Gender Gap in Earnings in Vietnam: Why Do Vietnamese Women Work in Lower Paid Occupations?

AuthorChowdhury, Iffat
  1. Introduction

    An earnings gap between women and men in the labour force is a common empirical feature of the labour market in countries around the world. This gender wage gap has been documented by economists for over half a century. While its magnitude has diminished in that time, it has seldom disappeared. To date, every country has some degree of wage inequality. Even in Iceland, ranked first in gender equality in the World Economic Forum's Global Gender Gap Index 2016, women's earnings are on average approximately 79 per cent that of men. In the country with the lowest ranking, Yemen, that figure is 63 per cent.

    One factor frequently cited to explain this gap was the difference in levels of education between male and female workers. Indeed, under the Millennium Development Goal on promoting gender equality and empowering women, one target was to "(e)liminate gender disparity in primary and secondary education ... by 2005, and in all levels of education by... 2015". Over the long term, perhaps in part as a result of such directed efforts, the global trend has been a narrowing of the education gap between men and women, and even a reversal in some countries including Vietnam (1) (World Bank 2012a).

    If differences in education levels do not explain the gender wage gap in East Asian countries today, what factors are responsible? One factor that has received increasing attention is occupational sorting. Petersen and Morgan (1995) find that wage differences between men and women are very small within an establishment for the same occupation. This study builds on their work by examining the role of occupational sorting in gender wage gaps in Vietnam. It also explores some potential channels for the emergence of occupational sorting. Vietnam offers a great setting for these analyses--gender gaps in educational enrolment have been more or less closed and female labour force participation is relatively high. From an analytical perspective, this implies that differences in human capital and differential selection into the labour force are less likely to be big drivers of the wage gap. From a policy perspective, this setting supports a focus on occupational sorting in the task of tackling inequalities in economic opportunities between genders rather than on first order issues of labour force participation and education.

    As a starting point, this paper shows that the gender wage gap in Vietnam persists despite the closing of the education gap, and demonstrates that a large fraction of this gap is associated with occupational sorting. Then, three hypotheses regarding occupational sorting are explored:

  2. Occupational sorting occurs once women are in the labour market and is explained by sorting on non-monetary characteristics of jobs. Women are more willing to forego monetary compensation for greater job security, insurance or leave since they have stronger preferences for these characteristics.

  3. Occupational sorting emerges much before women enter the labour market. Social norms about gender roles learned at an early age drive differences in career aspirations between young girls and boys. Career aspirations affect choices over level of education and field of study, and thus predetermine occupational choices when girls enter the labour force.

  4. Sorting into different occupations occurs during the school-to-work transition, because of gender-specific barriers to finding jobs within one's field of study. This hypothesis is only tested for individuals who have upper secondary, vocational or tertiary education, where specialization during study affects the types of jobs that are available.

    The study finds support for the first hypothesis; the analysis suggests that women have stronger preferences for non-monetary job characteristics. However, while women are concentrated in occupations and industries with higher non-monetary benefits, it is also found that women still face an additional penalty in those jobs. This wage differential is similar in magnitude to the unadjusted wage gap. The paper does not find evidence that differences in career aspirations of young boys and girls are a likely culprit for gender wage gaps in the future (the second hypothesis). Similarly, the results do not suggest that women face greater barriers in the school-to-work transition; they are not more likely compared to men to work outside of their field of study.

    The remainder of this paper is structured as follows. The next section provides an overview of the relevant literature. The third and fourth sections present the empirical strategy and data sources, respectively. The results are discussed in the subsequent section. The final section concludes.

  5. Literature Review

    This paper touches on several strands of literature: the dynamics of the gender wage gap in different parts of the world; the drivers behind the gender wage gap, including, the role of human capital versus sorting; and finally, explanations of why occupation and industry sorting emerge.

    Several studies have documented the decline in the gender wage gap in the United States and OECD countries since at least the 1970s (Petersen and Morgan 1995; Blau and Kahn 2008). In developing countries, the trend is less clear-cut--Weichselbaumer and Winter-Ebmer (2005) find conflicting trends in different parts of the world in a meta-analysis. A common driver has been the narrowing of the gap in human capital, particularly relative experience (O'Neill and Polacheck 1993; Blau and Kahn 1997) and education. Grant and Behrman (2010) show that conditional on ever being enrolled, girls have higher levels of educational attainment than boys, and across thirty-eight less developed countries. Becker, Hubbard and Murphy (2010) show that the gender gap in college attainment had reversed in sixty-seven of 120 countries between 1970 and 2010, including countries with below-median per capita GDP. In developing Asian economies (2) the gender gap in education shrank by 80 per cent among younger cohorts between 1960 and 2010. And in Vietnam, education poverty (3) was much higher among young males than young females as of 2010 (Framework of Inclusive Growth Indicators 2014).

    More generally, gaps in human capital now explain a smaller portion of the wage gap than in the past. Blau and Kahn (2016) show that human capital variables explained 27 per cent of the gender wage gap in the United States in 1980 but only about 8 per cent in 2010. In contrast, occupation and industry choice explained 27 per cent of the gap in 1980 and 49 per cent in 2010. This is in line with findings from other countries--on average, human capital characteristics explain 5.3 per cent of the wage gap in OECD countries and 6.4 per cent in developing countries, but when controlling for occupation this reduces significantly to 3.6 and 3.2 per cent, respectively (Oostendorp 2009). What then explains occupation-industry sorting?

    Sorting into industries has received less attention in the literature than sorting into occupations, but is important from a policy perspective. In the East Asian "miracle" economies, women were often clustered in the manufacturing sector, especially export manufacturing. When Taiwan, China experienced economic growth, manufacturing jobs were lost to countries with lower labour costs; as a result, the women enjoyed fewer benefits from that growth (Zveglich Jr and van der Meulen Rodgers 2004). David, Albert, and Vizmanos (2017) have also documented that there still exists a gender wage gap within many occupations that penalizes women in the Philippines despite the wage gap favouring women in aggregate.

    Occupational sorting has been tackled in a few different ways in the literature. A first set of explanations focused on differences in levels of human capital (Mincer and Polachek 1974). Specifically, greater human capital may raise productivity in some occupations more than in others. Individuals then are likely to sort across occupations based on their human capital. However, these differences have become less pronounced over time.

    A second set of explanations focuses on differences in preferences and constraints between women and men. Currie and Chaykowski (1992) find differences in benefit coverage between male- and female-dominated occupations. Female-dominated jobs had lower pension benefits but better paid and unpaid leave. They explain the variation using a model where women face a greater trade-off between time in household production and labour production. Van der Meulen Rodgers and Zveglich, Jr. (2012) and Bhalotra and Umana-Aponte (2010) also find that preferences and constraints help explain gender discrepancies in labour force participation rates in developing Asian countries. Women's labour supply decisions are largely influenced by economic need, social norms and the burden of caregiving. Both studies similarly find that economic need and income volatility both push women into the labour force while motherhood reduces women's employment.

    A third set of explanations centres on gender differences in educational paths. Since many occupations today require occupation-specific human capital that is acquired through education, gender streaming in education can have an effect on occupational choice and the gender pay gap (Black et al. 2008). Women continue to lag in the STEM fields and particularly in mathematically-intensive fields (Ceci et al. 2014). There is substantial evidence that mathematics test scores, maths-based curricula and maths as a college major are predictive of future income, while verbal abilities are not (Arcidiacono 2004).

    A plausible explanation of education streaming may be a performance gap between the genders that has been observed in different fields across different settings. In the United States, girls and boys show no differences in maths or verbal skills at the start of education, but girls fall behind in maths as early as fifth grade (Fryer and Levitt 2009)...

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