In the 1990’s, international activists launched an anti-sweatshop movement in order to improve working conditions and worker pay in overseas factories operated by Nike, Adidas, and Reebok. This project quantifies the effect of the anti-sweatshop movement on empowerment of female textile workers in Indonesia. I use a differences-in-difference design, by comparing differences among female textile workers before and after 1995-1996 in sub-districts with textile factories operated by targeted firms and in sub-districts with textile factories operated by other firms. Preliminary results indicate that women do not marry later nor have fewer children. However, women report using contraception at higher rates. These results suggest that worker reforms in industries with predominantly female labor forces can generate small improvements in female autonomy and empowerment.
I investigate the extent to which households in Colombia manipulate their eligibility for a social program. Eligibility is determined by a poverty score that is calculated from answers to a household survey, the formula for which was released four years after the start of the program. Because proxy-means testing systems can potentially predict household poverty poorly, households have incentives to manipulate their eligibility. I find that, as in Camacho and Conover (2011), there is a significant discontinuity at the eligibility cutoff and that one method households use to manipulate their eligibility is by having their poverty score overwritten. I then use machine-learning techniques to predict households’ actual poverty level. I find that households who manipulate their score are more likely to be poor than households with the same score and more likely to be poorly predicted by the government’s poverty score. These findings suggest that not all proxy-means testing systems predict household poverty well and when they do not, households self-target by manipulating their eligibility.