Infant mortality in Brazil, 1980-2000: A spatial panel data analysis

Background: Infant mortality is an important measure of human development, related to the level of welfare of a society. In order to inform public policy, various studies have tried to identify the factors that influence, at an aggregated level, infant mortality. The objective of this paper is to analyze the regional pattern of infant mortality in Brazil, evaluating the effect of infrastructure, socio-economic, and demographic variables to understand its distribution across the country. Methods: Regressions including socio-economic and living conditions variables are conducted in a structure of panel data. More specifically, a spatial panel data model with fixed effects and a spatial error autocorrelation structure is used to help to solve spatial dependence problems. The use of a spatial modeling approach takes into account the potential presence of spillovers between neighboring spatial units. The spatial units considered are Minimum Comparable Areas, defined to provide a consistent definition across Census years. Data are drawn from the 1980, 1991 and 2000 Census of Brazil, and from data collected by the Ministry of Health (DATASUS). In order to identify the influence of health care infrastructure, variables related to the number of public and private hospitals are included. Results: The results indicate that the panel model with spatial effects provides the best fit to the data. The analysis confirms that the provision of health care infrastructure and social policy measures (e. g. improving education attainment) are linked to reduced rates of infant mortality. An original finding concerns the role of spatial effects in the analysis of IMR. Spillover effects associated with health infrastructure and water and sanitation facilities imply that there are regional benefits beyond the unit of analysis. Conclusions: A spatial modeling approach is important to produce reliable estimates in the analysis of panel IMR data. Substantively, this paper contributes to our understanding of the physical and social factors that influence IMR in the case of a developing country.

Checking Assumptions in Latent Class Regression Models via a Markov Chain Monte Carlo Estimation Approach: An Application to Depression and Socio-Economic Status

Latent class regression models are useful tools for assessing associations between covariates and latent variables. However, evaluation of key model assumptions cannot be performed using methods from standard regression models due to the unobserved nature of latent outcome variables. This paper presents graphical diagnostic tools to evaluate whether or not latent class regression models adhere to standard assumptions of the model: conditional independence and non-differential measurement. An integral part of these methods is the use of a Markov Chain Monte Carlo estimation procedure. Unlike standard maximum likelihood implementations for latent class regression model estimation, the MCMC approach allows us to calculate posterior distributions and point estimates of any functions of parameters. It is this convenience that allows us to provide the diagnostic methods that we introduce. As a motivating example we present an analysis focusing on the association between depression and socioeconomic status, using data from the Epidemiologic Catchment Area study. We consider a latent class regression analysis investigating the association between depression and socioeconomic status measures, where the latent variable depression is regressed on education and income indicators, in addition to age, gender, and marital status variables. While the fitted latent class regression model yields interesting results, the model parameters are found to be invalid due to the violation of model assumptions. The violation of these assumptions is clearly identified by the presented diagnostic plots. These methods can be applied to standard latent class and latent class regression models, and the general principle can be extended to evaluate model assumptions in other types of models.

A model of factors determining levels of public support for health care policy in the United States

Public preferences for policy are formed in a little-understood process that is not adequately described by traditional economic theory of choice. In this paper I suggest that U.S. aggregate support for health reform can be modeled as tradeoffs among a small number of behavioral values and the stage of policy development. The theory underlying the model is based on Samuelson, et al.'s (1986) work and Wilke's (1991) elaboration of it as the Greed/Efficiency/Fairness (GEF) hypothesis of motivation in the management of resource dilemmas, and behavioral economics informed by Kahneman and Thaler's prospect theory. ^ The model developed in this paper employs ordered probit econometric techniques applied to data derived from U.S. polls taken from 1990 to mid-2003 that measured support for health reform proposals. Outcome data are four-tiered Likert counts; independent variables are dummies representing the presence or absence of operationalizations of each behavioral variable, along with an integer representing policy process stage. Marginal effects of each independent variable predict how support levels change on triggering that variable. Model estimation results indicate a vanishingly small likelihood that all coefficients are zero and all variables have signs expected from model theory. ^ Three hypotheses were tested: support will drain from health reform policy as it becomes increasingly well-articulated and approaches enactment; reforms appealing to fairness through universal health coverage will enjoy a higher degree of support than those targeted more narrowly; health reforms calling for government operation of the health finance system will achieve lower support than those that do not. Model results support the first and last hypotheses. Contrary to expectations, universal health care proposals did not provide incremental support beyond those targeted to “deserving” populations—children, elderly, working families. In addition, loss of autonomy (e.g. restrictions on choice of care giver) is found to be the “third rail” of health reform with significantly-reduced support. When applied to a hypothetical health reform in which an employer-mandated Medical Savings Account policy is the centerpiece, the model predicts support that may be insufficient to enactment. These results indicate that the method developed in the paper may prove valuable to health policy designers. ^

The determinants of improvements in health outcomes and of cost reduction in hospital inpatient care

This study aims to address two research questions. First, ‘Can we identify factors that are determinants both of improved health outcomes and of reduced costs for hospitalized patients with one of six common diagnoses?’ Second, ‘Can we identify other factors that are determinants of improved health outcomes for such hospitalized patients but which are not associated with costs?’ The Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS) database from 2003 to 2006 was employed in this study. The total study sample consisted of hospitals which had at least 30 patients each year for the given diagnosis: 954 hospitals for acute myocardial infarction (AMI), 1552 hospitals for congestive heart failure (CHF), 1120 hospitals for stroke (STR), 1283 hospitals for gastrointestinal hemorrhage (GIH), 979 hospitals for hip fracture (HIP), and 1716 hospitals for pneumonia (PNE). This study used simultaneous equations models to investigate the determinants of improvement in health outcomes and of cost reduction in hospital inpatient care for these six common diagnoses. In addition, the study used instrumental variables and two-stage least squares random effect model for unbalanced panel data estimation. The study concluded that a few factors were determinants of high quality and low cost. Specifically, high specialty was the determinant of high quality and low costs for CHF patients; small hospital size was the determinant of high quality and low costs for AMI patients. Furthermore, CHF patients who were treated in Midwest, South, and West region hospitals had better health outcomes and lower hospital costs than patients who were treated in Northeast region hospitals. Gastrointestinal hemorrhage and pneumonia patients who were treated in South region hospitals also had better health outcomes and lower hospital costs than patients who were treated in Northeast region hospitals. This study found that six non-cost factors were related to health outcomes for a few diagnoses: hospital volume, percentage emergency room admissions for a given diagnosis, hospital competition, specialty, bed size, and hospital region.^

Determinants of inbound tourists in Cambodia : a dynamic panel data approach

Understanding the determinants of tourism demand is crucial for the tourism sector. This paper develops a dynamic panel model to examine the determinants of inbound tourists to Siem Reap airport, Phnom Penh airport, and land and waterway borders in Cambodia. Consistent with the consumer theory of tourism consumption, a 10% increase in the origin country GDP per capita is predicted to increase the number of tourist visits to Siem Reap airport by 5.8%. A 10% increase in the real exchange rate between the origin country and Cambodia is predicted to decrease the number of tourist visits by 0.89%. In contrast, the number of foreign tourists in a previous period has little effect on the number of foreign tourists in the current period. Additionally, the determinants are different by the mode of entry to Cambodia.

Regularized model learning in EDAs for continuous and multi-objective optimization

Probabilistic modeling is the de�ning characteristic of estimation of distribution algorithms (EDAs) which determines their behavior and performance in optimization. Regularization is a well-known statistical technique used for obtaining an improved model by reducing the generalization error of estimation, especially in high-dimensional problems. `1-regularization is a type of this technique with the appealing variable selection property which results in sparse model estimations. In this thesis, we study the use of regularization techniques for model learning in EDAs. Several methods for regularized model estimation in continuous domains based on a Gaussian distribution assumption are presented, and analyzed from di�erent aspects when used for optimization in a high-dimensional setting, where the population size of EDA has a logarithmic scale with respect to the number of variables. The optimization results obtained for a number of continuous problems with an increasing number of variables show that the proposed EDA based on regularized model estimation performs a more robust optimization, and is able to achieve signi�cantly better results for larger dimensions than other Gaussian-based EDAs. We also propose a method for learning a marginally factorized Gaussian Markov random �eld model using regularization techniques and a clustering algorithm. The experimental results show notable optimization performance on continuous additively decomposable problems when using this model estimation method. Our study also covers multi-objective optimization and we propose joint probabilistic modeling of variables and objectives in EDAs based on Bayesian networks, speci�cally models inspired from multi-dimensional Bayesian network classi�ers. It is shown that with this approach to modeling, two new types of relationships are encoded in the estimated models in addition to the variable relationships captured in other EDAs: objectivevariable and objective-objective relationships. An extensive experimental study shows the e�ectiveness of this approach for multi- and many-objective optimization. With the proposed joint variable-objective modeling, in addition to the Pareto set approximation, the algorithm is also able to obtain an estimation of the multi-objective problem structure. Finally, the study of multi-objective optimization based on joint probabilistic modeling is extended to noisy domains, where the noise in objective values is represented by intervals. A new version of the Pareto dominance relation for ordering the solutions in these problems, namely �-degree Pareto dominance, is introduced and its properties are analyzed. We show that the ranking methods based on this dominance relation can result in competitive performance of EDAs with respect to the quality of the approximated Pareto sets. This dominance relation is then used together with a method for joint probabilistic modeling based on `1-regularization for multi-objective feature subset selection in classi�cation, where six di�erent measures of accuracy are considered as objectives with interval values. The individual assessment of the proposed joint probabilistic modeling and solution ranking methods on datasets with small-medium dimensionality, when using two di�erent Bayesian classi�ers, shows that comparable or better Pareto sets of feature subsets are approximated in comparison to standard methods.

Bayesian model selection of structural explanatory models: Application to road accident data

Using the Bayesian approach as the model selection criteria, the main purpose in this study is to establish a practical road accident model that can provide a better interpretation and prediction performance. For this purpose we are using a structural explanatory model with autoregressive error term. The model estimation is carried out through Bayesian inference and the best model is selected based on the goodness of fit measures. To cross validate the model estimation further prediction analysis were done. As the road safety measures the number of fatal accidents in Spain, during 2000-2011 were employed. The results of the variable selection process show that the factors explaining fatal road accidents are mainly exposure, economic factors, and surveillance and legislative measures. The model selection shows that the impact of economic factors on fatal accidents during the period under study has been higher compared to surveillance and legislative measures.

Essays in economic growth, political economy and institutions

This dissertation analyzes the effects of political and economic institutions on economic development and growth.^ The first essay develops an overlapping-generations political economy model to analyze the incentives of various social groups to finance human capital accumulation through public education expenditures. The contribution of this study to the literature is that it helps explain the observed differences in the economic growth performance of natural resource-abundant countries. The results suggest that the preferred tax rates of the manufacturers on one hand and the political coalition of manufacturers and landowners, on the other hand, are equal to the socially optimal tax rate. However, we show that owners of natural resources prefer an excessively high tax rate, which suppresses aggregate output to a suboptimal level.^ The second essay examines the relationship between the political influence of different social classes and public education spending in panel data estimation. The novel contribution of this paper to the literature is that I proxy the political power and influence of the natural resource owners, manufacturers, and landowners with macroeconomic indicators. The motivation behind this modeling choice is to substantiate the definition of the political power of social classes with economic fundamentals. I use different governance indicators in the estimations to find out how different institutions mediate the overall impact of the political influence of various social classes on public education spending. The results suggest that political stability and absence of violence and rule of law are the important governance indicators.^ The third essay develops a counter argument to Acemoglu et al. (2010) where the thesis is that French institutions and economic reforms fostered economic progress in those German regions invaded by the Napoleonic armies. By providing historical data on urbanization rates used as proxies for economic growth, I demonstrate that similar different rates of economic growth were observed in the regions of France in the post-Napoleonic period as well. The existence of different economic growth rates makes it hard to argue that the differences in economic performance in the German regions that were invaded by the French and those that were spared a similar fate follow from regional differences in economic institutions.^

Capital structure of Finnish firms – Estimating the effects of firm characteristics on capital structure

One of the most disputable matters in the theory of finance has been the theory of capital structure. The seminal contributions of Modigliani and Miller (1958, 1963) gave rise to a multitude of studies and debates. Since the initial spark, the financial literature has offered two competing theories of financing decision: the trade-off theory and the pecking order theory. The trade-off theory suggests that firms have an optimal capital structure balancing the benefits and costs of debt. The pecking order theory approaches the firm capital structure from information asymmetry perspective and assumes a hierarchy of financing, with firms using first internal funds, followed by debt and as a last resort equity. This thesis analyses the trade-off and pecking order theories and their predictions on a panel data consisting 78 Finnish firms listed on the OMX Helsinki stock exchange. Estimations are performed for the period 2003–2012. The data is collected from Datastream system and consists of financial statement data. A number of capital structure characteristics are identified: firm size, profitability, firm growth opportunities, risk, asset tangibility and taxes, speed of adjustment and financial deficit. A regression analysis is used to examine the effects of the firm characteristics on capitals structure. The regression models were formed based on the relevant theories. The general capital structure model is estimated with fixed effects estimator. Additionally, dynamic models play an important role in several areas of corporate finance, but with the combination of fixed effects and lagged dependent variables the model estimation is more complicated. A dynamic partial adjustment model is estimated using Arellano and Bond (1991) first-differencing generalized method of moments, the ordinary least squares and fixed effects estimators. The results for Finnish listed firms show support for the predictions of profitability, firm size and non-debt tax shields. However, no conclusive support for the pecking-order theory is found. However, the effect of pecking order cannot be fully ignored and it is concluded that instead of being substitutes the trade-off and pecking order theory appear to complement each other. For the partial adjustment model the results show that Finnish listed firms adjust towards their target capital structure with a speed of 29% a year using book debt ratio.

Validation of FHWA crash models for rural intersections - Lessons learned

A national-level safety analysis tool is needed to complement existing analytical tools for assessment of the safety impacts of roadway design alternatives. FHWA has sponsored the development of the Interactive Highway Safety Design Model (IHSDM), which is roadway design and redesign software that estimates the safety effects of alternative designs. Considering the importance of IHSDM in shaping the future of safety-related transportation investment decisions, FHWA justifiably sponsored research with the sole intent of independently validating some of the statistical models and algorithms in IHSDM. Statistical model validation aims to accomplish many important tasks, including (a) assessment of the logical defensibility of proposed models, (b) assessment of the transferability of models over future time periods and across different geographic locations, and (c) identification of areas in which future model improvements should be made. These three activities are reported for five proposed types of rural intersection crash prediction models. The internal validation of the model revealed that the crash models potentially suffer from omitted variables that affect safety, site selection and countermeasure selection bias, poorly measured and surrogate variables, and misspecification of model functional forms. The external validation indicated the inability of models to perform on par with model estimation performance. Recommendations for improving the state of the practice from this research include the systematic conduct of carefully designed before-and-after studies, improvements in data standardization and collection practices, and the development of analytical methods to combine the results of before-and-after studies with cross-sectional studies in a meaningful and useful way.

Probabilistic subgroup identification using Bayesian finite mixture modelling : a case study in Parkinson’s disease phenotype identification

This article explores the use of probabilistic classification, namely finite mixture modelling, for identification of complex disease phenotypes, given cross-sectional data. In particular, if focuses on posterior probabilities of subgroup membership, a standard output of finite mixture modelling, and how the quantification of uncertainty in these probabilities can lead to more detailed analyses. Using a Bayesian approach, we describe two practical uses of this uncertainty: (i) as a means of describing a person’s membership to a single or multiple latent subgroups and (ii) as a means of describing identified subgroups by patient-centred covariates not included in model estimation. These proposed uses are demonstrated on a case study in Parkinson’s disease (PD), where latent subgroups are identified using multiple symptoms from the Unified Parkinson’s Disease Rating Scale (UPDRS).

On the significance of omitted variables in intersection crash modeling

Advances in safety research—trying to improve the collective understanding of motor vehicle crash causes and contributing factors—rest upon the pursuit of numerous lines of research inquiry. The research community has focused considerable attention on analytical methods development (negative binomial models, simultaneous equations, etc.), on better experimental designs (before-after studies, comparison sites, etc.), on improving exposure measures, and on model specification improvements (additive terms, non-linear relations, etc.). One might logically seek to know which lines of inquiry might provide the most significant improvements in understanding crash causation and/or prediction. It is the contention of this paper that the exclusion of important variables (causal or surrogate measures of causal variables) cause omitted variable bias in model estimation and is an important and neglected line of inquiry in safety research. In particular, spatially related variables are often difficult to collect and omitted from crash models—but offer significant opportunities to better understand contributing factors and/or causes of crashes. This study examines the role of important variables (other than Average Annual Daily Traffic (AADT)) that are generally omitted from intersection crash prediction models. In addition to the geometric and traffic regulatory information of intersection, the proposed model includes many spatial factors such as local influences of weather, sun glare, proximity to drinking establishments, and proximity to schools—representing a mix of potential environmental and human factors that are theoretically important, but rarely used. Results suggest that these variables in addition to AADT have significant explanatory power, and their exclusion leads to omitted variable bias. Provided is evidence that variable exclusion overstates the effect of minor road AADT by as much as 40% and major road AADT by 14%.

Cost efficiency of Japanese steam power generation companies : a Bayesian comparison of random and fixed frontier models

This study analyses and compares the cost efficiency of Japanese steam power generation companies using the fixed and random Bayesian frontier models. We show that it is essential to account for heterogeneity in modelling the performance of energy companies. Results from the model estimation also indicate that restricting CO2 emissions can lead to a decrease in total cost. The study finally discusses the efficiency variations between the energy companies under analysis, and elaborates on the managerial and policy implications of the results.

Transdimensional sequential Monte Carlo using variational Bayes — SMCVB

A new transdimensional Sequential Monte Carlo (SMC) algorithm called SM- CVB is proposed. In an SMC approach, a weighted sample of particles is generated from a sequence of probability distributions which ‘converge’ to the target distribution of interest, in this case a Bayesian posterior distri- bution. The approach is based on the use of variational Bayes to propose new particles at each iteration of the SMCVB algorithm in order to target the posterior more efficiently. The variational-Bayes-generated proposals are not limited to a fixed dimension. This means that the weighted particle sets that arise can have varying dimensions thereby allowing us the option to also estimate an appropriate dimension for the model. This novel algorithm is outlined within the context of finite mixture model estimation. This pro- vides a less computationally demanding alternative to using reversible jump Markov chain Monte Carlo kernels within an SMC approach. We illustrate these ideas in a simulated data analysis and in applications.

Studies on Wage Differentials and Labour Market Transitions

This thesis consists of four studies. The first study examines wage differentials between women and men in the Finnish manufacturing sector. A matched employer-employee data set is used to decompose the overall gender wage gap into the contributions of sex differences in human capital, labour market segregation, and residual within-job wage differentials. The topic of the second study is the relationship between the extended unemployment benefits and labour market transitions of older workers. The analysis exploits a quasi-experimental setting caused by a change in the law that raised the eligibility age of workers benefiting from extended benefits. Roughly half of the unemployed workers with extended benefits are estimated to be effectively withdrawn from labour market search. The risk of unemployment declined and the re-employment probability increased among the age groups directly affected by the reform. The third study provides an empirical analysis of a structural equilibrium search model. Estimation results from various model specifications are compared and discussed. The last study is a methodological study where the difficulties of interpreting the results of competing risks hazard models are discussed and a solution for a particular class of models is proposed. It is argued that a common practice of reporting the results of qualitative response models in terms of marginal effects is also useful in the context of competing risks duration models.