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 Copyright 2007 by Nakavachara, Voraprapa All rights reserved UMI Microform 3291909 Copyright 2008 by ProQuest Information and Learning Company 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 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 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: Explaining the Gender Earnings Gap Trend in Thailand 1.1 Introduction 1.2 Socio-economic Background 1.2.1 Growth, Poverty, and Income Inequality 1.2.2 Expansion of Education 1.2.3 Gender Earnings Inequality in Neighboring Countries: The Literature 1.3 Data 1.4 The Thai Labor Market and Gender Inequality 1.5 Parametric Decomposition 1.5.1 Blinder-Oaxaca (1973): BO 1.5.2 Juhn-Murphy-Pierce (1991): JMP 1.5.3 BO Results 1.5.4 JMP Results 1.6 Nonparametric Decomposition 1.6.1 DiNardo-Fortin-Lemieux (1996): DFL 1.6.2 DFL Results 1.7 Conclusion 22 28 65 66 70 74 84 90 91 101 110 Chapter Two: Wrongful Discharge Laws and the Unexpected Substitution Effect 2.1 Introduction 2.2 Previous Literature: The Economics of Employment Law 2.2.1 Wrongful Discharge Laws (WDLs) 2.2.1.1 Implied Contract 2.2.1.2 Good Faith 2.2.1.3 Public Policy 2.2.2 Employment Consequences of Wrongful Discharge Laws 2.3 Theoretical Framework for Wrongful Discharge Laws 2.4 Data 2.5 Empirical Methodology 113 113 118 119 119 121 124 125 128 132 145 13 17 iv 2.6 2.7 Results 2.6.1 Good Faith 2.6.2 Implied Contract 2.6.3 Public Policy Conclusion 148 148 157 165 170 Bibliography 172 Appendix Appendix Table A.1 Appendix Table A.2 178 178 179 v List of Tables Table 1.1: Ratio of female to male earned income (selected countries) Table 1.2: Average hours worked per week for male and female workers (wage and salary sector) 28 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 (without occupations and industries) 85 Table 1.7B: Juhn-Murphy-Pierce (1991) results (with occupations and industries) 89 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) 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 (age:15-24) 39 Figure 1.4B: Female labor force participation and employment by sector (age:25-34) 40 Figure 1.4C: Female labor force participation and employment by sector (age:35-44) 41 Figure 1.4D: Female labor force participation and employment by sector (age:45-54) 42 Figure 1.4E: Female labor force participation and employment by sector (age:55-64) 43 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 (age: 15-40) 53 Figure 1.9B: Earnings of male and female workers and gender earnings gap (age: 41-65) 54 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-FortinLemieux (1996) when male wage structure is used as reference wage structure 94 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 (without occupations and industries) 102 Figure 1.17B: Modified DiNardo-Fortin-Lemieux (1996) results (with occupations and industries) 107 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-MurphyPierce 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 165 2.6.3 Public Policy The results from the Good Faith and Implied Contract exceptions have suggested, at some level of precision, that the employment of high-skilled labor is positively affected by these laws and that the employment of low-skilled labor is negatively affected by these laws I have already argued that the sector trend variables help to capture episodes of contraction for the low-skilled sector and of expansion for the high-skilled sector during the data period Thus, the coefficients of the adoption variables should be appropriate measures of the effects of these laws, rather than merely illustrating the employment trends of high-skilled and low-skilled labor As discussed in Section 2.2.1.3, the Public Policy exception does not constitute a new legal regime, but rather it restricts the actions of employers in order to protect existing public policy As shown in Figures 2.3 and 2.4, many states adopted this type of exception during the data period The group of states that adopted the Public Policy law is similar to the group that adopted the Implied Contract law Table 2.5A shows that the Public Policy law has virtually no effect on the employment of any type of labor, which is true across all training indices I obtain similar results for wages, as shown in Table 2.5B The employment results from the Public Policy exception act as a control for the employment results from the Good Faith and Implied Contract exceptions Basically, if the results from the Good Faith and Implied Contract exceptions are merely the illustration of employment trends for low-skilled and high-skilled labor, then I should %married %age36-55 %age18-35 %black %male HighXt LowXt High Low Adopt(PP)xHigh Adopt(PP)xMed Adopt(PP)xLow -0.19691 (0.16664) (0.18052) (0.12676) (0.21795) -0.24671 0.09880 (0.19331) (0.28487) -0.54024** -0.03160 (0.27208) (0.25233) -0.76758*** 0.61404** 0.63782** (0.18614) (0.00020) (0.16147) (0.00017) 0.00056*** (0.00020) -0.00108*** (0.07050) -0.96855*** (0.04877) -0.12720** (0.05031) -0.02697 (0.01298) -0.02509* (0.03114) 0.00904 (4) -0.00087 (0.00019) (0.00016) 0.00080*** (0.00021) -0.00101*** (0.03465) -0.71808*** (0.03270) -0.13855*** (0.05684) -0.01195 (0.01373) -0.01922 (0.03774) -0.00095 (3) School Training 0.11097 0.00109*** 0.00092*** (0.00026) (0.00027) (0.10074) -0.00096*** (0.02910) -0.00103*** -1.23363*** (0.07021) (0.04366) -0.67726*** 0.07601 (0.05013) (0.06022) -0.05914 -0.04489 (0.01444) (0.02028) -0.02086 -0.01586 (0.03436) (0.04547) -0.00631 0.00236 (2) -0.00959 (1) Any Training (0.00008) -0.00023*** (0.00027) -0.00143*** (0.01243) -0.82345*** (0.04357) -0.22457*** (0.03777) 0.01097 (0.02296) -0.02746 (0.04232) -0.00047 (5) (0.12078) -0.18206 (0.20934) -0.50323** (0.28449) -0.64143** (0.24741) 0.32000 (0.20706) 0.00103 (0.00009) -0.00040*** (0.00027) -0.00142*** (0.05231) -0.92423*** (0.07123) 0.12202* (0.02810) -0.01851 (0.01740) -0.03371* (0.03179) 0.00577 (6) Formal Training Table 2.5A: Public Policy and employment (0.00013) 0.00042*** (0.00016) -0.00048*** (0.02396) -0.40312*** (0.03240) -0.35291*** (0.02878) -0.00118 (0.01239) 0.00186 (0.02949) -0.03270 (7) (0.11962) -0.03093 (0.31620) -0.99223*** (0.34576) -0.71441** (0.25535) -0.25701 (0.26775) 0.67289** (0.00014) 0.00059*** (0.00020) -0.00067*** (0.03891) -0.28810*** (0.06506) -0.32709*** (0.02582) -0.00425 (0.01263) 0.00829 (0.02685) -0.03531 (8) Informal Training 166 0.41407 Prob > F -1.32772*** 0.33076 1.16994 0.86 21450 0.28633 1.29600 0.86 21450 (0.01054) Robust standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% 0.97087 0.83 F test 21450 (0.32463) (0.01699) R-squared -1.18537*** 0.27611 1.32761 0.88 21450 (0.13244) -1.36596*** (0.26277) (0.32440) (0.26657) 0.84222*** (0.28849) 1.81018*** -0.52821* (0.21957) (0.20994) -0.12362 0.25915 (0.48239) 0.45442** (0.48821) (4) 0.34902 (3) School Training -0.60541 (2) -1.36499*** Observations Constant %college education & higher %some college education %high school education %union (1) Any Training Table 2.5A, Continued 0.00601 4.66885 0.90 21450 (0.01949) -1.25036*** (5) 0.05399 2.72773 0.92 21450 (0.29074) -1.56819*** (0.29073) 2.17192*** (0.22819) 0.38391* (0.18398) 0.70454*** (0.43767) -0.55707 (6) Formal Training 0.37563 1.05771 0.82 21450 (0.01048) -1.34962*** (7) 0.28323 1.30546 0.84 21450 (0.26720) -0.48591* (0.34199) -0.55286 (0.32653) -0.86472** (0.20324) -0.51587** (0.28510) -0.22821 (8) Informal Training 167 %married %age36-55 %age18-35 %black %male HighXt LowXt High Low Adopt(PP)xHigh Adopt(PP)xMed Adopt(PP)xLow (0.02724) (0.02274) (0.03555) 0.09994*** (0.03027) 0.12932*** 0.04565 0.04693 (0.04668) (0.04115) (0.04467) -0.17896*** (0.03550) -0.16297*** -0.06470 -0.09594*** (0.02186) (0.00004) (0.01708) (0.00004) 0.00056*** (0.00005) -0.00039*** (0.01239) 0.00078 (0.01275) -0.16664*** (0.01119) -0.01354 (0.00749) -0.00684 (0.00933) -0.00674 (4) 0.29813*** (0.00004) (0.00005) 0.00041*** (0.00006) -0.00035*** (0.00705) 0.22047*** (0.00869) -0.28023*** (0.01251) -0.01236 (0.00706) -0.00376 (0.00962) -0.00380 (3) School Training 0.32715*** 0.00063*** 0.00048*** (0.00005) (0.00006) (0.01128) -0.00041*** (0.00983) -0.00039*** -0.00026 0.20221*** (0.01188) (0.00860) (0.01098) -0.18970*** (0.01376) -0.29825*** -0.01430 (0.00610) (0.00702) -0.01401 -0.00897 (0.00722) (0.00917) -0.00768 -0.00547 (2) -0.00151 (1) Any Training (0.00006) 0.00015** (0.00007) -0.00027*** (0.00815) 0.18569*** (0.00880) -0.37092*** (0.01001) -0.00201 (0.00827) -0.00602 (0.00889) -0.00366 (5) (0.02093) 0.13272*** (0.02661) 0.05149* (0.03436) -0.17622*** (0.03689) -0.05263 (0.01943) 0.22375*** (0.00006) 0.00014** (0.00006) -0.00025*** (0.00944) 0.09153*** (0.01122) -0.19735*** (0.00819) -0.00419 (0.00814) -0.00745 (0.00684) -0.00654 (6) Formal Training Table 2.5B: Public Policy and wages (0.00009) 0.00003 (0.00005) 0.00061*** (0.01215) 0.02744** (0.01115) -0.29176*** (0.01588) 0.00617 (0.00666) 0.00192 (0.01478) -0.02179 (7) (0.01188) 0.17900*** (0.02302) 0.10073*** (0.01967) -0.11790*** (0.04361) -0.11307** (0.01836) 0.24672*** (0.00006) 0.00007 (0.00005) 0.00050*** (0.00787) 0.02024** (0.01071) -0.20146*** (0.01074) -0.00191 (0.00554) -0.00053 (0.01096) -0.01825 (8) Informal Training 168 0.53722 Prob > F 2.01694*** 0.32973 1.17268 0.93 21450 0.40373 0.99351 0.92 21450 (0.00365) Robust standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% 0.73320 0.92 F test 21450 (0.03256) (0.00454) R-squared 1.41684*** 0.24205 1.44161 0.93 21450 (0.03362) 1.43696*** (0.05113) (0.05100) (0.05461) 0.73049*** (0.05129) 0.69130*** 0.41821*** 0.39453*** (0.05637) (0.05629) (0.04912) 0.36673*** 0.34283*** (0.04388) (4) 0.22734*** (3) School Training 0.28358*** (2) 2.02469*** Observations Constant %college education & higher %some college education %high school education %union (1) Any Training Table 2.5B, Continued 0.80452 0.32881 0.93 21450 (0.00562) 2.05068*** (5) 0.56918 0.67888 0.94 21450 (0.03237) 1.48768*** (0.04579) 0.70415*** (0.04523) 0.35348*** (0.04043) 0.27857*** (0.04170) 0.29296*** (6) Formal Training 0.19580 1.62417 0.84 21450 (0.00372) 2.00850*** (7) 0.09223 2.26831 0.87 21450 (0.02671) 1.39861*** (0.02879) 0.80379*** (0.03250) 0.37829*** (0.02226) 0.21685*** (0.02959) 0.26719*** (8) Informal Training 169 170 observe similar patterns here Since I not, this lends additional support to the hypothesis that the Good Faith and Implied Contract exceptions are associated with increases in employment of high-skilled labor and are associated with decreases in employment of low-skilled labor 2.7 Conclusion The common law doctrine of employment at will was established throughout the United States by 1953 The doctrine requires that a default employment relationship be at will, and thus can be unilaterally terminated by each party at any time for any or no reason and without any liability This at will doctrine although allows firms to effectively adjust their workforces, provides workers with no employment security During the 1970s and 1980s, state courts imposed limitations on this employment at will doctrine by allowing three classes of exceptions These exceptions are often referred to as Wrongful Discharge Laws (WDLs) These laws limit employers’ flexibility in dismissing workers and allow workers to litigate against wrongful discharge There are three categories of these laws – namely, Implied Contract, Good Faith, and Public Policy The literature reported negative (or at best zero) impacts of WDLs on overall employment These studies implicitly assumed that the overall labor force was homogeneous They failed to recognize that labor can be heterogeneous and firms may treat different types of labor as different forms of input Thus, the imposition of WDLs may influence the decisions of firms regarding not only the quantity of labor input but also the combination of different types of labor input In this paper, I utilize a simple model that acknowledges the heterogeneity of labor input Specifically, in the model, 171 firms acquire two types of labor input, namely low-skilled and high-skilled labor The model allows for firms to alter their production and to substitute between different types of labor input once WDLs are imposed The key finding of this paper is that WDLs, particularly in the case of the Good Faith law, are associated with increases in employment of high-skilled labor, a result that may have been unacknowledged in early studies WDLs, however, adversely affect the levels of employment for low-skilled workers This negative impact of WDLs on employment is consistent with the literature These results indicate that whether intended or not, WDLs can affect different groups of workers differently It will be interesting to see whether other types of labor laws also have different impacts on low-skilled and high-skilled workers It will also be interesting to investigate the subject matter in other countries that experienced similar changes in legal regimes 172 Bibliography Acemoglu, D & Angrist, J D (2001) Consequences of Employment Protection? 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Islands, FS Micronesia, Mongolia, Palau, Papua New Guinea, the Philippines, Samoa, Solomon Islands, Timor-Leste, Tonga, Vanuatu, and Vietnam 6 genders (residual gap) Second, the Juhn, Murphy, and