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NIDA Economic Review

วารสารเศรษฐศาสตรปริทรรศน ปที่ 9 ฉบับที่ 1 (มกราคม 2558)

NIDA Economic Review Volume 9 No. 1 (January 2015)

บรรณาธิการบริหาร ศาสตราจารย ดร.พิริยะ ศาสตราจารย ดร.ดิเรก ศาสตราจารย ดร.ณัฏฐพงศ ศาสตราจารย ดร.ตีรณ ศาสตราจารย ดร.อารยะ ศาสตราจารย ดร.โสภิณ รองศาสตราจารย ดร.เรณู รองศาสตราจารย สมพร

ผลพิรุฬห ปทมสิริวัฒน ทองภักดี พงศมฆพัฒน ปรีชาเมตตา ทองปาน สุขารมณ อิศวิลานนท

บรรณาธิการ ศาสตราจารย ดร.พิริยะ

ผลพิรุฬห

ประสานงาน นายสงคราม ไชยแกว songkram@nida.ac.th

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NIDA Economic Review


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NIDA Economic Review

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สารบัญ Determinants of Export Participation: Evidence from Thai Manufacturing Small and Medium-Sized Enterprises Yot Amornkitvikai and Teerawat Charoenrat

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Intrahousehold Bargaining Among Women Workers in Thailand's Northern Region Industrial Estate Gullinee Mutakalin

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Technological Investment of Thai Industries and Government Supports Vasu Suvanvihok

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NIDA Economic Review

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NIDA Economic Review

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วารสารเศรษฐศาสตรปริทรรศน สถาบันบัณฑิตพัฒนบริหารศาสตร NIDA Economic 9 ปที่ 9 ฉบับทีReview ่ 1 (มกราคม 2558)

Determinants of Export Participation: Evidence from Thai Manufacturing Small and Medium-Sized Enterprises Yot Amornkitvikai*and Teerawat Charoenrat** Abstract This paper empirically examines firm-specific factors that could affect the export participation of 65,111 Thai manufacturing SMEs. Three limited, dependent variable models (the probit model, the logit model and the linear probability model) were used to check the sensitivity of the results. With respect to the determinants that affect a firm’s export decision, a number of factors such as i) firm size, ii) productivity, iii) government assistance, iv) foreign investment (ownership), v) firm location (municipal or non-municipal area), vi) research and development, vii) firm age, and viii) workforce skill levels were found to be significantly and positively correlated with the export participation of Thai manufacturing SMEs. Beyond a certain threshold firm size, SME export decisions were found to have a significant and positive non-linear correlation. A significant and negative non-linear relationship between a firm’s age and its export decision, however, was found in the aggregate. Twenty-three sub-manufacturing sectors, classified into eight sub-manufacturing SME groups, were also investigated. Empirically evidence-based policies were also suggested to facilitate improvement the international competitiveness of Thai manufacturing SMEs in export markets.

Keywords: Export Participation; Small and Medium-Sized Enterprises; Thai Manufacturing *

Lecturer of Economics, School of Economics, Rangsit University, Phaholyothin Road, Pathum Thani, 12000, Thailand - Email: yot.a@rsu.ac.th. ** Lecturer of Economics, Faculty of Business Administration, Khon Kaen University (Nong Khai Campus) and Director of Centre of Entrepreneurship and Innovation for SME Development in ASEAN Region,Nong Khai, 43000, Thailand - Email: tc888.uowmail.edu.au.


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NIDA Economic Review

วารสารเศรษฐศาสตรปริทรรศน สถาบันบัณฑิตพัฒนบริหารศาสตร ปที่ 9 ฉบับที่ 1 (มกราคม 2558)

ปจจัยที่มีผลตอการเขารวมในการสงออก: กรณีศึกษาวิสาหกิจขนาดกลางและขนาดยอมใน ภาคอุตสาหกรรมการผลิตของประเทศไทย ยศ อมรกิจวิกัย*และธีระวัฒน เจริญราษฎร** š‡´—¥n°

Šµœª·‹´¥œ¸Êš—­°Á·Šž¦³‹´„¬r ž{‹‹´¥š¸É¤¸Ÿ¨˜n°„µ¦˜´—­·œÄ‹Äœ„µ¦­nŠ°°„…°Šª·­µ®„·‹…œµ—„¨µŠÂ¨³…œµ—¥n°¤ ÁŒ¡µ³£µ‡°»˜­µ®„¦¦¤„µ¦Ÿ¨·˜…°ŠÅš¥ ‹Îµœªœ 65,111 ª·­µ®„·‹ ×¥œÎµÂ‹Îµ¨°Š˜´ªÂž¦˜µ¤Á·Š‡»–£µ¡ ŗo  „n ‹Î µ ¨°ŠÃ¡¦· ˜ ‹Î µ ¨°ŠÃ¨‹· ˜ ¨³Â‹Î µ ¨°Š‡ªµ¤œn µ ‹³Áž} œ Á· Š Á­o œ Á¡ºÉ ° ª· Á ‡¦µ³®r  ¨³ Áž¦¸¥Áš¸¥Ÿ¨¨´¡›rš¸Éŗo „µ¦«¹„¬µœ¸Ê ¡ªnµ ž{‹‹´¥˜nµŠÇ ŗo„n 1) …œµ—…°Šª·­µ®„·‹ 2) Ÿ¨·˜£µ¡ 3) ‡ªµ¤ nª¥Á®¨º°‹µ„£µ‡¦´“ 4) „µ¦¨Šš»œ (‡ªµ¤Áž}œÁ‹oµ…°Š) ‹µ„˜nµŠž¦³Áš« 5) ­™µœš¸É˜´ÊŠ…°Šª·­µ®„·‹ (ĜÁ…˜ Áš«µ¨) 6) „µ¦ª·‹´¥Â¨³¡´•œµ 7) °µ¥»…°Šª·­µ®„·‹ ¨³ 8) ¦ŠŠµœš¸É¤¸š´„¬³ ¤¸‡ªµ¤­´¤¡´œ›rÁ·Šª„°¥nµŠ¤¸ œ´¥­Îµ‡´„´„µ¦˜´—­·œÄ‹Äœ„µ¦­nŠ°°„…°Šª·­µ®„·‹…œµ—„¨µŠÂ¨³…œµ—¥n°¤Äœ£µ‡°»˜­µ®„¦¦¤„µ¦Ÿ¨·˜Åš¥ ˜nÁ¤ºÉ°Áª¨µŸnµœÅž¦³¥³®œ¹ÉŠ ¡ªnµ‡ªµ¤­´¤¡´œ›r¦³®ªnµŠ…œµ—…°Šª·­µ®„·‹Â¨³„µ¦˜´—­·œÄ‹Äœ„µ¦­nŠ°°„ ¤¸ ‡ªµ¤­´¤¡´œ›rÁ·Šª„Å¤nÁž}œÁ­oœ˜¦Š °¥nµŠÅ¦„Șµ¤ ‡ªµ¤­´¤¡´œ›r¦³®ªnµŠ°µ¥»…°Šª·­µ®„·‹Â¨³„µ¦˜´—­·œÄ‹ Ĝ„µ¦­n Š °°„×¥¦ª¤ ¡ªn µ¤¸ ‡ ªµ¤­´ ¤ ¡´ œ ›r Á · Š ¨ÂÅ¤n Á ž} œ Á­o œ ˜¦Š œ°„‹µ„œ¸Ê Šµœª· ‹´ ¥ œ¸Ê Å —o «¹„ ¬µ 8 °»˜­µ®„¦¦¤¥n°¥ ¡¦o°¤š´ÊŠÁ­œ°œÃ¥µ¥š¸É­Îµ‡´ Á¡ºÉ°¥„¦³—´…¸—‡ªµ¤­µ¤µ¦™Äœ„µ¦Â…nŠ…´œÄœ˜¨µ—„µ¦ ­nŠ°°„…°Šª·­µ®„·‹…œµ—„¨µŠÂ¨³…œµ—¥n°¤…°Š£µ‡°»˜­µ®„¦¦¤„µ¦Ÿ¨·˜…°ŠÅš¥Äœ¦³—´œµœµµ˜·

‡Îµ­Îµ‡´: „µ¦Á…oµ¦nª¤Äœ„µ¦­nŠ°°„; ª·­µ®„·‹…°Š„¨µŠÂ¨³…œµ—¥n°¤; °»˜­µ®„¦¦¤„µ¦Ÿ¨·˜…°ŠÅš¥ *

°µ‹µ¦¥rž¦³‹Îµ‡–³Á«¦¬“«µ­˜¦r¤ r¤®µª·š¥µ¨´¥¦´Š­·˜™ ™œœ¡®¨Ã¥›·œ ‹´Š®ª´—žš»¤›µœ¸ 12000 - Email: yot.a@rsu.ac.th °µ‹µ¦¥rž¦³‹Îµ‡–³¦·®µ¦›»¦„·» ‹ ¤®µª·š¥µ¨´¥…°œÂ„nœ (ª·š¥µÁ…˜®œ°Š‡µ¥) ¨³Ÿ¼o°¼ 圪¥„µ¦«¼œ¼ ¥r¡´•œµ„µ¦Áž}œŸ¼ož¼ ¦³„°„µ¦Â¨³ œª´˜„¦¦¤Á¡ºÉ É° SMEsÄsĜ£¼¤·£µ‡°µÁŽ¸¥œ ¤®µª·š¥µ¨´¥…°œÂ„nœ- ‹´Š®ª´—®œ°Š‡µ¥ 43000 - Email: tc888@uowmail.edu.au

**


NIDA Economic Review

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1.Introduction Small and Medium Enterprises (SMEs) are key drivers of the Thai economy, contributing significantly to social and economic development (Brimble et al., 2002). They represented 99.5 percent of business establishments and 78.8 percent of employment between 2007 and 2012. SME production also accounted for 37.5 percent of GDP during the same period as shown in Table 1 (see OSMEP (2007-2012)).SMEs play crucial roles and functions in assisting large enterprises, particularly in regional production networks(Mephokee, 2003). In addition, they are key factors in linking all important units of industry and filling gaps in industrial clusters that may not be completed by large enterprises alone (Regnier, 2000). They are also key suppliers of goods, services, information and knowledge for large enterprises, and play a pivotal role in the production process of export goods (Tapaneeyangkul, 2001). As a subset, Thai manufacturing SMEs also played a leading role in the economy, accounting for 20 percent of business establishments, 20.7 percent of employment and 11.6 percent of GDP from 2007 to 2012 (see Table 1). Table 1: Contribution of manufacturing SMEs to the Thai economy, 2007 – 2012 Enterprises

2007

2008

2009

2010

2011

2012

99.6 28.2

99.7 19.2

99.8 18.9

99.6 18.6

99.8 17.8

98.5 17.4

76.0 29.6

76.2 29.6

78.2 26.8

77.9 25.9

83.9 24.8

80.4 26.3

38.2 11.7

38.1 11.8

37.8 11.5

37.1 12.0

36.6 11.4

37.0 11.4

All exports (% of GDP) SMEs (% of exports)

61.9 30.1

64.5 28.9

57.4 30.1

60.5 27.3

63.6 29.4

62.3 28.8

SMEs (% of GDP)

18.7

18.6

17.3

16.5

18.7

18.0

Business numbers SMEs (% of all firms) Manufacturing SMEs (% of all firms) SME employment SMEs (% of employment) Manufacturing SMEs (% of employment) GDP of SMEs SMEs (% of GDP) Manufacturing SMEs (% of GDP) SME exports

Source: Office of Small and Medium Enterprises Promotion (2007-2012).

Focusing on Thailand’s trade, exports substantially increased after the 1997 financial crisis due to the Baht depreciation, the easing of a reduction in the demand for labor, and reversal of persistent real wage growth experienced before the crisis (Lombaerde, 2008). The trade deficit disappeared after 1997, averaging surpluses of 353,910 million baht and 304,479 million baht during 1998-2000 and 2007-2009, respectively, as shown in Table 2. The country’s trade balance, however, moved into deficit at 82,754 million baht on average during 2004-2006 as a consequence of decelerating export growth along with faster growth in 2005 (Bank of Thailand, 2005). The trade deficit was also observed at 179,497 million baht on average between 2010 and 2013: a consequence of the decline in foreign demand resulting from weak global economic


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NIDA Economic Review

conditions as well as limitations from flood-damaged production capacity (Bank of Thailand, 2012). According to the direction of trade from 2010 to 2013, Thailand mainly exported to ASEAN, followed by the NAFTA nations, the EU and Japan. Thailand also mostly imported goods from Japan during 2010-2013, followed by ASEAN, the EU and the NAFTA nations (see Table 2). Table 2: Thailand’s merchandise trade value by countries and trade group, 1981 – 2008 (in million baht) 1995-1997 Exports Japan NAFTA EU 1/ ASEAN 2/ Rest of the World Total exports Imports Japan NAFTA EU 1/ ASEAN 2/ Rest of the World Total imports

1998-2000

2001-2003

2004-2006

2007-2009

2010-2013

248,130 305,723 256,213 333,867 397,775 1,541,707

343,200 564,019 416,166 452,738 636,320 2,412,443

446,270 631,606 481,888 608,292 876,703 3,044,758

589,440 745,408 623,307 952,512 1,505,918 4,416,584

607,494 708,414 713,561 1,184,992 2,234,901 5,449,362

689,538 782,217 694,208 1,722,196 2,996,296 6,900,330

516,299 253,809 284,783 242,383 542,959 1,840,233

500,178 279,979 239,180 328,056 711,140 2,058,533

669,500 316,091 328,872 478,426 1,095,765 2,888,654

977,916 363,864 416,188 805,236 1,936,133 4,499,338

988,158 368,036 436,240 908,448 2,444,002 5,144,884

1,364,852 467,295 625,824 1,223,736 3,805,865 7,079,826

Source: Customs Department (compiled by the Bank of Thailand (2009)) 1/ Beginning May 2004, the EU comprised 25 countries, adding Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Slovakia, Poland and Slovenia. In January 2007, the EU comprised 27 countries, adding Bulgaria and Romania. Since July 2013, the EU has comprised 28 countries, adding Croatia. 2/ Prior to 1999, ASEAN did not include Cambodia, Laos, Myanmar and Vietnam.

With respect to SME exports, the major export markets for Thai SMEs in 2011 were China, Hong Kong, Japan, United States, Switzerland, Malaysia, Indonesia, Vietnam, Australia and Singapore, accounting for 11.6 percent, 11.42 percent, 10.71 percent, 7.71 percent, 5.07 percent, 4.83 percent, 4.44 percent, 3.04 percent, 2.79 percent and 2.74 percent of SMEs’ total value of exports, respectively (OSMEP, 2011). Comparing export values between SMEs and large enterprises in 2011, SME export values were about 29.94 percent of the total value of exports, which were much lower compared with large enterprises’ export values, even though the number of exporting SMEs was much larger than that of large exporting enterprises. In addition, from the 2007 Thai Industrial Census, the number of exporting SMEs amounted to only 3,894 firms out of 70,355 SMEs, or 5.53 percent of total manufacturing SMEs. Punyasavatsut (2007) acknowledged that Thai manufacturing SMEs were not ready to face the rigors of international competition in global markets arising from the country’s increased opening and economic integration, as well as more intense competition from lower labor costs in other countries. According to the Office of Small and Medium Enterprises Promotion (2007), Thai business segments have fallen under the NutCracker Effect, implying that Thailand is trapped between countries with greater price


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competitiveness, such as China, Vietnam and Indonesia, and countries with more skilled labor and higher productivity that can differentiate their output by concentrating on higher value-added products and services, such as Japan, South Korea and Taiwan (OSMEP, 2007b). More importantly, the Organization for Economic Cooperation and Development (OECD) (2011) suggested that internationalization is one of the important SME policies that can overcome the problem of the “missing middle” for Thai SMEs, which refers to the lack of growth-oriented small enterprises and the relative under-representation of medium-sized enterprises that has lead to a distinctly smaller proportion of the SME population involved in export markets compared with OECD countries and other non-OECD Asian countries (OECD, 2011). According to Punyasavatsut (2007) and OECD (2011), Thai manufacturing SMEs have faced intense competition in competitive foreign markets, and they lack growthorientation. Therefore, they should be encouraged to participate in global markets as this can solve the “missing middle” problem as suggested by OECD (2011). To address these problems, this paper aims to examine the factors that affect the export participation of Thai manufacturing SMEs including eight sub-manufacturing SMEs, which have not been empirically examined in the existing literature. Jongwanich and Kohpaiboon (2008) used the 1997 Thai Industrial Census to empirically examine the factors of a firm’s export decision in Thailand’s manufacturing sector. Their study, however, focused only on manufacturing enterprises in the aggregate. More importantly, the most current data, from the 2007 Thai Industrial Census, is used for this study.This paper’s structure is organized as follows: section 2 provides a review of the literature; section 3 describes the data source and presents the empirical models used; and section 4 discusses the empirical results. Implications from these results and conclusions are provided in the final section. 2. Literature Review This section provides a review of the literature regarding the factors that affect the export decisions of firms, such as firm size, productivity, government assistance, foreign investment, municipal area, research and development (R&D), and the ratio of skilled workers to total workers. Several empirical studies have investigated both linear and non-linear relationships between a firm’s size and its export decision or export performance (see Jongwanich and Kohpaiboon, 2008;Dueñas-Caparas, 2006; Althukorala et al., 1995). Jongwanich and Kohpaiboon (2008) used the 1997 Thai Industrial Census to investigate the determinants of a firm’s export decision in Thailand’s manufacturing sector. They found that firm size as measured by sales has a positive and significant effect on a firm’s export decision, indicating that there are typically significant sunk costs related to entering export markets; therefore, larger firms are likely to gain greater advantages in doing so. A non-linear relationship between a firm’s size and its export decision, however, was not found in their study. Dueñas-Caparas (2006) examined the determinants of export performance in the Philippine manufacturing sector. They found both positive linear and negative non-linear relationships between a firm size’s and its export performance in the country’s clothing sector, but insignificant results in the food processing and electronics sectors. Athukorala et al. (1995) studied the Sri Lankan Survey of Manufacturing in 1981 and found that firm size is significantly and


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positively correlated with the export decisions of 111 Sri Lankan manufacturing firms. They suggested that firm size can be an important determinant of export participation where scale or size economies exist. Reaching an adequate size may be crucial for achieving success in export markets, since exporting is a costly and risky activity. Smaller firms, therefore, may be at a disadvantage in gathering market information, introducing overseas sales-promotion campaigns, withstanding exchange rate and other risks, and adapting their products to foreign markets. Firm age, indicated by a learning-by-doing experience, can be one of the factors that significantly affect export decisions, since old firms can compete with foreign companies due to their cumulative experience, business network and reputation. Aggrey et al. (2010), however, pointed out that young firms are more proactive, flexible and aggressive than old firms. As a result, they are willing to adopt modern technology, whereas old firms are stuck with outdated physical capital. Focusing on empirical studies, Jongwanich and Kohpaiboon (2008) found that firm age has a significant and positive linear effect on export decisions among Thai manufacturing enterprises, implying that older firms tend to have more operating experience and higher efficiency through a learning-by-doing process than younger firms. A negative and significant non-linear effect, however, was found among Thai manufacturing enterprises, indicating that beyond a certain threshold, a firm’s experience does not exert a positive effect on its export performance. In other words, a negative relationship between a firm’s age and its export activity may be observed, since firms firstly supply the local market, and diversification into exports does not occur until expansion of their domestic markets ends (Jongwanich and Kohpaiboon, 2008). However, Dueñas-Caparas (2006) found positive linear and negative non-linear associations between a firm’s age and its export performance in the Philippine clothing and electronics sectors, but insignificant results in the food processing sector. A number of empirical studies have found a significant and positive relationship between foreign investment (foreign ownership) and firm export decision (Greenaway et al., 2007; Jongwanich and Kohpaiboon, 2008; Aggrey et al., 2010). For example, Greenaway et al. (2007) found that foreign ownership had a significant and positive effect on firm export participation for 9,292 UK manufacturing enterprises between 1993 and 2003. For Thailand, Jongwanich and Kohpaiboon (2008) used the 1997 Thai manufacturing census and found that foreign ownership has a significant and positive impact on firm export participation among Thai manufacturing enterprises. This positive result implies that an increase in foreign participation encourages firms to participate in export markets since foreign partners bring new foreign markets and distribution, new products, managerial know-how and advanced production technology (Jongwanich and Kohpaiboon, 2008). Jongwanich and Kohpaiboon (2008, p. 21) also pointed out that foreign-owned firms can cover sunk costs and enter foreign markets more easily than domestically owned firms. Firm productivity is one of the factors that may affect a firm’s export decision. There exists strong evidence that the self-selection hypothesis, where only more efficient firms can participate in export markets, can be observed in several countries. For example, Bernard and Jensen (1999) used unbalanced panel data of U.S. manufacturing plants between 1984 and 1992 to investigate whether efficient firms become exporters or whether exporting improves a firm’s performance. In their results, total factor productivity (TFP) is found to be statistically significant in explaining a


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firm’s decision to export. Bernard and Wagner (1997) found that German manufacturing firms had to be successful before exporting. In other words, well-run firms most certainly became exporters. Baldwin and Gu (2003) also found that more productive Canadian manufacturing firms were likely to participate in export markets during the period 1990-1996. Their results reveal that firms that start exporting have higher labour productivity than non-exporters, and exporters that exit from export markets have lower labour productivity than continuing exporters. Hallward-Driemeier et al. (2002) studied the patterns of manufacturing productivity in Indonesia, Korea, Malaysia, the Philippines and Thailand between 1996 and 1998. They explained that firms can export after improving their technologies and production processes, investing to improve efficiency, training their work force and using external auditing. A series of these decisions, therefore, raise productivity. Jongwanich and Kohpaiboon (2008) also found that firm productivity has a significant and positive linear effect on export decisions, but such a significant and negative non-linear relationship between firm productivity and export decision-making was not found among Thai manufacturing enterprises. Similarly, skilled manpower is an important determinant in a firm’s export decision, since a higher proportion of skilled labour is associated with higher labour productivity, which will affect an export decision. Dueñas-Caparas (2006) found that skilled manpower as measured by the share of skilled workers to total workers has a significant and positive effect on a firm’s export decision in the Philippine food processing sector, but insignificant results were found in the country’s clothing and electronics sectors. Roper and Love (2002) also investigated the determinants of export performance in the Irish manufacturing sector between 1996 and 1999. They found that plants with more highly skilled workforces, especially those comprising more employees with graduate degrees, are likely to become more successful in export markets. Focusing on research and development (R&D), Dueñas-Caparas (2006) found that research and development as measured by the share of R&D expenditure to total sales has a significant and positive effect on a firm’s export decision in the Philippine electronics industry, but a significant and negative relationship was found in the country’s clothing industry. Roper and Love (2002) also found small plants’ export propensity was positively affected by informally and formally organized R&D activity, but only more formally organized R&D was useful for large plants. Location is another important factor, since firms in different locations may face differing transport costs and infrastructure, spillover effects and access to natural resources (Aggrey et al., 2010). Roper and Love (2002) revealed that plants in the Republic of Ireland have a significantly higher export propensity than similar plants in Northern Ireland. They also explained that plants in the Republic of Ireland may enjoy a better international image than those in Northern Ireland. Aggrey et al. (2010), however, found that location effects had no statistically significant association with the export decisions of manufacturing firms among cities in Kenya and Uganda. Finally, government assistance can affect a firm’s exporting decision. Such aid can be in the form of financial support (e.g., credit assistance, income tax exemption or reduction, and exemption from import duties on essential raw materials) and nonfinancial support (e.g., managerial and technical assistance, and training support). The coefficient estimates of the government support variable are positive. Wu and Cheng (1999) studied the determinants of export performance in China’s township-village


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enterprises, and found that government financial support contributes positively toward the international competitiveness of the export performance of township-village enterprises (TVEs). 3. Data and empirical models This study’s empirical analysis draws on the 2007 Thai Industrial Census of 73,931 firms across Thailand. This Industrial Census is conducted by the National Statistical Office (NSO) every 10 years. An enterprise that either employs between 51 and 200 workers or has fixed assets valued at 51-200 million baht is defined as a medium-sized enterprise. With respect to this criteria, enterprises with 200 or fewer workers were selected as SMEs for this study. As a result, 70,355 enterprises are defined as SMEs, accounting for 95.16 percent of all manufacturing enterprises.One of the firms’ specific factors used to determine export participation among Thai SMEs is labor productivity; therefore, 5,244 negative calculated value-added numbers were deleted. As a result, 65,111 Thai SMEs were used to conduct this study’s empirical analysis. The classification of sub-manufacturing sectors used in the empirical analysis is based on the Thailand Standard Industrial Classification (TSIC), with 23 divisions grouped into eight categories as summarized in Appendix 1. The binary variable for export participation1 is used as the dependent variable. Therefore, the Limited Dependent Variable Models, such as (i) the probit model, (ii) the logit model and (iii) the linear probability model, can be employed for this study, illustrated as follows (Wooldridge, 2006): ௭

‫ܩ‬ሺ‫ݖ‬ሻ ൌ ߶ሺ‫ݖ‬ሻ ൌ ‫ି׬‬ஶ ‫׎‬ሺ‫ݒ‬ሻ݀‫ݒ‬Probit Model ‫ܩ‬ሺ‫ݖ‬ሻ ൌ ܲሺ‫ ݕ‬ൌ ሺͳȁ‫ݔ‬ሻ ൌ ‫ܩ‬ሺߚ଴ ൅ ߚଵ ܺଵ ൅ ‫ ڮ‬൅ ߚ௞ ܺ௞ ሻ ൌ  ሺߚ଴ ൅ ‫ߚݔ‬ሻ

௘௫௣ሺ௭ሻ

ଵାୣ୶୮ሺ௭ሻ

ൌ ߉ሺ‫ݖ‬ሻLogit Model

Linear Probability Model

Where: ߶ሺ‫ݖ‬ሻis the standard normal density as given by ሺʹߨሻିଵȀଶ ‡š’ሺെ‫ ݖ‬ଶ Ȁʹሻ

For the probit and logit models, the relationship between dependent and independent variables is assumed to be an increasing function. For the binary response model, Wooldridge (2006, p. 256, 582) also mentioned that the probit and logit models can overcome certain drawbacks of the linear probability model (LPM), since the LPM violates the homoskedasticity assumption that is important for justifying the t and F statistics. The assumption of linear parameters between the dependent and independent variables is also generally required for the LPM under the OLS estimation. The probit model is also more popularly compared with the logit model, since economists are likely to favour the probit model’s normality assumption (Wooldridge, 2006, p. 385). In addition, the probit model’s method of maximum likelihood estimation automatically accounts for the heteroskedasticity problem. However, these three 1

It would be interesting to investigate the export performance of Thai manufacturing SMEs besides their export participation, thus using the ratio of exports as a continuous variable. However, it is beyond the scope of this study.


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estimation models are used to check the sensitivity of this study’s results(see Jongwanich and Kohpaiboon, 2008). Huber-White robust standard errors are used for the LPM as well as the probit and logit models, which can deal with a number of minor problems, such as non-normality, heteroskedasticity or observations that indicate large residuals, leverage or influence. With these robust standard errors, the estimates of the coefficients are exactly homogeneous as in ordinary OLS, but the robust standard errors take into account those problems. For the LPM, the problem of multicollinearity among regressors can lead to large variances and covariance, making precise estimation difficult. Correlation analysis is conducted to examine this problem as indicated in Appendix2, which shows no serious correlations among explanatory variables. For example, the correlation between two variables, such as productivity (labour productivity) and skilled labour, is about 0.1523 for all manufacturing SMEs, indicating a low level of collinearity. Applying the limited dependent variable models, three equations for the export decisions of all firms are identified as follows: Equation 1: ܼ௜௝ ൌ  ߚ଴ ൅ ߚଵ ܵ‫ܧܼܫ‬௜௝ ൅ ߚଶ ܴܱܲ‫ܻܶܫܸܫܶܥܷܦ‬௜௝ ൅ ߚଷ ‫ܶܵܫܵܵܣ̴ܶܰܧܯܴܧܸܱܩ‬௜௝ ൅ ߚସ ‫̴ܹܱܰܰܩܫܧܴܱܨ‬௜௝ ൅ ߚହ ‫ܣܧܴܣ̴ܮܣܲܫܥܫܷܰܯ‬௜௝ ൅ ߚ଺ ܴƬ‫ܦ‬௜௝ ൅ ߚ଻ ‫ܧܩܣ‬௜௝ ൅  ߚ଼ ܵ‫ܴܷܱܤܣܮ̴ܦܧܮܮܫܭ‬௜௝ ൅ ‫ݑ‬௜௝

Equation 2:

ܼ௜௝ ൌ ߚ଴ ൅ ߚଵ ܵ‫ܧܼܫ‬௜௝ ൅ ߚଶ ܴܱܲ‫ܻܶܫܸܫܶܥܷܦ‬௜௝ +ߚଷ ܴܱܲ‫ܻܶܫܸܫܶܥܷܦ‬௜௝ଶ ൅ߚସ ‫ܶܵܫܵܵܣ̴ܶܰܧܯܴܧܸܱܩ‬௜௝ ൅ ߚହ ‫̴ܹܱܰܰܩܫܧܴܱܨ‬௜௝ ൅ ߚ଺ ‫ܣܧܴܣ̴ܮܣܲܫܥܫܷܰܯ‬௜௝ ൅ ߚ଻ ܴƬ‫ܦ‬௜௝ ൅ߚ଼ ‫ܧܩܣ‬௜௝ ൅ ߚଽ ‫ܧܩܣ‬௜௝ଶ ൅ ߚଵ଴ ܵ‫ܴܷܱܤܣܮ̴ܦܧܮܮܫܭ‬௜௝  ൅ ‫ݑ‬௜௝

Equation 3:

ܼ௜௝ ൌ ߚ଴ ൅ ߚଵ ܵ‫ܧܼܫ‬௜௝ ൅ ߚଶ ܵ‫ܧܼܫ‬௜௧ଶ ൅ ߚଷ ܴܱܲ‫ܻܶܫܸܫܶܥܷܦ‬௜௝ ൅  ߚସ ܴܱܲ‫ܻܶܫܸܫܶܥܷܦ‬௜௝ଶ ൅ߚହ ‫ܶܵܫܵܵܣ̴ܶܰܧܯܴܧܸܱܩ‬௜௝ ൅ ߚ଺ ‫̴ܹܱܰܰܩܫܧܴܱܨ‬௜௝ ൅ ߚ଻ ‫ܣܧܴܣ̴ܮܣܲܫܥܫܷܰܯ‬௜௝ ൅ ߚ଼ ܴƬ‫ܦ‬௜௝ ൅ ߚଽ ‫ܧܩܣ‬௜௝ ൅ ߚଵ଴ ‫ܧܩܣ‬௜௝ଶ ൅ ߚଵଵ ܵ‫ܴܷܱܤܣܮ̴ܦܧܮܮܫܭ‬௜௝  ൅ ‫ݑ‬௜௝

Where ܼ௜௝ = Dummy for export participation: 1 if firm i in industry j exports in foreign markets 0, otherwise ܵ‫ܧܼܫ‬௜௝ = Size of firm i in industry j, represented by the book value of fixed assets in

the natural logarithm form ܴܱܲ‫ܻܶܫܸܫܶܥܷܦ‬௜௝ = Labour productivity of firm i in industry j, represented by the value added to total workers in the natural logarithm form ‫ܶܵܫܵܵܣ̴ܶܰܧܯܴܧܸܱܩ‬௜௝ = Dummy for government assistance:

= 1 if firm i in industry j receives privileges from theBoard of Investment (BOI) = 0, otherwise ‫̴ܹܱܰܰܩܫܧܴܱܨ‬௜௝ = Foreign Investment in firm iin industry j, represented by the

percentage of equity held by foreign investors


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‫ܣܧܴܣ̴ܮܣܲܫܥܫܷܰܯ‬௜௝ = Dummy for municipal area: ൌ1 if firm i in industry jis in a municipal area = 0, if firm i in industry j is in a non-municipal area ܴƬ‫ܦ‬௜௝ = Dummy for research and development (R&D), represented by the cost of research and development to administrative expenses of firm i in industry j. ‫ܧܩܣ‬௜௝ = Age of firm iin industry j, represented by the number of operating years. ܵ‫ܴܷܱܤܣܮ̴ܦܧܮܮܫܭ‬௜௝ = Proportion of skilled workforce of firm i in industry j, represented by the share of skilled workers to total workers.

4. Empirical results Three models estimated from different estimation methods provide empirical results as summarisedin Tables 3 to 11. Focusing overall on Thai manufacturing SMEs, firm size was found to have a significant and positive linear influence on export decisions. These results indicate large firms can afford the sunk costs necessary to enter export markets (Greenaway et al., 2007). In other words, large firms can earn sufficient profits to recover their sunk costs incurred during exporting (Jongwanich and Kohpaiboon, 2008). This result is consistent with the findings of Jongwanich and Kohpaiboon (2008). A significant and positive non-linear relationship between a firm’s size and its export decision was also found among Thai manufacturing SMEs, indicating that beyond a certain threshold, larger firms still have more advantages in participating in foreign markets. This empirical result contradicts the findings of Jongwanich and Kohpaiboon (2008) and Dueñas-Caparas (2006). Focusing on sub-manufacturing sectors, a significant and positive linear correlation between firm size and export decision was mainly found among SMEs classified in groups 2, 3, 4, 6, 7 and 8. Inconclusive results were found among SMEs in groups 1 and 5 due to the difference in significantly estimated signs. In addition, a positive non-linear relationship between firm size and export decision was found among SMEs classified in groups 1 and 5. An inconclusive result was found in group 8 due to the difference in significantly estimated signs. Insignificant results were also found in groups 2, 3, 4, 6 and 7. Productivity, as measured by labor productivity, was found to be significantly and positively related to export decisions among Thai manufacturing SMEs in the aggregate, including SMEs classified in all groups2, implying that only more efficient firms will self-select into export markets, since the most productive firms are best able to survive in highly competitive markets. The reason is that there exist additional costs in exporting to foreign countries, including transportation, marketing and production costs in developing existing products for foreign customers, which can keep small or less successful firms from becoming exporters (Wagner, 2005). However, beyond a certain threshold, a firm’s productivity was found to be significantly and negatively 1 Significant and negative results estimated by the OLS were found overall among Thai manufacturing firms, including SMEs in groups 1, 2 and 8. The LPM (the OLS estimation), however, violates the homoskedasticity assumption important for justifying the t and F statistics. Therefore, the empirical results obtained by the OLS estimation are unreliable compared with the probit and logit estimations.


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related to its export decision. These significant linear and non-linear results are consistent with the results of Jongwanich and Kohpaiboon (2008). A significant and negative non-linear relationship between productivity and a firm’s export decision was found among Thai manufacturing SMEs in the aggregate, including SMEs in groups 2, 3, 4, 5, 6 and 7. Significant and positive non-linear results, however, were found in the estimated models for SMEs in groups 1 and 8. Similarly, skilled labor was strongly found to be significantly and positively related to export decisions among Thai manufacturing SMEs. This result is consistent with the findings of Dueñas-Caparas (2006) and Roper and Love (2002), implying that higherskilled manpower is associated with higher productivity, which will affect export decisions. For sub-manufacturing sectors, a significant and positive linear relationship between productivity and firm export decision was mainly found among SMEs in groups 1, 2, 4, 6, and 8, but insignificant results were found among SMEs classified in other groups. In addition, government assistance has a significant and positive effect on export decisions among Thai manufacturing SMEs, since government assistance through the BOI’s privileges can help support their participation in foreign markets. This result is also consistent with the findings of Wu and Cheng (1999). For sub-manufacturing SME groups, significant and positive results between governance assistance and export decisions were strongly found among SMEs in all groups. Similarly, foreign investment (ownership) was found to be significantly and positively associated with export decisions among Thai manufacturing SMEs in the aggregate. This positive result implies that a rise in foreign participation encourages firms to participate in export markets, since foreign partners bring new foreign markets and distribution, new products, managerial know-how and advanced production technology (Jongwanich and Kohpaiboon, 2008). This result is also consistent with other empirical studies (Greenaway et al., 2007; Jongwanich and Kohpaiboon, 2008; Aggrey et al., 2010). Focusing on sub-manufacturing sectors, a significant and positive result was strongly found among SMEs in groups 2, 4 and 8, but insignificant results were found in groups 1, 3, 5, 6 and 7. Thai manufacturing SMEs in municipal areas were found to be more likely to enter foreign markets since they have more export advantages compared with firms in nonmunicipal areas. These advantages include better exporting position in terms of transport costs, infrastructure, spillover effects, labour and natural resources. With respect to sub-manufacturing sectors, a significant and positive relationship between municipal-based SMEs and their export decisions was also found in groups 1, 2, 4, 5, 6, 7 and 8. An insignificant result was found only in group 3. Research and development (R&D) has a significant and positive effect on a firm’s export decision, since it helps improve the quality of a firm’s products to compete in foreign markets. This positive result is consistent with the findings of Roper and Love (2002). Focusing on sub-manufacturing SMEs, a significant and positive relationship between R&D and a firm export’s decision was found in groups 1, 4, 5 and 8. A positive result was found in groups 3, 6 and 7, but it was insignificant. The significant and negative result estimated by the limited probability model (LPM) was found only in group 2.


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Firm age was also found to be statistically and positively related to a firm’s export decision, since old firms can compete with foreign companies due to their cumulative experience, business network and reputation. However, a significant and negative nonlinear relationship between a firm’s age and its export decision was found among Thai manufacturing SMEs. This is because after a certain threshold, old firms might be unable to enter foreign markets since they are stuck with outdated physical capital, while young firms are more proactive, flexible and aggressive (Aggrey et al., 2010). These results are consistent with the findings of Jongwanich and Kohpaiboon (2008). For sub-manufacturing sectors, a significant and positive linear relationship between a firm’s age and its export decision was found among SMEs in groups 1, 2, 3, 4, 5, and 6, but insignificant results were found in groups 7 and 8. Finally, a significant and negative non-linear relationship between a firm’s age and its export decision was found in groups 1, 2, 4, 5, 6, and 8, but insignificant results were found in groups 3and 7. To interpret the model, Marginal effects can be useful in summarizing how change in an explained variable is related to change in an explanatory variable. In the linear probability model (LPM), the marginal effect can be analyzed by the slope coefficient, which measures directly the change in the probability of an outcome occurring due to a unit change in the value of a control variable, with the effect of all other control variables constant (Gujarati, 2004). In the binary regression model, the marginal effect cannot be measured indirectly by the slope coefficient. However, the binary regression model aims to estimate the effects of the control variables (‫ݔ‬௜ ሻ on the response ෢଴ ൅ ‫ߚݔ‬መ൯ߚ෡ఫ ൧ο‫ݔ‬௝ , for probabilities, P(ሺ‫ ݕ‬ൌ ͳȁ‫ݔ‬ሻ. If ‫ݔ‬௜ is continuous, thenοܲሺ‫ݕ‬෣ ൌ ͳȁ‫ݔ‬ሻ ൎ ൣ݃൫ߚ small changes in ‫ݔ‬௝ . Therefore, this implies that if ‫ݔ‬௝ changes by 1 unit, then the change in the estimated success probability is approximately ݃൫ߚ෢଴ ൅ ‫ߚݔ‬መ൯ߚ෡ఫ . For probit and logit models, the Marginal Effect at the Mean (MEM) is often used in the literature and commonly estimated in econometrics packages. It replaces each control variable with its sample average, which can be written in the following equation (the adjustment factor): ݃൫ߚ෢଴ ൅ ‫ݔ‬ҧ ߚመ൯ ൌ ݃൫ߚ෢଴ ൅ ߚ෢ଵ ‫ݔ‬ҧ ൅ ߚ෢ଶ ‫ݔ‬ҧ ൅ ‫ ڮ‬൅ ߚ෢௞ ‫ݔ‬തതത൯ ௞ , where g(.) is the standard normal density in the probit model and g(z) = exp(z)/ሾͳ ൅ ‡š’ሺ‫ݖ‬ሻሿଶ in the logit model (Wooldridge, 2006, p. 590).


LPM -0.04454* (0.00249) 0.00221* (0.00018) 0.00146* (0.00019) 0.95426* (0.00294) 0.00033* (0.00008) 0.00708* (0.00081) 0.00094* (0.00026) 0.00035* (0.00005) 0.00689* (0.00102) 0.819

Model 1 Logit (0.30729) 0.25983* (0.01953) 0.24389* (0.02696) 8.31798* (0.17427) 0.01070* (0.0028) 0.89059* (0.0909) 0.02285* (0.00901) 0.02104* (0.00278) 0.84349* (0.11771) 0.779 Probit -5.52303* (0.15617) 0.11153* (0.0105) 0.09608* (0.01153) 4.21148* (0.06487) 0.00472* (0.00137) 0.33872* (0.03471) 0.01314* (0.00428) 0.00907* (0.00125) 0.34069* (0.04814) 0.780

LPM -0.01202* (0.00424) 0.00203* (0.00018) -0.00570* (0.00092) 0.00037* (0.00006) 0.95280* (0.00298) 0.00032* (0.00008) 0.00735* (0.00081) 0.00091* (0.00025) 0.00086* (0.00009) -0.00001* (0.000002 0.00596* (0.00103) 0.819

Model 2 Logit (2.22788) 0.25153* (0.01982) 1.60744* (0.36851) -0.05861* (0.01550) 8.37433* (0.18171) 0.01237* (0.00284) 0.83098* (0.09037) 0.03169* (0.00833) 0.08521* (0.01011) -0.00139* (0.00021) 0.82694* (0.11914) 0.781 Probit -7.34244* (1.00668) 0.10654* (0.01078) 0.40406* (0.16491) -0.01347* (0.00697) 4.21488* (0.06540) 0.00520* (0.00138) 0.32259* (0.03462) 0.01599* (0.00420) 0.03303* (0.00429) -0.00054* (0.00009) 0.33631* (0.04885) 0.781

LPM 0.01258* ( 0.00456) - 0.00344* (0.00057) 0.00026 * (0.00003) - 0.00419* (0.00093) 0.00025* (0.00006) 0.94920* (0.00307) 0.00027* (0.00008) 0.00748* (0.00081) 0.00082* (0.00025) 0.00075* (0.00009) - 0.00001* (0.000002) 0.00438* (0.00104) 0.820

Model 3 Logit (2.31766) -0.00981 (0.08840) 0.01033* (0.00316) 1.65961* (0.35740) -0.06262* (0.01500) 8.23438* (0.17134) 0.01116* (0.00291) 0.85232* (0.08986) 0.02895* (0.00837) 0.08165* (0.00991) -0.00133* (0.00020) 0.81119* (0.11966) 0.782

Probit -6.69871* (1.05951) -0.03848 (0.04827) 0.00548* (0.00169) 0.46634* (0.16208) -0.01670* (0.00682) 4.17724* (0.06406) 0.00474* (0.00138) 0.33326* (0.03489) 0.01456* (0.00423) 0.03176* (0.00426) -0.00052* (0.00009) 0.33001* (0.04807) 0.782

Note: Huber/White robust standard errors (S.E.) are in parentheses; * and ** indicate that the coefficients are statistically significant at the 5% level and 10% levels, respectively.

R2/Pseudo R2

Skilled_Labour

Age2

Age

R&D

Municipal_ Area

Foreign_Own

Government_Assist

Productivity2

Productivity

Size2

Size

Constant

Models

Table 3: Export Participation Equations for Thai Manufacturing SMEs (N=65,111)

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21


LPM -0.02175* (0.00500) 0.00089* (0.00034) 0.00104* (0.00032) 0.97330* (0.00662) 0.00020 (0.00019) 0.00113 (0.00118) 0.00143* (0.00065) 0.00006 (0.00005) 0.00636* (0.00179) 0.856

Model 1 Logit (1.38818) 0.25914* (0.10122) 0.27443* (0.09138) 9.02416* (0.50540) 0.01098 (0.01073) 0.47795** (0.25488) 0.04585* (0.02021) 0.00281 (0.00761) 1.43447* (0.41790) 0.822 Probit -5.26729* (0.57294) 0.07543* (0.04497) 0.10409* (0.03559) 4.54689* (0.20115) 0.00657 (0.00567) 0.17032** (0.09139) 0.02722* (0.00960) 0.00195 (0.00296) 0.58641* (0.14903) 0.823

LPM 0.00453 (0.00729) 0.00077* (0.00034) -0.00467* (0.00151) 0.00029* (0.00009) 0.97202* (0.00672) 0.00018 (0.00019) 0.00136 (0.00115) 0.00149* (0.00065) 0.00039* (0.00011) -0.00001* (0.000002 0.00546* (0.00179) 0.856

Model 2 Logit -11.25427* (3.63080) 0.23427* (0.10239) -0.14042 (0.50474) 0.01788 (0.02139) 9.23773* (0.56172) 0.00792 (0.01084) 0.44627** (0.25198) 0.08468* (0.01864) 0.09460* (0.02571) -0.00188* (0.00045) 1.46710* (0.42316) 0.825 Probit -3.99794* (0.85356) 0.06762 (0.04249) (0.09209) 0.01184* (0.00455) 4.61913* (0.21482) 0.00579 (0.00597) 0.16857** (0.09111) 0.04009* (0.00959) 0.03819* (0.01011) -0.00079* (0.00019) 0.57810* (0.14745) 0.826

LPM 0.02461* (0.01048) -0.00384* (0.00134) 0.00022* (0.00006) -0.00325* (0.00141) 0.00019* (0.00009) 0.96774* (0.00696) 0.00016 (0.00019) 0.00119 (0.00114) 0.00139* (0.00064) 0.00034* (0.00011) -0.00001* (0.000002) 0.00388* (0.00170) 0.857

Model 3 Logit -7.65277** (4.14346) -0.43661* (0.16971) 0.02647* (0.00690) 0.04471 (0.55001) 0.00523 (0.02190) 9.17055* (0.56451) 0.00810 (0.01114) 0.49217** (0.25664) 0.06762* (0.02228) 0.08954* (0.02594) -0.00180* (0.00046) 1.34995* (0.39651) 0.828

Probit -3.02394* (1.01615) -0.18144* (0.05502) 0.01077* (0.00235) -0.05083 (0.13635) 0.00514 (0.00589) 4.54919* (0.20746) 0.00581 (0.00630) 0.17939** (0.09269) 0.03373* (0.00991) 0.03452* (0.01046) -0.00073* (0.00020) 0.50690* (0.13884) 0.826

Note: Huber/White robust standard errors (S.E.) are in parentheses; * and ** indicate that the coefficients are statistically significant at the 5% level and 10% levels, respectively.

R2/Pseudo R2

Skilled_Labour

Age2

Age

R&D

Municipal_ Area

Foreign_Own

Government_Assis

Productivity2

Productivity

Size2

Size

Constant

Model

Table 4: Export Participation Equations for Thai manufacturing SMEs Classified in Group 1(N=14,929)

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LPM -0.07181* (0.00675) 0.00341* (0.00042) 0.00300* (0.00044) 0.92751* (0.00896) 0.00153* (0.00044) 0.00715* (0.00219) -0.00026 (0.00029) 0.00074* (0.00015) 0.00650* (0.00224) 0.738

Model 1 Logit (0.65203) 0.28876* (0.04189) 0.49934* (0.06080) 7.69021* (0.41637) 0.02743* (0.00762) 0.78361* (0.20857) -0.01552 (0.03852) 0.03353* (0.00622) 0.45196** (0.24765) 0.729 Probit -7.00201* (0.30726) 0.13844* (0.01883) 0.21390* (0.02727) 3.94475* (0.16304) 0.01417* (0.00372) 0.33033* (0.08419) -0.01462 (0.01617) 0.01504* (0.00293) 0.16361 (0.10414) 0.733

LPM 0.00816 (0.01052) 0.00292* (0.00041) -0.01613* (0.00270) 0.00111* (0.00018) 0.92217* (0.00924) 0.00147* (0.00044) 0.00630* (0.00220) (0.00029) 0.00139* (0.00026) -0.00002* (0.00001) 0.00410** (0.00226) 0.740

Model 2 Logit (5.31147) 0.27793* (0.04084) 2.02243* (0.92845) (0.03993) 7.85140* (0.47921) 0.02879* (0.00696) 0.74032* (0.21135) -0.01922 (0.03600) 0.09574* (0.01890) -0.00140* (0.00037) 0.45517** (0.25019) 0.732 Probit (2.03825) 0.13364* (0.01895) 0.77060* (0.35815) -0.02452 (0.01562) 3.96334* (0.17326) 0.01503* (0.00351) 0.31602* (0.08501) -0.01488 (0.01526) 0.04031* (0.00826) -0.00059* (0.00017) 0.15685 (0.10526) 0.736

LPM 0.05007* (0.01100) -0.00568* (0.00131) 0.00041* (0.00008) -0.01478* (0.00267) 0.00099* (0.00017) 0.91654* (0.00960) 0.00138* (0.00044) 0.00620* (0.00220) -0.00065* (0.00028) 0.00119* (0.00026) -0.00002* (0.00001) 0.00202 (0.00233) 0.740

Model 3 Logit -22.74736* (5.43400) 0.13365 (0.13436) 0.00552 (0.00528) 2.00881* (0.92425) -0.06727** (0.03979) 7.77463* (0.46375) 0.02847* (0.00702) 0.76063* (0.20858) -0.03100 (0.03079) 0.09428* (0.01883) -0.00138* (0.00036) 0.45906** (0.25009) 0.733

Probit -9.91475* (2.11861) 0.08848 (0.08321) 0.00166 (0.00310) 0.77294* (0.35823) -0.02476 (0.01563) 3.95197* (0.17172) 0.01492* (0.00352) 0.32100* (0.08461) -0.01774 (0.01334) 0.04009* (0.00827) -0.00059* (0.00017) 0.15826 (0.10539) 0.736

Note: Huber/White robust standard errors (S.E.) are in parentheses; * and ** indicate that the coefficients are statistically significant at the 5% level and 10% levels, respectively.

R2/Pseudo R2

Skilled_Labour

Age2

Age

R&D

Municipal_ Area

Foreign_Own

Government_Assist

Productivity2

Productivity

Size2

Size

Constant

Model

Table 5: Export Participation Equations for Thai manufacturing SMEs Classified in Group 2(N=12,721)

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23


LPM -0.02250* (0.00508) 0.00141* (0.00034) 0.00068 (0.00042) 0.97166* (0.00870) 0.00004 (0.00010) -0.00071 (0.00192) 0.00010 (0.00018) 0.00017 (0.00011) 0.00199 (0.00227) 0.841

Model 1 Logit -12.87376* (1.39945) 0.42031* (0.09200) 0.10391 (0.12053) 8.71709* (0.51249) -0.00500 (0.00874) -0.00048 (0.32120) 0.01526 (0.01242) 0.02072 (0.01368) 0.22947 (0.44856) 0.800 Probit -5.27356* (0.49881) 0.15194* (0.03658) 0.03554 (0.04038) 4.46134* (0.20734) -0.00136 (0.00370) -0.02299 (0.11768) 0.00493 (0.00579) 0.00867** (0.00503) 0.07761 (0.16848) 0.800

LPM -0.01675* (0.00717) 0.00135* (0.00033) -0.00080 (0.00167) 0.00008 (0.00011) 0.97147* (0.00874) 0.00003 (0.00010) -0.00076 (0.00193) 0.00009 (0.00018) 0.00051* (0.00021) -0.00001** (0.000004) 0.00160 (0.00238) 0.841

Model 2 Logit -23.35346* (6.51114) 0.38850* (0.09027) 2.00501** (1.13292) -0.08486** (0.04787) 8.86730* (0.49465) -0.00277 (0.00899) -0.09415 (0.30787) 0.01220 (0.01330) 0.09552* (0.04853) -0.00164 (0.00106) 0.18088 (0.45453) 0.802 Probit -8.73417* (2.66559) 0.14331* (0.03658) 0.67017 (0.46970) -0.02875 (0.02013) 4.50222* (0.20163) -0.00043 (0.00375) -0.05730 (0.11353) 0.00404 (0.00595) 0.03319** (0.01735) -0.00055 (0.00037) 0.06166 (0.17367) 0.801

LPM 0.00146 (0.00755) -0.00258* (0.00087) 0.00019* (0.00006) -0.00001 (0.00164) 0.00001 (0.00011) 0.96851* (0.00907) 0.00000 (0.00010) -0.00097 (0.00194) 0.00010 (0.00018) 0.00040* (0.00020) -0.00001 (0.000004) 0.00021 (0.00244) 0.841

Model 3 Logit -30.04197* (11.60962) 1.43850 (1.44092) -0.03400 (0.04507) 1.79576 (1.19707) -0.07607 (0.05009) 8.84596* (0.49029) -0.00084 (0.00929) -0.15822 (0.33030) 0.01357 (0.01294) 0.09766* (0.04834) -0.00167 (0.00105) 0.08218 (0.49385) 0.802

Probit -9.58618* (4.17025) 0.28057 (0.44559) -0.00454 (0.01417) 0.64161 (0.46388) -0.02750 (0.01980) 4.49843* (0.20126) -0.00020 (0.00381) -0.06432 (0.12025) 0.00412 (0.00591) 0.03343** (0.01742) -0.00056 (0.00037) 0.05138 (0.18410) 0.801

Note: Huber/White robust standard errors (S.E.) are in parentheses; * and ** indicate that the coefficients are statistically significant at the 5% level and 10% levels, respectively.

R2/Pseudo R2

Skilled_Labour

Age2

Age

R&D

Municipal_ Area

Foreign_Own

Government_Assist

Productivity2

Productivity

Size2

Size

Constant

Model

Table 6: Export Participation Equations for Thai manufacturing SMEs Classified in Group 3(N=7,710)

24 NIDA Economic Review


LPM -0.04096* (0.00732) 0.00339* (0.00071) -0.00085 (0.00075) 0.95293* (0.00653) 0.00029* (0.00013) 0.00844* (0.00376) 0.00141* (0.00070) 0.00104* (0.00026) 0.00609 (0.00426) 0.865

Model 1 Logit (0.79608) 0.33842* (0.06570) -0.02814 (0.06705) 8.37404* (0.47336) 0.01036* (0.00454) 0.61955* (0.24745) 0.02823* (0.01254) 0.03548* (0.00676) 0.56859** 0.30090 0.812 Probit -4.78929* (0.35132) 0.14389* (0.02928) -0.00647 (0.02818) 4.29992* (0.17445) 0.00515* (0.00215) 0.24208* (0.09851) 0.01458** (0.00747) 0.01694* (0.00337) 0.23840** (0.12550) 0.813

LPM (0.01251 0.00321* (0.00071 0.00111 (0.00234 -0.00012 (0.00014 0.95289* (0.00670 0.00030* (0.00012 0.00799* (0.00370 0.00144* (0.00071 0.00196* (0.00046 (0.00001 0.00581 (0.00426 0.865

Model 2 Logit -24.01782* (4.22813) 0.33226* (0.06739) 2.25598* (0.67806) -0.09751* (0.02786) 8.61252* (0.51280) 0.01267* (0.00446) 0.50217* (0.24141) 0.02708* (0.01231) 0.12245* (0.03241) -0.00157* (0.00063) 0.57454** (0.30514) 0.816 Probit -10.51384* (1.83867) 0.13667* (0.03041) 0.95719* (0.29548) -0.04106* (0.01222) 4.38215* (0.18757) 0.00628* (0.00211) 0.20138* (0.09858) 0.01436* (0.00731) 0.04872* (0.01298) -0.00060* (0.00025) 0.24323** (0.13001) 0.816

LPM -0.01215 (0.01146) -0.00423* (0.00161) 0.00032* (0.00009) 0.00247 (0.00239) -0.00023 (0.00015) 0.94967* (0.00707) 0.00026* (0.00013) 0.00868* (0.00371) 0.00138* (0.00070) 0.00185* (0.00045) -0.00002* (0.00001) 0.00536 (0.00428) 0.866

Model 3 Logit -23.23849* (4.41727) 0.20699 (0.24257) 0.00425 (0.00831) 2.28498* (0.68063) -0.09901* (0.02803) 8.58773* (0.50732) 0.01248* (0.00454) 0.50316* (0.24065) 0.02717* (0.01230) 0.12187* (0.03199) -0.00156* (0.00062) 0.57202** (0.30554) 0.816

Probit (1.96821) 0.07895 (0.14888) 0.00189 (0.00478) 0.97514* (0.30353) -0.04189* (0.01260) 4.37700* (0.18696) 0.00625* (0.00213) 0.20237* (0.09855) 0.01439* (0.00731) 0.04854* (0.01289) -0.00060* (0.00025) 0.24418** (0.12982) 0.816

ote: Huber/White robust standard errors (S.E.) are in parentheses; * and ** indicate that the coefficients are statistically significant at the 5% level and 10% levels, respectively.

R2/Pseudo R2

Skilled_Labour

Age2

Age

R&D

Municipal_ Area

Foreign_Own

Government_Assist

Productivity2

Productivity

Size2

Size

Constant

Model

Table 7: Export Participation Equations for Thai manufacturing SMEs Classified in Group 4(N=5,099)

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25


LPM -0.04522* (0.00709) 0.00167* (0.00036) 0.00191* (0.00049) 0.96868* (0.00691) 0.00012 (0.00009) 0.00600* (0.00139) 0.00066 (0.00046) 0.00044* (0.00010) 0.00028 (0.00210) 0.827

Model 1 Logit -13.83788* (0.86516) 0.27873* (0.05833) 0.28334* (0.07043) 9.48348* (0.61420) 0.00268 (0.00589) 1.12266* (0.25548) 0.03833* (0.01908) 0.04251* (0.00703) 0.21565 (0.35247) 0.793 Probit -5.95136* (0.45332) 0.11265* (0.03313) 0.10768* (0.03011) 4.62745* (0.19998) 0.00240 (0.00271) 0.41047* (0.09102) 0.01727** (0.00949) 0.01698* (0.00300) 0.13893 (0.13756) 0.793

LPM 0.00264 (0.02279) 0.00153* (0.00036) -0.00719 (0.00450) 0.00043** (0.00023) 0.96793* (0.00698) 0.00011 (0.00009) 0.00620* (0.00140) 0.00066 (0.00046) 0.00073* (0.00020) -0.00001 (0.00001) 0.00004 (0.00209) 0.827

Model 2 Logit -22.69210* (5.70233) 0.27319* (0.05915) 1.66498** (0.90633) -0.05680 (0.03788) 9.61033* (0.63780) 0.00378 (0.00613) 1.08900* (0.25521) 0.04098** (0.01981) 0.12876* (0.03624) -0.00186* (0.00079) 0.16165 (0.35511) 0.795 Probit -8.59412* (2.46941) 0.10720* (0.03471) 0.53207 (0.37540) -0.01777 (0.01574) 4.63821* (0.20439) 0.00326 (0.00277) 0.40702* (0.09139) 0.01847** (0.00945) 0.04744* (0.01278) -0.00069* (0.00028) 0.12536 (0.13936) 0.795

LPM 0.02160 (0.02302 (0.00115 0.00028* (0.00006 -0.00392 (0.00449 0.00022 (0.00023 0.96429* (0.00713 0.00006 (0.00009 0.00635* (0.00141 0.00058 (0.00045 0.00058* (0.00020 -0.00001 (0.00001 -0.00188 (0.00212 0.828

Model 3 Logit -19.76396* (5.77374) -0.36565* (0.17483) 0.02457* (0.00630) 1.95117* (0.90356) -0.07372** (0.03792) 9.29870* (0.52223) 0.00260 (0.00544) 1.09985* (0.25508) 0.03537 (0.02283) 0.12140* (0.03674) -0.00176* (0.00081) 0.20984 (0.35530) 0.798

Probit -7.97887* (2.30725) -0.17487* (0.05850) 0.01106* (0.00231) 0.75356* (0.36399) -0.02870** (0.01511) 4.58968* (0.19537) 0.00229 (0.00274) 0.42239* (0.09457) 0.01639 (0.01072) 0.04493* (0.01317) -0.00066* (0.00029) 0.10990 (0.13312) 0.780

Note: Huber/White robust standard errors (S.E.) are in parentheses; * and ** indicate that the coefficients are statistically significant at the 5% level and 10% levels, respectively

R2/Pseudo R2

Skilled_Labour

Age2

Age

R&D

Municipal_ Area

Foreign_Own

Government_Assist

Productivity2

Productivity

Size2

Size

Constant

Model

Table 8: Export Participation Equations for Thai manufacturing SMEs Classified in Group 5(N=18,037)

26 NIDA Economic Review


LPM (0.01968 0.00516* (0.00142 0.00409* (0.00137 0.94259* (0.00962 -0.00002 (0.00018 0.00799* (0.00422 0.00020 (0.00052 0.00073* (0.00023 0.00330 (0.00623 0.860

Model 1 Logit -12.95244* (1.01133) 0.22309* (0.03883) 0.33517* (0.08247) 8.14355* (0.42317) -0.00087 (0.00764) 0.67569* (0.30592) 0.02127 (0.02899) 0.03626* (0.00929) 0.93881* (0.40769) 0.812 Probit -6.27978* (0.44738) 0.11376* (0.01958) 0.14809* (0.03557) 4.22358* (0.17220) -0.00057 (0.00281) 0.29321* (0.12179) 0.00711 (0.01347) 0.01756* (0.00451) 0.38644* (0.15665) 0.814

LPM -0.12744* (0.05989) 0.00493* (0.00143) 0.00519 (0.01082) -0.00005 (0.00052) 0.94193* (0.00967) 0.00000 (0.00018) 0.00784** (0.00420) 0.00014 (0.00052) 0.00191* (0.00046) -0.00003* (0.00001) 0.00263 (0.00621) 0.860

Model 2 Logit (9.37417) 0.21073* (0.04606) 6.05570* (1.54000) -0.23017* (0.06406) 8.46788* (0.45716) 0.00215 (0.00806) 0.61470* (0.31196) 0.02222 (0.02828) 0.21078* (0.05031) -0.00420* (0.00122) 0.83631** (0.43537) 0.821 Probit (4.28807) 0.10241* (0.02116) 2.93726* (0.70578) -0.11234* (0.02935) 4.35120* (0.18630) 0.00081 (0.00293) 0.28785* (0.12559) 0.00522 (0.01387) 0.08751* (0.02085) -0.00174* (0.00051) 0.36103* (0.16757) 0.823

LPM (0.05976) 0.00015 (0.00485) 0.00022 (0.00019) 0.00625 (0.01104) -0.00013 (0.00053) 0.93948* (0.00957) -0.00004 (0.00018) 0.00851* (0.00425) 0.00005 (0.00052) 0.00182* (0.00042) -0.00003* (0.00001) 0.00078 (0.00599) 0.860

Model 3 Logit -46.87984* (8.36244) -0.00929 (0.15471) 0.01064 (0.00657) 5.85873* (1.36981) -0.22381* (0.05704) 8.26998* (0.43542) 0.00074 (0.00743) 0.66539* (0.31308) 0.01423 (0.03317) 0.19421* (0.04717) -0.00386* (0.00114) 0.75074** (0.43868) 0.822

Probit -23.15938* (4.13671) 0.01004 (0.07347) 0.00427 (0.00310) 2.91823* (0.68140) -0.11206* (0.02840) 4.29171* (0.18177) 0.00020 (0.00278) 0.30939* (0.12763) 0.00194 (0.01511) 0.08089* (0.01957) -0.00160* (0.00047) 0.33702* (0.17134) 0.823

Note: Huber/White robust standard errors (S.E.) are in parentheses; * and ** indicate that the coefficients are statistically significant at the 5% level and 10% levels, respectively.

R2/Pseudo R2

Skilled_Labour

Age2

Age

R&D

Municipal_ Area

Foreign_Own

Government_Assist

Productivity2

Productivity

Size2

Size

Constant

Model

Table 9: Export Participation Equations for Thai manufacturing SMEs Classified in Group 6(N=3,628)

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27


LPM -0.08998* (0.02678) 0.00321* (0.00130) 0.00523* (0.00219) 0.94076* (0.01865) 0.00006 (0.00030) 0.01521* (0.00758) -0.00090 (0.00172) -0.00034 (0.00028) -0.00243 (0.01086) 0.829

Model 1 Logit -9.91568* (1.15128) 0.16898* (0.06505) 0.27266* (0.09009) 7.52837* (0.66891) -0.00047 (0.01160) 0.86173** (0.45841) -0.06534 (0.07455) -0.02258 (0.01767) -0.05945 (0.57037) 0.761 Probit -4.87642* (0.51774) 0.08242* (0.02967) 0.12544* (0.04231) 3.94073* (0.28011) -0.00039 (0.00438) 0.36041* (0.17947) -0.02481 (0.03462) -0.01013 (0.00727) -0.02943 (0.23050) 0.762

LPM -0.27670* (0.11610) 0.00321* (0.00133) 0.03807* (0.01885) (0.00085) 0.94059* (0.01862) 0.00010 (0.00030) 0.01494* (0.00757) -0.00057 (0.00174) -0.00007 (0.00069) -0.00001 (0.00001) -0.00346 (0.01084) 0.830

Model 2 Logit (12.89421 0.18488* (0.07314) 4.80277* (2.07325) -0.18864* (0.08702) 7.77882* (0.80376) -0.00001 (0.01285) 0.90483* (0.46188) -0.02681 (0.08351) 0.00513 (0.05327) -0.00092 (0.00116) -0.31659 (0.57733) 0.766 Probit -18.23636* (5.20611) 0.08433* (0.03216) 2.35416* (0.83154) -0.09251* (0.03464) 4.00866* (0.30686) -0.00010 (0.00444) 0.38533* (0.18109) -0.00831 (0.03634) 0.00185 (0.02176) -0.00039 (0.00048) -0.13323 (0.23199) 0.767

LPM -0.27408* (0.10943) 0.00203 (0.00404) 0.00005 (0.00020) 0.03895* (0.02133) -0.00148 (0.00098) 0.94010* (0.01836) 0.00010 (0.00030) 0.01499* (0.00757) -0.00060 (0.00175) -0.00008 (0.00070) -0.00001 (0.00001) -0.00363 (0.01062) 0.830

Model 3 Logit -37.09832* (12.67210) 0.17234 (0.19393) 0.00054 (0.00956) 4.81317* (2.18478) -0.18922* (0.09364) 7.77039* (0.75899) -0.00001 (0.01282) 0.90515* (0.46109) -0.02722 (0.08363) 0.00520 (0.05294) -0.00093 (0.00116) -0.31939 (0.56374) 0.766

Probit -18.38370* (4.99078) 0.11797 (0.10362) -0.00133 (0.00437) 2.33956* (0.84996) -0.09165* (0.03586) 4.02858* (0.29266) -0.00006 (0.00448) 0.38438* (0.18142) -0.00753 (0.03642) 0.00159 (0.02169) -0.00038 (0.00048) -0.12756 (0.22910) 0.768

Note: Huber/White robust standard errors (S.E.) are in parentheses; * and ** indicate that the coefficients are statistically significant at the 5% level and 10% levels, respectively.

R2/Pseudo R2

Skilled_Labour

Age2

Age

R&D

Municipal_ Area

Foreign_Own

Government_Assis

Productivity2

Productivity

Size2

Size

Constant

Model

Table 10: Export Participation Equations for Thai manufacturing SMEs Classified in Group 7(N=3,628)

28 NIDA Economic Review


LPM -0.09619* (0.01163) 0.00388* (0.00060) 0.00458* (0.00094) 0.90875* (0.01129) 0.00135* (0.00042) 0.01629* (0.00311) 0.00436** (0.00243) 0.00035** (0.00019) 0.02399* (0.00411) 0.742

Model 1 Logit -13.81564* (0.99609) 0.36176* (0.06489) 0.30982* (0.08160) 7.30124* (0.53216) 0.03815* (0.00991) 1.01762* (0.21694) 0.11402* (0.02766) 0.00183 (0.01033) 1.33846* (0.26570) 0.722 Probit -6.21841* (0.45633) 0.16047* (0.02901) 0.12569* (0.03533) 3.65462* (0.20680) 0.01632* (0.00675) 0.37228* (0.08751) 0.05261* (0.01526) 0.00218 (0.00452) 0.53931* (0.11111) 0.718

LPM 0.02774 (0.02349) 0.00367* (0.00059) -0.02268* (0.00564) 0.00143* (0.00033) 0.90415* (0.01156) 0.00134* (0.00042) 0.01632* (0.00312) 0.00421** (0.00238) 0.00106* (0.00037) -0.00002* (0.00001) 0.02179* (0.00407) 0.743

Model 2 Logit -11.81662* (4.00832) 0.34810* (0.06394) -0.06932 (0.71250) 0.01673 (0.03137) 7.25657* (0.52863) 0.03952* (0.00923) 1.02503* (0.21537) 0.11243* (0.02762) 0.05129** (0.03071) -0.00125** (0.00074) 1.33523* (0.26703) 0.723 Probit -4.71237* (1.07060) 0.15556* (0.02899) -0.16118 (0.19020) 0.01319 (0.00871) 3.63791* (0.20607) 0.01653* (0.00690) 0.38124* (0.08806) 0.05151* (0.01503) 0.01742 (0.01144) -0.00038 (0.00026) 0.53021* (0.10947) 0.720

LPM 0.07535* (0.02309) -0.00795* (0.00171) 0.00059* (0.00011) -0.02046* (0.00558) 0.00125* (0.00033) 0.89329* (0.01240) 0.00131* (0.00042) 0.01625* (0.00311) 0.00411** (0.00233) 0.00083* (0.00036) -0.00001* (0.00000) 0.01677* (0.00426) 0.744

Model 3 Logit -19.33469* (6.68331) 1.47521** (0.85796) -0.03841 (0.02870) -0.17908 (0.67325) 0.02165 (0.02969) 7.26466* (0.53095) 0.03974* (0.00909) 1.00875* (0.21277) 0.12268* (0.02982) 0.05351** (0.03092) -0.00126** (0.00075) 1.25143* (0.25847) 0.723

Probit -5.65697* (2.33333) 0.29586 (0.31982) -0.00489 (0.01091) -0.16958 (0.19272) 0.01360 (0.00883) 3.64218* (0.20556) 0.01658* (0.00690) 0.37939* (0.08738) 0.05277* (0.01551) 0.01769 (0.01147) -0.00038 (0.00026) 0.52002* (0.10689) 0.720

Note: Huber/White robust standard errors (S.E.) are in parentheses; * and ** indicate that the coefficients are statistically significant at the 5% level and 10% levels, respectively.

R2/Pseudo R2

Skilled_Labour

Age2

Age

R&D

Municipal_ Area

Foreign_Own

Government_Assist

Productivity2

Productivity

Size2

Size

Constant

Model

Table 11: Export Participation Equations for Thai manufacturing SMEs Classified in Group 8(N=6,448)

NIDA Economic Review

29


LPM 0.00221* -0.00018 0.00146* -0.00019 0.95426* -0.00294 0.00033* -0.00008 0.00708* -0.00081 0.00094* -0.00026 0.00035* -0.00005 0.00689* -0.00102 0.819

Model 1 Logit 0.00181* -0.00015 0.00170* -0.00019 0.94603* -0.0077 0.00007* -0.00002 0.00675* -0.00073 0.00016* -0.00006 0.00015* -0.00002 0.00588* -0.00084 0.779 Probit 0.00238* -0.00021 0.00205* -0.00025 0.93924* -0.00715 0.00010* -0.00003 0.00776* -0.00083 0.00028* -0.00009 0.00019* -0.00003 0.00727* -0.00111 0.78

LPM 0.00203* -0.00018 -0.00570* -0.00092 0.00037* -0.00006 0.95280* -0.00298 0.00032* -0.00008 0.00735* -0.00081 0.00091* -0.00025 0.00086* -0.00009 -0.00001* -0.000002 0.00596* -0.00103 0.819

Model 2 Logit 0.00139* -0.00014 0.00888* -0.00155 -0.00032* -0.00007 0.93773* -0.00911 0.00007* -0.00002 0.00495* -0.00065 0.00018* -0.00005 0.00047* -0.00006 -0.00001* -0.000001 0.00457* -0.00077 0.781 Probit 0.00201* -0.00021 0.00763* -0.00259 -0.00025** -0.00011 0.93431* -0.00818 0.00010* -0.00003 0.00652* -0.00088 0.00030* -0.00008 0.00062* -0.0001 -0.00001* -0.000002 0.00635* -0.00116 0.781

LPM -0.00344* -0.00057 0.00026* -0.00003 -0.00419* -0.00093 0.00025* -0.00006 0.94920* -0.00307 0.00027* -0.00008 0.00748* -0.00081 0.00082* -0.00025 0.00075* -0.00009 -0.00001* -0.000002 0.00438* -0.00104 0.82

Model 3 Logit -0.00005 -0.00049 0.00006* -0.00002 0.00915* -0.0015 -0.00035* -0.00007 0.92983* -0.00948 0.00006* -0.00002 0.00509* -0.00066 0.00016* -0.00005 0.00045* -0.00006 -0.00001* -0.000001 0.00447* -0.00077 0.782

Probit -0.00073 -0.00094 0.00010* -0.00004 0.00882* -0.00251 -0.00032* -0.00011 0.92988* -0.00824 0.00009* -0.00003 0.00677* -0.00089 0.00028* -0.00008 0.00060* -0.00009 -0.00001* -0.000002 0.00624* -0.0011 0.782

Note: * and ** indicate that the coefficients are statistically significant at the 5% level and 10% level, respectively; for the probit model (*), dF/dx is for the discrete change of the dummy variable from 0 to 1; For the logit model (*), dy/dx for factor levels is the discrete change from the base level.

R2/Pseudo R2

Skilled_Labour

Age2

Age

R&D

Municipal_ Area

Foreign_Own

Government_Assist

Productivity2

Productivity

Size2

Size

Models

Table 12: Marginal Effect of Export Participation for Total Thai Manufacturing SMEs (N=65,111)

30 NIDA Economic Review


LPM 0.00089* -0.00034 0.00104* -0.00032 0.97330* -0.00662 0.0002 -0.00019 0.00113 -0.00118 0.00143* -0.00065 0.00006 -0.00005 0.00636* -0.00179 0.856

Model 1 Logit 0.00060* -0.0002 0.00064* -0.00023 0.93703* -0.0289 0.00003 -0.00003 0.00119** -0.0007 0.00011* -0.00005 0.00001 -0.00002 0.00334* -0.00127 0.822 Probit 0.00060** -0.0003 0.00083* -0.00032 0.94704* -0.02228 0.00005 -0.00005 0.00146** -0.00085 0.00022* -0.00009 0.00002 -0.00002 0.00470* -0.00167 0.823

LPM 0.00077* -0.00034 -0.00467* -0.00151 0.00029* -0.00009 0.97202* -0.00672 0.00018 -0.00019 0.00136 -0.00115 0.00149* -0.00065 0.00039* -0.00011 -0.00001* -0.000002 0.00546* -0.00179 0.856

Model 2 Logit 0.00052* -0.00019 -0.00031 -0.00115 0.00004 -0.00005 0.94562* -0.02804 0.00002 -0.00003 0.00105 -0.00066 0.00019* -0.00006 0.00021* -0.00007 -0.000004* -0.000001 0.00325* -0.00122 0.825 Probit 0.00051 -0.00028 -0.00120** -0.00081 0.00009* -0.00004 0.95220* -0.02127 0.00004 -0.00005 0.00136** -0.0008 0.00030* -0.00009 0.00029* -0.00009 -0.00001* -0.000002 0.00437* -0.00144 0.826

LPM -0.00384* -0.00134 0.00022* -0.00006 -0.00325* -0.00141 0.00019* -0.00009 0.96774* -0.00696 0.00016 -0.00019 0.00119 -0.00114 0.00139* -0.00064 0.00034* -0.00011 -0.00001* -0.000002 0.00388* -0.0017 0.857

Model 3 Logit -0.00095* -0.00044 0.00006* -0.00002 0.0001 -0.00119 0.00001 -0.00005 0.94127* -0.02901 0.00002 -0.00003 0.00115** -0.00065 0.00015* -0.00005 0.00019* -0.00006 -0.000004* -0.000001 0.00293* -0.00094 0.828

Probit -0.00123* -0.00044 0.00007* -0.00002 -0.00035 -0.00094 0.00003 -0.00004 0.94118* -0.02298 0.00004 -0.00004 0.00131** -0.00071 0.00023* -0.00007 0.00023* -0.00007 -0.000005* -0.000001 0.00344* -0.00098 0.826

Note: * and ** indicate that the coefficients are statistically significant at the 5% level and 10% level, respectively; for the probit model (*), dF/dx is for the discrete change of the dummy variable from 0 to 1; For the logit model (*), dy/dx for factor levels is the discrete change from the base level.

R2/Pseudo R2

Skilled_Labour

Age2

Age

R&D

Municipal_ Area

Foreign_Own

Government_Assist

Productivity2

Productivity

Size2

Size

Models

Table 13: Marginal Effect of Export Participationfor Thai manufacturing SMEs Classified in Group 1 (N=14,929)

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31


Model 2 Logit 0.27793* -0.04084 2.02243* -0.92845 -0.06709** -0.03993 7.85140* -0.47921 0.02879* -0.00696 0.74032* -0.21135 -0.01922 -0.036 0.09574* -0.0189 -0.00140* -0.00037 0.45517** -0.25019 0.732 Probit 0.13364* -0.01895 0.77060* -0.35815 -0.02452 -0.01562 3.96334* -0.17326 0.01503* -0.00351 0.31602* -0.08501 -0.01488 -0.01526 0.04031* -0.00826 -0.00059* -0.00017 0.15685 -0.10526 0.736

LPM -0.00568* -0.00131 0.00041* -0.00008 -0.01478* -0.00267 0.00099* -0.00017 0.91654* -0.0096 0.00138* -0.00044 0.00620* -0.0022 -0.00065* -0.00028 0.00119* -0.00026 -0.00002* -0.00001 0.00202 -0.00233 0.74

Model 3 Logit 0.13365 -0.13436 0.00552 -0.00528 2.00881* -0.92425 -0.06727** -0.03979 7.77463* -0.46375 0.02847* -0.00702 0.76063* -0.20858 -0.031 -0.03079 0.09428* -0.01883 -0.00138* -0.00036 0.45906** -0.25009 0.733 Probit 0.08848 -0.08321 0.00166 -0.0031 0.77294* -0.35823 -0.02476 -0.01563 3.95197* -0.17172 0.01492* -0.00352 0.32100* -0.08461 -0.01774 -0.01334 0.04009* -0.00827 -0.00059* -0.00017 0.15826 -0.10539 0.736

Note: * and ** indicate that the coefficients are statistically significant at the 5% level and 10% level, respectively; for the probit model (*), dF/dx is for the discrete change of the dummy variable from 0 to 1; For the logit model (*), dy/dx for factor levels is the discrete change from the base level.

R2/Pseudo R2

Skilled_Labour

Age2

Age

R&D

Municipal_ Area

Foreign_Own

Government_Assist

Productivity2

Productivity

Size2

Size

LPM 0.00292* -0.00041 -0.01613* -0.0027 0.00111* -0.00018 0.92217* -0.00924 0.00147* -0.00044 0.00630* -0.0022 -0.00049** -0.00029 0.00139* -0.00026 -0.00002* -0.00001 0.00410** -0.00226 0.74

Models Probit 0.13844* -0.01883 0.21390* -0.02727 3.94475* -0.16304 0.01417* -0.00372 0.33033* -0.08419 -0.01462 -0.01617 0.01504* -0.00293 0.16361 -0.10414 0.733

Table 14: Marginal Effect of Export Participation for Thai manufacturing SMEs Classified in Group 2(N=12,721)

Model 1 Logit 0.28876* -0.04189 0.49934* -0.0608 7.69021* -0.41637 0.02743* -0.00762 0.78361* -0.20857 -0.01552 -0.03852 0.03353* -0.00622 0.45196** -0.24765 0.729

NIDA Economic Review

LPM 0.00341* -0.00042 0.00300* -0.00044 0.92751* -0.00896 0.00153* -0.00044 0.00715* -0.00219 -0.00026 -0.00029 0.00074* -0.00015 0.00650* -0.00224 0.738

32


0.94033* -0.02954 -0.00002 -0.00003 0.00001 -0.00114 0.00005 -0.00004 0.00007 -0.00005 0.00082 -0.0016 0.841

LPM 0.00150* -0.00036 0.00037 -0.00042

0.97166* -0.0087 0.00004 -0.0001 -0.00071 -0.00192 0.0001 -0.00018 0.00017 -0.00011 0.00199 -0.00227 0.8

Model 1 Logit 0.00141* -0.00034 0.00068 -0.00042

0.95126* -0.0209 -0.00002 -0.00005 -0.00029 -0.00147 0.00006 -0.00007 0.00011** -0.00007 0.00097 -0.00212 0.8

Probit 0.00189* -0.00042 0.00044 -0.00051

LPM 0.00135* -0.00033 -0.0008 -0.00167 0.00008 -0.00011 0.97147* -0.00874 0.00003 -0.0001 -0.00076 -0.00193 0.00009 -0.00018 0.00051* -0.00021 -0.00001 -0.000004 0.0016 -0.00238 0.841

Model 2 Logit 0.00108* -0.0004 0.00557* -0.0025 -0.00024* -0.00011 0.93500* -0.03255 -0.00001 -0.00002 -0.00026 -0.00088 0.00003 -0.00004 0.00027** -0.00015 -0.000005 -0.000003 0.0005 -0.00127 0.802 Probit 0.00146* -0.00051 0.00684 -0.00377 -0.00029 -0.00016 0.94834* -0.02298 0.00001 -0.00004 -0.00058 -0.00119 0.00004 -0.00006 0.00034** -0.0002 -0.00001 -0.000004 0.00063 -0.0018 0.801

LPM -0.00258* -0.00087 0.00019* -0.00006 -0.00001 -0.00164 0.00001 -0.00011 0.96851* -0.00907 0.00001 -0.0001 -0.00097 -0.00194 0.0001 -0.00018 0.00040* -0.0002 -0.00001** -0.000004 0.00021 -0.00244 0.841

Model 3 Logit 0.00308 -0.00206 -0.00007 -0.00007 0.00385 -0.00301 -0.00016 -0.00013 0.91672* -0.04995 0.00001 -0.00002 -0.00034 -0.0007 0.00003 -0.00003 0.00021 -0.00014 -0.000004 -0.000003 0.00018 -0.00109 0.802 Probit 0.00264 -0.00336 -0.00004 -0.00012 0.00604 -0.00418 -0.00026 -0.00018 0.94500* -0.02777 0.00001 -0.00004 -0.0006 -0.00111 0.00004 -0.00005 0.00031** -0.00021 -0.00001 -0.000004 0.00048 -0.00182 0.801

Note: * and ** indicate that the coefficients are statistically significant at the 5% level and 10% level, respectively; for the probit model (*), dF/dx is for the discrete change of the dummy variable from 0 to 1; For the logit model (*), dy/dx for factor levels is the discrete change from the base level.

R2/Pseudo R2

Skilled_Labour

Age2

Age

R&D

Municipal_ Area

Foreign_Own

Government_Assist

Productivity2

Productivity

Size2

Size

Models

Table 15: Marginal Effect of Export Participation for Thai manufacturing SMEs Classified in Group 3(N=7,710)

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33


LPM 0.00339* -0.00071 -0.00085 -0.00075 0.95293* -0.00653 0.00029* -0.00013 0.00844* -0.00376 0.00141* -0.0007 0.00104* -0.00026 0.00609 -0.00426 0.865

Model 1 Logit 0.01173* -0.00216 -0.00098 -0.00231 0.96952* -0.00823 0.00036* -0.00016 0.02206* -0.00938 0.00098* -0.00044 0.00123* -0.00024 0.01971** -0.01087 0.812 Probit 0.01380* -0.00255 -0.00062 -0.0027 0.96759* -0.00828 0.00049* -0.00021 0.02355* -0.00996 0.00140** -0.00072 0.00162* -0.00032 0.02286** -0.01234 0.813

LPM 0.00321* -0.00071 0.00111 -0.00234 -0.00012 -0.00014 0.95289* -0.0067 0.00030* -0.00012 0.00799* -0.0037 0.00144* -0.00071 0.00196* -0.00046 -0.00002* -0.00001 0.00581 -0.00426 0.865

Model 2 Logit 0.00776* -0.00154 0.05271* -0.01521 -0.00228* -0.00064 0.96978* -0.01024 0.00030* -0.00012 0.01198** -0.00613 0.00063* -0.00031 0.00286* -0.00072 -0.00004* -0.00001 0.01342** -0.0076 0.816 Probit 0.00987* -0.00201 0.06912* -0.01906 -0.00297* -0.00079 0.96732* -0.01006 0.00045* -0.00018 0.01474* -0.00748 0.00104* -0.00055 0.00352* -0.00091 -0.00004* -0.00002 0.01756** -0.00984 0.816

LPM -0.00423* -0.00161 0.00032* -0.00009 0.00247 -0.00239 -0.00023 -0.00015 0.94967* -0.00707 0.00026* -0.00013 0.00868* -0.00371 0.00138* -0.0007 0.00185* -0.00045 -0.00002* -0.00001 0.00536 -0.00428 0.866

Model 3 Logit 0.00488 -0.00572 0.0001 -0.0002 0.05387* -0.01536 -0.00233* -0.00064 0.96941* -0.01024 0.00029* -0.00013 0.01211* -0.00614 0.00064* -0.00031 0.00287* -0.00073 -0.00004* -0.00001 0.01349** -0.00765 0.816

Probit 0.00577 -0.01075 0.00014 -0.00035 0.07123* -0.02024 -0.00306* -0.00085 0.96727* -0.00998 0.00046* -0.00018 0.01498* -0.00755 0.00105* -0.00056 0.00355* -0.00093 -0.00004* -0.00002 0.01784** -0.00991 0.816

Note: * and ** indicate that the coefficients are statistically significant at the 5% level and 10% level, respectively; for the probit model (*), dF/dx is for the discrete change of the dummy variable from 0 to 1; For the logit model (*), dy/dx for factor levels is the discrete change from the base level.

R2/Pseudo R2

Skilled_Labour

Age2

Age

R&D

Municipal_ Area

Foreign_Own

Government_Assist

Productivity2

Productivity

Size2

Size

Models

Table 16: Marginal Effect of Export Participation for Thai manufacturing SMEs Classified in Group 4(N=5,099)

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LPM 0.00167* -0.00036 0.00191* -0.00049 0.96868* -0.00691 0.00012 -0.00009 0.00600* -0.00139 0.00066 -0.00046 0.00044* -0.0001 0.00028 -0.0021 0.827

Model 1 Logit 0.00115* -0.00026 0.00117* -0.0003 0.97248* -0.01381 0.00001 -0.00002 0.00515* -0.00123 0.00016** -0.00008 0.00018* -0.00003 0.00089 -0.00148 0.793 Probit 0.00145* -0.00036 0.00139* -0.00043 0.96531* -0.01366 0.00003 -0.00004 0.00582* -0.00147 0.00022** -0.00013 0.00022* -0.00005 0.00179 -0.00187 0.793

LPM 0.00153* -0.00036 -0.00719 -0.0045 0.00043** -0.00023 0.96793* -0.00698 0.00011 -0.00009 0.00620* -0.0014 0.00066 -0.00046 0.00073* -0.0002 -0.00001 -0.00001 0.00004 -0.00209 0.827

Model 2 Logit 0.00093* -0.00021 0.00564* -0.0028 -0.00019 -0.00012 0.97118* -0.01468 0.00001 -0.00002 0.00409* -0.00111 0.00014** -0.00007 0.00044* -0.00013 -0.00001* -0.000003 0.00055 -0.00123 0.795 Probit 0.00121** -0.00031 0.00599 -0.00364 -0.0002 -0.00016 0.96270* -0.01491 0.00004 -0.00004 0.00505* -0.00147 0.00021** -0.00012 0.00053* -0.00016 -0.00001* -0.000003 0.00141 -0.0017 0.795

LPM -0.00442* -0.00115 0.00028* -0.00006 -0.00392 -0.00449 0.00022 -0.00023 0.96429* -0.00713 0.00006 -0.00009 0.00635* -0.00141 0.00058 -0.00045 0.00058* -0.0002 -0.00001 -0.00001 -0.00188 -0.00212 0.828

Model 3 Logit -0.00120** -0.00065 0.00008* -0.00003 0.00643* -0.00281 -0.00024* -0.00012 0.96134* -0.01655 0.00001 -0.00002 0.00403* -0.00108 0.00012 -0.00008 0.00040* -0.00012 -0.00001* -0.000003 0.00069 -0.00119 0.798

Probit -0.00181* -0.00071 0.00011* -0.00003 0.00778* -0.0034 -0.00030** -0.00014 0.95638* -0.01647 0.00002 -0.00003 0.00484* -0.00126 0.00017 -0.00012 0.00046* -0.00014 -0.00001* -0.000003 0.00114 -0.00141 0.78

Note: * and ** indicate that the coefficients are statistically significant at the 5% level and 10% level, respectively; for the probit model (*), dF/dx is for the discrete change of the dummy variable from 0 to 1; For the logit model (*), dy/dx for factor levels is the discrete change from the base level.

R2/Pseudo R2

Skilled_Labour

Age2

Age

R&D

Municipal_ Area

Foreign_Own

Government_Assist

Productivity2

Productivity

Size2

Size

Models

Table 17: Marginal Effect of Export Participation for Thai manufacturing SMEs Classified in Group 5(N=18,037)

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LPM 0.00516* -0.00142 0.00409* -0.00137 0.94259* -0.00962 -0.00002 -0.00018 0.00799** -0.00422 0.0002 -0.00052 0.00073* -0.00023 0.0033 -0.00623 0.86

Model 1 Logit 0.00555* -0.00143 0.00834* -0.00188 0.96113* -0.01125 -0.00002 -0.00019 0.01653* -0.00791 0.00053 -0.00071 0.00090* -0.00026 0.02337* -0.00887 0.812 Probit 0.00790* -0.00161 0.01028* -0.00245 0.95819* -0.01169 -0.00004 -0.00019 0.01996* -0.00832 0.00049 -0.00093 0.00122* -0.00032 0.02683* -0.01024 0.814

LPM 0.00493* -0.00143 0.00519 -0.01082 -0.00005 -0.00052 0.94193* -0.00967 0 -0.00018 0.00784** -0.0042 0.00014 -0.00052 0.00191* -0.00046 -0.00003* -0.00001 0.00263 -0.00621 0.86

Model 2 Logit 0.00311* -0.00086 0.08944* -0.02604 -0.00340* -0.00107 0.95696* -0.01475 0.00003 -0.00012 0.00893** -0.00513 0.00033 -0.0004 0.00311* -0.00078 -0.00006* -0.00002 0.01235* -0.0059 0.821 Probit 0.00435* -0.00103 0.12464* -0.02985 -0.00477* -0.00123 0.95127* -0.01532 0.00003 -0.00013 0.01197* -0.00572 0.00022 -0.00058 0.00371* -0.00089 -0.00007* -0.00002 0.01532* -0.00689 0.823

LPM 0.00015 -0.00485 0.00022 -0.00019 0.00625 -0.01104 -0.00013 -0.00053 0.93948* -0.00957 -0.00004 -0.00018 0.00851* -0.00425 0.00005 -0.00052 0.00182* -0.00042 -0.00003* -0.00001 0.00078 -0.00599 0.86

Model 3 Logit -0.00013 -0.00214 0.00015 -0.00009 0.08130* -0.0236 -0.00311* -0.00096 0.94753* -0.01738 0.00001 -0.0001 0.00908** -0.0048 0.0002 -0.00045 0.00269* -0.00064 -0.00005* -0.00002 0.01042** -0.0057 0.822

Probit 0.00041 -0.00298 0.00017 -0.00012 0.11821* -0.029 -0.00454* -0.00119 0.94400* -0.01712 0.00001 -0.00011 0.01228* -0.00553 0.00008 -0.00061 0.00328* -0.00077 -0.00006* -0.00002 0.01365* -0.00683 0.823

Note: * and ** indicate that the coefficients are statistically significant at the 5% level and 10% level, respectively; for the probit model (*), dF/dx is for the discrete change of the dummy variable from 0 to 1; For the logit model (*), dy/dx for factor levels is the discrete change from the base level.

R2/Pseudo R2

Skilled_Labour

Age2

Age

R&D

Municipal_ Area

Foreign_Own

Government_Assist

Productivity2

Productivity

Size2

Size

Models

Table 18: Marginal Effect of Export Participation for Thai manufacturing SMEs Classified in Group 6(N=3,628)

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LPM 0.00321* -0.0013 0.00523* -0.00219 0.94076* -0.01865 0.00006 -0.0003 0.01521* -0.00758 -0.0009 -0.00172 -0.00034 -0.00028 -0.00243 -0.01086 0.829

Model 1 Logit 0.00630* -0.00302 0.01017* -0.00324 0.95202* -0.01856 -0.00002 -0.00043 0.03175* -0.01574 -0.00244 -0.0028 -0.00084 -0.0007 -0.00222 -0.02119 0.761 Probit 0.00800* -0.00335 0.01217* -0.00407 0.94727* -0.02053 -0.00004 -0.00042 0.03435* -0.01618 -0.00241 -0.00334 -0.00098 -0.00073 -0.00285 -0.02233 0.762

LPM 0.00321* -0.00133 0.03807* -0.01885 -0.00143** -0.00085 0.94059* -0.01862 0.0001 -0.0003 0.01494* -0.00757 -0.00057 -0.00174 -0.00007 -0.00069 -0.00001 -0.00001 -0.00346 -0.01084 0.83

Model 2 Logit 0.00607* -0.00283 0.15775* -0.07567 -0.00620* -0.00317 0.95693* -0.02067 0.0000004 -0.00042 0.02939* -0.01355 -0.00088 -0.00272 0.00017 -0.00175 -0.00003 -0.00004 -0.0104 -0.01861 0.766 Probit 0.00703* -0.00295 0.19637* -0.07207 -0.00772* -0.00301 0.94832* -0.02287 -0.00001 -0.00037 0.03158* -0.01359 -0.00069 -0.00301 0.00015 -0.00181 -0.00003 -0.00004 -0.01111 -0.01918 0.767

LPM 0.00203 -0.00404 0.00005 -0.0002 0.03895** -0.02133 -0.00148 -0.00098 0.94010* -0.01836 0.0001 -0.0003 0.01499* -0.00757 -0.0006 -0.00175 -0.00008 -0.0007 -0.00001 -0.00001 -0.00363 -0.01062 0.83

Model 3 Logit 0.00566 -0.00625 0.00002 -0.00031 0.15798* -0.07822 -0.00621** -0.00335 0.95669* -0.01932 0.0000004 -0.00042 0.02938* -0.01354 -0.00089 -0.00273 0.00017 -0.00174 -0.00003 -0.00004 -0.01048 -0.01822 0.766

Probit 0.00985 -0.00865 -0.00011 -0.00036 0.19538* -0.07357 -0.00765* -0.00311 0.94990* -0.02093 0.0000005 -0.00037 0.03153* -0.01364 -0.00063 -0.00303 0.00013 -0.00181 -0.00003 -0.00004 -0.01065 -0.01898 0.768

Note: * and ** indicate that the coefficients are statistically significant at the 5% level and 10% level, respectively; for the probit model (*), dF/dx is for the discrete change of the dummy variable from 0 to 1; For the logit model (*), dy/dx for factor levels is the discrete change from the base level.

R2/Pseudo R2

Skilled_Labour

Age2

Age

R&D

Municipal_ Area

Foreign_Own

Government_Assist

Productivity2

Productivity

Size2

Size

Models

Table 19: Marginal Effect of Export Participation for Thai manufacturing SMEs Classified in Group 7(N=3,628)

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LPM 0.00388* -0.0006 0.00458* -0.00094 0.90875* -0.01129 0.00135* -0.00042 0.01629* -0.00311 0.00436** -0.00243 0.00035** -0.00019 0.02399* -0.00411 0.742

Model 1 Logit 0.00401* -0.00075 0.00343* -0.00094 0.90865* -0.03718 0.00042* -0.00013 0.01200* -0.00236 0.00126* -0.00033 0.00002 -0.00011 0.01483* -0.00342 0.722 Probit 0.00546* -0.00093 0.00427* -0.00123 0.88114* -0.03773 0.00055* -0.00025 0.01319* -0.00274 0.00179* -0.00054 0.00007 -0.00016 0.01834* -0.00437 0.718

LPM 0.00367* -0.00059 -0.02268* -0.00564 0.00143* -0.00033 0.90415* -0.01156 0.00134* -0.00042 0.01632* -0.00312 0.00421** -0.00238 0.00106* -0.00037 -0.00002* -0.00001 0.02179* -0.00407 0.743

Model 2 Logit 0.00392* -0.00079 -0.00078 -0.00807 0.00019 -0.00037 0.90650* -0.03782 0.00044* -0.00013 0.01229* -0.00256 0.00127* -0.00034 0.00058** -0.00035 -0.00001** -0.00001 0.01503* -0.00355 0.723 Probit 0.00545* -0.00091 -0.00564 -0.00682 0.00046 -0.00032 0.88036* -0.03798 0.00058* -0.00026 0.01392* -0.00289 0.00180* -0.00055 0.00061 -0.00041 -0.00001 -0.00001 0.01856* -0.00433 0.72

LPM -0.00795* -0.00171 0.00059* -0.00011 -0.02046* -0.00558 0.00125* -0.00033 0.89329* -0.0124 0.00131* -0.00042 0.01625* -0.00311 0.00411** -0.00233 0.00083* -0.00036 -0.00001* -0.000005 0.01677* -0.00426 0.744

Model 3 Logit 0.01250* -0.00441 -0.00033* -0.00017 -0.00152 -0.00573 0.00018 -0.00026 0.88342* -0.05336 0.00034* -0.00013 0.00909* -0.00294 0.00104* -0.00033 0.00045 -0.00028 -0.00001 -0.00001 0.01060* -0.00395 0.723

Probit 0.00965 -0.0085 -0.00016 -0.00032 -0.00553 -0.00633 0.00044 -0.0003 0.87565* -0.0425 0.00054* -0.00027 0.01292* -0.00352 0.00172* -0.00056 0.00058 -0.00038 -0.00001 -0.00001 0.01697* -0.00535 0.72

Note: * and ** indicate that the coefficients are statistically significant at the 5% level and 10% level, respectively; for the probit model (*), dF/dx is for the discrete change of the dummy variable from 0 to 1; For the logit model (*), dy/dx for factor levels is the discrete change from the base level.

R2/Pseudo R2

Skilled_Labour

Age2

Age

R&D

Municipal_ Area

Foreign_Own

Government_Assist

Productivity2

Productivity

Size2

Size

Models

Table 20: Marginal Effect of Export Participation for Thai manufacturing SMEs Classified in Group 8(N=6,448)

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Shown in Table 12-20, the effect of discrete change is also computed in this study due to the use of categorical variables such as government assistance and municipal area in the models. The marginal effects for categorical variables indicate how the response possibility, P(ሺ‫ ݕ‬ൌ ͳȁ‫ݔ‬ሻis predicted to change as ‫ݔ‬௝ alters from 0 to 1, holding all other control variables constant. For all Thai manufacturing SMEs, the magnitude of marginal effects predicted by the LPM, probit and logit models are similar. According to the second model in Table 12, the size of marginal effects predicted by the logit and probit models for firm size are 0.00139 and 0.00201, respectively. These results indicate that a one-unit increase in firm size will produce an increase of 0.00139 and 0.00201 in the probability of success in export participation under the logit and probit models, respectively. The magnitude of marginal effects predicted by the logit and probit models for firm size are 0.00888 and 0.00763, respectively. This evidence shows that a one-unit increase in labour productivity will produce an increase of 0.00888 and 0.00763 in the probability of success in export participation under the logit and probit models, respectively. However, after a certain threshold, a one-unit increase in labour productivity will lead to a decrease of 0.00032 and 0.00025 in the probability of success in export participation. Government assistance plays a significant role in increasing the probability of success in export participation due to the large size of its estimated marginal effect. The marginal effects estimated by the logit and probit models for government assistance are 0.93773 and 0.93431, respectively, indicating the predicted probability of success in export participation is 0.93773 and 0.93431 greater for SMEs that receive government assistance than for ones that do not receive government assistance, respectively. Differentiating SMEs by location in municipal and non-municipal areas, the predicted probability of success in export participation SMEs in municipal areas is 0.00495 and 0.00652 higher than that of SMEs in non-municipal areas under the logit and probit models, respectively. Moreover, the marginal effects estimated by the logit and probit models for research and development (R&D) are 0.00018 and 0.00030, indicating that a one-unit increase in R&D will lead to a 0.00018 and 0.00030 increase in the predicted probability of success in export participation of Thai manufacturing SMEs under the logit and probit models, respectively. Similarly, a one-unit increase in skilled labour will result in a 0.00457 and 0.00635 increase in the probability of success in Thai manufacturing SMEs’ export participation. A one-unit increase in firm age also results in a 0.00047 and 0.00062 increase in the probability in export participation of Thai manufacturing SMEs under the logit and probit models, respectively. However, beyond a certain threshold, a one-unit increase in firm age will lead to a 0.00001 decrease in the probability of success in Thai manufacturing SMEs’ export participation under both logit and probit models. Focusing on sub-manufacturing SMEs, the marginal effects are summarized in Tables 13 and 20. 5. Conclusions and policy implications This study employed the 2007 Thai Industrial Census to empirically examine the determinants for export decisions among 65,111 Thai manufacturing SMEs.The binary variable for export participation is used as the dependent variable. Three limited dependent variable models, (i) the probit model, (ii) the logit model and (iii) the linear probability model, were used to estimate both this study’s linear and non-linear equations. The use of these three models aims to check the


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sensitivity of the study’s results. Focusing on the significant factors affecting a firm’s export decision-making process, firm size, productivity, government assistance, foreign investment (ownership), municipal location, research and development, firm age and skilled labour were found to be significantly and positively related to the decisions of Thai manufacturing SMEs. Furthermore, a significant and positive non-linear relationship between firm size and export decision was also found for these SMEs, while a significant and negative linkage between a firm’s age and its export decision was revealed. With respect to sub-manufacturing sectors, a significant and positive linear association between a firm’s size and its export decision was mainly found among SMEs in groups 2, 3, 4, 6, 7 and 8. The empirical results in groups 1 and 5 were found to be inconclusive due to the difference in significantly estimated signs. Moreover, a significant and positive non-linear relationship between a firm’s size and its export decision was found in groups 1 and 5, but an inconclusive result was found in group 8 due to the difference in significantly estimated signs. The estimated results of other SME groups were found to be statistically insignificant. A significant and positive linear association between productivity and firm export decision was mainly found among SMEs in all groups, except that some significant and negative results estimated by the limited probability model (OLS) were rarely found in groups 1, 2 and 8. A significant and negative non-linear association between productivity and a firm’s export decision was found among SMEs in groups 2, 3, 4, 5, 6 and 7, but a significant and positive non-linear finding was found in groups 1 and 8. Government assistance has a significant and positive association with a firm’s export participation for all SME groups. With respect to the effect of foreign investment (ownership) on a firm’s export decision, a significant and positive result was strongly found in groups 2, 4 and 8, while empirical results in other groups were found to be statistically insignificant. In addition, SMEs in most groups in municipal areas were found to have their locations be a significant and positive effect on their export decisions, except for the finding in group 3, which was found to be statistically insignificant. Focusing on the effect of R&D on a firm’s export decision, a significant and positive finding was found among SMEs in groups 1, 4, 5 and 8. A positive result was also found in groups 3, 6 and 7, but it was statistically insignificant. A significant and negative result estimated by the limited probability model (OLS) was found only in group 2.Moreover, a significant and positive linear relationship between a firm’s age and its export decision was also found in groups 1, 2, 3, 4, 5 and 6, but insignificant results were found in groups 7 and 8. A significant and negative non-linear relationship between a firm’s age and its export decision was also found among SMEs in groups 1, 2, 4, 5, 6 and 8, whereas insignificant results were found in groups 3and 7. Finally, a significant and positive relationship between skilled labour and a firm’s export decision was found among SMEs in groups 1, 2, 4, 6, and 8, but the estimated results of other SME groups were found to be statistically insignificant. According to the empirical results, an increase in firm size should be promoted among Thai manufacturing SMEs including SMEs in groups 2, 3, 4, 6, 7 and 8, since large firms can earn sufficient profits to recover sunk costs incurred during exporting. In addition, larger firms may gain more advantages in collecting market information, launching overseas sales-promotion campaigns, bearing exchange rate and other risks, and adapting their products to foreign markets as suggested by Athukorala et al. (1995). A number of government policies should be


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implemented for Thai SMEs, such as (i) facilitating SMEs to obtain bank loans with affordable interest payments and an increase in equity via listings on the Market for Alternative Investment (MAI) or equity and knowledge aids from the Office of Small and Medium Enterprises, and (ii) encouraging an increase in their investments, which in turn can enlarge firm size. Promoting their investments can be encouraged via tax and non-tax incentives3 approved by the Board of Investment (BOI). These recommendations are aimed at helping Thai manufacturing SMEs reach an adequate size. Beyond a certain threshold, firm size can still be promoted for Thai manufacturing SMEs, including those groups 1 and 5. Labor productivity among Thai manufacturing SMEs should be enhanced, since the most productive firms can compete in highly competitive markets. Greater productivity can mitigate the additional costs associated with entering export markets, such as transportation, marketing and production costs in tailoring existing products for foreign customers. Beyond a certain threshold, it will not help a firm’s export participation for Thai manufacturing SMEs in the aggregate, including SMEs in groups 2, 3, 4, 5, 6 and 7, but training programs for workers and the use of capital to acquire and develop assets such as machinery and equipment might be promoted to maintain a firm’s extant export participation. Similarly, skilled labor should be promoted among Thai manufacturing SMEs including those in groups 1, 2, 4, 6 and 8. Therefore, government policies focused on improving the workplace environment and social welfare are recommended, such as i) launching worker training programs within SMEs4, ii) creating mentoring and consulting systems as well as labour standard certifications within formal and informal systems, iii) upgrading knowledge and skills of entrepreneurs and employees by developing learning mechanisms to improve their capabilities, and iv) providing facilities (e.g., on-the-job training programs, e-learning schools and university programs) to enhance knowledge among employees. Human resource development should also be implemented in line with the real demands of the manufacturing sector and cooperative networks should be built among educational institutions. In addition, government assistance through BOI privileges should be promoted for Thai manufacturing SMEs, including those in all groups, since such aid provides financial support (e.g., credit assistance, income tax exemptions or reductions, and exemptions from import duties on essential raw materials). Moreover, government non-financial support, such as managerial and technical assistance and training support, should be considered in line with financial support. This would contribute positively toward the international competitiveness of Thai manufacturing SMEs’ export performance.

3 Tax incentives are as follows: (i) exemption/reduction of import duties on machinery, (ii) reduction of import duties for raw or essential materials, (ii) exemption of juristic person’s income tax and dividends, (iii) a 50 percent reduction of juristic person’s income tax, (iv) double deductions for the costs of transportation, electricity and water supply, (v) additional 25 percent deduction for the cost of installation or construction of facilities, (vi) exemption from import duty on raw or essential materials for use in production for export. Moreover, non-tax incentives are as follows: (i) permit for foreign nationals to enter the Kingdom for the purpose of studying investment opportunities, (ii) permit to bring into the Kingdom skilled workers and experts to work in investment-promoted activities, (iii) permit to own land, and (iv) permit to take out or remit money abroad in foreign currency (see BOI (2014)). 4 This can be achieved, for example, by allowing worker training program expenditures to be doubled and used as a corporate tax deduction.


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Foreign investment (ownership) should also be promoted for Thai manufacturing SMEs, including those in groups 2, 4 and 8, since foreign partners bring new foreign markets and distribution, financial support, new products, managerial know-how and advanced production, all of which would help SMEs participate in export markets. Moreover, research and development (R&D) should be promoted among Thai manufacturing SMEs including those in groups 1, 4, 5 and 8, since it would contribute positively toward their export participation and competitiveness. Policies that aim to help young manufacturing SMEs to participate in foreign markets are also necessary, such as (i) promoting cross-learning between young firms and old firms, (ii) providing business training for young firms, and (iii) providing tax holidays5 for young firms. Finally, municipal-based Thai manufacturing SMEs have more advantages in terms of transport costs, infrastructure, spillover effects, labor and natural resources. Policies focused on improving the country’s infrastructure and facilities crucial for exporting, therefore, should be promoted for Thai manufacturing SMEs, except for those in group 3. Finally, policy implications addressed in this study are based upon the empirical results obtained from the 2007 Thai Industrial Census. Therefore, in-depth interviews of entrepreneurs or chief executive officers in specific submanufacturing SMEs should be conducted for further studies.


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References Athukorala, P., Jayasuriya, S., and Oczkowski, E. (1995) "Multinational Firms and Export Performance in Developing Countries: Some Analytical Issues and New Empirical Evidence", Journal of Development Economics, 46(1): 109-122. Aggrey, N., Eliab, L. and Joseph, S. (2010) "Determinants of Export Participation in East African Manufacturing Firms", Current Research Journal of Economic Theory, 2(2): 5561. Baldwin, J.R. and Gu, W. (2003) "Export-Market Participation and Productivity Performance in Canadian Manufacturing", The Canadian Journal of Economics, 36(3): 634-657. Bank of Thailand (2012) "Annual Report 2012", Bank of Thailand, retrieved June 2 2012, fromhttp://www.bot.or.th/English/ResearchAndPublications/Reports/DocLib_AnnualEcon Report/AW_BOT%20Final_ENG.pdf Bank of Thailand (2014) "Trade Classified by Country", Bank of Thailandretrieved 19 April, 2009 from http://www.bot.or.th/English/Statistics/ContactPerson/Pages/Contact.aspx Bernard, A.B. and Jensen, J.B. (1999) "Exceptional Exporter Performance: Cause, Effect, or Both?",Journal of International Economics, 47(1), 1-25. Bernard, A.B. and Wagner, J. (1997) "Exports and Success in German Manufacturing", Weltwirtschaftliches Archiv, 133(1): 134-157. BOI (2014).Incentives. Retrieved June 13, 2014, From Thailand Board of Investment Web site http://www.boi.go.th/index.php?page=incenive Brimble, P., Oldfield, D., and Monsakul, M. (2002) "Policies for SME recovery in Thailand". In Harvie, C. and Lee, B.C. eds. The role of SMEs in National Economies in East Asia. Cheltenham: Edward Elgar.

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In Thailand, a tax holiday currently refers to tax privileges that firms obtain from the Board of Investment (BOI).


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Dueñas-Caparas, M.T.S. (2006) "Determinants of Export Performance in the Philippine Manufacturing Sector", Discussion Paper Series, No. 2006-18, Makati City: Philippine Institute for Development Studies. Harvie, C. (2002) "China's SMEs: Their Evolution and Future Prospects in an Evolving Market Economy". In Harvie, C and Lee, B.C. eds.The Role of SMEs in National Economies in East Asia. Cheltenham: Edward Elgar. Lombaerde, P.A.A.D. (2008) "The Paradoxes of Thailand's Pre-crisis Export Performance", Global Economic Review, 37(2): 249-264. Clerides, S., Lach, S. and Tybout, J. (1996) "Is “Learning-by-Exporting” Important? MicroDynamic Evidence from Colombia, Mexico and Morocco", NBER Working Paper Series, 5715, Cambridge: National Bureau of Economic Research. Gujarati, D. (2004) Basic Econometrics, 4th ed., Singapore: The McGraw-Hill. Greenaway, D., Guariglia, A. and Kneller, R. (2007) "Financial Factors and Exporting Decisions", Journal of International Economics, 73(2): 377-395. Hallward-Driemeir, M., Iarossi, G. and Sokoloff, K.L. (2002) "Exports and Manufacturing Productivity in East Asia: A Comparative Analysis with Firm - Level Data", NBER Working Paper Series, 8894, National Bureau of Economic Research, Cambridge, MA. Jongwanich, J. and Kohpaiboon, A. (2008) "Export Performance, Foreign Ownership, and Trade Policy Regime: Evidence from Thai Manufacturing", ADB Economics Working Paper, 140, Manila: Asian Development Bank. OECD (2011) "Thailand: Key Issues and Policies, OECD Studies on SMEs and Entrepreneurship", Organization for Economic Co-operation and Development, retrieved May 1, 2014 from http://browse.oecdbookshop.org/oecd/pdfs/product/8511041e.pdf OSMEP (2012) "The White Paper on Small and Medium Enterprises of Thailand in 2011 and 2012", Office of Small and Medium Enterprises Promotion, retrieved April 17, 2014 from http://www.sme.go.th/Lists/EditorInput/DispForm.aspx?ID=1865 OSMEP (2010)"The White Paper on Small and Medium Enterprises of Thailand in 2010 and Trends 2011",Office of Small and Medium Enterprises Promotion, retrieved April 17, 2014 fromhttp://www.sme.go.th/Lists/EditorInput/DispF.aspx?List=15dca7fb%2Dbf2e%2D464 e%2D97e5%2D440321040570&ID=1430 OSMEP (2009) "The White Paper on Small and Medium Enterprises of Thailand in 2009 and Trends 2010", Office of Small and Medium Enterprises Promotion, retrieved April 17, 2014, retrieved fromhttp://www.sme.go.th/Lists/EditorInput/DispF.aspx?List=15dca7fb%2Dbf2e%2D464 e%2D97e5%2D440321040570&ID=664 OSMEP (2008) "The White Paper on Small and Medium Enterprises of Thailand in 2008 and Trends 2009", Office of Small and Medium Enterprises Promotion, retrieved April 17, 2014, retrieved from http://www.sme.go.th/Lists/EditorInput/DispF.aspx?List=15dca7fb%2Dbf2e%2D464e%2 D97e5%2D440321040570&ID=11 OSMEP (2007a)"The White Paper on Small and Medium Enterprises of Thailand in 2007 and Trends 2008", Office of Small and Medium Enterprises Promotion, retrieved April 17, 2014 , retrievedfrom http://www.sme.go.th/Lists/EditorInput/DispF.aspx?List=15dca7fb%2Dbf2e%2D464e%2 D97e5%2D440321040570&ID=10


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OSMEP (2007b) "The 2nd SMEs Promotion Plan (2007-2011)", Office of Small and Medium Enterprises Promotion, retrieved April 17, 2014 , retrievedfrom http://eng.sme.go.th/SiteCollectionDocuments/SMEs%20Promotion%20Plan/SMEMaster-Plan-2.pdf Punyasavatsut, C. (2007) "SMEs in The Thai Manufacturing Industry: Linking with MNEs", retrieved June 15, 2009 from http: //www.eria.org/research/images/pdf/PDF%20No.5/No,5-10-Thai.pdf Regnier, P. (2000) Small and medium enterprises in distress: Thailand, the East Asian crisis and beyond, Burlington: Ashgate Publishing. Roper, S. and Love, H.J. (2002) "The Determinants of Export Performance Panel Data Evidence fro Irish Manufacturing Plant", RP02024, Birmingham: Aston Business School Research Institute, Aston University. Tapaneeyangkul, P. (2001) Government policies in assisting SMEs for sustainable development, Bangkok: The Office of Small and Medium Enterprise Promotion. Wagner, J. 2005 "Exports and Productivity: A survey of the Evidence from Firm Level Data", Working Paper Series in Economics, 4, Hamburg: Institute of Economics, University of Luenburg. Wooldridge, J.M. (2006) Econometric Analysis of Cross Section and Panel Data, 3rd ed., London: The MIT Press. Wu, C. and Cheng, L.K. (1999), "The Determinants of Export Performance of China's Township-Village Enterprises", Working Paper, Kowloon: Department of Economics, School of Business and Management, Hong Kong University of Science and Technology. Xu, X. and Wang, Y. (1999), "Ownership Structure and Corporate Governance in Chinese Stock Companies", China Economic Review, 10(1): 75-98.


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NIDA Economic Review Appendix 1: Thailand standard industrial classification Division of 15 16 17 18 19 20

21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

Division of Industry Manufacture of food products and beverages Manufacture of tobacco products Manufacture of textiles Manufacture of wearing apparel; dressing and dyeing of fur Tanning and dressing of leather; manufacture of luggage, handbags, saddler, harness and footwear Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials Manufacture of paper and paper products Publishing, printing and reproduction of recorded media Manufacture of coke, refined petroleum products and nuclear Fuel Manufacture of chemicals and chemical products Manufacture of rubber and plastics products Manufacture of other non-metallic mineral products Manufacture of basic metals Manufacture of fabricated metal products, except machinery and equipment Manufacture of machinery and equipment n.e.c. Manufacture of office, accounting and computing machinery Manufacture of electrical machinery and apparatus n.e.c. Manufacture of radio, television and communication equipment and apparatus Manufacture of medical, precision and optical instruments, watches and clocks Manufacture of motor vehicles, trailers and simi-trailers Manufacture of other transport equipment Manufacture of furniture; manufacturing n.e.c. Recycling

Source: The 2007 Thai Industrial Census, National Statistic Office of Thailand

Group 1 1 2 2 2 3

3 3 4 4 4 5 5 5 6 6 6 6 6 7 7 8 8


วารสารเศรษฐศาสตรปริทรรศน สถาบันบัณฑิตพัฒนบริหารศาสตร NIDA Economic 47 ปที่ 9 ฉบับทีReview ่ 1 (มกราคม 2558)

Intrahousehold Bargaining Among Women Workers in Thailand’s Northern Region Industrial Estate Gullinee Mutakalin* Abstract This study specifically evaluated the effects of women’s participation in Export Processing Zones (EPZs) on women’s bargaining power by using Thailand’s Northern Region Industrial Estate (NRIE) as a case study. It explored how EPZ employment affects NRIE women workers’ bargaining power. The empirical findings show that NRIE work provides opportunities for women to be included in formal employment, which brings higher earning to NRIE women workers compared with lower earning of hired women workers. Therefore, NRIE employment decomposes women’s subordination by increasing the economic contribution of women within their households. However, the economic contribution of NRIE women workers does not radically increase NRIE women workers’ intrahousehold bargaining power vis-à-vis their husbands. Household income keeping, control and management as well as household decision making and housework allocation do not tend towards a more egalitarian status in households. Although NIRE women workers are relatively well-off compared to hired women workers, they are more subservient under an age hierarchy in households due to the strong influence of matrilineality and matrifocality.

Keywords:

Intrahousehold, Bargaining power, Women workers, Northern Region Industrial Estate, Thailand

*

Lecturer of Economics– Faculty of Economics, Chulalongkorn University, Phayathai Road, Bangkok 10330, Thailand. Email: Gullinee.M@Chula.ac.th This article is an excerpt from Mutakalin, Gullinee. 2008. The Effect of Women Workers’ Participation in Export Processing Zones on Women’s Bargaining Power in Households: The Case Study of Thailand’s Northern Region Industrial Estate.Ph.D. Dissertation.University of Utah.


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วารสารเศรษฐศาสตรปริทรรศน สถาบันบัณฑิตพัฒนบริหารศาสตร ปที่ 9 ฉบับที่ 1 (มกราคม 2558)

การศึกษาอํานาจตอรองภายในครัวเรือนของแรงงานหญิง: กรณีศึกษาแรงงานหญิงในนิคมอุตสาหกรรมภาคเหนือ จังหวัดลําพูน กุลลินี มุทธากลิน* š‡´—¥n° „µ¦«¹„¬µ·Êœœ¸Ê¤¸ª´˜™»ž¦³­Š‡rÁ¡ºÉ°«¹„¬µŸ¨„¦³š…°Š„µ¦šÎµŠµœÁž}œÂ¦ŠŠµœÄœœ·‡¤°»˜­µ®„¦¦¤š¸É¤¸˜n°°Îµœµ‹ ˜n°¦°Š…°ŠŸ¼o®·ŠÄœ‡¦´ªÁ¦º°œÃ—¥Äo¦ŠŠµœ®·ŠÄœœ·‡¤°»˜­µ®„¦¦¤£µ‡Á®œº° ‹´Š®ª´—¨Îµ¡¼œÁž}œ„¦–¸«¹„¬µ Ÿ¨ „µ¦«¹„¬µ¡ªnµ „µ¦šÎµŠµœÄœœ·‡¤°»˜­µ®„¦¦¤Ážd—ð„µ­Ä®oŸ¼o®·ŠÅ—oÁž}œÂ¦ŠŠµœÄœ¦³Â¨³šÎµÄ®o¦ŠŠµœ®·Š Á®¨nµœ¸Ê¤¸¦µ¥Å—oš¸É­¤ÉεÁ­¤°Â¨³­µ¤µ¦™œÎµ¦µ¥Å—oÁ…oµ¤µ­¼n‡¦´ªÁ¦º°œ¤µ„…¹ÊœÁ¤ºÉ°Áž¦¸¥Áš¸¥„´Ÿ¼o®·Š­nªœÄ®nĜ ®¤¼noµœš¸ÉÁž}œÂ¦ŠŠµœœ°„¦³ —oª¥Á®˜»œ¸Ê „µ¦šÎµŠµœÄœœ·‡¤°»˜­µ®„¦¦¤‹¹ŠÁ¡·É¤­™µœ£µ¡Â¨³°Îµœµ‹…°ŠŸ¼o®·Š Ĝ“µœ³…°ŠŸ¼oœÎµÁŠ·œ¦µ¥Å—o®¨´„¤µ­¼n‡¦´ªÁ¦º°œ °¥nµŠÅ¦„Șµ¤ „µ¦œÎµ¦µ¥Å—o®¨´„Á…oµ¤µ­¼n‡¦´ªÁ¦º°œ…°ŠŸ¼o®·ŠÅ¤nŗo Á¡·É¤°Îµœµ‹˜n°¦°ŠÄœ‡¦´ªÁ¦º°œ…°ŠŸ¼o®·ŠÄœš»„—oµœÁ¤ºÉ°Áš¸¥„´Ÿ¼oµ¥ Á¤ºÉ°¡·‹µ¦–µ„µ¦Á„ȝ¦´„¬µ „µ¦‡ª‡»¤ ¨³ „µ¦‹´—„µ¦ÁŠ·œ¦µ¥Å—o…°Š‡¦´ªÁ¦º°œ ¦ª¤™¹Š„µ¦˜´—­·œÄ‹Â¨³„µ¦‹´—­¦¦ŠµœoµœÄœ‡¦´ªÁ¦º°œ¥´Š¡ªnµ­™µœ£µ¡…°Š Ÿ¼o®·Š¥´ŠÅ¤nÁšnµÁš¸¥¤„´Ÿ¼oµ¥ ¨³Â¤oªnµÂ¦ŠŠµœ®·ŠÄœœ·‡¤°»˜­µ®„¦¦¤‹³¤¸­™µœ£µ¡Â¨³°Îµœµ‹˜n°¦°ŠÄœ ‡¦´ªÁ¦º°œš¸É—¸„ªnµÁ¤ºÉ°Áš¸¥„´Â¦ŠŠµœ¦´‹oµŠ®·ŠÄœ®¤¼noµœ ˜n¦ŠŠµœ®·ŠÁ®¨nµœ¸Ê„Ș„°¥¼n£µ¥Ä˜o„µ¦‡ª‡»¤…°Š ¦³Á‡¦º°µ˜·š¸ÉÁœoœÂ¤nÁž}œ«¼œ¥r„¨µŠÁœºÉ°Š‹µ„…o°‹Îµ„´—šµŠ—oµœ°µ¥»š¸Éœo°¥„ªnµ

‡Îµ­Îµ‡´: ‡¦´ªÁ¦º°œ, °Îµœµ‹˜n°¦°Š, ¦ŠŠµœ®·Š, œ·‡¤°»˜­µ®„¦¦¤£µ‡Á®œº°, ž¦³Áš«Åš¥ *

°µ‹µ¦¥rž¦³‹Îµ‡–³Á«¦¬“«µ­˜¦r‹¯» µ¨Š„¦–r¤®µª·š¥µ¨´¥ ™œœ¡µÅš „¦»ŠÁš¡ 10330, Email: Gullinee.M@Chula.ac.th

š‡ªµ¤·Êœœ¸Ê‡´—ÁœºÊ°®µ­Îµ‡´µŠ­nªœ¤µ‹µ„ª·š¥µœ·¡œ›r¦³—´ž¦·µÁ°„…°ŠŸ¼oÁ…¸¥œ – University of Utah ž¦³Áš«­®¦´“°Á¤¦·„µ Ĝ®´ª…o° The Effect of Women Workers’ Participation in Export Processing Zones on Women’s Bargaining Power in Households: The Case Study of Thailand’s Northern Region Industrial Estate.


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1. Introduction There has been a longstanding assumption in mainstream economics that the household is a site of altruism and cooperation. The resources within households are shared equally irrespective of gender or age hierarchies. The “new economics of the household,” spearheaded by Gary Becker (1981), holds such a vision of the household and economists working within this framework have produced what are called “unitary” models of the household (Barbara Bergmann, 1995; Cheryl R. Doss, 1996). Gender economics have challenged such models, while conceptualizing households as sites characterized both by cooperation and conflict. They have pointed to the importance of socially constituted power relations within households in the distribution of resources and assets as well as the distribution of the paid and unpaid work burden (Ann Whitehead, 1981;Rae Blumberg,1988; Carmen Deere and Magdalena León, 1982, 2003; NancyFolbre , 1986; Bina Agarwal, 1990, 1994, 1997; Daisy Dwyer and JudithBruce,1988; Jan Pahl, 1989; Susan Tiano, 1994;AgnesQuisumbing and John Maluccio, 1994, 1999; Sherri Grasmuck and Espinal Rosario, 2000; Greta Friedemann-Sanchez, 2002, 2006). Gender-based and age-based power relations are two of the crucial axes on which bargaining within household takes place. Accordingly, the household is not simply a site of altruism and co-operation, but it is also a site of negotiation, bargaining, and conflict. This is referred to as the “cooperative-conflict” models of the household (Amartya Sen, 1983, 1990). One of the determinants of women’s bargaining power within this framework is women’s access to paid labor (Diane Wolf, 1992; Halen Safa, 1995). However, women’s bargaining power is not simply determined by material factors such as economic factors, particularly women’s access to paid labor, but it is also determined by ideological factors such as views about the rights, the needs and the contributions of particular individuals and gender in society. In addition, the bargaining power of women is also determined by social structural factors such as the extrahousehold socioeconomic and legal institutions within the community and the state, which households are embedded in. For this reason, the complexity of the interaction of material, ideological and social structural factors in each society simultaneously determines the bargaining power of women (Gillian Foo and Linda Lim, 1989; Wolf, 1992; Quisumbing, 1994; Fernandez-Kelly, 1983; Nazli Kibria, 1995). At the same time, it means that the micro level of intrahousehold level is linked to the macro level of social structure in each society. Since the late 1960s and early 1970s, it has been recognized that capitalist development processes do not necessarily affect women and men in the same way. Women may in fact be left out of the development process. This was the marginalization thesis put forth by Ester Boserup (1970) in her pioneering work on women in the development process. Feminists advocate during this period argued that women had to be incorporated in the development process through participation in paid work. This was referred to as the Women in Development (WID) approach. The WID approach interpreted “development” mainly in terms of enhancing women’s


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participation in paid labor or the market economy. It was assumed that women’s well-being would improve alongside their incorporation in the paid labor force. In the late 1960s, the intensification of intercapitalist competition, particularly among industrialized countries, led to the relocation of labor-intensive manufacturing industries from industrialized to developing countries. This relocation created demand disproportionate to women’s paid employment, especially but not exclusively in export processing zones (EPZs) (Guy Standing, 1989). Gender economics, studying the conditions of work within such zones, have argued that this kind of integration of women in development processes does not necessarily eliminate women’s subordination or increase their bargaining power. In addition, this does not necessarily lead to a rise in their shares of the resources within households or reduce their unpaid domestic work burden (Diane Elson and Ruth Pearson, 1981a). The relocation of production of certain kinds of manufactured products from the developed countries to the Third World leads to rapid incorporation of Third World women into the labor market (June Nash, 1983; Standing, 1989, 1999; Lydia Kung, 1993). As a consequence, Women’s access to wage employment in the form of a “Global Assembly Factory,” particularly EPZ employment, dominated women’s employment in most developing countries for many decades since the 1960s (ILO, 2004).While there is agreement in the literature on a “Global Assembly Factory” that there is a rapid increase in women employed in the manufacturing industry, however there is disagreement on the implications of this employment for women workers (Aiwa Ong , 1987; Altha Cravey. 1998; Lim, 1990; Elizabeth Fussell, 2000; Kurt Alan Ver Beek, 2001; Bent Gehrt, 2002). The debates on a “Global Assembly Factory” lead to two contradictory theses. On the negative side, the exploitation thesis considers that EPZ employment in multinational factories takes advantage of the disadvantages of women workers because of their ages, education, and family status. In addition, they exploit women via harsh factory environments, where women workers suffer long hours, insecure, unhealthy, unsafe and poor working conditions with low wages (Fernandez-Kelly, 1983; Elson and Pearson, 1981b; Ong, 1987; Kung, 1983; Safa, 1995; Standing, 1989; Altha Cravey, 1998; Fussell, 2000). On the positive side, the integration thesis considers that EPZ employment creates an opportunity for women to enter the sphere of social production, particularly formal employment, at better wages compared to agricultural and domestic services, which most women are in. It means the economic independence of women through EPZ employment gives them economic leverage, which finally increases their autonomy (Sen, 1990; Lim, 1990; Wolf, 1992; Tiano, 1994; Safa, 1995; Friedemann-Sanchez, 2002). Nevertheless, the evidence of the effect of EPZ employment on women workers does not clearly situate in either thesis. On the contrary, most evidence shows that the effects of EPZs is mixed and nuanced. EPZ employment actually can, at the same time, decompose, intensify and recompose women’s subordination(Elson and Pearson, 1981a). Consequently, the effects of EPZs on women workers are far from homogeneous. Still, the universally generalized presumption on the situation of women workers in EPZs is questioned.


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For this reason, this study integrates feminists’ household economics theoretical framework with the literature of the global assembly factory and try to answer what are the effects of women’s participation in EPZs on their bargaining power within the household. In this study, Northern Region Industrial Estate (NRIE) women workers’ bargaining power is elaborated and considered relative to various intrahousehold dimensions. The considerations in this study range from intrahousehold income allocation, income control and management, intrahousehold decision making and housework allocation. NRIE women workers’ intrahousehold bargaining power is compared with males in households, particularly their husbands. In this study hire women workers are the representative of the majority of women workers in the villages. Also, NRIE women workers’ bargaining power is compared with hired women workers who combine various kinds of hired work with household agricultural as the comparison group. 2. Fieldwork Northern Region Industrial Estate (NRIE)6 located at Lamphun Province7 has been chosen as a case study based on 2 main reasons. First, the large majority of NRIE workers are women workers8. Second, the NRIE is located in a regional area where workers can remain at home with their households while they commute daily from their home villages to NRIE factories. Therefore, it would be relatively proper to see the interactions at the intrahousehold level compared to other EPZs, where most workers are migrants. This study focused on women workers in Vieng Nong Long Minor district9. Vieng Nong Long Minor district is divided in to three subdistricts with 16 villages. However, this study chose to concentrate on three villages; Dong Luang , Dong Charoen and Dong Nua. “Snowball sampling” was used in this study. As a result, the study started with one woman worker and then this woman worker generated additional female subjects by asking her to name the other women workers whom she knows. Therefore, this study is based on a small and purposively selected sample. It particularly focused on two groups of women workers; 24 NRIE women workers and 26 hired women workers. A small sample of women workers in three specific villages is based 6

The NRIE was established in response to the Thai governmental policy of spreading industrial development to the outlying regions as expressed in the Third Plan. It was the first IE established outside the Bangkok Metropolitan Region (BMR). It was expected to become the center of industrial development in Northern Thailand 7 The characteristics of Lamphun province location, which were perceived as the advantages of NRIE, can be categorized into five main reasons. First, its general location is in a cooler region compared to BMR; this makes it more attractive to electronic manufacturers who can reduce necessary temperature control costs. Second, the location is near an urban center and a large labor force. Third, the combination of being located near Chiang Mai but at the same time located outside it, allowed the investors to simultaneously take advantage of both Chiang Mai urban amenities and Lamphun nonurban advantages. Fourth, it is the location that has easy access to Chiang Mai international airport and Bangkok via a superhighway which is connected to Bangkok. Fifth, this specific location within Lamphun is close to required water supplies. However, despite these locational advantages, the NRIE did not originally attract strong investors’ interest (Glassman and Snedden, 2003: Glassman, 2004). 8 In 2006, there were 65 factories operating within the NRIE of which 43 factories are in the EPZ while 22 factories are in the GIZ. The GIZ in the NRIE factories employed 49,401 workers; 33,666 of the workers were females and 15,736 were males. However, within the EPZ, 43 factories employed a total of 37,514 workers, of which 25,565 were female and 11,949 were male (NRIE, 2007) 9 The administration of Lamphun province is divided into seven districts and one minor district. The seven districts are Muang Lamphun, Hya Chang, Li, Ban Hong, Ban Ti, Mae Tha, and Pasang. It has one minor district, which is Vieng Nong Long.


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on the necessity for controlling the common historical and socioeconomic dimension among women workers, which may reduce bias in intrahousehold analysis. Figure 1: Map of Lamphun Province

This study combined multiple qualitative methodologies which consisted of the documentary analysis, formal interviews via questionnaires, informalin-depth interview as well as partial participant observationto elaborate on and analyze the effects of NRIE factory work on women workers’ bargaining power within the intrahousehold level from October 2005 to May 2006. 3. Women Workers and Households The consideration of households encompasses various dimensions such as the composition, characteristics, lifecycles, number of household members in the labor force, earnings and assets of households, etc. The decomposition of the household was aimed at uncovering its underlying structure that incorporates gender and age bases in households. The underlying structure is not only the area in which the intrahousehold bargaining occurs, but it also affects the bargaining power or fallback position of household members, particularly women workers. Both NRIE and hired women workers’ households share very similar historical and socioeconomic backgrounds. Most households still were in the agricultural sector or claimed that they were in the agricultural sector even though agricultural earnings could not actually sustain their livelihoods. On the contrary, most households needed to combine various kinds of hired works with agricultural activities. Usually the larger proportion of total household income came from hired works rather than agricultural activities. The result is consistent with various former studies, which reported that large numbers of people in Thai rural areas remain in scarcely viable


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agricultural activities under a reduced agricultural sector (Phillip Hirsch, 1990; Peter Warr, 1993; Chris Dixon, 1999; Phongpaichit and Baker, 1995; 1996). Figure 2: Complete Nuclear Household

Husband

Wife

Children

Complete Nuclear Household

Figure 3: Extended Household

Extended Household

Woman worker’s nuclear household Husband

Wife

Children

Parents’ nuclear household Father

Mother


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NRIE and hired women workers’ households had a different composition, characteristics, and life cycle due to different ages between NRIE and hired women workers. The average age of NRIE women workers was 27 years, while the average age of hired women workers was 35 years. As a result, NRIE women workers were younger compared to hired women workers. Therefore, most NRIE women workers’ households were extended households, particularly (see figure 3), which usually consist of a woman worker and her parents’ nuclear household. Most hired women workers’ households were nuclear households (see Figure 2), which encompass a woman worker, her husband, and their children. Household structure is closely related to a pattern of postnuptial residence or a pattern of co-residence with parents10 particularly wife’s parents in form of matrilocal residence. This pattern is consistent with various studies in the Northern, Northeastern, and Central regions in Thailand (John De Young, 1955; Konrad Kingshill, 1965; Stanley Tambiah, 1970 ; Sulamith Potter, 1977; Chai Podhisita et al., 1990; Jennifer Gray,1990).Consequently, matrilineality and matrifocality, which dominate in the Northern region, strongly influence NRIE women workers’ households. All households owned land for a house and a house, while most of them owned land for longan as their major asset. Longan cultivation is the major agricultural activity and land for longan is the major source of income. NRIE and hired women workers’ households have different patterns of asset ownership. NRIE woman worker’s parents, either father or mother, was the major owner of NRIE women workers’ household assets. On the other hand, a wife or husband was the major owner of hired women workers’ household assets. As a result, NRIE women workers have weak fallback positions compared to their parents. There are different patterns of asset ownership between males and females.Usually a male in the household was the owner of more valuable assets or assets that related to occupation such as land for longan, car, and livestock. However, a female was the owner of less valuable assets or assets that do not relate to occupation such as a motorcycle, bicycle, land for a house, and a house. Therefore, males in households have strong fallback positions compared to females in households relative to valuable assets or assets which are related to occupation. Nevertheless, the ownership pattern of houses and land for houses strongly reflects matrilineality and matrifocality, which give women workers more opportunity to access household assets. Regarding household financial assets, usually a female in the household owned both informal and formal financial assets11. Both husband and wife jointly owned cash as a household flexible financial asset12. There are different ownership patterns of financial assets among the two groups of women workers. Most NRIE women workers owned formal financial assets in the form of bank saving accounts, but most hired women workers owned informal financial assets in the form of village saving or housewife saving accounts. Informal financial assets in this study are forced saving, which households need to keep as a guarantee for their borrowing. For this reason, hired women workers rely more on forced saving compared to NRIE women workers. 10

The coresidence pattern is an important factor in providing economic justification for new couples to accumulate savings before they can establish their own residences. 11 Formal financial assets mainly encompass bank savings accounts, while informal financial assets encompass a variety of informal saving groups, for example, village saving groups, and housewife saving groups. 12 Flexible financial assets encompass assets which have high liquidity, such as cash and gold.


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As a result, NRIE women workers have strong fallback positions compared to their husbands as well as hired women workers relative to the ownership of formal financial assets. According to the cooperative conflict model, a household is made up of multiple actors with varying preferences and interests. These actors have different abilities to pursue and realize their own interests. The multiple actors within a household have different bargaining power, which depends on his or her fall-back position. The bargaining power of each actor would be defined by a range of factors, particularly the strength of the person’s fall-back position or the threat point which are the outside options that determine how well-off he or she would be if cooperation fails. There are various factors which affect a person’s fall-back position. These factors can range from the quantifiable factors such as income and various kinds of property to unquantifiable factors such as social norms and perceptions about contributions and needs. However, different factors would carry different weight on a person’s fall-back position within the household. The different influences of various factors are also related to the socioeconomic context, from which cooperative conflict within a household can occur. In sum, the different influences of various factors among NRIE and hired women workers related to the socioeconomic context from which cooperative conflict within a household can occur shows that different household composition, characteristics, and life cycle betweenNRIE and hired women workers’ households affect the ownership pattern of various household assets. Matrilineality and matrifocality have more influence among NRIE women workers’ extended households. As a result, NRIE women workers have weak fallback positions compared to their parents within a household considering household composition and characteristics as well as life cycle. They also have weak fallback positions compared to their parents when considering household assets. On the contrary, NRIE women workers have strong fallback positions vis-à-vis their husbands when considering household composition, characteristics and household assets. This study also shows that gender-based and age-based power relations are two of the crucial axes on which bargaining within household takes place. But age-based power relations play crucial roles in NRIE women workers’ household due to the influence of matrilocality and matrilineality. However, age-based power relations have contradictory effects among NRIE women workers in the form that women workers needed to rely on their parents. 4. The Socioeconomic Background of Women Workers and the Conditions of Employment Study of the socioeconomic background of women workers showed that NRIE women workers do not start out poor compared to their husbands and hired women workers. NRIE women workers were younger and had higher educational levels vis-à-vis their husbands as well as hired women workers. The result also suggests that NRIE work occupies a relatively privileged place in the local work spectrum because of stability, better pay, and more fringe benefits. However, this well-off position is offset by long hours of work and unhealthy and unsafe working conditions in NRIE factories.


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The interview results showed that there was a trade-off between higher earnings as well as the stability of work and the various requirements of NRIE work such as the inflexible schedule of work, long hours of work, and repetitive work as well as the hazards of work. Various respondents had clearly compared the conditions of employment among NRIE work with agricultural-related and hired work. It is clear that NRIE women workers had less leisure time compared to hired women workers. This difference is dependent on the conditions of NRIE work, which requires longer working hours and more days of work. From the interviews, some NRIE women workers stated that they could freely choose when they would like to work or not to work overtime. Actually, they had to work on Saturday whether they preferred to choose to work or not. Moreover, during some periods, NRIE women workers had to work on Sunday because the factory needed to accelerate the production process when they received a large amount of orders. However, most women workers stated that they would like to work during weekends, particularly on Saturday, because they needed to make more money. There are different conditions of employment among NRIE work and agricultural as well as hired work that most husbands and hired women workers participate in. On the one hand, NRIE work can be categorized as formal employment because of contractual employment with stability of work, legal coverage, and various fringe benefits. On the other hand, most agricultural-related and hired work can be categorized as informal employment because it encompasses unstable work with lower pay and without legal coverage and fringe benefits. For this reason, NRIE women workers have strong fallback positions compared to their husbands as well as hired women workers considering the conditions of their employment. However, hired women workers do not have strong fallback positions vis-Ă -vis their husbands when considering the conditions of their employment. Different conditions of employment also lead to different positions of workers. Consequently, NRIE women workers are clearly economically better off compared to their husbands and hired women workers who participate in agricultural-related and hired work. NRIE women workers are included in formal employment under an export-oriented industrialization, which brings sources of foreign exchange to Thailand, but this economic development model at the same time is built on the back of women as Peter Bell (1997) reiterated. On the contrary, most workers in the village are pushed out of the agricultural sector because of the deterioration and unreliability of agricultural sector. Accordingly, these unqualified workers could only be included in informal employment. The unreliability of agricultural activities depended on the combination of various factors ranging from limited amounts of land, uncertainty, and vulnerability of agricultural-related activities, and particularly uneven development of the Thai state. Thai economic development policies for many decades have been streamlined toward a more urban and export orientation while discouraging the agriculture and rural sector. The economic development process not only changes the structure of the Thai economy, but it has significant consequences for a skewed distribution of economic activities via rising national income disparities. The results of this study


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help elaborate this explanation, particularly the unreliability of the agricultural sector and employment, while pushing most rural people into informal employment. It is clear that no single household in this study draws a livelihood entirely from agriculture. On the contrary, the means of household earning are a combination of a variety of activities. Most households in this study construct their livelihoods by diversifying their means of income ranging from agricultural activities, various kinds of hired work which are related to agricultural or not related to agricultural activities, to self-employed work. However, the major source of income in most households predominantly comes from various kinds of hired work rather than from household agricultural activities as should be expected in a rural area. The interviews show that most households used to work and invest in various kinds of agricultural activities in the past, particularly participating in longan, garlic, shallot or cabbage cultivation. Most of them stated that cultivation was a failure because the prices of these agricultural products were low and the costs of production were high. Currently, some of them still are in debt since they borrowed money to invest in these agricultural activities. As a result, some respondents mentioned the deterioration of agricultural activities that they perform or used to perform during the interviews. This finding is consistent with the situation of other rural areas in the Northern region as well as other regions in Thailand. Contrary to conventional beliefs, a large percentage of households in rural Thailand, who identify themselves as farmers, actually draw their main income from nonfarm activities. Office of Agricultural Economics (1999) found that income from nonagricultural sources among agricultural households was higher than the income from agricultural sources in every region, except for the South. The unreliability of agricultural income in this study is dependent on various factors. On the one hand, agricultural enterprises have met with a number of the limitations which are found in most rural areas. Major constraints of agriculture are associated with the small amount of land, the poor condition of agricultural activities, the fluctuation of agricultural production and prices, natural disasters, higher costs of production, etc. In addition, most agricultural areas in Thailand are primarily rain-fed, while only 22 percent of agricultural land is labelled as irrigated areas. These are coupled with low productivity, which has always been viewed as a major problem within the agriculture sector in Thailand (Na-Ranong, 2000). On the other hand, this phenomenon is also related to the effects of the Thai government’s economic and social development strategy and plans, which prioritize industrialization via export oriented strategy. Junichi Yamada (1997) found that the agricultural sector in Thailand grew by about 12.3 times in the 30 years during 1961-91, while non-agricultural sectors registered even more substantial increase at almost 55.9 times during the same period. As a result, the contribution of agricultural production to overall national GDP fell from 39.2 percent in 1961 to 12.4 percent in 1991 even though the labor share of agriculture sector was still high. In short, the existing evidence from this study strongly challenges the common assumption that export factory women workers homogeneously suffer from insecurity and lower wages. NRIE women workers are poor compared to women workers in developed countries. They are also


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poor compared to the skilled and professional Thai women workers, but they are relatively welloff compared to most of their peers in the villages, particularly their husbands and hired women workers as well as their parents. NRIE factory employment in this study is significant because it is generally superior to other forms of agricultural related employment in the villages due to the deterioration of agricultural sector as well as people in this sector. This result asserts that the extra-household dimension such as economic and social development strategy also affects women’s intrahousehold bargaining power. 5. Women Workers and Intrahousehold Bargaining 5.1 Income Allocation and Management The uneven development that prioritizes the industrial sector provides new opportunity for qualified workers, particularly NRIE women workers, to be able to be included in the formal economy. But the unqualified workers, who mostly are in agricultural-related and hired work, are pushed into informal employment instead. This situation certainly weakens the fallback positions of males in the household, who usually participate in agricultural-related and hired work. The results show that most NRIE women workers contributed more into the household economy as a mainstay provider, while males in households, particularly their husbands, contributed less to the household economy. The average percentage of NRIE women workers’ monthly earnings in total household income was 58. This average percentage of NRIE women workers’ earnings was higher than the average contribution that Thai women contributed to total household earnings, which was less than 50 percent (Kerry Richter and Napaporn Havanon, 1995). For this reason, the concept of “male breadwinner” is becoming a myth among NRIE women workers’ households. Most hired women workers contributed less to the household economy compared to their husbands. The average percentage of hired women workers’ monthly earning relative to total household income was 36. Even though hired women workers are secondary providers, their earnings are not merely supplementary. Most hired women workers perceived that their household livelihoods could not be sustained without their earnings. As a result, a male breadwinner concept still prevails in hired women workers’ households but it is certainly not solid. In addition, the interviews showed that both NRIE and hired women workers recognized the importance of their contributions to their household economy. Hence, most of them claimed that it was necessary for them to work because if they did not work, the well-being of their households would certainly be affected. NRIE women workers have strong fallback positions vis-à-vis their husbands and hired women workers considering their mainstay provider positions. On the contrary, hired women workers have weak fallback positions vis-à-vis their husbands considering their secondary provider positions. Still, the mainstay provider position of NRIE women workers does not automatically


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increase their bargaining power in household income allocation and management. The intrahousehold bargaining power of women does not depend only on the amount of earnings, the percentage of earnings relative to total household income and the pattern of household provider. For this reason, it is necessary to further clarify the details of household income allocation and management. This study also concentrated on three major dimensions of household income allocation and management: income information accessibility, household income keeping, and household management. Regarding income information accessibility, usually other household members, particularly husbands and parents, know about women workers’ income information. The reverse is true: women workers also know about other household members’ income information, particularly income information of their husbands or parents. The accessibility of income information among women workers and other household members is dependent on the nature and characteristics of both NRIE and agricultural-related as well as hired work. Households usually pool income from various household members while a female in the household is the income keeper and decision maker. The result is also consistent with previous studies which found that Thai women generally were the people who kept the household purse (Don Lauro, 1979; Potter, 1977; Christine Mougne, 1984; Phongpaichit, 1982; Nappaporn Chayovan, Vipan Ruffolo and Malinee Wongsith, 1996). However, what explains why household’s pool income from various members is economic necessity or the insufficiency of male income in a household. In addition, a woman keeping the household purse or income does not imply that women automatically control household income. Therefore, the study will further explore household income decision maker, income allocation patterns, and management to clarify how household income is kept, controlled, and managed. Figure 4: Pooled Income for Husband and Wife in a Nuclear Household (Wife as Income Keeper)

Husband’s income

% income

given to wife

Wife’s income

Personal expenses

Unknown % of money

HH pooled income for which the wife is income keeper and decision

hh expenditures

hh saving or hh borrowing


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Figure 5: Pooled Income from All Household Members in Extended Household (Wife as Income Keeper)

Husband’s income

% income

given to wife

Wife’s income

Other individual’s income

Personal expenses

Unknown % of money

HH pooled income for which the wife is income keeper and decision

hh expenditures

hh saving or hh borrowing


Personal expenses

Husband’s income

Unknown % of money

% income given to wife

Parents’ nuclear household

HH pooled income for which the wife is income keeper and

Wife’s income

Someoverlap of household income and expenditures

hh saving or hh borrowing

Household expenditures

Personal expenses

Husband’s income

Unknown % of money

given to wife

% income

HH pooled income for which the wife is income keeper and decision maker

Wife’s income

Woman worker’s nuclear household

Figure 6: Pooled Income Pattern in Each Nuclear Household (Extended Household)

hh saving or hh borrowing

Household expenditures

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There are different patterns of income keeper and decision maker among NRIE and hired women workers’ households. A woman worker or her mother was the income keeper and decision maker in most NRIE women workers’ extended households, but a hired woman worker, as a wife, was the income keeper and decision maker in most hired women workers’ nuclear households. (See Figure 4, 5, 6) As a result, matrilineality and matrilocality have more influence among NRIE women workers’ households compared to hired women workers’ households. The proportion of women workers’ income relative to total household income, particularly NRIE women workers, was more than 50 percent. In addition, the percentage of income which husbands gave to NRIE women workers was lower compared to hired women workers’ husbands. For this reason, women manage and control household pooled income under conditions of economic necessity which means not much money to keep, control, and manage. Moreover, large proportions of household incomes which women workers, particularly NRIE women workers, keep and control are their own incomes. The study also found that husbands do not totally withdraw from household income allocation and management because after they give money to women workers, they still request some money back for their personal use and other payments. (See Figure 4, 5, 6) The interviews also showed that even though most women workers stated that their husbands usually gave part of their earnings or all of their earnings to them, this did not reflect what has actually happened in reality. Further study found that usually a husband gave some or all of his earnings to his wife at first, but when he needed to use money to buy something even from his daily pocket money, he asked his wife to give money to him later from time to time. However, it was hard to ask women to compare the amount of money that their husbands gave to them to the amount that they gave back to their husbands because usually women did not know exactly how much money they gave back to their husbands; hence they did not notice or keep information about this. “I manage all household income. When my husband goes to work, I know that he get 150 baht for male hired work per day. All hired male workers get 150 baht equally. He gives all of his income to me but he requests it back when he wants to use it. When he requests for money I ask him how much money that he wants. Normally I give him 50 baht for motorcycle’s gasoline, 50 baht when he goes out and 20 baht for his alcohol something like that. I do not know how much I give him each month but just know that when he requests if I have money I give some to him. If I do not have it, I do not give it to him.” (A 28 year old hired woman worker) “I am the person who takes responsible for household income-expenditure management. My husband gives some of his daily earning to me. In case that he goes out to work, I know it and also know that he gets 150 baht per day. Every male worker doing hired works get 150 baht equally. I have to manage all the things because if not he spends all his money on alcohol. I am the person who can economize and know what should buy or not. If he wants to use money I give it to him. No, I do not know exactly how much I give money to him each month. I just recognize that I give him 30 baht


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or 40 baht per time if I have money that is all.” (A 30 year old hired woman worker) “My husband gives his earning to me; I am the one who take responsible for household income management. Yes, I know how much he receives from work each day and he also knows how much I get each month. We actually talk to each other. There is no reason why I take responsible for household income management, but we did it since we got married. He gives all his earning to me and then when he needs to use money, he can ask me and I will give it to him. Normally when he goes out to work I give him 50 baht for his daily expense.” (A 28 year old NRIE woman worker) “My wife is the person who is responsible for household income and expenditure management. I give all my daily earning to her in case that I go to work and receive money. And if I need to use some money I tell her. Normally she gives me 50 baht to 100 baht when I request her. I need to have some money in my pocket when I go out to work or meet some people outside.” (A 34 year old husband of hired woman worker) Moreover, it is found that there was a bias in women workers’ perceptions related to the money that they gave to other household members and their husbands. As the study showed earlier, after women workers received money from their husbands, normally women workers needed to give money back to their husbands. However, women workers did not think that giving money from pooled household income back to their husbands was giving money to others. On the contrary, women workers perceived that they already received money from their husbands; therefore, normally, the money that they gave back to husbands was also their husbands’ money. In addition, there were two points which pertain bargaining to between husband and wife. First, bargaining may exist between husband and wife on the amount of money that the husband gives to the wife after he received his earnings. Some respondents mentioned that the husband gave part of his earnings to his wife and kept some for his personal expenses. On the contrary, some respondents stated that the husband gave all of his earnings to his wife. Second, the bargaining may exist between husband and wife on the amount of money that the husband requests from household pooled income, when he needs it. This is because the amount of the money that a husband requests from his wife and the amount of money that the wife gives back to her husband are debatable. As a result, it can hardly be concluded that women workers have higher bargaining power compared to their husbands relative to household income allocation and management. Hence, women workers do not have higher bargaining power vis-à-vis their husbands when considering whose earning and under what conditions women workers actually keep, control, and manage household income. 5.2 Intrahousehold Decision Making There are different patterns of decision making among NRIE and hired women workers’ households related to different household composition, characteristics and life cycle. Usually a female in the household took responsibility in household decision making while women workers as wives were the major decision makers in most hired women workers’ nuclear


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households, and a woman worker and her mother were responsible as the decision makers in most NRIE women workers’ extended households. The study also found that there are different gender patterns in household decision making. The interviews showed that females in household usually are responsible for small issues related to daily life activities with inexpensive items such as food, products, and clothes used in households. On the contrary, males in household are jointly responsible for important issues or decision making related to expensive items such as durable goods, childcare, and health payment. This result is consistent with the previous study on household decision making in Thailand (Chayavan, Ruffolo and Wongsith, 1996). “Usually I make the decision for food, products and clothes used in household. However, when we would like to buy durable goods which are expensive, I and my husband have to talk together to make a decision. I specifically initiate in buying refrigerator and cloth washing machine. My husband initiates in buying television and car. For example, he is the person, who refers that we need to buy car because our land is situated in Chiangmai province, which is far away from the village. As a result, if we have car it is easier to commute to our land.” (A 28 year old hired woman worker) “I and my mother make the decision for food, products and clothes used in households. But usually I buy them from Lamphun city when I go to work because I commute to home by hired car so I can bring them back home even sometime they are heavy. But for my daughter’s clothes, I do not spend much money on them because my father complains that she will grow up later so do not buy many clothes, it is waste. For durable goods, we talk together before we buy them. My father initiates in buying television but actually we bought it before I got married. I initiates in buying radio, refrigerator something like that.” (A 26year old NRIE woman worker) This study also considered in detail whose income households usually used for various aspects of household payment. The results show that normally most households use pooled income to pay for these expenses. However, some NRIE women workers’ households did not use household pooled income but used NRIE women workers’ incomes to pay for these expenses instead. Some NRIE women workers were solely responsible for decision making on durable goods buying. But these NRIE women workers were also responsible for durable goods payment from their own incomes. On the contrary, no hired woman worker mentioned that she was solely responsible for durable goods buying. In sum, NRIE women workers do not really have more bargaining power vis-à-vis males in households pertaining to household decision making because NRIE works do not radically change gender patterns in household decision. Nevertheless, NRIE work provided more opportunity for some NRIE women workers to have more says in household decision making compared to hired women workers. However, this opportunity could not be separated from women workers ‘payment responsibility. Moreover, hired women workers’ households are more vulnerable compared to NRIE women workers’ households because they need to combine various sources of lending and various reasons for borrowing. However, the patterns of borrowing decision making among NRIE and hired women workers’ households are different because matriliniality and matrifocality


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dominate among NRIE women workers’ households. Both husband and wife jointly decided on household borrowing among hired women workers’ households, while a woman worker’s parents, either father, mother, or both of them, were responsible for borrowing decision making in most NRIE women workers’ households. This study found that there were various sources of credit market or sources of lending. However, they can be categorized in two forms: formal credit lenders and informal lenders.13 A bank was the only formal lender in this study. However, there were various types of informal lenders such as village funds, housewife saving group, and relatives. It was found that males and females borrowed from different sources of lending. Usually, males in a household borrowed from a formal source and females in a household borrowed from an informal source. Therefore, males are less vulnerable compare to females in households considering the access to sources of lending. 5.3 Housework Allocation Regarding housework allocation, there was a clear segregation among various kinds of housework in terms of which kinds were a female’s responsibility and which kinds was a male’s responsibility. Most women workers take responsibility for various kinds of housework. The various kinds of housework are female work, except repairing, which is male work. Even though more NRIE women workers compared to hired women workers did not mention that they did various kinds of housework as their main responsibility, the interviews also showed that most NRIE women workers stated that they also did various kinds of domestic housework. This result is consistent with information on the leisure time activities of women workers, which reveals that usually women workers stated that they did housework during their leisure time. “I do not have enough time at home and this is the reason that why my son does not familiar with me. Usually he stays with my mother and husband, who take turn taking care of him. When I come back from work I spend most time sleep and help doing some kinds of housework, such as cleaning dishes and house. But normally I have not enough time, my mother and husband taking care most of housework.” (A 25 year old NRIE woman worker) “Yes, I have leisure time. I take care of my child and do some housework if I have time on Sunday or after I come back from factory each day. I come back from factory around 8.45 in the morning. After I come back home, I take care of my children, preparing food for them, having breakfast with them and may be bring them to Sunday market on Sunday if they ask me. If not, I wash clothes and go to sleep. When I wake up in the afternoon I clean my house and prepare dinner. Then I prepare to go to work in the evening.” (A 26year old NRIE woman worker) “Normally I do various kinds of housework during leisure time. No, I do not take care of my son because he is grown up enough and likes to hang out with 13

A formal lender is a lender who frequently demands collateral in order to increase a borrower’s creditworthiness to increase their risk-adjusted return on the loan. Therefore, formal lenders require physical collateral such as land. On the other hand, informal lenders use collateralsubstitutes such as third-party guarantees, tied contracts, or threat of loss of future access to credit as common devices instead of physical collateral.


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his friends instead. I have a lot of available time, particularly after longan season. So, if no one asks me to work for them, I gather around with my sister and neighbors nearby. We talk, chat, and particularly complain to each other about this or that thing”. (A 32 year old hired woman worker) Normally if I have time I do weaving and if I do not weave, I do various kinds of housework instead. For example, in the morning I go to market, come back to prepare for breakfast. Then I take care of my child, send him to school and cleaning and then after seeing that everything is done then I start weaving. And during evening if my child come back home, I have to stop weaving and take care of him first. (A 30 year old hired woman worker) The pattern of activities which women workers did during their leisure time showed that most women workers did not think that doing domestic tasks such as doing housework or taking care of children was work. The concept of “work” in their sense is activities that bring money into the household. Therefore, leisure time for women workers is the time during which they did not perform activities which brought in money, but it is the time that they performed women’s duties in the form of doing various kinds of housework. In sum, this pattern of activities that women workers do during their leisure time showed that women workers have to combine public work and private work together as their responsibility. In addition, they internalize the idea that housework is not work but it is the responsibility of women in households. Moreover, it is clear that women workers face a double day. Therefore, both NRIE and hired women workers have weak fall-back positions compared to their husbands when considering their duty and responsibility related to both public and private work. Nevertheless, women workers themselves could not solely take responsibility for various kinds of housework. Various kinds of housework needed to be allocated among various household members. However, there are different patterns of housework allocation among NRIE and hired women workers’ households. Usually hired women workers were responsible for housework as their main responsibility, while NRIE women workers were responsible for housework but not as their main responsibility. This difference is related to the conditions of NRIE work, which requires longer working hours six days a week. Most hired women workers’ households were nuclear households, usually the husband in each household needed to help. On the contrary, most NRIE women workers’ households were extended households, usually other female members in the household needed to help. For this reason, matrilineality and matrifocality, which dominated in NRIE women workers’ households, help to reproduce the traditional sexual division of labor in households rather than challenge it. This result is consistent with the study of Halen Safa (1995), which reported that matrifocality actually encourages women’s attainments in the household and their domestic work because it enhances the importance of women in the household. As a result, NRIE works do not change or weaken kinship relations in the household. Moreover, the kinship relations via matrilineality help support women workers by shifting housework from women workers to other female relatives in the household. The reproductive


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labor, which is shifted to other female members, helps reproduce the productive labor of NRIE women workers. In brief, although women engage in paid work, women disproportionately take on more responsibility for housework compared to their husbands. Women’s participation in paid work actually does not change the housework allocation pattern. Thus, women have a “double day” of both paid and unpaid domestic work. Moreover, they also internalize the idea that housework is not work but it is the responsibility of women in households. Therefore, both NRIE and hired women workers have weak fall-back positions compared to their husbands when considering their duty and responsibility related to housework allocation. Certainly, work-life balance should be considered as one of the crucial aspects of women workers’ bargaining power. 6. Conclusions The result shows that the relocation of production of certain kinds of manufactured products from the developed countries to the developing countries such as Thailand leads to rapid incorporation of women into the labor market. This integration certainly affects women workers’ intrahousehold bargaining power. It certainly showed that NRIE work has both negative and positive impacts on women workers’ bargaining power vis-à-vis their husbands as well as hired women workers. This situation confirmed the mixed effects of EPZ employment on women workers as Elson and Pearson (1981a) have mentioned. On the one hand, NRIE work provides an opportunity for qualified women to enter into formal employment. It helps increase the contribution of NRIE women workers to total household income with their mainstay provider position. Therefore, NRIE women workers have strong fallback positions vis-à-vis their husbands. This existing evidence strongly challenges the common assumption that export factory women workers homogeneously suffer from insecurity and lower wages. On the contrary, hired women workers have weak fallback positions vis-à-vis their husbands considering their secondary provider positions. NRIE women workers are poor compared to women workers in developed countries and the professional Thai women workers. But they are relatively well-off compared to most of their counterparts in the villages including hired women workers. As a result, NRIE employment decomposes women’s subordination due to their contribution to household income. This economic contribution should create a material base that increases NRIE women’s bargaining power within households. On the other hand, while the mainstay provider position of NRIE women workers challenges the socially constructed “male breadwinner” role of their husbands, it does not radically increase women workers’ intrahousehold bargaining power. Usually NRIE and hired women workers did not actually have economic power over household income because they manage and control household pooled income under conditions of economic necessity which means not much money to keep, control, and manage. Moreover, their husbands do not totally withdraw from household income allocation and management. Consequently, NRIE employment recomposes women’s subordination related to household income control and management.


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In addition, this result also reiterates the worsening rural conditions in rural areas in terms of land availability, employment as well as earning opportunities in the unreliable agricultural sector, which are the main problem facing most rural households including women workers’ households. Thai economic development policies for many decades have been streamlined toward a more urban and export orientation while discouraging the agriculture and rural sector. The economic development process not only changes the structure of the Thai economy, but it has significant consequences for a skewed distribution of economic activities via rising national income disparities. The results of this study help elaborate this explanation, particularly the unreliability of the agricultural sector and employment, while pushing most rural people into informal employment. Therefore, NRIE women workers are included in formal employment, which brings sources of foreign exchange to Thailand. But this economic development model at the same time is built on the back of women while deteriorates other members of households.As a result, the struggle for a qualitatively different development model, which alters the socioeconomic context that women are positioned in, is required if we would like to increase women’s bargaining power. However, NRIE employment does not change various aspects of household decision making to any degree even though some NRIE women workers may have more say in household decision making compared to hired women workers.In this study, it is clear that NRIE employment does not alter housework allocation and the sexual division of labor in households. This subservience is related to matrilineality and matrifocality, which combine age-gender hierarchies in most NRIE women workers’ households.NRIE employment improves the economic status of women workers and their households but it makes little change to gender egalitarian direction in their households.Therefore, NRIE work intensifies women’s subordination as related to household decision making and housework allocation. For this reason, more specific policies, which help by increasing women’s bargaining power, need to be considered, for example, programs or policies which support comparable worth among females and males; programs or policies which help by relieving women’s double days; programs or policies which generate support directly in the area that women take responsibility for such as the area linked with household welfare or well-being; programs or policies which create egalitarian social value and gender relations atmosphere in society and the household, etc. In sum, the results of this study showed that a household is not a black box with harmonious interests but it contains a dynamic of various dimensions of controlling, managing, and a decision making process.This study also asserts that women’s participation in NRIE can intensify and decompose the existing forms of gender subordination while recompose new forms of gender subordination at the same time. While NIRE women workers are relatively well-off compared to hired women workers, they are more subservient under an age hierarchy in households due to the strong influence of matrilineality and matrifocality. The results affirmed that the effect of EPZ employment on women workers are mixed and nuanced.


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Friedemann-Sanchez, G. (2002) “Challenging Patriarchy in the Transnational Floriculture Industry: Household Economics Identity and Gender in Columbia.” Ph.D. Dissertation, University of Minnesota. Friedemann-Sanchez, G. (2006) “Assets in Intrahousehold Bargaining among Women Workers in Columbia’s Cut-Flower Industry”,Feminist Economics,12(1-2): 247-269. Fussell, E. (2000) “Making Labor Flexible: The Recomposition of Tijuana’s FemaleMaquiladora Labor Force.” Feminist Economics, 6,:59-79. Gehrt, B. (2002) “Balancing on the Margins: Workers Economic Decisions at the Northern Region Industrial Estate, Lamphun", Bangkok: Thailand Research Fund. Glassman, J. and Sneddon, C. (2003) “Chiang Mai and Khon Kaen as Growth Poles: Regional Industrial Development in Thailand and Its implications for Urban Sustainability.” The Annals of the American Academy of Political and Social Science, 590(1): 93-115. Glassman, J. (2004) Thailand at the Margins: Internationalization of the State and The Transformation of Labor. New York: Oxford University Press. Grasmuck, S. and Rosario, E. “Market Success or Female Autonomy?Income, Ideology, and Empowerment among Micro-Entrepreneurs in theDominican Republic.”,Genderand Society, 14(2): 231-55. Gray, J. (1990) “The Road to the City: Young Women and Transition in Northern Thailand.” Ph.D. Dissertation, Macquarie University. Hirsch, P. (1990)Development Dilemmas in Rural Thailand.Oxford: Oxford University Press: 93-121. International Labor Organization (ILO) (2004)ILO Database on Export Processing Zones.Geneva: International Labor Organization. Kibria, N. (1995) "Culture, Social Class, and Income Control in the Lives of WomenGarment Workers in Bangladesh",Gender and Society,9(3): 289-309. Kingshill, K. (1965)Ku-Daeng-The red Tomb,Bangkok: Bangkok Christian College. Kung, L. (1983)Factory Women in Taiwan, Michigan: UMI Research Press. Lauro, D. (1979) “The Demography of a Thai Village.”Ph.D. Dissertation, Australian National University. Lim, L. (1990) “Women’s Work in Export Factories: The Politics of a Cause,” in Irene Tinker, ed., Persistent inequalities: Women and World Development. Oxford: Oxford University Press. Mougne, C. (1984) “Women, Fertility and Power in Northern Thailand.”Paper presented at the International Conference of Thai Studies. Bangkok, Thailand. Na-Ranong, V. (2000) The Financial Crisis and Agricultural Productivity in Asia and the Pacific.” Report of the APOStudy Meeting on Effects of Financial Crisis on Productivity of Agricultural Sector.Bangkok, Thailand. Nash, J. (1983)“The Impact of the Changing International Division of Labor on Different Sectors of the Labor Force,” in June Nash and Maria Patricia Fernandez Kelly, eds., Women, Men, and the International Division of Labor. Albany: State University of New York Press, 3-38. Northern Region Industrial Estate (2007) Northern Region Industrial Estate, Lamphun: NRIE. Office of Agricultural Economics (1999)Agricultural Statistics of Thailand Year 1998/99.Bangkok: Ministry of Agriculture. Ong, A. (1987)Spirits of Resistance and Capitalist Discipline: Factory Women inMalaysia. Albany: State University of New York Press. Pahl, J. (1989)Money and Marriage. New York: St. Martin’s Press.


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วารสารเศรษฐศาสตรปริทรรศน สถาบันบัณฑิตพัฒนบริหารศาสตร ปที่ 9 ฉบับที่ 1 (มกราคม 2558)

Technological Investment of Thai Industries and Government Supports Vasu Suvanvihok* Abstract This study investigates the behavior of Thai industries’ R&D and innovation (RDI) activities, the technological investment, using the institutional framework in analyses. Using firm-level data from Thailand’s R&D and Innovation activities survey from theIndustrial sector 2009, the results show that most firms’ characteristic and business environment variablesare significantly related to the probabilities of carrying out RDI activities. R&Dexpenditures can be explained significantly by total sales, number of R&D staff, external cooperation with business partners and with universities or public research institutes (PRI). Forinnovations, the expenditures are significantlyrelated with more effect than on the R&D,to total sales and the export portion, experiences, and results of former activities. Decisionson effort allocation of firms’ R&D and innovation activities are different. Firms tend to carry out R&D onproducts more than on only processes, or carry outboth. But for innovation activities, firms tend to carry out both product and process innovation. Policy implications propose that government should support firms by providing appropriate information, facilitating cooperation both between the public to private sectors and among private firms, providing proper funding mechanisms, and supporting other related technological activities.

Keywords: R&D, Innovation, Institution, Government support, Thailand * Consultant, Technopreneur Development and Services Department, National Science and Technology Development Agency, 111 Thailand Science Park, Phahonyothin Road, Khlong Luang, Pathum Thani 12120, Thailand: E-mail - osk_107@yahoo.com


วารสารเศรษฐศาสตรปริทรรศน สถาบันบัณฑิตพัฒนบริหารศาสตร NIDA Review2558)73 ปที่ 9Economic ฉบับที่ 1 (มกราคม

การลงทุนดานเทคโนโลยีของภาคอุตสาหกรรมไทยและการ สนับสนุนของภาครัฐ วสุ สุวรรณวิหค*

š‡´—¥n° ª´˜™»ž¦³­Š‡r…°ŠŠµœª·‹´¥œ¸ÊÁ¡ºÉ°«¹„¬µ¡§˜·„¦¦¤Äœ„µ¦¨Šš»œ—oµœÁš‡ÃœÃ¨¥¸…°Š£µ‡°»˜­µ®„¦¦¤Åš¥ Ÿ¨ „µ¦«¹„¬µ¡ªnµ˜´ªÂž¦‡»–¨´„¬–³Â¨³­™µ´œ­nªœÄ®n¤¸œ´¥­Îµ‡´˜n°„µ¦˜´—­·œÄ‹—εÁœ·œ„·‹„¦¦¤ª·‹´¥ ¡´•œµ ¨³œª´˜„¦¦¤ ž{‹‹´¥š¸É¤¸Ÿ¨˜n°‡nµÄo‹nµ¥—oµœª·‹´¥Â¨³¡´•œµÅ—o„n ¥°—…µ¥ ‹Îµœªœ¡œ´„Šµœª·‹´¥ „µ¦¦nª¤¤º°„´ ¡´œ›¤·˜¦šµŠ›»¦„·‹ ¤®µª·š¥µ¨´¥®¦º°­™µ´œª·‹´¥ ­nªœž{‹‹´¥š¸É¤¸Ÿ¨˜n°‡nµÄo‹nµ¥Äœ„·‹„¦¦¤œª´˜„¦¦¤Å—o„n ¥°—…µ¥ ­´—­nªœ„µ¦­nŠ°°„ ž¦³­„µ¦–r ¨³Ÿ¨—εÁœ·œ„µ¦ÄœnªŠ„n°œ®œoµª´˜™»ž¦³­Š‡r…°Š„·‹„¦¦¤ª·‹´¥ ¡´•œµÂ¨³œª´˜„¦¦¤¤¸‡ªµ¤Â˜„˜nµŠ„´œ ¦·¬´š¤¸ÂœªÃœo¤‹³ª·‹´¥Â¨³¡´•œµÁŒ¡µ³Ÿ¨·˜£´–”r¤µ„„ªnµ‹³¡´•œµ „¦³ªœ„µ¦®¦º ° šÎ µ ‡ª‡¼n „´ œ ˜n ­Î µ ®¦´  „· ‹ „¦¦¤œª´ ˜ „¦¦¤¦· ¬´ š ¤¸  œªÃœo ¤ ‹³¡´ • œµš´Ê Š Ÿ¨· ˜ £´ – ”r  ¨³ „¦³ªœ„µ¦‡ª‡¼n„´œ…o°Á­œ°Âœ³Á·ŠœÃ¥µ¥Äœ„µ¦­œ´­œ»œ„·‹„¦¦¤ª·‹´¥ ¡´•œµÂ¨³œª´˜„¦¦¤Å—o„n ‹´—®µ …o°¤¼¨…nµª­µ¦°¥nµŠÁ®¤µ³­¤ ­œ´­œ»œÄ®o¤¸‡ªµ¤¦nª¤¤º°¦³®ªnµŠš´ÊŠ£µ‡¦´“¨³£µ‡Á°„œ ‹´—„¨Å„„µ¦Ä®o š»œ°»—®œ»œÂ¨³­œ´­œ»œ„·‹„¦¦¤š¸ÉÁ„¸É¥ª…o°ŠÄœ—oµœÁš‡ÃœÃ¨¥¸

‡Îµ­Îµ‡´:„µ¦ª·‹´¥Â¨³¡´•œµ œª´˜„¦¦¤ ­™µ´œ „µ¦­œ´­œ»œ…°Š£µ‡¦´“ ž¦³Áš«Åš¥ *

š¸Éž¦¹„¬µ iµ¥¦·„µ¦¡´•œµŸ¼ož¦³„°„µ¦šµŠÁš‡ÃœÃ¨¥¸ ­Îµœ´„Šµœ¡´•œµª·š¥µ«µ­˜¦r¨³Áš‡ÃœÃ¨¥¸Â®nŠµ˜· °»š¥µœª·š¥µ«µ­˜¦r 111 ž¦³Áš«Åš¥ ™œœ¡®¨Ã¥›·œ˜Îµ¨‡¨°Š®œ¹ÉŠ °ÎµÁ£°‡¨°Š®¨ªŠ ‹´Š®ª´—žš»¤›µœ¸ 12120: Email - osk_107@yahoo.com


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1. Introduction This study focuses on the Technological Investment or herein referred to as Research and Development14, and Innovation15 (RDI) activities, the role of business environment and institutional attributes of decisionson the activitiesof the Thai industrial sector. The problem was thatR&D activities inThailand are considered low. The R&D expenditure of Thailand, even increasing over time, averaged only 0.1 percent of GDP in the period from 1999 to 2008 (STI, 2009: 80). As shown in Figure 1, the ratio of Thailand’s Gross Expenditure on Research and Development and Gross Domestic Product (GERD/GDP) is considered very low compared to thosecountries whose ratiosare located abovethe trend line, and used R&D as a tool for encouraging economic growth (Ministry of Science and Technology, 2004:14). We can see that most countries having a higher GDP have higher values of GERD/GDP. Figure 1: Relation of GERD/GDP and GDP per Capita between 2002-2003

Source: Ministry of Science and Technology (2004: 14)

Thailand R&D were done by government, universities and state enterprises (61 percent), and 38 percent done by private sector or industrial firms (NRCT, 2010:3). This figure is however contrary to those of developed countries, where the activities are mostly done byprivate sector.Actually industrial firms are economic agents havingas a main objective the maximizing of profits. They always consider ways to improve their products or services for 14

“Research and experimental development (R&D) comprise creative work undertaken on a systematic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications”, Frascati Manual (OECD, 2002: 30)

“If the primary objective is to make further technical improvements on the product or process, then the work comes within the definition of R&D. If, on the other hand, the product, process or approach is substantially set and the primary objective is to develop markets, to do pre-production planning or to get a production or control system working smoothly, the work is no longer R&D” (OECD, 2002: 42). We could categorize the latter case as innovation activities. 15

“Technological product and process (TPP) innovations comprise implemented technologically new products and processes and significant technological improvements in products and processes. A TPP innovation has been implemented if it has been introduced on the market (product innovation) or used within a production process (process innovation), Oslo Manual (OECD, 1997: 31).


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higher added value and many of them know that one way to do so is to improve their technology for product or service innovation. Institutional factors of the National Innovation System (NIS)16, or we can simply call the technological environment of firms, are important factors in making decisions on the budget allocation between their normal operations and technological investment or R&D activities. As Patarapong Intarakamnerd, et al. (2002: 1455) proposed that for the studies of NIS in countries less successful in technological catching-up like Thailand, it should focus not only on how innovation related activities start and improve over time, but alsoon factors contributing to stagnancy and those contributing to the long-running perpetuation of weak and fragmented NIS systems. Then, the studies related to the factors encouraging firms to decide to carry out R&D activities are necessary inputs for government to improve its policies and mechanisms that motivate and facilitate firms to initiate and run their R&D projects efficiently. There were some studies related to firms’ R&D activities in Thailand, such as Peera Charoenporn (2005a: 89-122, 2005b: 15-34) and Direk Patmasiriwat (2010: 76 - 106). The studies are investigations of the firms’ decisions to carry out R&D activities as well as the R&D spending. Since the former works were rarely used and there are recommendation to explore the issues with, or related to institutional frameworks (Patarapong Intarakamnerd et al., 2002: 1455, Peera Charoenporn, 2005a: 117), which could explain more about thebehavior of firms, this study will apply an institutional framework to help in analyzing Thai industrial firms’ decision to pursueR&D and innovation activities. This study aims to investigate the behavior of Thai industries’ R&D and innovation activities so that it could suggest to the related parties how to understand and provide proper responses to encourage overall competencies. The specific objectives of the study are as follows: 1. To investigate and develop conceptual models, using new institutional economic approaches, explaining firms’ decisions on technical improvement or R&D activities. 2. To examine the factors contributing to Thai industries’ decision regardingR&D activities. 3. To examine the impact of institutional factors onThai industries’ expenditures for R&D activities. 4. To recommend the government support the policyfor R&D and innovation activities of Thai industries. The next section reviews the related literature of R&D and innovations, and institutional frameworks. Theoretical models explaining firm's decisions related to technological investment are presented in section 3. Section 4 describes methodologies employed and data description. Section 5 discusses the empirical results, the effect of socioeconomic and institutional factors on the probability of carrying out R&D or innovation activities and related decisions. Conclusion of the study and discussion are presented in section 6.

2. Literature Reviews 16

The NIS is the interactive system of existing institutions, private and public firms (either large or small), universities and government agencies, aiming at the production of science and technology (S&T) within national borders. Interaction among these units may be technical, commercial, legal, social and financial as much as the goal of the interaction may be development, protection, financing or regulation of new S&T (Niosi et al., 1993 quoted in Patarapong Intarakamnerd et al., 2002: 1446)


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Two interrelated issues are reviewed in this sectionto develop the the oretical framework for analyses, the technological development including R&D and innovation activities of industrial firms, and the institutional economics approach in analyses. The oretically, Amir (2000) described extensive comparison of the well-known R&D models of d’Aspremont-Jacquemin (AJ) and Kamien-Muller-Zang (KMZ). He analyzed the two models of R&D decisions, and summarized that the KMZ model is probably more appropriate as a model for strategic R&D with spillovers and can apply broadly to a generic industry, on the other hand, the AJ model may be adequate for certain industries with R&D processes. The group of works containing the largest portion of literature, is about the factors that influence the R&D activities. Some of which are reviewed here; those are Bae and Noh (2001), Zedtwitz and Gassmann (2002), Czarnitzki and Licht (2005) and Griffiths and Webster (2010). ( ) The factors found, from their studies, to have an effect on the decision for R&D activities are the degree of a firm’s multinationality, g y differences in R&D internationalization, distribution of public R&D subsidies, past profits, the rate of growth of the industry, and the level of R&D activity over the firm's industry Another group of studies are about the impact of R&D. Two examples of those are studied by Falk (2007) andCoccia (2012), they analyzed the association between R&D expenditure and the macro economic variables to see the impact. There were some studies related to firms’ R&D activities in Thailand. Peera Charoenporn (2005a: 89-122 and 2005b: 15-34) investigated the determinants of the firms’ decision to carry out R&D and innovation of firms, and found that competitive market conditions, the structure of industrial production, firm size, the availability of physical resources, human resources and technology resources influenced firms’ decisions to carry out R&D activities, and that contextual variables, business environment conditions, firm-internal competencies, strategic variables, and external communications are the determinants of success in innovation activities. Healso recommended that future studies should include evolutionary economics and a transaction cost economic perspective in the analytical framework. Another study was done by Direk Patmasiriwat (2010: 76 - 106). He investigated R&D spending of industrial enterprises in Thailand between 2001 and 2006, and found that only 8.4 percent of industrial enterprises had R&D budgets, the sum of R&D budgets over five years amounted to 16,316 million baht but the figure showed that R&D spending over time was increasing, and the research intensities varied significantly depending on the industry’s ISIC classification code. From this pointto the end of the section, New Institutional Economics (NIE) literatures and some applications are reviewed. As stated in many literatures, the NIE is one of the major developments of economic theory in the past few decades. Matthews (1986: 903) stated that the economics of institutions has become one of the liveliest areas in our discipline. It has brought us more closely in touch with a number of other disciplines within the social sciences. The reasons why NIE has grown are the limitations of mainstream neoclassical economics, of which assumptions are little concerned, such as frictionless and zero transaction costs, perfect individual rationality, institutions treated as exogenous, and not concerned with governance,


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property rights, and other necessities for controlling human behavior (Coase, 1998:72 and Somboon Siriprachai, 2004:82-83). The other criticized proposition of neoclassical economics was that firms are considered as a production function (Williamson, 1981:548). Marxian and Institutionalist school, both, criticized neoclassical economics for its lack of attention to institutions and hence to the important constraints(Nabli and Nugent, 1989:1336). Some related studies using the institutional economics approach were done by Bardhan (1989) and Nabli and Nugent (1989), they applied the concept to analysesof economic developmentand growth.Anotherappliedto R&D activities is done by Robertson and Gatignon (1998). They assess the factors explaining whether firms will engage in technological alliances or utilize the more traditional mode of internal R&D, up on which the hypotheses stem from a transaction cost conceptualization. They found that firms pursue technologi calalliances are likely to have less commitment to product category-specific assets, face higher technological uncertainty, to be more capable at measuring innovation performance, to have more successful technological alliance experiences, and to compete in lower growth product categories. As the relevant academic literatures reviewed above, the study aims to impose the institutional factors, associated with socioeconomic and business rational, to the theoretical framework, with the main-stream economic conceptual, for analyses of firms’ related decisions on carry out R&D or innovation activities 3. Theoretical Framework Technological investment is one of the strategic decisions of firms under the umbrella of their main objective, profit maximization. Such investment increases firms’ technology level, by both planned and unplanned discovery, and affects the revenue or profit by making new or improved processes for more efficiency, introducing new or higher performance of products and services solving their target customers’ problems. In this section, the conceptual model of firms’ decision on conducting R&D or technological investment to increase their technology level and the related decisions, are introduced. 3.1 Firms’ Decision on Technological Investment. The model developed in the study is a modification of a simplified version of a new growth theory developed by Romer (2006: 101-102). He internalized the technology level by introducing research and development, as a production of new technologies, in the original Solow growth model which take this as given. Then, the model of resources allocation between conventional goods production and R&D was constructed. The market here is considered to be an imperfectly competitive one, with a certain degree of competition varied by industries, so that prices can be different among the firms in the market. The competitive market can be considered as a case in this model, where the price is set to a constant, depending on demand and supply only, and that firms’ product differentiation is not affected. Firms’ objective is to maximize profit, t , over the operation period t. The profit of firms comes from its revenue, which is the product of price, Pt , and number of goods produced, Qt , minus the cost of capital and labors, Ct . The model can expressed primarily as follows:

S


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Max S a Kt ,a Lt

t

Pt [mt , qt ( gt ; At )].Qt [(1  a Kt ) K t ,(1  aLt ) Lt ; At ]  rt K t  wt Lt

(1)

with the budget constraint, Kt

Kt

(2)

Lt

Lt

(3)

The decision variables a Kt and aLt represents the portion of firms’ capital and labor used for R&D activities or technological investment. Firms’ decision, then, is to allocate their resources for production, (1  aKt ) Kt and (1  aLt ) Lt , and the rest for the R&D activities to maximize expected profit over the considered period. The more usage of resources in R&D activities, the less for production. But, it is expected to increase the technology level that makes for higher efficiency and revenue in return. The price, Pt , depends on the market demand and supply, and firms’ product or differentiation, m t , and the comparative quality or how differentiated of firms’ product or services value perceived by customers, qt , which is related to its general business management, gt , and technology level, At .The production function Qt (˜) indicates that firms use two main resources in producing goods or services to the customers, these are capital, K t , labor, Lt , and with technology level At . The change of technology level is a production function of new technologies or advancement, which is a function of labor, capital used in R&D activities and the level of technology in that period, Rt (aKt Kt , aLt Lt , At ) .The tangible resources K t and Lt are given as planned over the period, and cost of capital and labor at period t are rt and wt respectively. In this study, it is not specific whether technology is capital-or laboraugmenting, firms can under take both labor- and capital augmenting technological impro vements. The firms’ decision for each period t, will depend on the business or industry environment that reflect the price, the level of resources and technology, goods or service production function, and the capability of firms in developing their technologies. Theoretically, holding other conditions unchanged, firms will decide whether to invest in R&D or not by considering marginal profit through R&D investment (both in perspective ( a Kt ) of capital and labor ( aLt )). Virtually, firms will decide to invest in R&D in cases where

S

S

w t t 0 and waKt

w t t 0. waLt Firms will decide to allocate their capital and labor resources to R&D or technological investment activities for higher profit, but they will not do so without having enough confident on such conditions. The allocated resources affect the changes in technology level, wAt , which directly contribute to firms’ profit, but they cause the firms’ resources for their production or services reduced and may lead to the firms losses in total. Within the budget constraint, the allocations can be continued until the marginal profit from the allocation is


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zero. Firms can gradually increase their technology level and accumulate retained profit each year for more capital in later year investment. As the profit or revenue is the productof Pt (˜) and Qt (˜) , then we get:

S

w t waKt w t waLt

S

wQt wP wA  Qt ˜ t ] ˜ t wAt wAt waKt wQ wP wA [Pt ˜ t  Qt ˜ t ] ˜ t wAt wAt waLt [ Pt ˜

(4.1) (4.2)

Equations (6.1) and (6.2) explain how allocated resources affect the firms’ profit, via higher technology level, At , which contributed to both the price Pt through the function qt ( gt ; At ) that increase with the technology level,and the quantity Qt , of production. The higher technology level can make the products or services of firms more value differentiated for their customer such as higher quality, reliability, safety, more function of usages. This canenable firms to sell their products or services at higher prices. Technology level also reflects the efficiency of production, the higher level of which could make firms produce more products with the same resources, and making more revenue in turn. The results of the optimization, which is considered as business or fundamental rational, can be written as follow:

aKt

BKt ( Pt , Kt , Lt , At )

(5.1)

aLt

BLt ( Pt , Kt , Lt , At )

(5.2)

In this study, we use firms socioeconomic variables and some of business conditions or environment, referred as contextual variables (Peera Charoenporn, 2005b: 15-17), which would influence the decision of technological investment, as suggested in certain studies, those are industry group, ownership status, number of total employees (Wolfe, 1994), ownership status (Bae and Noh, 2001), experiences of firms in business (Nejad, 1997, Czarnitzki and Licht, 2005), total sales and export portions (Calvert et. al, 1996), categories of manufacturing (Peera Charoenporn, 2005b: 17), and the other relevant technological activities. 3.2 Institutional Attributes of Technological Investment In making a decision on R&D or technological investment, firms would consider institutional factors beside the fundamental business aspect. These include transaction and information costs in searching and acquiring new technologies, culture of firms or industry in adapting new technology, supporting or facilitated by government, competition within their industries, and cooperation among its related company or industrial association. For this reason, we could impose the institutional attributes to the solution equations (6) as follows

aKt aLt

D [P , K , L , A , I (i ,..., i )] D [P , K , L , A , I (i ,..., i )] Kt

t

t

t

t

t

1t

nt

(6.1)

Lt

t

t

t

t

t

1t

nt

(6.2)


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where I t (i1t ,..., int ) is the function of the institutional effects on the decision of a firm in allocating its resources for R&D or technological investment, and the institutional attributes may have positive or negative to the decision outcomes. In the study, some proxies that reflect firms’ institutionalization of R&D or innovation activities, including the government roles, to be explored are sources of fund used for investment, number of R&D staff, sources of information, cooperation with external parties, consequence of recent results (including sale of new products), limitations in carrying out, as referred by former studies (Czarnitzki and Licht (2005), Robertson and Gatignon (1998), Amir (2000). These variables will reflect the institutionalization of firm in carringout innovation activities. The limitations, such as lack of funds, human capital, information and others external limitations, would affect the decision on technological investment. Firms which lack of human capital may contract out other parties, such as research companies or public research institutes or universities to carry out their purposes, but in general we expect that the limitation would discourage firms in R&D or innovation activities. Then, firms’ resources allocated to R&D activities, or technological investment, can be calculated as a portion of their available capital and labor, a Kt K t and aLt Lt , and the total costor expenditures will be ra t Kt K t  wt a Lt Lt . 3.3 Decision on Products and Process Improvements Firms also have to decide to focus on the products, developing a new one or having the old one improved, or the process of production, having more efficiency or lower cost. The portion of each category basically decided by the rational that which part of development contributes more tohigher revenue or profits in the next period. We can use terms in equation (6.1) and (6.2), those are terms Pt ˜

wQt which represents the change in revenue or profit wAt

affected by more quantity produced resulted from R&D in process improvement, and term

wPt which represents such change affected by higher price resulted from R&D in new wAt wQ products or services. Term t represents the effect of technological change to the quantities wAt Qt ˜

of goods or services produced, given the tangible resources unchanged, or we can say it is the efficiency improved by technological change. Term

wPt wAt

represents the effect of

technological change to the price of products or services sold. Since

wPt wAt

wPt wqt ˜ , we can wqt wAt

explain this as the technological changes affect the price through the value perceived by customer, qt , which is directly related to the price at which firms can sell. Firms would focus on the product or service development when the condition is as follows,

Pt ˜

wQt wP  Qt ˜ t wAt wAt

(7.1)


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and will focus on the process improvement when the condition is as follows,

Pt ˜

wQt wP ! Qt ˜ t wAt wAt

(7.2)

However, in practice, firms can combine two portions when making decisions and also, can plan to improve the level of product quality or the process efficiency over successive years within the operation period. From the above conditions, and to maximize firms’ profit over the operation period, firms will decided to allocated there resources and select the proper allocation of development allott efforts, a Kt and a Ltallott , depending on the economic rational and institutional environment, which can be written as:

a

allott

a

allott

Kt

Lt

wQt wPt , , It (i1t ,..., int )] wAt wAt wQ wP allott DLt [Pt ,Qt , At , wAtt , wAtt , It (i1t ,..., int )]

D

allott Kt

[Pt , Qt , At ,

(8.1) (8.2)

Where allot trepresents the allocation of firms’ technology development efforts selected in period t, whether to do only process, only product, or both process and product development. 4. Empirical Estimations. The empirical studie saim to explore the factors affecting the firms’ decision to conduct technological investment, R&D and innovation activities. Related decisions in carrying out such activities, these are the amount of expenditures and effort allocation of investment, are to be included in the study. 4.1. Model Specification. Logistic regression is introduced for testinghypotheses about the factor affecting firms’ decision on conducting R&D or innovation activities. Referred to the results summarized at the end of section 3.2 that firms decide to allocated resources to R&D activities with the amount of ra t Kt K t  wt a Lt Lt , where a Kt and aLt are described with equation 6.1 – 6.2, we adapt for analyses in case that firms decide to conduct R&D activities when a Kt or aLt is positive and decide not to do when both of them are zero, where there is no resources allocated. Then, the outcome whether firms decide to do, or not to do, R&D or innovation activities are considered as a binary variable. In the basic logit model, the dependent or binary outcome variable, y , will be 1 if firms decided to carryout R&D or innovation activities, with probability, p , and be 0 if firms decided not to carry out R&D or innovation activities, with probability (1 - p ) . The probability mass function for the observed outcome, y 1 y y , is p (1  p) , with E ( y ) p , and Var ( y ) p (1  p ) .A regression model is formed by parameterizing p to depend on an index function of X cE , where X is a K x 1


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regressors vector and E is a vector of unknown parameters (Cameron and Trivedi, 2009: 446). The Logit model and variables are defined as per the following, e X ' Ei Pi { Pr( yi 1| x) F ( X icE ) , (1  e X ' E )

(9)

The study will test the hypothesis with another model,of which the alternative distribution for the disturbances to the normal or logistic distribution. The estimator called the scobit estimator, or "skewed-logit", allowing for a skewed response curve, was introduced by Nagler (1994). The model, relaxing assumption that individuals are with an initial probability of .5 of choosing either of two dichotomous alternatives, 0 or 1, and shown to be appropriate where individuals with any initial probability of choosing either of two alternatives are most sensitive to changes in independent variables (Nagler, 1994: 230). Cumulative Distribution for Scobit is shown in Figure 4.1, the model will be the same as another logit model when D , measure of skewness, is equal to one. By adding a parameter to the definition of the distribution, we may attempt to describe a set of distributions with the above criteria (Nagler, 1994: 234).The scobit or skewed logit model can be written here as,

Pi { Pr( yi

1 | x)

F ( X icE , D ) (

e X ' Ei D ) , 1  eX 'E

(10)

List of variables used in this and following models are described in Appendix A. The estimation for the binary model will be done by the maximum likelihood estimator (MLE) method. As indicated in Cameron and Trivedi (2009: 447), for a sample of N independent observations, the MLE, Eˆ , maximizes the associated log-likelihood function

Q( E )

N

ÂŚ[y ln F ( X cE )  (1  y )ln{1  F ( X cE )}] , i

i

i

i

(11)

i 1

The MLE is obtained by iterative methods and asymptotically normally distributed. For estimating firms’ expenditures in R&D and innovation activities, referred to the the oretical summarized in section 3.2 that the cost or expenditure of the activities is ra t Kt K t  wt a Lt Lt , where a Kt and aLt are described with the equation 6.1 – 6.2, we use linear instrumental-variables or IV regression model, which provide a solution for that we have both exogenous and endogenous independent variables in the model. The independent endogenous variable mentioned is firms’ revenue which is influenced directly by the main resources of a company, labor, which also is the important part of dependent variables R&D or innovation activities. The model called a structural equation can be written as

yi

X icE  ui ,

(12)

where the regressors vector X ic [ y 2c i X 1ci ] consists of both endogenous and exogenous variables. By combining instruments for these variables, then we get the vector of IV, zi c

[ X 1i c X 2c i ] , where X1 serves as the instrument for itself and X 2 is the instrument for


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y2 and the instruments z satisfy the conditional moment restriction, E (ui | zi ) and Trivedi, 2009: 173 - 174).

83

0 , (Cameron

The samples used for estimation in this and the next R&D or innovations related parts, are only firms those carried out R&D and innovation activities in Thailand, in 2008, and so, variables representing institution of firms’ activities are included in the estimation models, those are the limitations, information sources, funding sources and external coordination in carrying out the activities. Variables that reflect government support here are included in the institutional ones, here we refer to the universities and public research institutes as the government agents supporting firms in carrying out their tasks of R&D and innovations. In the study, the estimator for IV estimation to be used is the two-stage least-squares (2SLS) estimator, which is one the most efficient estimators, when ui are independent and homoskedastic. The 2SLS estimator can be written as

Eˆ2SLS {X cZ (ZcZ )1 ZcX }1 X cZ (ZcZ )1 Zcy ,

(13)

For testing the hypothesis about the factors affecting firms’ decision on allocation efforts of R&D and innovation activities, we referred to the the oretical models described by equations 8.1 – 8.2 in section 3.3, with some modifications adapting for empirical analyses. We use the unordered multinomial logit (MNL) model introduced by McFadden (1973), a widely used choice model due to its simple mathematical structure and ease of estimation for regressors specific case. In the MNL here, outcome yi is one of alternatives, among m choices, that firms choose or decide. The model specified probability that the outcome for individual i is alternative j, conditional on the regressors X i , is

Pij { Pr( yi

j | Xi )

Fj ( X icE )

e XicEi

¦

m l

e XicEl 1

,

j 1,..., m,

i 1,..., N

(14)

where X i are case-specific regressors. Only m - 1 of probability can be freely specified because probabilities sum to one. The model ensures that 0 < p ij < 1 and

¦

m j 1

pij

1 . To

ensure model identification, we set E j to zero for one the category, called the base category, and coefficients are interpreted with respect to that category (Cameron and Trivedi, 2009: 484, and Greene, 2009: 721). The estimation for MNLisalso by maximum likelihood (ML). The density for the ith individual is written as

f ( yi )

piyi1 1 u ... u pimyim

m

–p

yij ij

,

(15)

j 1

where yi1,..., yim are m indicator variables with y ij

1 if yi

j and otherwise y ij

0 . For

each individual, one of y1 ,..., yim will be non-zero. As indicated in Cameron and Trivedi


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(2009: 447) and Greene (2009: 721), for a sample of N independent observations, the MLE, Eˆ , maximizes the associated log-likelihood function

ln L( E )

N

m

¦¦ y

ij

ln Fj ( X icE ) ,

(16)

i 1 j 1

The hypotheses to be tested, using MNL, are factors explaining the firms’ decision to choose alternative efforts of R&D or innovation activities, whether to do R&D or innovate only in process17, or only product18, or both process and product19. 4.2. Data The study uses the firm-level data from Thailand R&D and Innovation activities survey in Industrial sector 2009, carried out by the National Science Technology and Innovation Policy Office (STI, 2009). The survey, using standard definitions of the R&D activities referring to Frascati Manual (OECD, 2002) and Innovation activities referring to Oslo Manual (OECD, 1997), had statistically sampling selected a total of 8,174 firms from 27,022 firms of 23 manufacturing sectors and 6 services sectors, whose revenues were greater than 12 million Baht in 2008 (STI, 2009: 11- 12). Of the 8,174 sampled, a total of 3,230 completed questionnaires or approximately 40% are used for analyses. The list of variables description is attached in Appendix A. A summary of sample profiles are presented here. Of the total 3,230 samples from different industries, we categorize, by ISIC code (International Standard Industrial Classification of All Economic Activities), into 5 groups,ISIC group 1–3 consist of a total of 2,613 firms from manufacturing industries and, ISIC group 6-7 consist of 617 firms fromthe services industries. Most of the samples or a total of 2,218 firms are wholly locally owned companies, where 425 are wholly foreign-owned and the rest of the 587companies are joint-ventures between local and foreign share holders. The overall average experiences of firms, years from establishment, are around 17 years. The highest value of 18.50 years is the firms in ISIC group no. 6 and the lowest value of 12.37 years is of the firms in ISIC group no.7. The average of firms’ total employees is 429 persons. The highest number is of firms in ISIC group no.6, with the value of 1,308 persons and the lowest are of firms in ISIC group no.2, with the value of 275 persons. The average sales of sample firms are 2,558 million THB. The highest value of 5,003 million THB is of firms in ISIC group no.6 and the lowest value of 278 million THB of firms in ISIC group no.7. We notice that the sales amount of firms is not a normal distribution.

17

In the study, firms carried out “R&D in process” are those distributed more than 60% of R&D expenditure to improve existing or develop new working process. Firms which carried out “process innovation” are those which introduced new or significantly improved process; such as methods of manufacturing, logistics, delivery, or distribution methods, or supporting activities of processes e.g. maintenance systems, operations for purchasing, accounting or computing. 18 In the study, firms which carried out “R&D in product” are those which distributed more than 60 percent of R&D expenditure to improve existing or to develop new product. Firms carried out “product innovation” are those which introduced new or significantly improved their products. 19 In the study, firms which carried out “R&D in process and process” are those which distributed more than 40 percent of R&D expenditure for each alternative. Firms which carried out “process and product innovation” are those which innovated both process and product.


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In a summary of firms which carried out R&D (either in Thailand or foreign) or innovation activities (in Thailand) (RDI) in year 2008, we have a total of 707 firms or 22 percent of samples pursued such activities. Firms in manufacturing industries (ISIC group no.1-3), averaged 23-29 percent, carried out the activities more than those in the service industries (ISIC group no.6 -7), averaged 11-14 percent. And when categorized by owner status, the wholly foreign-owned companies, averaged 41 percent, carried out RDI activities more than those of joint-venture, averaged 29-37 percent, and those of wholly locally owned companies, averaged 15 percent. 5. Results A summary of the estimated results of related firms’ decisions, the expenditures and allocation efforts of such activities are described here. Likewise R&D activities, studies of innovation activities were carried out but we skipped discussion in detail, the empirical results of innovation activities are presented in Appendix B. Table 1 reports the estimates of the firms’ decision to carry out R&D in Thailand in the year 2008, using the scobit model as equation (15). The coefficients of the model are exponentiated so that we can interpret them as oddsratios, which are easier for interpretation. The odds ratios indicate how one unit change of each independent variable would affect, increase or decrease by a certain factor, the odds that the outcome variable of being “1” versus “0”, here is that odds of firms which decided to carry out R&D in Thailand (rdlocal = 1) versus not to carry out (rdlocal = 0). Variable measuring skewness D ,isto be tested. The likelihood-ratio test at the bottom of the table, rejects Ha: D = 1, indicates that the model is significantly, at 1%, different from a normal logit model. The variables total_sale (in log)andwholly foreign-owned, significance at 1%, techact and1-50% locally owned, significant at 5%, affect the odds of firms decided to carry out R&D versus not to carry out. Holding other variables at a fixed value, odds ratio of variable total_sale (in log) indicates that a 1% increase in sale increases the odds of firms’ deciding to carry out R&D (versus not to carry out) increased by factor of 1.48. Having other technological activities in Thailand (techact = 1), the odds increase by a factor of 5.74 or 474% higher. Five other variables which significantly increases the odds, but with lower effect or less than 10%, are employee, experience, salese,sodm and,sobm. The results can be interpreted as per the following, holding other variables at a fixed value, one unit change inthe total number of employees, experiences of firms, export portion of firms’ revenue, portions of sales to parent company, sales as ODM, and sales OBM, increase the odds of firms’ deciding to carry out R&D (versus not to carry out) by factors of 1.0006, 1.0902, 1.0094, 1.0155 and 1.0196 respectively. The other variables significantly, but decreasingly, affecting the odds are two firms with group ownership status of 1-50% locally owned and wholly foreign-owned. Holding other variables with a fixed value, odds of firms’ with ownership status of 1-50 percent locally owned and wholly foreign-owned decided to carry out R&D (versus not to carry out) decrease from those of firms with wholly locally-owned (the reference group) by factors of 0.25and 0.24, or 75% and 76% lower, respectively.


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The estimated results of firms’ expenditure in R&D activities are presented in Table 2. The percentage difference of R&D expenditures among the firms can be significantly explained by four variables, those are total_sale (in log), rdstaff, exco1 andexco2. We can interpret that a percentage difference of firms’ total sales resulted in a 34 percent difference of R&D expenditures. The expenditures increase by 2 percent when firms acquire one more R&D staff, and also increased by 24 percent if firms cooperate intensely with business partners or universities or public research institutes in carrying out R&D or innovation activities. The information sources of firms’ R&D or innovations, either from within the company or associate companies, business partners, and universities or public research institutes, do not affect the difference in the R&D expenditures. Table 3 reports the estimated coefficients of the firms’ decision on the allocation effort (rdallot) of carrying out R&D in Thailand in the year 2008, by using multinomial logit model as presented in equation (19). The coefficients of the estimation are exponentiated so that we can interpret them as relative risk ratios, which indicate the factor ratio, for one-unit increase of predictor variable for being a certain outcome versus the base outcome. The outcomes include carrying R&D only process, only product, or both process and product.The decision to carry out R&D only in product (rd_prod) is assigned to be the base outcome, since most firms selected this alternative. The likelihood ratio chi-square of 87.33 with a P-value less than 0.001 indicates that model as a whole fits significantly better than an empty model. Five predictors which significantly affect the odds of firms deciding to carry out only R&D in process (rd_process) versus only R&D in product (base outcome) are 3.isic1, sobm, info1,info2 and urco. The relative risk ratio switching from ISIC group no.1 (reference group) to no.3 (3.isic1) is 3.74 for carrying only R&D in process versus only in product. The ratios of having their own company or associated (info1), business partners (info2) as the important sources of information, and having engagement frequently in R&D or innovation activities with universities/public research institutes (urco) are 3.51, 2.84 and 0.46 respectively, for carrying only R&D in process versus only in product. For a one-unit increase in portion of OBM sales (sobm), the relative risk ratio is 0.98 for carrying only R&D in process versus only in product. The odds of firms deciding to carry out both R&D in process and product (rd_product _process) versus only R&D in product (base outcome) are significantly affected by seven predictors, those are 2.isic1,3.isic1, 4.owner, sobm, rdfund1, info3 and exco3.The relative risk ratio switching from ISIC group no.1 (reference group) to no.2 (2.isic1), to no.3 (3.isic1), and from wholly locally-owned (reference group) to 1-50% locally owned (4.owner) are2.15, 2.92and 0.13 respectively, for carrying both R&D in process and product versus only in product.For a one-unit increase in the portion of OBM sales (sobm) and the portion of own funds used in R&D (rdfund1), the relative risk ratios are both 0.99 for carrying both types of R&D. The ratios of having universities, other higher educational institutes as important sources of information for R&D (info3), and cooperating intensely with institutes other than business partners and universities or public research institutes (exco3) are 0.46 and 2.26 respectively, for carrying both types of R&D in process and product versus only in product.


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Table 1:

Estimation of R&D Activities in Thailand(Skewed Logistic Regression) (N = 2,609)

rdlocal isic1 2 : (201 - 293) 3 : (300 - 372) Owner 71-99% locally owned 51-70% locally owned 1-50% locally owned Wholly foreign-owned employee techact experience total_sale (in log) salese sparent soem sodm sobm Constant /lnalpha alpha

Odds Ratio

Std. Err.

P-value

1.1333 0.5946

0.3819 0.2259

0.7100 0.1710

2.4968 1.9978 0.2518** 0.2368*** 1.0006* 5.7386** 1.0902*** 1.4842*** 1.0094** 1.013 0.9888 1.0155* 1.0196** 0.0001*** -2.3684*** 0.0936

1.8423 1.1411 0.1438 0.1163 0.0003 3.8955 0.0258 0.1781 0.0043 0.0084 0.0075 0.0083 0.0084 0.0001 0.3770 0.0353

0.2150 0.2260 0.0160 0.0030 0.0650 0.0100 0.0000 0.0010 0.0280 0.1200 0.1390 0.0600 0.0190 0.0000 0.0000 -

Notes : 1.) * significant at 10% ; ** significant at 5% ; ***significant at 1% Table 2:

Estimation of R&D expenditures (Instrumental variables (2SLS) regression)(N=369)

lnrdexp total sale (in log) experience salese rdfund1 rdstaff info1 info2 info3 info4 exco1 exco2 exco3 urco Constant

Coefficient 0.3450*** -0.0032 -0.0029 -0.0055 0.0212*** 0.0383 -0.0686 -0.2134 1.3728 0.2404* 0.2462* 0.0312 -0.0462 6.7759***

t-ratio 3.9300 -0.6300 -1.4800 -1.6300 6.4500 0.2900 -0.4300 -1.4400 1.2900 1.6800 1.6600 0.2300 -0.3300 3.5800

P-value 0.0000 0.5300 0.1390 0.1040 0.0000 0.7710 0.6660 0.1500 0.1990 0.0940 0.0980 0.8170 0.7390 0.0000

Notes : 1.) * significant at 10% ; ** significant at 5% ; ***significant at 1%

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Table 3:

Estimation of Firmsâ&#x20AC;&#x2122; allocation efforts of R&D activities (Multinomial logistic regression) (N = 331) Rdallot

RRR

Std. error

P-Value

1.0500 3.7366**

0.5437 2.0783

0.9250 0.0180

1.0142 1.2234 0.6351 0.6045 1.0000 833,610 1.0211 0.9789*** 1.00 1.00 0.9875 0.9960 3.5144** 2.8413* 0.8932 0.5413 0.6232 0.9963 0.4605* 0.0000 (base outcome)

0.8679 0.7907 0.5224 0.4204 0.0002 685,000,000 0.0151 0.01 0.14 0.01 0.0100 0.0104 1.8441 1.7510 0.4761 0.2674 0.3587 0.4859 0.2033 0.0004

0.9870 0.7550 0.5810 0.4690 0.9520 0.9870 0.1580 0.0000 0.9920 0.4960 0.2150 0.7010 0.0170 0.0900 0.8320 0.2140 0.4110 0.9940 0.0790 0.9860

2.1459* 2.9248**

0.8564 1.4229

0.0560 0.0270

0.8744 0.9899 0.1318** 0.5647 1.0001 2.7968 0.9803 0.9911** 1.0677 1.0067 0.9858* 1.0038 1.5069 1.3659 0.4558* 0.9438 0.7891 2.2622** 0.8169 0.0856

0.5093 0.4904 0.1173 0.2911 0.0001 3.1998 0.0137 0.0037 0.1085 0.0048 0.0079 0.0066 0.5480 0.5892 0.1933 0.3749 0.3263 0.7883 0.2982 0.2009

0.8180 0.9840 0.0230 0.2680 0.3180 0.3690 0.1550 0.0170 0.5190 0.1580 0.0730 0.5650 0.2590 0.4700 0.0640 0.8840 0.5670 0.0190 0.5800 0.2950

rd process isic1 2 : (201 - 293) 3 : (300 - 372) owner 71-99% locally owned 51-70% locally owned 1-50% locally owned Wholly foreign-owned employee techact experience sobm total_sale (in log) salese rdfund1 rdstaff info1 info2 info3 exco1 exco2 exco3 urco _cons rd product rd product process isic1 2 : (201 - 293) 3 : (300 - 372) owner 71-99% locally owned 51-70% locally owned 1-50% locally owned Wholly foreign-owned employee techact experience sobm total_sale (in log) salese rdfund1 rdstaff info1 info2 info3 exco1 exco2 exco3 urco Constant

Notes : 1.) * significant at 10% ; ** significant at 5% ; ***significant at 1%


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6. Conclusion and Discussions The study examines three hypotheses according to theoretical models; those are the variables of socioeconomic, business environment and institutional factors which significantly explain the firms’ decisions on carrying out R&D and innovation activities, the expenditures andallocation efforts of such activities. The results show that most of the firms’ characteristic and business environment variables; including ownership status, number of employees, experience in business, total sales and categories of sales, are significantly related to the probabilities to carry out R&D or innovation activities. Locally-owned firms tend to carry out R&D and innovation more than foreign majority firms. Firms having higher sales or carrying other technological activities are also highly tentative to carry out such activities. The expenditures for R&D can be explained significantly by total sales, number of R&D staff, and external cooperation with business partners and universities or public research institutes, all of which are in a positive direction. For innovations activities, the expenditures are significantly related to total sales, export portion, experiences, and results of former activities in recent years. Decisions on effort allocation of firms’ R&D and innovations activities are different. For R&D, firms tend to do product research more than process or carry out both of them. The factors that significantly relate to the firms’ decision to carry out process or both product and process are industry group, information from parent and associate companies,information from business partner, and intense cooperation with other institutes. For innovation activities, firmstend to carry out both product and process, except for some industry groups that tend to carry out more on product only. The reason may be they have to commercializequickly, so they have to plan and develop their production or service process to be ready oncethe product development is finished. In general, the institutional factors can explain some behavior of firms in carrying out R&D or innovation activities as described above, of which some are in the part of government roles. From the evidence, government support that significantly relates todecisions on R&D and innovation activities are information from universities, or public research institutes which affect the allocation effortsof R&D activities, cooperation with universities or public research institutes which affect the expenditure, and frequently engagement with universities or public research institutes which affect the allocation effort of R&D activities. From the study, there are policy implications which are proposed to be implemented. Firstly, as information is the important factor for firms in making decisions on R&D and innovation activities, government should provide, or make firms to be able to access enough information or proper knowledge for industries, the services or supporting mechanisms of government, and contacts of supporting agentsusing integrated communication to target firms or industries. Events such as public events for intellectual property marketing or R&D fairs should be arranged periodically. This would givemore chances for firms to explore, plan and decide to invest in proper technological development. Secondly, as the results show that external cooperation influences the decision onR&D and innovation, since cooperation or technology alliances can reduce R&D costs by dropping out redundant activities, sharing knowledgeand enhancing more outcomes effectively,


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government must continually support and play a more active role in facilitating the network and cooperation of both public and private,and among private industrial firms. The co-operation could be contracting or conducting joint research projects or other activities such as hiring academic consultants, technology licensing, using analytical and testing services or other technical infrastructure, training and personnel exchanges, meeting or conference, and even informal personal contacts. Government should also support firms to have more chances for asoft-loan, grantedin a specific field of technology, or having a tax rebate program for R&D and facilitate matching funds for technology investment. The available funds or cost reduction should make firms comfortable in making a decision on their choices of R&D and innovations. The important thing to be done is matching required technology or development of industrial firms to the researchers of universities or public research institutes. These will be the best answer to research problems for researchers since, if it succeeds, it will be fully used and commercialized in the real economic sectors. The public research institutes should also be enthusiastic to acquire research topics from outside-in or industries (users) oriented approach, instead of just following their interests or having competence without any requests from users. The government must play this important role of promoting and subsidizing the institu tionalization of joint R&D or innovation activities between public and private firms, setting its policy to target the real commercialization outcomes to the research institutes, and having effective control and monitoring procedures.In terms of individual joint R&D project, the government may have to subsidize or realize some loss in knowledge creation, but it will eventually have more benefit to the economy when fully considered in the long run. Finally, the government should encourage technological activities other than R&D, such as acquisition or adaptation of external technology, reverse engineering, basic and detail design, testing and quality control of products or processes, since they are significantly lead to the firmsâ&#x20AC;&#x2122; decision to carry out more R&D and innovations. Not with standing, there are some new development by public research institutes in Thailand in cooperation at more concrete levels than in the past. National Research Administration Network (NRAN), consisting of leading public research institutes relating to scientific fields20, was founded in 2012 according to the national research strategy to bring research outputs to solve problems and increase the competitiveness of the country. The NRAN members work together in national research project management, focusing on the same target issues and thoroughly supporting each other to bring the outputs to commercialization.At present, the five issues they focus onare rice,cassava, rubber, logistic and tourism. This is a good evolution in Thailand R&D society, but it has to be done continually and it will take length of time to see the effectiveness and sustainability of the operation. Related parties must support this for future success, not just be initiated by politics and to fade away in the later. There are two recommendations for further studies. Firstly, the next phase of study should be done by panel data analyses, since many R&D or innovation activities cannot be finished completely in one year and decisions are more proper to be considered as a dynamic model. It 20

The group consists of seven related public research institutes; National Research Council of Thailand, The Thailand Research Fund, Agricultural Research Development Agency (Public Organization), Office of the Higher Education Commission, Health Systems Research Institute, National Science Technology and Innovation Policy Office, and National Science and Technology Development Agency.


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is also required that data collection and recording are systematic and suitable for such analyses. Secondly, more institutional issues; socioeconomic conditions, political and business environment,specifically to industries or geographic region concerning firmsâ&#x20AC;&#x2122; decision on R&D and innovation activities should be explored to help handle the problems of institutionalization.


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References Amir, R. (2000) "Modeling Imperfectly Appropriable R&D via Spillovers International", Journal of Industrial Organization, 18: 1013–1032. Bae, S.C. and Noh, S. (2001) "Multinational Corporations versus Domestic Corporations: A Comparative Study of R&D Investment Activities",Journal of Multinational Financial Management, 11: 89–104. Bardhan, P. (1989)"The New Institutional Economics and Development Theory: A Brief Critical Assessment", World Development, 17(9): 1389-1395. Brousseau, E. and Glachant, J.M. (2008). New Institutional Economics, A Guide Book, Cambridge: Cambridge University Press. Coccia, M. (2012) "Political Economy of R&D to Support the Modern Competitiveness of Nations and Determinants of Economic Optimization and Inertia",Technovation, 32: 370–379. Czarnitzki, D. and Licht, G. (2005)Additionality of Public R&D Grants in a Transition Economy: the Case of Eastern Germany, Mannheim: Centre for European Economic Research. Direk Patmasiriwat. (2010) "R&D Expenditures by Industrial Enterprises in Thailand",Thammasat Economic Journal, 28(2): 76–106. Falk, M. (2007)"R&D Spending in the High-Tech Sector and Economic Growth",Research in Economics, 61: 140–147. Griffiths, W. and Webster, E. (2010)"What Governs Firm-Level R&D: Internal or External Factors?",Technovation, 30: 471–481. Matthews, R.C.O. (1986) "The Economics of Institutions and the Sources of Growth",The Economic Journal, 96(384): 903-918. McFadden, D. (1973) "Conditional Logit Analysis of Qualitative Choice Behavior", in Frontiers in Economics, New York: New York Academic Press, 105 - 142. McFadden, D. (1980) "Econometric Models for Probabilistic Choice Among Products",Journal of Business, 53(3): S13 - S29. Ministry of Science and Technology (MOST) (2004) National Science and Technology Strategic Plan (2004 – 2013), Bangkok: Ministry of Science and Technology. Nabli, M.K. and Nugent, J.B. (1989)"The New Institutional Economics and Its Applicability to Development", World Development, 17(9): 1333-1347. Nabli, M.K. and Nugent, J.B. (1989A) The New Institutional Economics and Development: Theory and Applications to Tunisia, Amsterdam: Elsevier. Nagler, J. (1994) "Scobit: An Alternative Estimator to Logit and Probit",American Journal of Political Science, 38(1): 230-255. National Research Council of Thailand (NRCT) (2010) National Survey on R&D Expenditure and Personnel of Thailand, Bangkok: NRTC. National Science Technology and Innovation Policy Office (STI) (2009).Thailand R&D and Innovation Activities Survey in Industrial Sector, Bangkok: STI. OECD (1997)Oslo Manual: Proposed Guidelines for Collecting and Interpreting Technological Innovation Data, 2nd Edition, OECD Publishing. OECD.(2002) Frascati Manual: Proposed Standard Practice for Surveys on Research and Experimental Development, The Measurement of Scientific and Technological Activities, OECD Publishing. Patarapong Intarakumnerd, Pun-arj Chairatana and Tipawan Tangchitpiboon.(2002) "National Innovation System in less Successful Developing Countries: The Case of Thailand", Research Policy, 31: 1445–1457.


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Peera Charoenporn. (2005a) "The Determinants of the Firmsâ&#x20AC;&#x2122; Decision to Carry out of R&D Activities in Thai Manufacturing Sector", Thammasat Economic Journal, 23(3): 89122. Peera Charoenporn. (2005b) "On the Determinants of Successful Innovative Firms in Thai Manufacturing Sector",Journal of International Development and Cooperation, 12(1): 15-34. Robertson, T.S. and Gatignon, H. (1998)"Technology Development Mode: A Transaction Cost Conceptualization",Strategic Management Journal, 19: 515 - 531. Romer, D. (2006).Advanced Macroeconomics, New York: McGraw-Hill/Irwin. Somboon Siriprachai. (2004) "New Institutional Economics: A New Approach to Pass Over the Mainstream Analyses?",Thammasat Economic Journal, 22(4): 82-108. Williamson, O.E. (1981) "TheEconomics of Organization: The Transaction Cost Approach", American Journal of Sociology, 87(3): 548-577. Williamson, O.E. (1985)The Economic Institutions of Capitalism, New York: The Free Press. Williamson, O.E. (1998) "The Institutions of Governance", American Economic Review, 88(2): 75-79. Zedtwitz, M. and Gassmann, O. (2002)"Market versus Technology Drive in R&D Internationalization: Four Different Patterns of Managing Research and Development", Research Policy, 31: 569â&#x20AC;&#x201C;588.


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APPENDIX A. Variables Description Table A.1 Variables Description Variables rdlocal inv techact

info1 info2 info3

exco1

exco2

exco3 urco

limf1 limf2 limf3 limf4 lnrdexp lninvexp employee experience total_sale (in log) salese soem sodm sobm rdfund1 rdstaff salenew

Description firms'decisionto carry R&D (dummy variable) firms'decisionto carryinnovation (dummy variable) firms carried out other technological activities in Thailand in 2008(dummy variable) (such as acquisition or adaptation of external technology, reverse engineering, basic design, detail design, testing and quality control) important information for R&D are within the company or from parent and associate companies (dummy variable) important information for R&D are from clients, or locally- or foreign-owned suppliers (dummy variable) important information for R&D are from universities, other higher education institutes or public research institutes (dummy variable) cooperation intensely with “business partners”, including customers, buyers, suppliers, or parent or associate company overseas, in R&D or innovation activities (dummy variable) cooperation intensely with “universities or public research institutes” in R&D or innovation activities (dummy variable) cooperation intensely with “other institutes” in R&D or innovation activities (dummy variable) engagement frequently in R&D or innovation activities with universities/public research institutes (dummy variable) “lack of funds” was an important limitation factors for innovation activities (dummy variable) “lack of human capital” was an important limitation factors for innovation activities (dummy variable) “lack of information” was an important limitation factors for innovation activities (dummy variable) “other external difficulties” was an important limitation factors for innovation activities (dummy variable) log of R&D expenditure (THB) log of innovation expenditure (THB) number of firms’ total employees (persons) experiences of firms measured by years from established (years) log of firms’ total sales in 2008 (thousand THB) export portion of firms’ total sales in 2008 (%) portion of sales as original equipment manufacturing or OEM (%) portion of sales as own design manufacturing or ODM(%) portion of sales as own brand manufacturing or OBM(%) portion of company’s own fund used for R&D expenditures (%) number of firms’ R&D staffs (persons) portion of sales from improved products introduced in 2006 - 2008 (%)

Obs. 3230 3230 3230

Means 0.1160991 0.1362229 0.8668731

Std. 0.3203933 0.3430782 0.3397645

707

0.4144272

0.4929716

707

0.4554455

0.4983635

707

0.1683168

0.3744123

707

0.4059406

0.4914208

707

0.1881188

0.3910837

707

0.1838755

0.3876569

707

0.4002829

0.4903025

440

0.4090909

0.4922257

440

0.4590909

0.4988909

440

0.5613636

0.4967851

440

0.4909091

0.5004864

371 307 3229 3230

14.92588 15.15484 429.3809 17.03746

1.434033 1.725243 1533.494 10.69363

3230 3230 2610

19.10132 25.30879 43.63897

1.965741 35.6069 45.94782

2610 2610 3227

13.01226 27.98123 10.79421

29.70785 41.81531 30.53398

3230 3211

1.939628 5.639069

10.44052 19.81895


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Table A.2 Variables Description (Category Variables) Variable rdallot

invallot

isic1

owner

Description alternatives that firms carried out R&D activities choose their 1 : only in process 2 : only product 3 : both process and product alternatives that firms carried out innovation activities choose 1 : only in process 2 : only product 3 : both process and product ISIC group 1: group of firms with ISIC code 151 - 192 2 : group of firms with ISIC code 201 - 293 3 : group of firms with ISIC code 300 - 372 6 : group of firms with ISIC code 641 - 660 7 : group of firms with ISIC code 721 - 749 Ownership status of firms 1 : Wholly locally owned 2 : 71-99% locally owned 3 : 51-70% locally owned 4 : 1-50% locally owned 5 : Wholly foreign-owned

Freq. 356 43 231 82 440 77 127 236 3,230 701 1,215 697 141 476 3,230 2,218 143 225 219 425

Percent 100 12.08 64.89 23.03 100 17.5 28.86 53.64 100 21.7 37.62 21.58 4.37 14.74 100 68.67 4.43 6.97 6.78 13.16


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APPENDIX B Empirical Results of Innovation Activities Table B.1: Estimation of innovation activities in Thailand (Skewed Logistic Regression) inv Odds Ratio Std. Err. isic1 2 : (201 - 293) 1.8876 0.9555 3 : (300 - 372) 1.613 0.8116 owner 71-99% locally owned 3.8334 4.2915 51-70% locally owned 3.9979 3.8354 1-50% locally owned 0.1585* 0.1524 Wholly foreign-owned 0.1115*** 0.0935 employee 1.0009 0.0006 techact 26.1863*** 30.8463 experience 1.0899** 0.0429 total_sale (in log) 1.6616** 0.3822 salese 1.0118* 0.0066 sparent 1.0248* 0.0147 soem 0.9828* 0.0099 sodm 1.0082 0.0098 sobm 1.0176 0.0115 _cons 0.0000*** 0.0000 -2.7899*** 0.5444 /lnalpha Alpha 0.0614 0.0334 Likelihood-ratio test of alpha=1: chi2(1) = 17.60 Prob > chi2 = 0.0000 Number of obs 2609 Zero outcomes 2199 Non-zero outcomes 410 Log likelihood -977.9375

P-value 0.2090 0.3420 0.2300 0.1490 0.0550 0.0090 0.1280 0.0060 0.0290 0.0270 0.0720 0.0880 0.0850 0.4000 0.1230 0.0060 0.0000


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Table B.2 Estimation of Expenditures of Innovation Activities (Instrumental variables (2SLS) regression) lninvexp total sale (in log) experience salese salenew invreslt limf1 limf2 limf3 limf4 info1 info2 info3 exco1 exco2 exco3 urco _cons Number of obs F( 16, 277) Prob > F R-squared Adj R-squared Root MSE

Coefficient 0.5593*** -0.0142* -0.007** 0.0003 -0.9636** 0.0673 0.2785 -0.3304 0.1787 0.1429 -0.183 -0.1452 0.349 -0.0987 0.1396 -0.1979 5.2717*** 292 2.96 0.0002 0.3012 0.2606 1.4916

t-ratio 5.9100 -1.6900 -2.4800 0.1300 -2.2000 0.3300 1.3000 -1.4600 0.9100 0.6600 -0.7000 -0.6400 1.4900 -0.4200 0.6500 -0.8700 3.0100

P-value 0.0000 0.0910 0.0140 0.8960 0.0280 0.7390 0.1950 0.1460 0.3660 0.5120 0.4830 0.5230 0.1370 0.6740 0.5190 0.3870 0.0030


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Table B.3: Estimation of Firmsâ&#x20AC;&#x2122; Allocation of Innovation Efforts(Multinomial logistic Regression) invallot

RRR

Std. error

P-Value

inv_process isic1 2 : (201 - 293) 3 : (300 - 372) 6 : (641 - 660) 7 : (721 - 749) owner 71-99% locally owned 51-70% locally owned 1-50% locally owned Wholly foreign-owned total_sale (in log) experience techact salese salenew invreslt info1 info2 info3 exco1 exco2 exco3 urco _cons

0.7600 1.9126 2.3633 0.0000

0.3105 0.8691 3,742.0250 0.0020

0.5020 0.1540 1.0000 0.9880

1.4220 0.7412 1.4657 1.5263 1.1358 0.9553*** 1.6389 0.9964 0.9677*** 0.4030 1.0491 0.5181 0.7907 0.4715* 0.5969 1.0292 0.8814 0.6487

0.8635 0.4027 0.9424 0.8327 0.1028 0.0164 2.1399 0.0047 0.0055 0.2243 0.4352 0.2362 0.3855 0.2042 0.2924 0.4276 0.3491 1.4079

0.5620 0.5810 0.5520 0.4380 0.1590 0.0080 0.7050 0.4540 0.0000 0.1020 0.9080 0.1490 0.6300 0.0830 0.2920 0.9450 0.7500 0.8420

1.4635 2.3221** 6.90E+07 32.7894***

0.5010 0.9195 5.91E+10 27.7907

0.2660 0.0330 0.9830 0.0000

1.4159 0.6100 1.4592 1.3237 0.9035 1.0025 1.5899 1.00 1.00 1.50 0.5815* 0.6726 1.1449 0.3449*** 1.3260 0.9591 0.6810 2.8303 (base outcome) 438 -325.3568 223.62 0.0000 0.2558

0.7395 0.2936 0.8617 0.6344 0.0683 0.0111 1.5664 0.0040 0.0036 0.7536 0.1778 0.2475 0.4219 0.1153 0.5173 0.3350 0.2241 4.8672

0.5060 0.3050 0.5220 0.5580 0.1790 0.8230 0.6380 0.9270 0.4150 0.4250 0.0760 0.2810 0.7130 0.0010 0.4690 0.9050 0.2430 0.5450

inv_product isic1 2 : (201 - 293) 3 : (300 - 372) 6 : (641 - 660) 7 : (721 - 749) owner 71-99% locally owned 51-70% locally owned 1-50% locally owned Wholly foreign-owned total_sale (in log) experience techact salese salenew invreslt info1 info2 info3 exco1 exco2 exco3 urco _cons inv_process___product Number of obs Log likelihood LR chi2(46) Prob > chi2 Pseudo R2


วารสารเศรษฐศาสตรปริทรรศน สถาบันบัณฑิตพัฒนบริหารศาสตร NIDA Review2558)99 ปที่ 9Economic ฉบับที่ 1 (มกราคม

บทบาทของการสะสมทุนมนุษยและการหลุดออกจากกับดัก รายไดปานกลางของไทย นิพิฐ วงศปญญา*

Šµœª·‹´¥·Êœœ¸Ê¡¥µ¥µ¤š¸É‹³«¹„¬µÁ„¸É¥ª„´šµš…°Š„µ¦­³­¤­˜È°„š»œ¤œ»¬¥r¨³„µ¦®¨»—°°„‹µ„„´—´„ ¦µ¥Å—ožµœ„¨µŠ…°ŠÅš¥Ã—¥Äoª·›¸ª·›¸—»¨¥£µ¡š´ÉªÅžš¸ÉÁž}œ¡¨ª´˜ (Dynamic stochastic general equilibrium) Ž¹ÉŠÂ‹Îµ¨°Šš¸ÉčoĜ„µ¦«¹„¬µ‡º°Â‹Îµ¨°Š„µ¦Á˜·Ã˜š¸ÉÁ„·—‹µ„£µ¥Äœ (The endogenous growth model) š¸Éž¦³„°—oª¥£µ‡„µ¦Ÿ¨·˜­·œ‡oµ­»—šoµ¥Â¨³£µ‡„µ¦«¹„¬µ š¸É¡´•œµ…¹ÊœÃ—¥ Uzawa (1964) ¨³ Lucas (1988) ‹Îµ¨°Šš¸Éŗo™¼„ž¦´Áš¸¥ (Calibration) „´…o°¤¼¨…°Šž¦³Áš«Åš¥nªŠže 2548-2555 „µ¦ª·Á‡¦µ³®r ‹Îµ¨°ŠšµŠÁ«¦¬“„·‹¤»nŠÁœoœÅžš¸ÉnªŠÁž¨¸É¥œŸnµœ (Transitional dynamic) …°ŠÁ«¦¬“„·‹ ¨³„µ¦œÎµÂ ‹Îµ¨°Šš¸Éž¦´Áš¸¥Â¨oª„´…o°¤¼¨…°Šž¦³Áš«Åš¥¤µž¦³¥»„˜rÁ¡ºÉ°«¹„¬µ™¹Š‡ªµ¤Áž}œÅžÅ—oĜ„µ¦šÎµÄ®ož¦³Áš« Ś¥®¨»—°°„‹µ„„´—´„¦µ¥Å—ožµœ„¨µŠŸ¨„µ¦«¹„¬µÅ—o­—ŠÄ®oÁ®Èœªnµ¤¸‡ªµ¤Áž}œÅžÅ—o­Îµ®¦´Á«¦¬“„·‹Åš¥ š¸É ‹ ³®¨» — °°„‹µ„„´  —´ „ ¦µ¥Å—o ž µœ„¨µŠ ×¥„µ¦Á¡·É ¤ ˜´ ª ¦„ªœš´Ê Š £µ‡„µ¦Ÿ¨· ˜ ­· œ ‡o µ ­» — šo µ ¥Â¨³£µ‡ „µ¦«¹„¬µ …œµ— 3 Ášnµ…°Š‡nµÁ¸É¥ŠÁœ¤µ˜¦“µœ ¨³Á¡·É¤…¹Êœ°¥nµŠ˜n°ÁœºÉ°ŠÁž}œÁª¨µ 60 ئ¤µ­ šÎµÄ®o ž¦³Áš«Åš¥®¨»—¡oœ°°„‹µ„„´—´¦µ¥Å—ožµœ„¨µŠÄœ 16 že®¦º°ž¦³¤µ– 64 ئ¤µ­ ‹Îµ¨°Š‡µ—„µ¦–r ªnµÁ«¦¬“„·‹Åš¥¤¸„µ¦…¥µ¥˜´ªÄœÅ˜¦¤µ­Â¦„Äœ°´˜¦µ¦o°¥¨³ 0.008 ®¦º°¦o°¥¨³ 3.2 ˜n°že ¨³„µ¦…¥µ¥˜´ª…°Š Á«¦¬“„·‹Á¡·É¤…¹ÊœÁ¦ºÉ°¥Ç ‹œ„¦³š´ÊŠ™¹Š¦o°¥¨³ 0.027 ˜n°Å˜¦¤µ­®¦º°¦o°¥¨³10 ˜n°žeĜئ¤µ­š¸É 60 ‹µ„œ´Êœ„µ¦ …¥µ¥˜´ª…°ŠÁ«¦¬“„·‹¨—¨Š¤µ°¥nµŠ¦ª—Á¦ÈªÂ¨³‡n°¥¨—¨Š­¼n°´˜¦µš¸É‡Šš¸É£µ¥®¨´Š‹µ„ئ¤µ­š¸É 80

‡Îµ­Îµ‡´: š»œ¤œ»¬¥r, „µ¦Á‹¦·Á˜·Ã˜š¸ÁÉ „·—‹µ„£µ¥Äœ„´—´„¦µ¥Å—ožµœ„¨µŠ, ž¦³Áš«Åš¥ *

°µ‹µ¦¥rž¦³‹Îµ‡–³Á«¦¬“«µ­˜¦r‹»¯µ¨Š„¦–r¤®µª·š¥µ¨´¥ ™œœ¡µÅš „¦»ŠÁš¡ 10330, Email:Nipit.W@Chula.ac.th


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วารสารเศรษฐศาสตรปริทรรศน สถาบันบัณฑิตพัฒนบริหารศาสตร ปที่ 9 ฉบับที่ 1 (มกราคม 2558)

Role of Human Capital Accumulation and Avoiding the Middle I Income Trap in Thailand Nipit Wongpunya* Abstract This paper investigates the role of human capital accumulation to avoid the middle income trap in Thailand using the dynamic stochastic general equilibrium approachwith the endogenous growth driven by human accumulation based on the Uzawa-Lucas framework. It is a two sector model with two production functions devoted respectively to produce human capital and physical capital. The model is calibrated for the Thai economy during 2005-2012. The transitional dynamics is the main feature to study the possibility of the Thai economy to escape from the middle income trap. The paper shows that output performs monotonically increasing when there are favorable disturbances in two sectors. It asserts that the three time consistent increases in standard deviation ofthe disturbancesfor 60 quarters in the educational sector and production sector could possibly move the Thai economy above the middle income level in 16 years. The model also predicts that the growth rate of Thai economy is 3.2 percent per year in the first quarter and it continuously rises to 10 percent per year in the 60th quarter. Then the growth rate of the Thai economy quickly declines to a constant level after 80 quarters.

Keywords: *

Human Capital, Endogenous Growth, Middle Income Trap, Thailand

Lecturer of Economics– Faculty of Economics, Chulalongkorn University, Phayathai Road, Bangkok 10330, Thailand. Email:Nipit.W@Chula.ac.th


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NIDA Economic Review

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NIDA Economic Review

Ĝ­nªœ…°ŠŠž¦³¤µ–—oµœ„µ¦«¹„¬µŽ¹ÉŠ­³šo°œÄ®oÁ®Èœ™¹Š£µ¡¦ª¤„µ¦Ä®o‡ªµ¤­Îµ‡´˜n°„µ¦¡´•œµ „µ¦«¹„¬µ…°Š¦´“µ¨Äœ“µœ³Ÿ¼o„ε®œ—œÃ¥µ¥ ¡ªnµž¦³Áš«Åš¥¥´Š‡Š¤¸­´—­nªœ…°ŠŠž¦³¤µ–—oµœ „µ¦«¹„¬µ˜n°¦µ¥Å—ož¦³µµ˜·°¥¼nĜ¦³—´˜É夵„Á¤ºÉ°Áš¸¥„´ž¦³Áš«°ºÉœ ץĜžeŠž¦³¤µ– 2552 ž¦³Áš«Åš¥¤¸Šž¦³¤µ–šµŠ„µ¦«¹„¬µ˜n°¦µ¥Å—ož¦³µµ˜·Ášnµ„´ 4.1 Ášnµ„´ž¦³Áš«¤µÁ¨ÁŽ¸¥ Ĝ…–³š¸Éž¦³Áš«Áª¸¥—œµ¤¤¸­´—­nªœ­¼Š„ªnµ ‡º° ¦o°¥¨³ 5.3 Á„µ®¨¸Ä˜o ¤¸­´—­nªœ¦o°¥¨³ 4.8 œ°„‹µ„œ¸Ê ¥´Š¡ªnµž¦³Áš«Åš¥¤¸„µ¦¨Šš»œÁ¡ºÉ°„µ¦ª·‹´¥Â¨³¡´•œµ (Research and Development: R&D) š¸É°¥¼nĜ¦³—´˜ÉεÁ¤ºÉ°Áš¸¥„´®¨µ¥ž¦³Áš«Äœ£¼¤·£µ‡ ¨³¤¸ÂœªÃœo¤¨—¨Š°¥nµŠ˜n°ÁœºÉ°ŠÄœ®¨µ¥ žeš¸ÉŸnµœ¤µÃ—¥…o°¤¼¨¨nµ­»—Äœže 2550 ¤¸­´—­nªœ„µ¦¨Šš»œ R&D Á¡¸¥Š¦o°¥¨³ 0.2 ˜n°¦µ¥Å—ož¦³µµ˜·26 Ž¹ÉŠ­³šo°œÄ®oÁ®Èœªnµž¦³Áš«Åš¥Ä®o‡ªµ¤­Îµ‡´œo°¥¤µ„˜n°„µ¦¡´•œµÂ¨³­´ÉŠ­¤°Š‡r‡ªµ¤¦¼o×¥ÁŒ¡µ³ °¥nµŠ¥·ÉŠ„µ¦ª·‹´¥Â¨³¡´•œµÁ¡ºÉ°­œ´­œ»œÄ®oÁ„·—‡ªµ¤„oµª®œoµšµŠÁš‡ÃœÃ¨¥¸ 4. š§¬’¸„µ¦Á‹¦·Á˜·Ã˜šµŠÁ«¦¬“„·‹Â¨³ª¦¦–„¦¦¤ž¦·š´«œr š§¬’¸„µ¦Á‹¦·Á˜·Ã˜šµŠÁ«¦¬“„·‹ž¦³„°Åž—oª¥­°Šš§¬’¸®¨´„Ž¹ÉŠÂ˜n¨³š§¬’¸„È‹³¤¸ª·›¸„µ¦°›·µ¥ „µ¦Á‹¦·Á˜·Ã˜š¸É˜„˜nµŠ„´œ š§¬’¸Â¦„š¸É°›·µ¥„µ¦Á‹¦·Á˜·Ã˜Á¦·É¤‹µ„Šµœ…°Š Solow (1965) Áž}œ ‹Îµ¨°Š„µ¦Á‹¦·Á˜·Ã˜š¸É„ε®œ—‹µ„ž{‹‹´¥£µ¥œ°„ (Exogenous Growth Model) š¸É˜´ÊŠ°¥¼nœ ­¤¤˜·“µœš¸É­Îµ‡´‡º° „µ¦¨—œo°¥™°¥¨Š…°ŠŸ¨˜°Âšœ‹µ„ž{‹‹´¥„µ¦Ÿ¨·˜š»œ (Diminishing Returns to Capital) ×¥˜o°Š„ε®œ—°´˜¦µ„µ¦°°¤Ä®o„´Â‹Îµ¨°Š (Exogenous Saving Rate) ¨³°´˜¦µ„µ¦ °°¤Áž}œ˜´ªÂž¦š¸É„ε®œ—‡»–­¤´˜· – ­£µª³‡Š˜´ª (Steady State) „µ¦Á˜·Ã˜…°Š¦µ¥Å—o˜n°®´ªš¸É š§¬’¸š¸É­°Šš¸É°›·µ¥„µ¦Á‹¦·Á˜·Ã˜Äo®¨´„„µ¦š¸É­Îµ‡´…°Š Endogenous Growth Model š¸É°›·µ¥ ‡ªµ¤­´¤¡´œ›r…°ŠÂ®¨nŠš¸É¤µ…°Š„µ¦Á‹¦·Á˜·Ã˜šµŠÁ«¦¬“„·‹Ž¹ÉŠ­µ¤µ¦™Â¥„Å—oÁž}œ 2 ®¨´„„µ¦ ŗo„n ž¦³„µ¦Â¦„„µ¦Á‹¦·Á˜·Ã˜š¸É„ε®œ—‹µ„ž{‹‹´¥£µ¥ÄœÃ—¥šµš„µ¦­³­¤…°Šš»œ„µ¥£µ¡Â¨³š»œ ¤œ»¬¥r×¥Á¦·É¤‹µ„Šµœ…°ŠUzawa (1964) ¨³¡´•œµ…¹ÊœÄœ£µ¥®¨´ŠÃ—¥ Lucas (1988) ž¦³„µ¦š¸É­°Š „µ¦Á‹¦· Á˜·Ã˜š¸É„ε®œ—‹µ„ž{‹‹´ ¥£µ¥ÄœÁœo œ‡ªµ¤­Îµ‡´…°Šœª´˜„¦¦¤šµŠÁš‡ÃœÃ¨¥¸ ¨³„µ¦ ‡oœ‡ªoµšµŠ—oµœª·š¥µ«µ­˜¦r ×¥„µ¦­œ´­œ»œ„µ¦¨Šš»œ—oµœŠµœª·‹´¥Â¨³¡´•œµÃ—¥ Romer (1990) Áž}œŠµœ®¨´„…°ŠÂœª‡·—œ¸ÊŽ¹ÉŠ°›·µ¥„µ¦Á‹¦·Á˜·Ã˜Áž}œŸ¨…°Š„µ¦¡´•œµšµŠÁš‡ÃœÃ¨¥¸šÉ¸¤¸š»œ¤œ»¬¥r Áž}œž{‹‹´¥­Îµ®¦´„µ¦ª·‹´¥Â¨³¡´•œµ š»œ¤œ»¬¥r (Human capital) ™º°Áž}œž{‹‹´¥„µ¦Ÿ¨·˜š¸É­Îµ‡´ž{‹‹´¥®œ¹ÉŠÄœ„µ¦­œ´­œ»œ„µ¦…¥µ¥˜´ªšµŠ Á«¦¬“„·‹ Ž¹ÉŠÂœª‡·—œ¸ÊÁž¦¸¥Áš¸¥„µ¦«¹„¬µªnµÁž}œš´ÊŠ„µ¦¦·Ã£‡Â¨³„µ¦¨Šš»œ Uzawa (1964) ¨³

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NIDA Economic Review

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Lucas (1988) ŗo¡´•œµÂ‹Îµ¨°Š Endogenous growth model …¹ÊœÃ—¥ÁœoœÄ®oÁ®Èœ™¹Š‡ªµ¤­Îµ‡´…°Š Ÿ¨˜°Âšœš´ÊŠ‹µ„„µ¦¨Šš»œÄœš»œ¤œ»¬¥r œª´˜„¦¦¤ ¨³‡ªµ¤¦¼o Ž¹ÉŠÁž}œž{‹‹´¥š»œ®œ¹ÉŠÄœ„µ¦Ÿ¨·˜Â¨³ šÎµÄ®oÁ„·—Ÿ¨Ÿ¨·˜Á¡·É¤¤µ„…¹Êœ˜µ¤Â˜n¨³®œnª¥‡ªµ¤¦¼oš¸ÉÄ­nÁ…oµÅž (Increasing Marginal Product) ­nŠŸ¨ Ä®o Á ¤ºÉ °Äo ž{ ‹‹´ ¥š» œ Á¡·É ¤ …¹Ê œ ŞÁ¦ºÉ °¥Ç Ÿ¨˜°Âšœ…°Šž{‹ ‹´¥ š»œ ¦ª¤‹³Áž}œ Ş°¥n µ Š‡Šš¸É (Constant Return to Capital) Ž¹ÉŠÁž}œŸ¨¤µ‹µ„Ÿ¨˜°Âšœ‹µ„„µ¦¨Šš»œÄœš»œ¤œ»¬¥rš¸ÉÁ¡·É¤…¹Êœ ŗo„n „µ¦«¹„¬µ ¨³„µ¦Á¡·É¤¡¼œ‡ªµ¤¦¼o‡ªµ¤­µ¤µ¦™Â¨³š´„¬³š¸ÉÁ¡·É¤…¹ÊœÅ—o˜¨°—Áª¨µ „µ¦Á˜·¤ž{‹‹´¥š»œ¤œ»¬¥rÁ…oµ¤µ Ĝ‹Îµ¨°Š‹¹Š°›·µ¥š¸É¤µ…°ŠÁš‡ÃœÃ¨¥¸š¸É°¥¼n£µ¥ÄœÂ‹Îµ¨°Šœ°„‹µ„œ¸Ê Á«¦¬“„·‹š¸Éčoš»œ¤œ»¬¥r œ“µœ‡ªµ¤¦¼o¥´ŠÁœoœ‡ªµ¤­Îµ‡´…°ŠŸ¨„¦³š£µ¥œ°„Á·Šª„š¸ÉÁ„·—…¹Êœ (Positive Externalities) Ž¹ÉŠÁ„·—‹µ„„µ¦­nŠŸnµœ‡ªµ¤¦¼o˜n°ÅžÅ—o (Spillover Effects) ¨³¤¸¤µ„¡°š¸É‹³­µ¤µ¦™œÎµÅž­¼n„µ¦¡´•œµ Á«¦¬“„·‹Äœ¦³¥³¥µªÅ—o Šµœª·‹´¥˜µ¤®¨´„„µ¦œ¸Ê °µš· Rebelo (1991) ®¦º° Caballe andSantos (1993) œ°„‹µ„œ¸Ê Romer (1990), Aghion andHowitt (1992), Grossman andHelpman (1991), Chenand Kee (2005) ŗoÁ¡·É¤š§¬‘¸„µ¦‡oœ‡ªoµª·‹´¥Â¨³¡´•œµ (R&D Theory) ¨³„µ¦Â…nŠ…´œÅ¤n­¤¼¦–rÁ…oµÅž ŪoĜ„¦°Âœª‡·—…°ŠÂ‹Îµ¨°Š„µ¦Á‹¦·Á˜·Ã˜ ץĜ‹Îµ¨°Š ­—ŠÄ®oÁ®Èœ™¹Š‡ªµ¤­´¤¡´œ›r ¦³®ªnµŠ‡ªµ¤„oµª®œoµšµŠÁš‡ÃœÃ¨¥¸š¸ÉÁ„·—‹µ„„µ¦¨Šš»œ—oµœª·‹´¥Â¨³¡´•œµ ¦ª¤š´ÊŠŸ¨‹µ„„µ¦—εÁœ·œ „·‹„µ¦ÂŸ¼„…µ— Ž¹ÉŠ‹³¥´Š‡ŠšÎµÄ®oÁ«¦¬“„·‹­µ¤µ¦™…¥µ¥˜´ªÅ—oĜ¦³¥³¥µª £µ¥Ä˜oœª‡·—œ¸Ê°´˜¦µ„µ¦ …¥µ¥˜´ªšµŠÁ«¦¬“„·‹…¹Êœ°¥¼n„´œÃ¥µ¥…°Š¦´“µ¨ °µš· œÃ¥µ¥—oµœ£µ¬¸ „µ¦Ä®o¦·„µ¦­µ›µ¦–¼ž„µ¦ „µ¦‡»o¤‡¦°Š¨·…­·š›·ÍšµŠž{µ ¨³œÃ¥µ¥„µ¦ÁŠ·œÂ¨³„µ¦‡oµ¦³®ªnµŠž¦³Áš« Áž}œ˜oœ Ž¹ÉŠ­³šo°œÄ®o Á®Èœ™¹Š‡ªµ¤­Îµ‡´…°Š„µ¦—εÁœ·œœÃ¥µ¥…°Š¦´“µ¨š¸É¤¸˜n°„µ¦…¥µ¥˜´ªšµŠÁ«¦¬“„·‹…°Šž¦³Áš«Äœ ¦³¥³¥µª ¤¸Šµœª·‹´¥š¸É«¹„¬µ‡»–­¤´˜·š¸É­£µª³‡Š˜´ªÂ¨³‡»–­¤´˜·„µ¦¨¼nÁ…oµÄœÂ‹Îµ¨°Š„µ¦Á˜·Ã˜š¸É„ε®œ—‹µ„ £µ¥ÄœÃ—¥¤¸š´ÊŠš»œ„µ¥£µ¡ š»œ¤œ»¬¥r ¨³„µ¦ª·‹´¥Â¨³¡´•œµ °µš·Ánœ Arnold (1998, 2000a) , Gomez (2005), Funke andStrulik (2000) ¨³ Sequeira (2007) Ž¹ÉŠÅ—o¤¸„µ¦™nµ¥Áš‡ªµ¤¦¼oœª´˜„¦¦¤ ‹µ„„µ¦ª·‹´¥Â¨³¡´•œµ ¡ªnµÂ‹Îµ¨°Š¤¸­£µª³‡Š˜´ª ¨³­µ¤µ¦™Ä®oÁ­oœšµŠ„µ¦Á‹¦·Á˜·Ã˜š¸É­¤—»¨ (Balanced growth path) Benhabib andPeri (1994) ­—ŠÄ®oÁ®ÈœªnµÂ‹Îµ¨°Š„µ¦Á˜·Ã˜š¸É„ε®œ— ‹µ„£µ¥Äœ„´„µ¦­³­¤š»œ¤œ»¬¥rš¸É¤¸Ÿ¨„¦³š‹µ„£µ¥œ°„ (Externalities) „n°Ä®oÁ„·—Á­oœšµŠ®¨µ¥ Á­oœš¸ÉŞ­¼n„µ¦Á‹¦·Á˜·Ã˜š¸É­¤—»¨ (Indeterminacy of Equilibria) Ladron de Guevara et al. (1997) «¹„¬µ¡¨ª´˜—»¨¥£µ¡…°ŠÂ‹Îµ¨°Š Uzawa ¨³ Lucas ¡ªnµÄœÂ‹Îµ¨°Šš¸É¤¸Áª¨µªnµŠ(leisure) ˜n Ťn¤¸Ÿ¨„¦³š‹µ„ž{‹‹´¥£µ¥œ°„‹³¤¸®¨µ¥—»¨¥£µ¡ „µ¦«¹„¬µÁ„¸É¥ª„´Ÿ¨‹µ„„µ¦¨Šš»œÄœš»œ¤œ»¬¥r˜n°„µ¦Á‹¦·Á˜·Ã˜šµŠÁ«¦¬“„·‹Ã—¥Äoª·›¸ Growth Accounting Method Ž¹ÉŠ¡ªnµ¤¸‡ªµ¤­´¤¡´œ›r¦³®ªnµŠ„µ¦Áž¨¸É¥œÂž¨Š…°Š¦³—´„µ¦«¹„¬µ˜n°„µ¦Á‹¦· Á˜·  ؚµŠÁ«¦¬“„· ‹ °µš· Šµœ…°Š Grilliches (1970) Ĝ¦³¥³˜n ° ¤µª· ›¸ „ µ¦«¹ „ ¬µ­n ª œÄ®n ® µ ‡ªµ¤­´¤¡´œ›r¦³®ªnµŠ°´˜¦µ„µ¦Áž¨¸É¥œÂž¨Š…°Šš»œ¤œ»¬¥r (Rate of Change) ¨³š»œ¤œ»¬¥r – ¦³—´


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Á¦·É¤˜oœ (Initial Level) ªnµ¤¸Ÿ¨˜n°„µ¦Á‹¦·Á˜·Ã˜šµŠÁ«¦¬“„·‹ ×¥°µ«´¥ª·›¸®µ‡ªµ¤­´¤¡´œ›rĜ¦¼ž ­¤„µ¦™—™°¥ (Regression) ‹µ„…o°¤¼¨˜´—…ªµŠ (Cross-section) …°Šž¦³Áš«˜nµŠÇ Ÿ¨‹µ„„µ¦«¹„¬µ …°ŠÂ‹Îµ¨°Š­nªœÄ®n­—ŠÄ®oÁ®Èœªnµš»œ¤œ»¬¥r¤¸‡ªµ¤­´¤¡´œ›rÁ·Šª„®¦º°ÅžÄœšµŠÁ—¸¥ª„´œ„´„µ¦ Á‹¦·Á˜·Ã˜šµŠÁ«¦¬“„·‹°µš· Šµœ…°Š Barro (1991), Benhabib and Spiegel (1994) ¨³ Barro and Sala-i-Martin (2004) °¥nµŠÅ¦„Șµ¤ Bernard ¨³ Durlauf (1995) ª·¡µ„¬r„µ¦Äoª·›¸šµŠÁ«¦¬“¤·˜·—oª¥ …o°¤¼¨˜´—…ªµŠ (Cross-section)Ĝ„µ¦«¹„¬µŸ¨„¦³š…°Šš»œ¤œ»¬¥r˜n°„µ¦Á‹¦·Á˜·Ã˜ ÁœºÉ°Š‹µ„ªnµ Áž}œ„µ¦¦ª¤…o°¤¼¨…°Š®¨µ¥ž¦³Áš«Ž¹ÉŠ°¥¼nĜnªŠ„µ¦¡´•œµš¸É˜nµŠ„´œÂ¨³¥·ÉŠÅž„ªnµœ´Êœœ…o°¤¼¨˜´— …ªµŠ¤¸­¤¤»˜·“µœš¸Éªnµž¦³Áš«š´ÊŠ®¤—¤¸˜´ªÂž¦Áš‡ÃœÃ¨¥¸Â¨³‡ªµ¤¡¹Š¡°Ä‹š¸ÉÁ®¤º°œ„´œ Jones (1995, 1997) ŗočo…o°¤¼¨°œ»„¦¤Áª¨µ (Time series) Ĝ„µ¦«¹„¬µš»œ¤œ»¬¥r˜n°„µ¦Á‹¦·Á˜·Ã˜ ¡ªnµš»œ ¤œ»¬¥rš¸ÉÁ¡·É¤…¹Êœ¤¸Ÿ¨„¦³šÄ®o„µ¦Á‹¦·Á˜·Ã˜­¼Š…¹Êœ °¥nµŠÅ¦„È—¸ Šµœª·‹´¥µŠÁ¦ºÉ°ŠÅ—oŸ¨„µ¦«¹„¬µš¸É˜„˜nµŠÂ¨³Ä®oŸ¨¨´¡›rš¸É…´—Â¥oŠÄœ˜´ªÁ°Š °µš· Šµœª·‹´¥ …°Š Barro (1991) š¸É«¹„¬µÃ—¥Äo…o°¤¼¨®¨µ¥ž¦³Áš« ¡ªnµ°´˜¦µ„µ¦Á…oµ«¹„¬µÄœ¦³—´ž¦³™¤«¹„¬µ¤¸ Ÿ¨Á·Šª„˜n°Ÿ¨Ÿ¨·˜˜n°®´ª °¥nµŠÅ¦„È—¸ „¨´¡ªnµ…o°¤¼¨ÄœnªŠže 1950 ®¦º° 1970 Ťn­µ¤µ¦™®µ‡nµ ‡ªµ¤­´¤¡´œ›rŗo Ĝ…–³š¸ÉŠµœª·‹´¥…°Š Mankiw, Romer, and Weil (1992) Ž¹ÉŠ«¹„¬µ‹µ„…o°¤¼¨…°Š ž¦³Áš«˜nµŠÇĜ„¨»n¤ OECD ¦ª¤22 ž¦³Áš« ¡ªnµ°´˜¦µ„µ¦Á…oµ«¹„¬µÄœ¦³—´¤´›¥¤¤¸‡ªµ¤­´¤¡´œ›r Á·Šª„˜n°Ÿ¨Ÿ¨·˜°¥nµŠ¤¸œ´¥­Îµ‡´ ˜n„¨´¡ªnµž{‹‹´¥„µ¦Ÿ¨·˜š´ÊŠš»œšµŠ„µ¥£µ¡Â¨³š»œ¤œ»¬¥rÄ®o Ÿ¨Ÿ¨·˜š¸É¨—œo°¥™°¥¨Š œ°„‹µ„œ¸Ê Šµœª·‹´¥…°Š Benhabib and Spiegel (1994) ŗo«¹„¬µ™¹Š‡ªµ¤ ­´ ¤ ¡´ œ ›r ¦ ³®ªn µ Š„µ¦«¹ „ ¬µÄœ¦³—´  ¤´ › ¥¤«¹ „ ¬µÂ¨³­¼ Š „ªn µ ¤´ › ¥¤«¹ „ ¬µ˜n ° °´ ˜ ¦µ„µ¦…¥µ¥˜´ ª …°Š ž¦³­·š›·£µ¡„µ¦Ÿ¨·˜Äœ®¨µ¥ž¦³Áš« ¨³¡ªnµž¦³Áš«‡ª¦‹³­³­¤š»œ¤œ»¬¥r™¹Š¦³—´®œ¹ÉŠÁšnµœ´Êœš¸É ‹³­µ¤µ¦™„n°Ä®oÁ„·—„µ¦Á‹¦·Á˜·Ã˜šµŠÁ«¦¬“„·‹ ×¥š»œ¤œ»¬¥r¤¸‡ªµ¤­´¤¡´œ›r„´¦³—´¦µ¥Å—oÁ¦·É¤˜oœ ˜n°´˜¦µ„µ¦Á¡·É¤…¹Êœ…°Š¦³—´„µ¦«¹„¬µÃ—¥ÁŒ¨¸É¥…°Šž¦³µ„¦„¨´Å¤n¤¸‡ªµ¤­´¤¡´œ›r°¥nµŠ¤¸œ´¥­Îµ‡´ ˜n°°´˜¦µ„µ¦Á‹¦·Á˜·Ã˜…°ŠŸ¨Ÿ¨·˜˜n°®´ª Ĝ­nªœ…°ŠŠµœª·‹´¥š¸ÉÁ„¸É¥ª…o°ŠÄœ¦·š…°Šž¦³Áš«„ε¨´Š¡´•œµÃ—¥ÁŒ¡µ³°¥nµŠ¥·ÉŠž¦³Áš«Åš¥ °µš· Šµœª·‹´¥…°ŠJimenez, Nguyen, and Patrinos (2012) ŗo«¹„¬µ™¹Ššµš…°Šš»œ¤œ»¬¥r˜n°„µ¦Á‹¦· Á˜·Ã˜š¸É¥´ÉŠ¥ºœÄœž¦³Áš«Åš¥Â¨³ž¦³Áš«¤µÁ¨ÁŽ¸¥¡ªnµ ¦³„µ¦«¹ „¬µš¸É—¸‡º°¡ºÊœ“µœš¸É‹³šÎµÄ®o ¦ŠŠµœ¤¸š´„¬³˜µ¤š¸É˜¨µ—Šµœ˜o°Š„µ¦ ž¦³µ„µ¦…°Šž¦³Áš«Åš¥Â¨³¤µÁ¨ÁŽ¸¥Â¤oªnµ‹³¤¸Á…oµ™¹Š „µ¦«¹„¬µ¤µ„…¹Êœ ˜n‡»–£µ¡…°Š„µ¦«¹„¬µ¥´Š‡ŠÁž}œž¦³Á—Èœš¸É­Îµ‡´Šµœª·‹´¥…°Š Hanushek (2013) ŗo«¹„¬µš»œ¤œ»¬¥r˜n°„µ¦Á‹¦·Á˜·Ã˜Äœž¦³Áš«„ε¨´Š¡´•œµ ¥ºœ¥´œªnµ‡»–£µ¡„µ¦«¹„¬µ‡º°ž{‹‹´¥ ­Îµ‡´¤µ„„ªnµ°´˜¦µ„µ¦Á…oµÁ¦¸¥œ ™oµÅ¤n¤¸„µ¦¡´•œµ‡»–£µ¡„µ¦«¹„¬µ ‹³Áž}œ„µ¦¥µ„­Îµ®¦´ž¦³Áš« „ε¨´Š¡´•œµ¥µ„š¸É‹³¡´•œµ«´„¥£µ¡Á«¦¬“„·‹Äœ¦³¥³¥µª œ°„‹µ„œ¸ÊŠµœ…°Š Vinod andKanushik (2007) ¡ªnµš»œ¤œ»¬¥rÁž}œž{‹‹´¥­Îµ‡´˜n°„µ¦Á‹¦·Á˜·Ã˜šµŠÁ«¦¬“„·‹…°Šž¦³Áš«„ε¨´Š¡´•œµ œÃ¥µ¥‡ª¦¤»nŠÁœoœÅžš¸É„µ¦«¹„¬µÂ¨³Áš‡ÃœÃ¨¥¸­Îµ®¦´ž¦³Áš«„ε¨´Š¡´•œµ


NIDA Economic Review

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5. ª·›¸„µ¦«¹„¬µÂ¨³Â‹Îµ¨°Š ‹Îµ¨°Š„µ¦Á˜·Ã˜š¸ÉÁ„·—‹µ„£µ¥Äœ (Endogenous Growth Model) š¸É­¦oµŠ…¹ÊœÃ—¥ª·›¸—»¨¥£µ¡š´ÉªÅž š¸ÉÁž}œ¡¨ª´˜œÎµ¤µÄoĜ„µ¦«¹„¬µ„µ¦®¨»—°°„‹µ„„´—´„¦µ¥Å—ožµœ„¨µŠ…°Šž¦³Áš«Åš¥ ‹Îµ¨°Š šµŠ—oµœÁ«¦¬“«µ­˜¦rœ¸Êž¦³„°—oª¥­°Š£µ‡„µ¦Ÿ¨·˜ ‡º°£µ‡„µ¦Ÿ¨·˜š»œ¤œ»¬¥r¨³£µ‡„µ¦Ÿ¨·˜­·œ‡oµ ¦· à £‡ ‹Î µ ¨°Šš¸É Ä o ª· Á ‡¦µ³®r „ µ¦®¨» — °°„‹µ„„´  —´ „ ¦µ¥Å—o ž µœ„¨µŠ…°Šž¦³Áš«Åš¥Áž} œ ‹Îµ¨°Šš¸É¡´•œµ‹µ„‹Îµ¨°Š„µ¦Á˜·Ã˜š¸ÉÁ„·—‹µ„£µ¥ÄœÃ—¥Äoš»œ¤œ»¬¥r(A Human Capital based Endogenous Growth Model) …°Š Uzawa (1965) ¨³ Lucas (1988) „µ¦®µ‹»——»¨¥£µ¡ š´ÉªÅžš¸ÉÁž}œ¡¨ª´˜Äo˜µ¤ÂœªšµŠ…°Š Novales, Fernandez, and Ruiz (2010) ץ‹Îµ¨°Š¤¸„µ¦ ž¦´Áš¸¥ (Calibration) „´…o°¤¼¨…°Šž¦³Áš«Åš¥ ˜´ª¦„ªœÄœ£µ‡„µ¦«¹„¬µÂ¨³Äœ£µ‡„µ¦Ÿ¨·˜ Á¡ºÉ°š¸É‹³nª¥Äœ„µ¦®µž¦·¤µ–…°Šš»œ¤œ»¬¥r¨³Áš‡ÃœÃ¨¥¸š¸Éž¦³Áš«Åš¥‹ÎµÁž}œ˜o°Š¤¸Äœ„µ¦­¦oµŠ¦µ¥Å—o …°Šž¦³Áš«Ä®o¤µ„„ªnµ¦³—´¦µ¥Å—ožµœ„¨µŠ 5.1 ‹Îµ¨°ŠÁ«¦¬“„·‹ ‹Îµ¨°ŠÁ«¦¬“„·‹ž¦³„°—oª¥­°Š£µ‡„µ¦Ÿ¨·˜ ­nªœÂ¦„…°ŠÂ‹Îµ¨°ŠÁ«¦¬“„·‹‡º°£µ‡„µ¦Ÿ¨·˜ ­·œ‡oµ¦·Ã£‡…´Êœ­»—šoµ¥Ž¹ÉŠœÎµ¤µÄo¦·Ã£‡ ®¦º°‹³œÎµ¤µ­³­¤°¥¼nĜ¦¼ž…°Šš»œ¤œ»¬¥r„Èŗo­nªœš¸É­°Š…°Š ‹Îµ¨°ŠÁ«¦¬“„·‹‡º°£µ‡„µ¦Ÿ¨·˜š»œ¤œ»¬¥r¦³Á«¦¬“„·‹ž¦³„°Åž—oª¥»—…°Šž¦³µ„¦Ž¹ÉŠ°µ«´¥ °¥¼nĜnªŠÁª¨µš¸ÉŤn¤¸š¸É­·Êœ­»— ‹Îµœªœ…°Šž¦³µ„¦ÄœÂ˜n¨³¦»nœ‡º° Nt ¨³¤¸„µ¦Á˜·Ã˜Äœ°´˜¦µ n ®¦º° Nt ent N0 ž¦³µ„¦Â˜n¨³‡œ¤¸®œ¹ÉŠ®œnª¥…°ŠÁª¨µš¸É­µ¤µ¦™Äoŗo „ε®œ—Ä®o ut Áž}œ­´—­nªœ…°ŠÁª¨µ š¸Éž¦³µ„¦ÄoĜ…ªœ„µ¦Ÿ¨·˜­·œ‡oµ¦·Ã£‡…´Êœ­»—šoµ¥ Áª¨µš¸ÉÁ®¨º° 1  ut ‡º°Áª¨µš¸Éž¦³µ„¦ÄoĜ „µ¦«¹„¬µ®¦º°Áª¨µÁ¡ºÉ°ÄoĜ„µ¦­³­¤š»œ¤œ»¬¥r Á«¦¬“„·‹œ¸Ê¥´Šž¦³„°Åž—oª¥»—…°Š¦·¬´šŽ¹ÉŠ¤¸„µ¦ …nŠ…´œ„´œ £µ‡„µ¦Ÿ¨·˜­·œ‡oµ­»—šoµ¥ Á«¦¬“„·‹œ¸ÊŸ¨·˜­·œ‡oµ¦·Ã£‡­»—šoµ¥®œ¹ÉŠœ·—™¼„šœ—oª¥ Yt Áš‡ÃœÃ¨¥¸š¸ÉčoĜ…ªœ„µ¦Ÿ¨·˜­·œ‡oµ ¦·Ã£‡…´Êœ­»—šoµ¥Âšœ—oª¥¢{Š„r´œ„µ¦Ÿ¨·˜ F ( Kt , H t ) Ž¹ÉŠ„ε®œ—¢{Š„r´œ„µ¦Ÿ¨·˜Ä®oÁž}œÂ CobbDouglas Á¤ºÉ°„ε®œ—Ä®o X t xt Lt Ž¹ÉŠ X t Ct , Kt , H t , Yt —´Šœ´Êœ­´¨´„¬–r£µ¬µ°´Š„§¬˜´ªÁ…¸¥œÁ¨È„ Ánœ xt ‡º°˜´ªÂž¦š¸É­—ŠÄœ¦¼ž˜n°®´ª Ž¹ÉŠÅ—o¤µ‹µ„„µ¦®µ¦ X t —oª¥‹Îµœªœž¦³µ„¦ —´Šœ´Êœ¢{Š„r´œ „µ¦Ÿ¨·˜Äœ¦¼ž˜n°®´ª‡º° yt

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108

NIDA Economic Review

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NIDA Economic Review

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110

NIDA Economic Review f

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NIDA Economic Review

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NIDA Economic Review

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NIDA Economic Review

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NIDA Economic Review

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NIDA Economic Review

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NIDA Economic Review

119

¦ª—Á¦ÈªÂ¨³‡n°¥¨—¨Š­¼n°´˜¦µš¸É‡Šš¸É®¨´Š‹µ„ئ¤µ­š¸É 80¦¼žš¸É 11 ­—Šªnµ„µ¦Á¡·É¤‡nµÄo‹nµ¥Äœ„µ¦«¹„¬µ Áž}œž¦·¤µ–™¹Š 1.5 Ášnµ…°Š‡nµÁ¸É¥ŠÁœ¤µ˜¦“µœ ®¦º°Áž}œ„µ¦Á¡·É¤ 1.61 ­œ¨oµœ˜n°že®¦º° 4 ®¤ºÉœ¨oµœ ˜n°Å˜¦¤µ­Äœ£µ‡„µ¦«¹„¬µÁª¨µ 80 ئ¤µ­ ¨³ 4 ¡´œ¨oµœµš˜n°že®¦º° 1 ¡´œ¨oµœµš˜n°Å˜¦¤µ­ Ĝ£µ‡„µ¦Ÿ¨·˜Áž}œÁª¨µ 80 ئ¤µ­ Ťn­µ¤µ¦™šÎµÄ®oŸ¨Ÿ¨·˜™¹Š¦³—´š¸É˜o°Š„µ¦‡º° 1.59 Ÿ¨Ÿ¨·˜¥´Š‡Š Á¡·É¤…¹ÊœoµÇ°¥nµŠ˜n°ÁœºÉ°Š™¹Š¦³—´š¸É 1.05 ®œnª¥ÄœÅ˜¦¤µ­š¸É 100 Á«¦¬“„·‹¤¸„µ¦…¥µ¥˜´ªÄœ°´˜¦µš¸É˜Éε Ĝئ¤µ­Â¦„Äœ°´˜¦µ¦o°¥¨³ 0.007 ®¦º°¦o°¥¨³ 2.8 ˜n°že ¨³„µ¦…¥µ¥˜´ª…°ŠÁ«¦¬“„·‹„ÈÁ¡·É¤…¹ÊœÁ¦ºÉ°¥ ‹œ„¦³š´ÉŠ™¹Š¦o°¥¨³ 0.015 ˜n°Å˜¦¤µ­®¦º° ¦o°¥¨³ 6 ˜n°žeĜئ¤µ­š¸É 80 ‹µ„œ´Êœ„µ¦…¥µ¥˜´ª…°Š ¦³Á«¦¬“„·‹¨—¨Š¤µ°¥nµŠ¦ª—Á¦ÈªÂ¨³‡n°¥¨—¨Š­¼n°´˜¦µš¸É‡Šš¸É®¨´Š‹µ„ئ¤µ­š¸É 120 ‹µ„„µ¦š—¨°Šš´ÊŠ 3 šÎµÄ®o­¦»žÅ—oªnµ„µ¦š—¨°ŠÂÂ¦„¤oªnµ‹³šÎµÄ®o¦³Á«¦¬“„·‹Åš¥®¨»—¡oœ °°„‹µ„„´—´„¦µ¥Å—ožµœ„¨µŠÄœ¦³¥³Áª¨µÅ¤n™¹Š 40 ئ¤µ­®¦º° 10 že ˜n‹ÎµÁž}œ˜o°ŠÄoŠž¦³¤µ– ¤µ„Á„·œÂ¨³Ä®o„µ¦Á˜·Ã˜š¸É­¼ŠÁ„·œ‡ªµ¤Áž}œ‹¦·Š ­Îµ®¦´„µ¦š—¨°Šš¸É­µ¤ „µ¦Á¡·É¤Šž¦³¤µ–¤¸œo°¥ Á„·œÅžš¸É‹³šÎµÄ®ož¦³Áš«®¨»—°°„‹µ„„´—´„¦µ¥Å—ožµœ„¨µŠ —´Šœ´Êœ„µ¦š—¨°ŠÄœÂš¸É­°Š¤¸‡ªµ¤ Áž}œÅžÅ—o¤µ„š¸É­»—­Îµ®¦´¦³Á«¦¬“„·‹Åš¥š¸É‹³®¨»—°°„‹µ„„´—´„¦µÅ—ožµœ„¨µŠ ‡º°„µ¦„µ¦Á¡·É¤ ‡nµÄo‹nµ¥Äœ„µ¦«¹„¬µ 3.21 ­œ¨oµœ˜n°že®¦º° 8 ®¤ºÉœ¨oµœ˜n°Å˜¦¤µ­Áž}œÁª¨µ 60 ئ¤µ­ ¨³8 ¡´œ¨oµœµš˜n°že®¦º° 2.01 ¡´œ¨oµœµš˜n°Å˜¦¤µ­Äœ£µ‡„µ¦Ÿ¨·˜Áž}œÁª¨µ 60 ئ¤µ­ šÎµÄ®ož¦³Áš« Ś¥®¨»—¡oœ°°„‹µ„„´—´¦µ¥Å—ožµœ„¨µŠÄœ 16 že®¦º°ž¦³¤µ– 64 ئ¤µ­ Á¤ºÉ°¡·‹µ¦–µ™¹Š„µ¦ Á˜·Ã˜šµŠÁ«¦¬“„·‹…°Šž¦³Áš«Åš¥Â¨oª ¡ªnµš¸ÉŸnµœ¤µ„µ¦Á‹¦·Á˜·Ã˜šµŠÁ«¦¬“„·‹…°ŠÅš¥°¥¼nš¸É ¦³—´˜Éε „µ¦Á˜·Ã˜Ã—¥ÁŒ¨¸É¥…°Šž¦³Áš« 11 že¥o°œ®¨´Š ‹µ„že 2543 ™¹Š 2554 °¥¼nš¸¦o°¥¨³ 3.18 —´Šœ´Êœ ‹¹Š¤¸‡ªµ¤­Îµ‡´Â¨³‹ÎµÁž}œÄœ„µ¦Á¡·É¤„µ¦Äo‹nµ¥Äœ„µ¦«¹„¬µ‡º°Á¡ºÉ°Áž}œ„¦³˜»oœÁ«¦¬“„·‹Â¨³„µ¦­¦oµŠ ¦µ¥Å—o…°Šž¦³Áš«Ä®o­¼Š…¹ÊœŸnµœ„µ¦­³­¤š»œ¤œ»¬¥r 10.…o°Á­œ°Âœ³ž¦³Á—ÈœÁ·ŠœÃ¥µ¥ Ÿ¨š¸Éŗo‹µ„„µ¦«¹„¬µÃ—¥Äo‹Îµ¨°Š„µ¦Á˜·Ã˜š¸ÉÁ„·—‹µ„£µ¥Äœš¸É­¦oµŠ…¹ÊœÃ—¥ª·›¸—»¨¥£µ¡š´ÉªÅžš¸É Áž}œ¡¨ª´˜­³šo°œÄ®oÁ®Èœªnµ„µ¦š¸Éž¦³Áš«Åš¥‹³­µ¤µ¦™Á¡·É¤¦³—´¦µ¥Å—o…°Šž¦³µ„¦Á¡ºÉ°Ä®o®¨»—°°„ ‹µ„„´—´„¦µ¥Å—ožµœ„¨µŠÅ—oœ´Êœ¤¸‡ªµ¤‹ÎµÁž}œ°¥nµŠ¥·ÉŠš¸É‹³˜o°Š­¦oµŠÂ¨³Á¡·É¤„µ¦­³­¤­˜È°„š»œ¤œ»¬¥r ×¥°µ«´¥„µ¦¨Šš»œšµŠ„µ¦«¹„¬µš´ÊŠ‹µ„£µ‡¦´“¨³Á°„œ Á¡ºÉ°­œ´­œ»œÂ¨³¦°Š¦´„µ¦ª·‹´¥Â¨³¡´•œµ šµŠÁš‡ÃœÃ¨¥¸Ä®o¤¸‡ªµ¤„oµª®œoµÂ¨³—εÁœ·œÅžÅ—o°¥nµŠ¤¸ž¦³­·š›·£µ¡—´Šœ´Êœ Ĝ„µ¦ªµŠÂŸœœÃ¥µ¥š¸É ­Î µ ‡´  š¸É ž ¦³Áš«Åš¥‹³˜o ° ŠÄ®o ‡ ªµ¤­Î µ ‡´  ¤µ„Áž} œ ¨Î µ —´  ¦„ ŗo  „n „µ¦¨Šš» œ Šž¦³¤µ–—o µ œ „µ¦«¹„¬µ Ž¹ÉŠÅ¤nÁ¡¸¥ŠÂ˜n­œ´­œ»œÄœÂŠn„µ¦Á¡·É¤ž¦·¤µ–„µ¦«¹„¬µ ˜n‹ÎµÁž}œ˜o°Š­œ´­œ»œ‡»–£µ¡šµŠ „µ¦«¹„¬µÄ®o°¥¼nĜ¦³—´š¸É­¼Š…¹Êœ¤µ„„ªnµš¸ÉŸnµœ¤µ ÁœºÉ°Š‹µ„…o°¤¼¨¸ÊÄ®oÁ®Èœ°¥nµŠ´—Á‹œªnµž¦³Áš«Åš¥¤¸ °´˜¦µ„µ¦Á…oµÁ¦¸¥œ­¼Š…¹Êœ°¥nµŠ˜n°ÁœºÉ°Š °¥nµŠÅ¦„È—¸ ¡ªnµ¥´Š¤¸­´—­nªœ°¥¼nĜ¦³—´˜ÉεÁ¤ºÉ°Áš¸¥„´ž¦³Áš« ×¥ÁŒ¡µ³°´˜¦µ„µ¦Á…oµ«¹„¬µ˜n°Äœ¦³—´¤´›¥¤«¹„¬µ ¨³¥´Š°¥¼nĜ¦³—´˜É優nµÁžjµ®¤µ¥…°Šž¦³Áš«š¸É „ε®œ—ŪoÄ®ož¦³µ„¦°µ¥» 12-14 že˜o°Š‹„µ¦«¹„¬µ£µ‡´Š‡´ Ĝ­nªœ…°Š‡»–£µ¡„µ¦«¹„¬µ‹µ„„µ¦ ž¦³Á¤·œŸ¨Ã—¥Áž¦¸¥Áš¸¥„´œµœµµ˜·„ÈÁž}œš¸É´—Á‹œªnµž¦³Áš«Åš¥¥´Š¤¸‡»–£µ¡„µ¦«¹„¬µš¸É˜É夵„


120

NIDA Economic Review

×¥ÁŒ¡µ³°¥nµŠ¥·ÉŠ—oµœª·š¥µ«µ­˜¦r¨³Áš‡ÃœÃ¨¥¸ —´Šœ´Êœ ĜŠž¦³¤µ–—oµœ„µ¦«¹„¬µ‹¹Š‡ª¦˜o°ŠÁœoœ „µ¦­nŠÁ­¦·¤„µ¦«¹„¬µ—oµœª·š¥µ«µ­˜¦r¨³Áš‡ÃœÃ¨¥¸ š´ÊŠœ¸Ê „µ¦¡´•œµ—oµœ„µ¦«¹„¬µÅ¤nÁ¡¸¥ŠÂ˜n„µ¦Ä®o ‡ªµ¤­Îµ‡´˜n°„µ¦«¹„¬µ­µ¥­µ¤´Äœ¦³ ˜n‡ª¦‹³˜o°ŠÄ®o‡ªµ¤­Îµ‡´˜n°„µ¦¡´•œµ„µ¦«¹„¬µÄœ ­µ¥°µ¸¡®¦º°„µ¦°µ¸ª«¹„¬µ ¨³„µ¦«¹„¬µœ°„¦³—oª¥ Á¡ºÉ°Ä®oœ´„Á¦¸¥œ¤¸šµŠÁ¨º°„¨³­µ¤µ¦™˜n° ¥°—°Š‡r‡ªµ¤¦¼o Á¡ºÉ°¡´•œµÁž}œÂ¦ŠŠµœš¸É¤¸š´„¬³ e¤º°Â¨³«´„¥£µ¡Á¡·É¤…¹Êœ ¨³˜°­œ°Š˜n°‡ªµ¤ ˜o°Š„µ¦…°Š˜¨µ—¦ŠŠµœ œ°„‹µ„œ¸Ê ¥´Š‡ª¦Ä®o‡ªµ¤­Îµ‡´„´œÃ¥µ¥„µ¦¡´•œµš´„¬³ e¤º°‡»–£µ¡Â¦ŠŠµœ ¨³„µ¦­œ´­œ»œ ¨Šš»œ—oµœŠµœª·‹´¥Â¨³¡´•œµ…°Šž¦³Áš«Ä®oÁ¡·É¤…¹Êœ Á¡ºÉ°Á˜¦¸¥¤‡ªµ¤¡¦o°¤Â„n¦ŠŠµœÄ®o­µ¤µ¦™¦°Š¦´ „µ¦™nµ¥š°—¨³˜n°¥°—„µ¦¡´•œµšµŠÁš‡ÃœÃ¨¥¸¦³—´­¼ŠÄ®oŗo°¥nµŠÁ˜È¤«´„¥£µ¡ Á¡ºÉ°Ä®ož¦³µ„¦ ­µ¤µ¦™‡·—‡oœ ¨³¡´•œµÁš‡ÃœÃ¨¥¸šµŠ„µ¦Ÿ¨·˜Á°ŠÁ¡ºÉ°nª¥­œ´­œ»œÄ®oÁ„·—„µ¦¥„¦³—´“µœ„µ¦Ÿ¨·˜ …°Šž¦³Áš«Ä®o­µ¤µ¦™Ÿ¨·˜­·œ‡oµÂ¨³¦·„µ¦š¸É¤¸¤¼¨‡nµÁ¡·É¤­¼Š…¹Êœ ¨³nª¥¨—„µ¦¡¹ÉŠ¡µ„µ¦œÎµÁ…oµ­·œ‡oµ ¨³Áš‡ÃœÃ¨¥¸ ‹ µ„˜n µ Šž¦³Áš« ¦ª¤š´Ê Š ­µ¤µ¦™¥„¦³—´  …¸ — ‡ªµ¤­µ¤µ¦™Äœ„µ¦Â…n Š …´ œ £µ¥Ä˜o Á«¦¬“„·‹Ã¨„š¸Éž¦´Áž¨¸É¥œÁž}œÁ«¦¬“„·‹“µœ‡ªµ¤¦¼oŗo ¦¼žš¸É 1: Impulse response ˜´ª¦„ªœÄœ£µ‡„µ¦Ÿ¨·˜Á¡·¤É …¹œÊ 1 Ášnµ…°Š‡nµÁ¸É¥ŠÁœ¤µ˜¦“µœ physical capital

output 0.55

1.795

0.548

1.79

0.546

human capital 1.001

1.785 1.0005

0.544

1.78 0.542

1.775 0.54

1

1.77

0.538

1.765

0.536 0.534

0

10

20

30

40 time

50

60

70

80

1.76

0

10

20

30

40

50

60

70

80

90

0.9995

0

10

20

30

40

time

time used in final good sector

0.474

50

60

70

80

90

time the wage rate

consumption

0.475

0.5085

1.04

0.508

1.038

0.5075

1.036

0.471

0.507

1.034

0.47

0.5065

1.032

0.473 0.472

0.469 0.506

1.03

0.5055

1.028

0.468 0.467 0.466

0

10

20

30

40 time

50

60

70

80

0.505

0

10

20

30

the rate of return

40 time

50

60

70

80

0.0222

0.31

0.022

0.308

0.0218

0.306

0.0216

0.304

1.026

0

10

20

30

40 time

50

60

70

80

50

60

70

80

k/h

y/k

1.795

1.79

1.785

1.78

1.775 0.0214

0.302

0.0212

0.3

1.77

0.021

0

10

20

30

40 time

50

60

70

80

0.298

1.765

0

10

20

30

40 time

50

60

70

80

1.76

0

10

20

30

40 time


NIDA Economic Review

121

¦¼žš¸É 2: Impulse response ˜´ª¦„ªœÄœ£µ‡„µ¦«¹„¬µÁ¡·¤É …¹œÊ 1 Ášnµ…°Š‡nµÁ¸¥É ŠÁœ¤µ˜¦“µœ -3

growth rate of output 0.03

6.35

0.025

6.3

-3

growth rate of human capital

x 10

12

growth rate of physical capital

x 10

11

10 0.02

6.25

0.015

6.2

0.01

6.15

0.005

6.1

9

8

7

0

0

10

20

30

40 time

50

60

70

80

6.05

6

0

10

20

30

40 time

50

60

70

80

5

0

10

20

30

40 time

50

60

70

80

¦¼žš¸É 3: Impulse response ˜´ª¦„ªœÄœ£µ‡„µ¦«¹„¬µÁ¡·¤É …¹œÊ 1 Ášnµ…°Š‡nµÁ¸¥É ŠÁœ¤µ˜¦“µœ output

physical capital

human capital

0.54

1.78

1.0045

0.538

1.775

1.004

1.77

1.0035

0.536

1.765

1.003

1.76

1.0025

0.534 0.532 1.755

1.002

1.75

1.0015

0.53 0.528 0.526 0.524

0

10

20

30

40 time

50

60

70

80

1.745

1.001

1.74

1.0005

1.735 0

10

20

30

40

time used in final good sector 0.47

50

60

70

80

90

0.46

0.455 0

10

20

30

40 time

50

60

70

1.03 1.0295

0.5065

1.029

0.506

1.0285

0.5055

1.028

0.505

1.0275

0.5045

1.027

0.504

1.0265

0

10

20

30

20

30

40

50

40 time

60

70

80

90

1.0305

0.507

80

10

time

0.5075

the rate of return

0

the wage rate

0.508

0.465

1

time consumption

50

60

70

80

1.026

0

10

20

30

y/k

40 time

50

60

70

80

50

60

70

80

k/h

0.31

1.765

0.0219 0.0218 0.0217

0.308

1.76

0.306

1.755

0.0216 0.0215

0.304

1.75

0.302

1.745

0.0214 0.0213 0.0212 0.0211 0.021

0.3

1.74

0.298

1.735

0.0209 0

10

20

30

40 time

50

60

70

80

0.296

0

10

20

30

40 time

50

60

70

80

1.73

0

10

20

30

40 time

¦¼žš¸É 4: Impulse response …°Š°´˜¦µ„µ¦Á˜·Ã˜Á¤ºÉ°˜´ª¦„ªœÄœ£µ‡„µ¦«¹„¬µÁ¡·¤É …¹œÊ 1 Ášnµ …°Š‡nµÁ¸¥É ŠÁœ¤µ˜¦“µœ -3

growth rate of output 0.015

7

-3

growth rate of physical capital

x 10

6.75

0.01

6.65 5

6.6

0.005

6.55

4 0

6.5 3

6.45

-0.005

6.4

2

6.35

-0.01

-0.015

growth rate of human capital

x 10

6.7

6

1

0

10

20

30

40 time

50

60

70

80

0

6.3 0

10

20

30

40 time

50

60

70

80

6.25

0

10

20

30

40 time

50

60

70

80


122

NIDA Economic Review

¦¼žš¸É 5: Impulse response ˜´ª¦„ªœÄœ£µ‡„µ¦Ÿ¨·˜Â¨³£µ‡„µ¦«¹„¬µÁ¡·¤É …¹œÊ 1 Ášnµ…°Š ‡nµÁ¸É¥ŠÁœ¤µ˜¦“µœ output

physical capital

0.5385

1.776

0.538

1.774

0.5375

1.772

human capital 1.005 1.0045 1.004

0.537

1.77

0.5365

1.768

0.536

1.766

0.5355

1.764

1.0035 1.003 1.0025 1.002 1.0015 1.001

0.535

0

10

20

30

40 time

50

60

70

80

1.762

1.0005 0

10

20

30

40

50

60

70

80

90

1

0

10

20

30

40

time

time used in final good sector 0.47

0.508

0.468

0.5075

0.467

0.507

0.466

0.5065

60

70

80

90

the wage rate

0.5085

0.469

50 time

consumption 1.04

1.035

1.03 0.465

0.506

0.464

0.5055

0.463

0

10

20

30

40 time

50

60

70

80

0.505

0

10

20

30

the rate of return

40 time

50

60

70

80

1.025

0

10

20

30

y/k

0.0216

0.3048

0.0216

0.3046

40 time

50

60

70

80

50

60

70

80

k/h 1.766

1.765

0.3044

0.0216

1.764

0.3042

0.0216

0.304

1.763

0.0216 0.3038 0.0215

1.762

0.3036 0.0215

0.3034

0.0215

1.761

0.3032

0.0215

1.76

0.303

0.0215 0

10

20

30

40 time

50

60

70

80

0.3028

0

10

20

30

40 time

50

60

70

80

1.759

0

10

20

30

40 time

¦¼žš¸É 6: Impulse response …°Š°´˜¦µ„µ¦Á˜·Ã˜Á¤ºÉ°˜´ª¦„ªœÄœ£µ‡„µ¦Ÿ¨·˜Â¨³£µ‡ „µ¦«¹„¬µÁ¡·É¤…¹Êœ 1 Ášnµ…°Š‡nµÁ¸É¥ŠÁœ¤µ˜¦“µœ -3

7.1

-3

growth rate of output

x 10

6.5

7

6.4

6.9

6.3

6.8

6.2

6.7

6.1

-3

growth rate of physical capital

x 10

6.6

growth rate of human capital

x 10

6.55

6.5

6.6

6

6.5

5.9

6.45

6.4

6.35 6.4

5.8

6.3 6.2

6.3

5.7

0

10

20

30

40 time

50

60

70

80

5.6

0

10

20

30

40 time

50

60

70

80

6.25

0

10

20

30

40 time

50

60

70

80


NIDA Economic Review

¦¼žš¸É 7: Pure temporary shock ˜´ª¦„ªœÄœ£µ‡„µ¦Ÿ¨·˜Â¨³£µ‡„µ¦«¹„¬µ ouput with changing persistency of shock

physical capial with changing persistency of shock

0.539

1.776 phi1=phi2=0.95 phi1=phi2=0

0.5385

phi1=phi2=0.95 phi1=phi2=0

1.774

0.538 1.772 0.5375 0.537

1.77

0.5365

1.768

0.536 1.766 0.5355 1.764

0.535 0.5345

0

10

20

30

40 time

50

60

70

80

1.762

0

10

20

30

40

50

60

70

80

90

time -3

human capial with changing persistency of shock 1.005

x 10

14

1.0045

growrth rate of output with changing persistency of shock

12

1.004 10 1.0035 8

1.003 1.0025

6

1.002

4

1.0015 2

phi1=phi2=0.95 phi1=phi2=0

1.001

1

phi1=phi2=0.95 phi1=phi2=0

0

1.0005 0

10

20

30

40

50

60

70

80

90

-2

0

10

20

30

40 time

time

50

60

70

80

¦¼žš¸É 8: Permanent shock ˜´ª¦„ªœÄœ£µ‡„µ¦Ÿ¨·˜Â¨³£µ‡„µ¦«¹„¬µ ouput with changing persistency of shock

physical capial with changing persistency of shock

0.544

1.785 phi1=phi2=0.95 phi1=phi2=0.99

0.543

phi1=phi2=0.95 phi1=phi2=0.99 1.78

0.542 0.541

1.775 0.54 0.539 1.77 0.538 0.537

1.765

0.536 0.535

0

10

20

30

40 time

50

60

70

80

1.76

0

10

20

30

40

50

60

1.014

80

90

12

x 10

growrth rate of output with changing persistency of shock

phi1=phi2=0.95 phi1=phi2=0.99

1.012

70

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human capial with changing persistency of shock

phi1=phi2=0.95 phi1=phi2=0.99

11

1.01 10 1.008 9 1.006 8 1.004 7

1.002

1

0

10

20

30

40

50 time

60

70

80

90

6

0

10

20

30

40 time

50

60

70

80

123


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¦¼žš¸É 9: ­—ŠŸ¨Ÿ¨·˜Â¨³„µ¦Á˜·Ã˜…°ŠŸ¨Ÿ¨·˜­Îµ®¦´„µ¦š—¨°Šš¸É 1 growth rate of output

output 2

0.055 0.05 0.045 0.04

1.5

0.035 0.03 0.025 1

0.02 0.015 0.01

0.5

0

10

20

30

40 time

50

60

70

80

0.005

0

10

20

30

40 time

50

60

70

80

¦¼žš¸É 10: ­—ŠŸ¨Ÿ¨·˜Â¨³„µ¦Á˜·Ã˜…°ŠŸ¨Ÿ¨·˜­Îµ®¦´„µ¦š—¨°Šš¸É 2 output

growth rate of output

2

0.028 0.026

1.8

0.024 1.6 0.022 1.4

0.02

1.2

0.018 0.016

1

0.014 0.8 0.012 0.6 0.4

0.01 0

10

20

30

40 time

50

60

70

80

0.008

0

10

20

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50

60

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80

¦¼žš¸É 11: ­—ŠŸ¨Ÿ¨·˜Â¨³„µ¦Á˜·Ã˜…°ŠŸ¨Ÿ¨·˜­Îµ®¦´„µ¦š—¨°Šš¸É 3 growth rate of output

output 1.2

0.016 0.015

1.1

0.014 1

0.013 0.012

0.9

0.011 0.8

0.01 0.009

0.7

0.008 0.6

0.5

0.007 0

20

40

60 time

80

100

120

0.006

0

20

40

60 time

80

100

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¦¦–µœ»„¦¤ Aghion, P. and Howitt, P. (1992) “A Model of Growth through Creative Destruction” ,Econometrica, 80(2): 323-351. Arnold, L. (1998) “Growth, Welfare and Trade in an Integrated Model of Human Capital Accumulation and Research”, Journal of Macroeconomics, 20(1): 81-105. Arnold, L. (2000a) “Endogenous Growth with Physical Capital, Human Capital and Product Variety: A Comment”, European Economics Review, 44: 1599-1605. Baranano, I. (2001) “On Human Capital Externalities and Aggregate Fluctuations”, Journal of Economics and Business, 53(5): 459-472. Baranano, I. Iza, A. and Vazquez, J. (2002) “A Comparison between the Parameterized Expectations and Log-Linear Simulation Methods”, Spanish Economic Review, 4: 4160. Barro, R.J. (1991) “Economic Growth in a Cross-Section of Countries”, Quarterly Journal of Economics, 106(2): 407-443. Barro, R.J. and Sala-i-Martin, X. (2004) Economic Growth, New York: McGraw-Hill. Benhabib, J. and Perli, R. (1994) “Uniqueness and Indeterminacy: On the Dynamics of Endogenous Growth”,Journal of Economic Theory, 63: 113-142. Benhabib, J. and Spiegel, M. (1994) “The Role of Human Capital in Economic Development: Evidence from Aggregate Cross-Country Data”, Journal of Monetary Economics, 34(2): 143-174. Bernard, A.B. and Durlauf, S.N. (1995) “Convergence in International Output”, Journal of Applied Econometrics, 71: 161-174. Caballe, J. and Santos, M.S. (1993) “On Endogenous Growth with Physical and Human Capital”, Journal of Political Economy, 106(1): 1042-1067. Chen, H.C. and Kee, H.L. (2005) “A Model on Knowledge and Endogenous Growth”, World Bank Policy Research Working Paper No. 3539, Washington, D.C., The World Bank. Fernandez, E., Ruiz, J. and Novales, A. (2010) Economic Growth: Theory and Numerical Solution Methods, Amsterdam:Springer. Funke, M. and Strulik, H. (2000) “On Endogenous Growth with Physical Capital Human Capital and Product Variety”, European Economics Review, 44: 491-515. Gomez, M. (2005) “Transitional Dynamics in an Endogenous Growth Model with Physical Capital, Human Capital, and R&D”, Studies in Non-linear Dynamics & Econometrics, 9(1), Article 5. Griliches, Z. (1992) “The Search for R&D Spillovers”, Scandinavian Journal of Economics, 94: 29-47. Grossman, G.M. and Helpman, E. (1991) Innovation and Growth in the Global Economy, Cambridge:MIT Press. Hanushek, E.A. (2013) “Economic Growth in Developing Countries: The Role of Human Capital”, Economic of Education Review, 37: 204-212. Jones, C.I. (1995) “R&D-Based Models of Economic Growth”, Journal of Political Economy, 103: 759-784. Jones, C.I. (1997) The Upcoming Slowdown in U.S. Economic Growth, Palo Alto:Stanford University Press. Jimenez, E., Nguyen, V., and Patrinos, H.A (2012) “Stuck in the Middle? Human Capital Development and Economic Growth in Malaysia and Thailand”, World BankPolicy Research Working Paper No. 6283, Washington, D.C.: The World Bank.


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วารสารเศรษฐศาสตรปริทรรศน สถาบันบัณฑิตพัฒนบริหารศาสตร NIDA Review2558)127 ปที่ 9Economic ฉบับที่ 1 (มกราคม

การพัฒนาทักษะฝมือแรงงานและการฝกอบรม: กรณีศึกษาภาคอุตสาหกรรมไทย พิริยะ ผลพิรุฬห* š‡´—¥n° „µ¦¡´•œµš´„¬³‹µ„„µ¦ f„°¦¤Äœ­™µœš¸ÉšÎµŠµœ¤¸‡ªµ¤­Îµ‡´˜n°„µ¦¡´•œµŸ¨·˜£µ¡…°ŠÂ¦ŠŠµœÃ—¥¦ª¤ ‹µ„„µ¦Äo…o°¤¼¨¦µ¥¦·¬´šÄœ£µ‡°»˜­µ®„¦¦¤„µ¦Ÿ¨·˜Äœž¦³Áš«Åš¥¡ªnµ …œµ—…°Š¦·¬´š­nŠŸ¨šµŠª„ °¥nµŠ¤¸œ´¥­Îµ‡´˜n°‡ªµ¤œnµ‹ÎµÁž}œÄœ„µ¦ f„°¦¤Â¦ŠŠµœ œ°„‹µ„œ¸Ê¥´Š¡ªnµ „µ¦ f„°¦¤‹³¤¸ÂœªÃœo¤­¼Š…¹Êœ Ĝ£µ‡„µ¦Ÿ¨·˜š¸ÉčoÁ‡¦ºÉ°Š‹´„¦ (®¦º°ž{‹‹´¥š»œ) Á…o¤…oœ œ°„‹µ„œ¸Ê‡»–£µ¡…°Šš»œ¤œ»¬¥rš¸É°Š‡r„¦‹oµŠŠµœ°¥¼nÁ°Š „È¥´Š­nŠŸ¨šµŠª„˜n°‡ªµ¤œnµ‹ÎµÁž}œÄœ„µ¦ f„°¦¤Ánœ„´œ Ÿ¨„µ¦«¹„¬µœ¸Ê¥´Š¸ÊÄ®oÁ®Èœªnµ „µ¦ f„°¦¤¤¸ œªÃœo¤…°Š„µ¦·—Áº°œÅž­¼n°Š‡r„¦š¸É¤¸Â¦ŠŠµœš¸É¤¸¦³—´…°Š‡»–£µ¡š»œ¤œ»¬¥rš¸É­¼Š°¥¼n¨oª Ž¹ÉŠ°µ‹‹³­nŠŸ¨šÎµ Ä®oÁ„·—‡ªµ¤Á®¨ºÉ°¤¨Êε…°Š„µ¦Å—o¦´„µ¦¡´•œµš´„¬³­Îµ®¦´Â¦ŠŠµœš¸É¤¸š´„¬³˜Éε œ°„‹µ„œ¸Ê¥´Š¡ªnµž{®µ ˜ÎµÂ®œnŠŠµœš¸ÉªnµŠÂ¨³„µ¦…µ—‡¨œÂ¦ŠŠµœ¥´ŠÁž}œ°¸„ž{‹‹´¥®œ¹ÉŠš¸É­nŠŸ¨˜n°‡ªµ¤œnµ‹ÎµÁž}œ…°Š„µ¦š¸É°Š‡r„¦ ‹³˜o°Š f„°¦¤Â¦ŠŠµœÁ¡·É¤…¹Êœ—oª¥Ánœ„´œ

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«µ­˜¦µ‹µ¦¥rž¦³‹Îµ‡–³¡´•œµ„µ¦Á«¦¬“„·‹ ­™µ´œ´–”·˜¡´•œ¦·®µ¦«µ­˜¦r ¨³¦°Š‡–—¸ª·š¥µ¨´¥œµœµµ˜·Â®nŠ­™µ´œ ´–”·˜¡´•œ¦·®µ¦«µ­˜¦r ™œœÁ­¦¸Åš¥ …ªŠ‡¨°Š‹´Éœ Á…˜µŠ„³žd „š¤ 10240Email: piriya@nida.ac.th š‡ªµ¤ª·‹´¥·Êœœ¸ÊÁž}œ­nªœ®œ¹ÉŠ…°ŠÃ‡¦Š„µ¦ª·‹´¥Á¦ºÉ°Š "„µ¦¡´•œµ¦³„µ¦«¹„¬µ°µ¸¡Â¨³„µ¦Á¦¸¥œ¦¼oœ°„¦³Á¡ºÉ°­¦oµŠ¦³„µ¦Á¦¸¥œ¦¼o ˜¨°—¸ª·˜…°Šž¦³Áš«Åš¥" ץŗo¦´š»œ­œ´­œ»œ‹µ„­Îµœ´„Šµœ‡–³„¦¦¤„µ¦ª·‹´¥Â®nŠµ˜·


128

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วารสารเศรษฐศาสตรปริทรรศน สถาบันบัณฑิตพัฒนบริหารศาสตร ปที่ 9 ฉบับที่ 1 (มกราคม 2558)

Skill Development and Training in the Workplace Empirical Evidences from Thai Manufacturers Piriya Pholphirul* Abstract Skill training in the workplace is a major concern among manufacturers in most developing countries. Using Thai manufacture firm-level data, this study finds a positive relationship between firm size and skill training. Skill training is found to be more prominent among firms that are more capital/technology intensive. Firms employing labor as a major input, on the other hand, provide less training opportunities for their workers. Firms employing lower educated workers or lower skilled workers are likely to provide less skill training while firms employing a higher percentage of technical staff show more interest on providing training courses. This suggests that training opportunities are rather biased toward higher skilled, better educated, rather than unskilled workers with low education that tend to amplify skill gaps among employees. Vacancies and general dissatisfaction with low-level workers may discourage firms from offering their own training, in favour of relying more on outside training.

Keywords: Training in Workplace, Job Vacancies, Labor Shortage, Manufacturing Sector *

Professor of Economics, Graduate School of Development Economics, National Institute of Development Administration and Associate Dean of the International College of National Institute of Development Administration, Serithai Road, Klong-Chan, Bangkapi, Bangkok 10240, Thailand. Email: piriya@nida.ac.thEmail: piriya@nida.ac.th

This paper is part of the project entitled "Developing Professional Education and Informal Education for Building a Life-Long Learning Framework in Thailand", funded by the National Research Council of Thailand.


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1. šœÎµ Ĝš«ª¦¦¬š¸ÉŸnµœ¤µ „¨¥»š›r®œ¹ÉŠš¸ÉšÎµÄ®o£µ‡°»˜­µ®„¦¦¤Åš¥­µ¤µ¦™¦´„¬µ…¸—‡ªµ¤­µ¤µ¦™Äœ„µ¦ …n Š …´ œ Ĝ˜¨µ—è„Å—o „È ‡º ° „µ¦‡ª‡» ¤ „µ¦Ÿ¨· ˜ ×¥¡¹É Š ¡µÂ¦ŠŠµœ¦µ‡µ™¼ „ œ´É œ ®¤µ¥™¹ Š „µ¦š¸É £µ‡°» ˜ ­µ®„¦¦¤‹³˜o ° Š¤¸ „ µ¦‹o µ ŠÂ¦ŠŠµœš¸É ¤¸ š´ „ ¬³˜ÉÎ µ Ĝ£µ‡„µ¦Ÿ¨· ˜ š¸É Ä o  ¦ŠŠµœÁ…o ¤ …o œ (Labor Intensive) Ž¹ÉŠÄœ°—¸˜Â¦ŠŠµœÁ®¨nµœ¸Ê™¼„—¹Š¤µ‹µ„£µ‡„µ¦Á„¬˜¦š¸É¤¸„µ¦¥oµ¥™·Éœ‹µ„œšÁ…oµ¤µÄœÁ¤º°Š °»˜­µ®„¦¦¤˜nµŠÇœ°„‹µ„œ¸Ê ¥´Š¤¸Â¦ŠŠµœ…oµ¤µ˜·‹µ„ž¦³Áš«Á¡ºÉ°œoµœ (×¥ÁŒ¡µ³‹µ„ž¦³Áš«¨µª „´¤¡¼µ ¨³¡¤nµ) š¸ÉÁ¨º°„š¸É‹³Á…oµ¤µšÎµŠµœÄœž¦³Áš«Åš¥š¸Éŗo¦´‡nµ‹oµŠ­¼Š„ªnµšÎµŠµœž¦³Áš«…°Š˜œ (Pholphirul, 2013) °¥nµŠÅ¦„Șµ¤ „µ¦š¸É‹³ÁŸ·®œoµ„´„µ¦Â…nŠ…´œÄœÁªš¸Ã¨„œ´Êœ‹µ„„µ¦Áœoœ˜oœš»œ¦µ‡µ™¼„¨³„—‡nµ‹oµŠ ¦ŠŠµœœ´ÊœÅ¤nŗošÎµÄ®oÁ«¦¬“„·‹…°Šž¦³Áš«Á„·—‡ªµ¤¥´ÉŠ¥ºœÂ˜n°¥nµŠÅ¦Äœ„µ¦š¸Éž¦³Áš«‹³­µ¤µ¦™„oµª Ş­¼n°¸„…´Êœœ´Êœ ž¦³Áš«Åš¥‹ÎµÁž}œ˜o°Š¡´•œµ„µ¦Ÿ¨·˜Åž¡¦o°¤„´„µ¦œÎµœª´˜„¦¦¤Ä®¤n¤µÄo¤µ„…¹Êœ ×¥ÁŒ¡µ³°¥nµŠ¥·ÉŠÄœ¥»‡ž{‹‹»´œš¸ÉÁž}œ¥»‡Áš‡ÃœÃ¨¥¸…o°¤¼¨…nµª­µ¦Â¨³„µ¦˜·—˜n°­ºÉ°­µ¦ (ICT) „µ¦š¸ÉŤn ­µ¤µ¦™ž¦´˜´ªÅ—oš´œšnªŠš¸œ´Êœ ›»¦„·‹°µ‹ž¦³­„´‡ªµ¤šoµšµ¥Á·Š„¨¥»š›r¨³ª·„§˜Å—o —´Šœ´Êœ‹¹ŠÁž}œ ­·ÉŠ‹ÎµÁž}œš¸É›»¦„·‹Åš¥‹³˜o°ŠÁ®Èœ‡ªµ¤­Îµ‡´…°Š„µ¦Á¡·É¤‡ªµ¤­µ¤µ¦™Äœ„µ¦Â…nŠ…´œÃ—¥„µ¦¨Šš»œÄœ œª´˜„¦¦¤ Ž¹ÉŠª·›¸®¨´„š¸É‹³œÎµÅž­¼n«´„¥£µ¡„µ¦Â…nŠ…´œš¸É­¼Š…¹Êœœ´Êœ ‡º° „µ¦­œ´­œ»œÄœ„µ¦­¦oµŠœª´˜„¦¦¤ ª·‹´¥Â¨³¡´•œµ£µ¥Äœ°Š‡r„¦ °¥nµŠÅ¦„Șµ¤Á¤ºÉ°Áš¸¥„´¤µ˜¦“µœ­µ„¨Â¨oª „µ¦¨Šš»œÄœ—oµœœª´˜„¦¦¤Â¨³—oµœ„µ¦ª·‹´¥Â¨³¡´•œµ (R&D) …°ŠŸ¼ož¦³„°„µ¦Åš¥¥´ŠoµÂ¨³¨oµ®¨´Š„ªnµ¤µ˜¦“µœ…°Š„¨»n¤ž¦³Áš« OECD ¦ª¤š´ÊŠµŠ ž¦³Áš«Äœ£¼¤£· µ‡ Ánœ ¸žÉ »iœ Á„µ®¨¸Ä˜o ­·Š‡Ãž¦r¨³¤µÁ¨ÁŽ¸¥ ×¥‹µ„¦¼žš¸É 1 Á®ÈœÅ—oªnµž¦³Áš«Åš¥¤¸ „µ¦¨Šš»œÄœ—oµœ„µ¦ª·‹´¥Â¨³„µ¦¡´•œµÁ¡¸¥ŠÂ‡n¦o°¥¨³ 0.24 ˜n°¦µ¥Å—ož¦³µµ˜· (GDP) Ášnµœ´Êœ Ĝ…–³š¸É‡nµÁŒ¨¸É¥…°Š„¨»n¤˜´ª°¥nµŠ°¥¼nš¦¸É o°¥¨³ 1 Ĝ…–³š¸Éž¦³Áš«Äœ„¨»n¤ OECD ‹³¤¸„µ¦¨Šš»œÄœ„µ¦ ª·‹´¥Â¨³„µ¦¡´•œµš¸Éž¦³¤µ–¦o°¥¨³ 3-4 …°Š¦µ¥Å—ož¦³µµ˜· °¥nµŠÅ¦„È—¸ ‹µ„„µ¦­Îµ¦ª‹ž¦³­· š›·£µ¡„µ¦Ÿ¨·˜Â¨³¦¦¥µ„µ«„µ¦¨Šš»œ (Productivity and Investment Climate survey: PICs) …°Šž¦³Áš«Åš¥Äœže 2007 ¸ÊÄ®oÁ®Èœªnµ (¦¼žš¸É 2) Á®˜»Ÿ¨š¸É­Îµ‡´š¸É ›»¦„·‹Åš¥Å¤nŗo¨Šš»œÄœ—oµœœª´˜„¦¦¤Â¨³„µ¦ª·‹´¥Â¨³¡´•œµ ŤnÁ¡¸¥ŠÂ‡nÁ®˜»Ÿ¨—oµœ„µ¦ÁŠ·œš¸É„µ¦‹´—®µ œª´˜„¦¦¤œ´Êœ¤¸˜oœš»œ­¼Š (¦o°¥¨³ 43.6) Ášnµœ´ÊœÂ˜n¥´ŠÁ„·—‹µ„„µ¦…µ—‡¨œ»‡¨µ„¦š¸É¤¸‡ªµ¤¦¼o¨³Ÿnµœ „µ¦ f„°¦¤ (¦o°¥¨³ 42.7) (World Bank, 2008)Ž¹ÉŠ„µ¦…µ—»‡¨µ„¦šµŠ—oµœÁš‡œ·‡Â¨³ f„°¦¤Äœ š¸Éœ¸Ê ®¤µ¥™¹Š „µ¦…µ—š´ÊŠš´„¬³¡ºÊœ“µœÂ¨³š´„¬³šµŠÁš‡œ·‡š¸É‹ÎµÁž}œÄœ›»¦„·‹Åš¥ °µ‹‹³šÎµÄ®oÁ„·—Ÿ¨ „¦³šš´ÊŠ¦³¥³­´ÊœÂ¨³¦³¥³¥µªÄœ„·‹„¦¦¤šµŠÁ«¦¬“„·‹ —´Šœ´ÊœÄœ¦³¥³­´Êœ¦·¬´š˜o°Š¤¸„µ¦Ÿ¨·˜š¸É˜Îɵ„ªnµ „ε¨´Š„µ¦Ÿ¨·˜Á˜È¤š¸É ­nªœÄœ¦³¥³¥µª›»¦„·‹‹³˜o°Š¡¥µ¥µ¤Á¡·É¤Ÿ¨Ÿ¨·˜Ÿnµœ„µ¦Äoœª´˜„¦¦¤Ä®¤n


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¦¼žš¸1É : ¦o°¥¨³…°Š„µ¦Äo‹µn ¥—o—oµœª·‹´¥Â¨³³¡´•œµ (R&D) ˜n°GDP¨³°´˜¦µ„µ¦Á‹¦·Á˜·Ã˜…°Š„µ¦ ¦Äo‹µn ¥—oµœª·‹¥´ ¨³¡´•œµµ (R&D) Áž¦¸¥Áš¸¥¦³®ªnµŠž¦¦³Áš«Åš¥Â¨³°´˜¦µ

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Ÿ¨„µ¦ª·Á‡¦µ³®r£µ¡¦ª¤…°ŠÁª¨µš¸ÉčoĜ„µ¦¦¦‹»˜ÎµÂ®œnŠŠµœªnµŠ¨nµ­»—¦µ¥£µ‡ ץ¥„Áž}œ¨´„¬–³ „¨»n¤Â¦ŠŠµœ Ž¹ÉŠ­nªœÄ®n‹³­³šo°œ™¹Š°Š‡rž¦³„°…°Š£µ‡„µ¦Ÿ¨·˜ÄœÂ˜n¨³£¼¤·£µ‡ Ánœ ¦ŠŠµœ¤¸ e¤º°Äœ£µ‡˜³ª´œ°°„ÁŒ¸¥ŠÁ®œº°®µ¥µ„š¸É­»—ץčoÁª¨µ™¹Š 15.5 ­´ž—µ®rŽ¹ÉŠ™º°ªnµ­¼ŠÁ¤ºÉ°Áš¸¥„´ ‡nµÁŒ¨¸É¥…°Šž¦³Áš« ‡º° 7.4 ­´ž—µ®r Ž¹ÉŠ„ªnµ‡¦¹ÉŠ®œ¹ÉŠ…°Š­™µœž¦³„°„µ¦°¥¼nĜ°»˜­µ®„¦¦¤ Á¢°¦rœ·Á‹°¦r¨³Ÿ¨·˜£´–”r‹µ„Ťo ¨³°»˜­µ®„¦¦¤Á­ºÊ°Ÿoµ­ÎµÁ¦È‹¦¼ž Ž¹ÉŠš´ÊŠ­°Š°»˜­µ®„¦¦¤œ¸ÊčoÁª¨µœµœ š¸É­»— Ĝ„µ¦®µÂ¦ŠŠµœ¤º °°µ¸ ¡ ¨³ÄœšÎ µ œ°ŠÁ—¸ ¥ ª„´ œ ¦ŠŠµœ e¤º ° ®µ¥µ„š¸É ­» — Ĝ£µ‡˜³ª´œ °°„ ÁœºÉ°Š‹µ„¤¸°»˜­µ®„¦¦¤·Êœ­nªœ¥µœ¥œ˜r ¨³°»˜­µ®„¦¦¤¥µŠÂ¨³¡¨µ­˜·„°¥¼n¤µ„¤µ¥ ‹¹Šž¦³­ ž{®µÂ¦ŠŠµœ e¤º°Å¤nÁ ¡¸¥Š¡°¤µ„„ªn µ°»˜­µ®„¦¦¤°ºÉœÇ Ĝ¦·¬´š˜nµŠµ˜·‹³ÄoÁª¨µÄœ„µ¦¦¦‹» ¡œ´„ŠµœÄœ˜ÎµÂ®œnŠªnµŠÅ—oÁ¦Èª…¹Êœ Á¡¦µ³¦·¬´š˜nµŠµ˜·‹³Ä®oÁŠ·œÁ—º°œÂ¨³Ÿ¨ž¦³Ã¥œr­¼Š„ªnµ¦·¬´š‡œ Ś¥ (World Bank, 2008) ž{®µ„µ¦…µ—‡¨œÂ¦ŠŠµœŸ¼oÎµœµŠµœÄœž¦³Áš«Åš¥Áž}œš¸É¡¦n®¨µ¥¤µ„…¹Êœ„ªnµÄœž¦³Áš«°ºÉœ Ç ‹µ„ 64ž¦³Áš«š¸É¤¸…o°¤¼¨Á®¤º°œ„´œ ×¥ÁŒ¨¸É¥Â¨oªÄoÁª¨µÄœ„µ¦®µÂ¦ŠŠµœŸ¼oÎµœµŠµœž¦³¤µ– 3.8 ­´ž—µ®rŽ¹ÉŠœo°¥„ªnµÁ¤ºÉ°Áž¦¸¥Áš¸¥„´ž¦³Áš«Åš¥š¸ÉčoÁª¨µ™¹Š 7.4­´ž—µ®r


NIDA Economic Review

135

˜µ¦µŠ1: ¦³¥³Áª¨µš¸ÉčoĜ„µ¦¦¦‹»¡œ´„Šµœ¨ŠÄœ˜ÎµÂ®œnŠŠµœªnµŠ‹ÎµÂœ„˜µ¤¦µ¥£µ‡ °»˜­µ®„¦¦¤Â¨³ž¦³Á£š›»¦„·‹

ž¦³Áš«Åš¥ „¦»ŠÁš¡²Â¨³ž¦·¤–”¨ £µ‡„¨µŠ £µ‡˜³ª´œ°°„ £µ‡Á®œº° £µ‡˜³ª´œ°°„ÁŒ¸¥ŠÁ®œº° £µ‡Ä˜o ·Êœ­nªœ¦™¥œ˜r Á‡¦ºÉ°ŠÄoÅ¢¢jµ ·Êœ­nªœ°·Á¨È„š¦°œ·„­r Ÿ¨·˜£´–”r°µ®µ¦Âž¦¦¼ž Á¢°¦rœ·Á‹°¦r Á­ºÊ°Ÿoµ­ÎµÁ¦È‹¦¼ž Á‡¦ºÉ°Š‹´„¦„¨ ¥µŠÂ¨³¡¨µ­˜·„ ­·ÉŠš° …œµ—Á¨È„ …œµ—„¨µŠ …œµ—Ä®n ˜nµŠž¦³Áš« Ĝž¦³Áš« Ťnčn„µ¦Ÿ¨·˜Á¡ºÉ°„µ¦­nŠ°°„ Ÿ¨·˜Á¡ºÉ°„µ¦­nŠ°°„ š¸É¤µ: Thailand PICS 2007

¦ŠŠµœ¤º°°µ¸¡ 7.4 7.7 6.3 6.6 6.0 15.5 9.0 7.6 7.1 6.6 6.8 10.1 8.2 8.0 6.1 7.2 6.9 7.9 7.0 5.5 7.7 6.6 7.9

¦ŠŠµœ¤¸ e¤º° 5.2 5.2 4.4 7.0 3.6 5.5 5.5 6.0 4.4 3.8 4.0 5.1 5.3 5.3 5.5 5.4 5.4 4.9 5.3 3.5 5.4 5.2 5.2

¦ŠŠµœÅ¦o e¤º° 2.2 2.0 2.0 2.7 2.6 1.7 3.2 1.9 2.1 1.8 2.7 2.1 2.2 2.2 2.1 2.2 2.4 2.0 2.0 1.3 3.3 2.3 2.0


136

NIDA Economic Review

Á®˜»Ÿ¨š¸É­Îµ‡´š¸ÉšÎµÄ®oÁ„·—˜ÎµÂ®œnŠŠµœªnµŠ¤µ„¤µ¥Â¨³¥µ„š¸É‹³®µ¡œ´„Šµœ¤µ¦¦‹»Äœ˜ÎµÂ®œnŠ„ȇº° „µ¦š¸É¦ŠŠµœÅ¤n¤¸‡»–£µ¡Ã—¥„ªnµ¦o°¥¨³ 40 …°ŠŸ¼o¦·®µ¦Äœ£µ‡°»˜­µ®„¦¦¤„¨nµªªnµ ˜ÎµÂ®œnŠŠµœ ªnµŠš¸ÉÁ„·—…¹Êœœ¸Ê¤µ‹µ„„µ¦š¸ÉŸ¼o­¤´‡¦‹Îµœªœ¤µ„¥´Š…µ—š´„¬³¡ºÊœ“µœÁnœ š´„¬³—oµœ£µ¬µ°´Š„§¬ ¨³ š´ „ ¬³„µ¦‡Î µ œª–¦ª¤š´Ê Š š´ „ ¬³šµŠÁš‡œ· ‡ Án œ —o µ œ‡°¤¡· ª Á˜°¦r  ¨³Áš‡ÃœÃ¨¥¸ ­ µ¦­œÁš« (IT)œ°„‹µ„œ¸Ê¥´Š…µ—š´„¬³—oµœ°Š‡r‡ªµ¤¦¼o Ánœ š´„¬³‡ªµ¤‡·—Á·Š­¦oµŠ­¦¦‡r¨³œª´˜„¦¦¤ „µ¦Áž}œ Ÿ¼oœÎµÂ¨³š´„¬³„µ¦˜·—˜n°­ºÉ°­µ¦ Ž¹ÉŠ¦µ¥Šµœœ¸Êŗo„¨nµª™¹Š‡»–£µ¡Â¦ŠŠµœÅš¥Å—oÁž}œ°¥nµŠ—¸ „µ¦…µ—‡¨œ»‡¨µ„¦š¸É¤¸‡ªµ¤­µ¤µ¦™‹³¤¸Ÿ¨˜n°„·‹„¦¦¤šµŠÁ«¦¬“„·‹š´ÊŠÄœ¦³¥³­´ÊœÂ¨³¦³¥³¥µª Ĝ ¦³¥³­´Êœ­™µœž¦³„°„µ¦‹³šÎµ„µ¦Ÿ¨·˜˜É優nµ„ε¨´Š„µ¦Ÿ¨·˜Á˜È¤š¸É Á¡¦µ³Å¤n­µ¤µ¦™®µÂ¦ŠŠµœš¸É¤¸ ‡ªµ¤­µ¤µ¦™Â¨³ž¦³­„µ¦–rÁ¡¸¥Š¡° Ĝ‡ªµ¤Áž}œ‹¦·ŠÁ„º°¦o°¥¨³ 20 …°Š­™µœž¦³„°„µ¦Äœ °»˜­µ®„¦¦¤Á­ºÊ°Ÿoµ­ÎµÁ¦È‹¦¼žÂ¨³°»˜­µ®„¦¦¤Á‡¦ºÉ°Š‹´„¦Â¨³°»ž„¦–r Áž}œÁ®˜»Ÿ¨š¸É­Îµ‡´š¸É˜o°ŠŸ¨·˜ £µ¥Ä˜o„ε¨´Š„µ¦Ÿ¨·˜š¸É¤¸°¥¼n ­nªœÄœ¦³¥³¥µª¤¸„µ¦…µ—‡¨œÂ¦ŠŠµœš¸ÉŸnµœ„µ¦ f„°¦¤¤µÁž}œ°¥nµŠ—¸ Ž¹ÉŠÁž}œ…o°‹Îµ„´—Äœ„µ¦Á¡·É¤Ÿ¨·˜£µ¡ ¨³Á„º°š»„›»¦„·‹ÁºÉ°ªnµ„µ¦¨Šš»œÄœ„·‹„¦¦¤„µ¦­¦oµŠœª´˜„¦¦¤ ‹³Å—oŸ¨˜°ÂšœÁŒ¨¸É¥­¼Š…¹Êœ ¦¼žš¸É 4: ­µÁ®˜»š¸É­µÎ ‡´…°Š˜ÎµÂ®œnŠŠµœªnµŠ (¦o°¥¨³…°Š…°Š­™µœž¦³„°„µ¦)

š¸É¤µ: Thailand PICS 2007


NIDA Economic Review

137

¦¼žš¸É 5: ¦o°¥¨³­™µœž¦³„°„µ¦š¸É¦ŠŠµœ¥´Š…µ—š´„¬³—oµœ˜nµŠÇ

š¸É¤µ: Thailand PICS 2004 and PICS 2007

—´Šœ´Êœ‹¹Š­µ¤µ¦™­¦»žÅ—oªnµ £µ‡°»˜­µ®„¦¦¤Åš¥„ε¨´Šž¦³­ž{®µ‡ªµ¤…µ—‡¨œÂ¦ŠŠµœšµŠ—oµœ °µ¸ª³ÄœÁ·Šž¦·¤µ–¤µ„„ªnµ„µ¦…µ—‡¨œÄœÁ·Š‡»–£µ¡ (Quantity Supply Constraint) œ´„«¹„¬µš¸É ‹‹µ„­™µ´œ°µ¸ª³‹Îµœªœ¤µ„Á¨º°„š¸É‹³«¹„¬µ˜n°Äœ¦³—´°»—¤«¹„¬µš¸É‹¤µšÎµŠµœš¸É­µ¥„ªnµÂ¨³ ŗo¦´Ÿ¨˜°Âšœš¸É­¼Š„ªnµ Ž¹ÉŠ¡ªnµÁž}œž{®µš¸É˜¦Š„´œ…oµ¤„´Â¦ŠŠµœš¸É‹Äœ¦³—´°»—¤«¹„¬µŽ¹ÉŠÅ¤nŗo ž¦³­ž{®µ…µ—‡¨œÄœÁ·Š…°Šž¦·¤µ– ˜n…µ—‡¨œÄœÁ·Š‡»–£µ¡ œ°„‹µ„¦ŠŠµœÄœ¦³—´´Êœ°µ¸ª³«¹„¬µÂ¨³¦³—´´–”·˜«¹„¬µÃ—¥£µ¡¦ª¤Â¨oª ‡ªµ¤­µ¤µ¦™Äœ„µ¦ …nŠ…´œ…°Š£µ‡°»˜­µ®„¦¦¤¥´Š…¹Êœ°¥¼n„´¦³—´„µ¦¨Šš»œÄœœª´˜„¦¦¤ ‹µ„š¸É¦³»Åªo˜´ÊŠÂ˜n˜°œ˜oœš¸Éªnµ ×¥£µ‡°» ˜ ­µ®„¦¦¤Åš¥¤¸ „ µ¦¨Šš» œ Ĝœª´ ˜ „¦¦¤Ä®¤n Ç š¸É œo ° ¥¤µ„ Ž¹É Š ¤¸ ­ µÁ®˜» ¤ µ‹µ„„µ¦š¸É £µ‡°»˜­µ®„¦¦¤¥´Š…µ—‡¨œÂ¦ŠŠµœš¸ÉšÎµ®œoµš¸ÉĜ„µ¦‡·—‡oœœª´˜„¦¦¤Ä®¤nÇ (World Bank, 2008) ×¥Á¤ºÉ°œÎµ…o°¤¼¨¤µ„ª·Á‡¦µ³®r¡ªnµ ¦ŠŠµœÁ·ŠÁš‡œ·‡ (Technical Staff) ®¦º°Â¦ŠŠµœÄœ­µ…µ STEM Field Ánœ ª·«ª„¦ œ´„ª·š¥µ«µ­˜¦r œ´„ª·Á‡¦µ³®r ¨³œ´„‡°¤¡·ªÁ˜°¦r¨³Å°š¸ ‡·—Áž}œ­´—­nªœ…°Š„µ¦ ‹oµŠŠµœÁ¡¸¥ŠÂ‡n¦o°¥¨³ 4 …°Š¡œ´„Šµœž¦³‹Îµš´ÊŠ®¤—…°Š¦·¬´šÁšnµœ´Êœ ץ¦ŠŠµœÁ·ŠÁš‡œ·‡œ¸Ê­nªœ Ä®n (¦o°¥¨³ 66) Áž}œª·«ª„¦ ¦°Š¨Š¤µÅ—o„nœ´„ª·š¥µ«µ­˜¦r (¦o°¥¨³ 12), œ´„‡°¤¡·ªÁ˜°¦r¨³Å°š¸ (¦o°¥¨³ 9) œ´„ª·‹´¥ (¦o°¥¨³ 6) ¨³œ´„ª·Á‡¦µ³®r (¦o°¥¨³ 4) ×¥°»˜­µ®„¦¦¤š¸É¤¸„µ¦‹oµŠÂ¦ŠŠµœÁ·Š Áš‡œ·‡š¸É­¼Šš¸É­»—¤´„Áž}œ°»˜­µ®„¦¦¤š¸Éčo„µ¦Ÿ¨·˜š¸ÉÁž}œš»œÁ…o¤…oœ ŗo„n °»ž„¦–rÅ¢¢jµ (¦o°¥¨³ 4.8) ¨³Á‡¦ºÉ°Š‹´„¦Â¨³°»ž„¦–r (¦o°¥¨³ 4.3)


138

NIDA Economic Review

¦¼žš¸É 6: ¦o°¥¨³…°Š¦·¬´šš¸É¦³»»ªnµœ´„«¹„¬µµ‹µ„­™µ´´œ°µ¸ª³«¹„¬µ¤¸š´„¬³š¸š¸É—¸Â¨³—¸¤µ„ 77.96 71.58

72.5

74.61 67.78 59.28

š¸É¤µ : ‡Îµœª–‹µ„ PICS-2007

Š ‡ ¦¼žš¸É 7: ­´—­nªœ…°ŠŠÂ¦ŠŠµœÁ·ŠÁš‡œ· mation Analysiss Staff Inform Techn ninians 4% % 9% % Research Staff 6 6% Scientissts 15%

š¸É¤µ : ‡Îµœª–‹µ„ PICS-2007

Engineers 66%

57.79


NIDA Economic Review

139

¦¼žš¸É 8: ¦o°¥¨³…°ŠÂ¦ŠŠµœÁ·ŠÁš‡œ·‡˜n°¡œ´„Šµœž¦³‹Îµš´ŠÊ ®¤—‹ÎµÂœ„¦µ¥°»˜­µ®„¦¦¤

4.8

2.3

4.3

2.2

4.0

1.8 0.6

0.3

0.8

0.6

š¸É¤µ : ‡Îµœª–‹µ„ PICS-2007

1. „µ¦‹´— f„°¦¤£µ¥Äœ°Š‡r„¦Â¨³„µ¦ f„°¦¤£µ¥œ°„°Š‡r„¦ œ°„‹µ„čož¦³Ã¥œr‹µ„š»œ¤œ»¬¥rš¸ÉŸnµœ¦³„µ¦«¹„¬µ‹µ„æŠÁ¦¸¥œÂ¨³¤®µª·š¥µ¨´¥Â¨oª ¦·¬´šÄœ £µ‡°» ˜ ­µ®„¦¦¤¥´ Š ¤¸  šµš­Î µ ‡´  Ĝ„µ¦Áž} œ Ÿ¼o ¨ Šš» œ Ĝš» œ ¤œ» ¬ ¥r Ÿn µ œ¦³…°Š„µ¦ f „ °¦¤ (Training) ×¥Šµœ«¹„¬µÁ·Šž¦³‹´„¬r­nªœÄ®n‹³šÎµ„µ¦ª·Á‡¦µ³®rŸ¨˜°Âšœ (Return) …°Š„µ¦  f„°¦¤ ÁnœŠµœ«¹„¬µ…°Š Blundell ¨³‡–³ (1996) ŗošÎµ„µ¦ž¦³¤µ–„µ¦Ÿ¨˜°Âšœ…°Š„µ¦  f„°¦¤˜n°‡ªµ¤­µ¤µ¦™Äœ„µ¦®µ¦µ¥Å—o¡ªnµ „µ¦ f„°¦¤­nŠŸ¨šÎµÄ®oŸ¼oš¸É¦´„µ¦ f„°¦¤¤¸¦µ¥Å—o Á¡·É¤­¼Š…¹Êœž¦³¤µ–¦o°¥¨³ 5-10 œ°„‹µ„œ¸Ê Šµœ«¹„¬µ‹Îµœªœ®œ¹ÉŠ¥´ŠÅ—ošÎµ„µ¦‹ÎµÂœ„ª·Á‡¦µ³®r¦³®ªnµŠ ¦ŠŠµœµ¥Â¨³Â¦ŠŠµœ®·ŠÃ—¥¡ªnµ ¦ŠŠµœ®·Š‹³Å—o¦´Ÿ¨˜°Âšœ‹µ„„µ¦ f„°¦¤¤µ„„ªnµ ¦ŠŠµœµ¥ (Booth, 1991; Greenhalgh and Stewart, 1987)Ĝ…–³š¸ÉŸ¨Å—o…°Š„µ¦ f„°¦¤¥´Š ˜„˜nµŠ˜µ¤˜ÎµÂ®œnŠŠµœÃ—¥¡ªnµ „µ¦ f„°¦¤Â¦ŠŠµœÄœ˜ÎµÂ®œnŠŸ¼o‹´—„µ¦ (Managerial Training) ¨³˜ÎµÂ®œnŠÂ¦ŠŠµœ¤º°°µ¸¡ (Professional and Technical Training) ‹³­¦oµŠŸ¨˜°ÂšœÅ—o­¼Š„ªnµ „µ¦ f„°¦¤Â¦ŠŠµœš¸É¤¸š´„¬³˜Éε (Lillard and Tan, 1992; Bartel, 1995)×¥„µ¦Á…oµ¦´„µ¦ f„°¦¤¤¸ œªÃœo¤…°Š„µ¦Á„·— Self-Selection ×¥Ÿ¼oÁ…oµ¦´„µ¦ f„ (­¤´‡¦Á…oµ¦nª¤„µ¦ f„) ­nªœÄ®n‹³Áž}œ ¦ŠŠµœš¸É¤¸š´„¬³­¼Š (Blendell ¨³‡–³, 1999). Ĝ…–³š¸ÉĜž¦³Áš«Åš¥ Šµœ«¹„¬µ…°Š ¡·¦·¥³ Ÿ¨¡·¦»¯®r ¨³ ž{Šž°œ—r ¦´„°Îµœª¥„·‹ (2558) ¡ªnµ ¦·¬´šš¸ÉšÎµ„µ¦ f„°¦¤£µ¥Äœ°Š‡r„¦ (In-house Training) ‹³¤¸Ÿ¨·˜£µ¡Â¦ŠŠµœ­¼Š„ªnµ¦·¬´šš¸ÉŤnŗo ‹´—„µ¦ f„°¦¤Äœ°Š‡r„¦°¥¼nž¦³¤µ–¦o°¥¨³ 13.6 ¨³¥´Š¤¸‡nµ‹oµŠÁŒ¨¸É¥­¼Š„ªnµ¦·¬´šš¸ÉŤnŗo‹´—„µ¦


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f„°¦¤Äœ°Š‡r „¦°¥¼n ž¦³¤µ–¦o° ¥¨³ 10.5 Ĝ…–³š¸É„µ¦Ä®o ¡œ´„ŠµœÅž f „°¦¤£µ¥œ°„°Š‡r „¦ (Outside Training) ‹³Å¤n­nŠŸ¨˜n°Ÿ¨·˜£µ¡Â¦ŠŠµœÂ¨³˜oœš»œ‡nµ‹oµŠ…°Š¦·¬´š°¥nµŠ¤¸œ´¥­Îµ‡´šµŠ ­™·˜· Ž¹ÉŠŸ¨š¸Éŗoœ¸Ê„È­—Šªnµ „µ¦Ä®o„µ¦ f„°¦¤Â¦ŠŠµœ‹³Áž}œ„·‹„¦¦¤š¸É¤¸‡ªµ¤‡»¤‡nµÄœ„µ¦­¦oµŠŸ¨Å—o (Benefits) ¤µ„„ªnµ˜oœš»œš¸ÉÁ¡·É¤…¹Êœ œ°„‹µ„œ¸Ê ‡nµÄo‹nµ¥Äœ„µ¦ f„°¦¤£µ¥œ°„¥´Š­nŠŸ¨šµŠª„˜n° Ÿ¨·˜£µ¡Â¦ŠŠµœÂ¨³‡ªµ¤­µ¤µ¦™Äœ„µ¦Â…nŠ…´œš¸ÉÁ¡·É¤…¹Êœ—oª¥Ánœ„´œ ×¥™oµ¦·¬´š¤¸„µ¦Äo‹nµ¥ÁŠ·œ„´ „µ¦ f„°¦¤£µ¥œ°„Á¡·É¤…¹Êœ°¸„¦o°¥¨³ 10 Ÿ¨·˜£µ¡Â¦ŠŠµœÄœ¦·¬´šœ´Êœ‹³Á¡·É¤…¹Êœž¦³¤µ–¦o°¥¨³ 0.16 ¨³­nŠŸ¨šÎµÄ®o‡nµ Unit Labor Cost ¨—¨Š (®¦º°¤¸…¸—‡ªµ¤­µ¤µ¦™Äœ„µ¦Â…nŠ…´œ…°ŠÂ¦ŠŠµœš¸É Á¡·É¤…¹Êœ) ž¦³¤µ–¦o°¥¨³ 0.12 ‹µ„„µ¦­Îµ¦ª‹¡ªnµ £µ‡°»˜­µ®„¦¦¤Åš¥­nªœÄ®n‹³Ä®o‡ªµ¤­Îµ‡´„´„µ¦ f„°¦¤Â¦ŠŠµœ¤µ„ ¡°­¤‡ª¦ ×¥­´ŠÁ„˜Å—o‹µ„­´—­nªœ¦o°¥¨³ 63.5 …°Š¦·¬´šš´ÊŠ®¤—š¸É¦³»ªnµ¦·¬´š…°Š˜œÅ—o¤¸„µ¦  f„°¦¤Â¦ŠŠµœ£µ¥Äœ°Š‡r„¦…°Š˜´ªÁ°Š (In-House Training) Ĝ…–³š¸É¦o°¥¨³ 64.1 ¦µ¥Šµœªnµ¦·¬´š ŗo¤¸„µ¦‹´—°¦¤£µ¥œ°„°Š‡r„¦®¦º°Ä®o®œnª¥Šµœ°ºÉœÁž}œŸ¼ošÎµ®œoµš¸É f„°¦¤ (Outsource Training ®¦º° Outside Training) œ°„‹µ„œ¸Ê¥´Š¡ªnµ ¦·¬´šš¸É¤¸„µ¦ f„°¦¤¤µ„š¸É­»—‹³Áž}œ¦·¬´šš¸É¡¹ÉŠ¡µ„µ¦Ÿ¨·˜š¸Éčoš»œ®¦º°Á‡¦ºÉ°Š‹´„¦ Áž}œ­Îµ‡´ Ánœ ·Êœ­nªœ¥µœ¥œ˜r °»ž„¦–rÅ¢¢jµ ¨³°µ®µ¦Âž¦¦¼ž ×¥„µ¦ f„°¦¤¤¸‡ªµ¤­Îµ‡´Áž}œ °¥nµŠ¤µ„„´„µ¦Á¡·É¤Ÿ¨·˜£µ¡Â¦ŠŠµœÄ®o­µ¤µ¦™Â…nŠ…´œÅ—oĜ˜¨µ—è„ ×¥‹µ„„µ¦­Îµ¦ª‹¡ªnµ ¦o°¥ ¨³ 75 …°Š¦·¬´šš¸ÉšÎµ„µ¦­nŠ°°„­·œ‡oµ (Exporting Firm) ‹³¤¸„µ¦ f„°¦¤Â¦ŠŠµœ Ĝ…–³š¸É¤¸Á¡¸¥Š ¦o°¥¨³ 25 …°Š¦·¬´šš¸ÉŤnŗoŸ¨·˜Á¡ºÉ°„µ¦­nŠ°°„š¸É¦³»ªnµ˜´ªÁ°ŠÅ—o¤¸„µ¦ f„°¦¤Â¦ŠŠµœ ˜µ¦µŠ2: ­´—­nªœ…°Š¦·¬´šÄœ£µ‡°»˜­µ®„¦¦¤š¸É¦³»ªnµ¤¸„µ¦ f„°¦¤Â¦ŠŠµœ °»˜­µ®„¦¦¤ ·Êœ­nªœ¥µœ¥œ˜r Á‡¦ºÉ°ŠÄoÅ¢¢jµ °»ž„¦–rÅ¢¢jµ Á¢°¦rœ·Á‹°¦r ž¦¦¼ž°µ®µ¦ Á‡¦ºÉ°Šœ»nŠ®n¤ Á‡¦ºÉ°Š‹´„¦Â¨³°»ž„¦–r ¥µŠÂ¨³¡¨µ­˜·„ ­·ÉŠš° ‡nµÁŒ¨¸É¥

In-house Training 89.9 57.1 83.1 51.0 71.3 50.9 54.2 62.4 59.4 63.5

š¸É¤µ : ‡Îµœª–‹µ„ PICS-2007

Outside Training 84.4 50.0 81.5 53.0 78.7 53.4 56.6 60.4 62.4 64.1


NIDA Economic Review

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Á¤ºÉ ° ‹Îµ œ„˜µ¤…œµ—…°Š¦·¬´ š ¡ªn µ ¦·¬´ š Ä®n ¤¸Â œªÃœo ¤š¸É ‹ ³Ä®o ‡ ªµ¤­Î µ‡´  …°Š„µ¦ f „ °¦¤ ¤µ„„ªnµ¦·¬´š…œµ—Á¨È„ ×¥¦o°¥¨³ 92.6 …°Š¦·¬´šš¸É¤¸…œµ—Ä®nš¸É¤¸„µ¦‹oµŠŠµœ¤µ„„ªnµ 200 ‡œ¦³» ªnµ¦·¬´š˜œÄ®o„µ¦ f„°¦¤Â¦ŠŠµœ£µ¥Äœ°Š‡r„¦ ×¥š¸É¦o°¥¨³ 88 …°Š¦·¬´šÄ®n¦³»ªnµ¦·¬´š¤¸„µ¦­nŠ ¦ŠŠµœÄ®oŞ f„°¦¤£µ¥œ°„ (Outside Training) Ĝ…–³š¸É ¦o°¥¨³ 29.1 ¨³¦o°¥¨³ 37.3 …°Š¦·¬´š š¸É¤¸…œµ—Á¨È„ (¤¸„µ¦‹oµŠŠµœœo°¥„ªnµ 50 ‡œ) Ášnµœ´Êœš¸É¦³»ªnµ¦·¬´š…°Š˜œ¤¸„µ¦ f„°¦¤£µ¥Äœ°Š‡r„¦ ¨³ f„°¦¤£µ¥œ°„°Š‡r„¦ ˜µ¤¨Îµ—´ ¦ŠŠµœ­nªœÄ®nš¸Éŗo¦´„µ¦ f„°¦¤£µ¥Äœ°Š‡r„¦Å—o„n ¦ŠŠµœš¸ÉšÎµŠµœÄœ£µ‡„µ¦Ÿ¨·˜ ץ¦ŠŠµœ š¸É¤¸š´„¬³˜Éε (Unskilled Production Workers) Áž}œ„¨»n¤š¸Éŗo¦´„µ¦ f„°¦¤¤µ„š¸É­»— ×¥‡·—Áž}œ¦o°¥ ¨³ 69.3 …°Š‹ÎµœªœÂ¦ŠŠµœš¸Éŗo¦´„µ¦ f„°¦¤š´ÊŠ®¤— Ĝ…–³š¸É¦ŠŠµœš¸Éŗo¦´„µ¦ f„°¦¤‹µ„ ®œnª¥Šµœ£µ¥œ°„×¥­nªœÄ®n„ÈÁž}œÂ¦ŠŠµœš¸É¤¸š´„¬³˜Éε (¦o°¥¨³ 31.4) ¨³Â¦ŠŠµœš¸É¤¸š´„¬³­¼Š (¦o°¥¨³ 33.3) Ž¹ÉŠ„µ¦Ä®o„µ¦ f„°¦¤Â„n¦ŠŠµœÄœ„¨»n¤œ¸Ê¤¸ª´˜™»ž¦³­Š‡rÁ¡ºÉ°Ä®oœnċªnµ ¦ŠŠµœ„¨»n¤œ¸Ê‹³ ŗo¦´„µ¦¡´•œµš´„¬³š¸É‹³­µ¤µ¦™˜°­œ°Š˜n°‡ªµ¤˜o°Š„µ¦…°Šœµ¥‹oµŠ Ĝ—oµœ®¨´„­¼˜¦ „µ¦ f„°¦¤£µ¥Äœ°Š‡r„¦­nªœÄ®n‹³Áž}œ®¨´„­¼˜¦¤µ˜¦“µœ‡ªµ¤ž¨°—£´¥ (¦o°¥¨³ 35.9) „µ¦‹´—„µ¦Â¨³„µ¦ÄoÁš‡ÃœÃ¨¥¸ (¦o°¥¨³ 25.1) ¨³—oµœÁš‡ÃœÃ¨¥¸„µ¦Ÿ¨·˜ (¦o°¥¨³ 24.9) Ĝ…–³š¸É®¨´„­¼˜¦š¸É‹´—×¥®œnª¥Šµœ£µ¥œ°„‹³Áž}œ—oµœÁš‡ÃœÃ¨¥¸„µ¦Ÿ¨·˜ (¦o°¥¨³ 27.8) ¦°Š¨Š¤µ ŗo„n ®¨´„­¼˜¦„µ¦‹´—„µ¦Â¨³„µ¦ÄoÁš‡ÃœÃ¨¥¸ (¦o°¥¨³ 27.2) ¨³®¨´„­¼˜¦¤µ˜¦“µœ‡ªµ¤ž¨°—£´¥ (¦o°¥¨³ 21.1) ˜µ¤¨Îµ—´ Ĝ…–³š¸É®¨´„­¼˜¦„µ¦ f„°¦¤Äœš´„¬³š´ÉªÅž (General Skill) Ánœ š´„¬³ šµŠ—oµœ£µ¬µ š´„¬³šµŠ—oµœ„µ¦˜¨µ— ¨³š´„¬³Äœ„µ¦—oµœÅ°š¸¥´Š¤¸šµš‡n°œ…oµŠœo°¥ Ž¹ÉŠ‹³Á®Èœ ªnµ£µ‡°»˜­µ®„¦¦¤‹³Ä®o„µ¦ f„°¦¤Ã—¥Áœoœš´„¬³š¸É‹³­µ¤µ¦™œÎµ¤µÄož¦³Ã¥œrĜš¸ÉšÎµŠµœ®¦º°Äœ 把µœÅ—o×¥˜¦Š Ĝ—oµœ˜´ªŸ¼o­°œ „µ¦ f„°¦¤£µ¥Äœ°Š‡r„¦ Ÿ¼o­°œ­nªœÄ®n‹³¤µ‹µ„ Ž»žÁž°¦rŪÁŽ°¦r/â¦r¤œ (¦o°¥ ¨³ 23) ¦°Š¨Š¤µÅ—o„n Ÿ¼o‹´—„µ¦Äœ°Š‡r„¦ (¦o°¥¨³ 20) ¦´“µ¨ (¦o°¥¨³ 19) ¦·¬´šš¸Éž¦¹„¬µ„µ¦ f„°¦¤ (¦o°¥¨³ 12) ¨³Â¦ŠŠµœš¸É°µ­µ­¤´‡¦Áž}œŸ¼o­°œ (¦o°¥¨³ 11) ˜µ¤¨Îµ—´ Ĝ…–³š¸É„µ¦ f„°¦¤‹µ„®œnª¥Šµœ£µ¥œ°„×¥­nªœÄ®n‹³Áž}œ®œnª¥Šµœ…°Š£µ‡¦´“µ¨ (¦o°¥¨³ 53) ×¥®œnª¥Šµœ®¨´„Ç …°Š£µ‡¦´“Å—o„n „¦¤¡´•œµ e¤º°Â¦ŠŠµœ „¦³š¦ªŠÂ¦ŠŠµœ, ­™µ´œÁ¡·É¤ Ÿ¨Ÿ¨· ˜ ®n Š µ˜· ­£µ°» ˜ ­µ®„¦¦¤ ­¤µ‡¤„µ¦‹´ — „µ¦Åš¥ ­¤µ‡¤Áš‡ÃœÃ¨¥¸ Å š¥-¸É ž»i œ Áž} œ ˜o œ ¦°Š¨Š¤µÅ—o  „n ¦· ¬´ š š¸É ž ¦¹ „ ¬µÂ¨³Á¸É ¥ ªµ—o µ œ„µ¦ f „ °¦¤ (¦o ° ¥¨³ 31) ¤®µª· š ¥µ¨´ ¥ ¨³ ­™µ´œ„µ¦«¹„¬µ¤¸šµšš¸Éœo°¥¤µ„Äœ„µ¦Áž}œŸ¼oÄ®o„µ¦ f„°¦¤ °¥nµŠÅ¦„È—¸ ¥´ŠÅ¤n¤¸Šµœ«¹„¬µ°°„¤µ ¦°Š¦´  ªn µ „µ¦ f„ °¦¤Äœ®œn ª ¥Šµœ£µ‡¦´ “ œ¸Ê ‹ ³n ª ¥Á¡·É ¤ Ÿ¨· ˜ £µ¡Â¦ŠŠµœÅ—o ° ¥nµ Š¤¸ œ´¥ ­Î µ‡´  ‹¦· Š


142

NIDA Economic Review

ÁœºÉ°Š‹µ„„µ¦­´¤£µ¬–rŸ¼ož¦³„°„µ¦µŠ¦µ¥Å—o¦³»ªnµ˜œ­nŠÂ¦ŠŠµœÅž f„°¦¤„´®œnª¥Šµœ£µ‡¦´“ ÁœºÉ°Š‹µ„Å—o¦´„µ¦¦o°Š…°‹µ„®œnª¥Šµœ£µ‡¦´“—´Š„¨nµªÃ—¥Å¤n˜o°ŠÁ­¸¥‡nµÄo‹nµ¥ ŤnŗoÁ„·—‹µ„‡ªµ¤ ­œÄ‹…°Š¦·¬´šš¸É‹³¡´•œµš´„¬³œ´Êœ°¥nµŠÂšo‹¦·Š œ°„‹µ„œ¸Ê¥´Š¡ªnµ ¦·¬´š¤¸ÂœªÃœo¤š¸É‹³­nŠÂ¦ŠŠµœš¸É¤¸š´„¬³­¼Š ¦ŠŠµœÄœ­µ¥¦·®µ¦Â¨³Â¦ŠŠµœ ª· µ¸ ¡Åž f „°¦¤£µ¥œ°„¤µ„„ªn µ„µ¦ f„°¦¤£µ¥Äœ ×¥­µÁ®˜»­Îµ‡´ Á„· —‹µ„„µ¦š¸É¦ŠŠµœ Á®¨nµœ´ÊœÅ¤nŗo‹ÎµÁž}œ˜o°Š¤¸„µ¦¡´•œµš´„¬³ÁŒ¡µ³Äœ„µ¦šÎµŠµœÄœÃ¦ŠŠµœ ˜nÁœoœ¡´•œµš´„¬³š´ÉªÅž ¤µ„„ªnµ Ĝ—oµœ‡nµÄo‹nµ¥ Ÿ¨‹µ„„µ¦­Îµ¦ª‹¡ªnµ¦·¬´šÄœ£µ‡°»˜­µ®„¦¦¤¤¸„µ¦Äo‹nµ¥ (Ĝže ‡.«. 2006) ­Îµ®¦´„µ¦ f„°¦¤Ã—¥Äo®œnª¥Šµœ£µ¥œ°„ž¦³¤µ– 82,216 µš˜n°že °¥nµŠÅ¦„È—¸Äœ°»˜­µ®„¦¦¤‹³ ¤¸‡nµÄo‹nµ¥Äœ­nªœœ¸Êš¸É­¼ŠÅ—o„nÁ‡¦ºÉ°ŠÄoÅ¢¢jµ (203,414 µš) ¦°Š¨Š¤µÅ—o„n °»˜­µ®„¦¦¤°µ®µ¦Âž¦ ¦¼ž (136,772 µš) ¨³·Êœ­nªœ¥µœ¥œ˜r (132,316 µš) ×¥‡nµÄo‹nµ¥ž¦³¤µ–¦o°¥¨³ 96 …°Š„µ¦  f„°¦¤°°„×¥¦·¬´š Ĝ…–³š¸ÉŸ¼o¦´„µ¦ f„°¦¤°°„‡nµÄo‹nµ¥Á¡¸¥Š¦o°¥¨³ 4 Ášnµœ´Êœ „µ¦¨Šš»œÄœš»œ¤œ»¬¥r‹µ„„µ¦ f„°¦¤Å¤nÁ¡¸¥ŠÂ˜n­nŠŸ¨šµŠª„˜n°¦·¬´šÄœ¦¼žÂ…°Š„µ¦Á¡·É¤Ÿ¨·˜ £µ¡Â¦ŠŠµœÂ˜nÁ¡¸¥Š°¥nµŠÁ—¸¥ª ˜n¥´ŠÁž}œ„µ¦­¦oµŠÃ°„µ­‡ªµ¤Á‹¦·„oµª®œoµÄœ®œoµš¸É„µ¦ŠµœÄ®o„´ Ÿ¼o¦´„µ¦ f„°¦¤ ×¥‹µ„Ÿ¨­Îµ¦ª‹¡ªnµ ž¦³¤µ–¦o°¥¨³ 8.6 …°ŠÂ¦ŠŠµœš¸Éŗo¦´„µ¦ f„°¦¤‹³ ŗo¦´‡nµ‹oµŠÁ¡·É¤…¹Êœš´œš¸ Ĝ…–³š¸É¦ŠŠµœ‹Îµœªœ®œ¹ÉŠš¸ÉŸnµœ„µ¦ f„°¦¤¥´ŠÅ—o¦´„µ¦…¥´˜ÎµÂ®œnŠÄ®o ­¼Š…¹Êœ


NIDA Economic Review

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˜µ¦µŠ 3: ­´—­nªœ…°Š¦·¬´šš¸ÉšµÎ „µ¦ f„°¦¤‹ÎµÂœ„˜µ¤…œµ—…°Š¦·¬š´ …œµ—…°Š¦·¬´š …œµ—Á¨È„ (‹oµŠŠµœœo°¥„ªnµ 50 ‡œ) …œµ—„¨µŠ (‹oµŠŠµœ¦³®ªnµŠ 50-200 ‡œ) …œµ—Ä®n (‹oµŠÂ¦ŠŠµœ¤µ„„ªnµ 200 ‡œ) ÁŒ¨¸É¥

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f„°¦¤£µ¥œ°„°Š‡r„¦ 37.3 70.0 88.0 64.1

š¸É¤µ : ‡Îµœª–‹µ„ PICS-2007

˜µ¦µŠ 4: „µ¦ f„°¦¤‹ÎµÂœ„˜µ¤˜ÎµÂ®œnŠŠµœ (¦o°¥¨³) ˜ÎµÂ®œnŠŠµœ Ÿ¼o¦·®µ¦/Ÿ¼o‹´—„µ¦ ¦ŠŠµœª·µ¸¡ ¦ŠŠµœ£µ‡„µ¦Ÿ¨·˜š¸É¤¸š´„¬³­¼Š ¦ŠŠµœ£µ‡„µ¦Ÿ¨·˜š¸É¤¸š´„¬³˜Éε ¦ŠŠµœš¸ÉŤnŗo°¥¼nĜ£µ‡„µ¦Ÿ¨·˜ ¦ª¤

f„°¦¤£µ¥Äœ°Š‡r„¦ 1.8 3.0 16.9 69.3 8.9 100

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˜µ¦µŠ 5: „µ¦ f„°¦¤‹ÎµÂœ„˜µ¤®¨´„­¼˜¦ (¦o°¥¨³) ®¨´„­¼˜¦„µ¦ f„°¦¤ Áš‡ÃœÃ¨¥¸„µ¦Ÿ¨·˜ ¤µ˜¦“µœ‡ªµ¤ž¨°—£´¥ „µ¦‹´—„µ¦ „µ¦˜¨µ— Áš‡ÃœÃ¨¥¸­µ¦­œÁš« £µ¬µ š¦´¡¥r­·œšµŠž{µ °ºÉœÇ ¦ª¤ š¸É¤µ : ‡Îµœª–‹µ„ PICS-2007

f„°¦¤£µ¥Äœ°Š‡r„¦ 24.9 35.9 25.1 2.8 2.2 1.2 0.4 7.7 100.0

f„°¦¤£µ¥œ°„°Š‡r„¦ 27.8 21.1 27.2 5.3 3.1 0.6 0.7 14.3 100.0


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¦¼žš¸ ž É 9 Ÿ¼oÄ®o„µ¦ f µ „°¦¤£µ¥Äœ°Š‡rr„¦ (¦o°¥¨³))

¦´“µ¨/NGO  19% š¸Éž¦¹„¬µ„µ¦ f„°¦¤ 12%

Supplier/Buyyer 3%

°ºÉœÇ 7%

°°µ­µ­¤´‡¦ 11%

¦ŠŠµœœŸ¼o¤¸ ž¦³­„„µ¦–r 5% %

Ÿ¼o‹´—„µ¦ (œ°„Áª¨¨µ Šµœ) 20%

Ž»žÁž°¦r¦rŪÁŽ°¦r 23%

š¸É¤µ : ‡Îµœª–‹µµ„ PICS-2007

¦¼žš¸ ž É 10: ®œnª¥Šµœ£µ¥œ ª œ°„š¸ÉšµÎ ®œo œoµš¸É f„°¦¤¤ ª ª·š¥µ¨´¥°µ¸ª³ 1%

¦·¬´šš¸Éž¦¹„¬µ„„µ¦ f„°¦¤ 31%

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¤¤®µª·š¥µ¨´¥ 5%

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®œnª¥Šµœ£µ‡¦´“ 53%

Joint-Venture Parttners 1%


NIDA Economic Review

145

¦¼žš¸É 11: ‡nµÄo‹µn ¥­Îµ®¦´„µ¦ f„°¦¤Ã—¥Äo®œnª¥Šµœ (Outside Training) že ‡.«.2006

203,814

136,772

132,316 90,511

74,500

45,038

40,029

Auto Parts

Electrical Electronic Furniture and Food Appliances Components Wood Processing Products

73,533

Garment

Machinery Rubber and and Plastics Equipment

53,898

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š¸É¤µ : ‡Îµœª–‹µ„ PICS-2007

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NIDA Economic Review

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NIDA Economic Review

147

ž¦³„°„µ¦…œµ—„¨µŠ¤¸ÂœªÃœo¤š¸É‹³Á­¸¥‡nµÄo‹nµ¥­Îµ®¦´„µ¦ f„°¦¤ž¦³¤µ– 44,236 µš˜n°že (ž¦³¤µ–¦o°¥¨³ 69) ¤µ„„ªnµ­™µœž¦³„°„µ¦…œµ—Á¨È„ Ž¹ÉŠŸ¨š¸É¡œ¸Ê¥´Š­°—‡¨o°Š‹µ„Ÿ¨…°Š„µ¦ ž¦³¤µ–„µ¦…°Š‡ªµ¤œnµ‹ÎµÁž}œÄœ„µ¦Ä®o„µ¦ f„°¦¤Â¦ŠŠµœ—oª¥Â‹Îµ¨°ŠÃ¡¦·˜ ×¥¦·¬´šš¸É¤¸ …œµ—Ä®n„ªnµ‹³¥·œ—¸ Á ­¸¥‡n µÄo‹nµ¥Äœ„µ¦ f„°¦¤¡œ´„Šµœ¤µ„„ªn µ¦· ¬´š š¸É¤¸…œµ—Á¨È „„ªnµ ×¥ ‡nµÄo‹nµ¥Á®¨nµœ¸Ê¤¸ÂœªÃœo¤Á¡·É¤…¹Êœ°¥nµŠ¤¸œ´¥­Îµ‡´Äœ„¦³ªœ„µ¦Ÿ¨·˜š¸Éčož{‹‹´¥š»œÁ…o¤…oœ œ°„‹µ„œ¸Ê ¥´Š¡ªnµ ¦·¬´šš¸Éž¦³­„´„µ¦…µ—‡¨œÂ¦ŠŠµœš¸É¤¸š´„¬³˜Éε‹³¤¸ÂœªÃœo¤š¸É‹³Á­¸¥‡nµÄo‹nµ¥Äœ„µ¦  f„°¦¤Á¡·É¤…¹Êœ°¥nµŠ¤¸œ´¥­Îµ‡´šµŠ­™·˜·˜·—oª¥Ánœ„´œ ×¥­¦»ž¡ªnµ ÁœºÉ°Š‹µ„ž{®µ„µ¦š¸É¦ŠŠµœš¸É‹„µ¦«¹„¬µ¤µœ´Êœ¤¸š´„¬³Å¤n˜¦Š˜µ¤‡ªµ¤˜o°Š„µ¦…°Š Ÿ¼ož¦³„°„µ¦ „µ¦¡´•œµš´„¬³Â¦ŠŠµœÄœ­™µœž¦³„°„µ¦‹¹ŠÁž}œ„¨¥»š›rš¸É­Îµ‡´Äœ„µ¦Á¡·É¤Ÿ¨·˜£µ¡ …°ŠÂ¦ŠŠµœÄœ°Š‡r„¦ ×¥Á¤ºÉ°ª·Á‡¦µ³®r®¨´„­¼˜¦„µ¦ f„°¦¤š´ÊŠ£µ¥ÄœÂ¨³£µ¥œ°„¡ªnµ „µ¦ f„ °¦¤‹³¤»nŠÁœoœÅžš¸É„µ¦­¦oµŠš´„¬³ÁŒ¡µ³ (Specific Skills) Ĝ—oµœ„µ¦Ÿ¨·˜®¦º°š¸ÉÁ„¸É¥ª…o°Š„´Ã¦ŠŠµœ Ánœ Áš‡ÃœÃ¨¥¸„µ¦Ÿ¨·˜Â¨³…´Êœ˜°œ‡ªµ¤ž¨°—£´¥ Áž}œ­Îµ‡´ Ĝ…–³š¸É„µ¦ f„°¦¤ž¦³Á£šš´„¬³ š´ÉªÅž (General Skill) Ánœ „µ¦°¦¤šµŠ£µ¬µ°´Š„§¬®¦º°šµŠ—oµœÁš‡ÃœÃ¨¥¸­µ¦³­œÁš« (IT) ¥´Š Ťnŗo¦´‡ªµ¤­œÄ‹‹µ„Ÿ¼ož¦³„°„µ¦Åš¥¤µ„Ášnµš¸É‡ª¦ œ°„‹µ„œ¸Ê „µ¦ f„°¦¤Ã—¥­nªœÄ®n‹³Á„·—…¹Êœ„´°Š‡r„¦š¸É¤¸…œµ—Ä®n¨³Äoš»œÂ¨³Á‡¦ºÉ°Š‹´„¦Äœ „¦³ªœ„µ¦Ÿ¨·˜ Ĝ…–³š¸É°Š‡r„¦°»˜­µ®„¦¦¤š¸É¤¸…œµ—Á¨È„¨³Äo¦ŠŠµœÁž}œž{‹‹´¥Á…o¤…oœÄœ„µ¦Ÿ¨·˜ ¥´Š‡ŠÄ®o‡ªµ¤­Îµ‡´„´„µ¦ f„°¦¤Â¨³¡´•œµÂ¦ŠŠµœš¸É˜É優nµ ×¥„µ¦ f„°¦¤‹³¤¸Ã°„µ­š¸É‹³Á„·—…¹Êœ ¤µ„„ªnµ„´¦·¬´šš¸É‹oµŠÂ¦ŠŠµœš¸É¤¸š´„¬³­¼Š®¦º°‹oµŠÂ¦ŠŠµœš¸É¤¸¦³—´„µ¦«¹„¬µ­¼Š ×¥ÁŒ¡µ³Â¦ŠŠµœ Á·ŠÁš‡œ·‡°¥nµŠÁnœ œ´„ª·‹´¥ ª·«ª„¦ œ´„ª·š¥µ«µ­˜¦r ¨³œ´„‡°¤¡·ªÁ˜°¦r Ž¹ÉŠŸ¨š¸É¡Äœšœ¸Êŗo­—Š ™¹Š„µ¦·—Á¸Ê¥ª (Biasness) …°Š„µ¦¡´•œµš»œ¤œ»¬¥rĜ°Š‡r„¦š¸É¤¸Ã°„µ­Á„·—‡ªµ¤Á®¨ºÉ°¤¨Êεš´ÊŠ£µ¥Äœ ¨³£µ¥œ°„°Š‡r„¦ ×¥‡ªµ¤Á®¨ºÉ°¤¨Êε£µ¥œ°„°Š‡r„¦Á„·—‹µ„„µ¦š¸É¦·¬´šÄ®n‹³Ä®o‡ªµ¤­Îµ‡´…°Š „µ¦ f„°¦¤¤µ„„ªnµ¦·¬´š…œµ—Á¨È„ °´œ­nŠŸ¨šÎµÄ®oÁ„·—‡ªµ¤Á®¨ºÉ°¤¨ÊεĜ„µ¦¡´•œµš´„¬³Â¨³…¸— ‡ªµ¤­µ¤µ¦™Äœ„µ¦Â…nŠ…´œ…°ŠÂ¦ŠŠµœÄœ¦·¬´š…œµ—Á¨È„Äœ¦³¥³¥µª œ°„‹µ„œ¸Ê „µ¦·—Áº°œ¥´Š­µ¤µ¦™Á„·—…¹ÊœÅ—o£µ¥Äœ°Š‡r„¦‹µ„„µ¦š¸É£µ‡›»¦„·‹Å¤nŗoÄ®o‡ªµ¤­Îµ‡´„´ „µ¦ f„°¦¤Â¦ŠŠµœš¸É¤¸š´„¬³˜É宦º°¤¸¦³—´š»œ¤œ»¬¥rš¸É˜Éε Ž¹ÉŠ„µ¦·—Áº°œ‹µ„£µ¥Äœœ¸Ê°µ‹­nŠŸ¨šÎµÄ®o ¦ŠŠµœš¸É¤¸š´„¬³˜É宦º°¤¸ª»•·„µ¦«¹„¬µ˜É優nµ°µ‹‹³…µ—ð„µ­Äœ„µ¦¡´•œµš´„¬³…°Š˜œš¸É‹³Á°ºÊ°˜n° „µ¦ž¦³„°°µ¸¡Äœ¦³¥³¥µª ×¥ÁŒ¡µ³Á¤ºÉ°š´„¬³š¸Éŗo¦´„µ¦ f„°¦¤­nªœÄ®n‹³Áž}œš´„¬³ÁŒ¡µ³ (Specific Skill) š¸Éčo­Îµ®¦´„µ¦šÎµŠµœÄœÃ¦ŠŠµœÁž}œ­Îµ‡´ Ĝ…–³š¸Éš´„¬³š´ÉªÅž°¥nµŠšµŠ—oµœ£µ¬µ ¨³šµŠ—oµœÁš‡ÃœÃ¨¥¸­µ¦­œÁš«Ž¹ÉŠÁž}œš´„¬³š¸É¦ŠŠŠµœÅš¥­nªœÄ®nž¦³­ž{®µ„µ¦…µ—‡¨œœ´Êœ „¨´Å¤nŗo¦´„µ¦¡´•œµ®¦º°Å—o¦´„µ¦ f„°¦¤¤µ„Ášnµš¸É‡ª¦


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NIDA Economic Review

œ°„‹µ„œ¸Ê „µ¦ž¦³­ž{®µ„µ¦„µ¦…µ—‡¨œÂ¦ŠŠµœ ×¥ÁŒ¡µ³„µ¦…µ—‡¨œš´„¬³Á·Š‡»–£µ¡ ¥´Š Áž}œ°¸„ž{‹‹´¥®œ¹ÉŠš¸É­nŠŸ¨Ä®o¦·¬´š‹³˜o°Š˜o°Š¡¹ÉŠ„µ¦ f„°¦¤š´ÊŠ£µ¥ÄœÂ¨³£µ¥œ°„¤µ„…¹Êœ—oª¥ ˜n ¤oªnµ‹³˜o°ŠÁŸ·„´˜ÎµÂ®œnŠªnµŠŠµœš¸ÉŤn¤¸Â¦ŠŠµœ¤¸‡ªµ¤Îµœµ Ž¹ÉŠ˜o°Š¡¹ÉŠ¡µ„µ¦ f„°¦¤‹µ„ £µ¥œ°„ ˜n­™µœž¦³„°„µ¦‹Îµœªœ¤µ„¤´„Ťnš¦µ™¹Šª·›¸„µ¦˜·—˜n°­™µ´œ„µ¦ f„°¦¤ªnµ‹³˜o°ŠšÎµ °¥nµŠÅ¦ (World Bank, 2008) ®œnª¥Šµœš¸É­œ´­œ»œ„µ¦¡´•œµš´„¬³Äœ„µ¦šÎµŠµœš´ÊŠ£µ‡¦´“¨³Á°„œ ‹¹Š‹Î µÁž}œ‹³˜o°Š¤¸ „µ¦ž¦´ž¦´ž¦»Š„µ¦ÁŸ¥Â¡¦nž¦³µ­´¤¡´œ›r®œnª¥ŠµœÂ¨³¤¸ „µ¦˜·—˜n°„´­™µœ ž¦³„°„µ¦Ä®o¤µ„…¹Êœ ¥nµŠÅ¦„È—¸ ÁœºÉ°Š‹µ„„µ¦ f„°¦¤Â¨³„µ¦¡´•œµš»œ¤œ»¬¥rĜ°Š‡r„¦ (°¥nµŠÁnœ„µ¦ªnµ‹oµŠÂ¦ŠŠµœš¸É¤¸ š´„¬³­¼Š®¦º°­ÎµÁ¦È‹„µ¦«¹„¬µÄœ¦³—´­¼Š) œ´Êœ„È¥´Š‡ŠÁž}œ­nªœ®œ¹ÉŠ…°Š„µ¦˜´—­·œÄ‹šµŠÁ«¦¬“«µ­˜¦r (Economics Decision) š¸É˜o°ŠÄ®o‡ªµ¤­Îµ‡´…°Š„µ¦ª·Á‡¦µ³®r®¨´„…°Š‡ªµ¤‡»o¤‡nµÃ—¥Áž¦¸¥Áš¸¥ ¦³®ªnµŠ˜oœš»œš¸ÉÁ„·—…¹Êœ‹µ„„µ¦‹oµŠŠµœ (Cost) „´Ÿ¨Å—ošÉ ¸É°Š‡r„¦‹³Å—o¦´ (Benefit) ¨³šÎµ„µ¦ Áž¦¸¥Áš¸¥„´œ Ž¹ÉŠÄœš˜n°Åž‹³Áž}œ„µ¦ª·Á‡¦µ³®r™¹Šž¦³Á—Èœ„µ¦«¹„¬µ—´Š„¨nµª


š¸É¤µ : ‡Îµœª–‹µ„ PICS-2007

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0.5043*** [0.028] 0.3647*** [0.031] 0.0010 [0.001] 0.0001 [0.002] 0.0005 [0.001] 0.3861* [0.233] 0.1158* [0.059] -0.0038*** [0.001] -0.0016 [0.001] 0.0143** [0.006] -0.0008 [0.001] 0.0011 [0.001] 928 0.322

„µ¦ f„°¦¤£µ¥Äœ 0.3721*** [0.032] 0.2522*** [0.033] 0.0008 [0.001] 0.0024 [0.002] 0.0017* [0.001] 0.4063 [0.250] 0.0922 [0.060] -0.0032*** [0.001] -0.0028*** [0.001] 0.0327*** [0.009] 0.0009 [0.001] 0.0013 [0.001] 928 0.231

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1.5549*** [0.184] 0.6915*** [0.167] 0.0025 [0.003] 0.0058 [0.006] 0.0095*** [0.003] 0.8984* [0.463] 0.0439 [0.181] -0.0046 [0.004] -0.0067* [0.004] 0.0652*** [0.016] -0.0010 [0.003] 0.0073* [0.004] 9.1862*** [0.400] 576 0.262

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126,485.8271*** [27,587.408] 44,236.5942* [25,013.426] 1,019.7943** [427.912] 573.1239 [980.702] 591.0673 [496.281] 78,097.9422 [69,005.864] 7,928.0855 [27,715.389] -41.5978 [637.910] -388.0663 [536.270] 5,477.9072** [2,447.508] 94.3778 [510.587] 87.1603 [605.037] 39,682.76 [61,392.338] 637 0.006

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150 NIDA Economic Review


NIDA Economic Review

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Á°„­µ¦°oµŠ°·Š Bartel, A.P. (1991) “Productivity Gain from the Implementation of Employee Training Programme”, NBER Working Paper No.3893, Cambridge: National Bureau of Economic Research Braga, C.A. (2010) Innovation and Growth: Policy Lessons from Catching up Experiences, PowerPoint presentation at the World Bank-Office of National Economics and Social Development Board (NESDB) Workshop on Skill for Innovation-Led Growth, December 7, 2010, Bangkok, Thailand. Brundell, R., Dearden, L., and Meghir, C. (1996) The Determinants of Work-Related Training in Britain, London: Institute for Fiscal Studies Brundell, R., Dearden, L., Meghir, C., and Sianei, B. (1999) “Human Capital Investment: The Returns from Education and Training to the Individual, the Firm, and the Economy”, Fiscal Studies, 20(1): 1-23. Booth, A.L. (1991) “Job-Related Formal Training: Who Receive it and What is it Worth?,Oxford Bulletin of Economics and Statistics, 53: 281-294. Greenhalgh, C.A. and Stewart, M.B. (1987) “The Effects and Determination of Training”, Oxford Bulletin of Economics and Statistics, 49: 171-189. Pholphirul, P. (2013) “Immigration, Job Vacancies, and Employment Dynamics: Evidencesfrom Thai Manufacturers”, Journal of Asian Economics, 24(1): 1-16


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Think Like A Freak Steven D. Levitt ¨³ Stephen J. Dubner 2013 William Morrow 268

Think Like A Freak˜¸¡·¤¡rÁ¤ºÉ°Á—º°œ¡§¬£µ‡¤¡.«. 2557 ˜nŠÃ—¥Steven D. Levitt «µ­˜¦µ‹µ¦¥r —oµœÁ«¦¬“«µ­˜¦r‹µ„¤®µª·š¥µ¨´¥·‡µÃ„ ¨³ Stephen J. Dubner œ´„Á…¸¥œž¦³‹Îµ The New York Times ®œ´Š­º° Think Like A Freak Áž}œ®œ´Š­º°Á¨n¤š¸É 3 š¸ÉŸ¼oÁ…¸¥œš´ÊŠ­°ŠÁ…¸¥œ¦nª¤„´œÃ—¥Á¨n¤ ¦„‡º°Freakonomicsŗo¦´„µ¦˜¸¡·¤¡rĜže ¡.«. 2548 ¨³Á¨n¤š¸É­°ŠSuperFreakonomics ˜¸¡·¤¡rĜ že ¡.«. 2552 ×¥š´ÊŠ­°ŠÁ¨n¤Áž}œ®œ´Š­º°š¸É×nŠ—´ŠÂ¨³Áž}œ®œ´Š­º°…µ¥—¸˜·—°´œ—´…°Š The New York Times Ĝ…–³š¸É Freakonomics ¨³ SuperFreakonomics Áž}œŠµœÁ…¸¥œš¸ÉœÎµÁ°µ…o°¤¼¨š¸Éœnµ­œÄ‹ ¨³ ž¨„˜„˜nµŠ‹µ„ŠµœÁ…¸¥œšµŠÁ«¦¬“«µ­˜¦r„¦³Â­®¨´„¤µÁ¦¸¥Š¦o°¥Áž}œÁ¦ºÉ°ŠÁ¨nµš¸É—¹Š—¼—‡ªµ¤­œÄ‹ *

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NIDA Economic Review

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šš¸É 3 °›·  µ¥ªn µ „µ¦˜´Ê Š ‡Î µ ™µ¤Ä®o ™¼ „ ˜o ° ŠÁž} œ­·É Š š¸É ­Î µ ‡´  ¤µ„ ÁœºÉ ° Š‹µ„®µ„˜´Ê Š ‡Î µ ™µ¤Ÿ· — ¨o ª ‡Îµ˜°š¸Éŗo‹³Å¤nčn­·ÉŠš¸É™¼„˜o°Š°¥nµŠÂœnœ°œ šœ¸ÊŸ¼oÁ…¸¥œÅ—o¥„˜´ª°¥nµŠÅªo°¥nµŠœnµ­œÄ‹¤µ„ Ánœ „µ¦ž’·¦¼ž„µ¦«¹„¬µš¸É¤»nŠÁœoœ„µ¦˜°‡Îµ™µ¤š¸Éªnµ “‡¦¼‡ª¦¤¸š´„¬³Ä—oµŠ” ®¦º° “‹Îµœªœœ´„Á¦¸¥œÄœ ®o°ŠÁ¦¸¥œ‡ª¦Áž}œÁšnµÄ—” ®µ„˜n™oµ¡·‹µ¦–µÄ®o¨¹„Ž¹ÊŠÂ¨oªœ´Êœ Á—ȄčoÁª¨µÄœ®o°ŠÁ¦¸¥œÁž}œ­´—­nªœš¸É œo°¥„ªnµÁª¨µœ°„®o °ŠÁ¦¸¥œ¤µ„ ¨oªÁ®˜»Ä —‹¹ŠÅ¤nÁž¨¸É¥œ‡Îµ™µ¤Áž}œ “šµš…°Š‡¦°‡¦´ª˜n ° „µ¦«¹„¬µ…°ŠÁ—È„‡ª¦Áž}œ°¥nµŠÅ¦” °¸„˜´ª°¥nµŠš¸Éœnµ­œÄ‹‡º° ª·›¸„µ¦˜´ÊŠ‡Îµ™µ¤…°Š ǝµ¥µ· œ´„„·œ‹» š¸É­¦oµŠ­™·˜·„µ¦„·œ…œ¤ž{ŠÅ­o„¦°„Äœª´œµ˜·­®¦´“°Á¤¦·„µš¸É­µ¤µ¦™„·œÅ—o¤µ„„ªnµÁž}œ­°ŠÁšnµ…°Š ­™·˜·Á—·¤Äœže„n°œ®œoµš¸ÉÁ…µ‹³šÎµ­™·˜·Ä®¤n šš¸É 4 ¤¸ÁœºÊ°®µÁ„¸É¥ª„´®¨´„„µ¦‡·—š¸É˜o°Š„µ¦¦³»ž{®µ ¨³Â„ož{®µš¸É˜oœÁ®˜» ˜´ª°¥nµŠš¸Éœnµ­œÄ‹ …°Ššœ¸Ê‡º° „µ¦¨—¨Š…°Š°´˜¦µ„µ¦„n°°µµ„¦¦¤ Ž¹ÉŠ™oµ—¼Ã—¥Ÿ·ªÁŸ·œ°µ‹ÁœºÉ°Š¤µ‹µ„ „‘®¤µ¥ „µ¦‡¦°‡¦°Šžgœ ®¦º°‡ªµ¤­µ¤µ¦™Äœ„µ¦‹´˜´ª‡œ¦oµ¥‡»¤…´ŠÁ¡·É¤…¹Êœ ˜nŸ¼oÁ…¸¥œÅ—ošÎµ„µ¦ª·Á‡¦µ³®r ¨³Á­œ°­µÁ®˜»š¸É­Îµ‡´…°Š„µ¦¨—¨Š…°Š°´˜¦µ„µ¦„n °°µµ„¦¦¤ ‡º° „µ¦°œ»µ˜Ä®o¥» ˜·„µ¦ ˜´ÊŠ‡¦¦£r×¥™¼„˜o°Š˜µ¤„‘®¤µ¥Á¤ºÉ°‡¦·­˜r«˜ª¦¦¬š¸É 70 œ°„‹µ„œ¸ÊŸ¼oÁ…¸¥œÅ—o„¨nµª™¹Š­µÁ®˜»š¸É‡œŸ·ª —Î µ Ĝž¦³Áš«­®¦´ “ °Á¤¦· „ µ¤¸ °´ ˜ ¦µ…°ŠŸ¼o ¤¸ £ µª³‡ªµ¤—´ œ è®· ˜ ­¼ Š „ªn µ ‡œŸ· ª …µªÄœž¦³Áš« ­®¦´“°Á¤¦·„µ ¨³‡œŸ·ª—εĜšª¸ž°´¢¦·„µŽ¹ÉŠÁž}œ˜´ª°¥nµŠš¸Éœnµ­œÄ‹—oª¥ šš¸É 5 ŗoœ³œÎµÄ®oŸ¼o°nµœ¨°Š‡·—Ĝš¸ÉÁ—È„‡·— Ž¹ÉŠ‡º° „µ¦‡·—Á¦ºÉ°ŠŠnµ¥š¸ÉŤn¥»nŠ¥µ„Ž´Žo°œ „µ¦‡·— °¥nµŠ­œ»„ ¨³„µ¦Å¤n„¨´ª„´­·ÉŠš¸É‹³Á„·—…¹Êœ „µ¦‡·—Á¦ºÉ°ŠŠnµ¥š¸ÉŤn¥»nŠ¥µ„Ž´Žo°œšÎµÄ®oÁ¦µ­µ¤µ¦™ ‡n°¥Ç „ož{®µš¸ÉŽ´Žo°œÅ—oš¸¨³­nªœš¸¨³˜°œ „µ¦‡·—°¥nµŠ­œ»„‹³šÎµÄ®o¤¸‡ªµ¤­»…Äœ„µ¦‡·—¨³ „µ¦šÎµŠµœ ¥„˜´ª°¥nµŠÁnœ „µ¦‹´—˜´ÊŠÄ®o¤¸ Prize-Link Savings Ž¹ÉŠÁž¦¸¥Å—o„´­¨µ„°°¤­·œÄœ ž¦³Áš«Åš¥ Ž¹ÉŠšÎµÄ®ož¦³µœ¤¸Â¦Š‹¼ŠÄ‹Äœ„µ¦°°¤¤µ„…¹Êœ Á¡¦µ³„µ¦°°¤Áž}œÁ¦ºÉ°Šš¸É“­œ»„” ¨³ „µ¦Å¤n„¨´ª„´­·ÉŠš¸É‹³Á„·—…¹ÊœŽ¹ÉŠÁž}œ¨´„¬–³œ·­´¥…°ŠÁ—È„š¸ÉšÎµÄ®o„¨oµ¨°Š ¨³šÎµÄœ­·ÉŠš¸Éž¨„Ä®¤nŽ¹ÉŠ œÎµ¨´„¬–³„µ¦‡·—Á—È„¤µÄoœ¸Ê°µ‹šÎµÄ®oŸ¼oÄ®n¤¸š´„¬³„µ¦‡·—š¸É—¸…¹ÊœÅ—o šš¸É 6 „¨nµª™¹ Š„µ¦Äo ¦Š‹¼ ŠÄ‹Äœ„µ¦Á¡·É ¤ž¦³­·š›·£µ¡Äœ¦³Á«¦¬“„· ‹ Ž¹ÉŠÂ¦Š‹¼ŠÄ‹œ´Êœ˜o°Š ‡Îµœ¹Š™¹Šš´ÊŠ…o°—¸Â¨³…o°Á­¸¥ ×¥Ÿ¼oÁ…¸¥œÅ—o¥„˜´ª°¥nµŠ„µ¦Äo¦Š‹¼ŠÄ‹…°Š¤¼¨œ·›·Â®nŠ®œ¹ÉŠ Á¡ºÉ°Ä®oŗo¦´ ÁŠ·œ¦·‹µ‡š¸É­¼Š…¹Êœ ץčoª·›¸„µ¦´„ªœÂÄ®¤nŽ¹ÉŠÂ˜„˜nµŠÅž‹µ„ª·›¸Á—·¤ œ°„‹µ„œ¸ÊŸ¼oÁ…¸¥œ¥´ŠÅ—o ¥„˜´ª°¥nµŠ‡ªµ¤¨o ¤Á®¨ªÄœ„µ¦Äo ¦Š‹¼ŠÄ‹°¸„—oª ¥ Ánœ œÃ¥µ¥„µ¦„ε‹´—Š¼ Á ®nµ…°Š°´Š„§¬Äœ ž¦³Áš«°·œÁ—¸¥Äœ¥»‡š¸É°´Š„§¬¥¹—‡¦°Š°·œÁ—¸¥ Ž¹ÉŠÄ®oŸ¨˜¦Š…oµ¤„´š¸É°´Š„§¬˜o°Š„µ¦‡º° šÎµÄ®o¤¸ ‹ÎµœªœŠ¼Á®nµÁ¡·É¤¤µ„…¹Êœ šÎµÄ®oŸ¼o°nµœÁ…oµÄ‹Â¨³‡Îµœ¹Š™¹Š„µ¦°°„Â¦Š‹¼ŠÄ‹š¸É¦´—„»¤ ¨³‡Îµœ¹Š™¹ŠŸ¨ š¸É‹³˜µ¤¤µ°¥nµŠ¦°—oµœ…¹Êœ


156

NIDA Economic Review

šš¸É 7 šœ¸ÊÁ ž} œšš¸É Ÿ¼o ª· ‹µ¦–r°¤µ„š¸É ­»— ×¥šœ¸Êŗo °›· µ¥™¹ Š„µ¦Äo ¦Š‹¼ ŠÄ‹Äœ„µ¦Â¥„ ž¦³Á£š…°Š‡œ ®¦º°°Š‡r„¦š¸É˜„˜nµŠ„´œ Ž¹ÉŠž¦³Ã¥œr…°Š‡ªµ¤­µ¤µ¦™Äœ„µ¦Â¥„ž¦³Á£šÅ—o‡º° šÎ µ Ä®o ‡ œ ®¦º ° ¦´ “ µ¨ ®¦º ° °Š‡r „ ¦ ž’· ´ ˜· ˜n ° ‡œÄœÂ˜n ¨ ³ž¦³Á£šÂ˜„˜n µ Š„´ œ Á¡ºÉ ° „n ° Ä®o Á „· — ž¦³­·š›·£µ¡Á¡·É¤…¹Êœ Ž¹ÉŠ­·ÉŠš¸ÉŸ¼oÁ…¸¥œ°›·µ¥‡º° „µ¦œÎµ®¨´„„µ¦…°Š Pooling Equilibrium ¨³ Separating Equilibrium ¤µÄo ×¥Ÿ¼oÁ…¸¥œÅ—oÁž¦¸¥Áš¸¥ „µ¦˜´—­·œ‡—¸…°Š KingSolomon ¨³„µ¦ Â¥„ž¦³Á£š…°ŠŸ¼o‹´—ŠµœÂ­—Š—œ˜¦¸š¸É‹´—ŠµœÁ¦¸¥¦o°¥Áž}œ¦³ ¨³Ÿ¼o‹´—Šµœš¸É˜o°Š¤¸„µ¦˜¦ª‹­° ¤µ„Áž}œ¡·Á«¬…°Š Devid Lee Roth Ÿ¼oÁž}œœ´„—œ˜¦¸š¸É°°„˜¦³ÁªœÂ­—Š—œ˜¦¸Äœš¸É˜nµŠÇ Áž}œž¦³‹Îµ šš¸É 8 ¤¸ÁœºÊ°®µÁ„¸É¥ª„´„µ¦‹¼ŠÄ‹Ÿ¼oš¸ÉŤn˜o°Š„µ¦Ä®o‹¼ŠÄ‹ ×¥˜o°ŠÁ…oµÄ‹ªnµ „µ¦‹¼ŠÄ‹Ÿ¼o°ºÉœÁž}œ­·ÉŠš¸É¥µ„ „µ¦‹¼ŠÄ‹ “Ÿ¼o°ºÉœ” ˜o°ŠÁ…oµÄ‹Â¨³Äo¤»¤¤°Š…°ŠŸ¼o™¼„‹¼ŠÄ‹¤·Änčo˜n¤»¤¤°Š…°Š˜œÁ°ŠÁ¡ºÉ°‹¼ŠÄ‹ „µ¦°„ Ÿ¼o°ºÉœªnµ­·ÉŠš¸É˜œÁ°Š„¨nµª™¹ŠÁž}œ­·ÉŠš¸É™¼„˜o°Šš¸É­»—œ´Êœ‹³šÎµÄ®oŸ¼o™¼„‹¼ŠÄ‹Å¤nÁºÉ°™º°ÁœºÉ°Š‹µ„Á¦ºÉ°Šš»„Á¦ºÉ°Š ¤¸š´ÊŠ—oµœª„¨³¨Á­¤°Â¨³„µ¦°›·µ¥®¦º°‹¼ŠÄ‹Ã—¥ÄoÁ¦ºÉ°Š¦µªŽ¹ÉŠž¦³„°—oª¥­™·˜·š¸ÉœnµÁºÉ°™º° Áž}œª·›¸š¸É¤¸ž¦³­·š›·£µ¡ ÁœºÉ°Š‹µ„šÎµÄ®oŸ¼o™¼„‹¼ŠÄ‹­œÄ‹ÄœÁ¦ºÉ°Šš¸É°›·µ¥ šš¸É 9 °›·µ¥ªnµ ĜµŠ‡¦´ÊŠ„µ¦¨o¤Á¨·„š¸É‹³šÎµ°³Å¦µŠ°¥nµŠ Ťnŗo®¤µ¥™¹Š‡ªµ¤¡nµ¥Â¡oÁ­¤°Åž ®µ„˜n­µ¤µ¦™œÎµ‡ªµ¤¦¼o®¦º°šÁ¦¸¥œÅžÄoĜ„µ¦Á¦·É¤šÎµ­·ÉŠ°ºÉœÅ—o˜n°Åž ץĜšµŠÁ«¦¬“«µ­˜¦rœ´Êœ Áž}œ„µ¦Áž¦¸¥Áš¸¥ Opportunity Costs ¨³ Sunk Costs œ´Éœ‡º° ®µ„ Opportunity Costs Ĝ„µ¦ ˜´—­·œÄ‹„¦³šÎµ„µ¦ „. ­¼Š„ªnµ Sunk Costs ¨oª „µ¦˜´—­·œÄ‹š¸É‹³Á¨·„„¦³šÎµ„µ¦ „. ‹³Áž}œ„µ¦˜´— ­·œÄ‹š¸É™¼„˜o°Š ×¥Ÿ¼oÁ…¸¥œÅ—o¥„˜´ª°¥nµŠž¦³­„µ¦–r˜¦Š…°Š˜´ªÁ°ŠÄœ„µ¦Á¨º°„­µ¥°µ¸¡¤µ°›·µ¥ Ä®oŸ¼o°nµœÁ…oµÄ‹ Ÿ¼o ª· ‹ µ¦–r ¤¸ ‡ ªµ¤Á®È œ ªn µ ®œ´ Š ­º ° Á¨n ¤ œ¸Ê ° µ‹‹³n ª ¥Ä®o Ÿ¼o °n µ œ¤¸ ¤» ¤ ¤°Š‡ªµ¤‡· — š¸É „ ªo µ Š…¹Ê œ ¨³œÎ µ œª‡ªµ¤‡· — šµŠÁ«¦¬“«µ­˜¦r š¸É ŗo ‹ µ„®œ´ Š­º ° œ¸ÊÅ žž¦³¥» „˜r Ä o„´  Šµœª· ‹´ ¥ ˜¨°—‹œž{  ®µÄœ ¸ª·˜ž¦³‹Îµª´œ…°ŠŸ¼o°nµœÅ—ooµŠÅ¤n¤µ„„Èœo°¥


Nida ebook 9 1 jul 2558  

NIDA ECONOMIC REVIEW ปีที่ 9 ฉบับที่ 1 กรกฎาคม 2558 คณะพัฒนาการเศรษฐกิจ สถาบันบัณฑิตพัฒนบริหารศาสตร์ เศรษฐศาสตร์ ปริทรรศน์

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