Jolliffe, Dean

Development Economics Data Group, The World Bank
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Fields of Specialization
Food security, Education economics, Health economics, Data collection methods, Measuring Poverty
Development Economics Data Group, The World Bank
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Last updated August 29, 2023
Dean Jolliffe is a lead economist in the Development Data Group at the World Bank. He is a member of the Living Standards Measurement Study team and co-lead of the team that works on global poverty measurement (PovcalNet). Previously, he worked in the Research Group and the South Asia region of the World Bank. Prior to joining the World Bank, he was a research economist with the Economic Research Service of the U.S. Department of Agriculture, an assistant professor at Charles University Center for Economic Research and Graduate Education in Prague, an adjunct professor at Johns Hopkins University School of Advanced International Studies, an adjunct professor at Georgetown University Public Policy Institute, and a postdoctoral fellow at the International Food Policy Research Institute. Dean holds appointments as a research fellow with the Institute for the Study of Labor, as a co-opted council member of the International Association for Research in Income and Wealth, and as a fellow of the Global Labor Organization. He received his PhD in economics from Princeton University.
Citations 324 Scopus

Publication Search Results

Now showing 1 - 3 of 3
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    From Tragedy to Renaissance : Improving Agricultural Data for Better Policies
    (World Bank Group, Washington, DC, 2015-01) Carletto, Gero ; Jolliffe, Dean ; Banerjee, Raka
    Agricultural development is an essential engine of growth and poverty reduction, yet agricultural data suffer from poor quality and narrow sectoral focus. There are several reasons for this: (i) difficult-to-measure smallholder agriculture is prevalent in poor countries, (ii) agricultural data are collected with little coordination across ministries of agriculture and national statistics offices, and (iii) poor analysis undermines the demand for high-quality data. This paper argues that initiatives like the Global Strategy to Improve Agricultural and Rural Statistics bode well for the future. Moving from Devarajan's statistical "tragedy" to Kiregyera's statistical "renaissance" will take a continued long-term effort by individual countries and development partners.
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    Food Insecurity and Rising Food Prices: What Do We Learn from Experiential Measures?
    (World Bank, Washington, DC, 2018-05) Jolliffe, Dean ; Seff, Ilana ; de la Fuente, Alejandro
    Throughout many countries in the world, the measurement of food security currently includes accounting for the importance of perception and anxiety about meeting basic food needs. Using panel data from Malawi, this paper shows that worrying about food security is linked to self-reports of having experienced food insecurity, and the analysis provides evidence that rapidly rising food prices are a source of the anxiety and experiences of food insecurity. This finding controls for individual-level fixed effects and changes in the economic well-being of the individual. A particularly revealing finding of the importance of accounting for anxiety in assessing food insecurity is that individuals report a significant increase in experiences of food insecurity in the presence of rapidly rising food prices even when dietary diversity and caloric intake is stable.
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    Do Different Types of Assets have Differential Effects on Child Education?: Evidence from Tanzania
    (World Bank, Washington, DC, 2017-05) Kafle, Kashi ; Jolliffe, Dean ; Winter-Nelson, Alex
    To assess the conventional view that assets uniformly improve childhood development through wealth effects, this paper tests whether different types of assets have different effects on child education. The analysis indicates that household durables and housing quality have the expected positive effects, but agricultural assets have adverse effects on highest grade completed and no effects on exam performance. Extending the standard agricultural-household model by explicitly including child labor, the study uses three waves of panel data from Tanzania to estimate the effects of household assets on child education. The analysis corrects for the endogeneity of assets and uses a Hausman-Taylor instrumental variable panel data estimator to identify the effects of time-invariant observables and more efficiently control for time-invariant unobservables. The negative effect of agricultural assets is more pronounced among rural children and children from farming households, presumably due to the higher opportunity cost of their schooling.