Sad statement, India, the country with the greatest physical labour requirements, exhibits the largest labour market gender inequality.
The author asks a good question.
Since WWII, women in the United States have greatly benefited from job requirements shifting away from physical attributes (“brain”) toward their comparative advantage in intellectual attributes (“brawn”) (seeRendall, 2010). While some of the shifting demand favoring women is related to job requirements changing intra-occupation, a large portion of the decrease in physical requirements is due to a structural shift toward services (Rendall, 2011). Hence, the last 60-years have seen an explosion in job opportunities for women in the United States, coupled with higher wages and relatively large returns to education. Can this structural transformation mechanism provide some insight into gender inequality in developing countries?
So how did they work out?
All the 4 countries, Brazil, Mexico, Thailand and India were ranked on their changes between brawn and brain. India, typically, didnt shift the brain demand upwards.
Given factor requirements, , and estimates of “prices,” , the decomposition of individual traits and factor effects on wage changes is given by,
where ω is the average (natural) log of wages, and bars without time subscripts denote averages over time, e.g., .
Thus, the wage gap from period 0 to t is decomposed into four categories:
Relative changes in standard male and standard female characteristics, given average returns across time.
Relative price changes, given average male and female standard characteristics (including changes in general gender discrimination λ).
Relative changes in skill endowments, given average skill prices.
Relative skill price changes, given average male and female skills.
I am not sure about the usage of average returns across time, that can actually hide quite a lot of rather interesting movements. Perhaps median returns would have been more accurate. Same with the average skill prices. There have been quite a lot of fertility changes as well so that impact needs to be considered.
The policy implications are good enough to be quoted in full
The wage analysis of Section 4 strongly suggests that falling brawn requirements and rising brain requirements can have a large impact on women’s relative wages. Section 5 provides evidence for policies related to improving the labor supply of women.
More specifically, Brazil is a clear example of rising brain requirements and falling brawn requirements significantly closing the gender wage gap. In contrast, India is a striking counterexample, where women work primarily in the highly brawn dependent agricultural sector. Thailand is a good example of a country that experienced major structural transformation within the labor market. This has allowed Thai women greater labor market flexibility and better opportunities to match innate abilities with skill requirements. While some of the gains in Mexico were undone by the rise of maquiladoras (low-brain jobs) in the 1990s, it could be argued that, as countries move along the development path and become richer, gender equality should naturally follow. However, at the same time, policy makers should promote an open environment with respect to education and employment opportunities for both genders. In particular, improving the quality of education will likely help strengthen and develop women’s comparative advantage in brain endowments. This policy recommendation is supported by findings in Pitt et al. (2010) for Bangladesh and Klasen and Pieters (2012)for India.
Mexico also provides a cautionary tale, as suggested by Gonzalez (2001). Mexico pursued manufacturing sector growth in the 1990s. This lead to diminishing returns to brain endowments, and was considered counterproductive when accounting for the effects on female employment opportunities. Thus, if improving gender equality is a goal, policy makers should encourage the development of service sector employment. Specific policies aimed at attracting firms or FDI in the knowledge industry would seem most beneficial in generating greater gender equality in terms of wages. India has started moving along this path relatively recently with its information technology focus (see Winters & Yusuf, 2007), and the results of this paper suggest this is a good first-step.
Note however, that policies just aimed at increasing the knowledge industry might not be enough to persuade women to participate in the labor market. Section 5 provides clear evidence in this regard. Specifically, fostering a good environment for high-quality education is extremely important. However, as can be seen from the predicted gender employment ratios in all countries, higher brain demands still favor men. This means that equal access to education is imperative for the strengthening of female employment opportunities.
In conclusion, a policy aimed at attracting firms to create employment in the service sector (knowledge industries) with good access to education for both men and women should result in greater gender equality in terms of labor market outcomes
net net, educate women, more and more and more….and reduce their fertility impact..