Reflecting the growing interest from both consumers and policymakers, and building on recent developments in Willingness to Pay (WTP) methodologies, we evaluate consumer preferences for an archetypal traditional food product. Specifically we draw on stated preference data from a discrete choice experiment, considering the traditional Hungarian mangalitza salami. A WTP space specification of the generalized multinomial logit model is employed, which accounts for not only heterogeneity in preferences but also differences in the scale of the idiosyncratic error term. Results indicate that traditional food products can command a substantial premium, albeit contingent on effective quality certification, authentic product composition and effective choice of retail outlet. Promising consumer segments and policy implications are identified. (authors' abstract)

This thesis addresses various food safety issues and investigates them from an economic perspective within four different, but related, studies. The studies are intended to provide policy-makers and other decision-makers in the industry with valuable information that will help them to implement better mitigation strategies and policies. The studies also present some applications of advancements in choice modelling, and thus contribute to the literature. To address these issues, various surveys were conducted in the UK.The first study investigates different stakeholder groups’ perceptions of responsibility among the stages of the meat chain for ensuring the meat they eat does not cause them to become ill, and how this differed with food types. The means by which this is achieved is novel, as we elicit stakeholders’ relative degrees of responsibility using the Best-Worst Scaling (BWS) technique. BWS is particularly useful because it avoids the necessity of ranking a large set of items, which people have been found to struggle with. The results from this analysis reveal a consistent pattern among respondents of downplaying the extent of their own responsibility. The second study explores people’s perceptions of various food and non-food risks within a framework characterised by the level of control that respondents believe they have over the risks, and the level of worry that the risks prompt. The means by which this is done differs from past risk perception analyses in that it questions people directly regarding their relative assessments of the levels of control and worry over the risks presented. The substantive analysis of the risk perceptions has three main foci concerning the relative assessment of (i) novel vs. more familiar risks, (ii) food vs. non-food risks, (iii) differences in the risk perceptions across farmers and consumers, with a particular orientation on E. coli. The third study investigates consumers’ willingness to pay (WTP) for reductions in the level foodborne health risk achieved by (1) nanotechnology and (2) less controversial manners in the food system. The difference between consumers’ valuations provides an implicit value for nanotechnology. This comparison is achieved via a split sample Discrete Choice Experiment study. Valuations of the risk reductions are derived from conditional, heteroskedastic conditional, mixed, and heteroscedastic mixed logit models. General results show the existence of heterogeneity in British consumers’ preferences and variances, and that the value of nanotechnology differs for different types of consumers. The fourth study investigates consumers’ perceptions of trust in institutions to provide information about nanotechnology and its use in food production and packaging. It is shown how the use of BWS and Latent Class modelling of survey data can provide in-depth information on consumer categories useful for the design of effective public policy, which in turn would allow the development of best practice in risk communication for novel technologies. Results show heterogeneity in British consumers’ preferences. Three distinct consumer segments are identified: Class-1, who trust “government institutions and scientists” most; Class-2, who trust “non-profit organisations and environmental groups” most; and Class-3, who trust “food producers and handlers, and media” most.

此篇文章探討，使用離散選擇模型(discrete choice model)中的邏輯模型(logit model)分析，若資料具有遺漏值(incomplete-data)，比較將具有遺漏值樣本值皆移除與使用多重插補方法補值之參數估計結果。 本文使用的多重差補法為Buuren(2007)等人所提出的Multiple Imputation by Chained Equation(MICE)多重插補方法進行補值，並使用Rubin(1987)所提出的方法合併參數估計結果。從模擬結果之參數偏誤盒狀圖可知插補後參數估計與設定參數差異不大，另外插補次數對於參數估計結果影響不大，且在遺漏比例(missing percentage)大時，參數估計結果比起將具有遺漏值樣本直接移除的參數估計較為穩定。 另外使用實際資料分析，發現具有遺漏值樣本直接移除的參數估標準差比起插補後參數估計標準差大的趨勢，與模擬結果相同。 / This paper focuses on using discrete choice logit model to analyze incompleted data. To deal with the incompleted data, complete case analysis and multiple imputation are used, and compare the result of parameter estimates of the two methods. The method of multiple imputation which this paper used is Multiple Imputation by Chained Equation (MICE). With the estimates from multiple imputed data sets, using Rubin’s method (1987) to pool the estimates. The simulation shows that after imputing the missing values, the estimates from the imputed data are not much difference from the real parameters. The number of imputation does not effect the estimates much. With larger missing percentage, the estimates from the imputed data is more robust than the estimates from the complete case analysis. In real data analysis, the standard deviation of estimates from using complete case analysis are bigger than imputed data, this result is the same with the simulation.

This thesis treats with the theory, interpretation and application of the Discrete Choice Models. The theoretical part contains the Fitting the Logistic Regression Model, Testing for the Significance of the Coefficients, Testing for the Significance of the Model. The Multiple Logistic Regression is mentioned too. The model was applied to interview data from the International research called Reflex.

Thesis advisor: Julie H. Mortimer / The unifying theme of this dissertation is the growing importance of pharmaceutical products in health care and in society more broadly. The first two chapters use structural and reduced-form models to study the effects of various policies on the choice and utilization of prescription drugs. The third chapter surveys the empirical literature on the competitive effects of a class of pricing arrangements used in the pharmaceutical and many other industries. Chapter 1. One of the criticisms leveled against direct-to-consumer advertising of prescription drugs is that it overemphasizes the use of pharmaceuticals at the expense of other forms of treatment. In “Choice of Depression Treatment: Advertising Spillovers in a Model with Complementarity,” I study how antidepressant TV ads affect demand for psychotherapy. Antidepressant advertising can increase demand for therapy if the products are complements or if advertising has spillover effects. To disentangle the different channels, I develop a discrete-choice demand model that allows for complementarity between products, advertising spillovers, and flexible unobserved preference heterogeneity. Individual-level panel data on treatment choices and price variation allow me to separately identify complementarity and correlated preferences, whereas the average price of TV advertising, used as an instrument, identifies the causal effect of antidepressant ads on demand for each product. The results indicate that even though antidepressants and psychotherapy are substitutes, drug advertising increases demand for therapy through a spillover effect. Allowing for time-invariant and time-varying unobservables that can be correlated across products critically affects the estimated degree of complementarity and advertising elasticities. Chapter 2. While prescription drugs have enabled the cost-effective treatment of a myriad of diseases, many pharmaceuticals come with potential for abuse. The growing use of opioid medications for chronic pain led to widespread misuse, addiction, and skyrocketing overdose death rates. In “Did Plain-Vanilla Prescription Drug Monitoring Programs Reduce Opioid Use? Evidence from Privately Insured Patients,” I explore whether prescription drug monitoring programs (PDMPs) with no registration or use mandates were effective in reducing the utilization of opioid prescription drugs. Exploiting the staggered introduction of such programs between 2008 and 2010, I use difference-in-differences to estimate their causal effect on the number of prescriptions, days supply, and dosage per capita. Based on data from privately insured adults, the estimation results reveal that PDMPs successfully reduced opioid utilization, especially of high-dosage prescriptions. A battery of robustness checks suggests that the estimated effects are caused by the PDMPs and not by confounding factors such as broader trends in health care, attrition, out-of-state purchases, or other anti-opioid policies. Chapter 3. The assumption that buyers pay the same price for each unit of the good they purchase underlies many economic analyses. However, linear pricing is one of many pricing arrangements used in practice. In “Empirical Evidence on Conditional Pricing Practices: A Review,” Julie Holland Mortimer and I review the existing empirical studies on the competitive impact of conditional pricing practices (CPPs), under which the price of a product may depend on a quantity, share, bundling, or other requirement. Examples of CPPs include all-units and loyalty discounts, full-line forcing contracts, and exclusivity arrangements. A common thread unifying the empirical literature is that CPPs often have both procompetitive and anticompetitive effects and that their net effect may depend on the details of the arrangements and the characteristics of the markets in which they are used. / Thesis (PhD) — Boston College, 2020. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

The thesis investigates the development of short-sea shipping (SSS) in the Southern African Development Community (SADC) region by studying the determinants of SSS, the stated choice preference of shippers and freight forwarders and the stated intentions of maritime carriers for SSS. It is purported the introduction of SSS in SADC could reduce socio-environmental problems currently faced such as road damage, road congestion, pollution and transport related accidents. Discrete choice modeling (DCM) is employed as the main methodology to study shipper and carrier behavior. Discrete choice modeling permits the construction of general utility functions incorporating various decision maker characteristics and choice attributes to elicit preference of respondents. The general postulate in DCM is that utility is derived from the properties of things rather than the actual thing per se. A particular benefit of DCM in this study is the elicitation of preference for services and interventions that have not been introduced by SSS. The first step in the study is a theoretical investigation of the potential of SSS in the SADC region. It highlights the policy initiatives, the barriers and enablers related to the development of SSS. The proposed SSS system would have three main roles: to offer an alternative mode of freight transport service between port cities, to serve as the main leg in an intermodal transport network, and to serve feeder services between hub-and-spoke ports. The findings reveal that, SSS has the theoretical potential to work in the SADC region, given the large geographic region, projected freight volumes and customs and trade policies the SADC region is pursuing. The second step in the study involves an a-priori study conducted to develop a general understanding of freight transport in SADC. For this purpose, a uniquely developed online survey was conducted across the SADC region to ascertain in particular: who the decision maker is in terms of freight mode choice; and what the significant attributes that influence freight mode choice are. The results reveal that both the shipper and the freight forwarder are involved in mode choice decisions, however the shipper being the dominant decision maker. Furthermore, the results of the exploded logit model reveal that the top five modal attributes that shippers consider most important are: reliability, transport cost, risk of damage, frequency of service and transit time. These results were subsequently employed to inform the shipper and carrier behavior studies. The third step entails the assessment of shipper behavior, where trip specific mode choice decisions are studied along five intra-urban origin-destination (O-D) paired routes (which would form the study corridors). Three of these corridors considered unimodal SSS, and the two considered intermodal SSS. Unimodal SSS was studied along the following corridors: Cape Town (South Africa)~ Walvis Bay (Namibia), Walvis Bay (Namibia) ~ Luanda (Angola) and Durban (South Africa) ~Beira (Mozambique); and intermodal SSS was studied along the following corridors: Durban (South Africa) ~ Harare (Zimbabwe) and Cape Town (South Africa) ~ Windhoek (Namibia). To develop the choice scenarios, d-efficient stated choice experiments were uniquely developed for each of the corridors with the following key modal attributes systematically varied and analyzed across respondents: service frequency, reliability in terms of arriving on time, expected delay, transport cost and transport time. Subsequently, the following choice models were developed: Binary Logit, Mixed Logit and Integrated Choice and Latent Variable Structure models for the unimodal corridors; and Multinomial Logit, Nested Logit and Cross Nested Logit models for the intermodal corridors. The results highlight that in addition to the modal attributes, mode choice decisions are driven by shipper characteristics and situational characteristics. Moreover, the unimodal SSS study reveals that underlying latent perceptions also influence freight mode choice decisions; while the intermodal SSS study reveal strong correlations in the intermodal SSS alternatives, which requires improved intermodal capability if SSS is to become competitive. The fourth step in the study entail the assessment of maritime carriers preference for SSS given varying levels of maritime conditions that include: dedicated freight volumes, income from freight, port dues discount, terminal handling fees discount and ship registration requirements. The results of an ordered logit model reveal that ship registration provisions and terminal handling charges are the most important to the development of SSS from a carrier side. Moreover, ship registration and maritime cabotage provisions require visitation to boost the participation of carriers in SSS. The last step of the study revisits the modeling results and considers their implications through the estimation of willingness-to-pay and attribute elasticities. The results were then employed to suggest policy actions and interventions to develop SSS.

No description available.

In recent decades, economists are gradually showing their interests in the study of happiness. They even put forward some challenges to the traditional theories. In contrast, studies on Chinese happiness problem are not enough in terms of breadth and depth.  This paper used the data provided by China General Social Survey to conduct an empirical analysis. The model author adopted is Ordered Discrete Choice model. In the empirical section, author analyzed the impact of income, macroeconomic variables, etc.  Ultimately, based on the empirical results, author proposed some policy recommendations and further study suggestions.

To achieve Sustainable Development Goal 3 in developing countries, Good health and wellbeing for all, the health workforce is vital however the unpopularity of rural medical practice results in widening healthcare inequalities between urban and rural areas. This study determined the heterogeneity in valuations for rural facility attributes by final year medical students at one South African public university to inform cost-effective recruitment policy recommendations. Focus groups conducted identified facility attributes, a D-efficient design was generated with 15 choice sets, each with two rural hospital alternatives and no opt-out option. An online, unlabelled discrete choice experiment (DCE) was conducted, the results effects coded, and mixed logit models applied. The final sample size was 193 (86,16% of the class), majority female 130 (66.33%), with urban origins 176 (89.80%), unmarried 183 (93.37%) and without children 193 (98.47%). Most had undergraduate rural medicine exposure 110 (56.12%) and intended to specialise 109 (55.61%). The main-effects mixed logit found advanced practical experience, hospital safety, correctly fitted personal protective equipment (PPE) and availability of basic resources the highest weighted attributes with their mean utilities increasing by 0.82, 0.64, 0.62 and 0.52 respectively (p=0.000). In contrast, increases in rural allowance and the provision of housing provided smaller mean utility increases of 0.001 (p<0.01) and 0.09 (p<0.05) respectively. The interaction terms; female, general practise and prior rural medicine exposure, were associated with higher weighting for hospital safety, mean utility increases 1.59, 1.82, 1.42 respectively (p=0.000). Participants were willing to pay ZAR 2636.45 monthly (95%CI: 1398.55;3874.355) to gain advanced practical experience (equivalent to 65.91% of current rural allowance). Medical students’ facility preferences have been found to be influenced by their gender, career aspirations and prior experienced with rural medicine. The policy recommendations derived from this research include publicising rural health facility “draw-cards” among medical graduates, such as the opportunity to gain practical experience, improving the physical and occupational safety at rural health facilities and providing greater transparency about rural facility attributes to medical graduates.

An understanding of habitat relationships is essential for managing hunted species, such as cougar, that are difficult to census. In the first phase of this study, I used aerial telemetry data to examine diurnal cougar resource selection at 2 scales, and compared results between 2 study sites, the Oquirrh Mountains and Monroe Mountain, Utah. In the second phase of this study, I used conditional logistic regression models and GPS collar data from the Oquirrh Mountains to determine whether cougar resource selection varied over 3 behaviors (prey caching, resting at a daybed site, and nocturnal activities) and 2 scales. Results from phase 1 indicated that in general, during diurnal hours cougars selected for woodland cover types, moderate to steep (20-70%) slopes, canyon and steep hillside landforms, and home ranges with a higher density of edge. However, selection for these resources was not consistent at both study sites, scales, or for every cougar. Small sample sizes and poor spatial accuracy of the aerial telemetry data likely precluded the ability to detect selection in every case. Results from phase 2 indicated that cougar resource selection varied by behavior, and selection of some resources was detected only at certain scales. Cougar cache sites were characterized by southern and eastern aspects; lower elevation; avoidance of edge; a greater diversity of land cover types; canyon landforms (ridges were avoided); riparian, deciduous, and coniferous woodland; and deciduous and coniferous forest cover types. Cougars selected daybed sites that avoided western aspects and edges, were further from roads, closer to streams, higher in elevation, on moderate to steep slopes, and in rocky, deciduous woodland, and riparian cover types. During nocturnal activities, cougars avoided northern aspects, and selected areas that were closer to streams and roads, on edges and in canyon landforms, with gentler slopes, and rock, riparian, and deciduous woodland cover types. Results from cross-validation procedures confirmed that the models were reliable and predictive of cougar resource selection. This study provides a comprehensive analysis of cougar resource selection over various scales and behaviors. Managers should use caution when using diurnal data to make conclusions about selection during other times of day or behaviors.