essays on labor and development economics

ESSAYS ON LABOR AND DEVELOPMENT ECONOMICS






by

Voraprapa Nakavachara

________________________________________________________________________




A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ECONOMICS)



December 2007

Copyright 2007 Voraprapa Nakavachara

UMI Number: 3291909
3291909
2008
Copyright 2007 by
Nakavachara, Voraprapa
UMI Microform
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by ProQuest Information and Learning Company.

ii
Acknowledgements


This is the page I have always wanted to write. There are many people whom I
owe my gratitude to.
First, I am indebted to John Strauss for giving me invaluable advice,
for patiently making sure that I do my work carefully, for caring about me, and for
believing in me. I am very fortunate to have him as my advisor.
Second, I am thankful for thoughtful suggestions and constructive comments from
my committee members: W. Bentley MacLeod, John Ham, and Gary Painter. I truly
appreciate their guidance.
Third, I am grateful to my parents for giving me this wonderful life, great
opportunities, as well as support. I would not have made it this far without them.
Fourth, I thank Sutham Saengpratoom, Numkrit Jeraputtiruk, Anon Juntavich,
David Autor, and Jean Roth. I barely know these people in real life yet their
overwhelming generosity contributed a great deal to the accomplishment of this work.
Last, I thank my wonderful friends at the University of Southern California. I am
blessed to have met many big-hearted people and to have become friends with them.
Good friendship makes troubles smaller and makes life more meaningful. Nayoung Lee,
Huseyin Gunay, Echu Liu, Heonjae Song, Serkan Ozbeklik, and Brijesh Pinto, I thank
you.



iii
Table of Contents

Acknowledgements ii

List of Tables v

List of Figures vii

Abstract ix

Chapter One: Superior Female Education: 1
Explaining the Gender Earnings Gap Trend in Thailand
1.1 Introduction 1
1.2 Socio-economic Background 8
1.2.1 Growth, Poverty, and Income Inequality 9
1.2.2 Expansion of Education 13
1.2.3 Gender Earnings Inequality in Neighboring Countries: 17
The Literature
1.3 Data 22
1.4 The Thai Labor Market and Gender Inequality 28
1.5 Parametric Decomposition 65
1.5.1 Blinder-Oaxaca (1973): BO 66
1.5.2 Juhn-Murphy-Pierce (1991): JMP 70
1.5.3 BO Results 74
1.5.4 JMP Results 84
1.6 Nonparametric Decomposition 90
1.6.1 DiNardo-Fortin-Lemieux (1996): DFL 91
1.6.2 DFL Results 101
1.7 Conclusion 110

Chapter Two: Wrongful Discharge Laws and the Unexpected Substitution Effect 113
2.1 Introduction 113

2.2 Previous Literature: The Economics of Employment Law 118
2.2.1 Wrongful Discharge Laws (WDLs) 119
2.2.1.1 Implied Contract 119
2.2.1.2 Good Faith 121
2.2.1.3 Public Policy 124
2.2.2 Employment Consequences of Wrongful Discharge Laws 125
2.3 Theoretical Framework for Wrongful Discharge Laws 128
2.4 Data 132
2.5 Empirical Methodology 145






iv
2.6 Results 148
2.6.1 Good Faith 148
2.6.2 Implied Contract 157
2.6.3 Public Policy 165
2.7 Conclusion 170

Bibliography 172

Appendix 178
Appendix Table A.1 178
Appendix Table A.2 179

v
List of Tables


Table 1.1: Ratio of female to male earned income (selected countries) 3

Table 1.2: Average hours worked per week for male and female workers 28
(wage and salary sector)

Table 1.3: Population and labor force structure in Thailand 33

Table 1.4A: Basic summary statistics of wage and salary workers 45

Table 1.4B: Basic summary statistics of wage and salary male workers 46

Table 1.4C: Basic summary statistics of wage and salary female workers 47

Table 1.5A: Earnings equation (without occupations and industries) 75

Table 1.5B: Earnings equation (with occupations and industries) 77

Table 1.6A: Blinder-Oaxaca (1973) results (without occupations and industries) 78

Table 1.6B: Blinder-Oaxaca (1973) results (with occupations and industries) 83

Table 1.7A: Juhn-Murphy-Pierce (1991) results 85
(without occupations and industries)

Table 1.7B: Juhn-Murphy-Pierce (1991) results 89
(with occupations and industries)

Table 2.1: Adoption dates 127

Table 2.2A: Any-training index 136

Table 2.2B: School-training index 138

Table 2.2C: Formal-training index 140

Table 2.2D: Informal-training index 142

Table 2.3A: Good Faith and employment 149

Table 2.3B: Good Faith and wages 158

Table 2.4A: Implied Contract and employment 160



vi

Table 2.4B: Implied Contract and wages 163

Table 2.5A: Public Policy and employment 166

Table 2.5B: Public Policy and wages 168

vii
List of Figures


Figure 1.1: Gender earnings gap in Thailand (1985-2005) 2

Figure 1.2: Labor force participation in Thailand by gender (1985-2005) 30

Figure 1.3: Female labor force participation by country (2005) 31

Figure 1.4A: Female labor force participation and employment by sector 39
(age:15-24)

Figure 1.4B: Female labor force participation and employment by sector 40
(age:25-34)

Figure 1.4C: Female labor force participation and employment by sector 41
(age:35-44)

Figure 1.4D: Female labor force participation and employment by sector 42
(age:45-54)

Figure 1.4E: Female labor force participation and employment by sector 43
(age:55-64)

Figure 1.5: Earnings of male and female workers (1985-2005) 50

Figure 1.6: Hourly wages of male and female workers (1985-2005) 50

Figure 1.7: Gender (hourly) wage gap in Thailand (1985-2005) 52

Figure 1.8: Gender earnings gap by percentile 52

Figure 1.9A: Earnings of male and female workers and gender earnings gap 53
(age: 15-40)

Figure 1.9B: Earnings of male and female workers and gender earnings gap 54
(age: 41-65)

Figure 1.10A: Earnings density estimation for male workers (1985-2005) 57

Figure 1.10B: Earnings density estimation for female workers (1985-2005) 58

Figure 1.11: Earnings density comparison (1985 VS 1995 VS 2005) 59

Figure 1.12: Earnings density comparison (male VS female) 61



viii

Figure 1.13: Hourly wage density comparison (male VS female) 62

Figure 1.14A: Earnings density comparison (male VS female, age: 15-40) 63

Figure 1.14B: Earnings density comparison (male VS female, age: 41-65) 64

Figure 1.15: Relationship between Blinder-Oaxaca (1973) and DiNardo-Fortin- 94
Lemieux (1996) when male wage structure is used as reference wage structure

Figure 1.16: Relationship between Blinder-Oaxaca (1973) and DiNardo-Fortin- 96
Lemieux (1996) when pooled wage structure is used as reference wage structure

Figure 1.17A: Modified DiNardo-Fortin-Lemieux (1996) results 102
(without occupations and industries)

Figure 1.17B: Modified DiNardo-Fortin-Lemieux (1996) results 107
(with occupations and industries)

Figure 2.1: Firing costs across economies (2007) 114

Figure 2.2: Firing costs across OECD countries (2007) 115

Figure 2.3: Number of states adopting Wrongful Discharge Laws 122

Figure 2.4: Pattern of adoption during 1983-1994 123

Figure 2.5: Theoretical framework 130

Figure 2.6: Employment per population for high-skilled and low-skilled labor 153
(categorized using any-training index) in states adopting the Good Faith exception





ix
Abstract

My dissertation consists of two essays on labor and development economics. The
first essay seeks to identify the main factors that contributed to the decline in gender
earnings gap in Thailand’s wage and salary sector from 1985-2005. Two parametric
methodologies, Neumark’s version of the Blinder-Oaxaca method and the Juhn-Murphy-
Pierce method, are implemented in order to decompose gender earnings gap at a point in
time and across time period. I also make a methodological contribution by proposing a
way to modify the DiNardo-Fortin-Lemieux nonparametric decomposition method so
that its results are comparable to those from Neumark’s version of the Blinder-Oaxaca
method. The key findings of this essay are as follows. First, I find that increases in female
education and changes in unobserved factors, which were concurrent with modernization,
were the main sources of the decline in gender earnings gap. Second, over time,
improvements in the education of females in this sector surpassed that of males.
However, the superior education of females did not result in higher female earnings
because of the overwhelming effect of the unexplained factors that supported higher male
earnings. Finally, the nonparametric investigation corroborated the results from the
parametric methodologies.
The second essay investigates how the Wrongful Discharge Laws (WDLs),
imposed during the 1970s and 1980s, affect workers in the United States.
Most
economists conjecture that WDLs increase firing costs for firms. In terms of employment,
the literature found a negative or at best zero impact. In terms of wages, most papers
found no impact. Thus the laws seemed to adversely affect an “average” worker. These



x
papers implicitly assumed that labor force was homogeneous. They did not recognize the
fact that labor can be heterogeneous and that firms may treat different types of labor as
different forms of input. My study attempts to overcome this limitation. I treat labor as
heterogeneous (high-skilled and low-skilled) thus allowing the laws to affect firms’

decisions regarding not only the quantity of labor input but also the combination of
different types of labor input. The key finding of this essay is that WDLs are associated
with increases in employment of high-skilled labor, a result unacknowledged in early
studies. WDLs, however, adversely affect employment of low-skilled labor, a result
consistent with the literature.

Chapter One

Superior Female Education:
Explaining the Gender Earnings Gap Trend in Thailand

1.1 Introduction
It is well-known and widely documented that Thailand has experienced a
remarkable increase in real income per capita during the past two decades. Regardless of
some setbacks during the late 1990s, from 1985-2005 the real per capita income more
than doubled.
1
Along with notable income growth, an impressive 35.1 percentage-point
reduction in poverty incidence was observed.
2
However, together with impressive socio-
economic development, many authors also reported an increase in overall income
inequality throughout the mid 1990s (Deolalikar, 2002; Motonishi, 2003; Warr, 2004;
Jeong, 2007). What is not documented in the literature is that, during the same time
period, the gender earnings inequality declined steeply regardless of the concurrent
increase in overall income inequality. Figure 1.1 shows that in 1985 an average male
worker earned 33.96% higher than an average female worker, whereas in 2005 an
average male worker earned only 8.98% higher than an average female worker.
The objective of this essay is to examine the decline in gender earnings inequality
in Thailand’s wage and salary sector from 1985-2005. Specifically, this paper seeks to

identify the main factors that contributed to the closing of this gender earnings gap,

1
Per capita GDP and CPI data from the bank of Thailand.
2
According to the headcount measure from Warr’s (2004) Table 1 (p. 4), in 1986, 44.9% of the population
were considered poor, however, in 2002, only 9.8% of the population were considered poor.
1

Source: author’s calculation from the Thai Labor Force Survey (Quarter 3)

Figure 1.1: Gender earnings gap in Thailand (1985-2005)

exploring whether these contributing factors were related to Thailand’s rapid
modernization and economic development during this time period.
The relationship between gender inequality and economic development has been a
controversial subject of interest in the literature. Xin Meng (1996) showed, using cross-
country data for selected Asian economies, that economic development and female
economic status, such as relative earnings of females compared to males did not have any
significant relationship. Specifically, Meng pointed out how economic inequality
according to gender was worse in richer countries like Japan and Korea than in poorer
countries. She concluded that the problem of gender inequality tended to stem from
social, political, and cultural structures rather than from economic development. Table
1.1 examines the ratio of female to male earnings for a broader range of countries. The
evidence seems to support the notion that economic prosperity cannot explain relative
2
Table 1.1: Ratio of female to male earned income* (selected countries)

Country F/M
Sweden 0.81

Norway 0.75
Cambodia 0.74
Denmark 0.73
Finland 0.71
Vietnam 0.71
Ghana 0.71
Australia 0.70
United Kingdom 0.65
Romania 0.65
France 0.64
Israel 0.64
Hungary 0.64
China 0.64
Canada 0.63
United States 0.62
Switzerland 0.61
Philippines 0.60
Ethiopia 0.60
Portugal 0.59
Poland 0.59
Thailand 0.59
Germany 0.58
Brazil 0.57
Greece 0.55
Argentina 0.53
Ukraine 0.53
Lao People's Dem. Rep. 0.52
Singapore 0.51
Czech Republic 0.51
Spain 0.50

Hong Kong, China (SAR) 0.49
Italy 0.46
Korea, Rep. of 0.46
Bangladesh 0.46
Indonesia 0.45
South Africa 0.45
Japan 0.44
Austria 0.44
Sri Lanka 0.42
Mexico 0.39
Iran, Islamic Rep. of 0.38
Kuwait 0.37
Malaysia 0.36
Turkey 0.35
India 0.31
Jordan 0.30
Pakistan 0.29
United Arab Emirates 0.24
Egypt 0.23
Saudi Arabia 0.15
Source: UNDP Human Development Report 2006

*Estimates are based on data for the most recent year available during 1991-2003

3
earnings of females to males. Although we can see from the table that wealthy
Scandinavian countries rank high on the list and that most Islamic countries rank low on
the list, we cannot draw useful conclusions regarding the rest of the countries. For
example, poorer countries like Cambodia, Vietnam, and Ghana rank very high in terms of
relative earnings of females to males. However, for richer countries like Italy, Korea,

Japan, and Austria, an average woman barely earns half of what an average man does.
Thus, within this context, income per capita and gender inequality do not seem to be
correlated.
However, within the boundaries of a national economy, growth and gender
equality have been seen as somehow positively related. Either growth leads to gender
equality, gender equality leads to growth, or both occur concurrently (United Nations
Development Programme [UNDP], 1995; World Bank, 2005). Growth can bring
prosperity to a country, it can create economic opportunities for women in terms of jobs
and education, and it can lead to women having more bargaining power within and
outside the households. Thus, women are able to raise social awareness about how they
should be treated as equals to men. The literature also argues that gender inequality can
exacerbate social, political, and cultural conflicts and thus obstruct economic
development, depressing the overall well-being of the population. Thus, efforts have been
made on the part of social and political organizations to raise awareness about the
importance of promoting gender equality.
The above evidence suggests that, in order to examine the issue of gender
inequality, one needs to look past the cross-country framework and investigate each
economy in and of itself. Gender status is deeply rooted in the socio-economic, political,
4
and cultural matrices of individual countries. Each country is unique. Thus, the results of
gender analysis are specific to specific countries.
Thailand is the country of interest in this paper. It is a good subject for case study
since it is a developing East Asian country
3
that has recently undergone modernization. In
the past, due to traditional beliefs, Thailand had a male-dominated social structure. The
unequal status of males and females, in terms of access to education and decent job
opportunities, could be observed. A gender gap in school enrollment was evident. A gap
in gender earnings was also apparent. However, many of these inequalities have faded
with the advent of modernization in Thailand. The roles of women and social attitudes

towards them have changed. The gender gap in the schooling of boys and girls has
virtually closed (Knodel, 1997). Also, as mentioned earlier, from 1985-2005, the gender
earnings gap, which was once considerable, has declined significantly. This paper will
analyze Thailand in terms of the relationship between factors of modernization and
gender earnings inequality.
In order to investigate the issue in question, the following methodologies are
implemented. First, Neumark’s (1988) version of the well-known parametric
decomposition proposed by Blinder (1973) and Oaxaca (1973) (hereafter, BO) is applied.
The BO decomposition helps identify, at any point in time, how much of the mean
earnings gap is caused by the differences in the observable characteristics of the two
genders (endowment gap) and by the differences in the pay structures faced by the two

3
According to the World Bank’s website, Thailand is categorized as a country residing in East Asia and
Pacific region. Other countries categorized to be in this region are Cambodia, China, Fiji, Indonesia,
Kiribati, Korea, Lao PDR, Malaysia, Marshall Islands, FS Micronesia, Mongolia, Palau, Papua New
Guinea, the Philippines, Samoa, Solomon Islands, Timor-Leste, Tonga, Vanuatu, and Vietnam.
5
genders (residual gap). Second, the Juhn, Murphy, and Pierce (1991) (hereafter, JMP)
decomposition is utilized to analyze the change in the earnings gap across time
parametrically. Over a period of time, the JMP method can distinguish whether the
increase or the decline in the overall gender earnings gap is due to [1] changes in the gap
of the observable characteristics between genders, [2] changes in the gap of the
unobservable characteristics between genders, [3] changes in the market returns to
observable characteristics, or [4] changes in the market returns to unobservable
characteristics. In this paper, I sometimes refer to the BO method as the time-point
analysis and the JMP method as the across-time analysis.
4
Third, a modified version of
the nonparametric decomposition proposed by DiNardo, Fortin, and Lemieux (1996)

(hereafter, DFL) is implemented. The DFL approach provides a full visualization of how
the differences in the entire earnings distributions of males and females can be
decomposed into two parts. The first part reflects the contributions of the difference in
the observable characteristics, while the second part reflects contributions of the different
pay mechanisms faced by males and females. I modify the standard DFL method to allow
the use of a more general form of the reference wage structure. This modification is
intended to make the DFL analysis comparable to Neumark’s (1988) version of the BO
analysis.
Although the issue of gender inequality has been widely discussed in
industrialized countries, rigorous empirical work using micro-datasets has been only
partially implemented in developing countries. In Thailand, hardly any papers have done


4
The BO method analyzes the earnings gap at a point in time, whereas the JMP method analyzes the
earnings gap across two time points. (Zveglich, Rodgers & Rodgers, 1997 referred to the BO method as the
level analysis and the JMP method as the trend analysis.)
6
rigorous empirical analyses regarding gender issues. This paper attempts to satisfy this
need by utilizing the Thai Labor Force Survey (Thai LFS), a large national micro-dataset
on demographic status and labor earnings of Thai workers, to investigate intensively
gender inequality in Thailand. In addition to applying existing methodologies to Thai
LFS data, I also make a methodological contribution by proposing a way to modify the
DFL method so that the results are comparable to those from Neumark’s (1988) version
of the BO method.
The key findings of the paper are as follows. First, I find that increases in female
education and reductions in the residual gap (difference in the unobservable
characteristics), which were concurrent with modernization, were the main sources of the
decline in male-female gender earnings inequality in Thailand’s wage and salary sector
from 1985-2005. Second, over time, improvements in the education of female workers in

this sector surpassed that of male workers. However, the superior education of females
did not result in higher female earnings because of the overwhelming effect of the
unexplained attributes that supported higher male earnings. Finally, when the analysis
was extended to account for the entire earnings distributions instead of just the mean
earnings, the nonparametric investigation (DFL) corroborated the results from the
parametric methodologies (BO and JMP).
The structure of the paper is organized as follows. Section 1.2 gives background
information regarding growth, poverty, and income inequality in Thailand. It also
discusses how education and gender gap in schooling have evolved during the economic
transition. It then touches upon gender inequality situations in other countries in the
region. Section 1.3 describes the Thai Labor Force Survey (Thai LFS), which is the main
7
dataset used in this study. Section 1.4 examines the Thai labor market, the gender
earnings inequality trends, and the changes in the earnings distributions of males and
females over the period of study (1985-2005). Section 1.5 describes and implements the
parametric methodologies (BO and JMP). Section 1.6 explains the relationship between
the parametric and the nonparametric decomposition methodologies (BO and DFL). The
section introduces my proposed modification to the standard DFL model. The modified
model is implemented and its results are discussed. Section 1.7 concludes the paper.
1.2 Socio-economic Background
In order to explore extensively the topic of gender earnings inequality in
Thailand, it is vital to comprehend the socio-economic background of Thailand and its
relationship to the issue in question. This section provides insights, regarding the Thai
economy. It elaborates the literature on GDP growth, poverty, and income inequality as
they occurred during periods of “miracle” growth, financial crisis, and economic
recovery. It then explores the roles of education and how education has expanded during
Thailand’s modernization. Education for women and the closing of the gender gap in
schooling in Thailand will be discussed. Finally, this section examines the existence and
the evolution of gender earnings inequality in neighboring countries. An elaboration of
Thailand’s labor market and a discussion of gender inequality in Thailand deserve a

separate section. These topics will be investigated in Section 1.4 after the main data
source (Thai Labor Force Survey) is discussed in Section 1.3.
8
1.2.1 Growth, Poverty, and Income Inequality
As recently as a few decades ago, agriculture was the most crucial component of
the Thai economy. A majority of labor was employed in this sector. The share of
agricultural employment was as high as 71% in 1980. However, this phenomenon has
changed dramatically over the past few decades. A majority of the labor force moved
towards manufacturing and service sectors, leaving only 39% employed in the
agricultural sector as of 2005.
5

Such a transition from agricultural employment to non-agricultural employment
was documented as the main source of poverty reduction during the “miracle” era prior to
the Asian financial crisis in 1997 (Jeong, 2007). Manufacturers benefited from the
abundance of cheap and low-skilled labor that had been transferred from the agricultural
sector. This inexpensive labor allowed the manufacturers to produce at low costs, giving
them an advantage in the export market. The growth in exports of labor-intensive
manufactured goods (footwear, textiles and garments) was considered one of the main
sources of overall growth for the Thai economy during that time. GDP growth was
remarkably high, averaging 8.8% per year during 1985-1996.
Many authors reported, using quantitative analyses, a positive relationship
between the rate of GDP growth and the rate of poverty reduction in Thailand during this
miraculous period. According to the headcount index of poverty measure, the proportion
of poor people in Thailand was 35.5% in 1981, declining significantly to 11.4% in 1996
(Warr, 2004). It can also be confirmed by any measure of poverty within the Foster-

5
The National Statistical Office’s calculation based on the Thai Labor Force Survey data.
9

Greer-Thorbecke family that the poverty reduction during this time period was robust
(Deolalikar, 2002; Jeong, 2007).
In spite of this tremendous GDP growth and remarkable poverty reduction,
income inequality among Thai households worsened. It has been pointed out that this
improvement in income and reduction in poverty occurred unevenly across the various
regions within Thailand (Deolalikar, 2002). Not surprisingly, the richest regions, such as
the Bangkok metropolitan area, experienced the highest reduction in poverty, while the
poorest regions, such as the Northeastern area, experienced the lowest reduction in
poverty. The decade of the 1990s also marked the rapid rise of the affluent middle class
population in Bangkok. These middle class people were not necessarily the elites but
were educated individuals from a variety of different socio-economic backgrounds
(Funatsu & Kagoya, 2003). These people gained their class status from the prestige of
their careers, from their growing wealth, and from their political connections. Their
knowledge and abilities allowed them to benefit a great deal from the growing economy.
Thus, they were the people in Bangkok who became wealthier during Thailand’s
industrialization.
This uneven development across regions, although crucial, was, however, not the
main source of Thailand’s increasing overall income inequality. According to Matonishi
(2003), the stimulus that underlay this surge in inequality stemmed from within each
region. Looking deeply into what economic factors actually caused the rise in inequality,
Jeong (2007) argued that the expansion of individuals’ access to credit and the increase in
education levels acquired by household heads were the main sources of this inequality.
10
After a long period of sustained growth, the Thai economy collapsed during mid-
1997. Many factors, such as flawed monetary policies, a lack of appropriate supervision
of financial institutions on the part of the central bank, reckless borrowing and investing
by private investors, and overconfident behavior by other participants in the market,
contributed to Thailand’s susceptibility to the financial crisis (Warr, 1999; Lauridsen,
1998; Tsurumi, 2000; Jansen, 2001). Negative export growth, observed in late 1996, also
contributed to the crisis.

Considering that Thailand’s previous “miracle” growth was largely due to
exports, it is not surprising that the negative export growth rate in 1996, which followed
years of positive and increasing export growth rates, signaled flaws in the economy.
6

Investors began to question the performance of the economy, leading to suspensions of
investment and speculation of a devaluation of the local currency. These speculative
attacks against the local currency in 1997 were often cited as the major cause of the
devastation of the Thai economy.
Starting at the end of 1995, one element that caused the slowdown of exports
appeared to be the appreciation of the Thai baht relative to the Japanese yen. At the time,
the Thai baht was tightly pegged to the US dollar. Thus, when the US dollar appreciated
against the Japanese yen, so did the Thai baht. This appreciation of the local currency
was detrimental to the export industry, since Japan was one of Thailand’s major
importers. Another important factor that caused Thai exports to lose competitiveness was
the concurring increase in the real wage rates of workers. Warr (1999) reported a
significant rise of real wages in the labor-intensive exporting sector during the 1990s. He

6
Warr (1999) argued that the negative export growth did not cause the crisis but triggered it.
11
also argued that Thailand used to maintain competitiveness in this market due to the
abundance of cheap unskilled labor. However, as the labor-intensive exporting sector
grew, cheap labor became scarce.
Thailand was the first country in East Asia to undergo the financial crisis. A large
number of businesses went bankrupt, the stock market crashed, several financial
institutions were closed, and many workers were laid off. The average GDP growth
during 1997-1998 dropped to -6%. The literature reported, however, only a moderate rise
in poverty incidence. Income inequality seemed to be stable regardless of the occurrence
of crisis.

Despite the severity of the financial crisis, Thailand managed to reform its
economy and recover from the financial turmoil. At the time of this writing, a decade has
passed since the crisis. The rate of recovery has been moderate yet steady. The economy
is generally considered to be in good shape. GDP growth has recovered and has remained
quite stable with an average annual growth rate of 4.9% (1999-2005).
7
Poverty incidence,
although slightly increased in the wake of the crisis, has decreased to a level comparable
to before the crisis and has continued to decline as of 2004. Likely, given the direction of
the trends, the level of poverty would have decreased to an even greater degree if there
had been no crisis. The levels of income inequality in Thailand have remained close to
those of the early 1990s (World Bank, 2006b).

7
Data from the Bank of Thailand.
12
1.2.2 Expansion of Education
It is indisputable that economic advancement and educational escalation are
interrelated. Once an economy has reached a certain benchmark of development, the
public, along with the policy makers, naturally turn their attention to the agenda of
promoting higher education. Conversely, expansion of education is also seen as a major
factor stimulating growth and thus generating economic advancement. The economy
benefits from better-educated workers since their superior skills and ability to adopt
newer technology allow them to excel with higher productivity. These educated workers
are also able to contribute more to the accumulation of human capital, leading to the
generation of even more able younger cohorts. The positive impact of education on
national economic development has been widely discussed in the literature. The benefits
of educating females, although not as elaborately examined, are of no less importance.
Besides the typical market benefits, educating females can also yield other positive
externalities such as enhancing the gains that result from educating males. However,

discrepancies in the schooling of boys and girls are still observed across the spectrum of
developing countries (Hill & King, 1991). In this section, I will explore the literature that
touches upon issues of female education and gender disparities in schooling. In East
Asian countries, traditional beliefs regarding gender roles are often to blame for such
disparities. However, this division in gender roles has abraded during modernization of
East Asia. As the country of interest, Thailand will be investigated in these respects. The
topics to be explored include the expansion of education, the closing of the gender gap in
schooling, and the ways in which the traditional views regarding gender roles have
altered during the transition periods of modernization.
13
The literature has emphasized how educating females can be beneficial.
Considering the labor market, Schultz (1991) demonstrated that in many East Asian
developing countries, such as Indonesia, Korea, Taiwan, and Thailand, the monetary
returns to female education were in fact higher than those for male education.
8
Besides
these superior market benefits, Hill and King (1991) demonstrated that educating females
also resulted in reduced fertility, better health, and improved living conditions for the
populace. These non-market benefits, although not measurable in terms of output or
income, have some positive impacts on other participants in the labor markets that allow
these participants to operate in a more efficient manner. Thus, investment in female
education has been empirically shown to be worthwhile, regardless of the women’s
decision to enter the labor market.
However, Hill and King (1991) observed a significant amount of discrepancies in
educational attainments for boys and girls in developing countries during 1960-1988. The
evidence pointed to how each of these countries failed to recognize the importance of
educating females and therefore under-invested in their education. It is interesting to look
into the causes of the underinvestment. Generally, decisions to invest in children’s
education belong to the parents. The literature pointed out how various factors could
hinder parents’ decisions to adequately invest in the education of girls. Most of the costs

of educating children were provided privately by the family in the form of tuition and
forgone labor. These costs could be easily measured in monetary terms. On the other
hand, the benefits of educating girls were mostly in the form of public benefits that might


8
The results from analyzing the Socio-economic Survey dataset (1976, 1981, and 1986) were also shown
to be robust for Thailand when the selection for participation in the labor force has been accurately adjusted
for.
14