Decomposing social indicators using distributional data

Are the poor less healthy? Does public health spending matter more to them? The authors decompose aggregate health indicators using a random coefficients model in which the aggregates are regressed on the population distribution by subgroups, taking account of the statistical properties of the error term and allowing for other determinants of health status, including public health spending. This also allows them to test possible determinants of the variation in the underlying subgroup indicators. They implement the approach with data on health outcomes and poverty measures for 35 developing countries. The authors find that poor people have appreciably worse health status on average than others - and that differences in public health spending tend to matter more to the poor. This is probably because the nonpoor are in a better position to buy private health care.Health Monitoring&Evaluation,Public Health Promotion,Health Systems Development&Reform,Health Economics&Finance,Early Child and Children's Health,Health Monitoring&Evaluation,Health Economics&Finance,Inequality,Health Systems Development&Reform,Poverty Assessment

How robust is a poverty profile?

Comparisons of poverty - indicating where or when poverty is greatest, for example - typically matter far more to policy choices than aggregate poverty measures, such as how many people are deemed"poor."So the author's examine how measurement practices affect empirical poverty profiles. They discuss the pros and cons of alternative approaches to developing a poverty profile and use those approaches on the same data set. In Indonesia, as in many countries, past methods of building poverty profiles have used the food-energy-intake method, defining the poverty line as the normal consumption spending at which a person typically attains a predetermined food-energy-intake in each subgroup. The author's argue that his method can yield differences in poverty lines (between urban and rural areas, for example) that exceed the cost-of-living differences the poor face. So, that method can mislead policy choices aimed at reducing absolute poverty. For comparison, they explore a cost-of-basic-needs methods, whereby an explicit bundle of foods typically consumed by the poor is valued at local prices, with a minimal allowance for non-food goods consistent with spending by the poor. This approach, though not ideal, is a conceptually transparent operational alternative that can be implemented with available data. They argue that this approach is more likely to generate a consistent poverty profile in that two people with the same measured standard of living - purchasing power of basic consumption needs - will be treated the same way. This refinement of past approaches retains some seemingly desirable features (such as concern for the tastes of the poor) and avoids others (such as the implicit use of a higher real poverty line in richer regions of the same country). For Indonesia, the cost-of-basic-needs methods finds more incidence, depth, and severity of poverty in rural areas, whereas the food-energy-intake method finds all measures of poverty worse in urban areas. The ranking of regions (provinces divided into rural and urban) by two methods has virtually zero correlation. The poverty profile by principal sector of employment is less sensitive to the choice of method, particularly in urban areas. This case study supports the conclusion that policymakers should be wary of underlying differences between methods of estimating poverty measures. The cost-of-basic-needs approach is fairly robust to severaly other methodological choices, notably changes in the composition of the basic need bundle (which determines the overall level of the poverty line), differences in the functional form of the poverty measure, and adjustment for spatial differences in prices, issues that have dominated debates on how to measure poverty. Ironically, the results of this study suggest that these issues matter less to poverty rankings (and hence to policy conclusions) than do the choices made in mapping a given specification of basic needs into monetary poverty lines.Poverty Lines,Poverty Assessment,Environmental Economics&Policies,Achieving Shared Growth,Poverty Reduction Strategies

Evaluating Job Training in Two Chinese Cities

Recent years have seen a surge in the evidence on the impacts of active labor market programs for numerous countries. However, little evidence has been presented on the effectiveness of such programs in China. Recent economic reforms, associated massive lay-offs, and accompanying public retraining programs make China fertile ground for rigorous impact evaluations. This study evaluates retraining programs for laid-off workers in the cities of Shenyang and Wuhan using a comparison group design. To our knowledge, this is the first evaluation of its kind in China. The evidence suggests that retraining helped workers find jobs in Wuhan, but had little effect in Shenyang. However, in terms of earnings impacts, retraining appears to have increased earnings in Shenyang but not in Wuhan. The study raises questions about the overall effectiveness of retraining expenditures, and it offers some directions for policymakers about future interventions to help laid-off workers.Active labor market programs, job training, impact evaluation, propensity score matching, China

Has Training Helped Employ Xiagang in China? A Tale from Two Cities.

Valuable contributions to the design of this study were made by Tamar Manuelyan Atinc, Wang Wei of the World Bank resident mission in Beijing, Chai Zhijian of the Chinese Ministry of Labor, Mr. Yu of the Chinese Labor Research Institute, and Li Huiming of the State Statistical Bureau. The expertise, resources, and courtesy of the municipal labor bureaus in Wuhan and Shenyang also contributed greatly to the successful completion of this design. Jeffrey Smith of the University of Maryland helped guide the evaluation. Superb research assistance was This study evaluates the effectiveness of training programs for workers retrenched from Chinese state-owned enterprises in the cities of Shenyang and Wuhan. A variety of impact estimators were applied, however ordinary least squares (OLS) controlling for observable characteristics was robust. We find that training dampens reemployment prospects in Shenyang but improves them in Wuhan. Training impact estimates computed by propensity score and logodds ratio matching imposing various support condition rules, yielded estimates very similar to those from the OLS. The estimates suggest that participation in training reduces the probability of being employed one year after participation by about 6 percentage points in Shenyang, bu

Evaluating Job Training in Two Chinese Cities *

matching; China Recent years have seen a surge in the evidence on the impacts of active labor market programs for numerous countries. However, little evidence has been presented on the effectiveness of such programs in China. Recent economic reforms, associated massive lay-offs, and accompanying public retraining programs make China fertile ground for rigorous impact evaluations. This study evaluates retraining programs for laid-off workers in the cities of Shenyang and Wuhan using a comparison group design. To our knowledge, this is the first evaluation of its kind in China. The evidence suggests that retraining helped workers find jobs in Wuhan, but had little effect in Shenyang. However, in terms of earnings impacts, retraining appears to have increased earnings in Shenyang but not in Wuhan. The study raises questions about the overall effectiveness of retraining expenditures, and it offers some directions for policymakers about future intervention