Correcting the bias in the estimation of a dynamic ordered probit with fixed effects of self-assessed health status

This paper considers the estimation of a dynamic ordered probit with fixed effects, with an application to self-assessed health status. The estimation of nonlinear panel data models with fixed effects by MLE is known to be biased when T is not very large. The problem is specially severe in our model because of the dynamics and because it contains two fixed effects: one in the linear index equation, interpreted as unobserved health status, and another one in the cut points, interpreted as heterogeneity in reporting behavior. The contributions of this paper are twofold. Firstly this paper contributes to the recent literature on bias correction in nonlinear panel data models by applying and studying the finite sample properties of two of the existing proposals to the ordered probit case. The most direct and easily applicable correction to our model is not the best one and still has important biases in our sample sizes. Secondly, we contribute to the literature that study the determinants of Self-Assesed Health measures by applying the previous analysis on estimation methods to the British Household Panel Survey.Dynamic ordered probit, Self-assessed health, Reporting bias, Panel data, Unobserved heterogeneity, Incidental parameters, Bias correction

Correcting the bias in the estimation of a dynamic ordered probit with fixed effects of self-assessed health status

This paper considers the estimation of a dynamic ordered probit with fixed effects, with an application to self-assessed health status. The estimation of nonlinear panel data models with fixed effects by MLE is known to be biased when T is not very large. The problem is specially severe in our model because of the dynamics and because it contains two fixed effects: one in the linear index equation, interpreted as unobserved health status, and another one in the cut points, interpreted as heterogeneity in reporting behavior. The contributions of this paper are twofold. Firstly this paper contributes to the recent literature on bias correction in nonlinear panel data models by applying and studying the finite sample properties of two of the existing proposals to the ordered probit case. The most direct and easily applicable correction to our model is not the best one and still has important biases in our sample sizes. Secondly, we contribute to the literature that study the determinants of Self-Assesed Health measures by applying the previous analysis on estimation methods to the British Household Panel Survey

Gender Differences in Major Choice and College Entrance Probabilities in Brazil

I study gender differences in major choice and college entrance probabilities in University of Campinas, a Brazilian public university dependent on the State of Sao Paulo. As with most Brazilian public universities, students select a major, and then compete for a place in that major by taking a major-specific entrance exam. This singular characteristic of the Brazilian case allows me to differentiate the effect of gender on major-specific entrance probabilities and preferences. I propose a model and econometric strategy which can account for two important issues, selectivity bias and the fact that expected utility depends on the probability of entering the different majors. I find evidence of gender differences in preferences and entrance probabilities. For most majors, gender differences in major choice are mostly explained by differences in preferences. However, for the most demanding majors (those that require higher grades from students), differences in major choice are explained in a large proportion by differences in entrance probabilities. Finally, I find that gender has important interactions with other variables. In particular, gender effects depend on education, socioeconomic characteristics and family background.Major choice, gender differences, college entrance, test, vestibular, brazilian universities

State dependence and heterogeneity in health using a bias corrected fixed effects estimator

This paper considers the estimation of a dynamic ordered probit of self-assessed health status with two fixed effects: one in the linear index equation and one in the cut points. The two fixed effects allow us to robustly control for heterogeneity in unobserved health status and in reporting behaviour, even though we can not separate both sources of heterogeneity. The contributions of this paper are twofold. First it contributes to the literature that studies the determinants and dynamics of Self-Assessed Health measures. Second, this paper contributes to the recent literature on bias correction in nonlinear panel data models with fixed effects by applying and studying the finite sample properties of two of the existing proposals to our model. The most direct and easily applicable correction to our model is not the best one, and has important biases in our sample sizesDynamic ordered probit, Fixed effects, Self-assessed health, Reporting bias, Panel data, Unobserved heterogeneity, Incidental parameters, Bias correction

State Dependence and Heterogeneity in Health Using a Bias Corrected Fixed Effects Estimator

This paper considers the estimation of a dynamic ordered probit of self-assessed health status with two fixed effects: one in the linear index equation and one in the cut points. The two fixed effects allow us to robustly control for heterogeneity in unobserved health status and in reporting behaviour, even though we can not separate both sources of heterogeneity. The contributions of this paper are twofold. First it contributes to the literature that studies the determinants and dynamics of Self-Assessed Health measures. Second, this paper contributes to the recent literature on bias correction in nonlinear panel data models with fixed effects by applying and studying the finite sample properties of two of the existing proposals to our model. The most direct and easily applicable correction to our model is not the best one, and has important biases in our sample sizes.Dynamic ordered probit, fixed effects, self-assessed health, reporting bias, panel data, unobserved heterogeneity, incidental parameters, bias correction

Estimating non-linear models with applications to health, labor and education economics

This dissertation is composed of three studies of non-linear econometric models, with applications to Health, Labor and Education Economics. Chapter 1 studies the differences in the proportion of temporary employees of domestic and foreign firms in the Spanish manufacturing sector. The objective of the chapter is to determine if, after controlling for a large set of observable firm characteristics and unobservable firm-specific time-invariant components, there is still a relationship between firm nationality and the type of employment contracts that firms offer.Chapter 2 considers the estimation of a dynamic ordered probit with fixed effects, and its application to the study of the determinants of self-assessed health status (SAH). SAH has been used as a proxy for true overall individual health status in many socioeconomic studies. Moreover, it has been shown to be a good predictor of mortality and of subsequent demand of medical care (see for example van Doorslaer, Koolman and Jones 2004). Finally, in Chapter 3, I study gender differences in major choice and college entrance probabilities in the University of Campinas, a Brazilian public university dependent on the State of Sao Paulo. As with most Brazilian public universities, candidates for entry into University of Campinas first select a major, and then compete for a place in that major by taking a major-specific entrance exam. This singular characteristic of the Brazilian case allows me to differentiate the effect of gender on major-speciffic entrance probabilities and preferencesEsta tesis se compone de tres trabajos de investigación en las areas de la Economía de la Salud, Trabajo y Educación. El capítulo 1 estudia las diferencias en la proporción de empleados temporales entre las empresas nacionales y extranjeras del sector manufacturero español. El objetivo del capítulo es determinar si existe una relación entre la nacionalidad de las empresas y el tipo de contratos de trabajo ofrecidos, a un después de controlar por un amplio conjunto de características observables y componentes no observables que no varían en el tiempo. El capítulo 2 estudia la estimación de un modelo probit ordenado dinámico con efectos fijos, y su aplicación al estudio de los determinantes del estado de salud autoreportado (ESA). El ESA se ha utilizado como sustituto del verdadero estado de salud en numerosos estudios socioeconómicos. Por otra parte, también se ha demostrado que es un buen predictor de la mortalidad y de la demanda de atención médica (v ease, por ejemplo van Doorslaer, Koolman y Jones 2004). Por ultimo, en el capítulo 3 estudio las diferencias de género en la elección de carrera y en la probabilidad de ingreso a la universidad en la Universidad de Campinas, una universidad pública brasileña dependiente del Estado de Sao Paulo. Como con la mayoría de las universidades públicas de Brasil, los candidatos para la entrada en la Universidad de Campinas eligen primero la carrera a la que desean entrar, y luego compiten por un lugar en esa carrera tomando un examen específico para dicha carrera. Esta característica singular del caso de Brasil me permite diferenciar el efecto del género sobre las probabilidades de entrada en cada carrera, y sobre las preferencias

State dependence and heterogeneity in health using a bias corrected fixed effects estimator

This paper considers the estimation of a dynamic ordered probit of self-assessed health status with two fixed effects: one in the linear index equation and one in the cut points. The two fixed effects allow us to robustly control for heterogeneity in unobserved health status and in reporting behaviour, even though we can not separate both sources of heterogeneity. The contributions of this paper are twofold. First it contributes to the literature that studies the determinants and dynamics of Self-Assessed Health measures. Second, this paper contributes to the recent literature on bias correction in nonlinear panel data models with fixed effects by applying and studying the finite sample properties of two of the existing proposals to our model. The most direct and easily applicable correction to our model is not the best one, and has important biases in our sample size

The Relation between Temporary Employment and Firm Ownership Nationality: Evidence from Spain

This paper analyzes the differences on the proportion of temporary employees in the Spanish manufacturing sector according to firm ownership nationality. Standard censored Tobit and Heckman sample selection models are estimated using data from the Survey on Managerial Strategies (ESSE) in the period 1991 to 2005. The results show there is a clear relation between the nationality of the owners of the firm and the type of labor offered, even after controlling for a large number of observable firm characteristics and unobservable fixed effects. In particular, the share of temporary employees is significantly lower for foreign firms and this effect decreases with firm size.Firm nationality, fixed-term contracts, temporary employment