Doctor of Philosophy, The Ohio State University, 2020, Evolution, Ecology and Organismal Biology

Critical social interactions between animals such as courtship and competition over resources are mediated by communication signals, which have evolved via natural or sexual selection. Signals may have evolved to transmit information about senders, to affect receiver responses, or both. Birds have long served as focal organisms in studies of signaling, as many avian signals have undergone extensive elaboration. The role of avian signals in breeding behavior is also well studied, as many signals evolved specifically for use in this context. Additionally, birds exhibit several types of mating systems and the reliability of signal information may vary between mating systems. Among birds, some grouse species are distinguished by a promiscuous mating system which is often centered on leks. Although these species exhibit the classic lek-mating system, several questions related to signals transmitted during breeding behavior remain scantly researched. The research in this dissertation focuses on signals employed in the breeding behavior of the lek-mating lesser prairie-chicken (Tympanuchus pallidicinctus), a North American grouse (sub-family Tetraonidae). Males display two sets of bright, conspicuous color ornaments used in visual signaling and sound production during breeding behavior. I tested the hypotheses that these ornaments are honest signals of age, condition (Chapter 2), and parasite loads (Chapter 3). Additionally, I considered the effects of ornament size and color properties on male mating success and the performance of male duets which are unique to the lesser prairie-chicken relative to other grouse. In Chapter 4, I tested the hypothesis that females rely on the interpretation of multiple male signals when choosing mates, and in Chapter 5 I explored three non-mutually exclusive hypotheses related to male duets: 1) duets serve as an endurance contest, 2) duets serve as a mechanism to de-escalate or prevent violent interactions between males, and 3) duets (open full item for complete abstract)

Committee: Jacqueline Augustine PhD (Advisor); Robert Gates PhD (Committee Member); Ian Hamilton PhD (Committee Member); Christopher Tonra PhD (Committee Member) Subjects: Animals; Biology; Ecology; Evolution and Development; Zoology

Doctor of Philosophy, The Ohio State University, 2018, Economics

The products offered in an industry determine the profits of firms and consumer welfare. In this paper, I estimate a model of product entry and exit in the U.S. yogurt industry from 2001-2011 using supermarket scanner data from the IRI Marketing Database. I use a two-step procedure. I first estimate yogurt industry demand and variable costs using the standard framework of Berry et al. (1995). In the second step, I estimate the fixed costs of offering a product. Estimation of the fixed cost is complicated because firms can offer any subset of the potential product lines in the industry, but I only observe in the sample a small number of the possible combinations of products. I apply the pairwise maximum score estimator of Fox (2007), which provides consistent estimates in settings with large choice sets. I use the first stage estimates to compute firms' expected variable profits from offering alternative sets of products and choose the fixed costs parameters to maximize the number of times the model predicts that the firms' observed choices were optimal. In a counterfactual analysis, I find that increases in market concentration do not increase the incentives to offer more unique products than the competitive industry offered and that the increased product variety is not enough to compensate consumers for the increases in prices.

Committee: Javier Donna (Advisor); Jason Blevins (Committee Member); Bruce Weinberg (Committee Member) Subjects: Economics

PhD, University of Cincinnati, 2009, Business: Business Administration

This dissertation investigates substantive questions developed from Kahneman and Tversky's behavioral choice theory. Behavioral choice theory postulates systematic departures from economically rational behavior when consumers face choices described incompletely or probabilistically. Previous research relies nearly exclusively on monetary options, which are intrinsically unidimensional and exhibit monotone utility. These special properties are likely to influence the frequency of preference reversals and other so-called non-rational behaviors in human decision-making. Four contributions emerge from this research. First, I extend the idea of risky choices from monetary to non-monetary options and build a theoretical framework with a foundation in prospect theory and reason-based choice. Second, I test the effect of multidimensional vs. unidimensional non-monetary options on choice focusing on both within- and between-dimensional risk. Third, I examine loss aversion across segments and relate an aggregation fallacy to contradictory results in the literature. Fourth, I suggest an extension of Kahneman and Tversky's behavioral choice theory by incorporating options with missing information. I use three discrete choice experiments to generate decision schema by segments of individuals sharing similar utility functions. Latent class discrete-choice models isolate the direction and magnitude of value for each attribute (level) of a set of multi-attribute options. They do so in choice domains involving both monetary and non-monetary attributes and operate effectively at both the aggregate and segment levels. As such, they support the rigorous design of experiments that circumvent the need to rely on monetary gambles. Study 1 investigates the influence of monetary (vs. non-monetary) goals on multidimensional risky choice when full information on reference points is available to an individual. Findings support goal-driven behavior, but reveal only limited evidence to supp (open full item for complete abstract)

Committee: David J. Curry PhD (Committee Chair); Frank R. Kardes PhD (Committee Member); Jordan J. Louviere PhD (Committee Member); James J. Kellaris PhD (Committee Member) Subjects: Marketing

Doctor of Philosophy, The Ohio State University, 2022, Agricultural, Environmental and Developmental Economics

US restaurant industry employs nearly 14 million people and contributes as much as 4% to national GDP. With heightened global attention and effort on promoting sustainability, creating a more sustainable and resilient business model in restaurant operations is become increasingly pertinent in both academic and industry discourse. However, gaps exist in our current understanding of this process. The first chapter utilizes both quantitative and qualitative survey data on back of house food waste to differentiate the causes of waste and amount of waste across different food categories. We then categorize the waste into actionable and non-actionable waste to provide practical recommendations for food waste reduction interventions. We find 40% of back of respondents reported a non-actionable cause for house food waste and attributed 50% of total back of house food waste to such cause. With the intention to allow practitioners to prioritize on actionable waste, we also propose several practices aiming to control the non-actionable portion of restaurant food waste. Technologies such as food waste management systems have been introduced in recent years and proven effective in reducing back of house food waste. It is important to investigate how restaurateurs value such systems. The second chapter examines the way independent restauranteurs make trade-offs between implementation cost and a variety of attributes associated with such food waste management systems. This is achieved by analyzing data obtained from a discrete choice experiment (DCE) administered in a nationally distributed online survey. The results suggest that most restaurateurs see value is such management systems. The cost of operating the system is negatively associated with restaurateur's willingness to adopt. Also, large variation in restaurateurs' preferences exists for various non-price attributes. This chapter offers a case demonstrating that restaurateur-specific opinions matter for the endorsement (open full item for complete abstract)

Committee: Wuyang Hu (Advisor); Brian Roe (Advisor); Wuyang Hu (Committee Chair); Frederick Michel (Other); Brent Sohngen (Committee Member); Andrew Hanks (Committee Member); Brian Roe (Committee Co-Chair) Subjects: Behavioral Sciences; Economics

Doctor of Philosophy, The Ohio State University, 2022, Economics

This dissertation is composed of three main papers. Each of these papers studies a different classical discrete choice model setting within the realm of Industrial Organization (IO) that now has the added complexity of containing a high-dimensional component that renders ineffective the traditional methods used and thus requires alternative approaches. In the first paper, I study a static single equilibrium market entry game of homogenous firms that contains a high-dimensional set of exogenous market characteristics that could enter a firm's profit function. In such type of high-dimensional setting we are at high risk of overfitting, i.e. estimating model parameters that are tailored too closely to the sample data available and thus don't generalize well to new data. The focus of this paper is exploring the use of different regularization techniques with the purpose of reducing overfitting when predicting market entry for a previously unobserved market. The second paper extends the previous market entry framework by now examining a static multiple equilibria market entry game of heterogeneous firms. The high-dimensional component in this setting arises from the way in which such a model is partially identified, which is through a set of moment inequalities that have to be met for a particular set of values of the parameters of interest to be consistent with the data. The number of moment inequalities that characterize this type of model can very easily grow beyond traditional sample sizes, thus requiring special attention from the researcher when testing whether a vector of values for the parameters of interest is indeed accepted by the model. This paper studies different approaches of high-dimensional testing applied to this market entry model and evaluates their performance. Finally, in the third paper I consider a different but still extremely relevant model of Industrial Organization, the aggregate discrete choice model with random coefficients for dema (open full item for complete abstract)

Committee: Jason Blevins (Advisor); Adam Dearing (Committee Member); Robert de Jong (Committee Member) Subjects: Economics

Doctor of Philosophy, The Ohio State University, 2022, Economics

This dissertation uses econometric methods to introduce an estimator and develop models to estimate the effect of the internet on bank branches. Chapter 1 introduces the nested pseudo likelihood estimator to estimate dynamic discrete choice models in continuous time. Chapter 2 uses this estimator to estimate the impact of the internet penetration on brick-and-mortar bank branches. In Chapter 3, I provide additional evidence on the effect of the internet in banking industry by examining the effect of Community Connect Broadband Grant Program, which helps rural areas to establish broadband service, on bank branches. In Chapter 1, we introduce a sequential estimator for continuous time dynamic discrete choice models (single-agent models and games) by adapting the nested pseudo likelihood (NPL) estimator of Aguirregabiria and Mira (2002; 2007), developed for discrete time models with discrete time data, to the continuous time case with data sampled either discretely (i.e., uniformly-spaced snapshot data) or continuously. We establish conditions for consistency and asymptotic normality of the estimator, a local convergence condition, and, for single agent models, a zero Jacobian property assuring local convergence. We carry out a series of Monte Carlo experiments using an entry-exit game with five heterogeneous firms to confirm the large-sample properties and demonstrate finite-sample bias reduction via iteration. In our simulations we show that the convergence issues documented for the NPL estimator in discrete time models are less likely to affect comparable continuous-time models. We also show that there can be large bias in economically-relevant parameters, such as the competitive effect and entry cost, from estimating a misspecified discrete time model when in fact the data generating process is a continuous time model. Chapter 2 examines the effect of the internet on market structure and consumer welfare in the US retail banking industry. The internet is (open full item for complete abstract)

Committee: Jason Blevins (Advisor); Adam Dearing (Committee Member); Matthew Weinberg (Committee Co-Chair) Subjects: Economics

MS, University of Cincinnati, 2019, Medicine: Genetic Counseling

Neurodevelopmental disorders (NDDs) are a group of conditions that include autism spectrum disorder (ASD), developmental delay (DD), and intellectual disability (ID). NDDs can be linked to a genetic etiology through genetic testing, which currently includes chromosomal microarray (CMA), Fragile X testing, PTEN sequencing, and MECP2 sequencing among others. Despite multiple genetic testing options, diagnostic yield from genetic testing remains around 15% on average (Miller et al. 2010). In addition to overall low and variable diagnostic yield, genetic tests for NDDs vary in turnaround time and cost. Algorithmic testing can simplify ordering genetic testing for NDDs and potentially decrease the cost of testing without risk of lowering the diagnostic yield, although time to get results is often increased. Currently, the relative value that parents of children with an NDD place on cost, time, and potential outcome when deciding on genetic testing for their child is unknown. This study used a discrete choice experiment to elicit relative parental value of diagnostic yield, cost, and turnaround time of genetic testing. This study found that parents in an NDD population place the most relative value on a test with a higher diagnostic yield, followed by a test that costs less out-of-pocket. Parents of children with intellectual disability (ID) or a known genetic cause for their diagnosis are more likely to prefer testing with a shorter turnaround time than lower out-of-pocket cost though yield is still their first priority. These results indicate that a clear discussion of potential diagnostic yield is important to these parents and a genetic testing approach that maximizes diagnostic yield should be considered.

Committee: Carrie Atzinger M.S. C.G.C. (Committee Chair); Janet Basil MS (Committee Member); Jennifer Glass MS (Committee Member); Hua He M.S. (Committee Member) Subjects: Genetics

Master of Science, The Ohio State University, 2016, Civil Engineering

Expanding travel choices by providing ridesharing can improve mobility and accessibility and reduce congestion and the negative externalities associated with single occupancy automobile use. To realize these benefits, sufficient demand must be generated by matching drivers and passengers with similar origins and destinations and who are willing to travel with potential strangers. Technological developments have facilitated the provision of real-time ridesharing programs, where travelers are matched to share a ride shortly before they travel. Real-time ridesharing offers additional flexibility and the possibility of occasional use that may be desirable in an increasingly complex society with varying schedules. While initial real-time travel options have been perceived as unattractive due to reliability and personal safety concerns, the growing success of real-time ride-sourcing services suggests that perceptions may be shifting. Furthermore, large employer-based ridesharing offers additional promise due to a network of co-workers with similar work locations facilitating good matches, increased familiarity with fellow travelers, and the ability to incentivize participation. A stated preference survey of The Ohio State University community was used to analyze willingness to participate in an idealized real-time employer-based ridesharing program. Individual characteristics and travel behaviors associated with unwillingness to participate in an ideal program are analyzed. Also, the characteristics and behaviors associated with interest in a passenger or driver role in such a program are identified. Many findings support results presented elsewhere and a priori expectations, for example an increased willingness of younger travelers to participate in ridesharing, an increased willingness of females to participate as passengers, and an increased willingness of those with experience driving to participate as drivers. In addition three findings provide important insights (open full item for complete abstract)

Committee: Mark McCord (Advisor); Rabi Mishalani (Advisor); Gulsah Akar (Committee Member) Subjects: Civil Engineering; Transportation

Doctor of Philosophy, The Ohio State University, 2012, Statistics

The first part of this dissertation is on A-optimal designs for stated choice experiments. Stated choice experiments are widely used in areas such as marketing, planning, transportation, medical care, etc. In such studies, a set of $n$ choice sets is presented to the subjects. Each choice set consists of two or more profiles. Subjects are asked to choose their favorite profile from each choice set. Therefore the outcomes of such studies are discrete and nonlinear models are usually used. The multinomial logit model (MNL) is one of the most frequently used models for stated choice experiments. There are discussions in literature about how to generate optimal designs with the MNL model but primarily with the assumption that all profiles are equally attractive. In this dissertation, a new approach is proposed to generate A-optimal designs by the local linearization of the MNL model. Under the assumption that all options are equally attractive, this approach gives the same A-optimal designs as in the literature under the same setting but in a wider class of designs. This approach is also extendable to more general settings when profiles are unequally attractive. The second part of this dissertation deals with sensitivity analysis for computer experiments. Sensitivity analysis is widely used for identifying influential input variables. Two approaches to evaluating sensitivity statistically are (1) estimating global sensitivity indices based on Sobol' variance decomposition, and (2) evaluating local sensitivity indices based on a gradient measure using a one-at-a-time sampling design. Although both approaches have been studied for (hyper-) rectangular input regions, they have not been considered carefully for the non-rectangular input region setting. In this dissertation, a more flexible gradient-based method is proposed to evaluate sensitivity indices for non-rectangular regions. In addition, the use of variable-length gradients is introduced and the importance of the st (open full item for complete abstract)

Committee: Angela Dean (Committee Co-Chair); Thomas Santner (Committee Co-Chair); William Notz (Committee Member) Subjects: Statistics

Doctor of Philosophy, The Ohio State University, 2012, Agricultural, Environmental and Developmental Economics

In this dissertation, a framework for modeling technological, economic, environmental, and social impacts over the life cycle of ten transportation fuels is developed. This is accomplished by linking engineering-based life cycle analysis of the fuels with choice analysis techniques for eliciting and understanding social preferences for multi-attribute consumption vectors. The use of life cycle data allows for a unique accounting of a broad range of environmental, natural resource, and health effects over the entire production and consumption life cycle of each transportation fuel. Combining these life cycle and stated choice analyses allows for social preferences to be established for the externalities resulting from the use of the different transportation fuels. This results in a unique model allowing for improved valuation of alternative fuel options and fuel policy design. The analysis is extended through the estimation of individual level preference parameters; allowing parameter, marginal price and total willingness-to-pay distributions to be derived from the individual level estimates. This creates a unique opportunity to test restrictions associated with traditional logit models used in discrete choice and welfare analysis, as well as compare parameter and welfare results across the two modeling frameworks.

Committee: Timothy Haab PhD (Committee Chair); Brent Sohngen PhD (Committee Member); H. Allen Klaiber PhD (Committee Member) Subjects: Energy; Environmental Economics

Doctor of Philosophy, The Ohio State University, 2010, City and Regional Planning

This dissertation presents five essays dealing with the utilization and abandonment of a city's assets, in particular two key assets: agglomeration economies and legacy capital. The first essay traces out the causes and effects of agglomeration economies by disentangling economies of agglomeration. It disentangles amenity and productivity effects of agglomeration; it decomposes aggregate scale effects into agglomeration factors of interest to policy makers; and it estimates own effects and spillovers to neighbors. It proposes a spatial simultaneous equations model in a spatial equilibrium framework with three agents – worker consumers and producers of traded goods and housing. Results for Ohio counties estimate economies resulting from population size, agglomeration causes, and public service quality and cost on each of the three agents in own and neighboring counties. The second essay theoretically models the abandonment and reuse of legacy capital in the process of industrial restructuring. It aims to identify the conditions for abandonment and the factors that determine the length of abandonment. The model is based on investment theory and game theory. It shows that abandonment is impacted by conversion costs of legacy capital, the rate of growth of industries involved in the restructuring, and policy variables such as tax rate. The third essay empirically verifies the theoretical model developed in the second essay, using data of industrial and commercial properties (ICPs) in the Cleveland city-region in Ohio. It shows that in declining industries or regions, ICPs experience tax delinquency of longer duration and are more likely to be abandoned than elsewhere. Also, ICPs with higher conversion costs are more likely to experience longer spells of tax delinquency and are more likely to be abandoned than others. Abandoned ICPs are spatially concentrated either as a result of negative spillovers or shared history. The fourth essay theoretically models the extern (open full item for complete abstract)

Committee: Burkhard von Rabenau (Committee Chair); Jean-Michel Guldmann (Committee Member); Philip Viton (Committee Member) Subjects: Economics; Urban Planning

Doctor of Philosophy, The Ohio State University, 2007, Economics

My dissertation research emphasizes estimation methods in evaluating the extent of social, strategic and spatial interactions among economic agents. My first essay, based on my joint research with Lung-fei Lee and John Kagel, generalizes Heckman's (1981) dynamic discrete-choice panel data models by introducing time-lagged social interactions and proposes simulation based methods to implement the maximum likelihood estimation. We use this generalized model to investigate learning from peers in experimental signaling games. We find that subjects' decisions are significantly influenced by the past decisions of their peers in the experiment. Hence the imitation of peers' strategies is an important component of the learning process of strategic play. My second essay explores the robustness of Guerre, Perrigne and Vuong's (2000) two-step nonparametric estimation procedure in first-price sealed-bid auctions with a large number of risk-averse bidders. With an asymptotic approximation of the intractable equilibrium bidding function of risk-averse bidders, I demonstrate that Guerre et al.'s two-step nonparametric estimator based on the equilibrium bidding behavior of risk-neutral bidders is still uniformly consistent even if bidders are risk-averse as long as the number of players in an auction is sufficiently large and derive the uniform convergence rate of the estimator. Furthermore, I show in Monte Carlo experiments that the two-step nonparametric estimator performs reasonably well with a moderate number of risk-averse bidders like six. In my third essay, which is based on my joint research with Lung-fei Lee and Christopher Bollinger, we consider the GMM estimation of the regression model with spatial autoregressive disturbances and the mixed-regressive spatial autoregressive model. We derive the best GMM estimator within the class of GMM estimators that are based on linear and quadratic moment conditions. Our best GMM estimator has the merit of computational simplicity an (open full item for complete abstract)

Committee: Lung-fei Lee (Advisor) Subjects: Economics, General

Doctor of Philosophy, The Ohio State University, 2007, Industrial and Systems Engineering

This dissertation explores two types of applications of applied statistics techniques to develop methods associated with bioinformatics and information retrieval. The first type relates to planning probably the most common type of genetics related experiment, i.e., co-hybridized microarray testing. The question addressed concerns how to deploy the samples to slides and select dye colors to improve the sensitivity and specificity without increasing the associated cost. A generalized A-optimality criterion called the expected squared errors of coefficient estimates (ESECE) is proposed to aid in experimental design selection. The proposed criterion also can be applied to any type of experimentation focused on parameter estimation. Heuristic methods to generate arrays using the proposed criterion are also suggested. The resulting “hybrid” designs constitute a compromise between the widely used “reference” designs and the “loop” designs. The proposed criterion and a study of 15,488 genes together suggest that reference designs are generally likely to foster more accurate estimation than loop designs. Also, the proposed “hybrid” designs likely offer further benefits in increased sensitivity and specificity with no added costs. The second type of application explored is the design of vector space search engines, which constitute perhaps the most common type of search technology in information retrieval. In this dissertation, two types of methods are explored separately and also combined to tune the selection of weights of the similarity distance function so that the search engine generates results of greater interest to users. The first type is so-called discrete choice analysis (DCA) methods to estimate the weights that putatively maximize the expected utility of users in the context of specific queries. The second type of method is the application of mixture modeling. Based on the fitting of specific types of mixture regression models, methods are proposed to enhance the (open full item for complete abstract)

Committee: Theodore Allen (Advisor) Subjects: Engineering, Industrial

Doctor of Philosophy, The Ohio State University, 2004, Business Administration

Marketing managers are interested in knowing how consumers will react to different product configurations. The product manager can change physical attributes through the design of the product and the perception of psychological attributes through promotion strategies. Because consumers are heterogeneous in their tastes and preferences, studies have focused on obtaining individual level estimates of attribute importance from a representative sample of consumers as opposed to just aggregate level estimates. Marketing researchers have procedural and statistical methods of obtaining measures of attribute importance for each respondent on each attribute. In laboratory or experimental choice settings, studies can be designed to help focus respondents' attention and processing of the product attributes. Bayesian methods of modeling heterogeneity shrink poorly measured individual level parameters to the overall or group level mean. However, it is erroneous to assume that consumers use all the product attributes in all brand choice situations. This thesis demonstrates that improved inference and predictive accuracy can be obtained by modeling which attributes are actually being used by consumers in different discrete choice situations. This thesis contributes new models for determining, at the individual level, which product attributes are being used by a consumer in a brand choice decision. The heterogeneous variable selection model extends current aggregate level models of Bayesian variable selection. This model assumes a distribution of heterogeneity with mass concentrated at 0 and away from 0. The pooled variable selection model allows the set of variables used by an individual to vary by choice context. Examples of separate contexts include partial and full profile choice experiments or choice experiments and actual market place transactions. A hybrid model combines the heterogeneous and pooled variable selection models. The threshold variable selection model incorporat (open full item for complete abstract)

Committee: Greg Allenby (Advisor) Subjects: Business Administration, Marketing