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Tracking poverty is predicated on the availability of comparable consumption data and reliable price deflators. However, regular series of strictly comparable data are only rarely available. Price deflators are also often missing or disputed. In response, poverty prediction methods that track consumption correlates as opposed to consumption itself have been developed. These methods typically assume that the estimated relation between consumption and its predictors is stable over time-an assumption that cannot usually be tested directly. This study analyzes the performance of poverty prediction models based on small area estimation techniques. Predicted poverty estimates are compared with directly observed levels in two country settings where data comparability over time is not a problem. Prediction models that employ either non-staple food or non-food expenditures or a full set of assets as predictors are found to yield poverty estimates that match observed poverty well. This offers some support to the use of such methods to approximate the evolution of poverty. Two further country examples illustrate how an application of the method employing models based on household assets can help to adjudicate between alternative price deflators.

Achieving Shared Growth --- Consumption Prediction --- Debt Markets --- Poverty Dynamics --- Poverty Reduction --- Price Deflator --- Regional Economic Development --- Rural Poverty Reduction --- Small Area Estimation

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Brazil's slow pace of poverty reduction over the last two decades reflects both low growth and a low growth elasticity of poverty reduction. Using GDP data disaggregated by state and sector for a twenty-year period, this paper finds considerable variation in the poverty-reducing effectiveness of growth-across sectors, across space, and over time. Growth in the services sector was substantially more poverty-reducing than was growth in either agriculture or industry. Growth in industry had very different effects on poverty across different states and its impact varied with initial conditions related to human development and worker empowerment. The determinants of poverty reduction changed around 1994: positive growth rates and a greater (absolute) elasticity with respect to agricultural growth contributed to faster poverty reduction. But because there was so little of it, economic growth played a relatively small role in accounting for Brazil's poverty reduction between 1985 and 2004. The taming of hyperinflation (in 1994) and substantial expansions in social security and social assistance transfers, beginning in 1988, accounted for a larger share of the overall reduction in poverty.

Agricultural Growth --- Economic Growth --- Human Capital --- Human Development --- Inequality --- Poor --- Poverty Dynamics --- Poverty Reduction --- Pro-Poor Growth --- Rural Development --- Rural Poverty Reduction --- Social Assistance --- Social Security

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Poverty estimates based on cross-section data provide static snapshots of poverty rates. Although a time series of cross-section data can offer some insights into poverty trends, it does not allow for an assessment of dynamics at the household level. Such a dynamic perspective on poverty generally calls for panel data and this kind of analysis can usefully inform poverty reduction policy, notably the design of social protection interventions. Absent actual panel data for Senegal, this paper applies new statistical methods to construct synthetic panel data from two rounds of cross-section household surveys in 2005 and 2011. These data are used to study poverty transitions. The results suggest that, in marked contrast to the picture obtained from cross-section data, there exists a great deal of mobility in and out of poverty during this period. More than half the population experiences changes in its poverty status and more than two-thirds of the extreme (food) poor move up one or two welfare categories. Factors such as rural residence, disability, exposure to some kind of natural disaster, and informality in the labor market are associated with a heightened risk of falling into poverty. Belonging to certain ethnicities and factors such as migration, working in the non-agriculture sector, and having access to social capital are associated with a lower risk of falling into poverty.

Chronic Poverty --- Education --- Macroeconomics and Economic Growth --- Mobility --- Poverty Dynamics --- Poverty Reduction --- Rural Poverty Reduction --- Senegal --- Services & Transfers to Poor --- Social Protections and Labor --- Synthetic Panel --- Transition

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November 1999 - Changes in Poland's family allowances and unemployment benefits have significant but different effects on different groups of households. In deciding on strategies to address long-term poverty, policymakers must take such differences into account. Okrasa analyzes how the incidence of household endowments and the allocation of social benefits affect families' transitions into and out of poverty. Using panel data for 1993-96 from Poland's Household Budget Survey, and a framework based on sample survival analysis techniques, Okrasa evaluates how various policies will affect households with specific characteristics that make them likely to become poor or to move out of poverty under different scenarios (including whether or not they receive a given amount of a particular type of social transfer). He also discusses how nonincome sources of welfare, such as savings, credits, and loans, affect the likelihood that families will become or stop being poor. He concludes that family allowances and unemployment benefits, the two major social programs analyzed, have significant but different effects on different groups of households (characterized in terms of the age, gender, marital status, and educational attainment of the head of household; the size, type, location, and sector of employment of the family or household; and the year in which the household fell into poverty). If the share of family allowances in total household income were reduced by 1 percent, for example, the average length of poverty would be increased by roughly 2 percent. But a 1 percent change in unemployment benefits would yield a 3 percent change in the average duration of poverty. Differences in hazard rates for various subgroups would be even greater. Households in villages were much more likely to fall into poverty than households in cities and large towns, but the poor in towns and cities had more difficulty exiting poverty. There was generally less poverty mobility among households headed by public sector employees than among those headed by employees in the private sector. Families with three or more children and one-parent families (and grandparents with children) faced the greatest risk of being poor; single-person households and childless married couples were the least endangered. Small nuclear families with one or two children and families without children fell between these two extremes. This paper - a product of Poverty and Human Resources, Development Research Group - is part of a larger effort in the group to analyze the dynamics of poverty and the effectiveness of the safety net. The study was funded by the Bank's Research Support Budget under the research project Household Welfare Change during the Transition (RPO 681-21). The author may be contacted at wokrasa@worldbank.org.

Chronically Poor --- Economic Growth --- Health, Nutrition and Population --- Household Budget --- Household Income --- Human Development --- Income --- Measures --- Poor --- Poor Households --- Population Policies --- Poverty --- Poverty Dynamics --- Poverty Index --- Poverty Profile --- Poverty Reduction --- Rural --- Rural Areas --- Rural Development --- Rural Poverty Reduction --- Safety Nets and Transfers --- Savings --- Services and Transfers to Poor --- Social Policies --- Social Programs --- Social Protections and Labor --- Temporarily Poor --- Unemployment

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This paper reviews both current practices and common challenges of measuring the causes, functioning, and consequences of violent conflict at the micro-level. The authors review existing conflict- and violence-related survey questionnaires, with a particular focus on the World Bank's Living Standard Measurement Surveys. Further, they discuss methodological challenges associated with empirical work in conflict-affected areas-such as operationalizing a definition of conflict, using the appropriate units of analysis, deciding on the timing of the survey, dealing with data biases and conducting surveys in an ethically sound manner-and propose ways to improve the usefulness of existing surveys to analyze conflict processes at the micro-level. Violent conflict, households, survey methods, questionnaire design.

Abuse. --- Access to Markets. --- Access to Services. --- Armed Conflict. --- Atrocities. --- Basic Needs. --- Bombing. --- Causes of Displacement. --- Child Mortality. --- Civil War. --- Civilian Populations. --- Combatants. --- Conflict and Development. --- Conflict Research. --- Conflict Resolution. --- Conflict. --- Conflicts. --- Constraints. --- Consultant. --- Contract. --- Crises. --- Death. --- Demobilization. --- Democracy. --- Developing Countries. --- Disarmament. --- Economic Development. --- Economic Growth. --- Economies. --- Ethnic Group. --- Evelopment Aid. --- Evelopment Policy. --- Ex-Combatant. --- Excess Mortality. --- Exposure to Violence. --- Extreme Poverty. --- Fertility. --- Fighting. --- Food Consumption. --- Food Policy. --- Foundations. --- Genocide. --- Guerrilla. --- Health Problems. --- Health, Nutrition and Population. --- Household Emographics. --- Household Heads. --- Household Survey. --- Household Welfare. --- Human Capital. --- Human Welfare. --- Hunger Insurance. --- Illness. --- Impact of Conflict. --- Impact of Shocks. --- Income. --- Inequality. --- Insurance. --- International Bank. --- International Committee. --- International Cooperation. --- Knowledge. --- Labor Market. --- Labor Supply. --- Lack of Infrastructure. --- Life Events. --- Living Standards. --- Local Conflict. --- Malnutrition. --- Measures. --- Migration. --- Military Service. --- Mortality Levels. --- Mortality. --- Nations. --- Needs Assessments. --- Negotiations. --- Number of Deaths. --- Nutrition. --- Observer. --- Peace Research. --- Peace. --- Peacebuilding. --- Peacekeeping. --- Polarization. --- Policy Iscussions. --- Policy Makers. --- Policy Research Working Paper. --- Policy Research. --- Policy. --- Political Constraints. --- Political Instability. --- Political Participation. --- Political Violence. --- Politics. --- Poor Households. --- Poor. --- Population Fund. --- Population Groups. --- Population Policies. --- Population. --- Post Conflict Reconstruction. --- Post-Conflict Reconstruction. --- Poverty Dynamics. --- Poverty Estimates. --- Poverty Reduction. --- Poverty. --- Primary School. --- Progress. --- Publications. --- Rape. --- Rebel. --- Reconstruction. --- Refugee Camps. --- Refugee. --- Revolutions. --- Right-Wing. --- Risks. --- Roads. --- Rural Households. --- Rural Poverty Reduction. --- Rural Roads. --- Rural. --- Sexual Abuse. --- Social Action. --- Social Conflict and Violence. --- Social Development. --- Social Science. --- Social Security. --- Soldiers. --- Substance Abuse. --- Terrorism. --- Training. --- Transfers. --- Trauma. --- Trust. --- Unemployment. --- United Nations Population Fund. --- University. --- Victims. --- Village Leaders. --- Violence. --- Violent Conflict. --- Violent Experiences. --- Vulnerability. --- Vulnerable Groups. --- War. --- Warfare. --- World Evelopment.