Overview

Abstract (English)

This thesis comprises three essays in labour economics, econometrics and health economics. The first essay uses educational quality outcomes in immigrants’ home countries to explain the variation of immigrants’ rates of return to education in Canada. The second essay explores an econometrics technique (i.e., generalized method of moments) that combines macro level data and micro survey data to reduce the bias and/or variance of estimates. The goal of this essay is to address nonresponse and attrition issues that are commonly encountered in surveys in health services and health economics. The last chapter is an empirical investigation on the association between diabetic patients’ hospitalizations and their family doctor’s payment model. The first chapter uses international test scores as a proxy for the quality of immigrants’ source country educational outcomes to explain differences in the rate of return to schooling among immigrants in Canada. The average quality of educational outcomes in an immigrant’s source country and the rate of return to schooling in the host country labour market are found to have a strong and positive association. However, in contrast to those who completed their education pre-immigration, immigrants who arrived at a young age are not influenced by this educational quality measure. Also, the results are not much affected when the source country’s GDP per capita and other nation-level characteristics are used as control variables. Together, these findings reinforce the argument that the quality of educational outcomes has explanatory power for labour market outcomes. The effects are strongest for males and for females without children. The second chapter explores a technique that combines macro and micro level data. Administrative data in the health sector normally provide censuses of relevant populations but the scope of the variables is often limited and frequently only aggregate summary statistics are publically available. In contrast, survey datasets have a broader set of variables but commonly suffer from nonresponse, attrition and small sample sizes. This paper explores a technique that combines complementary population and survey data using a method of moments technique that matches auxiliary moments of the two data sources in estimating micro-econometric models. We provide Monte Carlo evidence showing that the approach can give appreciable reductions in both bias and variance. We show an example looking at midwife training and another looking at an optometrist’s location of work, to illustrate its use in a health human resource context. This approach could have wide applicability in health economics and health services. The third chapter investigates the impact of a blended capitation model (Family Health Organizations — FHOs) compared to an enhanced fee-for-service model (Family Health Groups – FHGs) on diabetic patients in Ontario, Canada. Using comprehensive administrative data and primary care reform as a quasi-natural experiment, we construct a panel for diabetic patients and employ a difference-in-differences approach to identify the impact of a change in the general practitioner’s (GP’s) remuneration model on patients’ hospital admissions. We find that on both the intensive and extensive margins, the hospital admissions for senior female patients statistically significantly increased after their GP’s remuneration model changed from FHG to FHO. In contrast, the impacts on male patients and younger female patients were small and not statistically significant. The results provide a cautionary message regarding the differences in practice patterns towards senior diabetic patients between GPs as a function of their payment model.

Abstract (French)

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