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«Evaluating the Comparative Performance of Technical and Scale Efficiencies in Knowledge-Based Economies (KBEs) in ASEAN: A Data Envelopment Analysis ...»

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European Journal of Economics, Finance and Administrative Sciences

ISSN 1450-2275 Issue 51 (2012)

© EuroJournals, Inc. 2012

http://www.eurojournals.com/EJEFAS.htm

Evaluating the Comparative Performance of Technical and

Scale Efficiencies in Knowledge-Based Economies (KBEs) in

ASEAN: A Data Envelopment Analysis (DEA) Application

Munshi Naser Ibne Afzal

Corresponding Author, Faculty of Business, Economics and Policy Studies

Universiti Brunei Darussalam, Jalan Tungku Link, BE-1410, Brunei Tel: 673-7146601 E-mail: munshi.naser@gmail.com Roger Lawrey School of Accounting, Economics and Finance University of Southern Queensland, Toowoomba Qld 4350, Australia Tel: 61 7 4631 2100 E-mail: rogerlawrey@gmail.com Abstract The objective of this paper is to measure the technical and scale efficiencies of KBEs in the Association of South East Asian Nations (ASEAN) using Data Envelopment Analysis (DEA). DEA enables one to assess the efficiencies of firms, organizations, countries, and regions in converting inputs to outputs. For each country in each knowledge dimensions, the efficiency rating and a measure of returns to scale: increasing returns to scale (IRS), constant returns to scale (CRS) and decreasing returns to scale (DRS) are calculated. The two years 1995 and 2010 are considered to assess the cross-section performance of KBE dimensions. Data are collected from World Development Indicators (WDI), World Competitiveness Yearbook (WCY) and ASEAN publications. Indonesia in knowledge acquisition; Singapore, South Korea and Thailand in knowledge production; Singapore in knowledge distribution; the Philippines and S. Korea in knowledge utilization are the most productive and 100% efficient countries in either one or both of the years investigated. This is not the first study of its kind, although it is the first for ASEAN countries considering all KBE dimensions.

Keywords: Knowledge-based economy, knowledge economy dimensions, policy-focused framework, ASEAN, DEA, CRS, VRS, IRS, scale efficiency, technical efficiency O57, P17, O11

JEL Classification Codes:

Paper type: Research paper

1. Introduction The concept of the knowledge-based economy (KBE) was first introduced by the OECD, defining it as an economy which is directly based on the production, distribution and use of knowledge and information (OECD, 1996). Later APEC (2000&2004) and WBI (1999) referred to a KBE as an 82 European Journal of Economics, Finance and Administrative Sciences - Issue 51 (2012) economy in which the production, distribution and use of knowledge is the main driver of growth, wealth creation and employment across all industries. The advantage of KBE over a production-based (P-based) economy is that the former is considered an economy where knowledge, creativity and innovation play an ever-increasing and important role in generating and sustaining growth, whereas in a P-based economy growth is driven much more by the accumulation of the factors of production of land, labour and physical capital.

New ideas and innovations are the comparative advantage of KBEs. To produce new ideas, KBEs need a framework where knowledge and technical progress contribute in a measurable way to economic growth. Therefore different international development organizations and statistical departments of individual countries are trying to build a comprehensive KBE framework in order to quantify the performance of KBEs relative to other countries to assess their competitiveness. In this context, the OECD in its KBE framework report, The Growth Project (OECD, 2001), emphasized the importance of a stable and open macroeconomic environment with effective functioning markets;

diffusion of ICT; fostering innovation; development of human capital; and stimulating firm creation.

Under these core KBE dimensions they proposed a large set of indicators (Table 1A: Appendix: 01).

The World Bank Institute (1999) has also developed the Knowledge Assessment Methodology (KAM) as a KBE framework for its member states in order to indicate their level of knowledge-based economic development and as policy input to the achievement of sustainable economic growth. The WBI KAM is based on 83 structural and qualitative variables that serve as proxies for the four knowledge economy pillars (Table 1A: appendix: 01). These frameworks have one common trait in that they all give a basic analysis of the environment a KBE should possess and claim that a successful KBE should have the four core dimensions, namely, knowledge acquisition, knowledge production, knowledge distribution and knowledge utilization. However, it is interesting to note that none of the current methodologies explicitly divide the KBE indicators under these four core dimensions or extend their analysis to measure efficiency of the countries using the proposed variables. That is the approach taken in this paper where our first objective is to segregate the available KBE indicators under these four dimensions as knowledge input-output indicators for a better understanding of the performance of a KBE (see, for example, Lee, 2001; Tan, Hooy, Manzoni & Islam 2008 and Karahan, 2011). The second objective is to understand the efficiencies with which countries convert knowledge inputs to knowledge outputs as they develop as KBEs, using these indicators.

This paper tries to fulfil these two gaps in existing literature by building a policy-focused KBE framework and measuring the relative technical and scale efficiencies of the ASEAN countries by using the Data Envelopment (DEA) Analysis. DEA is chosen because, as an established quantitative tool, it provides researchers with the ability to measure and compare relative technical and scale efficiencies of the countries in transferring their KBE inputs to KBE outputs. DEA analysis has been widely used to assess operational efficiencies where traditional measures have been found wanting (Tan et al., 2008). This paper is organized as follows: Section 2 briefly discusses the existing literature of the use of DEA in country studies; Section 3 describes the research framework. The empirical results are presented and discussed in Section 4 and Section 5 presents conclusions.





2. Literature Review of DEA Cross-country Studies The use of the DEA method in cross-countries studies is not yet widely applied; particularly at state or country knowledge economy assessment levels (Tan et al., 2008). DEA involves the application of the linear programming technique to trace the efficiency frontier. It was originally developed to investigate the performance of various non-profit organizations, such as educational and medical institutions, which were not suitable for traditional performance measurement techniques like regression analysis due to the complex relations of multiple inputs and outputs, absence of price and non-comparable (www.worldbank.org/kam) 83 European Journal of Economics, Finance and Administrative Sciences - Issue 51 (2012) units. The principles of DEA date back to Farrel (1957). The recent series of discussions on this topic started with the article by Charnes, Cooper and Rhodes (1978). A good introduction to DEA is available in Norman and Stoker (1991). Cooper, Seiford and Tone (2000) provide recent and comprehensive material on DEA (Ramanathan, 2003). Studies on cross-country and knowledge economy performance assessment that employ the DEA method are given in Table1.

The DEA method in country’s macroeconomic and KBEs studies

Table 1:

–  –  –

In summary, these empirical studies using the DEA method reveal that research and development (R&D) expenditure, foreign direct investment inflows (FDI), trade openness and education expenditure can be considered as input variables, while real GDP growth, high-tech exports as a percentage of total manufacturing exports, computer users, patents, and scientific and technical journal articles are commonly considered as output variables for assessing the performance of a country’s macro as well as knowledge economy.

2.1. Need for Transformation to KBE in ASEAN Southeast Asia has been the world’s leading emerging market for several years. To promote economic, cultural and political cooperation in the region, the Association of Southeast Asian Nations (ASEAN) comprising Indonesia, Malaysia, The Philippines, Singapore and Thailand was established in 1967.

Brunei, Myanmar, Laos and Vietnam joined later. The ASEAN economies, particularly the ASEAN-5 (Indonesia, Malaysia, The Philippines, Singapore and Thailand, the first founder members), have been pursuing export-led and foreign direct investment-led development strategies. In earlier decades, the economic development of the ASEAN-4 (excluding Singapore) was largely resources-based and they competed in the world market as exporters of primary products, both agricultural and mineral. In the late 1980s, the ASEAN-5 began to move from resources-based to industrialized economies and steadily graduated to the World Bank’s middle income and high-income economies (Yue, 1999).

Growth in the ASEAN-5 has been accompanied by rapidly falling unemployment rates and poverty incidence. But in the light of the regional currency and financial crisis in 1996-1997, the ASEAN-5 was running out of steam. For instance Thailand’s annual export growth fell from 24% in 1995 to in 1996 and 3.2% in 1997; Malaysia’s from 26.6% in 1995 to 7.3% in 1996 and 6.0% in 1997;

Indonesia’s from 18.0% in 1995 to 5.8% in 1996, recovering to 11.2% in 1997 ( Lo, 2003). After the slowdown of economic growth during these years, those countries started to question the sustainability of their development policies. KBE can be considered as an alternative or complementary development policy option for long run, sustainable growth. In order to transform into KBE, countries should know the key KBE dimensions in which to invest.

3. Research Framework The reference period is determined by the start of the KBE framework concept by the OECD in 1995and ends at the availability of selected indicators at the national level in 2010. Accordingly, we use 1995 and 2010 as the two years for cross-section analysis to measure the efficiencies of the ASEAN-5 namely Indonesia, Malaysia, the Philippines, Thailand and Singapore in all KBE dimensions. Data are collected from WCY-2010, WDI-2010 and ASEAN statistical yearbooks. Before describing the DEA methodology, we first formulate our policy-focused KBE framework, with relevant input and output variables, in order to apply the DEA method. We build a policy-focused KBE framework based on the OECD (1996) KBE definition considering four knowledge dimensions under which there are four output variables and various selected input variables. The output variables are real GDP growth for knowledge acquisition, scientific and technical journal articles per 1000 populations for knowledge production, computer users per 1000 people for knowledge distribution and hightechnology exports as a percentage of total manufacturing exports for knowledge utilization.

The KBE input-output variables are selected from OECD, WBI KBE frameworks by observing time series data availability, literature surveys and the requirement that data preferably be available for all the study countries for the two reference years for the purposes of comparison (ABS, 2002; Afzal and Lawrey, 2012). This study applies the DEA approach by using the policy-focused KBE framework for ASEAN-5. Table 2 shows our policy focused KBE framework. All source of the variables are given in Appendix 1A, Table 2A.

85 European Journal of Economics, Finance and Administrative Sciences - Issue 51 (2012) Policy- Focused KBE framework

Table 2:

–  –  –

Table 2 is an example of variable segregation out of many KBE indicators depending on data availability. Many of the factors listed above define the knowledge economy and its effect on entrepreneurial activities and economic development (Kassicieh, 2010). For instance, Derek, Chen and Dahlman (2004) emphasized that education and skilled workers are key to efficient knowledge dissemination which tends to increase productivity when shared by information and communication technology (ICT) infrastructure. ICT infrastructure refers to the accessibility of computers, internet users, mobile phone users etc. Accordingly, we consider education expenditure and the school enrolment ratio as an input variable and computer users per thousand population as the output variable for the knowledge distribution dimension.

The World Bank Institute (1999) has stated that an effective innovation system depends on research and development (R&D) expenditure, foreign direct investment (FDI) inflows, and knowledge sharing between universities and industry. These variables are often considered as knowledge utilization inputs in order to produce domestic knowledge intensive products in a national innovation system (Poorfaraj, Samimi and Keshavarz, 2011). Hence, we consider FDI inflows and the knowledge transfer rate as input variables and high-tech exports as a percentage of total export as the output variable in the knowledge utilization dimension.

In many developing countries, knowledge and technology are nurtured from foreign sources and enter the country through FDI, imports of equipment and other goods which are promoted by trade openness and licensing agreements (Poorfaraj, Samimi and Keshavarz, 2011). These variables can make an enormous contribution to economic growth provided the existence of a sound, transparent legal and regulatory system in the individual countries. Therefore we consider FDI and trade openness as inputs while real GDP growth is the output variable in the knowledge acquisition dimension.

Dahlman and Andersson (2000) have stated that East Asian economies are weak in innovation activities compared to other, advanced economies, which account for nearly 90 per cent of global R&D expenditures and about the same proportion of patents granted and scientific and technical papers produced. They also argue that stronger protection of intellectual property rights enhances the efficiency of innovation systems in a KBE. Hence in our policy-focused framework, we include these variables under the knowledge production dimension.



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