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Fridays Academy: Gender and Economic Growth

Ignacio Hernandez's picture

From  Raj Nallari and Breda Griffith's lecture notes.

Empirical studies of gender inequalities and economic growth (and 3)

 By focusing on differential school attainment as the measure of gender inequality, Dollar and Gatti (1999) estimate the growth equation mentioned before, i.e. :

[?y/y]it=δ+ψgit+Xπ+ui

Growth of Per Capita Income (OLS Regressions)

 

Source:  Dollar and Gatti (1999); p. 33

 

The results, first using OLS, are given in the table. The authors find a weak negative coefficient on male education and a weak positive coefficient on female education. These results are dependent on the inclusion of regional dummies, especially for Latin America which has very high female secondary achievement and has a strong tendency to grow less rapidly than predicted by the other variables. Dividing the sample in half based on female secondary school achievement shows that for less developed economies – where female secondary attainment covers less than 10.35 percent of the population – there are insignificant coefficients on both male and female secondary attainment. For the more developed economies, there is a weak negative coefficient for male education and a significant positive coefficient for female secondary attainment. Dollar and Gatti (1999) comment on the strongly negative coefficient on the fertility variable and note that “female education may well contribute to per capita income growth by reducing fertility and hence population growth” (p. 19). The results of the 2SLS regressions confirm the pattern highlighted by the OLS estimation, using civil liberties and religion variables as instruments for the male education and female education (Dollar and Gatti,1999).

 

Turning to Volart (2004), in the short run, discrimination may act as a brake on economic growth and development. Volart (2004) develops a theoretical model of gender discrimination in India. Her hypothesis is that ‘gender discrimination against women in the market place reduces the available talent in an economy, which has negative economic consequences’ (p. 1). Concentrating on the labor market, Volart (2004) examines three possible scenarios:

1. the labor market equilibrium without discrimination

2. gender discrimination as an exogenous exclusion of females from managerial positions

3. gender discrimination as a complete exclusion of females from the labor market.    

 

Volart’s theoretical model relies on a given set of social norms and mores that influence the choices made by women in the three scenarios outlined based on their initial endowment of entrepreneurial talent affecting the level of human capital and its use; whether one will become a manager, a worker or engage in home production. Based on the second scenario, the model shows how discrimination affects the labor market, the equilibrium wage rate, the allocation of talent across working and managerial positions, the investment in education and economic growth. The results suggest that discrimination lowers equilibrium wages for both men and women workers and reduces investment in human capital by all men and women workers. Moreover, the average talent of managers is lower in the case of discrimination with knock-on effects for innovation, productivity and economic growth. In the third case scenario, women engage only in home production implying that the equilibrium wage rate is the same as in the case of the first scenario, no discrimination. However, per capita GDP is lower because of home productivity is lower than that of market productivity and because females decide not to invest in human capital.

 

The empirical model suggests that discrimination acts as a brake on economic development in India. A 10 percent increase in female-to-male managers ratio in India would increase total output per capita by 2-percent, while a 10 percent increase in the female-to-male workers ration would increase total output per capita by 8 percent. Moreover, the effects of gender discrimination are more serious for certain sectors in the economy, in particular for sectors where higher skills are needed. Lower ratios of female-to-male workers reduce output in both the agricultural and non-agricultural sectors, while lower ratios of female-to-male managers reduce output in non-agricultural sectors only. More worryingly, the modernization aspect of economic growth that suggests an undermining of discrimination as an economy grows does not hold in Volart’s study. The richer states continue to have lower ratios of female-to-male labor participation that suggests the existence of a binding discriminatory social norm. Targeted policies that change social norms are necessary to promote economic development in this case. For example, social policies that encourage education for women and their role in the labor market are critical to change the entrenched social norms.

 

At the other end of the spectrum, Seguino (2000) investigating the empirical impact of gender inequality on economic growth finds a positive relationship between gender inequalities and income growth.  Confining the analysis to a set of semi-industrialized countries over twenty-one years (1975 to 1995), the data capture countries that have adopted an export orientation with a large share of exports produced in female-dominated manufacturing industries. The principal hypothesis tested is that “gender inequality which works to lower women’s wages relative to men’s is a stimulus to growth in export-oriented economies.” (p. 1212) and is supported by the empirical results. A further hypothesis is that the “growth effect of gender wage differentials is transmitted via the stimulus to investment, serving as a signal of profitability” (p. 1212). This hypothesis is also supported by the empirical results. 

 

The hypotheses are derived from the economic growth literature and the emerging literature on gender and growth. From the 1980s, the growth literature has challenged the concept of the hitherto exogenous technical progress. Numerous empirical studies have examined the effects of trade and trade policies on technical progress and its effect on economic growth. While the results have been mixed, Seguino (2000) notes the importance of human capital in the growth equations suggesting that “exports are associated with gains in output primarily for those countries with sufficient human capital to absorb new technologies” (p. 1212). Furthermore, the economic structure of a country appears to influence the impact of trade on growth. In primary-commodity countries, exports to GDP do not have a positive effect on economic growth rates, whereas some studies find a positive relationship between the ratio of manufactured exports to GDP and economic growth (see Levin and Raut, 1997; Sachs and Warner, 1997). Building on the empirical literature that posits a positive relationship between exports, technical progress and growth, Seguino (2000) hypothesizes that gender inequality has a positive effect on technical progress and growth and summarizes the linkages as:

 

Gender inequality leads to export expansion that leads to technical change resulting in economic growth

The causal links in this chain leading to economic growth will depend upon:

  • The structure of the economy
  • The existence of a skilled labor force
  • A widening of the gender gap may increase investment

Seguino (2000) narrowly defines the causal link to investigate the “effects of discriminatorily low wages for women on: (a) exports, and therefore technological change and productivity growth, and (b) investment.” (p. 1223). The findings suggest that across countries and over time within countries there is a positive relationship between gender wage inequality and growth via (a) and (b).