B1204
Title: Assessing causal effects in a longitudinal observational study with truncated outcomes and nonignorable missing data
Authors: Alessandra Mattei - University of Florence (Italy) [presenting]
Michela Bia - Luxembourg Institute of Socio-Economic Research (Luxembourg)
Andrea Mercatanti - Luxembourg Institute of Socio-Economic Research (Luxembourg)
Abstract: Important statistical issues pervade the evaluation of effects of training programs for unemployed people. In particular the fact that offered wages are observed and well-defined only for subjects who are employed (truncation by death), and the problem that information on the employment status and wage can be lost over time (attrition) raise methodological challenges for causal inference. We present an extended framework for simultaneously addressing the aforementioned problems, and thus answering important substantive research questions in training evaluation observational studies with covariates, a binary treatment and longitudinal information on employment status and wage affected by the presence of missing data. There are two key features of this framework: we use principal stratification to properly define the causal effects of interest and we adopt a Bayesian approach for inference. The proposed framework allows us to partially answer an open issue in economics: the assessment of the trend of reservation wage over the duration of unemployment. We apply our framework to evaluate causal effects of foreign language training programs in Luxembourg, using administrative data on the labor force (IGSS-ADEM dataset). Our findings might be an incentive for the employment agencies to better design and implement future language training programs.
