Developing human resources for the technical workforce: a ...

Blackwell Publishing Ltd.Oxford, UK and Malden, USAIJTDInternational Journal of Training and Development1360-3736Blackwell Publishing Ltd. 2005March 2005917994ArticlesDeveloping human resources for the technical workforce

International Journal of Training and Development 9:1 ISSN 1360-3736

Developing human resources for the technical workforce: a comparative study of Korea and Thailand Joshua D. Hawley and Jeeyon Paek Asian countries face significant and growing shortages of technically skilled workers. Vocational-technical systems are key components of national human resource development. Using labor market data from Thailand and Korea, this paper analyzes the economic payoff for individual investment in vocational-technical education, and subsequent employment in a related occupation. The results are mixed, showing that relatively few men or women end up working in areas that they are trained for, but that for men in Korea and women in Thailand, employment in a related field pays off in terms of higher monthly earnings. As governments move toward workforce policy created in conjunction with firms and education, the results of this study reinforce the need for national development plans that address the relatively weak relationship between initial training and employment in Asian countries.

Introduction Asian countries face significant and growing shortages of technically skilled workers (APEC, 2004; Venter, 2003). The most recent APEC summit of human resource development ministers reported that basic education is not enough to prepare entry-level employees, and that countries need to work more effectively to build a culture of lifelong learning (APEC, 2001). Korea’s science and technical workforce grew from about 70,000 in 1990 to 128,000 in 1999. Even in 1999, during the economic crisis r Joshua D. Hawley, Assistant Professor, Workforce Development and Education, The Ohio State University, 283A Arps Hall, 1945 N. High Street, Columbus, OH 43210. Email: [email protected]. Jeeyon Paek, Doctoral Student, Workforce Development and Education, The Ohio State University, 287 Arps Hall, 1945 N. High Street, Columbus, OH 43210. Email: [email protected] © Blackwell Publishing Ltd. 2005, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main St., Malden, MA 02148, USA.

Developing human resources for the technical workforce 79

recovery, Korea faced labor shortages in key technical areas, such as manufacturing (Kang, 2002). The growth of jobs requiring highly technical skills is accelerating in Korea. Thailand’s need for technically skilled workers is also increasing. Thailand’s ninth National Social and Economic Development Plan (2002–2006) stresses the development of Human Resources to meet current economic challenges. The authors selected Korea and Thailand because they provide the most comparable data sets among those countries that have similar educational structures. Both countries have a substantial proportion of graduates from vocational secondary education in addition to those from academic secondary education. The growth of new economic sectors, and the success of HRD plans, depends on the ability of vocational-technical education systems to train entry-level workers for fields in demand. For a number of reasons, however, policy-makers have consistently questioned the necessity of providing vocational-technical education to youth. Vocational-technical education is historically more expensive than academic secondary schooling (Tsang, 1997), and parents consistently prefer to enroll students in academic secondary schools (Orapin, 1991). Additionally, scholars point out that vocationaltechnical education often fails to achieve its primary outcome: preparing youth for the labor market in specific fields of study (Adams, 2002). Scholars do show that youth completing vocational-technical education in specific fields who remain employed in related occupations have higher earnings than those who enter the labor market in a field unrelated to their schooling (Arriagada & Ziderman, 1992; Neuman & Ziderman, 1991, 1999). However, in making this determination about the relative effectiveness of vocational-technical education, existing studies have failed to examine differences between men and women. This paper selected two Asian countries, Korea and Thailand, to measure the impact of vocational education in fields related to areas of study. Korea and Thailand are both Asian countries that experienced a severe currency and economic crisis in 1998, but they are quite different in terms of cultural/political backgrounds and economic conditions. While Korea has a strong Confucian influence, placing great value on education and the humanities, Thailand is primarily a Buddhist country (Keyes, 1989). Korea is considered a high-income country, and is a member of the OECD. The GNI per capita stands at US$9,930, in comparison to the regional average of US$5,300. Thailand, in contrast, is considered a lower middle-income country with a lower level of GNI per capita, at US$1,980. Thailand has a larger population, with 61.2 million, compared to 47.3 million in Korea in 2001 (The World Bank, 2003). Over 80 percent of Korea’s population lives in urban areas, compared to 20 percent in Thailand. One of the most interesting differences between Thailand and Korea involves participation rates in the workforce. In 1990, 76 percent of women were in the workforce in Thailand, compared to 47 percent in Korea. This gender difference did not change fundamentally over the decade. In 2000, 49 percent of Korean women were in the labor force, in comparison to 79 percent of Korean men. In contrast, in 2000, 76 percent of Thai women were working. Currently, there are relatively small differences in terms of educational enrollments between Korea and Thailand, but this masks rapid change in the 1990s, especially in Thailand’s secondary education enrollments. In 2000/2001, Thailand had 634,872 people (34 percent of secondary school enrollments) enrolled in vocational-technical schooling. In contrast, in 2000/2001, Korea had 656,606 people (51 percent of secondary education enrollments) enrolled in vocational-technical schooling (Asia Development Bank, 2004). This paper uses data from the Korean Labor and Income Panel Study (KLIPS) and the Thai National Labor Force Survey (NLFS). The study examines the relationship between formal training programs and workers’ subsequent employment in Korea and Thailand. We will examine how technical education is relevant to employment in related occupational areas. Additionally, we will examine the relevance of technical education in terms of gender, country, and region. Moreover, the study’s methodology explores the relative utility of post-secondary vocational education and training. The conclusions can help determine the specific situations of vocationally-technically educated individuals, and provide guidance for future research on these important questions. 80 International Journal of Training and Development

© Blackwell Publishing Ltd. 2005.

Theoretical framework The starting point for the majority of formal policy statements on vocational-technical schooling is the 1991 World Bank policy paper on vocational education and training (Middleton et al., 1993; The World Bank, 1991). Since that time, we have witnessed scattered debates on the international stage about the role of vocational education and training. Most recently, the World Bank produced a series of case studies (Gill et al., 2000) highlighting the state of global vocational education and training. The OECD produced a large set of case studies and, more broadly, a summary document on school-to-work transitions in the late 1990s (Organization for Economic Co-operation and Development, 1998). This academic discussion is taking place at the same time as there are contradictory developments in commitments to vocational and technical education internationally. In general, the proportion of secondary education students involved in vocational education has remained high in Europe. Of the 16 countries with the highest proportion of vocational enrollments, 13 are in Europe (Hawley, 2005). Two of the largest vocational and technical education systems in Europe (in the United Kingdom and Germany) experienced small increases in enrollment. In contrast, Poland and France, each with over 1.5 million vocational and technical education students in 1998, experienced small declines in enrollment. Among Asian nations, which have a history of large numbers of vocational school completers, enrollments have declined in some of the larger systems, including Turkey, the Republic of Korea, Japan, and Israel. Japan’s system, which stood at 1.17 million in 1998, declined 3.8 percent to 1.12 million in 2001. In Korea, vocational-technical education enrollments declined 12 percent between 1998 and 2001. Longer-term trends are more varied. For example, in Indonesia, the proportion of secondary school students in vocational programs was 22 percent in 1970, 11 percent in 1980, and 13 percent in 1995. In contrast, Korea’s enrollments in technical-vocational schooling increased during the 1970s, but remained at approximately 20 percent of total secondary level enrollments during the 1980s and 1990s. In Thailand, the proportion of secondary school students in vocational schooling was 17 percent in 1995, remaining relatively constant since 1980. Data on China is incomplete, but publications document the emphasis that the Chinese government has placed on increasing their proportion of vocational and technical education completers during this period of economic growth (Yang, 1998). In China, growth in vocational enrollment is encouraged by increases in technical training in service and industry, which are in high demand in the private sector. Vocational-technical education is commonly considered successful if graduates from specific concentrations end up employed, specifically in an occupation or industry related to their field of study (Middleton et al., 1993). This is theoretically important, as vocational curricula tend to be highly specific, providing skills development in limited areas such as masonry or computer installation. If students are working in areas outside of their field, they are forced to apply skills learned in one occupation to another area. Therefore, students must acquire skills related to new occupations through additional formal training at the firm level, informal training via colleagues, or additional years in school for retraining. Likewise, firms have a built-in interest in hiring youth with formal training in specific areas, as one can assume that these individuals will require less time and fewer resources to be productive. Authors have focused on examining critical questions about the relationship between education and occupation. For example, Neuman and Ziderman (1991) investigated the relationship between education and employment in a related occupational area. In one study, they found that completing schooling and working in a related occupational area raises earnings by as much as eight percent monthly. Similarly, Arriagada and Ziderman (1992) analyzed data from Brazil, concluding that working in a matched occupation after completing vocational schooling led to a 37 percent earnings difference when compared with workers in non-matched occupations. A © Blackwell Publishing Ltd. 2005.


study on the United States by Hotchkiss (1993) offered a less favorable view of completing vocational schooling and working in a matched occupation. While the models used by both Hotchkiss (1993) and Neuman and Ziderman (1999) offer important methodological guidance for computing the advantages of working in a matched job, no previous study has used data on women. As far as we can tell, all conclusions about the relative proportion of vocational completers working in related fields have been based entirely on data from studies of men. This study examines the following research questions. 1. 2. 3. 4.

Does work within an occupation related to the field of study increase the monthly earnings of vocational completers? How do the estimates for the returns on employment in a related field of study differ between men and women? Does acquisition of a post-secondary degree increase the likelihood of completing a vocational-technical degree, and finding employment in a related area? How does the occupational transition for vocational education differ between Korea and Thailand?

One of the limitations of this study is the ability to generalize beyond Korea or Thailand. Because the data sets analyzed are from only two countries in specific time frames, the results cannot be generalized to every country at any point of time. Another limitation is that the primary variable of interest, matched vocational education and employment, might not be as precise as we would like due to the limited information available. The codes for vocational field of study are not mutually exclusive and are not clear enough to make an exact match with the codes for occupation. This is a common limitation in other studies of this nature, and reflects the inherent problems with linked occupation and education studies.

Methodology Samples The two data sets used for this research – Thailand’s National Labour Force Survey (NLFS) and Korea’s Labor and Income Panel Study (KLIPS) – are national labor market surveys, allowing comparisons between regions and genders. The data sets, sampling methodology, and descriptive statistics for Thailand and Korea are outlined below. Thailand The NFLS contains individual level data on education attainment, occupational status, and the earnings of workers. This analysis is limited to men and women between the ages of 24 and 35 who worked full-time from 1994 to 1998. The NLFS is administered four times a year throughout Thailand by the National Statistics Office. The sample is stratified by community and household, and is representative of the kingdom as a whole. The survey instrument used includes sections on economic status, labor market participation, and education, in addition to health. The measures for education have remained largely unchanged since the survey was first administered in 1963. The study focuses on 24–35-year-old workers because the economy has changed so dramatically over the period of study, and because one expects younger workers to experience greater changes in earnings due to shifts in supply and demand (Kim, 1994; Mincer, 1974; Wu, 1995). Using the pooled sample from Thailand produced samples of 7,696 men (57 percent of whom were in vocational education) and 6,130 women (64 percent of whom completed vocational schooling). The NLFS specifies the field of vocational-technical schooling completed. Table 1 provides information on fields of study, and the percentage of completers employed within a broad occupational area. One-digit codes from the National Labor Force Survey are used. As clarified in Table 1, the codes for vocational field of study are not mutually exclusive. Therefore, it would be incorrect to analyze the relative effectiveness of 82 International Journal of Training and Development


Table 1: Numbers of vocational-technical completers and the percentage employed in related occupational areas, by ‘broad’ field of study (Thailand) Subject of vocational study

Education, Nursing, Obstetrics and Health Education Education, Military Vocational, Obstetrics and Health Any occupational area* Education, Agriculture, Trade, Health and Obstetrics Totals

Number of vocational-technical completers (male/female)

Number employed in related field (male/female)

Percentage (male/female)

16/29

3/1

18.8%/3.5%

18/6 2,191/1,672

3/1 1,176/364

16.7%/16.7% 53.7%/21.8%

20/28 2,181/2,232

20/28 557/194

100.0%/100.0% 25.5%/8.7%

4,426/3,967

1,759/588

39.7%/14.8%

* This code did not provide information about the broad field of study. Recipients were coded as employed in a matched field. Given the small numbers of completers in this field this decision will likely have very little impact on the empirical estimates.

specific vocational concentrations in placing students in related employment. It is possible, however, to interpret the coefficient on the Matched variable. Interpreting the estimates from this variable provides a broad measure of the relative effect of enrolling and gaining employment in a broadly similar area of study. The most important disadvantage of this strategy is that it assumes that those who finished school in, for example, ‘health’, and then were employed in ‘education’ or ‘trade’ (as is the case in one subject area), are employed in a ‘matched’ occupation. Korea The KLIPS contains family-level data based on a composition of family members and the total earnings and expenses; and individual-level data on education attainment, occupational status, and the earnings of workers. The KLIPS have conducted the labor related panel study annually with 13,000 individuals in 5,000 families since 1998. The samples were selected by stratified random sampling, and the survey was implemented mainly through face-to-face interviews with supplemental phone interviews. The instruments were developed for family-level and individual-level data in 1998, and additional instruments for young adults’ behavior in the labor market were developed and implemented since 2000. The Korean case analysis is limited to men and women between the ages of 19 and 39 who worked full-time from 2000 to 2001. Full-time is defined as those whose selfreporting monthly wage is equal to or above 300,000 Korean won in year 2000. The sample is further restricted to individuals who work as employees in either government or private sector businesses, excluding individuals classified as employers, the self-employed, or individuals whose work is restricted to household work. In total, the data sets produced samples of 1,332 men (38 percent of whom were in vocational education) and 1,246 women (41 percent of whom completed vocational schooling). The percentage of vocational training or certificate represents those who have ever received vocational training regardless of site and fund source or those who have at least one or more industry certificate or license. The KLIPS data include a field specifying the type of vocational-technical schooling completed. Table 2 provides information on fields of study, and the percentage of completers employed within each broad occupational area. One-digit codes from the © Blackwell Publishing Ltd. 2005.


Table 2: Numbers of vocational completers and the percentage employed in related occupational areas, by ‘broad’ field of study (Korea) Subject of vocational study

Number of vocational completers (male/female)

Number employed in related field (male/female)

Percentage (male/female)

Farming Industrial or technical Commercial or business Marine General vocational education Foreign language Home economics Any occupational area

18/8 285/8 75/241 6/0 93/225 6/0 19/29 3/4

0/0 199/5 6/117 0/0 22/100 2/0 2/0 0/0

0.0%/0.0% 69.8%/62.5% 8.0%/48.6% 0.0%/0.0% 23.7%/44.4% 0.0%/0.0% 0.0%/0.0% 0.0%/0.0%

Total

505/515

232/222

45.0%/43.1%

KLIPS were used; these nine occupation codes were classified into three levels – high skill job, mid skill job, and low skill job – to increase statistical power. Although the codes of vocational field of study are not clear enough (Table 2), it is possible to interpret the coefficient on the Matched variable. Interpreting the estimates from this variable provides a broad measure of the relative effect of enrolling and gaining employment in a broadly similar area of study. All Korea data was computed with cross-sectional weights in order to represent the population. Analytical technique The data for Korea and Thailand are analyzed separately through Ordinary Least Squares regression methods. All computations are completed for men and women separately. Models include weights that adjust samples for proportions in the population. The basic model used regresses the log of monthly earnings on a vector of background characteristics and educational qualifications: ln earn = b0 + b1(Matched)i + b 2(Vocational)i + b3(PostSecVoc)i + b4(Edyrs)i + b5(Exp)i (1) + b6(Exp - SQ)i + b7(Occupation)i + b8(Region)i + b9(Public)i + m In this study, we were primarily interested in the coefficients on the variable Vocational, a dummy variable equal to 1 if the course of study was completed as a vocational degree, and 0 as an academic area. The variable Matched, a dummy variable, was equal to 1 if the vocational area of study was matched to an occupation in a related area. Additionally, the variable PostSecVoc provided estimates of the additional benefit/ reduction in log monthly earnings attributable to the completion of a post-secondary vocational certificate. All models include controls for years of education completed, levels of potential labor market experience (defined as age – the number of years of schooling – 6), the square of years of potential labor market experience, region of residence (north, northeast, south, central, or Bangkok), occupation, and whether or not an individual was employed in the public sector.

Results Descriptive findings The descriptive data (shown in Table 3) illustrate the similarities and differences in the status of technical-vocational schooling in Korea and Thailand. While vocational com84 International Journal of Training and Development


pleters comprise less than half of the total sample in the Korean case, vocational completers comprise more than half of the sample in Thailand. Interestingly, vocational education is highly concentrated in urban areas in Korea, but much less so in Thailand. Fifty-five percent of vocational graduates in Korea were from metropolitan cities in comparison to less than ten percent from Bangkok in Thailand’s case. Different wage distributions are observed from a comparison of the Korea and Thailand cases. Average monthly log earnings for men are higher than average earnings for women in both countries. However, average monthly log earnings for vocational completers are slightly lower than average earnings for academic school graduates in Korea while average earnings for vocational completers are slightly higher in Thailand. The average years of potential work experience for men are longer than the average years of experience for women in both countries. Korea, however, has much larger gender difference in average years of potential work experience. The gap in the average years of experience by gender is less than half a year for Thailand. In contrast, Korean women lag behind men in experience, by three years on average. Research on Korean women reported that housework, childcare, nursing, and other tasks still remain largely women’s responsibilities, thus making it difficult for women to remain employed after their late 20s and 30s (Kim, 1996). Regression results Thailand Table 4 presents the statistical results for Thailand. The coefficient on Vocational is positive and statistically significant for both genders at the one percent level, indicating that education in a vocational field provides higher average earnings for both men and women. This is consistent with earlier studies using the NLFS (Hawley, 2003a; Moenjak & Worswick, 2003). It is interesting to note that the private earnings differences reported by Thailand’s vocational education graduates are higher than those reported in other countries. Bennell (1996) showed that in only nine of 19 studies reviewed was the rate of return on general secondary education higher than for vocational schooling. It is important to remember, however, that the results reported here are focused on private rates of return, not on social rates that take into account the costs of schooling. Employment in a matched occupation The central research question examined the impact of completing vocational education and obtaining employment in a matched occupational area. The coefficient on Matched for both men and women is negative, and is statistically significant for males. The coefficient for men (-0.083, p < 0.05) can be expressed as a percentage of the log monthly earnings using the formula Exp(B) - 1. Using this formula, employment in a matched occupation is associated with an eight percent decrease in average log monthly earnings. However, after including an interaction term for Experience and Matched, the results shifted quite substantially. Table 4 presents an additional model including the interaction term (Model 2). As indicated, the coefficient on Matched is now positive and significant for women, and negative and significant for men. The coefficient on Matched for females (0.1670, p = 0.092) indicates that a matched occupation is associated with an 18 percent increase in initial years with zero years of experience. The coefficient on the interaction term (-0.019, p = 0.032) indicates that each additional year of experience is associated with a decrease in the increment to monthly log earnings due to employment in a matched occupation. The results for men are quite different. Using the interaction term for experience in the model, the coefficient on Matched is significant and negative (-0.18, p = 0.007). Using this specification, the difference between the monthly earnings of vocational graduates employed in a matched occupation and those employed in a non-matched field was -17 percent. However, the interaction term experience*matched (0.01, p = 0.106) illustrates that for each additional year of experience, the gap between male © Blackwell Publishing Ltd. 2005.


86 International Journal of Training and Development

Table 3: Definitions and means for full-time wage earners who completed vocational or academic high school Korea Vocational school completers (male/female)


Monthly earnings (log) Schooling Experience Experience squared Region of residence Metropolitan cities* Rural areas* Bangkok** North** South** Northeast** Center** Public sector employment Employment in a matched occupation Vocational schooling Post-secondary vocational schooling Vocational training or certificate* Sample size Sample size by gender % of sample vocational completers * Data available only in KLIPS (Korea data). ** Data available only in NLFS (Thailand data).

Thailand Academic school completers (male/female)

6.00/5.84 12.48/12.40 10.07/6.46 126.64/62.32

6.08/5.90 13.42/13.21 9.76/6.91 114.32/69.98

55%/55% 45%/45%

54%/56% 46%/44%

6%/6% 45%/43% 100%/100% 17%/13% 30%/40% 505/515 1332/1246 38%/41%

13%/14% 1%/2% 0%/0% 60%/40% 24%/23% 827/731

Vocational school completers (male/female)

Academic school completers (male/female)

8.79/8.64 12.98/13.12 10.27/9.91 118.36/11.75

9.56/8.41 12.25/12.47 10.10/9.58 111.48/102.22

8%/9% 17%/17% 20%/21% 21%/22% 35%/32% 40%/46% 40%/10% 100%/100% 50%/57%

6%/7% 19%/18% 18%/17% 21%/20% 35%/39% 47%/26% 0%/0% 0%/0% 0%/0%

4426/3967 7696/6130 58%/65%

3270/2163

Table 4: Regression of monthly earnings (log) for full-time workers, academic and vocational school completers in Thailand, 1995–1998 (n = 7696 (male), 6130 (female)) Male

Schooling Experience Experience squared Hours worked Region of residence North South Northeast Center Public sector employment Occupation Professional and academics Executive administration Clerk or typist Trade or sales Fisherman, farmer, logging Miner High skill without certificate Service, sport, and recreation Other Employment in a matched occupation Vocational schooling Post-secondary vocational schooling Experience*Matched Constant

Model 1

Model 2

Model 1

Model 2

0.104 (0.019)** 0.065 (0.015)** -0.002 (0.001)** 0.002 (0.001)**

0.098 (0.019)** 0.022 (0.003)**

0.085 (0.014)** 0.021 (0.003)**

0.002 (0.001)**

0.087 (0.014)** 0.056 (0.015)** -0.002 (0.001)** -0.001 -0.001

-0.001 -0.001

-0.407 (0.035)** -0.379 (0.033)** -0.36 (0.034)** -0.13 (0.031)** -0.023 -0.024

-0.407 (0.035)** -0.379 (0.033)** -0.36 (0.034)** -0.129 (0.031)** -0.026 -0.024

-0.49 (0.045)** -0.442 (0.029)** -0.447 (0.031)** -0.199 (0.026)** -0.032 -0.027

-0.492 (0.045)** -0.443 (0.030)** -0.449 (0.031)** -0.2 (0.026)** -0.033 -0.027

0.024 -0.05 0.285 (0.063)** 0.149 (0.037)** 0.164 (0.057)** -0.217 (0.060)** 0.599 (0.115)** 0.072 (0.036)* 0.085 (0.039)* 0 0 -0.083 (0.034)* 0.182 (0.035)** -0.016 -0.05

0.022 -0.049 0.29 (0.063)** 0.151 (0.038)** 0.171 (0.058)** -0.216 (0.061)** 0.592 (0.111)** 0.069 -0.036 0.087 (0.039)* 0 0 -0.183 (0.068)** 0.175 (0.035)** -0.008 -0.05 0.01 -0.006 7.221 (0.244)** 7696 0.27

-0.025 -0.09 0.403 (0.088)** -0.013 -0.09 -0.028 -0.102 -0.552 (0.169)** 0 0 -0.048 -0.095 -0.273 (0.098)** 0 0 -0.033 -0.042 0.161 (0.030)** -0.025 -0.037

-0.031 -0.09 0.386 (0.086)** -0.021 -0.09 -0.038 -0.103 -0.567 (0.167)** 0 0 -0.049 -0.095 -0.274 (0.098)** 0 0 0.167 -0.099 0.161 (0.030)** -0.027 -0.037 -0.019 (0.009)* 7.566 (0.236)** 6130 0.3

6.919 (0.259)** 7696 0.28

Observations R-squared

Female

7.387 (0.259)** 6130 0.3

Robust standard errors in parentheses. * significant at 5%; ** significant at 1%. © Blackwell Publishing Ltd. 2005.


vocational completers employed in matched and non-matched fields decreases. The results for men are not in line with other research, while the findings for women are comparable to previous findings using only men as the sample. For example, Neuman and Ziderman (1999) reported a four percent increase in earnings due to employment in an occupational area related to the field of study for men. They do not report findings for women. Likewise, Arrigada and Ziderman (1992) reported a 37 percent difference for men in broadly matched occupational fields. They also do not report data for women. Post-secondary education A major concern for individuals in Thailand is the relative value of a post-secondary credential. In this case, the variable on post-secondary vocational schooling shows that the earnings of post-secondary vocational completers do not differ significantly from the sample of both academic and vocational completers at secondary or postsecondary levels. The coefficients for both men and women in Table 4, Model 1 show that there is no statistically significant difference, controlling for years of schooling. The implication of this finding is that after controlling for the difference associated with vocational education (which is positive and significant), there is no additional benefit due to higher levels of schooling. This finding is in contrast to the positive difference found in an earlier study using a different sample of individuals from the NLFS (Hawley, 2003a). Korea Table 5 shows the results for Korea. Of particular interest are the results of regression analysis in Korea. Unlike many previous studies in other countries, the coefficient on vocational education is negative for men, with statistical significance at the five percent level, showing that vocational secondary education provides lower average earnings for men. However, the coefficient is positive and statistically significant for women at the five percent level, showing that vocational secondary education provides higher average earnings for women. Employment in a matched occupation The coefficient on a matched occupation is positive for males and negative for females, and for women is statistically significant at the five percent level. The coefficient for women on Matched occupation (-0.045, p = 0.015) indicates a decrease in log monthly earnings in Model 1. It can be interpreted that employment in a matched occupation is associated with a 4.5 percent decrease in average log monthly earnings for women. However, when we ran the data on full-time workers with final education at the secondary level, the result for men changed (Table 5, Model 2). The coefficient for men is positive and statistically significant at the five percent level. In Model 2, for men the coefficient on a matched occupation is 0.033 (p = 0.017), and for women is -0.039 (p = 0.015). This indicates that employment in a matched occupation is associated with a 3.3 percent increase in average log monthly earnings for men, and a 3.9 percent decrease in average log monthly earnings for women. Although the proportion decreased from 4.5 percent to 3.9 percent for women, it remained negative. Post-secondary education The coefficient on post-secondary education is positive and statistically significant at the one percent level for both men and women in Korea. Analysis indicates that the earnings of post-secondary graduates are significantly different from the sample of both academic and vocational completers. It can be interpreted that men who completed post-secondary education received higher earnings on average by 0.18 log earnings than those who did not, after controlling for years of experience, vocational schooling, and employment in a matched occupation. Women who completed postsecondary education received higher earnings by 0.19 log earnings than those who did not, after controlling for years of experience, vocational schooling, and employment in a matched occupation. As many previous studies have asserted, post-secondary 88 International Journal of Training and Development



Table 5: Model 1: Regression of monthly earnings (log) for full-time workers, academic or vocational school completers, 2000–2001 (n = 1332 (Male), 1246 (Female)); Model 2: Regression of monthly earnings (log) for full-time workers, only vocational school completers either having post-secondary vocational education or not, 2000–2001 (n = 505 (Male), 515 (Female)) (Korea) Male


Experience Experience squared Region of residence Metropolitan cities Public sector employment Occupation High skill job Mid skill job Low skill job Employment in a matched occupation Vocational schooling Post-secondary vocational schooling Vocational training or certificate Constant Observations R-squared Robust standard errors in parentheses. * significant at 5%; ** significant at 1%.

Female

Model 1

Model 2

Model 1

0.22 (0.004)** 0.00 (0.00)

0.20 (0.006)** 0.00 (0.00)

0.012 (0.003)** 0.00 (0.00)

-0.01 (0.009) 0.036 (0.015)*

-0.016 (0.016) 0.029 (0.034)

-0.003 (0.009) 0.106 (0.015)**

-0.006 (0.015) 0.123 (0.029)**

0.062 (0.033) 0.024 (0.029) -0.012 (0.030) 0.024 (0.015) -0.026 (0.012)* 0.181 (0.010)** -0.014 (0.011) 5.75 (0.035)** 1332 0.429

0.125 (0.073) 0.007 (0.055) 0.053 (0.056) 0.021 (0.016)

-0.017 (0.035) -0.039 (0.033) -0.070 (0.036) -0.045 (0.015)* 0.037 (0.012)* 0.190 (0.012)** -0.014 (0.010) 5.78 (0.035)** 1246 0.280

0.023 (0.064) 0.016 (0.051) -0.008 (0.054) -0.05 (0.016)*

0.168 (0.022)** 5.674 (0.063)** 505 0.371

Model 2 -0.003 (0.005) 0.00 (0.00)

0.118 (0.023)** 5.828 (0.053)** 515 0.118

education provided higher return in terms of individual earnings (without taking into account the expenses of post-secondary education).

Conclusions This study compared the return on vocational schooling in Thailand and Korea, using data from two national-level surveys. The descriptive data illustrated the similarities and differences in the status of technical-vocational schooling in Korea and Thailand. While vocational graduates comprised less than half of the total sample in the Korean case, that figure was more than half in Thailand. Vocational education graduates were 38 percent of the sample for men and 41 percent for women in Korea, and 58 percent for men and 65 percent for women in Thailand. Interestingly, vocational education is highly concentrated in urban areas in Korea, but much less so in Thailand. Fifty-five percent of vocational graduates in Korea were from metropolitan areas, compared to less than ten percent from Bangkok in the case of Thailand. Different wage distributions were observed between Korea and Thailand. Average monthly log earnings for men were higher than average earnings for women in both countries. However, average monthly log earnings for vocational completers are slightly lower than average earnings for academic graduates in Korea, while average earnings for vocational completers were slightly higher in Thailand. Although average years of potential work experience for men were longer than for women in both countries, Korea had a much larger difference between men and women. The gap in average years of experience by gender was less than a half year for Thailand. In contrast, the average experience gap was three years in Korea (with men ahead of women). Research on Korean women reported that housework, childcare, nursing, and other tasks still remain largely women’s responsibilities, making it difficult for women to continue to work after their late 20s and 30s (Kim, 1996). Korean data shows that both men and women with vocational education work in fields related to their vocational schooling by more than 40 percent. In Thailand, 40 percent of men work in a field related to their studies, while only ten percent of women had a matched occupation. Interestingly, 17 percent of Korean men and 13 percent of Korean women who completed vocational secondary education received postsecondary education, while for both men and women in Thailand, 50 percent of those who completed vocational secondary education received post-secondary education. Regression-based findings This study furthers research into a critical question for human resource development: Does the payoff of technical-vocational schooling increase with employment in the field for which one was trained? The evidence from Korea shows that men enjoy a benefit, but not women. For both men and women in Thailand, employment in a matched field does not offer a payoff. Matched fields and earnings payoffs In both cases, we were able to offer a more nuanced view of the circumstances that lead to a higher return on employment in a matched occupation. In the case of Thailand, experience seems important in ensuring that employment in a matched field leads to wage gains. In the case of women, in particular, employment in a matched field depends significantly on the number of years of experience in the labor market. Similarly, for Thai men, each additional year of experience led to a decrease in the negative coefficient associated with employment in a matched occupation. In the case of Korea, the key appears to be employment in a full-time occupation. For Korean women, employment in a matched full-time occupation led to a decrease in log monthly earnings, while for men employment in a matched full-time occupation led to an increase in the coefficient. These differences for Korea and Thailand are striking. It is important to recall that of the handful of studies completed on the outcomes of vocational education and employment in matched occupations, there are no studies using data on women. 90 International Journal of Training and Development


Therefore, while the coefficients on men contradict each other, they fit into the larger literature. In the case of Israel, Neuman and Ziderman (1991, 1999) found that there was an eight percent difference for employment in a matched occupation. In Brazil, Arriagada and Ziderman (1992) showed a 37 percent increase in earnings in matched occupations. The data from Korea show a much lower increment for earnings (3.3 percent), but are positive as well. In Thailand, the earnings of men in matched occupations were approximately 17 percent less than graduates of vocational schools in non-matched areas. Explanations for the striking difference in findings from Thailand come from a number of angles. The first is organized around data quality and sampling. The Thai data are representative and come from a high quality data source, the NLFS. However, the educational credential data in the NLFS is difficult to match with occupational data. In contrast, in the case of Korea and Brazil (Arriagada & Ziderman, 1992), educational credentials and occupational data are more closely aligned. The implication of this is that the coefficient in the Thai case represents a broader spectrum of matching than in the case of Korea, as in earlier studies on Brazil and Israel (Hawley, 2003b; Neuman & Ziderman, 1999). Gender and matching The Thai and Korean cases are the first attempts to measure the gender gap in terms of returns on vocational schooling. While Thailand and Korea offer fundamentally equal access to schooling at primary and secondary levels for boys and girls, the labor market participation rate for women in Korea is strikingly lower than for men, but relatively equal in Thailand. These differences in labor market participation rates can be found in the levels of experience that women and men had in our sample. Women had significantly fewer years of experience than men in Korea. In both countries, there was a significant earning difference between women who were employed in a matched field, and those who were not. In Thailand’s case, women benefited from employment in a matched field, while in Korea’s case there was a negative coefficient. These differences may be due to specific fields of vocational study. In Korea’s case, women are split between ‘Commerce or business’ and ‘General vocational education.’ In contrast, for Thailand, the vocational fields are more diversified into ‘Education, Health, Trade, and Agriculture’ (the emphasis here appears to be on professional fields). Therefore, the positive difference for women in Thailand could be due to the specialized nature of training offered by Thai vocational schools. Post-secondary schooling and matching The final research question focused on the relative impact of participation in postsecondary vocational schooling, and its effect on the outcomes of vocational-technical schooling. The data from Thailand and Korea offer fundamentally different conclusions. In the case of Korea, both women and men experienced a relatively significant difference in quarterly log earnings with the acquisition of a post-secondary degree. In contrast, in Thailand, there was no significant post-secondary coefficient. We would add, however, that in a previous study on Thailand, Hawley (2003a) showed a significant payoff to post-secondary schooling overall. These differences can likely be attributed to differences in the human resource systems in Thailand and Korea. While both countries have historically similar pay and education levels, Korean firms and government still provide salaries based primarily on educational level. In contrast, in Thailand, wages in the private sector are not a function of educational credential (as they are in the public sector). Moreover, the labor market in Thailand is still highly dependent on agriculture, and offers fewer opportunities for employment requiring a post-secondary credential than Korea.

Discussion: implications for human resource policy Vocational-technical education is increasingly important to Asian-Pacific countries as they struggle to develop their workforces to compete in the global marketplace © Blackwell Publishing Ltd. 2005.


(Ashton et al., 1999; The World Bank, 2000). In the human resource development field, scholars have begun to draw attention to the actions of government, stressing the importance of integrating the development of a technically skilled workforce with the business goals of firms. In this vein, scholars have developed the concept of National Human Resource Development (NHRD) (McLean, 2004). The concept of NHRD harkens back to more robust conceptions of the role of government, when training and development was primarily concerned with manpower planning or forecasting. This expanded view hopes to ensure that government, business, and education work together to promote economic development. The quintessential example comes from Singapore, where the government essentially directed economic growth towards international firms in the 1970s and 1980s through tax policy, repressing wages to attract firms, and supporting extensive training for entry-level workers and a system of firmlevel training and development. This Singapore system is known in the larger political economy literature as ‘corporatism’. Corporatism is a system of government that focuses on the interrelated roles of government, business, unions, and education in promoting economic and workforce development. Some studies of these collective capitalist systems have highlighted corporatist arrangements between peak associations, unions, and national governments (see, for example, Lynn & McKeown (1988) for Japan; Schmitter (1979) for Europe). In the European case, scholars investigated business associations, along with other collective economic institutions, in research that highlighted successful capitalist systems that operated differently from the prototypical free-market systems in the US and the UK (Dore et al., 1999). These systems – especially in Germany and Italy – privileged a role for collective firm action discouraged in the US through anti-trust regulations. Japanese business associations have also been included as part of this ‘new competition’ (Piore & Sabel, 1984). Collective institutions (such as business associations) allowed firms in these countries to outperform those in the US and the UK in the 1980s and 1990s, helping to innovate new products and production processes more quickly. The human resource development field is entering this debate with emerging literature on NHRD. However, the decisions on which HRD researchers and practitioners focus remain at the organizational level (Jacobs & Hawley, 2003; Swanson, 2001). As Swanson (2001) notes, the traditional domains of performance of HRD include organization, work processes, and group/individual levels. This emphasis restricts the typical focus of HRD. In contrast, the traditional view of vocational-technical schooling is intimately related to governmental action. In their classic text, Middleton et al. (1993) focus on the critical roles that government can play in strengthening vocationaltechnical education, and the generally problematic achievements of governments up to this point. The recent spate of policy literature on vocational-technical schooling (Castro et al., 2000; Herschbach & Campbell, 2000; Johanson, 2002; Calvert & AlShetaiwi, 2002) similarly concludes that employers are a critical component in vocational schooling. However, there is a lack of consensus on the role of government. This study places one decision of governmental policy in sharp contrast: the funding of entry-level training through public sector vocational-technical schooling. While vocational schooling in a matched field pays off for men and women in some countries, there are various challenges to the development of vocational-technical policy that can effectively promote a highly skilled workforce. First and foremost, the majority of individuals in both samples did not work in fields for which they are trained. This was particularly true for women in Thailand, where only ten percent of the sample worked in a matched occupation. Secondly, we found that post-secondary training offered potentially strong opportunities for reinforcing the impact of employment in a matched field. Finally, we described the gender differences in returns on vocationaltechnical schooling. These descriptions reveal the need for an increased focus on employer involvement in human resource policy, as described in corporatist systems. What this means practically for HRD scholars is difficult to know, because the traditional emphasis of HRD lies in organizational elements of training and development. Similarly, the concept of corporatism and larger scholarship on the relationship between business and govern92 International Journal of Training and Development


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