@workingpaper {643634,
	title = {Uncovering Household Self-Targeting with Machine Learning},
	year = {Working Paper},
	abstract = {I investigate the extent to which households in Colombia manipulate their eligibility for a social program.\&nbsp; 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.\&nbsp; Because proxy-means testing systems can potentially predict household poverty poorly, households have incentives to manipulate their eligibility.\&nbsp; 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.\&nbsp;\&nbsp; I then use machine-learning techniques to predict households{\textquoteright} actual poverty level.\&nbsp; 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{\textquoteright}s poverty score.\&nbsp; 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.},
	author = {Moya Chin}
}
