Commentary: Assessing long-run deworming impacts on education and economic outcomes: a comment on Jullien, Sinclair and Garner (2016)

Sarah Baird, Joan Hamory Hicks, Michael Kremer and Edward Miguel
2017
African DevelopmentHealthEducation and Human CapitalResearch Methodology
dyw350 2017.pdf
Jullien, Sinclair and Garner (2016) (henceforth JSG) state that they seek to ‘appraise the methods’ of three recent papers that estimate long-run impacts of mass deworming on educational or economic outcomes. This commentary focuses on their discussion of Baird, Hicks, Kremer and Miguel (2016) (henceforth Baird).We welcome scrutiny of our work, and appreciate the opportunity to discuss JSG. Baird finds evidence of gains in some educational and labour outcomes 10 years after a deworming programme in 75 Kenyan primary schools. Some gains are found in the full sample, and others among either males or females, in ways that are sensible given the context, e.g. there are gains in manufacturing employment among males but not females, fewer of whom work in this sector in Kenya. Below we discuss JSG’s claim that the evidence in Baird is unreliable. It is not surprising that any two scholars might interpret a body of results differently, but JSG make a series of claims that appear overstated or are somewhat misleading.

Transparent and Open Social Science Research – Summer 2017

Summer
2017
FutureLearn (Online Course)

For moe information, contact staff at the Berkeley Initiative for Transparency in the Social Sciences (bitss.org).

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Explore the causes of limited transparency in social science research, and tools to make your own work more open and reproducible. Demand is growing for evidence-based policy making, but there is growing recognition in the social science community that limited transparency and openness in research have contributed to widespread problems. Explore transparency issues in social science research – and how to solve them. In this free online course, we will discuss the major transparency and reproducibility issues across the social sciences today, including the problems of fraud, publication bias and data mining. We will also discuss many of the emerging solutions to these problems, including: pre-registering studies and writing pre-analysis plans; performing replications; conducting meta-analyses; making data open and available; visualizing data in ways that are honest and effective. The course has been developed by the Berkeley Initiative for Transparency in the Social Sciences (BITSS, bitss.org). Headquartered at UC Berkeley, BITSS has been leading the social science research transparency movement since 2012.