indicators and comparative dimensions following the bioeconomy 2020 targets.

1Bioeconomy

is a new area and thus so far no studies have been done in this sector using Structural Changes and it is unlikely to see similar studies are being done at other countries. Therefore, to support the Structural Changes (SC) with a rational scope of empirical analogy, a Constrained Multivariate Regression (CMR) and Likelihood Ratio Test (LRT) with the InputOutput (IO) tables of Malaysia are considered in this study additionally. CMR and LRT will be explored in the following sections accordingly. 6|P a g e


BIOECONOMY MALAYSIA REPORT

BIOECONOMY DEVELOPMENT IN MALAYSIA Malaysia was the first country in Southeast Asia to develop a holistic policy strategy. This includes action across multiple aspects of development spanning from policy measures, providing research and development funds, grants and soft loans as well as up to date overview of such measures. The dedicated and holistic bioeconomy strategy is geared towards industrial upgrading and the application of biotechnology in services, manufacturing, and agricultural processes. The Government has identified Bioeconomy as one of the key strategic drivers to elevate the nation’s socioeconomic development. Taking advantage of the climate and biodiversity of the country, Malaysia is in the right condition to create and support the development of a Bioeconomy ecosystem. In view of this, The National Biotechnology Policy (NBP) was launched in 2005 to oversee this emerging sector under the purview of Ministry of Science, Technology and Innovation (MOSTI). The support for the newly created industry is defined in three consecutive stages, first with a focus on Phase 1 (2005-2010) on capacity building; Phase 2 (2011-2015) on shifting science to business; and Phase 3 (2016-2020) on going global. The NBP are focused on the development of nine policy thrusts which are: 1) Agriculture Biotechnology Development  Transform and enhance the value creation of the agricultural sector through biotechnology. 2) Healthcare Biotechnology Development  Capitalise on the strengths of biodiversity to commercialise discoveries in natural products as well as position Malaysia in the bio-generics market. 3) Industrial Biotechnology Development  Ensure growth opportunities in the application of advanced bio-processing and biomanufacturing technologies. 4) R&D and Technology Acquisition  Establish Centres of Excellence, in existing or new institutions, to bring together multidisciplinary research teams in coordinated research and commercialisation initiatives. Accelerate technology development via strategic acquisitions. 5) Human Capital Development  Build the nation’s biotech human resource capability in line with market needs through special schemes, programs and training. 6) Financial Infrastructure Development  Apply competitive ‘lab-to-market’ funding and incentives to promote committed participation by academia, the private sector as well as government-linked companies. Implement sufficient exit mechanisms for investments in biotech. 7) Legislative and Regulatory Framework Development  Create an enabling environment through continuous reviews of the country’s regulatory framework and procedures in line with global standards and best practices. Develop a strong intellectual proper-ty protection regime to support R&D and commercialisation efforts. 8) Strategic Positioning  Establish a global marketing strategy to build brand recognition for Malaysian biotech and bench-mark progress. Establish Malaysia as a centre for contract research organisations and contract manufacturing organisations. 9) Government Commitment  Establish a dedicated and professional implementation agency overseeing the development of Malaysia’s biotech industry, under the aegis of the Prime Minister and relevant government ministries.

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Phases of development under the National Biotechnology Policy (NBP) are as follows: Phase 1: Capacity Building (2005-2010)  Adoption of policies, plans and strategies  Establishment of advisory and implementation Councils  Establishment of Malaysian Biotechnology Corporation Sdn. Bhd.  Capacity building in research and development  Industrial technology development  Develop agricultural, healthcare and industrial biotechnologies  Develop legal and intellectual property framework  Incentives  Business and corporate development through accelerator programmes  Bioinformatics  Skills development  Job creation  Regional Biotechnology hubs  Develop BioNexus Malaysia as a brand Phase 2: Science to Business (2011-2015)  Develop expertise in drug discovery and development based on biodiversity and natural resources  New products development  Technology acquisition  Promote Foreign Direct Investment (FDI) participation  Intensify spin-off companies  Strengthen local and global brands  Develop capability in technology licensing  Job creation Phase 3: Global Business (2016-2020)  Consolidate strengths and capabilities in technology development  Further develop expertise and strength in drug discovery and development  Leading edge technology business  Maintain leadership in innovation and technology licensing  Create greater value through global Malaysian companies  Rebranding of Malaysia as a Global Biotechnology Hub The Malaysian Bioeconomy Development Corporation (referred to as Bioeconomy Corporation; previously known as Biotechnology Corporation or BiotechCorp) was tasked as the main agency under the purview of Ministry of Science, Technology and Innovation (MOSTI) for executing the objectives of the National Biotechnology Policy (NBP) and acts to identify value propositions in both R&D and commerce and to support these ventures via financial assistance and developmental services. Under Bioeconomy Corporation, as part of the NBP implementation, BioNexus Status was created and awarded to qualified international and Malaysian biotechnology companies undertaking value-added biotechnology and/or life sciences activities. The status endows fiscal incentives, grants and other guarantees to assist growth. Apart from the overall benefits and supports, BioNexus Status companies are assured a list of privileges as stipulated in the BioNexus Bill of Guarantees. In 2012, as an additional initiative to support the NBP, the Prime Minister launched the Bioeconomy Transformation Programme (BTP) which is geared towards the private sector. It is a platform provided by the government for the private sector to 8|P a g e


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channel and maximise commercial opportunities in bio-based industries. Through BTP, the government and industry players work in tandem to achieve the objectives of increasing the application of biotechnology through catering for the required structural conditions and developing fundamental mechanisms to ensure that companies can flexibly adapt to the new opportunities. At its inception, 10 Entry Point Projects (EPPs) have been identified to kick-start the growth of Malaysia’s Bioeconomy. Within these 10 EPPs, 20 private-sector driven Trigger Projects have been assessed for potential benefit to the nation from the perspective of Gross National Income (GNI) generated, employment created and investment attracted. Bioeconomy Corporation has continued to identify and evaluate high potential proposed Trigger Projects to be added under the programme through BTP Workshops and Labs as well as ongoing engagement with the private sector. As of December 2016, a total of 12 EPPs and 61 Trigger Projects had been identified for inclusion into the BTP (Bioeconomy Corporation). In addition to the support created under the BTP, the policymakers also seek to create a complete support on the whole production value-chain. To achieve this, the Bioeconomy Malaysia Accelerator Programmes or Bio-Accelerators was introduced which focuses on community development, technological innovation, bio-entrepreneurship and market access. Key to the BioAccelerator programme is the Bioeconomy Community Development Programme whereby a community, farmer or association engage with an anchor company for the production of raw materials on the upstream with a promise of a guaranteed buy-back. This allows for the consistent supply of raw materials with improved yield and productivity standards as well as improving the quality of living for the affected communities, farmers or associations. The NBP are focused on the development of three major biotechnology sectors namely AgBiotech, BioMedical and BioIndustrial representing agriculture, medical, and industrial sectors respectively. The focus towards three major sectors allows for policymakers to narrow the expansive potential of biotechnology into areas where policy, monitoring, and implementation can be more targeted. Within each three subsectors, the focus on specific technology or processes are clearly defined and set so that opportunities within these sectors can be exploited effectively. The focus of the three sub-sectors are described in Table 1. Table 1: The breakdown of Bioeconomy focus areas by major sector according to the priorities set by the NBP AgBiotech

The sub-focus areas within this sector include:  Natural Products Biotechnology: Product development using biotechnology extraction processes using herbs, agricultural resources, flora, fauna or microbes.  Crop Biotechnology: Production of planting materials and propagation techniques that include validated hybrid seeds and tissue culture methods.  Livestock Biotechnology: Includes breeding of ruminants using biotechnology tools (i.e. artificial insemination, embryo transfer)  Aquaculture Biotechnology: Include breeding of both marine and freshwater species.

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BioIndustrial

The sub-focus areas within this sector include:  Biochemical: Derived from chemicals produced by bio-based feedstocks through fermentation, chemical or enzymatic conversion.  Biofuel: Renewable energy from bio-based feedstock such as plants and agricultural waste, food, domestic or industrial waste.  Biomaterial: Produced from cellulose, starch or sugar from bio-based feedstock used to create packaging, fabrics or plastic component.  Advanced Biogas: Gasses produced from organic matter such as waste produced through anaerobic digestion.  Biotransformation: process of performing large scale biosynthesis of drugs, materials, fuels or chemicals.  Bioremediation: waste management process using microorganism to remove pollutants from contaminated sites.

BioMedical

The sub-focus areas within this sector include:  Biopharmaceuticals: Consist of small and large molecule pharmaceutical products.  Medical Devices: include devices and diagnostics focusing on various product classes such as Orthopedics, Dialysis, and Surgical Devices.  Emerging Bio Sciences: Includes cellular medicine, cell and gene therapy, contract research organisation, bio-cosmeceuticals, wellness and pharma nutrition.

The impact of such programmes to the Bioeconomy can be seen in the data collected by BioNexus Status companies2. Table 2 to Table 4 highlights the annual realised investment, revenue and R&D spending respectively. Data has been normalised by dividing the actual published number with the number of BioNexus Status companies being added annually to get the average per company. Interestingly, the chart indicates that progress has been positive and consistent. Excluding outliers in 2005, realised investment (Table 2) has been stable to moderate, especially towards the latter stages which may indicate maturity of some of the companies. Meanwhile, revenue (Table 3) has continued to improve over the years excluding 2010 where data might be affected by economic downturn. An increasing R&D spending (Table 4) also show greater commitment of companies towards their growth and future development. Table 2: Realised Investment of BioNexus Status companies (Average per company, RM millions) Industry 2007 2008 2009 2010 2011 2012 2013 2014 2015 sector AgBiotech 5.57 2.97 4.60 2.01 1.75 1.27 0.69 1.72 0.79 BioIndustrial 65.65 4.26 15.11 4.63 6.69 2.49 0.38 1.71 1.24 BioMedical 13.37 2.38 0.17 1.73 1.22 1.33 0.68 1.46 0.46 Total 28.20 3.20 6.63 2.79 3.22 1.70 0.58 1.63 0.83 average

2

BioNexus Status companies are special status for biotechnology-based companies awarded by Bioeconomy Corporation which entail certain benefit to its holder (i.e. tax incentives and funding support). While it represents a small portion of the bio-based industry, it provides valuable information of the specific focus areas. 10 | P a g e


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Table 3: Revenue of BioNexus Status companies (Average per company, RM millions) Industry 2007 2008 2009 2010 2011 2012 2013 2014 sector AgBiotech 0.37 3.91 2.43 2.53 2.81 2.98 2.78 2.59 BioIndustrial 17.75 12.09 12.85 3.54 7.54 12.86 14.91 16.71 BioMedical 2.43 2.72 2.59 2.52 3.16 3.77 4.51 4.11 Total 6.85 6.24 5.96 2.86 4.50 6.54 7.40 7.80 average

2015 -

Table 4: R&D expenses of BioNexus Status companies (Average per company, RM millions) Industry 2007 2008 2009 2010 2011 2012 2013 2014 2015 sector AgBiotech 0.07 0.13 0.27 0.37 0.25 0.33 0.38 0.35 BioIndustrial 0.35 0.50 0.31 0.17 0.33 0.19 0.21 0.26 BioMedical 0.17 0.28 0.43 0.53 0.76 0.93 0.75 0.74 Total 0.20 0.31 0.33 0.35 0.45 0.48 0.44 0.45 average Source: Bioeconomy Corporation, internal calculations

Judging from the analysis of the BioNexus Status companies, performance of the bioeconomy industry has indicated that policies and progress has been positive and effective in improving the outcome and delivery of the bioeconomy companies to some extent. This is encouraging and indicates that the developmental efforts so far have bear fruition. Yet, comparing the achievements against the earlier targets specified during the planning of the NBP shows that there are still areas in which it can be improved. The table below highlights the NBP targets as specified during their commencement compared with their recent or projected achievements. Comparison of NBP has shown somewhat a mixed level of progress. Despite over 10 years of implementation, NBP manages to generally achieve only half of their specified target in revenue and job opportunities, while achievements might only be slightly higher in terms of GDP contribution and investments. Meanwhile, the NBP aimed for the creation of 20 global biotechnology-based companies by the year 2020. So far, no such companies have yet to make the mark although recent efforts have been made through nurturing of such companies under the BioNext programme3. If projected growth of these targets are linear, it may be a challenge that these can be achieved in just under 3-4 years, although, under exponential growth, this projection can be achieved anchored upon what the future policy step would be. Table 5: NBP targets vs. current achievements Targets by year 2020 Achievements National Biotechnology Policy (launched in 2005) 

Biotechnology to contribute at least 5% of GDP



Bioeconomy estimated at 2.2% of GDP under Phase 1 (2010)

3

BioNext programme is intended to create home grown global champions through targeted approach. The programme involves undertaking tangible activities, that contributes towards the following outcomes in 2020: (1) At least 70% of the participating companies achieve an average of 20% annual growth rate for 3 consecutive year starting from 2017, (2) penetration into at least two new international markets and, (3) enhanced brand presence in domestic and international markets. 11 | P a g e


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To generate RM15 billion in investments

To generate RM100 billion in revenue

 

To create 160,000 job opportunities Creation of 20 global Malaysian companies

 

RM11.1 billion investments generated by 2015 RM52.3 billion revenue generated by 2015 84,153 jobs created by 2015 To be accomplished through nurturing of companies under the BioNext programme

Source: Bioeconomy Corporation internal calculation

We have seen that so far the achievement, while encouraging, fell short of the initial target specified during the policy planning stage. Thus, this study seeks to look deeper at the underlying linkages of the sub-sectors and how it influences each other over time in the aim to understand whether the development of the industry is consistent of the expected growth and how the forward and backward linkages seems to aid or hinder the development of the sub sectors. The following section will discuss on the methodology of estimation and the discussion of the result.

3. METHOD AND DATA SOURCES 3.1 Material Structure: The methods and techniques used in this study are referred to as the Constrained Multivariate Regression (CMR) with Likelihood Ratio Test (LRT) and Structural Changes (SC) which used the Malaysian Input-Output (IO) tables with a reclassification of sectors to include bioeconomy-related sectors. Structural Changes 4 considers a special econometric modelling to oversee the shift (or change) of economic resources (labour, land and capital) within the production process over time. Thus, the measurement of Structural Changes (SC) by a market demand shift determines how a country had pass-through the resources (labour, land and capital) through the growth process over the years or decades (Pasinetti, 1983; Laitner, 2000; Fan et a. 2003). Importantly, the demand shift is measured by SC in this study is viewed through the elasticity in the production process and indicated by the transformation of changes of economic resources (labour, land and capital) that influence the bioeconomy from 2005 to 2015. In other words, the SC analyses the interrelationships between two sectors (or many sectors) to look at how the interrelationships have changed over time. A stronger interrelationship, marked by an increasing coefficient, between the two sectors indicate that there are more interlinkages. These interlinkages can be through backward linkages (which describe the process of how a company in a given sector purchases its goods, products, or supplies from a different sector), or forward linkages (the process of how a company in a given sector sells its goods, products, or supplies to a different sector). Greater interlinkages imply improved sophistication of the sector and indicate improving “capability set” (these includes inputs, knowledge, technology, and institutions) that allow for the sector to grow. A declining or stagnating interrelationships highlights the opposite, where the sector are not being effective in utilising its resources and indicate little contribution from the “capability set”. To understand the nature of the influences of the bioeconomy, Malaysian economic sectors are reclassified into 23 industries (Al-Amin, 2015) for the model construction and the sectors under study are aggregated and rearranged as three sub-sectors: (a) AgBiotech, (b) BioIndustrial and (c) BioMedical, to conform with the bioeconomy estimation (Table 6)5. This study notably conducted three

4More details of SC can be found by the book of ‘Structural change and economic growth: a theoretical essay on the dynamics

of the wealth of nations (Pasinetti, 1983). 5 The earlier works estimated Bioeconomy Contribution Index (BCI) in 2015 and considered 23 sectors from Malaysian economy to capture the bioeconomy structure. This preset study tied with BCI, however the 23 sectors again reclassified to 3 sub-sectors and aggregated to (1) AgBiotech, (2) BioIndustrial and (3) BioMedical to find the nature of coefficient of 12 | P a g e


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different analyses for the SC for each sector (i.e. three sub-sectors against each other and against the whole economy) and primarily oversees the standard deviation within the historical and estimated IO coefficients for three explanatory variables. This explores the magnitude of the dynamic changes between the IO coefficients for year 2005, 2010 and 2015. On the other hand, the Constrained Multivariate Regression (CMR) is a common tool to understand the relationship between the variables and parameters in an empirical study and important in analysing two or more dependent or outcome variables in the main analyses (Chamberlain, 1982; Binder, 198; Burnham et al. 1996; Liu et al. 1997; De'Ath, 2002; Keith, 2014 6. Fundamentally, CMR is a technique that evaluates a single econometric regression model with more than one variable (e.g. outcome) in the assessment of analyses. The constraints used ensure that when identifying trends, the important model-fitted concentrations are maintained, while accounting for between variable dependencies. In this context, there are three explanatory variables which are (i) AgBiotech, (ii) BioIndustrial and (iii) BioMedical sectors. The purpose of CMR in the study is to set a limit on how the three explanatory variables are interrelated (e.g. multiple or partially correlated) between the dependent (Bioeconomy) and interdependent variables (AgBiotech, BioIndustrial BioMedical sectors). The CMR ultimately is to generate an amount of effect to the overall bioeconomy by the interdependent variables as a form of intermediate inputs and final goods (IO coefficient matrices) from year 2005 to 2015. Thus, CMR refers to how multiple outcomes of the AgBiotech, BioIndustrial and BioMedical sectors (e.g. predictor variables) influence the impact to bioeconomy (predicted variables) over time. CMR is conducted along with the SC study to show the shift or changes from one point to another point of time. Meanwhile, Likelihood Ratio Test (LRT)7 is a tool in the econometric analysis that explains the comparison of the ‘Goodness of Fit’ by observing data8 using two techniques; (i) null hypothesis, and (ii) the alternative hypothesis in modelling. The purpose of LRT test on this study is to test whether the influence of the three sectors (AgBiotech, BioMedical and BioIndustrial) on explanatory variables (labour, land and capital) are significantly different than the assumption of ‘no influence’. A result that indicated ‘no influence’ implied insignificant contribution on the structural changes to Malaysian overall bioeconomy in the year under study. Whereas, an opposite result would indicate positive contribution pattern of the explanatory variables. The pioneer in the studies that analysed the initial theory development on Likelihood Ratio Test (LRT) using the ‘Goodness of Fit’ are Neyman et al. (1933), Wilks (1938), Riedwyl (1967) and Tallis (1983). The logarithm of the Likelihood Ratio (LR) uses a p-value which is a statistical probability to the overall econometric estimation (MacKinnon et al 1999) and pvalue compares the probability distribution in the test statistic which this study’s scope referred by three explanatory variables. Importantly, the known independent variables as considered as “no unknown parameters should exist’ in the modelling as by using Likelihood Ratio Test (LRT) so called postulated by Pearson Lemma (Neyman and Pearson, 1992). Initially, Miller and Blair (2009) approach is used to obtain the IO coefficient matrices from year 2005 to 2015 and the revised estimation was necessary due to reclassification of 23 industries and further aggregation to three sub-sectors which are estimated as follows 9:

variance and historical & estimated comparative variation of technical coefficient over time. A detailed information of economic sectors is given in the Appendix. 6More details of Constrained Multivariate Regression (CMR) and degrees of freedom can be found by the latest books by ‘Multiple regression and beyond: An introduction to multiple regression and structural equation modelling, Routledge (Keith, T.Z., 2014)’ and ‘Handbook of Applied Multivariate Statistics and Mathematical Modeling, Academic Press (Tinsley, H. and Brown, S. 2000)’. 7More details of Likelihood Ratio (χ2) can be found by the latest article of “Evaluating Goodness-of-Fit indexes for testing measurement invariance (Cheung and Rensvold, 2002)”. 8The extent to which observed data match the values expected by theory. 9a as new estimation represents overall IO coefficient from sector i to sector j. ij 13 | P a g e


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aij 

zij Xj

(1)

Where, aij, zij, and Xj are the input needed by sector j from sector i to produce one unit of the product, the inter-industry sales by sector i to sector j, and the total production of the sector j, respectively. This study method and technique computes vectors of IO coefficient to estimate the influences of the explanatory variables namely (1) AgBiotech, (2) BioIndustrial and (3) BioMedical to bioeconomy after obtaining the IO coefficient matrices following over time ‘t’. Thus, IO coefficient matrices from equation (1) uses the time periods as a(t) t = 1…T. In addition, the explanatory variables follows the time periods as x(k,t) k = 1…k and extended as:

a(i, t )  b0(i )  k b(i, k )  x(k , t )  e(i, t ) a(i, t )  0,

 a(i, t )  1.0 i

(2)

where b0(i) and b(i,k) describe the regression coefficients and e(i,t) estimates the change between historical and estimated values over time with Likelihood Ratio Test (LRT) and CMR by utilising of 2N(ln S – ln S0)10 and χ2 distribution11 and coefficient matrix of A_est(t,i,j)12.

3.2 Data source: The related data to the bioeconomy estimation are gathered from the Bioeconomy Corporation, Economic Planning Unit, Household Income & Expenditure Survey, Labour Force Survey and industrial classification prepared by Department of Statistics Malaysia (DOSM). Among the data that are used primarily are bioeconomy to the national economy are Intermediate Inputs, Final Goods and Services, Domestic Production, Total National Demand, Total Supply, Export and Import, Labour, Capital and Value-Added (DOS, 2005, 2010; DOS, 2013a & b; MDP, 2006 & 2010). Data for the three sectors are derived from actual production of the related sectors using available data obtained from DOSM and related agencies (i.e. Sustainable Energy Development Authority Malaysia (SEDA)). The data aims to capture the elements specified in the focus areas of the NBP, which is highlighted in Table 1. Note that available data made no distinction between normal and biotechnologybased productions hence we will look at the industry as a whole. Table 6 summarises the breakdown of the sectors. Table 6: Malaysian aggregated bioeconomy sub-sectors No. 1 2 3

Sector name AgBiotech: Livestock, Aquaculture, Paddy, Vegetables, Other Agriculture, and Other Food Sectors BioIndustrial: Biochemicals, Biogas and Water Utilities (Bioremedation) BioMedical: Pharmaceuticals and Medical Devices

10Where

S and N are the results of the performance function optimization and numbers of data respectively 0.05 level of the χ2 distribution 12General Algebraic Modeling System (GAMS) software is used to estimate the study findings 11The

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4. RESULTS AND DISCUSSIONS 4.1. Likelihood Ratio Test: On our first stage, we conduct the LRT test to determine the influence of the three sectors against the explanatory variables (labour and capital). This study first calculates the LRT by using the level of 0.05 confidence interval within the chi-square (χ2) distribution (Table 7)13. The result of the LRT estimation is then compared to the standard χ2 table. The estimations indicate that AgBiotech and BioIndustrial have significant influences on the overall bioeconomy contribution in year 2005, 2010 and 2015 respectively. In addition, the influence of AgBiotech has a greater overall influence over time than BioIndustrial in the period of analysis given the higher coefficient. In contrast, BioMedical has insignificant influences on the structural changes to Malaysian overall bioeconomy contribution in year 2005 and 2010; although it had significant influences in later years particularly in 2015. Table 7: LRT estimation (χ2) with the combination of explanatory variables No.

2005 Influence 406 33 1.7

Sector name

1 2 3

2010 Influence 740 46 6

AgBiotech BioIndustrial BioMedical Rejection at χ2 value below 12.59 known as critical value in statistics. * Insignificant estimation indicates for 2005 and 2010

2015 Influence 1339 67 412

Remarks/ over periods Significant Significant *Significant

4.2. Structural Changes (Historical IO14): On the second stage, we conduct SC analysis using historical IO coefficients. Table 8 defines the historical IO coefficients of AgBiotech sector (intermediate input and final demand) and its overall influence to the explanatory variables on BioIndustrial, BioMedical and Value-Added sector from 2005 to 2015. The first row of Table 8 indicates that in 2005, AgBiotech industry is taking 0.1315 amount of resources from BioIndustrial to produce 1 additional unit of AgBiotech output. The industry improved in 2010 with increased coefficient as AgBiotech is drawing 0.300867 amount of resources from BioIndustrial. This however moderated in 2015 as the coefficient stagnates. Similar pattern can be seen on second and third row of Table 8. The result indicates that the influence from BioIndustrial had higher and increased pattern of influence than BioMedical coefficients to the AgBiotech sector in the period of the analysis. Thus, findings justify that the BioIndustrial contribution is greater than those of BioMedical sector. On the other hand, the pattern of Value-added in the period of analysis is found to be increasing in 2005 to 2010 but steady from 2010 to 2015. The figures show the appealing indication to the Malaysian bioeconomy contribution pattern as it is obvious that input from BioIndustrial sector required more and increased share of output to yield AgBiotech related economic activities than BioMedical in the period of analysis. Table 8: AgBiotech - Historical IO coefficients and its influences on intermediate input and final demand

Sector name

AgBiotech

2005

2010

2015

Influence

Influence

Influence

Remarks/ over periods: Overall Influence to:-

0.131502 0.038020 0.309571

0.300867 0.063886 0.375466

0.300882 0.063901 0.375460

BioIndustrial BioMedical Value-Added

13The

value of the degree of freedom is referred in this study as 3x1x2=6 and cutoff value is χ20.05 (6) = 12.59 Historical Input-Output uses the original IO table provided by Department of Statistics Malaysia without any alterations. Further explanations on Historical and Estimated IO are explained in the following sections. 14

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Table 9 describes the historical IO coefficients of BioIndustrial sector (both intermediate input and final demand) and its overall influence to AgBiotech, BioMedical and Value-Added from 2005 to 2015. Value-Added refers to the influence of the sector on labour. The findings indicate that the influence by BioIndustrial from AgBiotech is much higher in terms of influence and impact than BioMedical coefficients in 2005. The finding shows that BioMedical sectors contributed less towards BioIndustrial sector compared to what is required by AgBiotech over time. On the other hand, the pattern of Value Added in the period of analysis is indicated as increasing in trends. However, the contribution share is less than those by AgBiotech sectors. Table 9: BioIndustrial - Historical IO coefficients and its influences on intermediate input and final demand

Sector name

BioIndustrial

2005

2010

2015

Influence

Influence

Influence

Remarks/ over periods: Overall Influence to:-

0.91819 0.01436 0.04134

0.867519 0.022506 0.102813

0.867526 0.022680 0.102577

AgBiotech BioMedical Value-Added

Table 10 describes the historical IO coefficients of BioMedical sector (both on intermediate input and final demand) and its overall influence to explanatory variables on AgBiotech, BioIndustrial and Value-Added from 2005 to 2015. This study indicates that the influence from AgBiotech had higher and increased pattern of contribution than the BioIndustrial coefficients. Thus BioMedical sector draws more from AgBiotech sectors to produce its output and consistently increase over the years while drawing less from BioIndustrial sector. Meanwhile, the pattern of Value-Added in the period of analysis is found steady although decreasing in trends over time. This implies that BioMedical sectors draw more of its influences from other non-bioeconomy related sectors and greater inter-linkages exist compared to intra-linkages within the industry. Table 10: BioMedical - Historical IO coefficients and its influences on intermediate input and final demand

Sector name

BioMedical

2005

2010

2015

Influence 0.274490 0.248557 0.458745

Influence 0.395351 0.190754 0.384225

Influence 0.395351 0.190754 0.384227

Remarks/ over periods: Overall Influence to:AgBiotech BioIndustrial Value-Added

4.3. Structural Changes (Estimated IO): The third stage is to look at the SC using estimated15 IO coefficient. This allows for the analysis solely on the impact of additional capital investment. Table 11 shows the AgBiotech sector and its estimated IO coefficients with influences on intermediate input and final demand from 2005 to 2015. The figures show the influence from BioIndustrial and BioMedical in the analysis period. Importantly, the influence from BioIndustrial sector had higher impacts than 15Historical

IO coefficients are the fixed coefficients that are estimated by statistical department from the national accounts for a given time period. In contrast, estimated IO coefficients refers to additional assessment of historical IO coefficients by using beta (β) = 0 (e.g. no relationship between dependent and independent variables) and uses additional capital investment alone (e.g. investment by Bioeconomy Corporation to AgBiotech, BioMedical and BioIndustrial sub-sectors) to estimate the share of intermediate consumption in the factors of production to the final demand in the national economy. Thus, once additional capital goes to the national accounts from one period to other period, the sectoral selected coefficients should be consistent over time (e.g. uprising if capital investment increases over time) and in this case estimated changes are considered a return to capital. With the background value of beta (β)=0 and additional capital influx in the estimated IO coefficients, this reflect the expected shifting (estimated) to the prior- IO coefficients. 16 | P a g e


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BioMedical coefficients for 2005, 2010 and 2015 respectively. Apparently, the findings from estimated IO coefficients show less influence than the earlier historical IO coefficients from BioIndustrial and BioMedical in 2005 and 2010 respectively. It is shown from the result that the AgBiotech industry could not utilise the input from BioIndustrial and BioMedical efficiently to yield the expected output which would strengthen the bioeconomy contribution. Table 11: AgBiotech - Estimated IO coefficients and its influences on intermediate input and final demand

Sector name

AgBiotech

2005

2010

2015

Influence 0.187251 0.046533 0.331263

Influence 0.222558 0.051929 0.344996

Influence 0.304782 0.064497 0.376977

Remarks/ over periods: Overall Influence to:BioIndustrial BioMedical Value-Added

Table 12 shows the BioIndustrial sector and its estimated IO coefficients with influences to final demand and intermediate input from 2005 to 2015. The findings indicate the influence of AgBiotech and BioMedical sectors over time overall is similar in direction of trend compared to earlier historical IO estimations. The findings show declining coefficients from 2005 to 2015 (e.g. even though the estimated IO findings showing progress) and the overall estimated IO coefficients show less influence than historical IO coefficients by BioMedical sector in 2010 except in Value-Added. Thus, the findings seemingly indicate less impact to those sectors that utilise of input from AgBiotech sector in 2005 and BioMedical sectors and Value-Added in 2010. Table 12: BioIndustrial - Estimated IO coefficients and its influences on intermediate input and final demand

Sector name BioIndustrial

2005 Influence 0.901509 0.017019 0.061607

2010 Influence 0.89095 0.018772 0.074345

2015 Influence 0.866359 0.022866 0.103995

Remarks/ over periods: Overall Influence to:AgBiotech BioMedical Value-Added

Table 13 shows the BioMedical sector and its estimated IO coefficients with influences on intermediate input and final demand from 2005 to 2015. The progress and influence such as direct contribution from AgBiotech is steady in 2005 and 2015; however, those contributions were less than from 2010 for BioIndustrial and Value-Added. The estimated IO coefficients of BioIndustrial sector in 2005 and 2010; Value-Added in 2005 and 2015 are not showing sound progress. The estimated IO coefficients thus indicate less influence than historical IO coefficients of the input utilisation. Essentially, those contributions supposed to be correctly employed from AgBiotech and BioIndustrial sectors to contribute more to BioMedical sector as expected. Table 13: BioMedical - Estimated IO coefficients and its influences on intermediate input and final demand Sector name BioMedical

2005 Influence 0.314275 0.229530 0.434214

2010 Influence 0.339467 0.217481 0.418682

2015 Influence 0.398134 0.189423 0.382510

Remarks/ over periods: Overall Influence to:AgBiotech BioIndustrial Value-Added

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4.4. Comparison of Historical and Estimated Changes: Figure 1 and Figure 1a shows the comparison of AgBiotech between Historical IO and Estimated IO. To analyse the comparison, two considerations must take place, 1) both historical and estimated IO need to be trending up which signifies increased interrelationships over time, and 2) the coefficient of estimated IO must be higher than historical IO. When these two conditions are fulfilled, it implies that the performance of the sector are encouraging and efficient capital investment yield a positive influence on the bioeconomy related industries. However, the findings show that even though estimated IO structural changes well followed the trend in historical changes from 2005 to 2015; the structural changes are not steady from 2005 to 2010 except for Value-Added. Thus, it can be said that the combination of explanatory variables had weaker influence to AgBiotech from the sub-sectors until mid of 2010 and 2015. The phenomenon on AgBiotech indicated weaker overall performance of the industry over time against the expected policy target. There are several reasons such as the lack of an efficient source of utilisation, policy orientation, action, strategy and implementation which can be the causes toward less-steady circumstances. Figure 1: AgBiotech: Comparison of Historical and Estimated Changes (2005-2015) 0.6 0.5 0.4

Historical IO

0.3 0.2

Estimated IO

0.1 0 2005

2010

2015

Figure 1a: AgBiotech: Value –Added comparison of Historical and Estimated Changes (20052015) 0.4 0.3 Historical IO

0.2

Estimated IO

0.1 0 2005

2010

2015

Figure 2 and Figure 2a explain the changes of BioIndustrial to the explanatory variables and its influence to value-added from 2005 to 2015. The sectoral and sub-sectoral comparisons of historical and estimated structural changes of time variation in the factors and volatilities over time are explored with correlations patterns. The findings explored are similar in nature and trend with the historical and estimated structural changes from 2005 to 2015 as what has been specified earlier in the study found for AgBiotech sectors. The findings are showing less influence to the explanatory variables over time except for value-added. There are several reasons such as lacking of an efficient source of utilisation such as from related products, facility service, policy implementation, management action, rapid price change, inflationary effects and policy implementation which can be the cause.

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Figure 2 BioIndustrial: Comparison of Historical and Estimated Changes (2005-2015) 0.03 0.02

Historical IO

0.01

Estimated IO

0 2005

2010

2015

Figure 2a BioIndustrial: Value-added comparison of Historical and Estimated Changes (20052015) 0.12 0.1 0.08

Historical IO

0.06 0.04

Estimated IO

0.02 0 2005

2010

2015

Figure 3 and Figure 3a explain the changes of BioMedical to the explanatory variables and its influence to value-added from 2005 to 2015. Both figures show the historical and estimated changes of coefficients, correlations and trends over time on the period of study. The trends of time variation in the factors and volatilities over time show the impact between the sectoral and sub-sectoral associations of historical and estimated structural changes over time. The findings explored the comparative trends from 2005 to 2015 and specify the influences quantitatively on BioMedical (including value-added) toward the structural changes in the bioeconomy contributions. It can be understood that the combination of historical and estimated changes of BioMedical had strong influence after 2005 (e.g. LRT estimations (χ2) was insignificant), and shows steady trends over time. There are several factors such as efficient resource utilisation, action, strategy, policy orientation, and implementation on time leads the positive trend late periods. Figure 3 BioMedical: Comparison of Historical and Estimated Changes (2005-2015) 0.04 0.03 Historical IO

0.02

Estimated IO

0.01 0 2005

2010

2015

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Figure 3a BioMedical: Value-added comparison of Historical and Estimated Changes (20052015) 0.48 0.46 0.44 0.42 0.4 0.38 0.36 0.34

Historical IO Estimated IO

2005

2010

2015

5. POLICY IMPLICATIONS Policy implication is always a crucial issue for national governments or related agencies in order to place a sound policy action related to many macroeconomic factors that are involved in the national, regional, or structural plans. The question on policy selection and action is constantly an issue to find a pathway to explore a sound strategy and act to reach the national target. Several types of theoretical literatures, including renowned Neoclassical, Keynesian, and post-Keynesian studies and approaches in the evolutionary economics have shown that higher targeted aim is possible in an economy focusing on certain sectors given the presence of certain action plans, and if and only if the right policy action is in the right place. Thus the right plan will be crucial in making progress on targeted goal such as using structural changes analysis as it has been proven in the earlier decades. A number of studies that used structural changes analysis are able to provide important feedback for their policies as it is known that estimation of structural changes can help to improve the accuracy of national macroeconomic targets. Thus, this study has considered the structural changes analysis to the bioeconomy industry as one of our initiative. Likewise, this study followed the same notion of idea and considered explorations on AgBiotech, BioIndustrial and BioMedical to understand the importance in determining the correct bioeconomy policy targets and goals and what should be done in the future to meet the target. However, the findings of this study do not justify the impressive achievements of the bioeconomy progress. Instead, this study shows mixed result toward the influences of the bioeconomy and its structural changes over time. Those can be seen by Tables 7 to Table 13 and Figures 1, 2 and Figure 3a. The ideas of this study is to illustrate the impacts in the national bioeconomy and progress over time using structural changes as a way forward for national policymakers and related agencies, such as Bioeconomy Corporation, to better understand the development of the industry. Particularly, the focus was for those sectors or industries which are still underperform to fast-track towards the year 2020 targets or beyond. The structural changes analysis demonstrated the institutional changes and their relevancies toward the bioeconomy progress which were touched briefly in earlier studies. There are several reasons such as correct policy identification, investment strategy, efficient resource utilisation, orientation of targeted plan, approach, and policy implementation which can be the causes of mixed progress found in the results. It can be seen from the findings that some explanatory variables are progressing earlier and some of them are declining unfortunately on later periods (Tables 7 to Table 13 and Figure 1a, Figure 2 and Figure 3). Particularly, BioMedical influences and contributions by LRT estimation (χ2) in 2015 and 2010 had been found insignificant whereas AgBiotech and BioIndustrial show that the impact of structural changes are significant. Moreover, the SC analysis also show incoherent result as there is no discernible sign that the bioeconomy is generally improving as expected, i.e. AgBiotech has a decreasing influences to BioIndustrial, similarly, BioMedical has a decreasing influence on BioIndustrial. The only promising indication is by AgBiotech sector where

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influence from BioIndustrial, BioMedical and Value-Added all show better integration and interlinkages over time. This may highlight future potential avenue to explore in terms of policies and strategy. Meanwhile, much can be said about bioeconomy performance on Phase 1 (2005-2010) and Phase 2 (2011-2015) of the NBP masterplan. Comparison on historical estimation on the IO on all the three sectors shows that the changes in the coefficient are much more drastic during Phase 1 than Phase 2. This is especially true for AgBiotech production that draws from BioIndustrial (henceforth simplified as AgBiotech – BioIndustrial), but also true for AgBiotech – BioMedical, BioIndustrial – BioMedical, and BioMedical – AgBiotech. In these relationships, the scale of changes in the coefficient during Phase 1 easily eclipsed the relatively benign changes during Phase 2. Looking at the Estimated IO however, the pattern is not so evident. In fact, on Estimated IO, greater changes in coefficient are seen during Phase 2. This can be seen in AgBiotech – BioIndustrial, AgBiotech – BioMedical, BioIndustrial – BioMedical and BioMedical – AgBiotech. Since the Estimated IO accounts for only the additional capital accumulation, it carries substantial inferences from the result. In particular, AgBiotech – BioIndustrial, AgBiotech – BioMedical, BioIndustrial – BioMedical and BioMedical – AgBiotech relationships seems to be contributed by other elements (such as labour, GDP, tax, government and other economic variables) in Phase 1, while greater capital accumulation is the reason for coefficient changes in Phase 2. The occurrence of the 2010 recession may also play a role in the decline in the coefficient in Phase 2 and might causes many economic leakages to non-bioeconomy related sectors. It is important to know why some explanatory variables and their influences to bioeconomy are less than previous periods and why some of them are not steady as expected as assessed over time. The sectoral and sub-sectoral comparisons of historical and estimated structural changes are explored in detail to understand the nature and phenomenon further. It indicates certain action plans and policy action must be in the right place by looking at the finding on the structural changes over time. Importantly, according to the findings the estimated structural changes are shown to be downward trending. Thus, it can be concluded that additional investment did not add to supplementary contribution to the sub-sectors as classified by the bioeconomy sectors. This study observes that estimated IO coefficients of both BioIndustrial and BioMedical show less influence than historical IO coefficients in 2005 and 2010 respectively, even though additional investments are put in place to those sub-sectors from 2005 to 2015. The findings suggest that the relevant investments into the bioeconomy related sectors yield less efficient returns than expected and may be due to issues involving resource utilisation, policy design, implementation and the right timing. The consequence of these downward trends in both Historical and Estimated IO indicates less impact and contribution toward steady progress than expected. These downward trends indicate that it may be the right opportunity to reassess the existing NBP strategies. In fact, the policies and targets that the NBP introduced in 2005 has never been reviewed since its inception. The policies remain the same for the last 10 years irrespective of the changing economic environment and structure. If the Government is serious in developing the industry much is needed to be done in terms of aligning its focus based on current trend in biotechnology, the country’s current scientific capabilities, opportunities that exists, and whether certain policies still applies. Even the Eleventh Malaysia Plan (2015-2020) has no specific focus on biotechnology although elements of it are embedded in other relevant chapters i.e. application of biotechnology in modernising agriculture. Thus the question begs, how can the Government through MOSTI and Bioeconomy Corporation as the implementation agency, identify what strategy needs to be looked at in order to have a positive and rising capital accumulation within the sector? Answering this question is not an easy feat as it requires deeper understanding of the Bioeconomy linkages on the micro level. However, we can start to look beyond the existing targeted operations and took a drastic change in order to have a larger impact or hasten the pace of the existing policies and programmes. These can be done as follows:

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1) Align existing policies with current targets and goals Bioeconomy policies should be flexible and adaptive to the current focus of the Government. This can be done through putting emphasis on sectors or activities that is currently being pursued by Government policymakers. A good step in the right direction is in the case of one of the Bio-Accelerator programme, which is the inclusion of BCDP into the National Blue Ocean Strategy (NBOS). Being under the focus of the administration allows for easier implementation and continuous support from relevant ministries and agencies. Other NBP focus areas especially the AgBiotech, BioIndustrial and BioMedical industries could realign its focus with other initiatives such as those that are being pursued under the 11th Malaysia Plan. This includes increased need for certified high yield food varieties, leverage on the intensified research in genetic enhancement or focus on green technologies. This allows for the industry to benefit from additional financing options and better support. 2) Further enhance inter-ministerial and inter-agency engagements Bioeconomy by nature linked across many sectors of the economy hence involves the jurisdiction of many different ministries. We believe that the Ministry of Science Technology and Innovation (MOSTI) as the main overarching supervisory body would further enhance the collaboration with other ministries such as through shared KPI under the NBOS platform. Even on the agency level, Bioeconomy Corporation, Malaysian Green Technology Corporation (under the Ministry of Energy, Green Technology and Water (KeTTHA)) and Sustainable Energy Development Authority (SEDA) should be working closely for the implementation of more effective BioIndustrial green technologies framework such as biofuel or biodiesel. Similarly, in the AgBiotech sector, closer cooperation with Ministry of Agriculture (MOA) and its agencies and so forth could help to support further the Bioeconomy agenda. Meanwhile, BioMedical sector can be more enhanced through increased collaboration with the Ministry of Health (MOH) and its agencies. 3) Leverage on novel discovery in biotechnology The constant change in the technological environment makes for a dynamic industry. Same goes for the bio-based industry. To leverage on the changes, policymakers could focus on getting the first-mover advantage in a specific scientific discovery rather than pursuing an already trending technology. While this may pose a higher risk of success as infant technology might not become the mainstream, it may well be the feasible action to be more dynamic for the industry to go forward. 4) Commitment for interim review Bioeconomy Corporation monitors and tracks the development of NBP against its targets (i.e. progress of BioNexus Status companies, BTP Trigger Projects, other bio-based companies, and upon the end of Phase 1, Phase 2 and Phase 3). To enhance its operations, an advisory council was established under the NBP framework to monitor and decide upon implementation plans and strategies. However, there is a need to re-evaluate the framework at specific milestones. Future policy or extension of the policy post-2020 should have options to reassess the policy framework done so that it adapts to the changes in the economic structure and current conditions. The European Union’s Bioeconomy Strategy is a good example. It has specified selected years for reviews of its polices with next review in 2017 which will provide a major opportunity for new political impetus and orientation.

This study is an indication for the direction to Government, through MOSTI and Bioeconomy Corporation as the implementation agency, as to how to relook and revisits the NBP. Nevertheless, to answer the issue effectively requires a careful examination and deeper research in the bioeconomy linkages and relationships all the way to the micro level which this study did not focus on due to resource

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limitations. The gaps in the present actions could be detected by experimenting on deeper studies on the backward and forward IO linkages using the concept of multiplier effects. Nevertheless, multiplier effects alone cannot provide suitable strategy unless there is further understanding on the nature of the industry which can be achieved through (a) bottom-up approach, and (b) top-down approach. The bottom-up approach thus would consider firm-level issues to be quantified to analyse the gaps to meet the firm or industry level targets. Meanwhile, top-down approach would necessitate directional guides for the industry to further nurture the Malaysian bioeconomy. All bioeconomy related firms or industries would be grouped in to a cluster by the Bioeconomy Corporation with a short- to medium-term vision and the relevant experiments would be carried out to address their (AgBiotech, BioIndustrial and BioMedical sectors) shortcomings and intervene later stage with a catching up strategy compared to other advanced economies by the top-down approach.

5. CONCLUSIONS The study analysed the influences of the bioeconomy and its structural changes to understand the nature of bioeconomy progress. The bioeconomy in Malaysia is divided by three sectors as by (1) AgBiotech, (2) BioIndustrial and (3) BioMedical to be analysed. The influences of the three sectors are estimated between 2005 and 2015 in addition to the value-added and final demand of historical changes and deviations. The Likelihood Ratio and Constrained Multivariate Regression (CMR) modelled with different types of analyses by the coefficient of variance and historical and estimated comparative variation of technical coefficient. Malaysian total economic sectors are aggregated to 23 sectors following on the national bioeconomy classifications to investigate the impacts quantitatively over time. This study shows mixed results on the bioeconomy contribution and do not justify the achievements expected by the NBP. Thus, it is important for MOSTI through Bioeconomy Corporation to consider what strategy needs to be looked further and what additional policy action needs to be placed in order to meet the year 2020 targets. This study illustrates the development of Malaysian Bioeconomy and the gaps it has in meeting its target and the implication it has on the Bioeconomy structure over the implementation years. Thus, overall NBP achievements should be re-visualised through a micro-oriented focus looking at NBP with special attention to (i) aligning existing policies with current targets and goals, (ii) leverage on novel discovery in biotechnology, (iii) further enhance inter-ministerial and inter-agency engagements, and (iv) consistent interim review. The findings help to understand the phenomenon of related firms and industries to explain the nature of bioeconomy progress over time and what needs to be done for further progress in the industry.

6. LIMITATIONS There are some limitations in this study such as this study considered the analyses from 2005 to 2015 and did not perform the forecast of structural base until 2020, 2030 or 2050 that is duly acknowledged. Although many variables were analysed to meet the objective of the study, the exclusion of some other variables might limit the findings and some biasness or usual suspects or other related on the findings may arise in the analyses. Thus, future studies should be considered more in the further scopes and can be carried out relevant analyses for a better understanding of the influences of the bioeconomy and its structural changes explored here.

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Laitner, J., 2000. Structural change and economic growth. The Review of Economic Studies, 67(3), pp.545-561. Liu, J., Wu, S. and Zidek, J.V., 1997. On segmented multivariate regression. Statistica Sinica, pp.497525. MacKinnon, J.G., Haug, A.A. and Michelis, L., 1999. Numerical distribution functions of likelihood ratio tests for cointegration. Journal of applied Econometrics, 14(5), pp.563-577. McCormick, K. and Kautto, N., 2013. The bioeconomy in Europe: An overview. Sustainability, 5(6), pp.2589-2608. McCormick, K., 2014. The bioeconomy and beyond: visions and strategies. Biofuels, 5(3), pp.191193. 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. Miller, R.E. and Blair, P.D. 2009. Input–Output Analysis: Foundations and Extensions–2ndEdition, Cambridge University Press, New York. Neyman, J. and Pearson, E.S., 1992. On the problem of the most efficient tests of statistical hypotheses. In Breakthroughs in Statistics (pp. 73-108). Springer New York. Neyman, Jerzy; Pearson, Egon S. 1933. "On the Problem of the Most Efficient Tests of Statistical Hypotheses" (PDF). Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 231 (694-706): 289–337. Pasculea, M., 2015. Perspectives on Developing The Bioeconomy Secotor In Romania. Annales Universitatis Apulensis: Series Oeconomica, 17(1), p.39. Pasculea, M., 2015a. Strategic options of public policy for developing the bioeconomy sector in Romania. Revista Romana de Economie, 41(1). Pasinetti, L.L., 1983. Structural change and economic growth: a theoretical essay on the dynamics of the wealth of nations. Cambridge University Press, UK. Philp, J., 2014. OECD Policies for Bioplastics in the Context of a Bioeconomy, 2013. Industrial Biotechnology, 10(1), pp.19-21. Riedwyl, H., 1967. Goodness of fit. Journal of the American Statistical Association, 62(318), pp.390398. Staffas, L., Gustavsson, M. and McCormick, K., 2013. Strategies and policies for the bioeconomy and bio-based economy: An analysis of official national approaches. Sustainability, 5(6), pp.27512769. Tallis, G.M., 1983. Goodness of fit. Wiley Encyclopedia of Clinical Trials. Tinsley, H. and Brown, S. 2000. Handbook of Applied Multivariate Statistics and Mathematical Modeling. Academic Press. Wilks, S.S. 1938. "The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses". The Annals of Mathematical Statistics. 9: 60–62.

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APPENDIX 1. AGGREGATED INDUSTRIAL SECTORS: SEC1-A Paddy SEC2-A Food Crops SEC3-A Vegetables SEC4-A Fruits SEC5-A Rubber SEC6-A Oil Palm SEC7-A Livestock SEC8-A Forestry and logging SEC9-A Fishing SEC10-A Other Agriculture SEC11-A Crude Oil & Natural Gas & Mining and Quarrying SEC12-A Oil & Fat Processing SEC13-A Food Processing SEC14-A Beverage Processing SEC15-A Textiles & Apparel SEC16-A Wood Products SEC17-A Paper products SEC18-A Refined Petroleum products SEC19-A Chemicals & Chemical products SEC20-A Other Industrials SEC21-A Transportation & Communication SEC22-A Financial services SEC23-A Services 2. STRUCTURAL CHANGES: COMPARISON OF HISTORICAL AND ESTIMATED 1 column sector 2005 2010 2015 AgBiotech Historical 0.520907 0.259781 0.259757 Estimated 0.434953 0.380517 0.253743 BioIndustrial Historical 0.131502 0.300867 0.300882 Estimated 0.187251 0.222558 0.304782 BioMedical Historical 0.03802 0.063886 0.063901 Estimated 0.046533 0.051929 0.064497 Value added Historical 0.309571 0.375466 0.37546 Estimated 0.331263 0.344996 0.376977 2 AgBiotech BioIndustrial BioMedical Value added

column sector Historical Estimated Historical Estimated Historical Estimated Historical estimated

2005 0.02611 0.019865 0.91819 0.901509 0.01436 0.017019 0.04134 0.061607

2010 0.007161 0.015933 0.867519 0.89095 0.022506 0.018772 0.102813 0.074345

2015 0.007216 0.00678 0.867526 0.866359 0.02268 0.022866 0.102577 0.103995

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BIOECONOMY MALAYSIA REPORT

3

AgBiotech BioIndustrial BioMedical Value added

column sector

Historical Estimated Historical Estimated Historical Estimated Historical Estimated

2005 0.018209 0.021982 0.27449 0.314275 0.248557 0.22953 0.458745 0.434214

2010 0.02967 0.02437 0.395351 0.339467 0.190754 0.217481 0.384225 0.418682

2015 0.029669 0.029932 0.395351 0.398134 0.190754 0.189423 0.384227 0.38251

3. BIOECONOMY SECTORS (BI: DCGE Sectors (Al-Amin, 2015)): SEC7-A Livestock SEC9-A Fishing SEC10-A Other Agriculture SEC1-A Paddy, SEC2-A Food Crops, SEC3-A Vegetables, SEC4-A Fruits SEC11-A Crude Oil & Natural Gas & Mining and Quarrying SEC23-A Services SEC19-A Chemicals & Chemical products 4. BTP ENTRY POINT PROJECTS: AgBiotech: a. BioBased Farm Inputs (Livestock) b. BioBased Farm Inputs (Aquaculture) c. High-Value Local Bioingredients d. High-Value food varieties BioIndustrial: a. Industrial Bioeconomy Upstream Inputs b. Bioremediation BioMedical a. Chemicals b. Pharmaceutical

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The Influences of Bioeconomy and its Structural Changes in Malaysia: 2005-2015  

This study is to analyse the influences of bioeconomy and its structural changes to the Malaysian economy from the year 2005 to 2015. Struct...

The Influences of Bioeconomy and its Structural Changes in Malaysia: 2005-2015  

This study is to analyse the influences of bioeconomy and its structural changes to the Malaysian economy from the year 2005 to 2015. Struct...

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