The Gender Wage Gap in the Vietnamese Transition, 1993 – 2008 Huong Thu Le
Ha Trong Nguyen *
This paper uses data from the Vietnam Living Standard Surveys to examine wages and the gender wage gap between 1993 and 2008. During this period of accelerated transition and opening up the economy, real wages of Vietnamese workers nearly tripled. Wage growth was highest for low wage workers and wage inequality declined. By gender, wage growth was higher for women than men. As a result, the gender wage gap reduced significantly from 1993 to 2008. Our results also reveal a slight increase in the mean of the gender wage gap from 2002 to 2008, which is mainly driven by a sharp increase in the gender wage gap for low wage workers. The Oaxaca-Blinder decomposition method applied to the unconditional quantile regression is employed to isolate factors contributing to the gender wage gap and the wage growth across the whole wage distribution. The results suggest that the major part of the gender wage gap attributes to the gender discrimination. While the gender discrimination decreases for high wage workers, it increases for low wage workers and this is a major explanation for an emergence of the stickyfloor phenomenon in 2008. Over the period, wage growth is partly explained by changes in average characteristics but mainly due to increasing returns.
JEL classification: J31; J71; P20; C13. Keywords: Gender wage gap; Vietnam; transition; unconditional quantile regression; decomposition.
*
Huong Thu Le, School of Mathematical Sciences, Queensland University of Technology and School of Population and Global Health, University of Western Australia. Ha Trong Nguyen (corresponding author; email:
[email protected]; Tel: +61 8 9266 5711), Bankwest Curtin Economics Centre, Curtin University. Acknowledgements: The authors gratefully acknowledge constructive comments provided on an earlier draft by the Co-editor, Liam Kelley, and three anonymous referees of this journal. We are also grateful to Alison Booth, Bob Breunig, Tue Gørgens, Amy Liu and Mathias Sinning for helpful comments on earlier drafts of this paper. We would like to thank participants at the 34th Pacific Trade and Development Conference (PAFTAD) for comments and suggestions. Any errors are our own.
1.
Introduction
The gender gap in labor market outcomes has now been intensively explored (ILO 2016; Blau & Kahn 2017). Due to significant changes occurred during the transition from a centrally planned economy to a market economy, the issue of the gender gap in transitional economies has also been received a lot of attention. One of the particular research and policy interests is how the gender wage gap evolves during the transition. While there is a rich literature on gender differences in wages accrued from the last four decades, studies on transitional economies provide rather mixed results. 1 Specifically, studies by Brainerd (2000), Jolliffe (2002), Jolliffe and Campos (2005), Keane and Prasad (2006), Newell and Socha (2007) have consistently found that relative wages of female workers have improved significantly and discrimination has declined remarkably in most Eastern European countries during the transition process. In contrast, a substantial decline in women’s relative wage was found during transition in Russia and Ukraine (Brainerd 1998, 2000; Ganguli & Terrell 2006). The existing evidence for China is also mixed. While Maurer-Fazio and Hughes (2002) and Chi and Li (2008) find that the gender wage gap in urban China increased over the course of the reform, Meng (1998) finds that the marketization helped to reduce the gender wage discrimination in the newly developed rural industrial sector. This paper aims to contribute to the literature by investigating the gender wage gap in the transitional economy of Vietnam. Over the last three decades, Vietnam has made a remarkable transition from a centrally planned subsidy economy to an open market economy. The country has experienced strong economic 1
The gender wage gap is the average difference between a man's and a woman's remuneration. For concentration purposes, the
literature review only focuses on gender wage gap in transitional economies. For recent reviews of the global gender wage gap, see, for example, ILO (2016) and Blau and Kahn (2017).
1
growth, substantial improvements in living standards and one of the greatest reductions in poverty in the world (Pincus & Sender 2008; Rama 2010). The transition away from a centrally planned economy and the concurrent strong economic growth have both triggered enormous changes in the functioning of the Vietnamese labor market. Although it seems likely that these changes have had different effects on the labor market success of men and women, very little is known about gender differences in the Vietnamese labor market. Due to the changes that took place since the renovation started in 1986, Vietnam is an interesting transitional country on which to base a study of the gender wage gap. So far, only a few studies have examined the problem. Using data from the Vietnam Living Standard Surveys in 1993 and 1998, Liu (2004) finds that the gender wage gap declined significantly during the period and most of the reductions in the gender wage gap at the mean come from improvements in observed characteristics. Pham and Reilly (2007) examine the gender wage gap along the distribution and extend the studied period to 2002. They also find a reduction of the gender wage gap at the mean. Along the distribution, the narrowing of the gender wage is greatest at the top of the distribution. Their quantile-regression-based decomposition results show that the treatment effect is stable across the distribution. This study provides a threefold contribution to the literature, with the first two being methodological while the last relates to the extended data window used for Vietnam. The first contribution is the use of the newly developed unconditional quantile regression method of Firpo et al. (2009) to examine the determinants of hourly wages at selected quantiles along the distribution. Different from the traditional conditional quantile regression method of Koenker and Bassett (1978), the newly developed unconditional quantile regression method does not require the rank-preserving condition. Its estimated coefficients are explained as the impact of 2
changes in the distribution of explanatory variables on the quantiles of the unconditional marginal distribution of the dependent variable (Firpo et al. 2009). Therefore, the OaxacaBlinder type decomposition can be applied directly to the estimation results of the unconditional quantile regression in order to isolate the contributing factors to the overall gender wage gap, without having to do many simulations as in the quantile regression based decomposition method developed by Machado and Mata (2005). This represents the second contribution to the literature. The third contribution is the examination of a period longer than previous studies on the Vietnamese gender wage gap, from 1993 up to 2008. As discussed above, this period is important for Vietnam not only because of its continuously high economic growth, but also because of its significant restructuring inside the economy and its accelerated opening and integrating into the world. 2 Additionally compared to previous studies on the gender wage gap in Vietnam our study controls for a richer set of covariates including working industry and working sector by ownership structure. These variables are important in explaining wage determinants, wage growth as well as the gender wage gap during the country’s transition with significant structural changes. The paper’s main findings are that as the Vietnamese economy becomes more marketized, opening and integrated into the world economy, wages increased and women have benefited relative to men in the labor market. For a 15-year transitional period, the average real wage of Vietnamese workers nearly triples. Wage inequality reduces significantly. Interestingly, wage 2
Vietnam resumed relations with the International Monetary Fund and the World Bank in 1992. The US trade embargo against
Vietnam was removed in 1994. Vietnam established political normalization with the United States in 1995, became a member of the Association of Southeast Asian Nations (ASEAN) in 1995, ASEAN Free Trade Area (AFTA) in 1996, Asia-Pacific Economic Cooperation (APEC) in 1998, signed the Bilateral Trade Agreement with the US in 2000, and joined the World Trade Organization in 2007.
3
growth is higher for low wage workers. By gender, wage growth is higher for female than for male workers and this resulted in a significant reduction of the average gender wage gap. In all years, most of the gender wage gap attributes to the gender difference in return to observed characteristics and to unobserved factors, also known as the ‘unexplained gap’ or ‘discrimination’. Over the studied period, improvements in observed productivity related characteristics of Vietnamese women relative to men play an important role in narrowing the gender wage gap. While gender ‘discrimination’ decreases for high wage workers, it increases for low wage workers and this is a major explanation for an emergence of the sticky-floor phenomenon in 2008. 3 Wage growth is partly explained by changes in average characteristics but is mainly due to increasing returns. The remainder of the paper is structured as follows. Section 2 summarizes the Vietnamese transition and gender differences in the labor market during the transition. Section 3 describes the data and provides overall descriptive statistics. Section 4 investigates wage determinants, both at the mean and at selected quantiles along the distribution using the OLS and the unconditional quantile regression method of Firpo et al. (2009). Section 5 identifies factors contributing to wage growth and the gender wage gap using the Oaxaca-Blinder decomposition method with application to the unconditional quantile regression. Section 6 presents concluding remarks.
3
Booth et al. (2003) define “glass-ceiling” as the phenomenon when the gender wage gap is wider at the top of the distribution.
The opposite phenomenon when the gender wage gap is wider at the bottom of the distribution is called “sticky-floor”.
4
2.
Background of the Vietnamese transition and gender differences in the labor
market During a long period as a centrally planned economy, Vietnam did not have a ‘labor market’ in the sense of market-determined employment and payment. Jobs and wages were planned and assigned by the government, the only employer in the economy. Once taken, employment was for life (ADB 2006). The non-existence of competition in the labor market and the egalitarian distribution mechanism created no incentive for people to work hard or to be innovative. The country’s transition and accelerated integration into the world economy have created opportunities and challenges which have had different effects on the labor market success of men and women. On one hand, the elimination of the centrally determined wage system may have contributed to a widening gender wage gap. The state-owned enterprises (SOE) restructuring in the late 1980s and early 1990s resulted in a large number of workers losing their jobs - about two third of the laid-off workers were women (Dollar & Litvack 1998; Bales & Martin 2002). The shrinking of the state sector also reduced a variety of support services and job guarantees, such as generous maternity benefits, extensive provision of childcare centers, lower effort levels and a more generous old-age pension regime. These services were particularly relevant for female workers. As a result, female workers were supposed to be vulnerable to the massive downsizing of the state sector (Pham & Reilly 2007). In addition, between 1988 and 1992 about a half million soldiers were demobilized and returned to the civilian labor force (Dollar 1994; McCarty 2001). This phenomenon created employment pressure and also the potential for the development of both the formal and informal private sectors. On the other hand, Vietnam’s shift from its centrally planned and self-reliance economy to a market-oriented economy along with its exposure to the international markets is likely to 5
offer better prospects for women. For example, the development of newly emerged exportoriented light industries is more likely to create jobs for women (Rama 2001). In order to regulate the newly emerged labor relations in transition and to protect women, Vietnam has improved its legal framework significantly. The Vietnamese labor code, first issued in 1994 and amended two times in 2002 and 2006, includes a separate chapter on provisions concerning female workers (“Chapter X”). According to this regulation, female workers are entitled to four to six months of maternity leave (article 114). During the period of maternity leave, female workers who have paid social insurance contributions are entitled to social security benefits equivalent to 100% of their wage and to an additional allowance of one month’s wage for the first and second maternity period (article 144). Employers are not allowed to dismiss or terminate the employment contract of female workers unilaterally because of marriage, pregnancy, maternity leave or breast-feeding a child under 12 months of age, except in cases where the enterprise ceases its activities (article 111). With these regulations, the employment of female workers produces extra costs. Employers would prefer to employ male workers (Pham & Reilly 2007) and thus a potential for widening the gender wage gap. From the country’s demographic characteristics, it can be seen that Vietnamese women have been over-represented in the country’s population as consequences of the Vietnam War and because of higher life expectancy of women compared to men. In addition, Vietnam had a big jump in its birth rate after the Vietnam War in 1975. As a result, there was a remarkable increase in the number of young people entering the labor force during the period between the late 1990s and early 2000s (MOLISA & ILO 2010). This phenomenon created a strong competitive pressure for the youth in the labor market. Given the above channels, it is uncertain whether
6
Vietnamese women have lost or gained from the country’s transition and international market integration. 3.
Data and overall descriptive statistics
3.1.
Data
We use data from six waves of the nationally representative Vietnam Living Standard Surveys. The first two waves were undertaken in 1992/1993 and 1997/1998, called VLSS-1993 and VLSS-1998, respectively. The respective sample size of VLSS-1993 and VLSS-1998 is 4,800 and 6,002 households. The next four waves were undertaken biennially between 2002 and 2008, and called VHLSS-2002, VHLSS-2004, VHLSS-2006 and VHLSS-2008. These are nationally representative surveys conducted by Vietnam’s General Statistics Office (GSO) with technical assistance from the World Bank. Although the subsequent VHLSS questionnaires were simplified compared to the first two waves of VLSS, the questionnaire design in both is based on the Living Standard Measurement Surveys of the World Bank. In our study, we use the core model of the VHLSS which includes details and comparable information on an individual’s human capital and current labor market activity. The sample size for the core model is 29,532 households for VHLSS-2002, 9,188 for VHLSS-2004 and 9,189 for VHLSS-2006 or VHLSS2008. There are panel samples between the first two waves – VLSS 1993 and 1998 and also panel samples among the last four waves VHLSS 2002, 2004, 2006 and 2008. However, households are not re-interviewed between the VLSS and the VHLSS. For the purpose of observing the whole period and making the observed sample nationally representative, all waves are analyzed in separate cross-sections.
7
To give an overall picture of the labor force behavior by gender, we firstly use all six waves. Then we particularly focus on three waves: VLSS-1993 (the earliest survey), VHLSS2002 and VHLSS-2008 (the most recent available survey) to examine wage growth as well as the gender wage gap. The observed wage sample includes those who are in the labor age (aged between 15 and 55 years) working and having their most time consuming job in the last 12 months as paid employment. 4 We use spatial and temporal price indexes given by the surveys and Consumer Price Indexes (CPI) given by GSO to convert nominal wages to the real values of January, 2008. 3.2.
Labor force participation by gender, 1993-2008
Labor force participation rate Table 1 presents the overall labor force participation (LFP) rates and the rates by gender. Over the studied period as the economy becomes more market-oriented, the LFP rates declined. This is true for both men and women. Particularly, the national LFP rate reduces from 88% in 1993 to 80% in 2008. By gender, the female LFP rate reduces from 87% in 1993 to 78% in 2008; the male (LFP) rate reduces from 90% in 1993 to 81% in 2008. In all years, the LFP rate of Vietnamese women is just slightly lower than that of men. Even with a rate of 78% in 2008, the female LFP rate of Vietnam is still much higher than the rate of other countries in the world as well as in the Asia region (ILO 2010). This characteristic of a high female LFP rate observed in Vietnam is similar to the situation in other transitional countries departing from the centrally planned mechanism (Brainerd 2000).
4
Article 6 of the Vietnamese Labor Code (1994) regulates that employees are persons who are at least 15 years old, able to work
and who have a labor contract. The current retirement age in Vietnam is 60 years for men and 55 years for women. To allow comparisons between the two groups, we restrict the sample to men and women below 55 years.
8
[Table 1 about here] Daily working hours The average daily working hours of wage workers increased from 7.7 hours in 1993 to 8.1 hours in 2008 (See Figure 1). By gender, average male workers in 1993 worked 0.5 hours a day longer than female workers. The gap decreases to 0.2 hours a day in 2008 due to the increase in daily working hours of female workers. [Figure 1 about here] The share of wage employment Table 1 also shows the share of wage employment from 1993 to 2008. Over time, as the economy becomes more marketized, the private sectors are recognized and the Vietnamese labor market becomes more formalized. Consequently, the informal sector shrinks and the proportion of wage employment increases. Particularly, in 1993, around 17% of working people are wage employed in their main job. The rate increases to 20% in 1998, 28% in 2002 and nearly 30% in 2008. 5 By gender, the proportion in wage employment for men is considerably higher than that for women in the initial observed year. The rates are 20% and 14% for men and women, respectively. Between 1993 and 2008, the proportion of men in wage employment increases faster than that of women. As a result, at the end of the observed period, in 2008, there are 35% of women but only 24% of men working in wage employment. 3.3.
Wage distribution, wage growth and the raw gender wage gap
Wage distribution
5
There are some people who report a work wage but this wage work is not their most time consuming job in the last 12 months.
If we include these people in the wage sample, the proportion of those in paid employment is 17% in 1993, 20% in 1998, 30% in 2002 and up to 34% in 2008.
9
Over time, the returns to education Table 2 shows real hourly wages at the mean and at selected quantiles for men and women separately in 1993, 2002 and 2008. A graph of the real wage distributions by gender is in Appendix B. There are some interesting points to note. First, in all years - 1993, 2002 and 2008 - the male wage density lies to the right of the female one, indicating that male wages are higher than female wages at almost all points of the distribution. Second, from 1993 to 2008, the wage distribution of both men and women becomes more and more concentrated, suggesting that the wage distribution becomes more equal within groups of male and female wage earners. This is consistent with the evolution of the 90th over 10th percentile ratio, presented in columns 11 and 12 of Table 2, and the wage inequality indexes presented in Appendix C. Particularly, the wage level of workers at the 90th percentile is more than six times the wage level of workers at the 10th percentile in 1993. This declines to about four times for men and more than five times for women in 2008. The Gini coefficient also decreases from 0.44 in 1993 to 0.37 in 2008. By gender, the Gini coefficient of women decreases from 0.44 in 1993 to 0.40 in 2008. The Gini coefficient of men decreases even more, from 0.43 in 1993 to 0.35 in 2008. Wage growth Between 1993 and 2008, average real wages of Vietnamese workers nearly triples. By gender, wage growth is higher for women than for men at the mean. This is also true for the middle and the upper parts of the wage distribution. However, at the bottom of the wage distribution, women have a lower rate of wage growth than men over the whole period and this is a major cause for an emergence of the sticky floor phenomenon in 2008. Table 2 also shows that wage growth is higher in the initial observed transitional period, 1993 to 2002 than in the latter observed
10
transitional period, 2002 to 2008. Along the distribution, for both men and women, wage growth is highest for low wage workers, the bottom 10th percentile. The raw gender wage gap Columns 13 to 16 of Table 2 present the male-female wage ratios at the mean and at selected points along the distribution. It can be seen that in 1993, at the mean, male workers receive 24% higher wages than female workers. Along the distribution, the rates are 19% at the 10th quantile and 28% at the 50th and the 90th quantiles. From 1993 to 2002, the average gender wage gap decreases by more than half with most of the reduction occurs in the middle and the upper parts of the distribution. This is consistent with Pham and Reilly (2007). However, analyzing the gender wage gap for a longer period, up to 2008, we find that from 2002 to 2008, while the gender wage gap is relatively stable at the mean, it evolves differently along the distribution (Figure 2). In particular, from 2002 to 2008, the gender wage gap continues to decrease for high wage workers, but it increases remarkably for low wage workers and this is a major explanation for an emergence of the sticky-floor phenomenon in 2008. [Table 2 and Figure 2 about here] 4.
Wage determinants along the distribution
4.1.
Unconditional quantile regression method
Our descriptive statistics show that the mean wage is always higher than the median, that the shape of the wage distribution is right skewed, and that it contains extreme values. These characteristics of the data suggest the need for exploring wage determinants not only at the mean but also along the distribution. Quantile regression technique is employed for our purpose. While the traditional (conditional) quantile regression method of Koenker and Bassett (1978) allows us to evaluate the effects of a change in the covariates on the entire distribution of 11
the response variables, the method is restrictive in that a change in the distribution of the covariates may change the interpretation of the estimated coefficients (Firpo et al. 2009). Without the rank-preserving condition, the estimated coefficients from the (conditional) quantile regression are not explained as the impacts of a change in the covariates on the outcome variable of interest for those at a specific point of the distribution. In this paper we use the unconditional quantile regression method of Firpo et al. (2009) because of its advantage over the traditional (conditional) quantile regression method in evaluating the impact of changes in the distribution of explanatory variables on the unconditional marginal distribution of the outcome variable. The unconditional quantile regression method does not require the rank-preserving condition. As a result, its estimated coefficients can be interpreted as the impacts of changes in the distribution of the covariates on quantiles of the outcome variable. Therefore, we can apply the Oaxaca-Blinder decomposition directly to the results of the unconditional quantile regression to separate factors contributing to wage growth and the gender wage gap without having to do many simulations as in the quantile regression based decomposition proposed by Machado and Mata (2005).6 The central idea of the unconditional quantile regression introduced by Firpo et al. (2009) is the re-centered influence function(RIF). The RIF is the sum of the value function and the influence function (IF). For example, let T be the value function and F be the probability for which T is defined. Then a slight perturbation of F by a point mass in y is the mixture
distribution from which an observation has probability (1 − ε) of being generated by F and probability (𝜀𝜀 ) of being an arbitrary value 𝛿𝛿𝑦𝑦 , is written as: 6
See Le and Booth (2014) and Le & Nguyen (2018) for more details about the unconditional quantile regression method.
12
𝐹𝐹𝜀𝜀 (𝑦𝑦) = (1 − 𝜀𝜀 ). 𝐹𝐹 + 𝜀𝜀. 𝛿𝛿𝑦𝑦
(1)
The influence function of an estimator 𝑇𝑇 with probability distribution 𝐹𝐹 at point 𝑦𝑦 is given by: 𝐼𝐼𝐼𝐼𝑇𝑇,𝐹𝐹 (𝑦𝑦) = 𝑙𝑙𝑙𝑙𝑙𝑙𝜀𝜀→0
{𝑇𝑇[𝐹𝐹𝜀𝜀 (𝑦𝑦)]−𝑇𝑇[𝐹𝐹(𝑦𝑦)]} 𝜀𝜀
= 𝑙𝑙𝑙𝑙𝑙𝑙𝜀𝜀→0
�𝑇𝑇�(1−𝜀𝜀).𝐹𝐹+𝜀𝜀.𝛿𝛿𝑦𝑦 �−𝑇𝑇(𝐹𝐹)� 𝜀𝜀
(2)
if the limit exists. Being a form of the Gâteaux derivative, the influence function measures the extent to which an estimator is influenced by adding an additional observation (Hampel 1974). Given the influence function, the re-centered influence function (RIF) of an estimator T with probability distribution 𝐹𝐹 at point 𝑦𝑦 is: 7
𝑅𝑅𝑅𝑅𝑅𝑅(𝑦𝑦) = 𝑇𝑇[𝐹𝐹(𝑦𝑦)] + 𝐼𝐼𝐼𝐼𝑇𝑇,𝐹𝐹 (𝑦𝑦)
Following the definition, the 𝑅𝑅𝑅𝑅𝑅𝑅 of a given 𝜏𝜏 𝑡𝑡ℎ quantile, is:
𝑅𝑅𝑅𝑅𝑅𝑅(𝑦𝑦; 𝑞𝑞𝜏𝜏 ) = 𝑞𝑞𝜏𝜏 + 𝐼𝐼𝐼𝐼 (𝑦𝑦; 𝑞𝑞𝜏𝜏 ) = 𝑞𝑞𝜏𝜏 +
(3)
𝜏𝜏−1{𝑦𝑦≤𝑞𝑞𝜏𝜏 } 𝑓𝑓𝑌𝑌 (𝑞𝑞𝜏𝜏 )
(4)
where 1{𝑦𝑦 ≤ 𝑞𝑞𝜏𝜏 } indicates the dummy variable for whether the value of 𝑦𝑦 is below 𝑞𝑞𝜏𝜏 ; 𝑓𝑓𝑌𝑌 (𝑞𝑞𝜏𝜏 ) is the density of Y evaluated at 𝑞𝑞𝜏𝜏 .
The estimation of 𝑅𝑅𝑅𝑅𝑅𝑅 at a given τth quantile involves two estimated components. The
first component: q� τ - the τth population quantile - is estimated as in Koenker and Bassett (1978).
The second component: f̂Y (q� τ ) is density estimator of Y at point q� τ - is estimated using the
7
+∞
Because the expected value of the IF equals to zero: ∫−∞ IF�y, T(F)� dF(y) = 0 so the expected value of the RIF is exactly the +∞
+∞
value function: ∫−∞ RIF�y, T(F)�dF(y) = ∫−∞ �T(F) + IF�y, T(F)��dF(y) = T(F). Therefore, the law of iterated expectations +∞
applied to the conditional mean: Ex [f(y|x)] = ∫−∞ f(y|x)f(x)dx = f(y), can also be applied to the RIF : Ex [RIF(y|x)] =
T�F(y)�. This important characteristic enables the estimated coefficient from the unconditional quantile regression using RIF to
be explained similarly as the estimated coefficient from the ordinary least squares (OLS) regression but applied to any statistics of interest (Firpo et al. 2009).
13
kernel density: f�Y (q� τ ) = bandwidth.
1
N.b
. ∑N i=1 K Y �
Yi −q �τ b
� where: K Y (z) is the Gaussian kernel and b is the
The method of unconditional quantile regression can be done through one of three estimation techniques: OLS (called RIF-OLS), logistic (called RIF-logit) or nonparametric (called RIF-nonparametric). However, three estimation techniques are shown to give similar estimation results (Firpo et al. 2009). For simplicity, in this application we use the RIF-OLS. Consistent estimates of this method is obtained under the assumption that: 𝑃𝑃𝑃𝑃[𝑌𝑌 > 𝑞𝑞𝜏𝜏 |𝑋𝑋 = 𝑥𝑥 ] is linear in 𝑥𝑥. The estimation of RIF-OLS is similar to OLS:
�𝜏𝜏 = (𝑋𝑋 ′ 𝑋𝑋)−1 𝑋𝑋 ′ 𝑅𝑅𝑅𝑅𝑅𝑅 � (𝑌𝑌, 𝑞𝑞�𝜏𝜏 ) 𝛽𝛽
(5)
The only difference is the replacement of the estimated values of 𝑅𝑅𝑅𝑅𝑅𝑅 at a given quantile as a new dependent variable. 4.2.
Model specifications and estimation results
We investigate how the wage determination differs between men and women at the mean and at various quantiles of the wage distribution by estimating a series of the OLS and unconditional quantile regressions for the pooled male and female sample for each of the observed years 1993, 2002 and 2008 of the form: 𝑌𝑌𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽𝑋𝑋𝑖𝑖 + 𝛾𝛾𝑀𝑀𝑖𝑖 + 𝛿𝛿𝑀𝑀𝑖𝑖 ∗ 𝑋𝑋𝑖𝑖 + 𝜀𝜀𝑖𝑖
(6)
where: 𝑌𝑌𝑖𝑖 is the natural log of the hourly wage of individual 𝑖𝑖, 𝑀𝑀𝑖𝑖 is a gender dummy, 𝑋𝑋𝑖𝑖 is the
vector of explanatory variables for individual 𝑖𝑖 including productivity related characteristics, job characteristics and regional dummies, 𝑀𝑀𝑖𝑖 ∗ 𝑋𝑋𝑖𝑖 is the interaction between the gender dummy and the explanatory variables. The vector of coefficients 𝛽𝛽 are the returns to characteristics, and 𝛾𝛾 and 𝛿𝛿 denote the intercept and the slope differential by gender, respectively. 14
Our paper exploits the rich and comparable information across VLSSs and VHLSSs to construct a set of explanatory variables to include in the augmented Mincerian wage equations, which control for worker human capital, ethnicity and other characteristics. These variables have been widely used in studies on Vietnam (Liu 2004; Pham & Reilly 2007) and other transitional countries (Brainerd 2000; Jolliffe 2002; Jolliffe & Campos 2005; Keane & Prasad 2006; Newell & Socha 2007). For example, to proxy for human capital, we include variables reflecting years of schooling and general experience (Liu 2004). In addition, a variable reflecting whether an individual belongs to the minority group is included to proxy for social exclusion (Pham & Reilly 2007). Furthermore, we include a marital status indicator to capture worker characteristics (Liu 2004; Pham & Reilly 2007). To control for heterogeneity of job characteristics, we include two dummies reflecting employment industries (Manufacturing or Service) and two dummies reflecting employment sectors (Public sector or SOE). To adjust for the seasonal effects in the wage determination, we include a set of dummies for the interview quarters (Pham & Reilly 2007). We also include an urban dummy indicating if an individual is living in urban areas to gauge the possible differences in labor market conditions or costs of living between rural and urban areas. Finally, we include six dummy variables to control for seven regional differences. Detailed descriptions and summary statistics of variables are presented in Table 3. To begin with, we estimate a restricted version of equation (6) that only includes the intercept, the gender dummy and a set of all explanatory variables at the mean using OLS and at selected quantiles using an unconditional quantile regression. The estimation results suggest that all the gender dummies have positive coefficients and are highly significant, implying that, ceteris paribus, male workers are paid more than female workers.
15
Next, we estimate a full specification of equation (6) including the intercept, the gender dummy, the set of explanatory variables, plus the interaction terms of the gender dummy with the set of explanatory variables. We carry out an F test for the hypothesis that all the coefficients of interaction terms are jointly significant. The test results reject the null hypothesis, suggesting that there are indeed significant differences in the return to characteristics between male and female workers. We then use the OLS and the unconditional quantile regression to estimate the wage determinants, both at the mean and at selected quantiles, for males and females separately. Tables 4 and 5 report the regression results for female and male workers, respectively. 8 There are some interesting points to note from the regression results. [Tables 4 and 5 about here] First, education is highly significant with a positive impact on wages. Other things being equal, having one more year of education increases a worker’s wage by 3% in 1993. Over time, the returns to education increase remarkably and the rate of increase is highest for high wage workers. For example, as illustrated in Figure 3, between 1993 and 2008, at the 90th quantile, the return to education doubles for females and increases up to five times for males. The
8
Wages are only observed for those people in paid employment who account for around 17% of Vietnamese workers in 1993 and
30% of workers in 2008. Wage workers might be a selective group. For example, they could be more talented or from a better family background with a better social network. Following Heckman (1979), we estimated a sample selection model. The exclusion restrictions for the wage participation equation are the dependency ratio, daily hours of doing housework, per capita cultivated land area, non-labor income and the number of people in paid employment in the household. The estimation results show that the sample selections are not statistically significant in most of the cases, suggesting that either the exclusion restrictions are weak or that the wage samples are already a random subset of the overall population of workers. To ensure comparability, all estimations and decompositions are carried out without sample selection (see Appendix A).
16
phenomenon of substantial increase in return to education during transition observed in Vietnam is consistent with that observed in other transitional economies (Campos & Jolliffe 2007). [Figure 3 about here] Second, experience has no significant effect on wages in 1993 but has a significantly positive impact on wages in 2002 and 2008. The estimated coefficients of experience squared are statistically significant with negative signs in 2002 and 2008, suggesting diminishing returns to experience. That is marginal return to experience will be maximized after a certain period of working then reduces. Third, by sectors, on average, female workers working as public servants received significantly higher wages than those in the private sector. The wage premium for female public servants increases remarkably over the studied period, especially in the latter years, 2002 and 2008. The same trend happens for male workers. While in 1993, male public servants receive significantly lower wage than their counterparts in the private sector, in 2008, male public servants receive significantly higher wage than their counterparts in the private sector. Our result is consistent with Imbert (2013) that in the 1990s public servants were under paid in comparison with their counterparts in the private sector and the wage premium of public servants improved significantly in the 2000s. There is almost no difference in hourly wage rates between workers in state-owned enterprises (SOE) and those in private enterprises in 1993. However, by 2008, average female workers in the private sector receive 14% lower wage than those in the SOE while average male workers in the private sector receive 2% higher wage than those in SOE. The results suggest that the private sector favors men over women as the economy becomes more market oriented. Given that female workers in the private sector do not only receive lower wages than their counterparts 17
in the state sector, many of them also do not receive health and social insurance contributions from their employers (GSO 2009). They are thus the most vulnerable group of wage laborers. Fourth, by industries, in 1993, average male workers in the manufacturing industry earn 15% more than their counterparts in the agricultural industry. Over the studied period, there is a decline in the returns to working in the manufacturing industry in comparison to the agricultural industry. By 2008, average male workers in the manufacturing industry earn 9% less than their counterparts in the agricultural industry and average female workers in the manufacturing industry earn up to 21% less than average female workers in the agricultural industry. A possible explanation for the trend of deterioration in the return to working in the manufacturing industry comes from the fact that, at the beginning of the transition period, the manufacturing industry was highly protected by the government. However, as the Vietnamese economy becomes more liberalized and integrated into the world economy, the protection rate for the manufacturing industry declines and return for those workers working in the manufacturing industry decreases (Vo 2005). 5.
Factors contributing to wage growth and the gender wage gap
5.1.
Decomposition method
We examine the factors contributing to wage growth, along with the factors contributing to the gender wage gap at the mean and at selected points along the wage distribution. We do this by using the following variations of the Oaxaca-Blinder (1973) decomposition: �𝑡𝑡 �𝜇𝜇̂ 𝑡𝑡 − 𝜇𝜇̂ ∗ � + 𝑋𝑋�𝑡𝑡 �𝜇𝜇̂ ∗ − 𝜇𝜇̂ 𝑡𝑡 �� �𝑋𝑋�𝑡𝑡� �𝜇𝜇̂ ∗ + �𝑋𝑋 𝑌𝑌�𝑡𝑡1 − 𝑌𝑌�𝑡𝑡0 = �� − 𝑋𝑋�𝑡𝑡� ��������������������� 1 ��� 0 �� 1 1 0 0 "𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸"
"𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈"
18
(7)
�𝑚𝑚𝑚𝑚 (𝜇𝜇̂ 𝑚𝑚𝑚𝑚 − 𝜇𝜇̂ 𝑡𝑡∗ ) + 𝑋𝑋�𝑓𝑓𝑓𝑓 �𝜇𝜇̂ 𝑡𝑡∗ − 𝜇𝜇̂ 𝑓𝑓𝑓𝑓 �� �𝑋𝑋�𝑚𝑚𝑚𝑚 − 𝑋𝑋�𝑓𝑓𝑓𝑓 �𝜇𝜇̂ 𝑡𝑡∗ + �𝑋𝑋 𝑌𝑌�𝑚𝑚𝑚𝑚 − 𝑌𝑌�𝑓𝑓𝑓𝑓 = ��������� ��������������������� "𝐸𝐸𝑥𝑥𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝"
(8)
"𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈"
where: 𝑌𝑌� is the natural log of hourly wages; 𝑋𝑋� is a vector of the mean observed characteristics; 𝜇𝜇̂
is a vector of the estimated coefficients in the regression model of log of hourly wages on a set of explanatory variables, including the constant; 𝜇𝜇̂ ∗ is a vector of the estimated coefficients from the
pooled sample (of two consecutive years in equation (7) and of both male and female workers in equation (8)) with other explanatory variables and a year dummy for the wage growth (equation (7)) or a gender dummy for the gender wage gap model (equation (8)); 𝑡𝑡 is time indicator with 𝑡𝑡1
is the latter time and 𝑡𝑡0 is the initial time; 𝑚𝑚 and 𝑓𝑓 are indicators for males and females,
respectively. We choose to include the gender dummy for the gender wage gap model or the year
dummy for the wage growth model when estimating the reference wage structure to obtain unbiased estimates of other variables as suggested by Fortin (2008) and Jann (2008). Equation (7) decomposes factors contributing to the wage growth for male and female workers separately, while equation (8) decomposes factors contributing to the gender wage gap in each of three years 1993, 2002 and 2008. In both equations, the first term on the right-hand side is the difference due observed productivity related characteristics – the ‘explained part’. The second term on the right-hand side is the difference in factors other than the observed characteristics – the ‘unexplained part’, which is often interpreted as labor market discrimination. In the presence of categorical variables, the decomposition results will be sensitive to the choice of the reference group. Some solutions are proposed to transform the estimated coefficients by adding restrictions. However, doing so will lose the simple meaningful interpretations and preclude comparisons across years (Fortin et al. 2011). Therefore, we
19
perform all decompositions with the same reference groups to facilitate the interpretation and ensure the compatibility. 5.2.
Factors contributing to wage growth from 1993 to 2008
The decomposition results of factors contributing to the wage growth between 1993 and 2008 for female and male workers are presented in Table 6. It can be seen from the table that for both male and female workers, at the mean, wage growth comes from both the improvements in characteristics and returns. Furthermore, the improvements in returns explain the major share. This is true for both male and female workers, both at the mean and at all selected points along the distribution. [Table 6 about here] Consider the contribution of some explanatory variables; here, education plays the most important role. At the mean, the better education contributes 7% (0.07 log points) and 9% (0.10 log points) to wage growth for female and male workers, respectively. Along the distribution, for both male and female workers, the contribution of better education to wage growth increases from the bottom to the top of the distribution. The improvement in return to education significantly contributes 26% (0.29 log points) and 23% (0.24 log points) to wage growth at the mean for female and male workers, respectively. Along the distribution, the contribution of improving returns to education to wage growth is biggest for high wage workers. Over the studied period, while the change in the ownership structure lowers wage of female workers, it has little impact on wage of male workers. At the mean, the change in returns by ownership structure increases wages by 7% (0.08 log points) and 9% (0.09 log points) for female and male workers, respectively. Along the distribution, the change in return by ownership
20
structure has the biggest impact on increasing worker’s wage for high wage workers, those at the top of the distribution. The change in the industrial structure and its related returns lowers wages of both male and female workers, both at the mean and all selected points along the distribution. The biggest impact is for low wage workers, the bottom 10th percentile. A reasonable explanation for the trend is that most of the recent newly established firms in Vietnam have been in the light manufacturing export-oriented industries, such as food processing, leather, textile and garment, pottery, simple metal parts, furniture and others. These are labor-intensive, employing lowskilled workers with simple technologies and thus low wages (MIT & UNIDO 2011). A significant part of the unexplained gap lies in the inter-period difference in the constant. These are differences in other factors not being captured in the model. These factors may be the improvements in the market transparency during the Vietnamese transition. Our results suggest that a more transparent market significantly helps to improve wages of workers at the lower and middle parts of the distribution and reduces wages of workers at the top of the distribution and thus reduces wave inequality. 5.3.
Factors contributing to the gender wage gap
Table 7 reports the decomposition results of factors contributing to the gender wage gaps at three points in time: 1993, 2002 and 2008. In 1993, the average gender wage gap is 0.23 log points. Along the distribution, the gender wage gap is lowest for low wage workers, the bottom 10th percentile. Contributions to the gender wage gap come from both the gender differences in average characteristics and their returns. The contribution of gender differences in characteristics to the gender wage gap is stable along the distribution. Most of the variation in the gender wage
21
gap comes from the gender difference in returns to observed characteristics and unobserved factors. [Table 7 about here] In 2008, the average gender wage gap reduced to 0.17 log points. Along the distribution, the gender wage gap is higher for workers with low wages. Over the studied period, there had been a significant improvement in observed productivity related characteristics of female workers, especially those at the middle and the top of the wage distribution. As a result, in 2002 and 2008, female workers even have better observed productivity-related characteristics than male workers. The gender differences in observed productivity related characteristics lower the gender wage gap. For example, in 2008, the better education of average women than men reduces the gender wage gap by 0.03 log points at the mean and the median, and up to 0.06 log points at the 90th percentile. In both years, 2002 and 2008, the entire gender wage gap comes from the gender difference in return to characteristics and unobserved factors. This is true both at the mean and along the entire distribution. In all years, for low wage workers, the gender differences in returns across industries are highly significant and contribute positively to the gender wage gap. Between 1993 and 2008, along the wage distribution, while the ‘unexplained’ part increases sharply for low wage workers, it decreases remarkably for high wage workers. This sharp increase in gender ‘discrimination’ at the bottom of the distribution is a major explanation for an emergence of the sticky-floor observed in 2008. 6.
Concluding remarks
This paper uses the Vietnam Living Standard Surveys to analyze changes in the labor force behavior, wages and the gender wage gap in Vietnam between 1993 and 2008. For the observed 22
15-year transitional period, the average real wage of the Vietnamese workers nearly tripled. Interestingly, wage growth was higher for low wage workers and wage inequality reduced significantly. By gender, wage growth was higher for women than men and this resulted in a significant reduction of the average gender wage gap from 1993 to 2008. The improvements in observed productivity related characteristics of Vietnamese women relative to men played an important role in narrowing the average gender wage gap over this period. Consistent with Pham and Reilly (2007), we find a remarkable reduction in the gender wage gap from 1993 to 2002. However, our investigation for a longer period shows a slight increase in the mean of the gender wage gap from 2002 to 2008, which is mainly driven by a sharp increase in the gender wage gap for low wage workers. Our results of the Oaxaca-Blinder decomposition method suggest that, in all years and at almost all points along the wage distribution, most of the gender wage gap attributes to the gender difference in returns to observed characteristics and to unobserved factors, also known as the ‘unexplained gap’ or ‘discrimination’. While gender ‘discrimination’ decreases for high wage workers, it increases for low wage workers and this resulted in an emergence of the sticky-floor phenomenon in 2008. It is challenging to isolate the factors behind the evolution in the gender wage gap observed in this study, as has also been shown in the international literature (ILO 2016; Blau & Kahn 2017). Pham and Reilly (2007) speculate that the selective withdrawal from wage employment of low skilled females, in terms of both observables and unobservables, could be one of the factors explaining the reduction of the gender wage gap from 1993 to 2002 in Vietnam. This hypothesis appears to be supported in our results as we noticed a significant improvement in observed productivity related characteristics of female workers between 1993 and 2002. This 23
conjecture is also mentioned in the study by Hunt (2002) when explaining the dramatic reduction in the gender pay gap among East German. If this hypothesis holds, it also helps explain a slight increase in the gender wage gap observed between 2002 and 2008 in our study as our results indicate that the contribution of the observed productivity related characteristics of female workers to the total gender wage gap is stable during the same period while that of the “unexplained” part increases slightly. 9 Another possible explanation for the slight increase in the mean of the gender wage gap observed between 2002 and 2008 is that this period is marked by rapid growth of the private sector and the drastic move of employment away from agriculture toward light manufacturing and service sectors (McCaig & Pavcnik 2013). Specifically, Vietnam implemented numerous institutional reforms during this period to acquire the World Trade Organization (WTO) membership in 2007 (Vo & Nguyen 2009). This resulted in a surge in the development of the private sector. The expansion of the private sector when viewed with one of our findings that the private sector favors men over women during the same period thus help explain the widening gender wage gap. Furthermore, the economic integrations into the world economy including the signing of the USVietnam Bilateral Trade Agreement in 2000 led to the expansion of export markets for products in the light labor-intensive manufacturing industries (Fukase 2013). This gives opportunities for workers previously self-employed in the agriculture sector or in the “informal” sector to be
9
Minimum wage policies that bring up the bottom of the distribution will disproportionately affect females and
influence the gender wage gap (Blau & Kahn 2017). Therefore, increases in the minimum wages in 1997, 2000 and 2002 in Vietnam may contribute to the decrease in the gender wage gap observed between 1993 and 2002 (Sakellariou & Fang 2014). However, subsequent increases in the minimum wages in 2004, 2006 and 2008 cannot help explain the increasing gender wage gap observed between 2002 and 2008.
24
employed in the “formal” sector. However, these newly established jobs are mostly laborintensive and more likely to employ low skilled female workers with low wages. Therefore, the disproportionate increase of females in low paid jobs during this period may be another factor explaining the increase in the gender wage gap. Similarly, one could also link the rapid increase of the service sector (especially financial and banking) occurred during this period to the increasing gender wage gap (McCaig & Pavcnik 2013). The last two explanations are consistent with one of our findings that, between 2002 and 2008, the evolution of gender differences in returns across industries contributed significantly to the widening of the gender wage gap. It should be noted that while some of our results are supportive of the explanations described above, the explanations themselves are largely conjectural and by no means exhaustive. To this end, more research aimed at better understanding the underlying reasons for the gender wage gap and changes in the gap in Vietnam is needed. The growing availability of panel data or more recent data would facilitate such research.
25
Reference ADB, 2006. Economic Transition in Vietnam: Doi Moi to WTO. Public Policy Tranning Program ADB (Ed.). Asian Development Bank, Manila Bales, S., Martin, R., 2002. Are Public Sector Workers Underpaid? Appropriate Comparators in a Developing Country. The World Bank Working Paper No 2741 Blau, F., Kahn, L., 2017. The Gender Wage Gap: Extent, Trends, and Explanations. Journal of Economic Literature 55, 789-865 Booth, A.L., Francesconi, M., Frank, J., 2003. A Sticky-floors Model of Promotion, Pay, and Gender. European Economic Review 47, 295-322 Brainerd, E., 1998. Winners and Losers in Russia's Economic Transition. The American Economic Review 88, 1094-1116 Brainerd, E., 2000. Women in Transition: Changes in Gender Wage Differentials in Eastern Europe and the Former Soviet Union. Industrial and Labor Relations Review 54, 138162 Campos, N., Jolliffe, D., 2007. Earnings, Schooling, and Economic Reform: Econometric Evidence From Hungary (1986–2004). The World Bank Economic Review 21, 509526 Chi, W., Li, B., 2008. Glass-ceiling or Sticky-floor? Examining the Gender Earnings Differential across the Earnings Distribution in Urban China, 1987-2004. Journal of Comparative Economics 36, 243-263 Dollar, D., 1994. Macroeconomic Management and the Transition to the Market in Vietnam. Journal of Comparative Economics 18, 357-375 Dollar, D., Litvack, J., 1998. Vietnam's Renovation - A Unique Growth Path. In: The Newsletter about Reforming Economies Transition B (Ed.), pp. 1-3. The World Bank, Washington Firpo, S., Fortin, N.M., Lemieux, T., 2009. Unconditional Quantile Regressions. Econometrica 77, 953-973 Fortin, N., Lemieux, T., Firpo, S., 2011. Chapter 1 - Decomposition Methods in Economics. In: Ashenfelter O & Card D (eds.) Handbook of Labor Economics. Elsevier, pp. 1-102. Fortin, N.M., 2008. The Gender Wage Gap among Young Adults in the United States: The Importance of Money vs. People. The Journal of Human Resources 43, 886-920 Fukase, E., 2013. Export Liberalization, Job Creation, and the Skill Premium: Evidence from the US–Vietnam Bilateral Trade Agreement (BTA). World Development 41, 317-337 Ganguli, I., Terrell, K., 2006. Institutions, Markets and Men's and Women's Wage Inequality: Evidence from Ukraine. Journal of Comparative Economics 34, 200-227 GSO, 2009. The 2008 Population Change, Labour Force and Family Planning Survey: Major Findings. Statistical Publishing House, Hanoi. Hampel, F.R., 1974. The Influence Curve and Its Role in Robust Estimation. Journal of the American Statistical Association 69, 383-393 Heckman, J.J., 1979. Sample Selection Bias as a Specification Error. Econometrica 47, 153162 Hunt, J., 2002. The Transition in East Germany: When Is a Ten Point Fall in the Gender Wage Gap Bad News? Journal of Labor Economics 20, 148-169 ILO, 2010. Key Indicator of the Labour Market. International Labour Organization, Geneva ILO, 2016. Women at Work Trends 2016. International Labour Organization, Geneva Imbert, C., 2013. Decomposing the Labor Market Earnings Inequality: The Public and Private Sectors in Vietnam, 1993–2006. The World Bank Economic Review 27, 55-79 Jann, B., 2008. The Blinder-Oaxaca decompositon for linear regression models The Stata Journal 8, 453-479 Jolliffe, D., 2002. The Gender Wage Gap in Bulgaria: A Semiparametric Estimation of Discrimination. Journal of Comparative Economics 30, 276-295
26
Jolliffe, D., Campos, N.F., 2005. Does Market Liberalisation Reduce Gender Discrimination? Econometric Evidence from Hungary, 1986-1998. Labour Economics 12, 1-22 Keane, M.P., Prasad, E.S., 2006. Changes in the Structure of Earnings during the Polish Transition. Journal of Development Economics 80, 389-427 Koenker, R., Bassett, G., 1978. Regression quantiles. Econometrica 46, 33-46 Le, H.T., Booth, A.L., 2014. Inequality in Vietnamese Urban–Rural Living Standards, 1993– 2006. Review of Income and Wealth 60, 862-886 Le, H.T., Nguyen, H.T., 2018. The evolution of the gender test score gap through seventh grade: New insights from Australia using unconditional quantile regression and decomposition. IZA Journal of Labor Economics forthcoming, DOI: 10.1186/s40172018-0062-y Liu, A.Y.C., 2004. Gender wage gap in Vietnam: 1993 to 1998. Journal of Comparative Economics 32, 586-596 Machado, J.A.F., Mata, J., 2005. Counterfactual Decomposition of Changes in Wage Distributions Using Quantile Regression. Journal of Applied Econometrics 20, 445465 Maurer-Fazio, M., Hughes, J., 2002. The Effects of Market Liberalization on the Relative Earnings of Chinese Women. Journal of Comparative Economics 30, 709-731 McCaig, B., Pavcnik, N., 2013. Moving out of agriculture: structural change in Vietnam. NBER Working Paper No 19616 McCarty, A., 2001. The Employment and Social Consequences of Vietnam's International Economic Intergration. In: paper presented at UNIDO/ UNDP/ CIEM conference on 31st March 1999. EconWPA Meng, X., 1998. Male-female Wage Determination and Gender Wage Discrimination in China's Rural Industrial Sector. Labour Economics 5, 67-89 MIT, UNIDO, 2011. Viet Nam Industrial Competitiveness Report 2011. Ministry of Industry and Trade of Vietnam and United Nations Industrial Development Organization, Hanoi MOLISA, ILO, 2010. Labour and Social Trends in Vietnam 2009/10. Ministry of Labour Invalids and Social Affairs (MOLISA) and International Labour Organization (ILO), Hanoi Newell, A., Socha, M.W., 2007. The Polish Wage Inequality Explosion. Economics of Transition 15, 733-758 Oaxaca, R., 1973. Male-Female Wage Differentials in Urban Labor Markets. International Economic Review 14, 693-709 Pham, T.-H., Reilly, B., 2007. The Gender Pay Gap in Vietnam, 1993-2002: A Quantile Regression Approach. Journal of Asian Economics 18, 775-808 Pincus, J., Sender, J., 2008. Quantifying Poverty in Viet Nam: Who Counts? Journal of Vietnamese Studies 3, 108-150 Rama, M., 2001. The Gender Implications of Public Sector Downsizing. In: The Reform Program of Vietnam. Development Research Group - The World Bank Rama, M., 2010. Poverty Reduction and Social Protection in Vietnam. In: Paper Presented at the Second Economic Workshop on Vietnam 02/03/2009, Australian National University Sakellariou, C., Fang, Z., 2014. The Vietnam reforms, change in wage inequality and the role of the minimum wage. Economics of Transition 22, 313-340 Vo, T.T., 2005. Vietnam's Trade Liberalization and International Economic Integration: Evolution, Problems, and Challenges. ASEAN Economic Bulletin 22, 75-91 Vo, T.T., Nguyen, D.A., 2009. Vietnam after Two Years of WTO Accession. What Lessons Can Be Learnt? ASEAN Economic Bulletin 26, 115-135
27
TABLE 1: Labor Force Participation and Share of Wage Employment, 1993-2008 1993
1998
2002
2004
2006
2008
All sample
88.27
87.62
83.76
81.98
80.44
79.59
Male
89.83
87.81
84.71
83.09
81.71
81.39
Female
86.79
87.44
82.80
80.82
79.11
77.71
All sample
16.66
20.18
27.81
27.68
28.72
29.76
Male
19.58
25.27
34.10
33.67
34.17
35.33
Female
13.89
15.22
21.45
21.51
23.04
23.91
Labor force participation rate (%)
Share of wage employment (%)
Notes:
(a) Samples are weighted. (b)LFP rate is the ratio between the labor force and the size of the population in the labor age. Thus the participation excludes those who are in the labor force age group but not working because they are studying, being ill or disabled, doing home duties, and others. (c) Wage earners are those whose main job in the past 12 months was paid employment. Source: VLSS 1993/1998, VHLSS 2002/2004/2006 and VHLSS2008, own calculations.
28
TABLE 2: Wages and Gender Wage Gap at Mean and Selected Percentiles, 1993 and 2008 Wages at Mean and selected Percentiles (Unit: 1000 VND)
Male – Female Wage Ratios Ratio
% Mean
Female
Q10th
Q50th
Q90th/Q10th
Q90th
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Mean
Q10th
Q50th
Q90th
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
1993
44%
4.03
5.00
1.22
1.45
2.75
3.53
7.05
9.04
5.76
6.22
1.24
1.19
1.28
1.28
2002
39%
8.22
9.11
2.59
3.10
5.30
6.08
12.47
14.32
4.82
4.62
1.11
1.20
1.15
1.15
2008
40%
11.80
12.98
4.13
5.58
8.30
9.98
21.72
21.83
5.25
3.91
1.10
1.35
1.20
1.01
2.93
2.60
3.38
3.84
3.02
2.83
3.08
2.41
Ratio 2008/1993 Notes:
(a) Samples are weighted. (b) Money values are adjusted by spatial and temporal price indexes, converted to the real value of Jan 2008. (c) Wage earners are those whose main job in the past 12 months was paid employment. (d) Colum 2 is the percentage of females in the sample of both male and female wage earners Source: VLSS 1993, VHLSS 2002, and VHLSS 2008, own calculation.
29
TABLE 3: Variable Descriptions and Summary Statistics Variable
Variable description
Female
Male
1993
2002
2008
1993
2002
2008
Lnwage
= Natural log of hourly wage
7.96
8.58
9.08
8.20
8.72
9.24
Ethnicity
= 1 if minority ethnic, = 0 if majority
0.10
0.08
0.07
0.12
0.07
0.09
Marital status
= 1 if married, = 0 if single
0.54
0.58
0.63
0.62
0.65
0.65
Potential experience
= age-schooling-6
15.86
17.06
17.44
16.59
18.03
18.57
Potential experience square
= Potential experience square/100
3.61
4.06
4.39
3.68
4.27
4.70
School year
= Years of schooling
8.24
8.72
9.94
8.22
8.66
9.42
Public servant
= 1 if working as public servant
0.29
0.26
0.24
0.20
0.18
0.18
SOE
= 1 if working in state owned enterprises
0.18
0.16
0.11
0.15
0.12
0.09
Private
= 1 if working in private enterprises
0.53
0.58
0.65
0.65
0.69
0.73
Manufacturing
= 1 if working in manufacturing
0.34
0.35
0.39
0.42
0.46
0.45
Service
= 1 if working in service sector
0.40
0.43
0.47
0.32
0.33
0.39
Agriculture
= 1 if working in agriculture
0.26
0.22
0.14
0.26
0.22
0.16
Urban
= 1 if living in urban
0.50
0.41
0.42
0.47
0.36
0.35
Northern Upland
= 1 if living in Northern Uplands
0.09
0.12
0.13
0.08
0.12
0.13
Red River Delta
= 1 if living in Red River Delta
0.17
0.19
0.24
0.18
0.22
0.24
North Central Coast
= 1 if living in North Central Coast
0.08
0.06
0.06
0.07
0.08
0.08
South Central Coast
= 1 if living in South Central Coast
0.10
0.09
0.11
0.12
0.11
0.10
Central Highlands
= 1 if living in Central Highlands
0.02
0.05
0.04
0.02
0.04
0.04
South East
= 1 if living in South East
0.27
0.22
0.20
0.27
0.18
0.19
Mekong River Delta
= 1 if living in Mekong River Delta
st
0.27
0.27
0.23
0.27
0.25
0.22
st
0.24
0.24
-
0.24
0.24
-
nd
0.23
0.26
0.34
0.21
0.26
0.35
rd
0.25
0.26
0.55
0.26
0.26
0.54
th
0.28
0.23
0.11
0.28
0.24
0.11
915
7,409
2,704
1,181
11,840
4,106
=1 if interviewed in 1 quarter
1 Quarter nd
=1 if interviewed in 2 quarter
2 Quarter rd
=1 if interviewed in 3 quarter
3 Quarter th
=1 if interviewed in 4 quarter
4 Quarter No. observations
Notes:
(a) Vietnam has 54 ethnic groups; the Kinh group is the majority one; (b) Public servants include those who work for the government, the communist party or social organizations; Private includes those who work for private companies, foreign invested firms, collectives or other households and in paid employment. (c) Money values are adjusted by spatial and temporal price indexes, converted to the value of Jan 2000. (d) No household being surveyed in the first quarter in 2008.
Source: VLSS 1993, VHLSS 2002 and VHLSS 2008, own calculations.
30
TABLE 4: Determinants of Female Wage at Mean and Selected Quantiles in 1993, 2002 and 2008 Variables
Q10th 1993
Q50th 1993
Q90th
2002
2008
1993
2002
OLS
2002
2008
2008
0.08 (0.09) 0.07 (0.08) 0.01 (0.01) -0.01 (0.03) 0.01 (0.01)
0.05 (0.05) 0.04 (0.03) 0.03*** (0.00) -0.06*** (0.01) 0.05*** (0.00)
-0.03 (0.09) 0.06 (0.05) 0.02*** (0.01) -0.07*** (0.02) 0.04*** (0.01)
0.12 (0.09) 0.10* (0.06) -0.00 (0.01) 0.01 (0.02) 0.02** (0.01)
0.06* (0.03) 0.07*** (0.02) 0.02*** (0.00) -0.03*** (0.01) 0.06*** (0.00)
0.01 (0.05) 0.11*** (0.03) 0.01*** (0.00) -0.03*** (0.01) 0.05*** (0.00)
0.05 (0.16) 0.14 (0.12) -0.02 (0.02) 0.04 (0.03) 0.05*** (0.02)
0.15*** (0.05) 0.06* (0.03) 0.02*** (0.00) -0.01 (0.01) 0.09*** (0.01)
0.21** (0.09) 0.03 (0.05) 0.03*** (0.01) -0.03 (0.02) 0.11*** (0.01)
0.36** (0.14) 0.16 (0.11)
0.27*** (0.04) 0.25*** (0.03)
0.34*** (0.06) 0.05 (0.07)
0.19** (0.09) 0.11 (0.08)
0.32*** (0.03) 0.28*** (0.03)
0.50*** (0.04) 0.19*** (0.05)
0.05 (0.18) -0.09 (0.15)
-0.06 (0.06) 0.12*** (0.04)
-0.10 (0.10) -0.35** (0.14)
-0.15*** (0.04) -0.30*** (0.05)
-0.46*** (0.08) -0.59*** (0.09)
0.10 (0.09) 0.16* (0.09)
0.02 (0.03) 0.02 (0.03)
-0.11** (0.04) -0.11** (0.05)
0.09 (0.15) 0.00 (0.16)
-0.05 (0.09) 0.45** (0.20) 0.25 (0.24) 0.64*** (0.21) 0.94*** (0.18) 0.93*** (0.18) 0.85*** (0.19)
0.13*** (0.03) -0.16*** (0.05) -0.14** (0.06) 0.15*** (0.05) -0.07 (0.07) 0.23*** (0.04) 0.25*** (0.05)
0.03 (0.04) -0.05 (0.07) -0.05 (0.11) 0.07 (0.08) -0.00 (0.10) 0.12* (0.07) -0.08 (0.08)
-0.20*** (0.06) 0.23** (0.10) -0.03 (0.12) 0.11 (0.11) 0.00 (0.21) 0.64*** (0.09) 0.38*** (0.10)
0.10*** (0.02) -0.12*** (0.03) -0.16*** (0.04) 0.05 (0.04) -0.10** (0.04) 0.26*** (0.03) 0.16*** (0.03)
-0.06** (0.03) 0.02 (0.04) -0.01 (0.06) 0.10* (0.05) 0.10 (0.07) 0.26*** (0.05) 0.09* (0.05)
0.03 (0.09) -0.01 (0.10) -0.10 (0.10) 6.32*** (0.22) 0.09 915
-0.09** (0.04) -0.02 (0.03) 0.08** (0.03) 7.07*** (0.08) 0.08 7,422
0.00 (0.00) -0.08 (0.07) -0.06 (0.07) 8.20*** (0.14) 0.08 2,704
-0.09 (0.07) -0.10 (0.07) -0.10 (0.07) 7.38*** (0.14) 0.10 915
-0.01 (0.02) 0.01 (0.02) 0.06*** (0.02) 7.44*** (0.05) 0.25 7,422
0.00 (0.00) -0.05 (0.04) -0.02 (0.04) 8.18*** (0.08) 0.29 2,704
1993
2002
2008
0.10 (0.09) 0.09* (0.05) -0.00 (0.01) 0.01 (0.02) 0.03*** (0.01)
0.06** (0.03) 0.06*** (0.02) 0.02*** (0.00) -0.03*** (0.01) 0.07*** (0.00)
0.03 (0.04) 0.09*** (0.02) 0.02*** (0.00) -0.03*** (0.01) 0.06*** (0.00)
0.34*** (0.09) 0.18* (0.09)
0.20** (0.09) 0.08 (0.07)
0.22*** (0.03) 0.22*** (0.02)
0.43*** (0.04) 0.14*** (0.04)
-0.05* (0.03) 0.18*** (0.04)
-0.24*** (0.06) -0.14* (0.08)
0.06 (0.08) -0.03 (0.09)
-0.03 (0.02) -0.01 (0.02)
-0.21*** (0.04) -0.19*** (0.04)
-0.01 (0.11) 0.04 (0.16) -0.01 (0.19) 0.15 (0.19) 0.12 (0.18) 0.52*** (0.17) 0.60*** (0.19)
0.12*** (0.03) 0.12** (0.06) 0.06 (0.07) 0.09 (0.06) 0.36*** (0.09) 0.50*** (0.06) 0.42*** (0.06)
-0.16*** (0.05) 0.31*** (0.09) 0.23* (0.12) 0.34*** (0.10) 0.68*** (0.17) 0.53*** (0.09) 0.47*** (0.09)
-0.12** (0.06) 0.17* (0.10) 0.02 (0.12) 0.17 (0.11) 0.19 (0.16) 0.69*** (0.10) 0.51*** (0.11)
0.11*** (0.02) -0.06* (0.03) -0.10*** (0.04) 0.09*** (0.03) 0.03 (0.04) 0.33*** (0.03) 0.24*** (0.03)
-0.05** (0.02) 0.05 (0.04) 0.04 (0.06) 0.14*** (0.05) 0.25*** (0.07) 0.27*** (0.04) 0.12*** (0.04)
-0.40*** (0.13) -0.37*** (0.14) -0.29** (0.14) 8.41*** (0.27) 0.05 915
0.09** (0.04) 0.12*** (0.04) 0.11*** (0.04) 7.80*** (0.09) 0.13 7,422
0.00 (0.00) -0.04 (0.08) -0.04 (0.07) 8.23*** (0.16) 0.18 2,704
-0.14** (0.06) -0.16** (0.07) -0.12* (0.07) 7.35*** (0.13) 0.13 915
0.01 (0.02) 0.04** (0.02) 0.10*** (0.02) 7.43*** (0.04) 0.29 7,422
0.00 (0.00) -0.05 (0.04) -0.03 (0.03) 8.14*** (0.07) 0.37 2,704
Worker’s characteristics:
Ethnicity Marital status Potential experience Potential experience squared School year Sectors:
Public servant State-owned Enterprise Industries:
Manufacturing Service Regions: Urban Red River Delta North Central Coast South Central Coast Central Highlands South East Mekong River Delta Surveyed quarters:
First quarter Second quarter Third quarter Constant R-squared Observations
Notes:
(a) Private sector is the base group for sectors; agriculture is the base group for industries; Northern Uplands is the base group for regions, fourth quarter is the based group for surveyed quarters, no household surveyed in the first quarter in 2008. (b) Robust standard errors are in parentheses, P values for two-side test: *** p
