Barbara Sianesi

An evaluation of the Swedish system of active labour market programmes in the 1990s

We investigate the presence of short- and long-term effects from joining a Swedish labor market program vis--vis more intense job search in open unemployment. Overall, the impact of the program system is found to have been mixed. Joining a program has increased employment rates among participants, a result robust to a misclassification problem in the data. On the other hand it has also allowed participants to remain significantly longer on unemployment benefits and more generally in the unemployment system, this being particularly the case for those entitled individuals entering a program around the time of their unemployment benefits exhaustion.

The Returns to Education: Macroeconomics

We offer an extensive summary and a critical discussion of the empirical literature on the impact of human capital on macro-economic performance, with a particular focus on UK policy. We also highlight methodological issues and make recommendations for future research priorities. Copyright Blackwell Publishing Ltd, 2003.

Quasi-experimental study designs series – Paper 7: assessing the assumptions

Quasi-experimental designs are gaining popularity in epidemiology and health systems research-in particular for the evaluation of health care practice, programs, and policy-because they allow strong causal inferences without randomized controlled experiments. We describe the concepts underlying five important quasi-experimental designs: Instrumental Variables, Regression Discontinuity, Interrupted Time Series, Fixed Effects, and Difference-in-Differences designs. We illustrate each of the designs with an example from health research. We then describe the assumptions required for each of the designs to ensure valid causal inference and discuss the tests available to examine the assumptions.

Misclassified Treatment Status and Treatment Effects: An Application to Returns to Education in the United Kingdom

We study the impact of misreported treatment status on the estimation of causal treatment effects, focusing on applications where no additional information or repeated measurements are available. We first characterize the bias introduced by misclassification on the average treatment effect on the treated (ATT) under a conditional independence assumption, in both a binary and a multiple-treatment setting. We find that the bias of matching-type estimators computed from misclassified data cannot in general be signed. We subsequently provide easily implementable methods to bound the ATT of interest semiparametrically, in particular allowing for very general forms of impact heterogeneity and of the no-treatment outcome equations, as well as for some dependence of the misreporting probabilities on individual characteristics. The empirical problem that motivates our paper is the estimation of the wage returns to a number of educational qualifications in the United Kingdom, allowing for misreporting in attainment. We investigate the sensitivity of the raw estimates to the presence of misclassification and explore the identification power of plausible restrictions on the nature and extent of misclassification. We show that the resulting bounds are sometimes wide but generally point to reasonable ranges of positive values for average returns to schooling among the schooled. For the range of educational qualifications considered, we further show that the claim sometimes made that measurement error bias roughly cancels out selection bias is not supported. More generally, our results show that under relatively mild restrictions, we can obtain strong conclusions regarding our questions of interest.