Three Essays on Policy Evaluation
Creators: name: Ingino, Francesco
Publisher: name: Universita degli studi di Salerno
Year: 2016
Subjects: subject: SECS-P/01 ECONOMIA POLITICA
subject: Medical marijuana law
subject: Policy evaluation
subject: Employment protection legislation
subject: Apprentices

description: 2014 - 2015
description: Proposito della tesi di dottorato, dal titolo “Three Essays on Policy Evaluation”, è quello di sottolineare come l’impatto di una politica (economica e non) possa essere valutato secondo un approccio rigoroso, quasi-sperimentale, quando architettata adeguatamente a tale scopo. A tal proposito, sono mostrati tre esempi di valutazione delle politiche, nel corso dei quali si espongono ed affrontano le principali problematiche legate a questo tipo di esercizio. L'interesse dell'approccio adoperato nella presente tesi è dato dall'ampia utilizzazione del metodo difference-in-difference che consente di stimare, in vari contesti, gli impatti che politiche di differente natura possono avere sulle variabili socio-economiche. Ciascuno degli esercizi di valutazione costituisce un capitolo della tesi. Ogni uno di essi ha comportato una rassegna della letteratura in materia (allo scopo di inquadrare il tema trattato), la ricerca dei dati e l’elaborazione di uno specifico modello econometrico finalizzato all’identificazione del nesso causale. .. [a cura dell'Autore]
description: Over the last two decades there has been a proliferation of literature on program evaluation. Many researches in economics look at the causal effect of exposure of units to programs on some outcomes through econometric and statistical analysis. The units are typically economic agents such as individuals, households, markets, firms, counties, states or countries. The programs can be job search assistance programs, educational programs, vouchers, laws or regulations, drug therapies, environmental exposure or technology shocks. Rubin potential outcomes framework seems to be the dominant framework in which the aim is to compare the two potential outcomes for the same unit when he or she is exposed and not exposed to the program (or treatment). However, each unit can be only exposed to one levels of program: an individual may enrol or not in a training program or he (or she) may be subjected or not to policy. We can refer to this as the fundamental problem of causal inference (Holland, 1986; Imbens and Wooldridge, 2008). The impossibility to compare the same individual at different treatment status induces to resolve the issue thinking in term of counterfactual. We need to compare distinct units at different levels of treatment. This means to compare different physical units or the same physical unit observed at different times. But each individual or unit who chooses to enrol in a program is (by definition) different from that who chooses not to enrol. These differences may invalidate causal comparison of outcomes by treatment status. Indeed, the fear in this econometrics literature is traditionally related to endogeneity, or self-selection, issues. The simplest case for analysis is when assignment to treatment is randomized, and thus independent from the covariates as well as the potential outcomes. It is straightforward to obtain attractive estimators for the average effect of treatment in randomized experiments (e.g. the difference in means by treatment status). Although there have been some example of experimental evaluations, they remain relatively rare in economics. More common is the case where economists analyse data from observational studies. Observational data generally create challenges in estimating causal effects referred to unconfoundedness, exogeneity, conditional independence, or selection on observable characteristics. Estimation and inference of causal effect under unconfoundedness assumption requires that conditional on observed covariates there are no unobserved factors that are associated both with the assignment and with the potential outcomes. Without unconfoundedness assumption there is no general approach to estimating treatment effects and various methods have been proposed (for a review, see Imbens and Wooldridge 2008). .. [edited by Author]