报告摘要: FoodAPS (this is an NBER working paper and forthcoming at Southern Economic Journal)
This study uses propensity score matching and causal forest (a machine learning method) to derive the average treatment effect on the treated (ATT) using a food purchase data (FoodAPA) recently released by USDA. We addressed the selection issue of WIC (a major nutrition program for women infants and children) participation. We found WIC food package to be the reason for program success.
报告人简介:
Di Fang is Assitant Professor at the University of Arkansas at Fayetteville. She received a Ph.D. in Business Administration (Agribusiness) from W.P. Carey School of Business at Arizona State University and her BS in Economics from Nankai University in China. Fang’s research interest includes Food and Health Economics, Consumer Economics and Marketing, and issues related to Obesity. She has published in the Southern Economic Journal, Canadian Journal of Agricultural Economics, Journal of Agricultural and Resources Economics, Contemporary Economic Policy, American Journal of Public Health, JAMA Network Open, NBER working papers and others. She is a reviewer for USDA's AFRI grants in 2018, Agricultural and Applied Economics Association (AAEA) and the American Public Health Association (APHA).
本次学术报告为北京食品安全政策与战略研究基地和食物与健康经济研究中心(C’FHER)系列学术报告第四十期,报告题目及内容简介见下文。该系列报告由C’FHER和北京食品安全政策与战略研究基地共同主办,欢迎广大老师同学届时参加。
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