# **3.2 Sorting Patterns: Where Do Women Work?**

Figures 1A-F plot kernel weighted polynomial bivariate regressions of the gender composition on firm-size, capital intensity, value-added per worker, wages and share of output exported, to document sorting patterns by gender. They reveal a very strong association between firm-size and the share of women in the workforce; small firms tend to be majority-male firms, while large firms tend to be majority female-firms. In part because they tend to be larger, firms that employ a larger proportion of women are also more likely to export. Yet, the share of women in the workforce is negatively correlated with capital intensity, value-added per worker and firm average wages, variables that are positively correlated with firm-size. Firms that use proportionately more blue-collar workers also employ proportionately more women (see figure 1F). The coexistence of these patterns points towards the importance of sorting by gender into different types of industries and firms.

Table 1 presents descriptive statistics by industry, ranking them on the basis of the share of women they employ. Industries which are highly labor-intensive pay relatively low wages and have a high export propensity, such as the tobacco and apparel industry, employ a lot of women. By contrast, capital intensive industries, such as transport and motor vehicles, basic machinery and equipment employ relatively few women. These patterns are by no means unique to Indonesia; very similar gender sorting patterns have been documented in a host of other countries (see e.g. World Bank forthcoming, Chapter 5 and the references therein). Possible explanations for these sorting patterns are that women have a comparative advantage in industries that require fine motor skills, women may be less likely to be involved in labor disputes which can be a particular concern in large firms, and that women may lack more favorable outside opportunities (relative to men).<sup>15</sup> Given the sorting of women into larger firms, we will present weighted regressions. Unweighted statistics reflect average outcomes for firms, while weighted statistics reflect outcomes for workers, better capturing average outcomes for women.<sup>16</sup> While women represent 46 percent of blue-collar workers in manufacturing, only about a third of all white-collar workers are women.

> 15 For example, according to study of manufacturing firms in Bangladesh (Paul Majumder and Begum, 2000, p3) ― _women sort into garment firms because a) women are more patient and nimble; (b) women are more controllable than men; (c) women are less mobile and less likely to join a trade union; and (d) women can do better in sewing because this job coincides with their traditional jobs. Most of the garment employers interviewed, reported that benefits arising from these qualities amply compensate the cost of employment of women in terms of maternity leave, high absenteeism, and other factors”._

> 16 The results obtained using unweighted regressions are in general very similar to those obtained using weighted regressions. An interesting difference in weighted and unweighted pertains to differential treatment, the evidence for which is somewhat stronger in unweighted regressions. This suggests that differential treatment is more prevalent in small firms.  Results are not presented to conserve space, but available from the authors upon request.

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