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 - 7 of 7
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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
A Global Count of the Extreme Poor in 2012: Data Issues, Methodology and Initial Results
(World Bank, Washington, DC, 2015-10)The 2014 release of a new set of purchasing power parity conversion factors (PPPs) for 2011 has prompted a revision of the international poverty line. In order to preserve the integrity of the goalposts for international targets such as the Sustainable Development Goals and the World Bank’s twin goals, the new poverty line was chosen so as to preserve the definition and real purchasing power of the earlier $1.25 line (in 2005 PPPs) in poor countries. Using the new 2011 PPPs, the new line equals $1.90 per person per day. The higher value of the line in US dollars reflects the fact that the new PPPs yield a relatively lower purchasing power of that currency vis-à-vis those of most poor countries. Because the line was designed to preserve real purchasing power in poor countries, the revisions lead to relatively small changes in global poverty incidence: from 14.5 percent in the old method to 14.1 percent in the new method for 2011. In 2012, the new reference year for the global count, we find 12.7 percent of the world’s population, or 897 million people, are living in extreme poverty. There are changes in the regional composition of poverty, but they are also relatively small. This paper documents the detailed methodological decisions taken in the process of updating both the poverty line and the consumption and income distributions at the country level, including issues of inter-temporal and spatial price adjustments. It also describes various caveats, limitations, perils and pitfalls of the approach taken. -
Publication
Toward Successful Development Policies: Insights from Research in Development Economics
(World Bank, Washington, DC, 2020-01)What major insights have emerged from development economics in the past decade, and how do they matter for the World Bank? This challenging question was recently posed by World Bank Group President David Malpass to the staff of the Development Research Group. This paper assembles a set of 13 short, nontechnical briefing notes prepared in response to this request, summarizing a selection of major insights in development economics in the past decade. The notes synthesize evidence from recent research on how policies should be designed, implemented, and evaluated, and provide illustrations of what works and what does not in selected policy areas. -
Publication
Food Security and Wheat Prices in Afghanistan : A Distribution-sensitive Analysis of Household-level Impacts
(World Bank, Washington, DC, 2012-04)This paper investigates the impact of increases in wheat flour prices on household food security using unique nationally-representative data collected in Afghanistan from 2007 to 2008. It uses a new estimator, the Unconditional Quantile Regression estimator, based on influence functions, to examine the marginal effects of price increases at different locations on the distributions of several food security measures. The estimates reveal that the negative marginal effect of a price increase on food consumption is two and a half times larger for households that can afford to cut the value of food consumption (75th quantile) than for households at the bottom (25th quantile) of the food-consumption distribution. Similarly, households with diets high in calories reduce intake substantially, but those at the bottom of the calorie distribution (25th quantile) make very small changes in intake as a result of the price increases. In contrast, households at the bottom of the dietary diversity distribution make the largest adjustments in the quality of their diets, since such households often live at subsistence levels and cannot make large cuts in caloric intake without suffering serious health consequences. These results provide empirical evidence that when faced with staple-food price increases, food-insecure households sacrifice quality (diversity) in order to protect calories. The large differences in behavioral responses of households that lie at the top and bottom of these distributions suggest that policy analyses relying solely on ordinary least squares estimates may be misleading. -
Publication
Under What Conditions Are Data Valuable for Development?
(World Bank, Washington, DC, 2021-10)Data produced by the public sector can have transformational impacts on development outcomes through better targeting of resources, improved service delivery, cost savings in policy implementation, increased accountability, and more. Around the world, the amount of data produced by the public sector is increasing at a rapid pace, yet their transformational impacts have not been realized fully. Why has the full value of these data not been realized yet This paper outlines 12 conditions needed for the production and use of public sector data to generate value for development and presents case studies substantiating these conditions. The conditions are that data need to have adequate spatial and temporal coverage (are complete, frequent, and timely), are of high quality (are accurate, comparable, and granular), are easy to use (are accessible, understandable, and interoperable), and are safe to use (are impartial, confidential, and appropriate). -
Publication
Food Insecurity and Rising Food Prices: What Do We Learn from Experiential Measures?
(World Bank, Washington, DC, 2018-05)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. -
Publication
What Makes Public Sector Data Valuable for Development?
(Published by Oxford University Press on behalf of the World Bank, 2023-04-14)Data produced by the public sector can have transformational impacts on development outcomes through better targeting of resources, improved service delivery, cost savings, increased accountability, and more. Around the world, the amount of data produced by the public sector is increasing rapidly, but we argue the full potential of data to improve development outcomes has not been realized yet. We outline 12 features needed for data to generate greater value for development and present case studies substantiating these features. We argue that a key reason why the transformational value of data has not yet been realized is that suboptimal data—data not satisfying these 12 features—are being supplied. The features are that the data should be of adequate spatial and temporal coverage (complete, frequent, and timely), should be of high quality (accurate, comparable, and granular), should be easy to use (accessible, understandable, and interoperable), and should be safe to use (impartial, confidential, and appropriate).