THE LONG‐TERM EFFECTS OF CONDITIONAL CASH TRANSFERS ON CHILD LABOR AND SCHOOL ENROLLMENT

This paper investigates the long‐term effects of conditional cash transfers on school attainment and child labor. To this end, we construct a dynamic heterogeneous agent model, calibrate it with Brazilian data, and introduce a policy similar to the Brazilian Bolsa Família. Our results suggest that this type of policy has a very strong impact on educational outcomes, sharply increasing primary school completion. The conditional transfer is also able to reduce the share of working children from 22% to 17%. We then compute the transition to the new steady state and show that the program actually increases child labor over the short run, because the transfer is not enough to completely cover the schooling costs, so children have to work to be able to comply with the program's schooling eligibility requirement. We also evaluate the impacts on poverty, inequality, and welfare. (JEL O11, I25, J24)     

Health care privatization processes in Europe: Theoretical justifications and empirical classification

This article analyses the health care system reform process in Europe based on the concept of privatization. This notion is understood from two perspectives. First, privatization may concern the health care financing or the provision of health services. Second, privatization can be “imposed” on individuals or be “internalized” and then introduced by individuals (patients and doctors). So we emphasize the diversity that privatization can assume. We classify privatization mechanisms used by different countries and identify which of the perspectives presented are more common in 14 European Union countries since the 1980s. The article shows that even if privatization processes are widespread, they assume different patterns in each country.     

Seeing is Believing? Evidence from Property Prices in Inundated Areas

We use hedonic property models to estimate the changes in implicit flood risk premium following a large flood event. Previous studies have used flood hazard maps to proxy flood risk. In addition to knowing whether a property lies in the floodplain, we use a unique data set with the flood inundation map. We find that the price discount for properties in the inundated area is substantially larger than in comparable properties in the floodplain that did not get inundated. This suggests that, in addition to capturing an information effect, the larger discount in inundated properties reflects potential uninsurable flood damages, and supports a hypothesis that homeowners respond better to what they have visualized (“seeing is believing”).     

Modified inference function for margins for the bivariate clayton copula-based SUN Tobit Model

This paper extends the analysis of the bivariate Seemingly Unrelated Regression (SUN) Tobit model by modeling its nonlinear dependence structure through the Clayton copula. The ability to capture/model the lower tail dependence of the SUN Tobit model where some data are censored (generally, left-censored at zero) is an useful feature of the Clayton copula. We propose a modified version of the (classical) Inference Function for Margins (IFS) method by Joe and XP [H. Joe and J.J. XP, The estimation method of inference functions for margins for multivariate models, Tech. Rep. 166, Department of Statistics, University of British Columbia, 1996], which we refer to as Modified Inference Function for Margins (MIFF) method, to obtain the (point) estimates of the marginal and Clayton copula parameters. More specifically, we employ the (frequenting) data augmentation technique at the second stage of the IFS method (the first stage of the MIFF method is equivalent to the first stage of the IFS method) to generate the censored observations and then estimate the Clayton copula parameter. This process (data augmentation and copula parameter estimation) is repeated until convergence. Such modification at the second stage of the usual estimation method is justified in order to obtain continuous marginal distributions, which ensures the uniqueness of the resulting Clayton copula, as stated by Solar's [A. Solar, Fonctions de répartition à n dimensions et leurs marges, Publ. de l'Institut de Statistique de l'Université de Paris 8 (1959), pp. 229–231] theorem; and also to provide an unbiased estimate of the association parameter (the IFS method provides a biased estimate of the Clayton copula parameter in the presence of censored observations in both margins). Since the usual asymptotic approach, that is the computation of the asymptotic covariance matrix of the parameter estimates, is troublesome in this case, we also propose the use of resampling procedures (bootstrap methods, such as standard normal and percentile, by Efron and Tibshirani [B. Efron and R.J. Tibshirani, An Introduction to the Bootstrap, Chapman & Hall, New York, 1993] to obtain confidence intervals for the model parameters.     

Skew-normal distribution for growth curve models in presence of a heteroscedasticity structure

In general, growth models are adjusted under the assumptions that the error terms are homoscedastic and normally distributed. However, these assumptions are often not verified in practice. In this work we propose four growth models (Morgan–Mercer–Flodin, von Bertalanffy, Gompertz, and Richards) considering different distributions (normal, skew-normal) for the error terms and three different covariance structures. Maximum likelihood estimation procedure is addressed. A simulation study is performed in order to verify the appropriateness of the proposed growth curve models. The methodology is also illustrated on a real dataset.     

Bivariate Tail Dependence and the Generation of Multivariate Extreme Value Distributions

We define, in a probabilistic way, a parametric family of multivariate extreme value distributions. We derive its copula, which is a mixture of several complete dependent copulas and total independent copulas, and the bivariate tail dependence and extremal coefficients. Based on the obtained results for these coefficients, we propose a method to build multivariate extreme value distributions with prescribed tail/extremal coefficients. We illustrate the results with examples.     

New Bootstrap Applications in Supervised Learning

Some bootstrap and boosting methods for problems related to classification are introduced in this article. The first method chooses better boosting weights by using a bootstrap search algorithm. The second method is a good way to define a classification frontier. A new formulation for boosting in linear discriminant analysis is given. Since in this new formulation the uncertainty is represented by the weighted covariance matrix, it is more appropriate from the conceptual point of view. Simulation results show that the proposed methods perform well in data analysis.     

The Role of Stakeholders in the Efficiency of Nonprofit Sports Clubs

This article analyzes the efficiency levels of nonprofit sports clubs through the data envelopment analysis methodology and specifically evaluates how efficient and inefficient clubs perceive the distinct contribution of stakeholders in attaining their respective levels of output efficiency. The results distinguish the varying levels of efficiency between such clubs and highlight significant differences in the roles of the local government and of associations and federations in attaining these efficiency levels. The study further suggests best practices that can be adopted by officials at inefficient clubs toward reducing or eliminating their shortfalls in efficiency.