Discussion Papers In Economics
And Business
Earnings gaps among higher-educated workers withinmain cities in semi-industrialized and newly industrialized
Asian countries
Mamiko Takeuchi

Discussion Paper 19-06-Rev.2

July 2019

Graduate School of Economics
Osaka University, Toyonaka, Osaka 560-0043, JAPANEarnings gaps among higher-educated workers within main cities in semiindustrialized and newly industrialized Asian countries *Mamiko Takeuchi †Kyushu University
Abstract

This paper comparatively analyzes determinants of earnings and the gender earnings gap among
higher-educated workers in nine main cities in seven semi-industrialized and newly
industrialized Asian countries. The analysis focuses on effects of specific qualifications or skills
beyond a bachelor’s degree; such as a postgraduate degree, specific field of degree, or
experience working or studying abroad. The results show some such attributes positively affectearnings, although these varied by city and gender. A decomposition analysis also reveals there
is no gender gap among higher-educated workers in Delhi and Mumbai, whereas endowment
or coefficient effects on gender gaps are detected in other cities.

Keywords: Asian countries, Higher-educated workers, Specific abilities, Comparative analysis,
Gender earnings gap
JEL Classification Codes:

*

I26

J24

J71

I appreciate a letter of recommendation from Hisakazu Matushige to submit my manuscript. This paper is
supported by JSPS KAKENHI Grant Number 18K01652 and Kyushu University Research Activity
Support Program. The data for this secondary analysis, “Gloval Career Survey” conducted by Recruit
Works Institute, was provieded by the Social Science Japan Data Archive, Center for Social Research andData Archives, Institute of Social Science, The University of Tokyo.
† Corresponding author: Mamiko Takeuchi, Kyushu University, Fukuoka, Japan.
E-mail: takeuchi[@]danjyo.kyushu-u.ac.jp

I. Introduction
Completion of higher education among young workers has been growing in semi-industrialized
and newly industrialized Asian countries1, though employment opportunities are often
unfavorable in these countries. Notably, higher unemployment rates among younger workers
and demand–supply mismatch for higher-educated workers have emerged as major concerns.
Against this backdrop, the present analyses focuses on higher-educated younger workers in nine
main cities in China, India, Indonesia, South Korea, Malaysia, Thailand, and Vietnam. These
countries share some common challenges and areas of focus with regard to younger and/or
higher-educated workers in their labor markets.
In China, the government has expanded the number of universities and increased enrollment,
based on its policy of improving human resources to sustain national economic growth (Meng,
2018). However, the full-time employment rate has been declining because of a demand–supply
mismatch in the labor market (Meng, 2018; Ministry of Health, Labour and Welfare, 2017).
The problem of university graduates’ employment was defined as the first priority on thegovernment’s agenda in 2011 (Ministry of Health, Labour and Welfare, 2014). In India, theuniversity enrollment rate increased by 13.3% from 2013 to 2017, and holders of doctoral
degrees also increased during this period (Government of India, Ministry of Human Resource
Development, 2017). However, the unemployment rate2 for those aged <30 years outpaced theoverall unemployment rate, including among those with university degrees or higher. A
demand–supply mismatch in the labor market has occurred among higher-educated workers
because of this group’s general preference of working in white-collar jobs (Ministry of Health,Labour and Welfare, 2017).
A similar situation has been observed in Indonesia, where the national unemployment rate
was 5.5% in August 2017, and has been as high as 27.54% among people aged 15–19 years(Statistics Indonesia, 2018). Employment measures for young people are the topic of key policy
initiatives in this country (Ministry of Health, Labour and Welfare, 2017). In South Korea, theunemployment rate among those aged 15–29 years increased by 10.4% in 2017 (Korean
Statistical Information Service, 2018). University enrollment has remained high, leading to a
problematic lack of jobs for university-educated workers, causing higher unemployment andrapid turnover (Kim, 2013). The government has announced an employment-promotion
program, including job creation and an internship initiative for younger people (Ministry of
Health, Labour and Welfare, 2018). In Malaysia, the unemployment rate among those aged 15–1

24 years was approximately 10% in 2016, higher than the total unemployment rate. The lack ofjobs to keep pace with the rapid progress in higher education has been pointed out (Ministry of
Health, Labour and Welfare, 2017).
Similarly, in Thailand, the unemployment rate has held at around 1% in recent years, though
the unemployment rate among people aged <25 years, at around 5%, surpassed the total rate in
the second quarter of 2016 (National Statistical Office, Thailand, 2017). Vietnam’s labor force
has continually grown (General Statistics Office of Vietnam, 2016) and the unemployment rate
has remained at a low level; 2.1% in the fourth quarter of 2016. However, the unemployment
rate among those aged <25 years was 7.28%, accounting for 50.9% of the total unemployment
rate (General Statistics Office of Vietnam, 2016). The younger generation, aged 0–24 years,
comprises 40.0% of Vietnam’s population, and 23.1% of the population is <14 years old;
therefore, job creation will be an important topic in coming years.
Noteworthy in the above descriptions of these countries’ employment situations is that eachcountry faces one or more issues regarding unemployment in urban areas, unemployment ofyoung people, and job mismatch among higher-educated workers. Thus, the working
environment for the higher-educated younger generation has grown unfavorable in the urban
areas of these countries. Their governments all face certain challenges in supporting theseworkers. Furthermore, working conditions are thought to differ for men and women in eachcountry3. Under these circumstances, the main concern of this study is what skills provide anadvantage among higher-educated workers amid competition for work. From this perspective,
this study analyzes the determinants of earnings among higher-educated young workers in nine
main cities in seven Asian countries; Shanghai in China, Delhi and Mumbai in India, Jakarta inIndonesia, Seoul in South Korea, Kuala Lumpur in Malaysia, Bangkok in Thailand, and Hanoi
and Ho Chi Minh City in Vietnam. We particularly focus on the effects of these workers’
qualifications or skills in addition to just having an undergraduate degree—such as having a
postgraduate degree, a specific field of degree, and experience working or studying abroad—on their earnings. Earnings function is estimated by country and gender, and a comparative
analysis of these estimations is conducted, as well as a decomposition analysis of the earnings
function.
The rest of this paper is organized as follows. The next section presents a review of theliterature. Section III provides the analytical methodology. This is followed by a summary of
the source of data and descriptive statistics of variables in section IV. Section V discusses theresults, while the final section concludes the paper.
2II. Literature Review
A large number of studies have investigated the return associated with higher education,
specific majors, and graduate school, and some have shown these factors to explain gender gaps
(Daymont and Andrisani, 1984; Gerhart, 1990; Grogger and Eide, 1995; Finnie and Frenette,
2003; Machin and Puhani, 2003; Black et al., 2004; O’Leary and Sloane, 2005; Furno, 2014;
Zafar, 2016; Walker et al., 2017). García-Arail (2008) points out that when the fields of study
are considered, mathematics, engineering, and medical sciences graduates earn more than
graduates from other fields. Such choice of field is also found to affect the gender gap among
European higher-education graduates. Mclntosh and Lindley (2015) show that, in the United
Kingdom, most of the increase in graduate wage inequality relates to subjects studied, and thatvariance of childhood cognitive test scores and the widening variety of jobs graduates
performed potentially also affect this inequality. Lindley and Machin (2016) argue that the wage
gap between workers with postgraduate and only undergraduate degrees substantially widened
from 1980 to 2012 in the United States. Parey and Waldinger (2011) find that European
university students studying abroad tend to be more-skilled workers and increase theirlikelihood of later working abroad.
Some studies have focused on Asian countries (Bauer et al., 1992; Gustafsson and Li, 2000;
Sebastian, 2008; Su, 2011; Chang, 2015; Qin et al., 2016; Lee and Wie, 2017). Lee et al. (2016)
analyze return to overeducation across fields of study in South Korea, and find the return for
university graduates in health and welfare, engineering and manufacturing, social sciences,business, and law are relatively high compared with return for those in agriculture, humanities
and arts. Though very few studies have investigated effects concerning Asians working or
studying abroad, Counsell (2011) points out that earning a degree and working in the United
Kingdom, United States, or Australia tend to favorably impact future careers for workers in
China. Rani (2014) discusses that return to education for workers in India is greater for thosewith fluent English. Azam et al. (2013) also point out the advantages of English proficiency,
specifically among younger educated workers in India.
Gender gaps have also been explored in Asian countries. Yoo (2003) confirms the genderwage gap in South Korea decreased from 1988 to 1999; however, discrimination against female
workers replaced human capital endowment as the main gender gap. Cho and Lee (2015)
investigate the gender wage gap under the stratified structure of the labor market in South Korea
and confirm gender skewness in employment distribution in that market structure. Milanovic
3

(2006) confirms wage discrimination against women, based on Oaxaca decomposition (Oaxaca,1973), for workers with educational levels ranging from primary school to university in
Malaysia. Fang and Sakellariou (2011) investigate the gender monthly earnings gap in Thailandusing quantile regression combined with Oaxaca–Blinder decomposition, and find most of thegender earnings gap is due to discrimination, especially at the lower tail of the distribution. Lee
and Wie (2017) examine the gender wage gap in urban China and urban India and find awidening gap in China but a dramatically reduced one in India.
Few studies, however, have focused on investigating the determinants of earnings among
higher-educated workers in Asian countries and the gender earnings gap among these workers,
especially in urban areas. The present research contributes with an international comparative
analysis focusing on young workers with higher education in nine main cities in seven Asian
countries. By using data from a unique survey carried out by a Japanese think tank, this study
is able to focus on workers who are leading growth and development of Asian countries, where
the popularization of higher education is increasing. This is in contrast to the employment
situation, which remains rigid, in recent years. In particular, we investigate the effects of theseworker’s additional qualifications and skills—such having a postgraduate degree, specific fieldof degree, and experience working or studying abroad—on their earnings. A decomposition of
the gender earnings gap is also conducted among these workers for investigating the gender gap
among higher-educated workers.

III. Analytical Method
III.1. Estimation of determinants of earnings by country and genderFirst, the earnings function by city and gender is investigated as follows:=
Yij X ij' βij + ε ij
= Z ij' γ ij + V ′δ ij + ε ij ,E( ε ij ) = 0 , Shanghai, Delhi and Mumbai, Jakarta, Seoul, Kuala Lumpur i∈ Bangkok ,Hanoi and Ho Chi Minh Cityj ∈ {male, female}(1)

where X ij' is a vector of personal characteristics and βij contains the corresponding vector ofmodel parameters and the intercept, and ε ij is the error term. The dependent variable Yij is theyearly earnings in 2011 by city and gender.
The logarithm of hourly wage would have been a better measure of the dependent variable,4but the data set used herein does not contain information on hours worked. We also determined
we would use earnings without the logarithm based on the residual sum of squared and adjusted
R-squared4. For the independent variables, all available work-related information from the data
set is used. Postgraduate degree, field of degree (dummy variables for social science, naturalscience, or other, compared with the reference group of the humanities), experience working
abroad, and experience studying abroad are used as the attributes of interest. These variables
are appropriate for investigating the effects of workers’ additional qualifications and skills.
However, these variables could only be treated as the exogeneous variables, not as endogenous
variables. This owes to the lack of necessary information in this survey. Moreover, the small
data sample size used in this study prevent attempting a more developed estimation. For thesereasons, the fundamental Mincerian-type equations are estimated using the ordinary leastsquares method.

III.2. Decomposition of gender earnings differentials
A popular decomposition method, the Blinder–Oaxaca decomposition technique (Blinder,
1973; Oaxaca, 1973), is used to investigate the gender earnings differentials. This method
divides the gender differential in wages into a part that is essentially explained by differences
between men and women in productivity characteristics such as education or work experienceand a residual part that cannot be accounted for by such differences in wage determinants. This
unexplained part is often used as a measure of discrimination, but it also includes the effects ofgroup differences in unobserved predictors. Most applications of this technique can be foundin the labor market and discrimination literature (Stanley and Jarrell, 1998; Weichselbaumer
and Winter-Ebmer, 2005). The question of this study is how much of the mean outcome
difference is accounted for by group differences in the predictors. Given the two groups (menand women), an outcome variable for earnings Y, and a set of predictors in each city i, the meanoutcome difference is expressed as
R = E (Yi  male ) − E ( Yi  female )where E(Yi  male ) and(2)

E(Yi  male ) denote the expected values of the outcome variable. Basedon the linear model (1), the mean outcome difference can be expressed as the difference in thelinear prediction at the group-specific means of the regressors:
R =E( Yi  male ) − E( Yi  female ) =E( X i  male )' βi  male − E( X i  female )' βi  female

This is so because5(3)

E ( Y=E ( X ij' βij + ε=
E ( X ij' β ij ) + E ( ε=E (X ij )' βij ,
ij )
ij )
ij )

with
=
E( βi ) β=
0 by assumption. To identify the contribution of groupi and E( ε i )differences in predictors to the overall outcome difference, equation (3) can be rearranged, forexample, as follows (Daymont and Andrisani, 1984; Jones and Kelley, 1984; Winsborough andDickinson, 1971):' E( X i  male ) − E( X i  female ) β i  female + E( X i  female )' ( β i  male − β i  female )
R=

(4)

'

+  E( X i  male ) − E( X i  female ) ( β i  male − β i  female )This is a threefold decomposition; that is, the outcome difference is divided into three parts:
R=E+C+I

The first part,'=E  E ( X i  male ) − E ( X i  female ) βi  female
is the part of the differential that is attributable to group differences in the predictors (the“endowment effect”). The second component,=
C E ( X i  female )' ( βi  male − βi  female )measures the contribution of differences in the coefficients (including differences in theintercept). The third part,' E ( X i  male ) − E ( X i  female ) ( βi  male − βi  female )I =is an interaction term accounting for the fact that differences in endowments and coefficients
exist simultaneously between the two groups. The decomposition (4) is formulated from theviewpoint of female workers; that is, the group differences in the predictors are weighted by
the coefficients of female workers to determine the endowment effect (E). This means the (E)component represents what the expected change in women’s mean outcome would be if women
had men’s predictor levels. Similarly, for the second component (C), the differences incoefficients are weighted by women’s predictor levels; therefore, the second component
represents what the expected change in women’s mean outcome would be if women had men’s
coefficients.

IV. Data
The dataset used in the present analysis is from the Global Career Survey, which was conducted
online September 14–21, 2012, by Recruit Works Institute, a leading Japanese think tank in the6field of labor market research. This survey (Recruit Works Institute 2013) was conducted toinvestigate the differing and common factors of the actual work situations of higher-educatedyounger workers. It is a multinational survey aimed at comparative analysis of urban areas in
Asian countries. The target population was workers with a bachelor’s degrees or higher, and
who were aged 20–39 years and worked in Shanghai, Delhi, Mumbai, Jakarta, Seoul, Kuala
Lumpur, Bangkok, Hanoi, or Ho Chi Minh City. The sample size was set to select 150 workers
in each age and gender group in each country, yielding a total sample size of nearly 600 in eachcountry. This survey is unique because it allows the investigation of younger educated workers,
who are leading growth and development in the main cities of these seven Asian countries. The
survey also allows investigation of the effects of additional qualifications and skills—such as
having a postgraduate degree, specific field of degree, and experience working or studying
abroad—on earnings among workers with higher education.
The dependent variable is earnings in the previous year, 2011. For this reason, respondents
who changed jobs from 2011 to the point of the survey from the analytical sample were
excluded because information about their former jobs was not unavailable. One year was also
subtracted from the time-related variables such as length of employment so that all variables
showed the workers’ attributes in 2011.
The responses on earnings were in the currency of each country: Chinese yuan, Indian rupee,
Indonesian rupiah, South Korean won, Malaysian ringgit, Vietnamese dong, and Thai baht. We
converted these currencies to United States dollars (USD) at the exchange rate on September
20, 2011, 1 year before the survey. In addition, we excluded observations with earnings overthree standard deviations higher than the average earnings in each country as outliers.
Figure 1 shows the kernel density distributions of men’s and women’s earnings in the ninecities of seven countries. The distributions for men tend to be skewed slightly to the right, andthe gender earnings gap is confirmed. However, in Delhi and Mumbai, both genders’distributions are almost matched, with the peaks of the distributions at almost the same point in
the graph. In Bangkok, only the distribution for men is skewed to the right.
Tables 1 and 2 present the descriptive statistics for male and female workers by country.
Regarding the definition of independent variables, a postgraduate degree indicate that workers
had a master’s or doctoral degree, and field of degree is divided into four broad fields. The
broad field social science includes, among others, the disciplines of economics, businessadministration, law, and political science. Natural science includes engineering, agriculture,
medicine, and physical science. “Other” includes art, architecture, and other such disciplines.
7

Humanities is the reference category in the analyses, and includes disciplines such as literature,
psychology, and education.
For workers’ information, the independent variables include years of tenure at the same
company and several dummy variables, such as whether the individual had held a managerial
position, experience changing jobs, worked as a full-time employee (part-time employee as thereference group), permanent employee (fixed-term employee as the reference group), or worked
for a foreign-affiliated company5. Also included is a set of dummy variables gauging whether
the worker is a public employee, company executive or director, self-employed or family
employee, with company employees as the reference group. Additionally, dummy variables
assessing the workers’ lifestyle attributes—such as marital status, the presence of children, andliving with parents—are included.
Regarding the comparative examination, columns in Tables 1 and 2 include each country’s
rank for each variable, and gray boxes are used to indicate the top-ranked country for eachvariable. Average earnings in each country varied, reflecting the exchange rate to the dollar.
Delhi and Mumbai rank first among the seven countries in terms of the percentage of workers
with a postgraduate degree (40.0% for men, 40.8% for women). Jakarta has the highest
percentage of male workers with a social science degree (37.4%), and Shanghai has the highest
percentage of female workers with a social science degree (45.3%). Hanoi and Ho Chi Minh
City have the highest percentage of workers with a natural science degree among men (45.4%),
while Delhi and Mumbai have the highest percentage among women (40.5%). Delhi andMumbai also have the highest percentage of men with experience working abroad (31.0%). For
women, this percentage is highest in Shanghai (15.1%). Seoul has the highest percentages ofboth men (21.3%) and women (22.3%) who had studied abroad.
Tendencies are very similar for men and women within each country for the other examined
attributes. Bangkok ranks first in average years of tenure for both men (5.52 years) and women(4.73 years). The highest percentages of managerial positions among both men (59.6%) andwomen (52.0%) are observed in Shanghai. The highest percentage of male workers changing
jobs is in Kuala Lumpur (61.9%), followed by Bangkok (53.9%). The highest percentage offemale workers changing jobs is found in Bangkok (63.0%), with Kuala Lumpur (60.2%)
second. For both men and women, the highest percentages of public employees are in Hanoi
and Ho Chi Minh City (25.3% for men, 22.8% for women). The highest percentages of thoseworking for foreign-affiliated companies and of full-time employees are in Shanghai (97.8%
and 26.9% for men, 98.7% and 36.4% for women). The highest percentages of permanent8

employees are in Seoul (91.5% for men, 82.8% for women). The highest percentages who arecompany executives or directors were in Kuala Lumpur (14.4% for men, 14.5% for women).
The highest percentages of self-employed workers are in Jakarta (12.1% for men, 20.5% forwomen). The highest percentages of those who are married were in Shanghai (72.6% for men,
70.7% for women).
For other characteristics, the top-ranking countries differ by gender. The highest percentage
with children is seen in Jakarta for men (51.1%) and in Delhi and Mumbai for women (56.8%).
The highest percentage living with their parents is seen in Delhi and Mumbai for men (52.5%)
and in Seoul for women (45.9%).

V. Results
V.1. Estimation results of the earnings function by gender
Tables 3 and 4 show the estimation results of men’s and women’s earnings functions by thecities in each country. All employment status dummy variables were combined into “Type of
employment” at the bottom of these tables; this includes full-time employee, permanent
employee, and a set of dummy variables of public employee, company executive or director,
self-employed or family employee, with company employee as the reference group. Threedummy variables—marital status, having children, and living with one’s parents—were alsogrouped into “Lifestyle attribute” in these tables.
First, we examine the four categories of focus in this research: postgraduate degree, field ofdegree, experience working abroad, and experience studying abroad.
Table 3 shows that having a postgraduate degree has a positive effect on men’s earnings inboth Shanghai and Seoul. The coefficient is 9,011.89 in Shanghai and 13,000.00 in Seoul. Thesefigures are large relative to the average earnings for men in Shanghai (USD 21,168.91, shownin Table 1) and Seoul (USD 28,767.46).
Regarding field of degree, the dummy variables for social science, natural science, and“other” fields are compared with the reference group of the humanities. In Seoul, a social
science degree is more greatly associated with higher earnings than are degrees in other fields,with natural science degrees ranking second. In Hanoi and Ho Chi Minh City, a social science
degree is also associated with higher earnings, at the 10% level of significance. In contrast, in
Kuala Lumpur, a humanities degree is associated with the highest earnings. These results differ
from those in most of the previous studies analyzing other countries (e.g., García-Arail [2008]
and Lee et al. [2016]), providing evidence of the earnings premium for the fields of science and9businesses.
In Shanghai, experience working abroad is found to have a coefficient of 7798.44, which issignificant at the 5% level. This effect is smaller than the effect of having a postgraduate degree.
Similarly, in Hanoi and Ho Chi Minh City, experience working abroad has the coefficient of3552.93, which is significant at the 5% level; this effect is large, at 70% of men’s average
earnings of USD 5832.04 (Table 1). Only in Seoul is the effect of studying abroad significant
for predicting earnings; there, the coefficient of 9052.85 is significant at the 5% level.
Several other variables are shown to have noteworthy effects on men’s earnings. Holding amanagerial position has a significantly positive effect in all seven countries. Employment inforeign-affiliated companies has a positive impact in Shanghai and Jakarta.
Table 4 shows the results for women’s earnings function. Having a postgraduate degree has
a positive effect on women’s earnings in Shanghai and Bangkok, with coefficients of 9868.97
and 6638.16, respectively. These coefficients are quite large compared with women’s average
earnings in Shanghai (USD 17521.51) and Bangkok (USD 6566.61). Notably, this effect islarger for women than for men in Shanghai and is significant only for women in Bangkok. In
contrast, in Hanoi and Ho Chi Minh City, having a postgraduate degree negatively affects
women’s earnings; significant at the 10% level. However, this effect is likely explained by thesmall sample of female postgraduate workers (4.4%) and should therefore be interpreted more
moderately—as a postgraduate degree having no positive effect on women’s earnings in Hanoi
and Ho Chi Minh City.
Having a natural science degree has a positive impact on women’s earnings in Shanghai,
Delhi and Mumbai, and Seoul. Experience working abroad has no effect in any of the surveyedcountries, but experience studying abroad has a significant positive effect on women’s earnings
in Seoul, Bangkok, and Hanoi and Ho Chi Minh City. For female workers in Bangkok, bothhaving a postgraduate degree and experience studying abroad have positive effects on earnings.
This indicates the importance of additional learning opportunities in this country for higher
earnings among educated female workers.
Several other variables have noteworthy effects on women’s earnings. Years of tenure at thesame company has a positive effect in Shanghai, Delhi and Mumbai, Jakarta, and Kuala
Lumpur; representing four countries. Holding a managerial position also has a significant
positive effect on women’s earnings in the cities of four countries; in this case, Shanghai, Delhi
and Mumbai, Bangkok, and Hanoi and Ho Chi Minh City6. These results differ from those formen. It appears more important for women to continue to work for the same company during
10the early stages of their careers.

V.2. Results of decomposition of gender earnings differentials
In Table 5, Predictions 1 and 2 show men’s and women’ average earnings, respectively. Thesefigures correspond exactly to the figures for earnings presented as descriptive statistics in Tables
1 and 2. The “Difference” column indicates the difference between male and female average
earnings, and another column indicates women’s earnings as a percentage of men’s earnings
within each country. The significance level shows the statistical results of mean comparison
tests (t-tests of equality of means). The earnings differential between men and women is not
significant in India, where the average for women’s earnings is 85.6% of that for men’s earnings.
However, the earnings differentials in all other cities are significant at the 5% or 10% level. In
Bangkok, earnings for women are, on average, 70.7% of those for men. This percentage is thelowest among the nine cities in seven countries. Considering the graph for Bangkok presented
in Figure 1, the right tail of the distribution for men widen the gender gap in this country, evenafter dropping outliers for earnings7.
In the results of the three parts of the decomposition, endowments significantly affect thegender differentials in Shanghai, Seoul, and Hanoi and Ho Chi Minh City, and coefficients(evaluations of endowments) significantly affect the gender differentials in Shanghai, Jakarta,
Kuala Lumpur, and Bangkok at the 5% or 10% significance level. Thus, there is a possibility
that both effects contribute to the gender gap only in Shanghai. The last column of Table 5shows no significant interaction effects; therefore, we specially focus on the endowment andcoefficient effects in Tables 6-1 and 6-2. Those tables show the breakdown details of the two
effects from the total decomposition results. Variables with positive values positively
contributed to the total decomposition results, and variables with negative values reduced them.
Looking at cities where the gender differential in earnings is significantly affected by
endowment factors in Table 6-1, the proportion of workers with a degree in natural science
contributes to the gender differentials in Shanghai and Seoul, at USD 811.01 and USD 1810.49,
respectively. These results reflect the descriptive findings in Tables 1 and 2 that 44.8% of men
and 30.2% of women in Shanghai and 44.0% of men and 21.7% of women in Seoul have adegree in natural science. In the same way, the gender difference in the proportion of workers
holding managerial positions contributes the most to the gender earnings differential in Hanoi
and Ho Chi Minh City. The extent of the contribution is USD 346.76, which reflects thedescriptive finding that 46.0% of male workers and 31.1% of female workers are managers
11(Tables 1 and 2). However, the gender differences in postgraduate degrees, degrees in naturalscience, and managerial positions also significantly contribute to the earnings difference in
Bangkok, but the total value of USD 794.14 is not significant. These results could be explained
as each significant variable contributing to the gender earnings differentials, but, on the whole,being offset by other variables.
For the cities where coefficient effects significantly affect the gender earnings differential(Table 6-2), experience working abroad contributes to the coefficient effects at USD 114.09 in
Jakarta and USD 407.06 in Bangkok. Experience studying abroad also has a significant positiveeffect on the total coefficient effect result in Kuala Lumpur. Holding a managerial position has
a significant impact on the total results, at USD 874.87 in Jakarta and USD 2418.56 in Kuala
Lumpur. However, years of tenure at the same company in Shanghai, Delhi and Mumbai, andJakarta, as well as having a postgraduate degree and experience studying abroad in Bangkok,
have significant negative effects on the earnings differentials. Notably, the coefficient effect ofa postgraduate degree in Bangkok nearly cancels out the positive endowment effect. In Seoul,
having a postgraduate degree possibly effects on the differentials, but the total coefficient effectis not significant.
In sum, one particularly noteworthy result shown in these two tables is that the proportional
differences in study fields between men and women contribute to widening the gender gap in
the cities of three countries (Table 6-1), whereas years spent working at the same company
contribute to reducing the gender gap in the cities of three countries (Table 6-2). However,holding a managerial position is a barrier for women to gain the same level of earnings as theirmale counterparts from the viewpoints of endowment or evaluation components in the cities offour countries (Tables 6-1 and 6-2).
Finally, from the results of the earnings estimation and decomposition analysis, we also findthat the variables other than tenure at the same company that contribute to higher earnings
among women, such as earning a degree in a natural science field or holding a managerial
position, do not contribute to reducing the earnings gap between men and women.

VI. Conclusions
This study explored the determinants of earnings among workers with higher education in nine
main cities in seven Asian countries: Shanghai, Delhi and Mumbai, Jakarta, Seoul, Kuala
Lumpur, Bangkok, Hanoi and Ho Chi Minh City. The survey used for this study was conducted
in 2012 by a Japanese think tank. That survey’s respondents are almost 600 workers aged 20–
1239 years in each country. The sample in this survey can be seen as representing workers withpotential to lead the Asian economy in the large industrial cities, and this study is the firstattempt at a comparative analysis among these cities, using this unique dataset.
The present analysis predicted individual earnings, focusing on the effects of having a
postgraduate degree, field of degree, experience working abroad, and experience studying
abroad, to examine the impact of additional qualifications or experience beyond a university
degree.
First, we found positive effects of having a postgraduate degree on male workers’ earnings
in Shanghai and Seoul. Additionally, holding a social science degree in Seoul or Hanoi and Ho
Chi Minh City and a humanities degree in Kuala Lumpur has a significantly positive effect onmale workers’ earnings. For men in Shanghai and in Hanoi and Ho Chi Minh City, experience
working abroad positively impacts earnings, while experience studying abroad has a positiveeffect in Seoul.
Second, we found that having a postgraduate degree has a positive impact on women’s
earnings in Shanghai and Bangkok. Holding a degree in natural science has a significant
positive effect on earnings for women in Delhi and Mumbai, Shanghai, and Seoul. We could
not confirm that having experience working abroad affected women’s earnings. However,experience studying abroad has a positive effect in Seoul, Bangkok, Hanoi and Ho Chi MinhCity.
Finally, we performed a decomposition analysis of gender earnings differentials. The results
do not detect a gender gap in earnings in Delhi and Mumbai, although there are significant
gender earnings gaps in the cities of the other countries. The decomposition results show thatendowment effects significantly influence the gender earnings differentials in Shanghai, Seoul,and Hanoi and Ho Chi Minh City, whereas coefficient effects significantly influence the genderdifferentials in Shanghai, Jakarta, Kuala Lumpur, and Bangkok.
As for the endowment effects, the proportional difference in having a degree in naturalscience in Shanghai, Seoul, and Bangkok and the proportional difference in holding amanagerial position in Bangkok and in Hanoi and Ho Chi Minh City have positive effects on
the earnings differentials in each country.
Based on the results of the coefficient effects analysis, experience working abroad
contributes to the coefficient effects in Jakarta and Bangkok, and holding a managerial position
significantly affects the total results for coefficient effects in Jakarta and Kuala Lumpur. Yearsof tenure at the same company in Shanghai, Delhi and Mumbai, and Jakarta, and holding a13

postgraduate degree and having experience studying abroad in Bangkok, significantly reduce
the earnings differentials. This means these attributes are more evaluated for women than formen.
In conclusion, the results show that earnings competition among higher-educated workers
has been widening in the main cities of seven Asian countries. Although the extent of this variedamong the examined cities, additional abilities and qualifications may become increasingly
important in the future. In these contexts, gender earnings gaps persist among workers withhigher education. Especially for women, holding a natural science degree and serving in a
managerial position are recognized more productive human resources within the female group(in Table 4), but these attributes possibly contribute to widening the gender earnings gaps
through endowment effects in Shanghai, Seoul, and Bangkok (in Table 6-1). This paradox might
owe to men dominating at the level of proportion or evaluation for these attributes.
The present results should be carefully comprehended as evidence derived from highereducated workers specifically in main Asian cities. Especially, the positive impact of humanities
on men’s earning in Kuala Lumpur, the slightly negative impact of a postgraduate degree on
women’s earnings in Hanoi and Ho Chi Minh City, and a wider gender earnings gap in Bangkok
generally differ from findings in previous studies and reports. Further research is needed toclarify these findings.
Additionally, the sample used in this analysis had 150 respondents for each age and gendercategory in each country, for a total sample size of 600 in each country. The distributions in thesample are therefore not proportional to the real distributions of workers in the examined cities.
Additionally, the sample sizes are insufficiently large to guarantee robust estimation results.
Insufficient information on respondents also restricts more extended analyses, such as
instrumental variable estimations dealing with omitted variables and quantile regressions
investigating distributions. These issues will be explored in future research.

14

Notes
1. UNESCO (2014) reported that, over the past four decades, global higher education
enrollment increased from 32.6 million in 1970 to 182.2 million in 2011, 46% of which
was in the East and South Asian region as of in 2011.
2. The definition of unemployment rate differs across the studied countries. The
information presents here refers to the definition used by the Japan Institute for Labour
Policy and Training (2015).
3. Recent statistical features concerning women’s working conditions are reported. For all
female workers in India, who have a higher unemployment rate than male workers, thegovernment has implemented women’s vocational training to encourage them to workin society (Ministry of Health, Labour and Welfare, 2017). In South Korea, the genderwage difference has generally declined since 2010 and women’s hourly wages were
66.4% of men’s in 2015 (Chan, 2016). In Malaysia, the female labor force participation
rate was 48.8% in 2012, which was lower than that in neighboring countries (The Japan
Institute for Labour Policy and Training, 2013). The female labor participation rate in
Thailand has been among the highest of the Association of Southeast Asian Nations(ASEAN) member countries, and female workers there who give birth generally returnto their workplace (Ministry of Health, Labour and Welfare, 2017).
4. In fact, the earnings-age profile should not gradually decrease for those aged 20–39
years. This is not the case in a normal wage profile.
5. The survey used for this study did not include certain information about the workers,
such as their occupation, industry, or employer size. Years of tenure at the same
company was used as an independent variable because it was found to be more suitable
than worker’s age or working experience years, based on the adjusted R-squared and tstatistic.
6. Unexpectedly, marriage and childbearing had no effect on women’s earnings in any of
the surveyed cities. Because the sample in this study consisted of workers with higher
education and working in urban areas, these results indicate the possibility that womenwith a certain level of ability and qualifications receive relatively sufficient support tocontinue to work, and experience no negative impact on their earnings from thehousehold tasks associated with marriage and having children.
7. Recruit Works Institute (2013) suggested the earnings differences between men andwomen in India and Vietnam were smaller than in other countries; however, the15difference in Thailand was not mentioned as indicating a wide gender gap. This mightreflect different treatment of outliers. That study dropped the top and bottom 2.5% of
earnings distribution in its analysis.
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19

Figure 1. Earnings distributions for men and women in nine main cities in seven Asian countries

Density.00005.0001
00Density.00001 .00002 .00003 .00004
0

20000

80000

60000
40000
Earnings

100000

0

20000

40000

60000
Earnings.00006
100000

0

20000

40000
Earnings

80000

60000

0

.00005

Density.0001 .00015

.0002

20000

60000
40000
Earnings
Men
Women

Hanoi_and_Ho_Chi_Minh_City

30000

0

kernel = epanechnikov, bandwidth = 2.6e+03

kernel = epanechnikov, bandwidth = 3.5e+03

20000
Earnings

Bangkok

Men
Women

Kernel density estimate
kdensity Earnings

10000

40000

30000

0

0
80000

40000
60000
Earnings

kernel = epanechnikov, bandwidth = 6.4e+03

0

20000
Earnings

kernel = epanechnikov, bandwidth = 2.0e+03

Density.00002.00004

Density.00001.00002
0

20000

10000

Men
Women

Kuala_Lumpur

Soeul

0

0

100000kernel = epanechnikov, bandwidth = 2.5e+03

kernel = epanechnikov, bandwidth = 4.3e+03.00003

80000

Men
Women

Men
Women

Density.00002 .00004 .00006 .00008

0

Jakarta.00015

Delhi_and_Mumbai

Density.00001.00002.00003.00004.00005

Shanghai

40000

Men
Women
kernel = epanechnikov, bandwidth = 1.1e+03

20

80000

100000Table 1. Descriptive Statistics for Male Workers

Variable

China

India

Indonesia

South Korea

Malaysia

Thailand

VietnamShanghai

Delhi and Mumbai

Jakarta

Seoul

Kuala Lumpur

Bangkok

Hanoi and Ho Chi
Minh City

Mean
Earnings (in USD)
Postgraduate degree
Social science degree
Natural science degree
Other degree
Experience working abroad
Experience studying abroadYears of tenure
Manager
Experience changing jobs
Full-time employee
Permanent employee
Foreign-affiliated company
Public employee
Company exective/director
Self-employed/family employee
MarriedChildren
Living with parents
Sample size

21168.910
0.099
0.372
0.448
0.103
0.197
0.072
4.511
0.596
0.538
0.978
0.726
0.269
0.063
0.139
(omitted)
0.726
0.507
0.256
223

Ranking
2
3
2
2
7
3
4
4
1
3
1
6
1
5
2
1
2
7

Mean
12982.990
0.400
0.310
0.235
0.380
0.310
0.045
4.260
0.485
0.520
0.855
0.795
0.230
0.040
0.110
0.115
0.615
0.445
0.525
200

Ranking
3
1
4
7
1
1
5
6
2
5
5
3
2
7
3
3
3
3
1

Mean

Ranking6349.266
0.038
0.374
0.363
0.225
0.143
0.088
4.434
0.291
0.527
0.841
0.764
0.104
0.121
0.011
0.121
0.698
0.511
0.264
182

6
5
1
6
4
4
2
5
5
4
7
4
6
3
7
1
2
1
6

21

Mean
28767.460
0.099
0.270
0.440
0.135
0.064
0.213
3.652
0.220
0.404
0.936
0.915
0.014
0.064
0.021
0.043
0.468
0.312
0.340
141

Ranking
1
2
6
3
6
7
1
7
7
7
3
1
7
4
6
6
6
6
3

Mean
10869.630
0.033
0.232
0.420
0.337
0.215
0.083
4.608
0.326
0.619
0.939
0.890
0.199
0.171
0.144
0.050
0.575
0.425
0.271
181

Ranking
4
6
7
4
2
2
3
2
4
1
2
2
3
3
1
5
5
4
5

Mean
9283.308
0.139
0.272
0.367
0.283
0.122
0.028
5.522
0.228
0.539
0.850
0.756
0.133
0.078
0.028
0.133
0.322
0.172
0.406
180

Ranking
5
7
5
5
3
6
7
1
6
2
6
5
5
6
5
2
7
7
2

Mean
5832.040
0.092
0.310
0.454
0.201
0.138
0.029
4.517
0.460
0.454
0.925
0.592
0.178
0.253
0.063
0.052
0.575
0.414
0.333
174

Ranking
7
4
3
1
5
5
6
3
3
6
4
7
4
1
4
4
4
5
4

Table 2. Descriptive Statistics for Female Workers

Variable

China

India

Indonesia

South Korea

Malaysia

Thailand

VietnamShanghai

Delhi and Mumbai

Jakarta

Seoul

Kuala Lumpur

Bangkok

Hanoi and Ho Chi
Minh City

Mean
Earnings (in USD)
Postgraduate degree
Social science degree
Natural science degree
Other degree
Experience working abroad
Experience studying abroadYears of tenure
Manager
Experience changing jobs
Full-time employee
Permanent employee
Foreign-affiliated company
Public employee
Company exective/director
Self-employed/family employee
MarriedChildren
Living with parents
Sample size
Note: USD: United States dollars

17521.510
0.089
0.453
0.302
0.093
0.151
0.036
4.680
0.520
0.564
0.987
0.707
0.364
0.040
0.089
0.009
0.707
0.467
0.347
225

Ranking
2
3
1
2
7
1
7
2
1
3
1
5
1
7
3
7
1
2
6

Mean
11122.140
0.408
0.333
0.305
0.197
0.127
0.042
4.535
0.469
0.432
0.883
0.798
0.197
0.075
0.094
0.085
0.671
0.568
0.441
213

Ranking
3
1
5
1
4
2
6
3
2
7
4
4
2
6
2
4
2
1
2

Mean

Ranking4745.406
0.068
0.415
0.273
0.193
0.080
0.114
4.057
0.284
0.557
0.733
0.705
0.108
0.108
0.028
0.205
0.500
0.313
0.364
176

7
5
4
4
5
4
3
7
4
4
7
6
6
3
5
1
5
5
5

22

Mean
23543.870
0.076
0.261
0.217
0.140
0.032
0.223
4.153
0.178
0.478
0.898
0.828
0.038
0.108
0.013
0.057
0.382
0.185
0.459
157

Ranking
1
2
7
6
6
7
1
6
6
5
3
1
7
3
7
5
6
7
1

Mean
7887.929
0.042
0.313
0.295
0.349
0.102
0.175
4.229
0.253
0.602
0.867
0.801
0.187
0.133
0.145
0.120
0.542
0.446
0.265
166

Ranking
4
7
6
3
1
3
2
5
5
2
6
3
3
2
1
2
4
4
7

Mean
6566.612
0.071
0.429
0.196
0.261
0.076
0.060
4.734
0.158
0.630
0.875
0.821
0.114
0.098
0.027
0.103
0.277
0.174
0.375
184

Ranking
5
4
2
7
2
5
4
1
7
1
5
2
5
5
6
3
7
6
4

Mean
4887.806
0.044
0.428
0.222
0.200
0.072
0.044
4.500
0.311
0.450
0.933
0.544
0.156
0.228
0.033
0.139
0.606
0.456
0.400
180

Ranking
6
6
3
5
3
6
5
4
3
6
2
7
4
1
4
6
3
3
3

Table 3. Estimation Results of the Earnings Function for Men
China

India

Indonesia

South Korea

Malaysia

Thailand

VietnamShanghai

Delhi and Mumbai

Jakarta

Seoul

Kuala Lumpur

Bangkok

Hanoi and Ho Chi
Minh City

9011.891**
2724.278
1578.252
13000.000***-552.557(4001.168)(2141.296)(4515.029)(4759.983)(4060.643)
5734.232-1200.000-976.111
9604.570*-6700.000*
Social science degree(5493.985)(2924.869)(3176.503)(5109.244)(3775.119)5502.276-695.938-740.618
8377.389*-5900.000*
Natural science degree(5147.245)(3293.575)(3114.753)(4979.68)(3529.076)
1745.245
2892.035-727.983
1807.79-5700.000
Other degree(6150.562)(3845.998)(3180.090)(5932.248)(3619.766)
7798.449**
1986.732
1877.896-8000.000
427.247
Experience working abroad(3314.220)(2350.536)(2065.597)(6009.037)(2858.852)
2157.289
1010.600
1125.943
9052.859**
8700.371
Experience studying abroad(4443.955)(5266.363)(3809.222)(2461.507)(4016.344)
208.660-271.937
162.6781053.917*
691.203*
Years of tenure(291.983)(334.603)(160.254)(623.466)(407.659)
5391.039**
5879.370***4224.392***
8769.911*
13000.000***
Manager(2140.523)(2063.515)(2640.264)(1476.055)(4661.302)
4060.756**-149.151-98.078
1876.205
1868.876
Experience changing jobs
(2048.915)(1878.714)(1137.705)(4096.798)(1967.345)
5934.978**531.0215356.315**
1031.734
1662.877
Foreign-affiliated company(2369.036)(2681.947)(2317.014)(4324.263)(2667.730)
7338.186
995.637-1300.000
15000.000
15000.000
Intercept(8335.488)(4461.832)(9413.445)(3287.767)(14000.000)
Yes
Yes
Yes
Yes
Yes
Type of employment
Yes
Yes
Yes
Yes
Yes
Lifestyle attribute
223
200
182
141
181
N
2
0.302
0.141
0.221
0.275
0.292
R
Notes: Robust standard errors are in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

308.076(3036.590)
2809.066(3389.532)
2339.351(2964.947)
2878.978(3676.114)
4935.002(4064.817)-2500.000(3250.378)-308.016(256.398)
8477.620**(3490.379)-1300.000(1929.206)
3710.133(2960.074)
3299.842(3885.142)
Yes
Yes
180
0.173

-559.896(1233.077)
2590.977*(1402.919)
1758.684(1335.872)
1415.033(1698.993)
3552.931**(1414.433)
68.399(1363.787)
131.602(166.486)
1415.737*(718.402)
869.853(740.198)
850.780(1146.759)
7762.502(6614.715)
Yes
Yes
174
0.235

Independent variable
Postgraduate degree

23

Table 4. Estimation Results of the Earnings Function for Women
China

India

Indonesia

South Korea

Malaysia

Thailand

VietnamShanghai

Delhi and Mumbai

Jakarta

Seoul

Kuala Lumpur

Bangkok

Hanoi and Ho Chi
Minh City

9868.972**-437.498
33.617-4300.000-2200.000(4995.168)(1803.944)(2015.460)(4531.231)(2354.726)
3160.3073397.228
1433.444
3698.870-4100.000
Social science degree(2693.009)(2599.459)(1211.692)(3370.990)(5600.420)
5546.998*
3343.702**
940.7228113.129**-3700.000
Natural science degree(2879.766)(1517.711)(1171.723)(3852.830)(5465.831)
5394.440
3776.467*
345.6037218.046-5200.000
Other degree(3973.910)(2094.634)(1094.804)(5160.409)(5388.839)
5501.105130.747443.612-7800.000-1600.000
Experience working abroad(3812.461)(2459.741)(1191.833)(9267.388)(2891.195)
4440.053
4810.136-261.556
5607.397*
1639.703
Experience studying abroad(4137.705)(3059.288)(1338.245)(3304.737)(2263.309)
1126.845***612.965*578.989***765.049762.509***
Years of tenure(422.451)(312.096)(197.576)(496.230)(250.522)
4078.502**
4726.055***
1144.819
3980.985
3060.849
Manager(2047.275)(1660.944)(1215.976)(5180.966)(1873.605)
3460.593-3300.000*
1108.551
71.3653252.916**
Experience changing jobs
(2105.805)(1617.302)(1889.180)(997.820)(2928.635)
12000.000
1246.281
1602.316-4800.000
458.347
Foreign-affiliated company(5539.748)(2064.782)(1471.648)(5826.321)(2227.162)-7700.000
4419.148-694.415
3215.723
9528.351
Intercept(2683.548)(6705.164)(1780.537)(6400.071)(5969.324)
Yes
Yes
Yes
Yes
Yes
Type of employment
Yes
Yes
Yes
Yes
Yes
Lifestyle attribute
225
213
176
157
166
N
2
0.266
0.200
0.216
0.173
0.130
R
Notes: Robust standard errors are in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

6638.164*(3411.526)
1033.277(1741.010)
3179.187(2110.537)
2824.435(1787.836)-414.992(2096.704)
8267.531*(4675.002)-19.932(166.049)
6242.411**(2529.683)-48.411(1101.715)
1466.835(2398.346)
1269.474(2368.737)
Yes
Yes
184
0.239

-2300.000*(1230.053)
177.936(953.843)
441.121(1134.148)
78.574(988.310)
2116.977(1742.575)
2977.485**(1474.938)
126.542(96.566)
2332.607***(785.579)-193.767(592.262)
1063.500(826.367)
1916.614*(1142.587)
Yes
Yes
180
0.185

Independent Variable
Postgraduate degree24Table 5. Results of the Decomposition of Gender Earnings Differentials
Difference% offemale/maleearnings

Endowment

CoefficientInteraction

17521.520***3647.396***

82.77

1716.212**2458.272*-527.087(1159.595)(1081.170)(1585.430)(949.159)(1542.152)(995.648)

12982.990***11122.140***

1860.850-489.668

844.5681505.951(1063.986)(846.156)(1359.429)(839.725)(1545.025)(1207.199)

6349.266***4745.406***

1603.860***-218.607

1289.948*

532.519(593.771)(500.500)(776.573)(441.183)(839.543)(615.750)

28767.460***

23543.870***5223.589***2349.719*

1857.073

1016.797(1705.079)(1348.488)(2173.870)(1481.569)(2584.586)(2245.965)

10869.630***

7887.929***2981.696***147.0662404.497**

430.132(1093.696)(778.334)(1342.377)(601.140)(1460.394)(1072.129)

9283.308***

6566.612***2716.696***794.147

1927.475*-4.920(939.990)(591.194)(1110.447)(576.748)(1257.366)(932.333)

5832.040***4887.806***

944.234**493.200*

343.539

107.495(402.487)(309.143)(507.509)(305.209)(580.482)(457.533)

City

Total
sample size

Male income
prediction

Female income
prediction

Shanghai

448

21168.910***

Delhi and Mumbai
Jakarta
Seoul
Kuala Lumpur
Bangkok
Hnoi and Ho Chi Minh City

413
358
298
347
364
354

85.66
74.73
81.84
72.56
70.73
83.80

Notes: Standard errors are in parentheses. ***, **, and * indicate significance at the 5%, 10%, and 15% levels, respectively.

25

Table 6-1. Results for Endowment Effects
Variable
Postgraduate degree
Social science degree
Natural science degree
Other degree
Experience working abroad
Experience studying abroadYears of tenure
Manager
Experience changing jobs
Foreign-affiliated company
Type of employment
Lifestyle attribute
Total endowment effects

China

India

Indonesia

South Korea

Malaysia

Thailand

VietnamShanghai

Delhi and
MumbaiJakarta

Seoul

Kuala Lumpur

Bangkok

Hanoi and Ho
Chi Minh City

96.378(274.457)-256.414(280.676)
811.016*(524.603)
52.896(157.377)
254.141(237.079)
160.700(215.164)-190.199(351.551)
311.648(250.584)-91.109(173.235)-304.284(243.659)
Yes
Yes
1716.212**(949.159)

3.697(25.536)-79.268(167.011)-234.608(227.200)
690.402(509.659)
23.958(451.616)
13.210(97.633)-168.694(212.092)
73.331(234.536)-288.409(225.926)
40.899(86.51336)
Yes
Yes-489.668(839.725)-0.999(57.440)-58.980(101.922)
84.580(163.984)
11.091(58.985)
28.085(113.042)
6.728(40.853)
218.422(201.496)
8.148(55.504)-32.531(65.833)-5.702(52.620)
Yes
Yes-218.607(441.183)-98.989

19.421(56.282)
333.792(379.410)-459.879(527.817)
64.1040(269.417)-175.893(314.226)-150.567(195.503)
288.852(286.830)
223.303(199.740)
53.266(173.278)
5.568(31.677)
Yes
Yes
147.066(601.140)

452.966**(259.806)-162.354(285.627)
543.687*(374.956)
63.447(139.003)-19.145(96.901)-264.600(192.058)-15.716(132.474)
438.024*(284.790)
4.431(107.097)
28.167(60.934)
Yes
Yes
794.147(576.748)-110.524(97.035)-20.895(106.382)
102.252(229.382)
0.090(3.557)139.104(102.776)-46.772(64.053)2.181(45.299)346.763***(157.242)-0.779(10.606)
24.040(46.372)
Yes
Yes
493.200*(305.209)(187.443)
30.911(192.331)
1810.492**(932.824)-38.801(290.096)-248.996(307.556)-56.993(271.255)-382.818(364.154)
165.267(237.260)-5.241(207.085)
116.289(185.723)
Yes
Yes
2349.719*(1481.569)

Notes: Standard errors are in parentheses. ***, **, and * indicate significance at the 5%, 10%, and 15% levels, respectively.

26

Table 6-2. Results for Coefficients Effects
Variable
Postgraduate degree
Social science degree
Natural science degree
Other degree
Experience working abroad
Experience studying abroadYears of tenure
Manager
Experience changing jobs
Foreign-affiliated company
Intercept
Type of employment
Lifestype attribute
Total coefficient effects

China

India

Indonesia

South Korea

Malaysia

Thailand

VietnamShanghai

Delhi and
MumbaiJakarta

Seoul

Kuala Lumpur

Bangkok

Hanoi and Ho
Chi Minh City

-76.184(439.587)
1166.846(2252.198)-13.515(1529.893)-340.591(605.942)
347.154(587.829)-81.164(241.469)-4297.105**(2320.351)
682.519(1639.778)
338.759(1833.003)
1000.382(1152.825)
15022.260(17771.000)
Yes
Yes
2458.272*(1542.152)

1291.429(1116.264)-1523.069(1620.484)-1232.754(1525.071)-174.395(960.688)
235.265(439.057)-160.543(277.000)-4013.217**(2291.972)
541.462(1292.754)
1349.951(1313.229)-141.037(668.474)-3423.512(9272.962)
Yes
Yes
844.568(1545.025)

105.316(253.535)-999.417(1444.400)-458.547(934.034)-207.397(687.364)
114.090*(193.854)
157.670(292.383)-1688.899**(1025.779)
874.878**(492.518)-671.872(889.324)
405.261*(281.093)-567.396(4727.505)
Yes
Yes
1289.948*(839.543)

1297.358**(679.535)
1542.253(1610.857)
57.228(1298.774)-758.125(1026.538)-6.816(318.325)
768.096(1127.322)
1199.633(3247.482)
854.076(1014.626)
862.184(2154.293)
224.355(564.836)
11727.560(14312.730)
Yes
Yes
1857.073(2584.586)

67.500(282.586)-813.742(3238.724)-647.092(2995.651)-168.147(3572.654)
203.078(381.762)
1233.490**(750.577)-301.546(1833.183)
2418.568***(805.046)-833.759(1707.867)
224.942(616.713)
4990.465(15542.590)
Yes
Yes
2404.497**
1460.394-447.234*(273.244)
762.431(1774.043)-164.315(818.109)14.228(1103.054)
407.064*(280.919)-644.840*(415.101)-1363.702(1498.071)
352.288(456.514)-789.563(1453.011)
256.028(375.918)
2030.368(8807.616)
Yes
Yes
1927.475*(1257.366)

78.509(95.203)1032.245(974.774)
292.791(509.331)
267.291(478.405)
103.707(120.565)-129.292(128.064)
22.771(802.768)-285.248(324.989)
478.629(485.887)-33.089(208.428)
5845.888*(3724.669)
Yes
Yes
343.539(580.482)

Notes: Standard errors are in parentheses. ***, **, and * indicate significance at the 5%, 10%, and 15% levels, respectively.

27