We examine the effect of the Worker Profiling and Reemployment Services system. This program "profiles" Unemployment Insurance (UI) claimants to determine their probability of benefit exhaustion and then provides mandatory employment and training services to claimants with high predicted probabilities. Using a unique experimental design, we estimate that the program reduces mean weeks of UI benefit receipt by about 2.2 weeks, reduces mean UI benefits received by about $143, and increases subsequent earnings by over $1,050. Most of the effect results from a sharp increase in early UI exits in the treatment group relative to the control group. (JEL J650)

The effects of public financing of health expenditures, insurance coverage and other factors on health outcomes are examined within health production models estimated using 1960-1992 data across 20 OECD countries. Mortality rates are found to depend on the mix of health care expenditures and the type of health insurance coverage. Increases in the publicly financed share of health expenditures are associated with increases in mortality rates. Increases in inpatient and ambulatory insurance coverage are associated with reduced mortality. The effects of GDP, health expenditures and age structure on mortality are similar to those in previous studies. Tobacco use, alcohol use, fat consumption, female labour force participation, and education levels are also significantly related to overall mortality rates. Increases in income inequality are associated with lower mortality rates, suggesting that the negative relationship between inequality and health outcomes suggested by some previous studies does not remain when a more complete model is estimated. The result that increases in public financing increase mortality rates is robust to a number of changes in specifications and samples. Thus, as countries increase the level of their health expenditures, they may want to avoid increasing the proportion of their expenditures that are publicly financed.

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Using Data from the National Longitudinal Survey of Young Men, the author of this paper examines the relationship between predicted future earnings for five broad fields of study and college students' choice of major. Conditional logit models of major choice that incorporate alternative predicted earnings variables are specified and estimated. The results indicate that, holding family background characteristics constant, individuals are likely to choose majors offering greater streams of future earnings rather than, as some have argued, majors with higher beginning earnings at the time of the choice. It is also found that earnings profiles corrected for self-selection bias have flattened for more recent graduates in business, liberal arts, and education. The life-cycle earnings in these disciplines appear to be more severely depressed than those in science and engineering.How do individuals predict future earnings when choosing a college major? Several studies have addressed this question using aggregated time-series data without reaching a clear consensus. The typical approach has been to estimate models that incorporate beginning or average earnings in some fashion. Freeman (1975Freeman ( , 1976, for example, estimated models that assume individuals base predictions of future earnings opportunities in various fields on the starting salaries when the choice is made. Others, such as Hoffman and Low (1983), Siow (1984),

We develop a model 4 optimal employer search strategy when i@mzation about match quality is endogenous. The model is tested using four data sets, two 4 which have not previously been used. As theory predicts, wefind that whenfilling positions requiring more training, employers search more intensively and extensively. Employers also search more extensively when hiring workers with more education and with prior experience. These findings provide strong evidence qf systematic variation in search strategies based on the characteristics 4 the positions and job applicants. Factors that influence employer search also affect the duration $a vacancy.

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