The Occupational Cost of Being Illegal in the United States: Legal Status,Job Hazards,and Compensating Differentials

Considerable research and pervasive cultural narratives suggest that undocumented immigrant workers are concentrated in the most dangerous, hazardous, and otherwise unappealing jobs in U.S. labor markets. Yet, owing largely to data limitations, little empirical work has addressed this topic. Using data from the 2004 and 2008 panels of the Survey of Income and Program Participation, we impute legal status for Mexican and Central American immigrants and link their occupations to Bureau of Labor and Statistics (BLS) data on occupational fatalities and occupational hazard data from the U.S. Department of Labor to explore racial and legal status differentials on several specific measures of occupational risk. Results indicate that undocumented workers face heightened exposure to numerous dimensions of occupational hazard – including higher levels of physical strain, exposure to heights, and repetitive motions – but are less exposed than native workers to some of the potentially most dangerous environments. We also show that undocumented workers are rewarded less for employment in hazardous settings, receiving low or no compensating differential for working in jobs with high fatality, toxic materials, or exposure to heights. Overall, this study suggests that legal status plays an important role in determining exposure to job hazard and in structuring the wage returns to risky work.     

Face à l'économie informelle au Mozambique: volontarisme ou maintien du statu quo?

L'économie informelle fournit l'essentiel de l'emploi dans nombre de pays émergents. Selon la théorie de la régulation, l'activité économique est régie par un ensemble complexe de règles formelles et informelles. Les auteurs examinent, à partir du cas du Mozambique, les éléments qui favorisent ou entravent la transition vers l'économie formelle, ainsi que les diverses formes de régulation, en s'appuyant sur une enquěte qualitative conjuguant observation directe et entretiens avec des personnalités locales. Les données recueillies font apparaître un rôle ambivalent de l'Etat, qui se montre parfois volontariste et soucieux de réformer la réglementation et les institutions et parfois partisan du statu quo.     

Do Interactions between Unemployment Insurance and Sickness Insurance Affect Transitions to Employment?

Previous research suggests that there are substantial interactions between the unemployment and sickness insurance in Sweden. Moral hazard arises in the interplay between these insurance systems, as by reporting sick an unemployed person can postpone unemployment insurance expiration and sometimes receive considerably higher benefits. I examine whether these interactions affect the transition rate to employment. To do this I use a reform that greatly reduced the incentives to transfer to sickness benefits. Although there is evidence that this reform effectively lowered the incidence of sick reports among the unemployed, I do not find that the reduced sick report rate in turn affected the transition rate to employment.     

Inference in Arch and Garch Models with Heavy–Tailed Errors

ARCH and GARCH models directly address the dependency of conditional second moments, and have proved particularly valuable in modelling processes where a relatively large degree of fluctuation is present. These include financial time series, which can be particularly heavy tailed. However, little is known about properties of ARCH or GARCH models in the heavy–tailed setting, and no methods are available for approximating the distributions of parameter estimators there. In this paper we show that, for heavy–tailed errors, the asymptotic distributions of quasi–maximum likelihood parameter estimators in ARCH and GARCH models are nonnormal, and are particularly difficult to estimate directly using standard parametric methods. Standard bootstrap methods also fail to produce consistent estimators. To overcome these problems we develop percentile–t, subsample bootstrap approximations to estimator distributions. Studentizing is employed to approximate scale, and the subsample bootstrap is used to estimate shape. The good performance of this approach is demonstrated both theoretically and numerically.