The Role of Child Health and Economic Status in Educational, Health and Labour Market Outcomes in Young Adulthood

The Ontario Child Health Study provides the first opportunity in Canada to assess directly the relationship between socio-economic and health status in childhood and levels of completed schooling, health status and labour market success in young adulthood. We find that childhood health problems are negatively associated with educational attainment, especially the probability of a university degree, and the health status of young adults. Our results also imply that childhood health problems influence adult labour force outcomes, especially for males, mainly through adult levels of schooling and health.Child Health; Adult Outcomes

Simulation-based Inference in Dynamic Panel Probit Models: an Application to Health

This paper considers the determinants of a binary indicator for the existence of functional limitations using seven waves (1991-1997) of the British Household Panel Survey(BHPS). The focal point of our analysis is a consideration of the relative contributions of state dependence, heterogeneity and serial correlation in expanding the dynamics of health. To investigate these issues we apply static and dynamic panel probit models with flexible error structures. To estimate the models we show strong positive state dependence, with the effect for men around 150% of the effect for women.

Using Simulation-based Inference with Panel Data in Health Economics

Panel datasets provide a rich source of information for health economists, offering the scope to control for individual heterogeneity and to model the dynamics of individual behaviour. However the qualitative or categorical measures of outcome often used in health economics create special problems for estimating econometric models. Allowing a flexible specification of the autocorrelation induced by individual heterogeneity leads to models involving higher order integrals that cannot be handled by conventional numerical methods. The dramatic growth in computing power over recent years has been accompanied by the development of simulation-based estimators that solve this problem. This review uses binary choice models to show what can be done with conventional methods and how the range of models can be expanded by using simulation methods. Practical applications of the methods are illustrated using data on health from the British Household Panel Survey (BHPS).

Using Simulation-Based Inference with Panel Data in Health Economics

Panel datasets provide a rich source of information for health economists, offering the scope to control for individual heterogeneity and to model the dynamics of individual behaviour. However the qualitative or categorical measures of outcome often used in health economics create special problems for estimating econometric models. Allowing a flexible specification of individual heterogeneity leads to models involving higher order integrals that cannot be handled by conventional numerical methods. The dramatic growth in computing power over recent years has been accompanied by the development of simulation estimators that solve this problem. This review uses binary choice models to show what can be done with conventional methods and how the range of models can be expanded by using simulation methods. Practical applications of the methods are illustrated using on health from the British Household Panel Survey (BHPS)Econometrics, panel data, simulation methods, determinants of health

Socioeconomic Status, Health and Lifestyle

The role of lifestyle in mediating the relationship between socio-economic characteristics and and health has been discussed extensively in the epidemiological and economic literatures. Previous analyses have not considered a formal framework incorporating unobservable heterogeneity. In this paper we develop a simple economic model in which health is determined (partially) by lifestyle, which depends on preferences, budget and time constraints and unobservable characteristics. We estimate a recursive empirical specification consisting of a health production function and reduced forms for the lifestyle equations using Maximum Simulated Likelihood for a multivariate probit model with discrete indicators of lifestyle choices and self-assessed health (SAH) on British panel data from the 1984 and 1991 Health and Lifestyle Survey. We find that prudent drinking and not smoking in 1984 have dramatic positive effects on the probability of reporting excellent or good SAH in 1991. The failure of epidemiological analyses to account for unobserved heterogeneity can explain their low estimates of the relevance of lifestyle in the socio-economic status-health relationship. Accounting for unobserved heterogeneity also leads us to conclude that indicators for sleep, exercise and breakfast in 1984 are unimportant for SAH in 1991.Determinants of health, lifestyles, simulation-based inference, panel data.

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 impact of health on professionally active people's incomes in Poland. Microeconometric analysis

The outcome of the research confirms the occurrence of positive interaction between professionally active people's incomes and the self-assessed state of health. People declaring a bad state of health have incomes by 20% on average lower than people who enjoy good health (assuming that the remaining characteristics of the surveyed person are the same). In case of men, the impact of health state on incomes is slightly greater than in case of women.Wyniki badań potwierdzają istnienie pozytywnej zależności dochodów osób aktywnych zawodowo od stanu zdrowia mierzonego jego samooceną. Osoby deklarujące zły stan zdrowia osiągają dochody przeciętnie o 20% niższe niż osoby, które cieszą się dobrym stanem zdrowia (przy założeniu, że pozostałe charakterystyki badanej osoby są takie same). W przypadku mężczyzn zależność dochodów od stanu zdrowia jest nieznacznie silniejsza niż w przypadku kobiet

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

This paper estimates a dynamic ordered probit model of self-assessed health with two fi xed effects: one in the linear index equation and one in the cut points. This robustly controls for heterogeneity in unobserved health status and in reporting behavior, although we cannot separate both sources of heterogeneity. We fi nd important state dependence effects, and small, but signi cant effects of income and other socioeconomic variables. Having dynamics and f exibly accounting for unobserved heterogeneity matters for those estimates. We also contribute to the bias correction literature in nonlinear panel models by comparing and applying two of the existing proposals to our modelThe first author gratefully acknowledges that this research was supported by a Marie Curie International Outgoing Fellowship within the 7th European Community Framework Programme, by grants ECO2009-11165 and SEJ2006-05710 from the Spanish Minister of Education, MCINN (Consolider- Ingenio2010) and Consejería de Educación de la Comunidad de Madrid (Excelecon project)Publicad

Simplified implementation of the Heckman Estimator of the Dynamic Probit Model and a comparison with alternative estimators

This paper presents a convenient shortcut method for implementing the Heckman estimator of the dynamic random effects probit model and other dynamic nonlinear panel data models using standard software. It then compares the estimators proposed by Heckman, Orme and Wooldridge, based on three alternative approximations, first in an empirical model for the probability of unemployment and then in a set of simulation experiments. The results indicate that none of the three estimators dominates the other two in all cases. In most cases all three estimators display satisfactory performance, except when the number of time periods is very small