Person:
Jolliffe, Dean
Author Name Variants
Fields of Specialization
Food security,
Education economics,
Health economics,
Data collection methods,
Measuring Poverty
Degrees
ORCID
External Links
Departments
Development Economics Data Group, The World Bank
Externally Hosted Work
Contact Information
Last updated
August 29, 2023
Biography
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.
Publication Search Results
1 - 3 of 3
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Publication
Earnings, Schooling, and Economic Reform
(World Bank, 2007-09-30)Earnings, Schooling, and Economic Reform: Econometric Evidence From Hungary (1986 2004) Nauro Campos and Dean Jolliffe How does the relationship between earnings and schooling change with the introduction of comprehensive economic reform? This article sheds light on this question using a unique data set and procedure to reduce sample-selection bias. The principal assumptions are that sample-selection bias was minimal in 1986 and that the decision to participate in the wage market after 1986 is correlated with age, gender, and schooling demographics. Once corrected for sample selection on observables, the increase in returns is smaller, suggesting the existence of the positive correlation between education and the decision to participate in the wage sector that was discussed above. 16 Comparing the panels shows that sample-selection bias is positive and quite large throughout the period of analysis. An advantage of the Wage and Earnings Survey design is that the sample was selected in a single stage, and thus there is no need to correct estimates of the sampling variance for any design-induced dependence. Returns to Years of Schooling, 1986 2004: Spatial and Industry Fixed-effects Estimation of Equation (1) 1986 Panel A: Selection-corrected estimates Years of schooling Gender dummy variable (male 1) Potential experience Experience squared/100 Firm size dummy (300 employees 1) Number of observations R2 Panel B: Uncorrected estimates Years of schooling Gender dummy variable (male 1) Potential experience Experience squared/100 Firm size: 300 employees Number of observations R2 1989 1992 1995 1998 Although the Wage and Earnings Survey data include no direct measures of school quality, it is possible to provide limited supporting evidence. Studies that are based on multiple survey instruments for temporal analysis face the difficult question of whether the observed change results from changes in the examined population or changes in the survey instrument. The analysis showed that the 75 percent increase in returns to a year of schooling between 1986 and 2004 is evidence that the planned economy Campos and Jolliffe 525 undervalued education and that liberalization has allowed markets to correct this. -
Publication
Snapshot of Poverty and Labor Market Outcomes in Lebanon Based on Household Budget Survey 2011-2012
(World Bank, Washington, DC, 2015-12-08)This brief is based on analysis of the 2011-12 household budget survey (HBS) implemented by Central Administration for Statistics (CAS) with technical assistance from the World Bank. The survey was conducted during the period of September 2011 to November 2012, and was stratified across nine regions. The sample was designed to cover 4,805 households, but due to high non-response, it only includes 2,476 participating households. Poverty numbers presented in this note are not comparable with poverty estimates for other years due to differences in the instruments, fieldwork implementation and to some extent sample design; and also due to differences in the methodology for constructing welfare aggregate and the poverty line. All regional estimates in this report should be viewed with caution given concerns about significant levels of nonresponse and relatively small sample sizes within regions. CAS and the World Bank are working together to improve the quality of future surveys. -
Publication
Do Different Types of Assets have Differential Effects on Child Education?: Evidence from Tanzania
(World Bank, Washington, DC, 2017-05)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.