Prison Rehabilitation Programs: Efficiency and Targeting
William Arbour, Guy Lacroix, Steeve Marchand
Increasing evidence suggests that incarceration, under certain circumstances, can improve inmates' social reintegration upon release. Yet, the mechanisms through which incarceration can lead to successful rehabilitation remain largely unknown. This paper finds that participation in social rehabilitation programs while incarcerated can significantly reduce recidivism. This result is entirely driven by inmates whose risk and needs were evaluated by a widely used assessment tool identifying their criminogenic needs. For this group, we estimate that participation in these programs reduces recidivism by about 9 percentage points within three years following release. Our results suggest targeting criminogenic needs is crucial for successful rehabilitation. We also find considerable heterogeneous program treatment effects: inmates with a high overall risk score, or who exhibit procriminal attitudes, benefit little if at all from program participation. We investigate the stability of the treatment effect coefficients and conclude they unlikely suffer from an omitted variable bias.
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Immigrants' Economic Performance and Selective Outmigration: Diverging Predictions from Survey and Administrative Data
Charles Bellemare, Natalia Kyui, Guy Lacroix
We show that survey and administrative data-based estimates of a panel data model of earnings, employment, and outmigration yield very different qualitative and quantitative predictions. Survey-based estimates substantially overpredict outmigration, in particular for lower performing immigrants. Consequently, employment and earnings of immigrants who remain in the country are overpredicted relative to model predictions from administrative data. Importantly, estimates from both data sources find opposite self-selection mechanisms into outmigration. Differences hold despite using the same cohort, survey period, and observable characteristics. Differences in predictions are driven by difficulties of properly distinguishing non-random sample attrition from selective outmigration in survey data.
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Infections, Accidents and Nursing Overtime in a Neonatal Intensive Care Unit (published, European Journal of Health Economics)
Marc Beltempo, Georges Bresson, Jean-Michel Etienne, Guy Lacroix
The paper investigates the effects of nursing overtime on nosocomial infections and medical accidents in a neonatal intensive care unit (NICU). The literature lacks clear evidence on this issue and we conjecture that this may be due to empirical and methodological factors. We model the occurrences of both events using a sample of 3,979 neonates who represents over 84,846 observations (infant/days). We exploit an important change in workforce arrangement that was implemented in June 2012, and which aimed at reducing overtime hours to identify a causal impact between the latter and the two outcomes of interest. We contrast the results using a standard mixed-effects logit model with those of a semiparametric mixed-effects logit model. Contrary to the mixed-effects logit model, the semiparametric model unequivocally shows that both adverse events are impacted by nursing overtime as well as being highly sensitive to infant and NICU-related characteristics. Furthermore, the mixed-effects logit model is rejected in favour of the semiparametric one.
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Can Recidivism Be Prevented From Behind Bars? Evidence From a Behavioral Program
William Arbour
Incarcerated offenders are offered a wide range of programs to encourage their chances of successful reintegration into society. Little is known, however, about the degree to which such programs improve prisoners’ reentry. In this paper, I study the effects of a cognitive-behavioral program implemented in Quebec, Canada, with a rich micro-level dataset. To manage the econometric issue of inmates’ self-selection into the program, I exploit inmates’ random assignment to probation officers who exhibit varying propensities to recommend the rehabilitation measure. I find large, negative, and significant effects of the program on recidivism, as measured by an inmate’s probability of serving a subsequent sentence: within one year following release, the program reduces recidivism by up to 18 percentage points. Moreover, the program is shown to decrease the number of future offenses. Further analyses indicate that the most plausible mechanism can be attributed to the program’s success in altering offenders’ preferences towards crime.
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Évaluation de l'impact de la générosité des prestations sur la demande de congés parentaux au Québec
Nathalie Havet, Guy Lacroix, Morgane Plantier
Dans cette étude, nous nous intéressons à l'impact de la générosité des prestations parentales sur la demande de congés suite à une naissance au Québec. À partir des données administratives du ministère du Travail, de l'Emploi et de la Solidarité sociale (MTESS), couplées aux données fiscales de Revenu Québec (RQ), pour la période de 2006 à 2017, nous proposons pour la première fois d'évaluer l'effet causal du niveau des prestations familiales accordées sur la demande de congés des mères à bas revenus. En utilisant l'approche par Regression Kink Design (RKD), nos résultats concluent à un impact positif de la générosité des prestations sur la durée des congés parentaux demandés par les mères québécoises les plus modestes. Ils montrent également que cet effet varie selon la situation familiale de la mère, et en particulier qu'il tend à diminuer avec l'implication croissante du père. Notre étude permet ainsi de valider la pertinence de mesures basées sur un mécanisme d'incitation financière à destination des mères les plus modestes, mais également de quantifier l'effet attendu de ce type de dispositif.
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Intrahousehold Resource Allocation and Individual Poverty: Assessing Collective Model Predictions against Direct Evidence on Sharing (published in The Economic Journal)
Olivier Bargain, Guy Lacroix, Luca Tiberti
Welfare analyses conducted by policy practitioners around the world usually rely on equivalized or per-capita expenditures and ignore the extent of within-household inequality. Recent advances in the estimation of collective models suggest ways to retrieve the complete sharing process within families using homogeneity assumptions (typically preferences stability upon exclusive goods across individuals or household types) and the observation of exclusive goods. So far, the prediction of these models has not been validated, essentially because intrahousehold allocation is seldom observed. We provide such a validation by leveraging a unique dataset from Bangladesh, which contains information on the fully individualized expenditures of each family member. We also test the core assumption (efficiency) and homogeneity assumptions used for identification. It turns out that the collective model predicts individual resources reasonably well when using clothing, i.e., one of the rare goods commonly assignable to male, female and children in standard expenditure surveys. It also allows identifying poor individuals in non-poor households while the traditional approach understates poverty among the poorest individuals.
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The impact of tobacco tax reforms on poverty in México (published in Springer Nature: Business & Economics)
Luis Huesca, Abdelkrim Araar, Linda Llamas, Guy Lacroix
This paper investigates the impact of increasing the tobacco taxes on the poverty rate in Mexico. Unlike most LMIC countries, the prevalence of smoking in Mexico is higher among the well-off than among the poor. Yet, tobacco tax rates in Mexico are lower than those in most LMIC countries. There is room, thus, to implement tax reforms and compensating policies to mitigate their impact on the poor. Our analysis is based upon the stochastic dominance approach. More precisely, several tax reforms are analyzed through the so-called Consumption Dominance curves. In addition, the reforms are assumed to be revenue neutral and to give rise to compensating subsidies on specific goods. Our results show that if the Mexican government were to implement a WHO-type reform, poverty among households with at least one smoking member would increase by 2.6 percentage points. Yet, the deleterious effects are entirely mitigated by price subsidies on staple foods.
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Robust dynamic panel data using epsilon-contamination (forthcoming, Advances in Econometrics)
Badi H Baltagi, Georges Bresson, Anoop Chaturvedi, Guy Lacroix
This paper extends the work of Baltagi et al (2018) to the popular dynamic panel data model. We investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, we consider the epsilon-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the epsilon-contamination priors use Zellner’s (1986) g-priors for the variance-covariance matrices. We propose a general ``toolbox'' for a wide range of specifications which includes the dynamic panel model with random effects, with cross-correlated effects à la Chamberlain, for the Hausman-Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using a Monte Carlo simulation study, we compare the finite sample properties of our proposed estimator to those of standard classical estimators. The paper contributes to the dynamic panel data literature by proposing a general robust Bayesian framework which encompasses the conventional frequentist specifications and their associated estimation methods as special cases.