essays on labor and development economics

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 Tab

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

3291909 2008

Copyright 2007 by Nakavachara, Voraprapa

UMI Microform Copyright

All rights reserved This microform edition is protected against unauthorized copying under Title 17, United States Code.

ProQuest Information and Learning Company

300 North Zeeb Road P.O Box 1346 Ann Arbor, MI 48106-1346 All rights reserved.

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

Explaining the Gender Earnings Gap Trend in Thailand

2.2 Previous Literature: The Economics of Employment Law 118

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

(without occupations and industries)

(with occupations and industries)

vi

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 1.9A: 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 2.2: Firing costs across OECD countries (2007) 115 Figure 2.3: Number of states adopting Wrongful Discharge Laws 122

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 economic development, many authors also reported an increase in overall income

socio-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,

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

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

Sweden 0.81 Norway 0.75 Cambodia 0.74 Denmark 0.73 Finland 0.71 Vietnam 0.71 Ghana 0.71 Australia 0.70

Romania 0.65 France 0.64 Israel 0.64 Hungary 0.64 China 0.64 Canada 0.63

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

Japan 0.44 Austria 0.44

Mexico 0.39

Kuwait 0.37 Malaysia 0.36 Turkey 0.35 India 0.31 Jordan 0.30 Pakistan 0.29

Egypt 0.23

Source: UNDP Human Development Report 2006

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

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,

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 country3

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

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.)

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

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

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

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

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

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.6Investors 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

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

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

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

be non-pecuniary The benefit-cost analysis, according to this scenario, would make investment in female education costly for families (Hill & King, 1991) Future job

opportunities and the expected income of females also play a role The evidence is clear (see Table 1.1) that in most economies, men on average earned a higher income than women in the labor market There are two ways to explain this fact One way is to

understand how the foreseen disparities in the labor market, such as females earning less than males, or females clustering in lower-paying industries, influence parents to invest more in the education of boys rather than girls Parents may also be more eager to

persuade boys, rather than girls, to be diligent in their studies and to major in subjects that will lead them to respectable careers Thus, future expectations influence parents’

decisions regarding educational investment in their children A second way to explain this gender inequality is to understand how traditional beliefs, such as those in East Asian cultures, push men and women into assuming different roles and responsibilities Boys are to become income earners and girls are to become care-takers When boys become adults, they earn income to support the family, whereas girls may get married and

become a member of another family Thus, parents usually prefer sending boys to school and encourage them to put a significant amount of effort into their studies These

behaviors result in men having better opportunities and higher income than women when they enter the labor market These arguments show how traditional perceptions regarding roles of males and females in society, particularly in East Asian countries, prevent girls from receiving adequate investment in their education

East Asian countries, although very different from one another in their historical foundations, share a similar ground in terms of gender roles The extent of gender

segregation, however, differs across countries According to Cameron, Dowling and Worswick (2001), this segregation of gender roles is considered to be moderate in

Thailand compared to other countries in the region During the “miracle” boom, like other East Asian countries, Thailand went through modernization and the level of

education within the population moderately increased In Thailand, achieving higher education not only yields higher economic gain but also allows the individuals to attain a higher social status (Knodel, 1997) Thus, education has been a subject matter that has received public attention The Thai government and related organizations have made significant attempts to encourage higher education for the country’s populace

According to Knodel (1997) and Hawley (2004), the Thai government imposed compulsory education of 6 school years in 1978 I believe that they might be referring to the Primary Education Act (No 5) B.E 2521 which required children aged 8 years old to enroll in primary education until they turned 15 or until they completed 6th grade

(primary education) In 1999, the National Education Act B.E 2542 was imposed The act mandated children aged 7 years old to enroll in primary and secondary education until they turned 16 or until they completed 9th grade (lower secondary education) According

to Hawley (2004), the average number of years of education achieved by members of the population has increased rather significantly during Thailand’s modernization In the late 1980s, the country reached near universal primary education (6th grade) Similar to other East Asian countries, Thailand used to harbor the traditional preference of sending boys

to school However, as modernization occurred, this traditional view eroded According

to Knodel (1997), more and more Thai parents have expressed indifference as to whether

to send boys or girls to school These parents revealed that with limited resources in the family, the most intelligent children should be allowed to pursue further schooling, regardless of their gender Asian Development Bank [ADB] (1998) also documented the attempts of the Thai government and of many other non-profit agencies to launch various programs to support girls to pursue higher education The purpose of these programs was

to prevent girls from entering into prostitution when they grew up Accordingly, the gender gap in the education of boys and girls, which used to be evident, has been seen to

be narrowing as a result of modernization (ADB, 1998; Schultz, 1991) In fact, Knodel (1997), using the 1990 Thai Census data, documented the closing of the gender gap in educational attainment for the primary and secondary levels

1.2.3 Gender Earnings Inequality in the Neighboring Countries: The

Literature

East Asian countries are economically and culturally interrelated In order to understand where Thailand stands in terms of gender inequality issues, it is interesting to explore the extent of gender inequality in neighboring countries Unlike in developed Western countries, the literature regarding gender inequality in East Asia has yet to emerge The difficulty of gaining access to rigorous datasets for some East Asian

countries has been the major hindrance for scholars studying the subject matter Only a few studies have managed to analyze the gender earnings gap using available datasets Most of the analyses were done parametrically The East Asian countries that will be discussed here are Indonesia, Japan, Taiwan, Korea, China, and Vietnam

Indonesia’s miracle growth, like that of Thailand, occurred in conjunction with a massive transition of the workforce from the agricultural sector to the formal wage and salary sector An increase in real wages, a reduction in poverty, and an increase in

education levels occurred along with this impressive growth Smith, Thomas,

Frankenberg, Beegle, and Teruel (2002) documented a doubling of female real hourly earnings and a 50% increase in male real hourly earnings during 1987-1997 According

to Thomas, Beegle, and Frankenberg (2003), the 1997 crisis did not affect employment so much as it caused a large drop in real earnings As for the matter of male-female

inequality in the labor market, Pirmana (2006), using the SAKERNAS data (Survei Tenaga Kerja Nasional or the Indonesian Labor Force Survey), reported a slight decline

in the gender earnings gap during 1996-2004 More specifically, in 1996, the average male earned 32.64% higher than the average female However, in 2004, the average male earned only 23.34% higher than the average female The World Bank (2006a)

documented the Indonesian government’s imposition of labor laws to enforce equal pay for equal work However, violations of these laws were still reported to be prevalent The article also reported a narrowing of the gender gap in school enrollment Using the

SUSENAS 2002 data (Survei Sosial Ekonomi Nasional or the Indonesian National Economic Household Survey), World Bank (2006a) found that at the primary and lower secondary levels, the number of girls enrolled was greater than the number of boys

Socio-enrolled However, as reported by Pirmana (2006), the gender earnings gap still existed Pirmana’s decomposition showed that the majority of the gender earnings gap was

accounted for, not by the observable characteristic differences, but by differences in how the skills of males and females were compensated

In Japan, labor laws prohibit gender discrimination in the labor market However, evidence of males earning more than females was prevalent (Daly, Kawaguchi, Meng & Mumford, 2006) Kawaguchi (2004) documented a narrowing of the gender earnings gap during 1990-2000 In 1990, the average male earned approximately 48.6% higher than the average female The gap decreased to 39.2% in 2000 The decomposition results showed that the reduction of the returns to job tenure was one of the main contributors to the decline in the gender earnings gap Generally the job tenure of men was longer than that of women Thus, the lower returns to job tenure reduced the advantage that men had and narrowed the gap The author reasoned that the transition from a seniority-based to a result-based compensation system in Japan during the 1990s was the main cause of the decline in the returns to job tenure

Unlike other countries previously discussed, Taiwan experienced a stagnant gender earnings gap during 1978-1992 Zveglich, Rogers, and Rogers (1997), using Taiwan’s Manpower Utilization Survey, reported the total earning gaps to have slightly fluctuated around 0.43 log-points throughout 1978-1992 That is, the average male earned 43% higher than the average female According to the time-point decomposition analysis, the authors reported that about half of the gap was explainable by the observable

characteristic differences between the genders Estimates of the contribution of the

explained gap (62.6% in 1978 and 42.3% in 1992) were close to the estimates given by Gannicott (1986) Besides a time-point decomposition analysis, Zveglich, Rogers, and Rogers (1997) also carried out an across-time decomposition analysis They observed the improvement of Taiwanese female characteristics, especially in terms of education and experience, throughout the period of study However, the residual gap was also shown to

be growing, and its growth rate was substantial enough to offset totally the improvement

in female characteristics The counteraction of the two opposing forces resulted in a stagnant male-female earnings gap during the study period

Similar to Taiwan, Korea also experienced a stagnant gender earnings gap

However, Rodgers (1998), using Korea’s Occupational Wage Survey (OWS),

documented that the rigidity of Korea’s gender earnings gap lasted only until 1983 After

1983, the gap began to narrow The average male earned 75% higher than the average female in 1983, but the average male earned only 55.7% higher than the average female

in 1992 At any given point in time within the study period, the decomposition showed that the majority of the gap could be explained by the observed differences between genders (ranging from 66.8% to 83.8%) Across time, females’ observed characteristics were initially inferior to those of males’, however, they improved significantly

(especially in terms of education) during 1983-1992 These improvements were the major source of the narrowing of the total earnings gap from 1983-1992 Like Taiwan, Korea also experienced a growing residual earnings gap during this period However, the

improvement in observed characteristics of females during 1983-1992 was large enough

to outweigh the increase in the unobserved differences between genders, resulting in an overall decline in the gap

Since the late 1970s, China, the most populous country in the world, has been undergoing a process of major reformation from a centralized economy to a market economy According to Liu, Meng, and Zhang (2000), China’s major economic reforms started in 1978 These reforms allowed most firms to gain some flexibility regarding the production process in terms of output and technology It was not until 1992, however,

when an updated agenda was ratified, that employers were given more flexibility in dealing with workers in terms of pay, employment, and promotions Gustafsson and Li (2000), using data from the Urban Household Income Survey, documented a slight increase in the gender earnings gap during 1988-1995 The gender gap, indicating how much the average male earned compared to the average female, stood at 15.6% in 1988 and 17.5% in 1995 For 1988-1995, the time-point decomposition analysis showed that slightly more than half of the gender gap was accounted for by differences in the market returns to skills of males and females Across time, the growing residual gap was the major cause of the worsening of the overall gender earnings gap throughout the period The authors argued that the rising residual gap could be explained by Chinese economic reforms Prior to these reforms, under the central planning regime, Chinese workers, both male and female, were assigned jobs and earnings by government agencies The

centralized regime called for equality for both male and female workers Thus, when the system became decentralized, workers were hired and compensated according to how their characteristics were valued by the employers Within a patriarchal society such as China, female workers might not have been as equally preferred as male workers It is also possible that some characteristics of females that were not observed in the data prevented females from earning as much as males

In Vietnam, Liu (2004), using the Vietnam Living Standard Surveys (1992/1993 and 1997/1998), reported a slight decline in the gender earnings gap from 1993-1998 The size of the gap in terms of log differentials was 0.257 in 1993 and 0.194 in 1998 That is, the average male earned 25.7% and 19.4% higher than the average female in

1993 and 1998, respectively The decomposition at each time point showed that the

majority of the gap was accounted for by the unequal rates of returns to the workers’ skills In 1998, the estimated endowment gap was negative, indicating that female

workers had more favorable observable characteristics than male workers However, the unexplained portion of the total gap was larger, resulting in males earning more than females Across time, the decomposition results showed that the residual gap had

widened The author pointed out how the growing residual gap could have stemmed from the erosion of the centralized system, as in China Once employers faced lower

limitations in their ability to determine wages, their attitudes towards certain

unobservable characteristics became important It is possible that these preferences may have worked in a way that was not favorable for females The World Bank (2006c) reported the existence in Vietnam of cultural beliefs regarding gender stereotypes The study reported how illustrations in school materials put fixed ideas into children as to how each gender should have different roles in the household and workplace Regardless

of the growth of the residual gap, the data demonstrated a decline of 6.3% in the overall gender earnings gap This is because female observable characteristics were improving so quickly that they outweighed the opposite effects from the growing residual gap

1.3 Data

The main dataset used in this paper is from the Thai Labor Force Survey (Thai LFS) The Thai LFS has been conducted by the Thai National Statistical Office (NSO) since 1963 Currently, actual raw data are available only from 1985, whereas from 1963

to 1984, only aggregate statistics are available.9 The main purpose of the Thai LFS is to assess the labor force characteristics of the country The households covered are private households and special households (persons living in groups or in quarters within the compound of a factory) Institutional households (jails, college or school dormitories, and military bases) are excluded

Before 1994, the number of households in the sample size was 27,780

(approximately 84,000 persons) From 1994 to 2000, the sample size was 60,492

(approximately 170,000 persons) From 2001 onwards, the sample size increased to 79,560 (approximately 200,000 persons) From 1971-1983, two rounds of surveys were conducted, namely, Q1 (dry or non-agricultural season) during January-March, and Q3 (wet or agricultural season) during July-September From 1984-1997, a third round, Q2, was established Q2 was conducted during April-June, the period in which a reasonably large group of new workers enters the labor force right after graduating from school In

1998, the fourth and final round, Q4, was established This was conducted from December Since 2001, the survey has been administered monthly, with the data grouped into quarters The data structure was initially constructed as a repeated cross-section, but since 2001, a rotating panel structure was used

A two-stage sampling methodology has been utilized in the survey process Thailand is comprised of 76 provinces, and each province is divided into municipal areas and non-municipal areas.10 The primary sampling units in the first stage are blocks for municipal areas and villages for non-municipal areas The number of blocks and villages sampled is determined by employing the probability proportional to size scheme where size is the total number of households in the areas In the second stage, private

households are the ultimate sampling units Fifteen households are chosen for each municipal area and twelve households are chosen for each non-municipal area Once sampled, the head of each household is interviewed

The survey collects information regarding the individuals’ employment situation and demographic background Previously, questionnaires came in two types: short form and long form The short form included questions relating to demographic, education, and employment (age, gender, marital status, highest education level, location, type of workers, occupation, industry, hours worked, etc.) In addition to the questions asked in the short form, the long form also included income and migration questions

(compensation type, wage rate, previous location, etc.) The long form was used in Q1 and Q3, while the short form was used in Q2 and Q4 Since 1999, the long form has been used for all quarters However, the survey questions were not always consistent In some quarters, additional questions were added These questions included information

regarding individual literacy, activities during the non-agricultural season, smoking

10

Initially, Thailand was comprised of 73 provinces In 1994, 3 new provinces, Nong Bua Lam Phu, Am Nat Charoen, and Sra Kaeo were added These new provinces were formerly parts of other existing provinces

habits, computer usage, etc Unfortunately, some of the information was removed from the Thai LFS dataset and assembled separately as other dataset projects.11

Previously, in regards to Thai LFS documentation, individuals age 13 and older were classified according to whether they were in the total labor force or not.12

The total labor force is composed of the current labor force and of seasonally inactive laborers, with the current labor force comprised of employed and unemployed individuals In

1998, the government issued the Labor Protection Act B.E 2541, which stated that the legal minimum age for child labor was 15 years of age (previously, the legal age was 13).13

Thus, in 2001, the NSO re-defined the labor force population as anyone age 15 or older An individual is defined as employed if he or she is working for pay; not working but receiving pay or holding a job that he or she will be returning to; or working without pay Employed workers are classified as private employees, government employees, employers, self-employed workers, family business workers, and state-enterprise

The difference between employer and self-employed worker was not explained explicitly in the

questionnaire Usually for a family that owns either a business or a farm will have the oldest employed member reported himself as either an employer or a self-employed worker and have the rest of the family members (who work) reported themselves as family business workers

15

The co-op worker (cooperative) category was added in 2001

In 2001, there was a major change in the Thai LFS questionnaires Instead of quarterly, the survey was conducted monthly The repeated cross-section structure was replaced by the rotating panel structure Industry, occupational, and educational codes were modified.16

Actual hours worked were recorded differently Prior to 2001, if an individual had a permanent job but was not working during the survey week, the usual hours worked was recorded However, from 2001 onwards, zero was recorded for such a case The structure of the questions relating to income was also modified In the surveys conducted prior to 2001, all wage and salary workers were asked to identify their

compensation types as hourly, daily, weekly, monthly, other, or unknown If the worker reported one of the first four categories, then he or she would be asked to report his or her wage accordingly If he or she categorized the compensation type as “other”, then he or she would be asked to report the average daily wage If he or she answered that the compensation type was “unknown”, then he or she would be asked to select a salary range from a set of pre-defined salary ranges From 2001 onwards, this set of questions was altered Specifically, hourly, daily, and weekly workers were asked to report their wages accordingly All wage and salary workers, including hourly, daily, and weekly workers, were also asked to report their average monthly earnings

In this study, I focus the analyses on full-time17

wage and salary workers aged between 15 and 65 years old The Q3 Thai LFS data from 1985 to 2005 are utilized.18

16

I attempted to make the educational categories compatible for the data before and after 2001 in order to estimate statistics relating to the data The industry codes can be converted with only trivial errors, but the occupational codes cannot be converted I consulted this with an NSO officer who was in charge of the Thai LFS data project

Since most workers are paid monthly and from 2001 onwards every worker has been asked to report their average monthly earnings, the analyses in this paper are based on monthly earnings Other types of reported earnings are converted to monthly earnings according to Appendix Table A.2.19

The earnings are deflated using the year 2002 as the base year

It is reasonable to argue that hourly wages may constitute a more appropriate measure of income compared to monthly earnings Monthly earnings can be affected by the number of hours each worker decides to work and one may expect male workers to work more hours than female workers Table 1.2 compares average hours worked per week for male and female workers in Thailand’s wage and salary sector.20 The table shows that hour differences between the two genders are trivial and thus should not cause any concern For most years, less than 1% of the workers reported their hourly wages From 1985 to 2000, about 50% of the workers reported their monthly earnings and from

2001 onwards all workers reported their monthly earnings By using monthly earnings, I introduce minimum inaccuracies associated with the unit conversion process I will also show in the next section that the gender gap in income, in terms of mean gap and density gap, estimated using monthly earnings and hourly wages are very similar to each other

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

Average hours worked per week Year

1.4 The Thai Labor Market and Gender Inequality

This section describes the Thai labor market and discusses various issues

regarding gender inequality in the market Statistics and figures presented in this section (and beyond) are derived from the Thai LFS dataset, unless otherwise noted This section

is organized as follows First, the labor force participation of both genders is explored A discussion regarding why the participation rate of females in Thailand has been

substantial is conducted Next, the segregation of the labor market into a formal sector (wage and salary) and an informal sector (non-wage and salary) is discussed Since the main analyses of the paper will focus on workers in the formal sector, the overall

characteristics of male and female workers in this sector are thoroughly investigated I then explore the situations of female workers and the formal legislation that protects female workers’ rights Finally, the average gender earnings gap and the earnings density gap between male and female workers are carefully explored I address how these gaps evolved over time and how they evolved differently for the younger and the older

workers

The labor force participation rate21 of Thai workers segregated by gender is

shown in Figure 1.2 The male labor force participation was 89% in 1985 It declined during the 1990s and has stayed at 85% since 2000 For females, the labor force

participation was about 77% in 1985, increasing slightly during the late 1980s, but

diminishing during the 1990s and stabilizing at around 70% since 2000 For those who are familiar with the levels of female labor force participation in other countries, it is not difficult to perceive that the participation rate for Thai females has for many years been rather large compared to that of other countries The high participation rate of females still prevails in Thailand Figure 1.3 compares the female labor force participation for selected countries using data from the International Labor Organization.22

The figure confirms that Thailand falls into the category of countries with high female labor force participation rate