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 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? The Case of the Americans with Disabilities Act Journal of Political Economy, 109(5), 915-957 Arrow, K (1972) The Theory of Discrimination In O Ashenfelter & A Rees (Eds.), Discrimination in Labor Markets Princeton, NJ: Princeton University Press Asian Development Bank (1998) Women in Thailand Manila, the Philippines Autor, D H., Kerr, W R & Kugler, A D (2007) Does Employment Protection Reduce Productivity? Evidence from US States The Economic Journal, 117(521), 189-217 Autor, D H (2003) Outsourcing at Will Journal of Labor Economics, 21(1), 1-42 Autor, D H., Donohue, J J & Schwab, S J (2004) The Employment Consequences of Wrongful-Discharge-Laws: Large, Small, or None at all? American Economic Review Papers and Proceedings, 94(2), 440-446 Autor, D H., Donohue, J J & Schwab, S J (2006) The Costs of Wrongful Discharge Laws Review of Economics and Statistics, 88(2), 211-231 Barsky, R., Bound, J., Charles, K K & Lupton, J P (2002) Accounting for the BlackWhite Wealth Gap: A Nonparametric Approach Journal of the American Statistical Association, 97(459), 663-673 Becker, G (1957) The Economics of Discrimination Chicago, IL: University of Chicago Press Bertrand, M., Duflo, E & Mullainathan, S (2004) How Much Should We Trust Differences-in-Differences Estimates? Quarterly Journal of Economics, 119(1), 249275 Besley, T & Burgess, R (2004) Can Labor Regulation Hinder Economic Performance? Evidence from India Quarterly Journal of Economics, 119(1), 91-134 Black, D., Haviland, A., Sanders, S & Taylor, L (2006) Why Do Minority Men Earn Less? A Study of Wage Differentials among the Highly Educated The Review of Economics and Statistics, 88(1), 300-313 Blinder, A S (1973) Wage Discrimination: Reduced Form and Structural Estimates The Journal of Human Resources, 8(4), 436-455 173 Botero, J C., Djankov, S., La Porta, R., Lopez-de Silanes, F & Shleifer, A (2004) The Regulation of Labor Quarterly Journal of Economics, 119(4), 1339-1382 Cameron, L A., Dowling, J M & Worswick, C (2001) Education and Labor Market Participation of Women in Asia: Evidence from Five Countries Economic Development and Cultural Change, 49(3), 459-477 Cotton, J (1988) On the Decomposition of Wage Differentials The Review of Economics and Statistics, 70(2), 236-243 Daly, A., Kawaguchi, A., Meng, X & Mumford, K (2006) The Gender Wage Gap in Four Countries The Economic Record, 82(257), 165-176 DeLeire, T (2000) The Wage and Employment Effects of the Americans with Disabilities Act Journal of Human Resources, 35(4), 693-715 Deolalikar, A B (2002) Poverty, Growth, and Inequality in Thailand ERD Working Paper Series, No 8, Asian Development Bank, Manila, the Philippines Dertouzos, J N & Karoly, L A (1992) Labor Market Responses to Employer Liability Santa Monica, CA: Rand, Institute for Civil Justice DiNardo, J (2002) Propensity Score Reweighting and Changes in Wage Distributions Meimo DiNardo, J., Fortin, N M & Lemieux , T (1996) Labor Market Institutions and the Distribution of Wages, 1973-1992: A Semiparametric Approach Econometrica, 64(5), 1001-1044 Funatsu, T & Kagoya, K (2003) The Middle Classes in Thailand: The Rise of the Urban Intellectual Elite and Their Social Consciousness The Developing Economies, 41(2), 243-263 Gannicott, K (1986) Women, Wages, and Discrimination: Some Evidence from Taiwan Economic Development and Cultural Change, 34(4), 721-730 Garen, J & Jeraputtiruk, N (2005) Mandated Fringe Benefits in an Economy with Covered and Uncovered Sectors: The Case of Labor Market Regulation in Thailand Meimo Grubb, D & Wells, W (1993) Employment Regulation and Patterns of Work in EC Countries OECD Economic Studies, 21, 7-58 Gustafsson, B & Li, S (2000) Economic Transformation and the Gender Earnings Gap in Urban China Journal of Population Economics, 13, 305-329 174 Hawley, J D (2004) Changing Returns to Education in Times of Prosperity and Crisis, Thailand 1985-1998 Economics of Education Review, 23, 273-286 Heckman, J J & Pages, C (2000) The Cost of Job Security Regulation : Evidence from Latin American Labor Markets NBER Working Papers, No 7773, National Bureau of Economic Research Heckman, J J & Pages, C (2000) Law and Employment: Lessons from Latin America and the Caribbean Chicago, IL: University of Chicago Press Hill, M A & King, E M (1991) Women’s Education in Developing Countries: An Overview In M A Hill & E M King (Eds.), Women’s Education in Developing Countries: Barriers, Benefits and Policies (pp 1-50) Washington, DC: World Bank Jansen, K (2001) Thailand, Financial Crisis and Monetary Policy Journal of the Asia Pacific Economy, 6(1), 124-152 Jeong, H (2007) Assessment of Relationship between Growth and Inequality: Micro Evidence from Thailand Macroeconomic Dynamics, Forthcoming Jeraputtiruk, N (2004) The Effects of Labor Regulation in Developing Countries: The Evidence on Hourly Earnings Compensation from Thailand Meimo Jolls, C & Prescott, J J (2004) Disaggregating Employment Protection: The Case of Disability Discrimination NBER Working Papers, No 10740, National Bureau of Economic Research Juhn, C., Murphy, K M & Pierce, B (1991) Accounting for the Slowdown in BlackWhite Wage Convergence In M Kosters (Ed.), Workers and Their Wages Washington, DC: AEI Press Kawaguchi, A (2004) Changes in the Japanese Gender Wage Gap in the 1990s paper presented to the 2004 Conference on Feminist Economics, Oxford, August Knodel, J (1997) The Closing of the Gender Gap in Schooling: The Case of Thailand Comparative education, 33(1), 61-86 Krueger, A B (1991) The Evolution of Unjust-Dismissal Legislation in the United States Industrial and Labor Relations Review, 44(4), 644-660 Kugler, A D & Saint-Paul, G (2004) How Do Firing Costs Affect Worker Flows in a World with Adverse Selection? Journal of Labor Economics, 22(3), 553-583 175 Lauridsen, L S (1998) The Financial Crisis in Thailand: Causes, Conduct and Consequences? World Development, 26(8), 1575-1591 Lazear, E P (1990) Job Security Provisions and Employment Quarterly Journal of Economics, 105(3), 699-726 Lemieux, T (2002) Decomposing Changes in Wage Distributions: A Unified Approach The Canadian Journal of Economics, 35(4), 646-688 Liu, A Y C (2004) Gender Wage Gap in Vietnam: 1993 to 1998 Journal of Comparative Economics, 32, 586-596 Liu, P W., Meng, X & Zhang, J (2000) Sectoral Gender Wage Differentials and Discrimination in the Transitional Chinese Economy Journal of Population Economics, 13, 331-352 Meng, X (1996) The Economic Position of Women in Asia Asian-Pacific Economic Literature, 10(1), 23-41 Miles, T J (2000) Common Law Exceptions to Employment at Will and US Labor Markets Journal of Law, Economics & Organization, 16(1), 74-101 Motonishi, T (2003) Why Has Income Inequality in Thailand Increased? – An Analysis Using 1975-1998 Surveys ERD Working Paper Series, No 43, Asian Development Bank, Manila, the Philippines Muhl, C J (2001) The Employment-at-Will Doctrine: Three Major Exceptions Monthly Labor Review, 124(1), 3-11 National Statistical Office of Thailand (2003a) Report of the Labor Force Survey, Whole Kingdom, Quarter (April – June 2003) Bangkok, Thailand National Statistical Office of Thailand (2003b) Report of the Labor Force Survey, Whole Kingdom, Quarter (October – December 2003) Bangkok, Thailand Neumark, D (1988) Employers’ Discriminatory Behavior and the Estimation of Wage Discrimination The Journal of Human Resources, 23(3), 279-295 Oaxaca, R L (1973) Male-Female Wage Differentials in Urban Labor Markets International Economic Review, 14(3), 693-709 Oyer, P & Schaefer, S (2000) Layoffs and Litigation Rand Journal of Economics, 31(2), 345-358 176 Phananiramai, M (1993) Women’s Economic Roles in Thailand TDRI Working Paper, Thailand Development Research Institute, Bangkok, Thailand Pirmana, V (2006) Earnings Differential between Male-Female in Indonesia: Evidence from Sakernas Data Working Paper in Economics and Development Studies, No 200608, Department of Economics, Padjadjaran University, Bandung, Indonesia Reimers, C W (1983) Labor Market Discrimination Against Hispanic and Black Men The Review of Economics and Statistics, 65(4), 570-579 Rodgers, Y M (1998) A Reversal of Fortune for Korean Women: Explaining the 1983 Upward Turn in Relative Earnings Economic Development and Cultural Change, 46(4), 727-748 Rosen, S (1968) Short-Run Employment Variation on Class-I Railroads in the US, 1947-1963 Econometrica, 36, 511-529 Schanzenbach, M (2003) Exceptions to Employment at Will: Raising Firing Costs or Enforcing Life-Cycle Contracts? American Law and Economics Review, 5(2), 470504 Schultz, T P (1991) Returns to Women’s Education In M A Hill & E M King (Eds.), Women’s Education in Developing Countries: Barriers, Benefits and Policies (pp 5199) Washington, DC: The World Bank Tiliak, J B G (1991) East Asia In M A Hill & E M King (Eds.), Women’s Education in Developing Countries: Barriers, Benefits and Policies (pp 247-284) Washington, DC: The World Bank Schafgans, M M A (2000) Gender wage differences in Malaysia: parametric and semiparametric estimation Journal of Development Economics, 63, 351-378, Silverman, B W (1986) Density Estimation for Statistics and Data Analysis London, England: Chapman & Hall Smith, J P., Thomas, D., Frankenberg, E., Beegle, K & Teruel, G (2002) Wages, Employment and Economic Shocks: Evidence from Indonesia Journal of Population Economics, 15, 161-193 Thomas, D., Beegle, K & Frankenberg, E (2003) Labor Market Transitions of Men and Women During an Economic Crisis: Evidence from Indonesia, In B Garcia, R Anker & A Pinnelli (Eds.), Women in the Labour Market in Changing Economies: Demographic Issues (pp 37-58) Oxford: Oxford University Press 177 Tsurumi, H (2000) Asian Financial Crisis Prologue and the Case of Thailand AsiaPacific Financial Markets, 7, 1-9 United Nations Development Programme (1995) Human Development Report New York, NY: Oxford University Press United Nations Development Programme (2006) Human Development Report New York, NY: Oxford University Press US Bureau of the Census (1989) The Relationship between the 1970 and 1980 Industry and Occupation Classification Systems Technical Paper 59 Washington, DC: US Government Printing Office Warr, P (1999) What Happened to Thailand? The World Economy, 22, 631-650 Warr, P (2004) Globalization, Growth, and Poverty Reduction in Thailand ASEAN Economic Bulletin, 21(1), 1-18 World Bank (2005) East Asia Update, Special Focus: Gender Equality in East Asia Washington, DC World Bank (2006a) Indonesia Gender Assessment Washington, DC World Bank (2006b) Thailand Economic Monitor Washington, DC World Bank (2006c) Vietnam Gender Assessment Washington, DC Zveglich, J E., Rodgers, Y M & Rodgers, W M (1997) The Persistence of Gender Earnings Inequality in Taiwan, 1978-1992 Industrial and Labor Relations Review, 50(4), 594-609 178 Appendix Appendix Table A.1: Actual raw data availability of the Thai LFS from 1985-2005 1985 (2528) 1986 (2529) 1987 (2530) 1988 (2531) 1989 (2532) 1990 (2533) 1991 (2534) 1992 (2535) 1993 (2536) 1994 (2537) 1995 (2538) 1996 (2539) 1997 (2540) 1998 (2541) 1999 (2542) 2000 (2543) 2001 (2544) 2002 (2545) 2003 (2546) 2004 (2547) 2005 (2548) Y: available Q1: Jan-Mar (Feb round) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Q2: Apr-Jun (May round) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Q3: Jul-Sep (Aug round) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Q4: Oct-Dec (Nov round) Y Y Y Y Y Y Y Y 1985-2000 monthly earnings = (reported) hourly wage * hours of work per week * 4.2 monthly earnings = (reported) daily wage * days of work per week * 4.2 monthly earnings = (reported) weekly wage * 4.2 monthly earnings = (reported) monthly earnings monthly earnings = (reported) average daily wage * days of work per week * 4.2 monthly earnings = mid point of chosen salary range (highest and lowest salary ranges were dropped) N/A Wage Type Hourly Daily Weekly Monthly Other Unknown NonCash 2001-2005 monthly earnings = (reported) average monthly earnings monthly earnings = (reported) average monthly earnings monthly earnings = (reported) average monthly earnings monthly earnings = (reported) average monthly earnings monthly earnings = (reported) average monthly earnings monthly earnings = (reported) average monthly earnings monthly earnings = (reported) average monthly earnings Appendix Table A.2: Conversion Methodology for Monthly Wages 179 ... 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... 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... 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