Essays in nonparametric instrumental variable regression

In my PhD thesis, I develop nonparametric instrumental variable methods for two different settings. First, in a duration setup, I develop an approach to deal with dynamic and static selection, two important problems in econometric research. The method can deal with censoring and is based on a comparison of cohorts. I also construct a framework to analyse the static endogeneity of a model. In addition to these identification results, I also provide the necessary estimation procedures and derive their asymptotic properties. Second, I show that a class of constrained penalised minimum distance estimators can be viewed as projections. In a simulation, I study their properties under monotonicity constraint. I construct a test for monotonicity and use it to analyse the effect of class size on test scores in a Minnesota dataset. The effect is revealed to be non-monotone. I suggest an economic approach to explain this result

Nonparametric instrumental variable methods for dynamic treatment evaluation

We develop a nonparametric instrumental variable approach for the estimation of average treatment effects on hazard rates and conditional survival probabilities, without model structure.We derive constructive identification proofs for average treatment effects under noncompliance and dynamic selection, exploiting instrumental variation taking place during ongoing spells. We derive asymptotic distributions of the corresponding estimators. This includes a detailed examination of noncompliance in a dynamic context. In an empirical application, we evaluate the French labor market policy reform PARE which abolished the dependence of unemployment insurance benefits on the elapsed unemployment duration and simultaneously introduced additional active labor market policy measures. The estimated effect of the reform on the survival function of the duration of unemployment duration is positive and significant. Neglecting selectivity leads to an underestimation of the effects in absolute terms

Nonparametric Instrumental Variable Methods for Dynamic Treatment Evaluation

We develop an instrumental variable approach for identification of dynamic treatment effects on survival outcomes in the presence of dynamic selection, noncompliance, and right-censoring. The approach is nonparametric and does not require independence of observed and unobserved characteristics or separability assumptions. We propose estimation procedures and derive asymptotic properties. We apply our approach to evaluate a policy reform in which the pathway of unemployment benefits as a function of the unemployment duration is modified. Those who were unemployed at the reform date could choose between the old and the new regime. We find that the new regime has a positive average causal effect on the job finding rate

Behavioral Spillovers

What is a behavioral spillover? How can a spillover be uncovered from the data? What is the precise link between the underlying psychological theory of a spillover and the econometric assumptions which are necessary to estimate it? This paper draws on recent advancements in causal inference, behavioral economics, psychology, and neuroscience to develop a framework for the causal evaluation and interpretation of behavioral spillovers. A novel research design is suggested. The paper challenges existing empirical strategies and reevaluates of existing empirical results

Essays in nonparametric instrumental variable regression

In my PhD thesis, I develop nonparametric instrumental variable methods for two different settings. First, in a duration setup, I develop an approach to deal with dynamic and static selection, two important problems in econometric research. The method can deal with censoring and is based on a comparison of cohorts. I also construct a framework to analyse the static endogeneity of a model. In addition to these identification results, I also provide the necessary estimation procedures and derive their asymptotic properties. Second, I show that a class of constrained penalised minimum distance estimators can be viewed as projections. In a simulation, I study their properties under monotonicity constraint. I construct a test for monotonicity and use it to analyse the effect of class size on test scores in a Minnesota dataset. The effect is revealed to be non-monotone. I suggest an economic approach to explain this result

The effect of local monitoring on nuclear safety and compliance: Evidence from France

International audienc

Are older nuclear reactors less safe? Evidence from incident data in the French fleet

This paper studies the impact of age and reactor technology on safety in the French nuclear fleet between 1997 and 2015. We use a novel dataset encompassing over 19,000 nuclear safety events declared by plant managers to the French regulatory agency. A major problem for evaluating the effects of age and technology is that declarations of safety events are influenced by the plant managers’ level of transparency. We deal with this problem by restricting the analysis to the occurrences of particular types of events, such as automatic shut-downs. These events, due to their technical specifics, exhibit perfect detection and declaration rates. We obtain the following results. First, technology has a strong impact on reactor safety. Second, age has a significant and technology-specific effect on reactor safety. For instance, while in 1997 one additional year of age led to a 15% increase in the expected number of automatic shut-downs among the 900 MW reactors, this number was reduced to only 6% in 2014. In comparison, the 1450 MW reactors undergo a significantly larger number of automatic shut-downs, but age has a smaller effect on their rate of occurrence. Finally, we find that local transparency, defined as both detection abilities, reporting behaviours and declaration guidelines, plays a significant role in the explanation of the observed variations of declarations of events

Testing the regulatory threat hypothesis: Evidence from Sweden

International audienc

Implicit yardstick competition between heating monopolies in urban areas: Theory and evidence from Sweden

International audienc

Methods for strengthening a weak instrument in the case of a persistent treatment

International audienceWhen evaluating policy treatments that are persistent and endogenous, available instrumental variables often exhibit more variation over time than the treatment variable. This leads to a weak instrumental variable problem, resulting in high bias or uninformative confidence intervals. We evaluate two new estimation approaches that strengthen the instrument. We derive their theoretical properties and show in Monte Carlo simulations that they outperform standard IV-estimators. We use our procedures to estimate the effect of public utility divestiture in the US nuclear energy sector. Our results show that divestiture significantly increases production efficiency