Men and Women

and, in some regions, from the expansion of global innovator services, even though men and women tend to work in different types of occupations.

Figure 2.25 highlights the occupational distribution across sectors. In transportation, women are in the minority, and those working in the sector are much less likely than men to be drivers or machine operators. In financial and business services, women are much more likely than men to perform clerical tasks. And even among occupations with similar shares of female and male employment, women’s lower labor participation means that men continue to outnumber women in these roles.

Gender disparities also translate into job quality in terms of wages, where large gender gaps persist. Data from the United States suggest that these wage gaps might be the largest among the global innovator services. Women in financial services and professional services, respectively, earn 39 percent and 29 percent less than men (BLS 2019).

FIGURE 2.25 Across Sectors in LMICs, the Occupational Distribution Differs between Men and Women

Decomposition of occupational roles of females and males, by sector, latest available year, 2005–17

Share of employment (%) 100 90 80 70 60 50 40 30 20 10 0 73 76 15

64 29

49

9 5 11 5 3 14 8 12 5 15

Female (37%) Male (63%) Female (45%)

Male (55%) Manufacturing Commerce and hospitality 16 10

26

47 20

18

12

48

Female (44%) Male (56%)

Financial and business services 23

19 65

24

32 12 5 17

Female (17%)

Male (83%) Transportation and communications

Management, professionals, and technicians Sales and personal services workers Clerical support workers Production workers (incl. drivers)

Source: Calculations based on World Bank’s International Income Distribution Dataset (I2D2). Note: The I2D2 is a global harmonized household survey database. The data cover 89 low- and middle-income countries (LMICs) across all regions for the latest available year between 2005 and 2017. (Under World Bank income group classifications, LMICs had 1994 gross national income of less than US$8,955.) Percentages in parentheses represent each gender’s share in overall employment in that sector. Employment covers both paid and unpaid forms (for example, contributing family members). “Commerce and hospitality” includes the services subsectors of wholesale and retail trade as well as hotels and restaurants. Occupational groups are defined under the International Labour Organization’s International Standard Classification of Occupations (ISCO), as follows: “Management, professionals, and technicians” covers ISCO major groups 1, 2, and 3. “Clerical support workers” covers major group 4. “Sales and personal services workers” covers major group 5. “Production workers (including drivers)” covers major groups 7, 8, and 9 (respectively craft workers, plant and machine operators, and elementary occupations). Within the transportation sector, “production workers” includes drivers. Major groups 6 (agricultural workers) and 10 (armed forces) are not reported but have values near 0 percent.

Gaps in low-skill services tend to be narrower; for example, in food services, women earn 15 percent less than men. Many LMICs see similar wage gaps (World Bank 2011).

Part, but not all, of the wage gap occurs because women tend to work in lower-skill occupations within higher-skill services. In the financial sector, for example, men are more likely to be in managerial positions while women are more likely to be bank tellers. In the United States, occupational differences within an industry explain roughly a third of the observed gender wage gap (Blau and Kahn 2017). Barrowman and Klasen (2020) document similar occupational gaps in LMICs, even though the gaps tend to be smaller in countries with larger services sectors.

Moreover, even when men and women have the same job in the same subsector, women’s earnings are often still not equal. Data consistently show that “adjusted” wage gaps—measures that control for differences in education, occupation, and industry— remain present, although some of the gaps are narrowing (Blau and Kahn 2017; Olivetti and Petrongolo 2008, 2016; Oostendorp 2004). Analysis based on the World Bank’s I2D2 labor force survey data suggest that when occupation is controlled for, women earn roughly 25 percent less than men.15

Chapter 1 highlighted similar gaps for women entrepreneurs, with female business owners being more likely than men to be in lower-skill services, especially retail, and more likely than men to run an informal business.

Skills for Productive Jobs

Human capital plays an important part in service delivery. Those services subsectors with the highest productivity, particularly the global innovator services, are also those with the highest needs for worker skills. Skill building is therefore crucial to allow for movements into the higher-productivity services and firms that offer high wages and higher-quality jobs.

A key question is, how much evidence is there for skills acquisition—and transferability of skills across sectors—in understanding the likely time it will take to expand jobs in more productive services? Some of these skills are obtained through formal education (the role of which will be further explored in chapter 5), but data show that more informal forms of training also play an important role in building skills.

Just as in manufacturing, some of the skills relevant to services are learned “on the job.” Learning by working—broadly defined as human capital accumulation through experience in the labor market—is an important driver of rapid productivity growth. Variants of this process feature prominently in several leading theories of international trade and economic growth (Grossman and Helpman 1991; Krugman 1987; Lucas 1988; Redding 1999). Arguably, the movement of labor from farms to factories might have induced such learning by doing and boosted productivity growth during East Asia’s growth miracle. Yet there is little direct empirical evidence on the extent to which learning opportunities differ systematically across sectors.

Some evidence suggests that the scope for on-the-job training is in fact larger in services than in manufacturing. Based on cross-sectional household survey data across 145 countries between 1990 and 2016, Islam at el. (2018) estimate that wages increase by 2.6 percent for each extra year of experience in the services sector compared with 2 percent in industry and 1.3 percent in agriculture.16 In both HICs and LMICs, the returns to experience are higher in services than in industry and higher in industry than in agriculture.17 The wage gap between services and nonservices sectors appears after only five years of experience and tends to widen over time. Furthermore, the returns to experience are higher in highincome economies than in low- and middle-income economies for all three sectors, and the sector with the highest returns in LMICs (services, at 2.4 percent) has lower returns than the sector with the lowest returns in HICs (agriculture, at 3.1 percent).

Looking across subsectors, the returns to experience in commerce (wholesale and retail); transportation and communications (the latter comprising post and telecommunications); finance; real estate; and business services exceed those in manufacturing in both HICs and LMICs. These subsector aggregates might conceal variations across constituent occupations. For example, evidence suggests that alongside agricultural workers, elementary service occupations (involving manual labor) have the lowest returns.

A more direct way to measure learning by doing is to measure returns to experience by following workers over time and across jobs. Yet most Mincer-type analyses of the relationship between age and wages do not fully capture learning by working in a particular sector because people may change jobs or sectors over their working lives. In a recent study based on 2003–15 longitudinal employer-employee panel data from the universe of formal sector workers and firms in Brazil, Artuç and Bastos (2020) circumvent this measurement challenge because they can track individual workers over time, as they move across firms and sectors. They show significant wage returns associated with experience, especially in global innovator services. Relative to all other subsectors (in manufacturing and services), the percentage wage change from one more year of experience was 2.2 percent in ICT services, compared with only 0.6 percent in accommodation and food services. The corresponding changes in apparel and automotive manufacturing, respectively, were 0.4 percent and 1.5 percent.18

In addition, learning by working might be more transferable in services than in manufacturing. In high-skill manufacturing industries, job experience only translates into higher wages when workers keep working in those industries, whereas in global innovator services, earnings associated with experience remain high even if workers move to a different industry (Artuç and Bastos 2020).19 Relative to all other subsectors (in manufacturing and services), the percentage wage change from one more year of experience when the worker had changed industries was about 1.9 percent in global innovator services (figure 2.26). In manufacturing, the corresponding change in wages was negative.