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This paper deals with the compensation for non-fatal accident risk in Switzerland and presents empirical estimates of the value of a statistical injury. We approach the problem of endogenous sorting of workers into jobs with different accident risks based on unobserved productivity differences twofold. First, we have access to the number of accidents not only at the level of industries, but within cells defined over industry×skill-level of the job, which allows us to estimate risk compensation within groups of workers defined over the same cells. Second, we capitalize on the partial panel structure of our data which allows us to empirically isolate the wage component specific to the employer. Our different approaches to identification in fact yield very different estimates of the value of a statistical injury. Our preferred estimate gives an estimate of about 40,000 Swiss francs (per prevented injury per year). JEL classification: J31, J17Keywords: Compensating Wage Differentials, Value of a Statistical Injury, Risk Measurement, Unobserved Productivity * Olivier Steiger, Rainer Winkelmann and Josef Zweimüller provided many helpful comments and suggestions which significantly improved this study. We thank Alois Fässler from the Swiss Accident Insurance Fund (Suva) for providing us with the data and helpful guidelines concerning the data. We also thank Simon Büchi for reviewing parts of the code for the empirical analysis. Simone Gaillard and Jonathan Lorand proof-read the manuscript.

This paper deals with the compensation for non-fatal accident risk in Switzerland and presents empirical estimates of the value of a statistical injury. We approach the problem of endogenous sorting of workers into jobs with different accident risks based on unobserved productivity differences twofold. First, we have access to the number of accidents not only at the level of industries, but within cells defined over industry×skill-level of the job, which allows us to estimate risk compensation within groups of workers defined over the same cells. Second, we capitalize on the partial panel structure of our data which allows us to empirically isolate the wage component specific to the employer. Our different approaches to identification in fact yield very different estimates of the value of a statistical injury. Our preferred estimate gives an estimate of about 40,000 Swiss francs (per prevented injury per year). JEL classification: J31, J17Keywords: Compensating Wage Differentials, Value of a Statistical Injury, Risk Measurement, Unobserved Productivity * Olivier Steiger, Rainer Winkelmann and Josef Zweimüller provided many helpful comments and suggestions which significantly improved this study. We thank Alois Fässler from the Swiss Accident Insurance Fund (Suva) for providing us with the data and helpful guidelines concerning the data. We also thank Simon Büchi for reviewing parts of the code for the empirical analysis. Simone Gaillard and Jonathan Lorand proof-read the manuscript.

This paper analyzes labor market success of workers who are displaced in boom versus recession periods. Moreover, the empirical analysis contrasts workers from small firms and large firms. The idea is that displacement carries no information about workers' productivity in large firms but is a signal of low productivity in small firms. This signal is stronger when the plant closure occurs in a boom period than in a recession period. Results indicate that the (i) state of the business cycle is important for influence the effect of displacement on labor market success and (ii) the effect differs by the size of the firm. In large firms, displaced workers suffer from larger earning losses when displacement occurs in recession compared to boom, the opposite result is found for workers displaced from small firms. JEL-Classification: E32, J64, J65