Three Essays In Spatial Econometrics And Labor Economics

This dissertation is a combination of three essays on spatial econometrics and labor economics. Essays 1 and 2 developed double length regression (DLR) tests for testing functional form and spatial dependence, which includes spatial error dependence and spatial lag dependence. More specifically, these essays derive the DLR joint, DLR one-direction, and DLR conditional tests for testing functional forms and spatial dependence. The essays also provide empirical examples and Monte Carlo simulations to examine how the DLR tests perform in the empirical work and how the power of the DLR test depends on changes in functional form and spatial dependence. The results suggested that DLR tests work similarly to its Lagrangian Multiplier (LM) counterpart for testing functional form and spatial dependence in the empirical example and simulations. The DLR tests do not require the second-order derivatives of the log-likelihood function, so they provide practitioners an easy-to-use method to test for functional forms and spatial dependence. Essay 3 investigates the effects of fertility on parental labor force participation and labor supply in Vietnam. The essay uses instrumental variable (IV) probit models to estimate the effects of fertility on parental labor force participation and the IV models to estimate the effects of fertility on parental labor supply. Using the gender of the first child and the same gender of the first two children as two instrumental variables, this essay found negative effects of fertility on maternal labor force participation and labor supply. It also found positive effects of fertility on paternal labor force participation and labor supply. The results suggest that fertility had the specialization effect on parental labor force participation and labor supply in Vietnam. The homogeneity test results indicate that the magnitude of the effects of fertility on parental labor force participation and labor supply is different among parents and locations.

My job market paper, "Bayesian Estimation of a Spatial Autoregressive Model with an Unobserved Endogenous Spatial Weight Matrix and Unobserved Factors", examines the specification and estimation of the SAR model with new features. Motivated by the spillover effects of state medicaid spending on welfare programs, we combine all these new features together for the first time in the SAR model. Specifically, we focus on two ways of defining neighborliness (a source of unobserved spatial weight matrix W): one based on geographical distance and the other on "economic" distance. In this particular application, endogeneity of W comes from the correlation of economic distance and the disturbances in the SAR equation. Unobserved factors are introduced to control for common shocks to all states. For the estimation of the model, the Bayesian MCMC method is employed, which is also supported by simulation results. We find that a dollar increase in a state's neighbors' Medicaid related spending will increase its own Medicaid related spending by about 52 cents. Both geographical and economic distances are shown to have significant effects on the interaction strength of state Medicaid related spending. Our results suggest that in the context of Medicaid spending, welfare motivated move and yardstick competition are both sources of strategic interactions among state governments.

Abstract: The first essay explores the consequences of misspecified spatial interdependence structure in SAR models with a row-normalized weight matrix. I provide the analytical formulae for the asymptotic biases of the OLS estimator when a spatial weight matrix is over-specified, under-specified, or omitted in a simple linear regression model. I then design Monte Carlo experiments to study how a misspecified spatial weight matrix in the SAR model might impact the finite sample properties of the 2SLSE and MLE. The major finding is that an "over-specification" of the weight matrix causes less bias in 2SLSE and MLE as well as lower RMSE than an "under-specification." The results also strongly suggest that goodness of fit measures such as adjusted R-square and log-likelihood can serve as selection criteria for the choice of a spatial weight matrix. In the second essay, I consider the effectiveness of Wald, distance difference, minimum Chi-square, and gradient tests within GMM framework in selecting different specifications of spatial weights in SAR models. The two major results I obtain are (1) that for each of the five tests, GMM framework significantly improves the empirical power of the tests over 2SLS framework, and (2) that when performed in GMM framework, all five tests have suitable empirical size and power with similar performance outcomes. Finally, the third essay investigates the nature of competition in the retail gasoline market using a two year panel data of weekly prices for gas stations in San Diego County. I use IV methods to estimate several spatial autoregressive (SAR) models of stations' price reaction functions after specifying spatial weights based on distance between stations. By using the SAR model, I am able to identify that the brand of competing stations and their relative geographic proximity to the original station are important factors in explaining price variation across gasoline stations, as opposed to just the number of competing stations.

This dissertation presents three essays on the health economics of the Quebec population,including newborn health. It introduces the spatial econometrics tools into the analyses,paying particular attention to the spatial correlation between socioeconomic characteristics(notably income) and the health of individuals (obesity) and newborns. The unit of analysis is the census tracts and individuals living in the province of Quebec. In our study, we propose a much more refined analysis of the determinants of income and health outcomes among new borns. The main motivation for our research comes from the fact that it is generally accepted that the consumption of health services and goods that are harmful to health vary significantly with the income of individuals. Richer or poorer individuals will tend to consume these goods and services to a greater or lesser extent. However, income itself varies overtime and space. These spatial and temporal dimensions need to be taken into account when modelling the relationships between demographic, socioeconomic and health characteristics of individuals. The introduction of spatial and temporal analyses into health economics issues will allow us to take into account spatial and temporal correlations in order to better estimate and determine the likely reasons for the variation in outcomes of interest across at different geographic scales. From a policy perspective, this approach will allow to anticipate and take preventive measures in the future with respect to the health of the population of Quebec.Health status modelling is innovative in that it integrates the characteristics of individuals ’immediate vicinity as well as spatial correlations in unobservable characteristics. These two components of the determinants of health are generally neglected or omitted in economic and econometric studies of public health issues.The main objective of this dissertation is therefore to introduce the modelling of spatial interactions into the analysis of public health issues. The idea is that, as stated in the first law of geography, everything interacts with everything, but two things that are close are more likely to interact than two things that are far away. The chapters of this dissertation are linked by the integration of a spatial dimension in the analysis of correlations. Specifically,the first chapter focuses on the relationship between obesity, measured through body mass index (BMI) and labour market outcomes (income and number of hours worked) from a spatiotemporal perspective. We use data from the National Population Health Survey (NPHS). The results of this chapter allow us to observe the presence in Quebec of a correlation between the income of individuals and that of their neighbours. This shows the spatial influence that neighbourhood has on an individual. Failure to take this aspect into account could have led to an incorrect conclusion about the relationship between income level and obesity. This also allowed us to separately test for the presence of the two spatial phenomena of autoregression and autocorrelation by explicitly modelling the fact that the residuals may be related to each other. In addition, we find that the marginal effects of the non-spatial model were underestimated compared to the spatial model. This underestimation ranges from 5% to17%. A refinement of the estimates was therefore made using the spatial model. With respect to the relationship between obesity and labour market outcomes, our analyses show that, inQuebec, obese individuals work more hours than individuals with a normal BMI. Obesity has no impact on the income of those who work there. The estimated models use continuous dependent variables.The second chapter assesses the relationship between income in the census tract where mothers live and adverse birth outcomes (premature and low birth weight). This chapter,to the best of our knowledge, differs from the existing literature in that it uses two different models: in addition to a spatial bivariate probit model, we also use a spatial SUR model, which is compatible with the introduction of a lagged spatiotemporal variable in the modelling. Our analysis was performed with data from neonates admitted and born at the neonatology unit of the Centre Hospitalier de l’Université Laval (CHUL). Our results show the existence of a positive correlation between premature newborns and low birth weight babies at the individual and census tract levels. The introduction of the delayed spatiotemporal variable in the SURspatial modelling shows that the spatially and temporally lagged gestational age positively affects the gestational age of newborns. The results also show that the gestational age and weight of a newborn are positively influenced by those of their neighbours. This shows the spatial influence that neighbourhood has on mothers. Failure to take into account the effect of the influence of some mothers’ behaviours on others could have resulted in the omission of possible spatial correlations in unobservable characteristics that influence adverse birthout comes. The fact that these results were obtained by estimates of both the bivariate probitmodel and the spatial SUR model indicates the robustness of the correlation. A comparison of the results of the spatial bivariate probit model and the spatial SUR model was performed.In addition, we note that regardless of the model used in the study, low income in the censustract is associated with a high risk of preterm birth and low birth weight.The third chapter deals with the likelihood of newborns contracting nosocomial infections or suffering medical incidents when their mothers live in the Quebec census metropolitan area. The objective of this last chapter is to determine whether the child’s mother’s nearby environment and characteristics can explain the risks associated with nosocomial infections and medical incidents. These relationships have barely been analyzed in the literature. The main contribution of this chapter lies in the fact that the literature on this question has typically failed to consider the impact of the proximity of some mothers on others. These interactions could have effects on nosocomial infections in their newborns. We conduct our analysis with the data from the previous chapter. A spatial bivariate probit model was incorporated to better capture the correlation between medical incidents and nosocomial infections acquired by newborns. The results of this chapter show that factors at the census tract level that are not observed and captured by spatial effects do not influence nosocomial infections and medical incidents in related ways. Specifically, census tracts where newborns suffer from nosocomial infections are not likely to have the same newborns with medical incidents. The results alsos how that premature and low birth weight infants are more likely to have both hospital acquired infections and medical incidents. Infants transferred from the maternity ward to theNeonatal Intensive Care Unit (NICU) are more likely to develop a nosocomial infection and experience a medical incident. In addition, children born in high income census tracts tend to be less vulnerable to infections.

Spatial Econometrics is a rapidly evolving field born from the joint efforts of economists, statisticians, econometricians and regional scientists. The book provides the reader with a broad view of the topic by including both methodological and application papers. Indeed the application papers relate to a number of diverse scientific fields ranging from hedonic models of house pricing to demography, from health care to regional economics, from the analysis of R&D spillovers to the study of retail market spatial characteristics. Particular emphasis is given to regional economic applications of spatial econometrics methods with a number of contributions specifically focused on the spatial concentration of economic activities and agglomeration, regional paths of economic growth, regional convergence of income and productivity and the evolution of regional employment. Most of the papers appearing in this book were solicited from the International Workshop on Spatial Econometrics and Statistics held in Rome (Italy) in 2006.

World-renowned experts in spatial statistics and spatial econometrics present the latest advances in specification and estimation of spatial econometric models. This includes information on the development of tools and software, and various applications. The text introduces new tests and estimators for spatial regression models, including discrete choice and simultaneous equation models. The performance of techniques is demonstrated through simulation results and a wide array of applications related to economic growth, international trade, knowledge externalities, population-employment dynamics, urban crime, land use, and environmental issues. An exciting new text for academics with a theoretical interest in spatial statistics and econometrics, and for practitioners looking for modern and up-to-date techniques.

This dissertation explores various innovative approaches that can be used in development economics in order to more closely examine economic realities of the developing world. This dissertation provides guidance on how methods not used up to this point can be best used to provide a better understanding of the potential impacts of development projects, and also highlights potential areas of improvement in existing methods and practices of data collection and project evaluation. The first essay examines two major channels of social networks influence on the gender norms of young men and women, using the interviews conducted with members of ten camps in Dar es Salaam, Tanzania. Inequitable gender norms have been identified as one of the major factors to negatively influence HIV-related behavior, domestic violence, and parenting. There is a need for a better understanding of the factors that contribute to inequitable gender norms, and one potential method to change individual gender norms is through social networks. While there are numerous studies dealing with the social networks influence, there are not many that examine the social networks influence on personal norms. Social network influence is examined using the network autoregressive model that takes into account interdependencies among network members, and the results imply that the gender norms of the network actors are correlated with the gender norms of their alters, indicating a similarity of genders norms among closest network members. When different types of network relationships are pooled, actors' attitudes are not correlated with those of their network contacts. Network actors' and their alters' attitudes are significantly correlated in work and problem-solving relationships. The second essay explores whether and how can spatial econometric methodology be used to examine the spatial spillovers of conditional cash transfers. Conditional cash transfer programs are considered to be one of the most efficient and cost-effective ways to support the poor in the developing countries. Many studies have been performed that show positive impact of conditional cash transfer on beneficiaries' consumption, health, education, etc. However, spatial inter-village spillovers of these programs are potential impacts of spillovers are mostly neglected. The results of the analysis indicate that there are spatial spillovers that reinforce the effects of the program, and there are benefits in using spatial econometrics methods as additional tool in the impact evaluation of conditional cash transfers and other programs. Using the experimental setup of the Progresa-Oportunidades program in Mexico, we find evidence of positive effects of program density on junior and senior high school enrollment among the poor beneficiaries in treated villages. The third essay analyses the impact of gender and marriage perceptions on reporting about labor outcomes using a survey conducted in Ghana. When the standard surveys are conducted in developing countries, they mostly rely only on household heads to provide information about all household members. An alternative approach is taken within the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) initiative which attempts to conduct interviews with all household members above a certain age. However, there are only a few empirical studies that attempt to provide a framework for understanding the potential advantages and disadvantages of using self and proxy reporters in developing countries. The impact of different factors on labor reporting is examined using the standard models for corner solutions and ordinary least squares. The results of the estimations provide evidence of the influence of gender and marriage perceptions on labor reporting.