Caitlin Brown 
Ph.D. Candidate
Department of Economics
Georgetown University 
email: cb575 at

I am a Ph.D. Candidate in the Department of Economics at Georgetown University and a Consultant at the Development Research Group (DECRG) at the World Bank. You can find 
my CV here 

Research Interests: 
Primary Fields: Development Economics, Applied Microeconomics
Secondary Fields: Education, Poverty, Health

Current Research: 
Social Effects on Schooling in Rural India (Job Market Paper)  [paper]
While efforts to promote schooling in poor families have tended to focus more on individualized parental incentives, social effects may play an important role. This paper investigates whether these effects matter in the school enrollment decisions of rural Indian households. Two social effects are considered: a peer effect and a 'role model' effect, whereby mothers of a child's peer group can influence his or her enrollment. Role model effects are found to be negligible. An instrumental variables strategy using average peer mother education and village-level fixed effects is used to identify the peer effect. Peers are found to significantly influence child enrollment: on average, a one percent increase in peer enrollment increases the probability of a school-aged child enrolling by 0.22 percent. The magnitude of the effect varies by caste group, as well as by age and gender. Peer enrollment is also found to positively influence the number of hours per day spent studying, and negatively influence the amount of time spent on farm work, household chores and leisure activities.
A Poor Means Test? On Econometric Targeting in Africa (with Martin Ravallion & Dominique 
van de Walle)  [paper]  [addendum]
NBER Working Paper No. 22919
Proxy-means testing (PMT) is a popular method of poverty targeting with imperfect information. In a now widely-used version, a regression for log consumption calibrates a PMT score based on chosen covariates, which is then implemented for targeting out-of-sample. In this paper, the performance of various PMT methods is assessed using data for nine African counties. Standard PMT helps filter out the nonpoor, but excludes many poor people, thus diminishing its impact on poverty. Some methodological changes perform better, with a poverty-quantile method dominating in most cases. Even so, either a basic-income scheme or transfers using a simple demographic scorecard are found to do as well, or almost as well, in reducing poverty. However, even with a budget sufficient to eliminate poverty with full information, none of these targeting methods bring the poverty rate below about three quarters of its initial value. The prevailing methods are particularly deficient in reaching the poorest.  

How well do Household Poverty Data Identify Africa's Nutritionally Vulnerable Women and Children? (with Martin Ravallion & Dominique van de Walle)  [paper]  [addendum]
A comprehensive assessment for Sub-Saharan Africa generally confirms the expected positive household wealth effects on individual nutritional status. But it also reveals that undernourished women and children are spread quite widely across the distribution. On average, roughly three-quarters of underweight women and under-nourished children are not found in the poorest 20% of households when judged by a wealth index or consumption, and around half are not found in the poorest 40%. The mean joint probability of being an underweight woman and living in the poorest wealth quintile is only 0.03. Countries with higher overall rates of undernutrition tend to have a higher share of undernutrition on non-poor households.

White or Black Hat? An Economic Analysis of Computer Hacking [paper]
GCER Working Paper, 15-15-04
Cyber attacks have increased sharply in recent years. This paper investigates the 
decision a profit-motivated hacker makes between working as a malicious hacker, called a black hat, or in cybersecurity as a white hat hacker. A key component of the model is the contest between white hats and black hats for some part of firm output that is vulnerable to attack. White and black hat earnings are increasing, nonlinear functions of the proportion of black hats. Multiple equilibria exist. Increasing the cost of switching from black to white hat work is found to reduce the equilibrium proportion of black hats. Laws requiring firms to share information about breaches leads to a free rider problem, with firms employing fewer white hats.