Growth regressions have serious limitations, and those that use gender-disaggregated data are no exception. One serious limitation is the ad hoc nature of extensions to the augmented Solow model. Variables have been added to capture economic openness, levels of government spending, political instability, ethnic diversity, and a host of other potential determinants of growth—frequently with little or no justification in economic theory. A second weakness is the problem of endogeneity: gender equality affects growth, but growth presumably also affects gender equality. Finding valid instrumental variables to correct for this endogeneity is challenging to say the least. Only one empirical paper employing growth regressions explicitly addresses this simultaneity by instrumenting. In a cross-country panel regression of over 100 countries for the 1975-1990 period, Dollar and Gatti (1999) find that increases in per capita income are associated with increases in gender equality along three dimensions: secondary attainment, wage gaps and women in parliament. The effect of income on gender equality becomes stronger as countries move from low-middle income to high income.

Inequalities in opportunities are not limited to education. Numerous studies document large gaps in wages or hourly earnings between men and women, even after controlling for education and other forms of human capital.<sup>34</sup> One explanation frequently given for these wage gaps is the occupational segregation of women into low-paying and precarious employment.

What are the impacts of wage/hourly earnings gaps and occupational segregation on growth? The allocation of talent and entrepreneurial skills to productive activities is a powerful source of growth; conversely, if this talent is dedicated to rent-seeking behavior, long-run growth will suffer (Murphy et al., 1991). An analogous argument can be applied to occupational segregation by gender: to the extent that the concentration of women in low productivity occupations is nonvoluntary, the misallocation of talent may have large growth costs via efficiency losses.

Surprisingly, only one study has looked at the impact of occupational segregation on growth rates. Tzannatos (1999), using data from the 1980s, performs a simple simulation for 11 Latin American and Caribbean countries in which he calculates the impact on women’s wages, men’s wages and output of the elimination of occupational differentials within industries. While men’s wages fall by between 6 and 13 percent, women’s wages rise by significantly more: from 24 to 96 percent. Output increases range from 2 to 9 percent of GDP. Tzannatos interprets these impacts as what “can happen in the long run when: a) women and men are equally endowed with human capital; b) there is no employer discrimination; c) family constraints are no more binding upon women than men; and d) the gender specific effects of social norms and other institutional factors have withered away. (Tzannatos, 1999: 559).” Gender wage gaps per se have an ambiguous relationship with growth rates. On the one hand, one analysis based on panel data found that gender wage inequality in export-oriented middleincome countries boosts economic growth presumably via its effect on firm profits and investment (Seguino, 2000). On the other hand, greater wage inequality may be associated with lower aggregate saving in these countries, which is likely to hamper long-run growth rates (Seguino and Floro, 2003). Both these results should be viewed as tentative and preliminary,

> 34 Wage gaps are both an opportunity and an outcome measure.  To the extent that they are signals from labor markets about returns to investment in human capital, they measure opportunities.  To the extent that they are concrete labor market outcomes for women, they measure outcomes.

36