Indicator Four: Earnings Gaps between Men and Women

The story here may be that apparel jobs are at least good enough to keep women in the workforce but are less appealing to younger generations—and there are few, if any, alternative options for middle-aged women. Given the lack of other employment opportunities and often minimal reskilling programs in LMICs, females who start in the apparel industry are likely to stay in it while they remain in the workforce.

The male-female wage gap is one of the most pervasive and widespread labor market characteristics, given that on average, worldwide, men earn more than women. Wage gaps not only send a discouraging signal but also provide a financial disincentive for women to invest in labor market experience, training, and education. Exporting apparel increases the demand for female workers and, as a result, may affect the wage gap. Labor market experience, industry and occupation segregation, and government policies also play vital roles as determinants of pay gaps for equal work.

FACTORS BEHIND THE WAGE GAPS

In the United States, wage gaps were persistent even after the quiet revolution started (Goldin’s Phase IV). In 1980, women’s earnings were about 60 percent of men’s wages (Goldin 2006) and by 2020 were close to 84 percent (Barroso and Brown 2020). The wage gap does not necessarily close when women start entering the labor market (Kabeer and Natali 2013); indeed, if women enter the labor market for some reason besides wages, the increase in the relative supply of women can even widen the gap. Furthermore, as women enter the labor market, they often start in lower-ranked occupations or are simply paid less than men for the same work, both of which can lower the average wage earned by women. In any given country, the factors driving gender wage gaps can be numerous, entrenched, and interconnected.

Experience and skills requirements. In the labor market, occupations within an industry differ in terms of activities, skills, education, and wages. Technical and managerial positions (which men usually hold) often earn higher salaries than operators or machinists, and they require more (and different) skills and education.

Gender norms. Moreover, gender norms exist in many industries and occupations; that is, men and women often choose to work in different industries and occupations. For example, men are more likely to take jobs in construction, industries that require using capital equipment or handling heavy loads, chemical production and use, engineering, and software development. Women tend to work in health services, education, domestic services, or assembly-line work associated with activities performed in the home (such as sewing and food preparation).

Employment segregation. In the United States, employment segregation by occupation accounted for 33 percent of the gender wage gap, employment segregation by

industry for 18 percent, and experience for 14 percent, according to the Oaxaca-Blinder decomposition used by Das and Kotikula (2019). In fact, employment segregation is one of the primary contributors to gender wage gaps around the world. Women tend to be more concentrated in low-wage employment and in fewer sectors; men are more evenly distributed across sectors, occupations, and job types (Christian, Evers, and Barrientos 2013; Fontana 2009; ILO 2016). As a result, gender wage gaps can be significant.

Unequal pay for equal work. Furthermore, women may be paid less than men for the same work. This may be because of discrimination or because women’s subordination is deeply entrenched. For example, men are more likely to switch jobs or ask for higher wages—either of which often leads to higher earnings over time, even in the same position (Pearlman 2019; Schultz 2019).

EMPIRICAL ANALYSIS OF WAGE GAPS

There are many different dimensions of both labor market characteristics (industry and occupation mix, output prices, and geographic differences) and individual worker characteristics (age, education, experience, industry, occupation, and others). Therefore, we apply a commonly used empirical approach—Mincerian equations—to explore the components that explain the wage gaps driving gender wage differentials and to see whether education or other policies might support the transition toward better-paid jobs and careers.

Previous literature has decomposed the gap between male and female workers and estimated the effect of the growth in female-intensive industries on increasing demand for female workers (Blau and Kahn 2017; Kis-Katos, Pieters, and Sparrow 2017; Sauré and Zoabi 2014). To analyze the evolution of the gender wage gap in each sample country, we use a Mincerian wage equation, where the dependent variable is “real monthly wages” and the explanatory variables are the different observable characteristics of both the labor market and the individual. We interpret the estimated coefficients as the contribution that each of the represented elements makes to total earnings. In our estimation, we include variables that identify female workers, the apparel industry (which includes textiles and leather), the interaction between females and the apparel industry variable, and variables representing discrete education levels by gender. We also include control covariates such as age, age squared, education, weekly hours worked, and industry sectors. Our estimation equation for worker i at time t is

1 2 4 5 6 7 2

lnWageit α β= + Femaleit 3Apparel FemalexApparelit itβ β+ + Education Educationit itβ β + + xFemale Ageit β+ it Age Hoursit 8β β+ + it Industry ek kit it∑δ + +

it

k (2.1)