Investigating the gender wage gap in vietnam by quantile regression sticky floor or glass ceiling

MINISTRY OF EDUCATION A UNIVERSITY OF ECONOMICS H MS: UNIVERSITY-LEVEL RESEA TOPIC: INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUAN Investigator: TRẦN THỊ TUẤN ANH TP.HCM, JULY - 20 INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING? TABLE OF CONTENTS TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES CHAPTER 1: INTRODUCTION 1.1 INTRODUCTION What is glass ceiling? What is sticky floor? 1.2 THE NECESSARY OF INVESTIGATING THE STICKY FLOOR AND GLASS CEILING IN VIETNAM 1.3 OBJECTIVES 10 1.4 CONTRIBUTIONS 10 1.5 STRUCTURES 11 CHAPTER 2: LITERATURE REVIEW 12 2.1 BACKGROUND 12 2.1.1 Mincer-type wage equation 12 2.1.2 .Quantile regression 14 2.2 LITERATURE REVIEW 17 2.3 THE RESEARCH GAPS 20 CHAPTER 3: METHODOLOGY 21 3.1 DATA 21 3.2 VARIABLES AND MINCER-TYPE WAGE EQUATION 21 3.3 QUANTILE REGRESSION OF WAGE EQUATION 24 CHAPTER 4: RESULTS AND DISCUSSION 26 INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING? 4.1 DESCRIPTIVE STATISTICS 26 4.2 RESULTS 28 4.2.1 The distribution of wage: Kernel density wage estimation 28 4.3 THE GENDER WAGE DIFFERENTIALS ACROSS THE DISTRIBUTION 34 a.Entire sample 35 b By urban – rural areas 38 c.By sectors 43 d By education 48 e By occupations 51 4.4 RESULTS OF THE STICKY FLOOR AND GLASS CEILING EFFECTS IN THE VIETNAM LABOUR MARKET 52 CHAPTER 5: CONCLUSION 56 5.1 CONCLUSION 56 5.2 POLICY IMPLICATIONS 56 REFERENCES 60 APPENDIX A: QUANTILE REGRESSION OF WAGE EQUATION BY EDUCATION 63 APPENDIX B: QUANTILE REGRESSION OF WAGE EQUATION BY OCCUPATION 69 LIST OF TABLES Table 1: List of variables 21 Table 2: The percentage of male and female labourers in entire sample and in each subsample 26 Table 3: Comparison of lnwage between male and female groups 27 Table 4: Quantile wage regression in entire sample 36 Table 5: Quantile wage regressions in urban areas 39 Table 6: Quantile wage regressions in rural areas 41 Table 7: Quantile wage regressions in state sector 44 Table 8: Quantile wage regressions in private sector 46 Table 9: Summary about stick floor and glass ceiling in Vietnam 53 LIST OF FIGURES Figure 1: Density functions of male and female (log) hourly wages 30 Figure 2: Density functions of male and female (log) hourly wages in urban and rural 30 Figure 3: Density functions of male and female (log) hourly wages in state sector and private sector 31 Figure 4: Density functions of male and female (log) hourly wages by qualifications 32 Figure 5: : Density functions of male and female (log) hourly wages by occupations 34 Figure 6: Gender wage gap in entire sample by OLS and quantile regression .38 Figure 7: Gender wage gap in urban area by OLS and quantile regression 40 Figure 8: Gender wage gap in rural by OLS and quantile regression .43 Figure 9: Gender wage gap in state sector by OLS and quantile regression 46 Figure 10: Gender wage gap in private sector by OLS and quantile regression .48 Figure 11: Gender wage gap by education .50 Figure 12: Gender wage gap by occupation 52 CHAPTER 1: INTRODUCTION 1.1 INTRODUCTION Inequality between men and women in the labour market is one of the issues that are of great interest in labour economics Many empirical studies have shown that wages of males are higher than for females This happens in most countries around the world Most of these studies focus on the average gender wage gap However, in modern labour economics, an interesting phenomenon also attracts the attention of researchers, that is the gender wage gap at the upper and lower tails of wage distribution are usually higher than that at middle If the gender wage gap at lower tail quantiles is wider than gap at the middle quantiles, it will result in a sticky floor effect If the gender wage gap at upper quantiles is higher than the middle units, the glass ceiling is called to be existed What is glass ceiling? Glass ceiling can be interpreted as the phenomenon whereby women quite well in the labour market up to a point after which there is an effective limit on their prospects Glass ceiling implies that there seems to be an invisible barrier to female workers in occupation, in promotion or in wage that prevents females to reach the top compared to male workers who have the same productivity characteristics The glass ceiling effect in wage existed if the gender wage gap at the top of the wage distribution is wider than other position, suggesting that females in wage ceiling have lower pay than their male counterparts What is sticky floor? The sticky floor effect occurs when the gender wage gap widen at the lower tail of the wage distribution This mentions to the case where women at the bottom of the wage distribution are more discriminated against than men and they may face greater disadvantages than at other quantiles Why gender wage gap, sticky floor and glass ceiling effects exist? There are many reasons for existence of gender wage gap in which women often receive a lower wage: - Due to differences of labour characteristics such as education level, health, etc - Due to the occupational segregation - Due to the discrimination against women, especially in some Asian countries where the male – dominated thought still exists - And other reasons The sticky floor effect may be occurred due to: - Low - paid careers are often associated with women, such as maids, secretaries, housekeepers, clerks, tailors, etc Even if doing jobs of equal value, women earn less than men One of the main reasons is the way females' competences are valued compared to males' - Men and women with their own characteristics are often suitable for different industries In fact, the male-dominated industries often pay more than femaledominated industries Men are still able to participate in the dominant women's sector, but they may demand higher compensation than women receive to the job - Getting married and having children can affect female workers' productivity and hence lead to income diversification between males and females After marriage, men may feel more responsibility for the family and work hard to support their families Meanwhile, women will be responsible for housework and caring for children, so women may reduce their participation in labour force and their productivity will be reduced In addition, women have tendency to choose less demanding jobs and lose the opportunity to find or maintain good occupations The glass ceiling effect that exists may be due to: - Men still perceive more promotions than women - It is popular to disregard the women‟s potential to fulfil senior or managerial positions amongst women themselves as well as their male colleagues - Women often find that it is hard to obtain the education and training required to be promoted into leader or managerial positions - The prejudices either conscious or unconscious in society regarding gender still exist that may limit the women‟s opportunity to get promotions Sometimes, women may get promoted but with lower wage than men counterparts - An employer may care about a woman‟s marital status as signal of family responsibilities, less flexibility and less productivity And this may reduces the employment prospects of married women and lower the level of wages that women can command One should pay attention to gender wage gap, sticky floor and glass ceiling effects Firstly, low wages increase the dependence of women on men in the home, so the role of women in the family may be overlooked, which lead to the case that women shoulder almost the entire burden of family planning or lead to domestic violence The fact that women are responsible for housework also contributes to lower productivity in women's main work Or long term violence can affect physical health and mental health This in turn decreases the productivity of women Secondly, wages in the workplace of women are lower than men, so the pension will also be lower Women retire earlier than men, while the average life expectancy of women is higher than that of men Thus, women will experience a longer retirement period than men with lower wages, and women will face economic difficulties in their old age The presence of sticky floor and glass ceiling is also one of the important sign of gender inequality in particular and social inequality in general This can be seen as a consequence of the development progress of a country Therefore, it is necessary to investigate the existence of sticky floor effect and glass ceiling effect 1.2 THE NECESSARY OF INVESTIGATING THE STICKY FLOOR AND GLASS CEILING IN VIETNAM Nowadays sustainable development is a global concern In development process, most regions and countries encounter many common challenges One of the most popular challenges is the problem of increasing inequality in society along with economic growth Therefore, gender equality is one of the important criteria for assessing the sustainable development of a country As other countries, Vietnam is also oriented towards sustainable development Therefore, the improvement of gender wage gap is also one of the urgent requirements in global integration context Investigating the existence of the glass ceiling sticky floor effect will determine the segments where the gender wage inequality actually occurred, and thereby help the government to build strategies for improving the gender inequality efficiently and effectively In addition, many studies reveal that inequality hurts economic growth Overcoming the effect of sticky floor and glass ceiling will create conditions for both men and women to contribute significantly to country‟s development The fact that female workers are stuck in low-income or bound with invisible barriers in high-income workers may limit th their ability to contribute The 17 sustainable development goals of United Nation mention that “Achieve gender equality and empower all women and girls” In Vietnam, there are some empirical studies that demonstrate statistical evidence of gender wage gap Liu (2004) uses data from VHLSS 1992-1998 to investigate gender wage inequality in Vietnam by multiple linear regression and the Oxaca – Blinder (1973) decomposition Hung PT (2007) employs quantile regression to analyze the gender wage differential with the data for the period from 1992 to 2002 Anh T.T.T (2015) also uses quantile regression and Machado- Mata (2015) analyzed the gender wage gap All above studies show the existence of gender wage inequality in Vietnam with strong statistical evidence However, none of these papers has really focused on analyzing glass ceiling and sticky floor effects In addition, it is important to know at which quantiles of wage distribution the wage inequality is stronger If the existence of the glass ceiling and sticky floor effects are cofirms, this will provide important guidance for policy makers to focus specifically on specific income groups where the gender wage inequality is most serious 1.3 OBJECTIVES The study aims to achieve the following objectives: - Investigate the existence of glass ceiling and sticky floor on Vietnam‟s labour market - Investigate the floor stickiness and glass ceiling effects by groups which formed by living areas (urban – rural), by sectors (state - private), by education and by occupations 1.4 CONTRIBUTIONS By employing quantile regression on the VHLSS 2014, the results of this research project have helped the article to contribute as follow - Firstly, with the latest data available, this study reinforces the empirical evidence of the existence of gen wage inequality in Vietnam This is consistent with previous research in Vietnam - Secondly, this paper sheds light on the overview of gender wage inequality in Viet Nam By investigating the existence of glass ceiling and sticky floor of wages we confirm that the gender wage inequality mainly occurs in the low wage group (sticky floor effect) and be less severe in high wage group (no glass ceiling effect) - This study also clarifies the glass ceiling and sticky floor effect in each group of labour (urban - rural, state - private, educational, occupational groups Specifically, in terms of urban and rural areas, the sticky floor exists in both regions, but the glass ceiling exists only in rural areas In terms of state and private sectors, the glass ceiling exists in both sectors, while the stick floor is only present in the private sector The cause may be that males are often assigned senior or important position than females Females are still able to participate in high-level leadership but in fact Such cases are quite rare If this happens, females SkillLabourer 0.165 -0.0821 [0.74] [-0.43] ManualLabourer 0.134 0.355** [1.63] [2.41] OperationWorker 0.230*** 0.522*** [2.61] [3.11] Intercept 0.00979 -3.010*** [0.03] [-5.33] Observations 683 683 *,**,*** : significant at 10%, 5%, 1% respectively 0.196 [0.64] 0.153 [1.51] 0.321*** [2.83] -0.387 [-0.95] 683 0.238 [1.09] 0.0939 [1.24] 0.169** [2.00] 1.139*** [4.05] 683 0.0882 [0.50] 0.0463 [0.79] 0.0496 [0.73] 1.617*** [7.66] 683 0.151 [0.83] 0.0621 [0.48] 0.0522 [0.36] 1.936*** [4.18] 683 Vocational degree Variables male age age2 married staterun private foreign race urban Manager OLS 0.1 0.25 Quantile regression 0.5 0.168*** [4.35] 0.0837*** [5.66] 0.229*** [2.89] 0.0960*** [3.45] 0.157*** [2.58] 0.0791*** [3.78] 0.179*** [4.72] 0.0604*** [4.51] -0.00094*** -0.0012*** -0.00091*** [-4.84] [-3.37] [-3.37] [-3.55] [-2.44] [-2.81] 0.184*** [4.04] 0.296*** [4.86] 0.176*** [3.17] 0.291*** [3.86] -0.0314 [-0.49] 0.0780** [2.16] 0.540*** [6.29] 0.228** [1.96] 0.153 [1.39] 0.127 [0.86] 0.0778 [0.56] 0.113 [1.58] 0.158** [2.41] 0.245*** [2.90] 0.0991 [1.25] 0.250** [2.33] 0.0170 [0.15] 0.0804 [1.48] 0.109** [2.46] 0.242*** [4.45] 0.173*** [3.42] 0.306*** [4.55] -0.0820 [-1.20] 0.103*** [2.97] 0.111* [1.91] 0.273*** [3.81] 0.170*** [2.60] 0.305*** [3.41] -0.0372 [-0.42] 0.0865* [1.88] 0.155*** [2.91] 0.255*** [3.79] 0.0778 [1.30] 0.286*** [3.43] 0.0299 [0.40] 0.0734* [1.77] -0.0636 [-0.39] [1.18] 0.210 [0.73] [1.77] -0.150 [-0.68] [1.49] 0.133 [0.92] [3.17] -0.0203 [-0.11] [0.07] -0.0239 [-0.14] [1.09] 66 -0.00061*** 0.75 0.9 0.161*** [3.30] 0.0560*** [3.14] 0.170*** [3.81] 0.0581*** [3.45] -0.00056** -0.00061*** HighLevelExpert 0.128 [1.17] 0.395* [1.90] 0.263 [1.57] 0.176 [1.65] 0.0259 [0.19] 0.108 [0.82] AverageLevelExpert 0.100 0.287* 0.174 0.238*** 0.00694 0.105 [1.18] [1.77] [1.49] [3.17] [0.07] [1.09] 67 OfficeStaff -0.0369 [-0.37] 0.0241 [0.23] 0.0979 [0.53] 0.0357 [0.19] -0.00263 [-0.02] 0.0697 [0.52] 0.0284 [0.33] 0.175** [2.07] -0.0596 [-0.52] -0.0566 [-0.52] 0.0238 [0.23] 0.0478 [0.47] SkillLabourer 0.0142 [0.11] 0.292 [1.62] 0.125 [0.53] 0.0523 [0.35] -0.0122 [-0.06] -0.154 [-0.88] ManualLabourer 0.0594 [0.72] 0.282* [1.69] 0.116 [1.00] 0.138* [1.89] -0.0813 [-0.85] 0.0107 [0.12] OperationWorker 0.132 [1.58] 0.331** [2.01] 0.217* [1.88] 0.244*** [3.32] -0.00621 [-0.07] 0.129 [1.42] 0.913*** [3.30] 912 -0.339 [-0.63] 912 0.740* [1.90] 912 1.341*** [5.38] 912 1.858*** [5.62] 912 1.962*** [6.32] 912 Service Intercept Observations *,**,*** : significant at 10%, 5%, 1% respectively Bachelor Variables male age age2 married staterun private foreign OLS Quantile regressi on 0.25 0.5 0.75 0.121*** 0.168*** 0.176*** [4.11] [5.36] [5.52] 0.106*** 0.0937*** 0.0949*** [8.43] [6.96] [6.90] 0.177*** [5.61] 0.145*** [9.18] 0.1 0.0900 [1.34] 0.194*** [6.55] -0.00153*** -0.00211*** -0.00105*** -0.000922*** -0.000935*** -0.000759** [-7.85] 0.0838* [1.72] 0.168* [1.73] 0.257*** [2.61] 0.405*** [3.43] [-5.71] 0.401*** [5.22] 0.326** [2.18] 0.355** [2.42] 0.418** [2.36] [-6.62] 0.148*** [3.98] 0.382*** [4.85] 0.373*** [4.71] 0.537*** [6.02] [-5.42] 0.0691* [1.66] 0.223*** [2.64] 0.303*** [3.55] 0.477*** [4.94] [-5.36] -0.0130 [-0.30] 0.274*** [3.04] 0.438*** [4.90] 0.674*** [6.76] [-2.19] 0.0325 [0.38] 0.107 [0.67] 0.210 [1.33] 0.499*** [2.82] [2.80] [1.07] [2.76] [1.70] [1.39] [1.31] 67 0.9 0.205*** [3.56] 0.0793*** [2.90] race urban Manager 0.000354 [0.01] 0.0634* [1.83] 0.243*** 0.207 [1.52] 0.139** [2.05] 0.198 0.0335 [0.54] 0.120*** [3.88] 0.244*** -0.0490 [-0.74] 0.0807** [2.45] 0.166* -0.0736 [-1.06] 0.0604* [1.81] 0.138 -0.0366 [-0.28] -0.00593 [-0.10] 0.236 [2.80] [1.07] [2.76] [1.70] [1.39] [1.31] 68 HighLevelExpert 0.152** 0.0676 [2.16] [0.47] AverageLevelExpert -0.0113 0.0581 [-0.15] [0.37] OfficeStaff -0.164* -0.211 [-1.70] [-1.14] Service -0.444*** -0.552*** [-4.25] [-2.68] SkillLabourer -0.828** -1.253*** [-2.14] [-5.38] ManualLabourer -0.311** -0.363 [-2.39] [-1.58] OperationWorker -0.494*** -0.579** [-2.92] [-2.48] Intercept -0.0677 -2.190*** [-0.22] [-3.62] Observations 1373 1373 *,**,*** : significant at 10%, 5%, 1% respectively 0.152** [2.07] 0.0891 [1.13] -0.0808 [-0.90] -0.486*** [-5.10] -1.413*** [-8.96] -0.284** [-2.55] -0.598*** [-4.95] 0.0839 [0.34] 1373 0.0458 [0.56] -0.0534 [-0.61] -0.194** [-1.98] -0.481*** [-4.66] -0.683** [-2.51] -0.471*** [-3.91] -0.421*** [-3.23] 0.992*** [3.71] 1373 -0.00267 [-0.03] -0.198** [-2.28] -0.285*** [-2.92] -0.460*** [-4.42] -0.794*** [-4.52] -0.441*** [-3.58] -0.682*** [-5.14] 1.340*** [4.96] 1373 Postgraduate Variables male age age2 married staterun private foreign urban OLS Quantile regression 0.25 0.5 0.75 -0.0775 0.256 0.376 [-0.18] [1.18] [1.23] 0.0370 0.0511 0.0650 [0.18] [0.53] [0.53] 0.142 [1.21] 0.0627 [1.02] 0.1 -0.0738 [-0.09] 0.0273 [0.04] -0.000704 -0.000284 -0.000428 -0.000608 -0.000780 0.000393 [-0.95] -0.236 [-1.57] 2.188*** [6.55] 2.412*** [5.90] 2.334*** [6.97] 0.385** [-0.03] -0.200 [-0.30] 2.385 [0.75] 2.653 [0.71] 2.842 [0.88] 0.527 [-0.17] -0.209 [-0.40] 2.683** [2.26] 2.836** [2.10] 2.938** [2.43] 0.249 [-0.54] -0.0829 [-0.28] 2.074*** [3.73] 2.070*** [3.09] 2.167*** [3.54] 0.498* [-0.54] -0.283 [-0.73] 1.795** [2.37] 1.939** [2.06] 1.879** [2.38] 0.620 [0.46] -0.195 [-1.63] 2.443*** [5.54] 2.657*** [4.73] 2.235*** [4.69] 0.373** 68 0.9 0.137 [0.82] -0.0262 [-0.37] 0.169 [1.18] -0.115 [-0.74] -0.133 [-0.78] -0.419** [-2.24] -1.036*** [-4.69] -0.147 [-0.71] -0.402* [-1.83] 1.964*** [3.69] 1373 [2.57] [0.60] [0.46] Manager -0.00880 -0.476 -0.381 [-0.07] [-0.48] [-0.91] HighLevelExpert -0.0266 -0.641 -0.464 [-0.24] [-0.54] [-1.19] AverageLevelExpert 0.320 0.209 -0.101 [1.36] [0.12] [-0.12] OfficeStaff -0.337** -0.491 -0.601 [-2.33] [-0.44] [-0.94] Service -0.0395 -0.611 -0.445 [-0.18] [-0.28] [-0.53] Intercept 0.228 0.799 0.659 [0.21] [0.07] [0.18] Observations 77 77 77 *,**,*** : significant at 10%, 5%, 1% respectively [1.84] 0.137 [0.59] 0.0395 [0.22] 0.479 [1.19] -0.189 [-0.42] 0.188 [0.37] 0.298 [0.18] 77 [1.41] 0.251 [0.74] 0.344 [1.37] 0.727 [1.27] -0.0594 [-0.13] 0.138 [0.27] 0.263 [0.13] 77 [2.25] 0.428*** [3.37] 0.438** [2.33] 0.265 [0.88] -0.293 [-1.47] 0.212 [0.62] 1.688 [1.34] 77 APPENDIX B: QUANTILE REGRESSION OF WAGE EQUATION BY OCCUPATION Manager Variables male age age2 married staterun private foreign 0.185* [1.77] 0.137*** [3.23] 0.1 -0.138 [-0.23] 0.156 [0.87] Quantile regressi on 0.25 0.5 0.75 -0.00660 0.137 0.253 [-0.06] [0.85] [1.24] 0.150*** 0.0691 0.0858 [3.31] [1.21] [1.34] -0.0014*** -0.00176 -0.0015*** -0.000585 -0.000740 -0.00101 [-2.83] 0.237 [1.09] 0.321 [1.60] 0.824*** [3.73] 0.490** [-0.83] 0.634 [0.67] -0.430 [-0.66] -0.0865 [-0.10] 0.379 [-3.00] 0.679*** [3.60] -0.106 [-0.48] 0.317 [1.32] 0.506* [-0.89] 0.406* [1.68] 0.171 [0.82] 0.685*** [2.80] 0.571 [-1.02] 0.176 [0.58] 0.550 [1.53] 1.017** [2.56] 0.599 [-0.57] 0.165 [0.18] 0.838 [1.27] 1.571** [2.05] 0.605 OLS 0.9 0.324 [0.87] 0.111 [0.72] [1.99] [0.41] [1.67] race 0.0524 -0.173 -0.0717 [0.46] [-0.19] [-0.39] urban 0.0358 0.0271 0.0207 [0.30] [0.04] [0.17] Secondary -0.518 0.165 0.606* [-0.81] [0.19] [1.77] Highschool 0.559 0.974 1.668*** [1.41] [1.11] [5.96] Vocational -0.0507 0.940 0.913*** [-0.14] [1.10] [3.79] Bachelor 0.700* 1.434 1.637*** [1.97] [1.54] [7.48] Postgraduate 0.707* 1.542* 1.770*** [1.92] [1.86] [6.94] Intercept -0.889 -1.367 -2.067** [-1.08] [-0.37] [-2.19] Observations 135 135 135 *,**,*** : significant at 10%, 5%, 1% respectively [1.26] 0.0706 [0.31] -0.0176 [-0.10] -0.520 [-0.95] 0.361 [0.75] -0.270 [-0.59] 0.570 [1.29] 0.637 [1.34] 0.646 [0.53] 135 [1.27] 0.217 [0.82] -0.0411 [-0.20] 0.661 [1.26] 0.999** [2.32] 0.339 [0.89] 0.974*** [2.81] 0.831** [2.12] -0.214 [-0.16] 135 [0.61] 0.292 [0.61] -0.0459 [-0.07] 0.524 [0.66] 1.289* [1.67] 0.436 [0.50] 1.249 [1.55] 1.073 [1.14] -1.191 [-0.47] 135 High level expert Variables male age age2 married staterun private foreign race 0.145*** [3.85] 0.112*** [5.67] 0.1 0.0275 [0.46] 0.151*** [5.11] Quantile regression 0.25 0.5 0.75 0.140*** 0.164*** 0.122** [4.89] [4.63] [2.32] 0.0949*** 0.0759*** 0.0521** [6.96] [4.51] [2.13] -0.0012*** -0.0015*** -0.00096*** -0.00074*** -0.00045 -0.00028 [-4.84] 0.123** [2.12] -0.264 [-1.47] -0.219 [-1.19] 0.0208 [0.10] -0.154 [-4.22] 0.469*** [7.91] -0.214** [-2.42] -0.268*** [-2.72] -0.347** [-2.57] -0.216 [-5.63] 0.138*** [3.74] -0.260** [-2.21] -0.279** [-2.32] -0.0509 [-0.39] -0.159** [-3.51] 0.0818 [1.64] -0.274* [-1.93] -0.183 [-1.26] 0.0161 [0.10] -0.158* [-1.46] 0.0469 [0.61] -0.210 [-0.96] -0.108 [-0.48] 0.271 [1.13] -0.111 [-0.57] 0.00539 [0.04] -0.0841 [-0.26] 0.0102 [0.03] 0.352 [0.99] -0.159 OLS 0.9 0.134* [1.68] 0.0377 [0.96] [-1.61] [-1.50] [-2.26] urban 0.187*** 0.173*** 0.188*** [4.34] [2.58] [5.76] Primary 1.160* 2.632*** 2.719*** [1.77] [15.01] [16.55] Highschool 0.849 1.777*** 2.204*** [1.26] [7.28] [11.68] Vocational 0.677 1.385*** 1.985*** [1.02] [6.66] [11.07] Bachelor 0.973 1.678*** 2.272*** [1.48] [10.23] [13.99] Postgraduate 1.248* 1.817*** 2.524*** [1.89] [9.22] [14.73] Intercept 0.260 -2.100*** -0.960*** [0.36] [-3.88] [-3.53] Observations 865 865 865 *,**,*** : significant at 10%, 5%, 1% respectively [-1.79] 0.166*** [4.15] 0.639** [2.28] 0.363 [1.19] 0.213 [0.74] 0.409 [1.47] 0.702** [2.45] 1.551*** [4.02] 865 [-0.84] 0.211*** [3.57] 0.416 [1.33] 0.451 [1.28] 0.275 [0.85] 0.481 [1.57] 0.812** [2.51] 2.094*** [4.33] 865 [-0.79] 0.167* [1.88] 0.364 [0.92] 0.632 [1.34] 0.365 [0.85] 0.763* [1.93] 1.058** [2.50] 2.421*** [3.22] 865 AverageLevelExpert Variables male age age2 married staterun private foreign race urban 0.130** [2.58] 0.141*** [6.23] 0.1 0.0885 [0.70] 0.237*** [4.64] Quantile regression 0.25 0.5 0.75 0.0547 0.0872* 0.184*** [1.02] [1.89] [4.13] 0.120*** 0.0981*** 0.109*** [5.59] [5.24] [5.89] 0.9 0.242*** [3.78] 0.0905*** [3.36] -0.0015*** -0.0026*** -0.0012*** -0.0009*** -0.0011*** -0.00089** [-5.19] 0.112* [1.85] 0.0474 [0.26] 0.132 [0.71] 0.507** [2.07] -0.160** [-2.29] -0.0188 [-4.01] 0.263** [2.28] 0.185 [0.67] 0.344 [1.17] 0.570 [1.44] 0.0263 [0.13] 0.000892 [-4.33] 0.0831 [1.34] 0.356*** [2.83] 0.399*** [3.03] 0.703*** [4.25] -0.104 [-1.17] -0.00627 [-3.86] 0.0553 [0.97] 0.149 [1.21] 0.189 [1.49] 0.415*** [2.68] -0.190** [-2.43] -0.00239 [-4.69] -0.0588 [-1.08] 0.0201 [0.20] 0.116 [1.11] 0.358** [2.57] -0.233*** [-3.12] -0.0220 [-2.57] 0.0230 [0.29] -0.757*** [-5.64] -0.744*** [-5.24] -0.440** [-2.18] -0.0791 [-0.73] -0.118** OLS INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING? [-0.37] [0.01] [-0.12] Primary 0.191 0.323 0.218 [1.04] [0.72] [0.98] Secondary 0.191 -0.0326 -0.0181 [0.73] [-0.09] [-0.08] Highschool 0.218 -0.476 -0.153 [1.58] [-1.39] [-0.77] Vocational 0.0599 -0.430 -0.220 [0.82] [-1.53] [-1.27] Bachelor 0.201*** -0.218 -0.00565 [2.88] [-0.78] [-0.03] Postgraduate 0.741*** 0.800*** 0.694*** [12.04] [2.97] [4.08] Intercept 0.0893 -2.340** 0.171 [0.19] [-2.15] [0.37] Observations 479 479 479 *,**,*** : significant at 10%, 5%, 1% respectively [-0.05] 0.310 [1.12] -0.0341 [-0.10] 0.191 [0.73] 0.0380 [0.15] 0.189 [0.77] 0.713*** [2.91] 0.889** [2.00] 479 [-0.51] 0.259 [1.37] 0.698*** [3.58] 0.475*** [2.86] 0.364** [2.50] 0.469*** [3.23] 0.804*** [5.65] 0.937** [2.36] 479 [-1.99] 0.516** [2.35] 0.882*** [4.99] 0.674*** [4.11] 0.673*** [4.70] 0.857*** [5.82] 0.847*** [6.05] 1.780*** [3.26] 479 OfficeStaff Variables male age age2 married staterun private foreign race urban 0.148** [2.05] 0.169*** [5.27] 0.1 0.226 [1.19] 0.290*** [3.97] Quantile regression 0.25 0.5 0.75 0.0391 0.136** 0.113 [0.38] [2.50] [1.32] 0.153*** 0.0853*** 0.101*** [3.76] [4.28] [3.13] -0.0019*** [-4.70] -0.0033*** [-3.69] -0.0018*** [-3.50] -0.00091*** [-3.65] -0.0010** [-2.53] -0.0013 [-1.65] -0.0815 [-0.84] -0.0927 [-0.70] 0.103 [0.74] 0.439** [2.59] -0.0587 [-0.33] 0.151* -0.163 [-0.73] -0.363 [-0.62] -0.185 [-0.33] 0.284 [0.46] -0.0883 [-0.21] 0.259 -0.0234 [-0.18] -0.181 [-0.65] -0.00685 [-0.02] 0.440 [1.42] -0.0196 [-0.10] 0.244** -0.0198 [-0.29] 0.0560 [0.43] 0.131 [1.01] 0.652*** [4.45] -0.249** [-2.54] 0.240*** -0.161 [-1.44] 0.0695 [0.32] 0.377* [1.71] 0.570** [2.35] -0.263* [-1.81] 0.0550 -0.153 [-0.79] 0.240 [1.20] 0.495** [2.50] 0.656** [2.47] -0.172 [-0.56] 0.0648 OLS 0.9 0.101 [0.62] 0.123* [1.96] [1.83] [1.23] [2.27] Primary 0.131 -0.840* 0.0147 [0.57] [-1.93] [0.04] Secondary 0.0854 -0.979* -0.00108 [0.32] [-1.80] [-0.00] Highschool 0.401* -0.401 0.293 [1.77] [-0.85] [0.96] Vocational 0.429* -0.469 0.380 [1.96] [-0.99] [1.26] Bachelor 0.518** -0.237 0.498 [2.32] [-0.51] [1.64] Postgraduate 0.799** 0.210 0.775* [2.38] [0.35] [1.92] Intercept -0.824 -2.742** -0.577 [-1.51] [-2.25] [-0.80] Observations 281 281 281 *,**,*** : significant at 10%, 5%, 1% respectively [4.30] 0.718*** [2.75] 0.899*** [3.57] 1.061*** [4.41] 1.146*** [4.78] 1.252*** [5.21] 1.725*** [5.26] 0.0876 [0.23] 281 [0.62] [0.40] -0.0609 0.0969 [-0.22] [0.22] 0.182 0.489 [0.68] [1.26] 0.379 0.668** [1.62] [2.06] 0.409* 0.615* [1.76] [1.82] 0.496** 0.742** [2.12] [2.30] 0.863*** 0.779* [2.79] [1.91] 0.899 0.333 [1.56] [0.33] 281 281 Service Variables male age age2 married staterun private foreign race urban OLS 0.0856 [1.35] 0.127*** [6.22] -0.0016*** [-6.02] 0.142** [2.06] 0.172** [2.21] 0.120* [1.65] 0.360*** [2.73] 0.359** [1.98] 0.121** [1.97] Quantile regression 0.1 0.25 0.5 0.75 0.9 -0.0350 0.113 0.160*** 0.174** 0.164* [-0.33] [1.61] [2.77] [2.23] [1.81] 0.208*** 0.155*** 0.113*** 0.0817*** 0.0758*** [6.19] [7.39] [6.44] [3.49] [2.82] -0.0027*** -0.0019*** -0.0014*** -0.0010*** -0.00091** [-6.34] [-7.27] [-6.21] [-3.34] [-2.50] 0.369*** 0.201*** 0.106 0.0705 0.0793 [2.95] [2.66] [1.52] [0.78] [0.80] 0.307** 0.0735 0.0623 0.115 0.0865 [2.20] [0.80] [0.78] [1.09] [0.70] 0.187 0.0950 0.0888 0.119 0.102 [1.54] [1.24] [1.33] [1.36] [0.95] 0.666*** 0.236 0.231* 0.198 0.334 [2.69] [1.49] [1.69] [1.13] [1.47] 0.944*** 0.394** 0.277* 0.332* 0.157 [4.35] [2.33] [1.91] [1.81] [1.53] 0.214** 0.142** 0.105* 0.129* 0.103 [2.05] [2.06] [1.86] [1.80] [1.16] Primary 0.161 0.518** 0.0881 0.0307 0.00117 -0.0159 [1.07] [2.14] [0.54] Secondary 0.409*** 0.927*** 0.371** [2.70] [4.02] [2.34] Highschool 0.401** 0.500** 0.311* [2.53] [2.14] [1.95] Vocational 0.610*** 0.888*** 0.541*** [3.76] [3.66] [3.19] Bachelor 0.384** 0.720*** 0.340** [2.40] [2.94] [2.00] Postgraduate 1.575*** 2.402*** 1.928*** [7.99] [7.19] [8.94] Intercept -0.588 -3.788*** -1.369*** [-1.42] [-5.80] [-3.37] Observations 536 536 536 *,**,*** : significant at 10%, 5%, 1% respectively [0.22] 0.216 [1.63] 0.269** [2.02] 0.532*** [3.77] 0.262* [1.81] 1.523*** [8.11] -0.0609 [-0.19] 536 [0.01] 0.359** [2.11] 0.361** [2.14] 0.569*** [3.17] 0.413** [2.27] 1.284*** [5.28] 0.683 [1.58] 536 [-0.09] 0.341** [2.00] 0.375** [2.23] 0.480*** [2.65] 0.348* [1.90] 0.863*** [3.13] 1.250*** [2.72] 536 ManualLabourer Variables male age age2 married staterun private foreign race urban OLS Quantile regressio n 0.75 0.25 0.5 0.480*** 0.347*** 0.276*** [8.25] [11.88] [7.93] 0.0898*** 0.0489*** 0.0420*** [5.24] [5.58] [3.84] 0.9 0.225*** [4.84] 0.0275** [2.02] 0.406*** [9.82] 0.0802*** [5.99] 0.1 0.602*** [6.98] 0.119*** [4.90] -0.000964*** -0.00152*** -0.00116*** -0.000576*** -0.000452*** -0.000241 [-5.43] 0.131*** [3.24] 0.405*** [6.27] 0.228*** [5.40] 0.320*** [5.85] -0.0863 [-1.06] 0.0887*** [2.69] [-4.80] 0.280*** [3.48] 0.310* [1.91] 0.219** [2.29] 0.234* [1.88] 0.126 [0.86] 0.125 [1.64] [-5.09] 0.209*** [3.38] 0.395*** [3.58] 0.161** [2.52] 0.319*** [3.72] 0.149 [1.19] 0.0893* [1.74] [-4.88] 0.109*** [3.31] 0.424*** [7.42] 0.216*** [6.73] 0.281*** [6.56] -0.0281 [-0.43] 0.0844*** [3.15] [-3.02] 0.0587 [1.48] 0.332*** [4.92] 0.190*** [4.95] 0.220*** [4.36] -0.0632 [-0.81] 0.0409 [1.27] [-1.31] 0.0460 [0.90] 0.195** [2.40] 0.0617 [1.19] 0.103 [1.55] -0.0281 [-0.27] 0.0749* [1.71] Primary 0.0729 0.0776 0.0643 [1.12] [0.54] [0.66] Secondary 0.171*** 0.327** 0.191** [2.84] [2.30] [1.98] Highschool 0.152** 0.258 0.116 [2.16] [1.61] [1.08] Vocational 0.209*** 0.352** 0.168 [3.13] [2.24] [1.58] Bachelor 0.0644 -0.0698 0.0212 [0.54] [-0.31] [0.13] Intercept 0.769*** -1.010** 0.134 [3.19] [-2.28] [0.42] Observations 1141 1141 1141 *,**,*** : significant at 10%, 5%, 1% respectively 0.0903* [1.77] 0.150*** [2.96] 0.105* [1.86] 0.163*** [2.89] 0.00609 [0.07] 1.430*** [8.83] 1141 0.0598 [0.96] 0.0618 [1.00] 0.108 [1.55] 0.156** [2.26] 0.0513 [0.51] 1.943*** [10.00] 1141 0.137 [1.61] 0.107 [1.24] 0.206** [2.13] 0.347*** [3.77] 0.43*** [3.23] 2.38*** [9.45] 1141 OperationWorker Variables male age age2 married staterun private foreign race urban Primary Secondary 0.153*** [3.71] 0.0672*** [4.36] 0.1 0.134 [0.97] 0.114*** [2.90] Quantile regression 0.25 0.5 0.75 0.9 0.113** 0.101** 0.159*** 0.191*** [2.34] [2.27] [4.56] [3.56] 0.0743*** 0.0435*** 0.0337*** 0.00549 [5.85] [3.26] [3.01] [0.29] -0.00075*** -0.0014*** -0.00091*** -0.00051*** -0.00029* 0.000080 [-3.60] 0.0494 [0.98] 0.279*** [4.09] -0.0129 [-0.23] 0.121** [2.04] 0.108 [0.90] 0.00577 [0.14] 0.117 [1.06] 0.247** [-2.64] 0.0255 [0.17] 0.329 [1.44] -0.0236 [-0.14] 0.0267 [0.14] 0.288 [0.96] 0.0710 [0.59] -0.0930 [-0.28] 0.212 [-5.27] 0.0343 [0.74] 0.328*** [4.22] -0.0150 [-0.28] 0.132** [2.18] 0.266*** [2.63] 0.0499 [1.29] -0.0278 [-0.26] 0.127 [-2.75] 0.0315 [0.67] 0.266*** [3.47] -0.00698 [-0.13] 0.0997 [1.62] 0.127 [1.23] 0.0239 [0.61] 0.0575 [0.53] 0.162 [-1.90] 0.0428 [1.10] 0.237*** [3.75] 0.0396 [0.85] 0.138*** [2.66] 0.176** [1.99] 0.0168 [0.51] 0.163* [1.67] 0.252*** [0.30] 0.147** [2.26] 0.398*** [3.73] 0.0475 [0.59] 0.158* [1.84] -0.0956 [-0.64] -0.0687 [-1.24] 0.211 [1.29] 0.277* OLS [2.29] [0.65] [1.22] [1.52] [2.60] [1.71] Highschool 0.163 0.193 0.0371 [1.45] [0.56] [0.34] Vocational 0.267** 0.155 0.0841 [2.51] [0.49] [0.80] Bachelor -0.1000 -0.536 -0.223* [-0.52] [-1.35] [-1.70] Intercept 1.231*** -0.157 0.983*** [4.50] [-0.21] [4.15] Observations 706 706 706 *,**,*** : significant at 10%, 5%, 1% respectively 0.113 [1.03] 0.219** [2.03] 0.000780 [0.01] 1.837*** [7.63] 706 0.180* [1.82] 0.323*** [3.34] 0.190 [1.60] 1.949*** [10.09] 706 0.158 [0.95] 0.422*** [2.69] 0.263 [1.39] 2.787*** [8.41] 706 ... gap in the rural and urban areas in India Their findings show evidence of the sticky floor effect in the urban sector and evidence of the glass ceiling effect in the rural sector The gender wage. .. studies have investigated the sticky floor and glass ceiling effects, that mean they not determine whether the wage gap is stronger at low quantiles (sticky floor) or at high quantiles (ceiling effect)... but the glass ceiling exists only in rural areas In terms of state and private sectors, the glass ceiling exists in both sectors, while the stick floor is only present in the private sector The