ESSAYS IN THE SOCIAL DETERMINANTS OF HEALTH AND PUBLIC HEALTH POLICY

There is broad-based qualitative evidence arguing that stress is a public health concern. In the search for causality however, economists investigating public health concerns are often limited to a narrow subset of socio-economic status (SES) factors which are not well suited to the evaluation of stress/health effects. As a result, economists have a difficult time answering the “how” and “why” questions which emerge from the well-respected SES/health gradient. This dissertation focuses on a subset of stressors credibly affiliated with SES to address two pathways underling the established SES/Health gradient. Chapter 2 uses the Panel Study of Income Dynamics to test financial strain (FS) and high-frequency job switching (JS) effects on health. Using dynamic panel random effects ordered probit specifications, we find support for FS and JS effects on self-assessed health status. Of course, policy recommendations that follow are unimaginably vast. In chapters 3 and 4, we focus on breaking down the grand policy consideration of reducing financial strain into politically viable actions. First, we develop and test a pragmatic policy analysis method against observable outcomes from two health policies made by the state of Alaska. In terms of reducing individual year-over-year health insurance premium volatility, the presented method performs better than the commonly relied upon actuarial methods. The implication is that adoption of such analytical methods may result in improved population health. Chapter 4 evaluates the effects of childhood retrospective circumstances on the experiences of adult financial strain. Results suggest school experiences, feelings of loneliness, parental work habits, and maternal relationships are key predictors of adult financial strain

Applications of robust optimal control to decision making in the presence of uncertainty

This thesis is concerned with robustness of decision making in financial economics. Feedback control models developed in engineering are applied to three separate though linked problems in order to examine the role and impact of robustness in the creation and application of decision rules. Three problems are examined using robust optimal control techniques to evaluate the impact of robustness and stability in financial economic models. The first problem examines the use of linear models of robust optimal control in the pricing of castastrophe based derivatives and finds its relative performance to be superior to the popular jump diffusion and stochastic volatility models in the pricing of these emerging instruments. The novelty of the approach arises from the examination of the impact of robustness and stability of the pricing solution. The second problem involves robustness and stability of hedging. An alternative method of creating hedging rules is developed. The method is based on robust control Lyapunov functions that are simple, robust and stable in operation, yet in practice are not so conservative that they eliminate all trading gains. The third problem involves the development of robust control policies for managing risk, using non-linear robust optimal control techniques to provide clear evidence of superior performance of robust models when compared with existing VAR and EVT approaches to risk management. The novelty in the approach arises from the development of a simple and powerful risk management metric

The suitabilty of conceptual graphs in strategic management accountancy

The hypothesis of the research is "conceptual graphs are a suitable knowledge-base decision support tool for use by managemenat ccountants in strategic planning", explained as follows. Knowledge-based approaches can help accountants apply their skills in the direction of strategic management problems. Such problem domains cannot be modelled effectively by computer alone, hence we are only interested in those advanced knowledge-based methodologies that can be adequately reviewed by strategic management accountants in the light of their own continually changing tacit and implicit knowledge. Structured diagram techniques, such as flowcharting, are well known by accountants and are a clearly understandable yet important aid in problem review. Apart from being founded on a logically complete reasoning system, the knowledge-based methodology of conceptual graphs was formulated to be an enhancement of these other methods. Furthermore the graphical form of conceptual graphs enjoy an apparent similarity to the 'negating' brackets in the accountant's traditional bookkeeping model. After conducting a comparative study with two similar methodologies in current use showing the technical advantages of conceptual graphs, the Conceptual Analysis and Review Environment computer software was devised and implemented. CARE was used to test the - accepted graphical form of conceptual graphs through a series of user evaluation sessions. The evaluations started out with subjects from the conceptual graphs community itself, then key business school staff, and culminated in a session with senior practising accountants. In addition, CARE was enhanced iteratively in accordance with the results of each evaluation session. Despite their strong prima facie attractiveness and positive response from the conceptual graphs community session, as the user evaluations progressed it became increasingly evident that the inherent complexity of conceptual graphs fundamentally undern-tined them as a viable tool, other than for very trivial problems well below the level needed to be viable for strategic management accountancy. Therefore the original contribution of this research is that its hypothesis turns out to be false

Risk Sharing and Risk Aggregation via Risk Measures

Risk measures have been extensively studied in actuarial science in the guise of premium calculation principles for more than 40 years, and recently, they have been the standard tool for financial institutions in both calculating regulatory capital requirement and internal risk management. This thesis focuses on two topics: risk sharing and risk aggregation via risk measures. The problem of risk sharing concerns the redistribution of a total risk among agents using risk measures to quantify risks. Risk aggregation is to study the worst-case value of aggregate risks over all possible dependence structures with given marginal risks. On the first topic, we address the problem of risk sharing among agents using a two-parameter class of quantile-based risk measures, the so-called Range-Value-at-Risk (RVaR), as their preferences. The family of RVaR includes the Value-at-Risk (VaR) and the Expected Shortfall (ES), the two popular and competing regulatory risk measures, as special cases. We first establish an inequality for RVaR-based risk aggregation, showing that RVaR satisfies a special form of subadditivity. Then, the Pareto-optimal risk sharing problem is solved through explicit construction. We also study risk sharing in a competitive market and obtain an explicit Arrow-Debreu equilibrium. Robustness and comonotonicity of optimal allocations are investigated, and several novel advantages of ES over VaR from the perspective of a regulator are revealed. Reinsurance, as a special type of risk sharing, has been studied extensively from the perspective of either an insurer or a reinsurer. To take the interests of both parties into consideration, we study Pareto optimality of reinsurance arrangements under general model settings. We give the necessary and sufficient conditions for a reinsurance contract to be Pareto-optimal and characterize all such optimal contracts under more general model assumptions. Sufficient conditions that guarantee the existence of the Pareto-optimal contracts are obtained. When the losses of an insurer and a reinsurer are measured by the ES risk measures, we obtain the explicit forms of the Pareto-optimal reinsurance contracts under the expected value premium principle. On the second topic, we first study the aggregation of inhomogeneous risks with a special type of model uncertainty, called dependence uncertainty, in individual risk models. We establish general asymptotic equivalence results for the classes of distortion risk measures and convex risk measures under different mild conditions. The results implicitly suggest that it is only reasonable to implement a coherent risk measure for the aggregation of a large number of risks with dependence uncertainty. Then, we bring the well studied dependence uncertainty in individual risk models into collective risk models. We study the worst-case values of the VaR and the ES of the aggregate loss with identically distributed individual losses, under two settings of dependence uncertainty: (i) the counting random variable and the individual losses are independent, and the dependence of the individual losses is unknown; (ii) the dependence of the counting random variable and the individual losses is unknown. Analytical results for the worst-case values of ES are obtained. For the loss from a large portfolio of insurance policies, the asymptotic equivalence of VaR and ES is established, and approximation errors are obtained under the two dependence settings

Complex adaptive systems based data integration : theory and applications

Data Definition Languages (DDLs) have been created and used to represent data in programming languages and in database dictionaries. This representation includes descriptions in the form of data fields and relations in the form of a hierarchy, with the common exception of relational databases where relations are flat. Network computing created an environment that enables relatively easy and inexpensive exchange of data. What followed was the creation of new DDLs claiming better support for automatic data integration. It is uncertain from the literature if any real progress has been made toward achieving an ideal state or limit condition of automatic data integration. This research asserts that difficulties in accomplishing integration are indicative of socio-cultural systems in general and are caused by some measurable attributes common in DDLs. This research’s main contributions are: (1) a theory of data integration requirements to fully support automatic data integration from autonomous heterogeneous data sources; (2) the identification of measurable related abstract attributes (Variety, Tension, and Entropy); (3) the development of tools to measure them. The research uses a multi-theoretic lens to define and articulate these attributes and their measurements. The proposed theory is founded on the Law of Requisite Variety, Information Theory, Complex Adaptive Systems (CAS) theory, Sowa’s Meaning Preservation framework and Zipf distributions of words and meanings. Using the theory, the attributes, and their measures, this research proposes a framework for objectively evaluating the suitability of any data definition language with respect to degrees of automatic data integration. This research uses thirteen data structures constructed with various DDLs from the 1960\u27s to date. No DDL examined (and therefore no DDL similar to those examined) is designed to satisfy the law of requisite variety. No DDL examined is designed to support CAS evolutionary processes that could result in fully automated integration of heterogeneous data sources. There is no significant difference in measures of Variety, Tension, and Entropy among DDLs investigated in this research. A direction to overcome the common limitations discovered in this research is suggested and tested by proposing GlossoMote, a theoretical mathematically sound description language that satisfies the data integration theory requirements. The DDL, named GlossoMote, is not merely a new syntax, it is a drastic departure from existing DDL constructs. The feasibility of the approach is demonstrated with a small scale experiment and evaluated using the proposed assessment framework and other means. The promising results require additional research to evaluate GlossoMote’s approach commercial use potential

Three Essays on Strategic Behavior and Policy.

This dissertation contains three chapters that study the strategic behavior of economic agents in environments shaped by legal and regulatory policy. Two chapters examine strategic behavior as it pertains to the market outcomes of firms competing as oligopolists; a third studies the negotiation outcomes of a firm and consumer bargaining in the shadow of the law. Each chapter extends or applies fundamental models of strategic behavior to develop testable implications. It then exploits the historical change induced by a policy intervention to empirically test the model's mechanisms, conditions, and predictions. Finally, it interprets the meaning of empirical estimates for policy toward antitrust and other types of law. The first chapter analyzes the sustainability and consequencs of collusion in a high-technology industry with learning-by-doing. It shows how the dynamic cost savings that occur through learning, combined with multiproduct competition, can reverse the standard predictions of static supergame models of collusion. It illustrates this by building an oligopolistic model of learning-by-doing embedded within a supergame. It estimates the price change attributable to collusion by using firm-level data before, during, and after explicit collusion in the electronic memory chip market. The second chapter assesses the predictions of the Priest-Klein model of pre-trial settlement bargaining. It applies the model to an auto insurance negotiation setting to show how parties' uncertainty in a legal standard drives the likelihood of claim settlement. It then exploits state- and time-level variation in states' adoption of tort liability for insurance bad faith law to find that tort liability significantly increased the likelihood of a claim to settle with litigation in the short-term, but significantly reduced this likelihood in the long-term. The third chapter uses data from a price fixing cartel in the Korean petrochemical industry to test empirical screens of collusion. It exploits the information disclosed by the Korean antitrust regulator to formulate a hypothesis of the effect of collusion on price-cost asymmetry. It uses price data before and after the breakdown of the cartel to find evidence consistent with a change from high to low price-cost asymmetry before and after the cartel's collapse.PhDBusiness AdministrationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113443/1/dasmat_1.pd

Current Topics on Risk Analysis: ICRA6 and RISK2015 Conference

Peer ReviewedPostprint (published version

Individual preferences and farm-level decisions : experimental evidence from Costa Rica

This thesis contributes to the understanding of individual preferences and farm-level decision making, in interaction with the institutions and government policies that can influence these decisions. Decisions on investments and input used, decisions on whether or not to support local agricultural cooperatives, decisions the demand of credit with or without insurance, and decisions of voluntary certification schemes that have an impact on the livelihood of small farmers in developing countries. I used standard econometric methods to analyze the survey and data collected from three lab-in-the-field experiments, and a one quasi-experiment all implemented with real subjects: coffee farmers in rural Costa Rica. Chapter 1 starts with an overview of the main topics: individual preferences and farm-level decisions. It introduces the concept of risk preferences, social preferences, formal market-based strategies and certification schemes, that can be adopted by farmers to manage risks. More specifically, it presents the research questions, methodology, empirical strategy the study area and explains how the data was collected for the subsequent main chapters of the thesis. In Chapter 2, we evaluate a survey-based method to other methods that elicit farmers’ risk attitudes. For both researchers and practitioners, surveys can be easier to implement than field experiments in developing countries. We first assess correlations between a context-free survey estimate of risk-taking and context-specific risk preferences. Then, we test whether survey data predicts risk-taking behavior in an incentivized experiment. Finally, we show how the different estimates of survey risk preferences relate to real-life farming choices in a population of coffee farmers in Costa Rica. Our results indicate that one should be careful when extrapolating risk attitudes across contexts. Context-neutral and context-specific survey questions elicit different risk preferences. While the context-free survey estimate of risk preferences predicts risk-taking behavior in a context-free risk experiment, and context-specific estimates are associated with risk-taking in the same agricultural real-life context , the context-free survey estimate of risk-taking is not associated with actual risk-taking behavior in the agricultural setting. Connecting these methods to farm practices, we find that higher willingness to take risk is associated with the implementation of agricultural practices that require more farm investment. In contrast, farmers who report less willingness to take risks are more likely to have higher expenditures on fertilizer use. Researchers interested in using risk preferences as inputs into the design of policy instruments should make sure that preferences are elicited in the specific context targeted by the potential policy instrument. Chapter 3 tests the internal and external validity of the typology of a conditional cooperator classified by using a public goods game together with the strategy method. Individuals categorized as conditional cooperators adapt their behavior to the group to which they belong. In Costa Rica, coffee farmers are traditionally organized in agricultural cooperatives, a setting very similar to the scenario presented to an individual facing the strategy method in a public goods game: how much to cooperate, given what others do. Our results show that conditional cooperators believe they contribute to the public good by matching the contribution of others in the experiment. However, we find no evidence that those classified as conditional cooperators in the experiment also behave this way when it comes to bringing coffee to the local cooperative in real life. We show supporting evidence to conclude that the typology of a conditional cooperator is internally consistent, but do not find evidence that the typology of conditional cooperators is externally valid. Our paper is a contribution to the external validity of context-free experiments and helps in understanding cooperative behavior relevant to the sustainability of agricultural cooperatives in the developing world. In Chapter 4, we examine the effect of farmers’ liability on demand for credit with and without insurance. We test predictions of a theoretical model in a lab in the field experiment with coffee farmers in Costa Rica. Farmers choose how much to invest in six different settings, described on the one hand by whether the loan is insured or not, and on the other by their liability. Our results show that the uptake of loans bundled with insurance is significantly higher than uptake of loans without insurance, both when farmers are liable for sure for their debt, and interestingly when there is uncertainty about their liability. When farmers are not liable for their debt, i.e. under limited liability, the uptake of credit is high irrespective of whether the loans are insured or not. Our results suggest that in order to increase the uptake of insurance as a strategy to increase private investment and reduce the vulnerability of farmers to weather events which may lead to serious crop failure, it is important that farmers are liable with at least some probability. In terms of policy design, our results show that the “principle” of limited liability does not have be abandoned altogether in order to generate an increase in the uptake of insured credit. Chapter 5 evaluates the environmental impacts of organic coffee certification. Eco-certification of coffee, timber and other high-value agricultural commodities is increasingly widespread. In principle, it can improve commodity producers' environmental performance, even in countries where state regulation is weak. But eco-certification will have limited environmental benefits if, as one would expect, it disproportionately selects for producers already meeting certification standards. Rigorous evaluations of the environmental effects of eco-certification in developing countries that control for selection bias are virtually nonexistent. To help fill this gap, we use detailed farm-level data to analyze the environmental impacts of organic coffee certification in central Costa Rica. We use propensity score matching to control for selection bias. We find that organic certification improves coffee growers' environmental performance. It significantly reduces chemical input use and increases adoption of some environmentally friendly management practices. To conclude, Chapter 6 presents a synthesis and the previous core chapters. Conclusions, lessons learned and policy recommendations of individual chapters are reconsidered. Subsequently, results are placed in a thesis-wide perspective and finally, general conclusions made.</p

Current Topics on Risk Analysis: ICRA6 and RISK2015 Conference

Artículos presentados en la International Conference on Risk Analysis ICRA 6/RISK 2015, celebrada en Barcelona del 26 al 29 de mayo de 2015.Peer ReviewedPostprint (published version