Treatment evaluation in the case of interactions within markets

We extend the standard evaluation framework to allow for interactions between individuals within segmented markets. An individualâs outcome depends not only on the assigned treatment status but also on (features of) the distribution of the assigned treatments in his market. To evaluate how the distribution of treatments within a market causally affects the average effect within the market, averaged over the full population, we develop an identification and estimation method in two steps. The first one focuses on the distribution of the treatment within markets and between individuals and the second step addresses the distribution of the treatment between markets. We apply our method to data on training programs for unemployed workers in France. We use a rich administrative register of unemployment and training spells as well as the information on local labor demand that is used by unemployment agencies to allocate training programs. The results show that the average treatment effect on the employment rate causally decreases with respect to the proportion of treated in the market. Our analysis accounts for unobserved heterogeneity between markets (using the longitudinal dimension of the data) and, in a robustness check, between individuals.Treatment evaluation; equilibrium effects; matching estimators

Active Labor Market Policy Effects in a Dynamic Setting

This paper implements a method to identify and estimate treatment effects in a dynamic setting where treatments may occur at any point in time. By relating the standard matching approach to the timing-of-events approach, it demonstrates that effects of the treatment on the treated at a given date can be identified even though non-treated may be treated later in time. The approach builds on a "no anticipation" assumption and the assumption of conditional independence between the duration until treatment and the counterfactual durations until exit. To illustrate the approach, the paper studies the effect of training for unemployed workers in France, using a rich register data set. Training has little impact on unemployment duration. The contamination of the standard matching estimator due to later entries into treatment is large if the treatment probability is high.treatment, program participation, unemployment duration, matching, training, propensity score, contamination bias

Analyzing the Anticipation of Treatments Using Data on Notification Dates

When treatments may occur at different points in time, most evaluation methods assume – implicitly or explicitly – that all the information used by subjects about the occurrence of a future treatment is available to the researcher. This is often called the “no anticipation” assumption. In reality, subjects may receive private signals about the date when a treatment may start. We provide a methodological and empirical analysis of this issue in a setting where the outcome of interest as well as the moment of information arrival (notification) and the start of the treatment can all be characterized by duration variables. Building on the "Timing of Events" approach, we show that the causal effects of notification and of the treatment on the outcome are identified. We estimate the model on an administrative data set of unemployed workers in France which provides the date when job seekers receive information from caseworkers about their future treatment status. We find that notification has a significant and positive effect on unemployment duration. This result violates the standard "no anticipation" assumption and rules out a "threat effect" of training programs in France.evaluation of labor market programs, training, duration model, timing of events, anticipation