The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Demombynes and Ozler examine the effects of local in police station jurisdictions that are the wealthiest inequality on property and violent crime in South Africa.among their neighbors, suggesting that criminals travel to Their findings are consistent with economic theories neighborhoods where the expected returns from burglary relating inequality to property crime, and also with are highest. The authors do not find evidence that sociological theories that imply that inequaliry leads to inequality between racial groups fosters interpersonal crime in general. Burglary rates are 20-30 percent higher conflict at the local level.This paper-a product of the Poverty Team, Development Research Group-is part of a larger effort in the group to understand the relationship between income inequality and various outcomes, such as crime, health, and pro-poor growth.Copies of the paper are available free from the World Bank,

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The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 9313A greater share of reported COVID-19 deaths occur at younger ages in low-and middle-income countries (LMICs) compared to high-income countries (HICs). Based on data from 26 countries, people age 70 and older constitute 37 percent of deaths attributed to COVID-19 in LMICs on average, versus 87 percent in HICs. Only part of this difference is accounted for by differences in population age structure. In this paper, COVID-19 mortality rates are calculated for each age group by dividing the number of COVID-19 deaths by the underlying population. The resulting age-mortality curves are flatter in countries with lower incomes. In HICs, the COVID-19 mortality rate for those ages 70-79 is 12.6 times the rate for those ages 50-59. In LMICs, that ratio is just 3.5. With each year of age, the age-specific mortality rate increases by an average of 12.6 percent in HICs versus 7.1 percent in LMICs. This pattern holds overall and separately for men's and women's mortality rates. It reflects some combination of variation across countries in age patterns of infection rates, fatality rates among those infected, and under-attribution of deaths to COVID-19. The findings highlight that experiences with COVID-19 in wealthy countries may not be generalizable to developing countries.

Poverty maps, spatial descriptions of the distribution of poverty in any given country, are most useful to policy-makers and researchers when they are finely disaggregated, i.e. when they represent small geographic units, such as cities, towns, or villages. Unfortunately, almost all household surveys are too small to be representative at such levels of disaggregation, and most census data do not contain the required information to calculate poverty. The 1996 South African census is an exception, in that it does contain income information for each individual in the household. In this paper, we show that the income from the census data provides only a weak proxy for the average income or poverty rates at either the provincial level or at lower levels of aggregation. We also demonstrate a simple method of imputing expenditures for every household in the census, using information in the October Household Survey (OHS) and the Income Expenditure Survey (IES) in 1995. The resulting predicted household consumption values are plausible and provide a good fit with the IES data. We also provide an example, which demonstrates that poverty headcount can be imputed with fair precision for Magisterial Districts and for Transitional Local Councils. Finally, our paper serves as a reminder of the value of comparing various data sources for external validation and it underlines the need to make more use of census data that seems to be underutilized in most developing countries.