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on Discrete Choice Models |
| By: | Wong, Stephen D. Ph.D.; Yu, Mengqiao; Kuncheria , Anu; Shaheen, Susan A. Ph.D.; Walker, Joan L. Ph.D. |
| Abstract: | Recent technological improvements have greatly expanded the sharing economy (e.g., Airbnb, Lyft, and Uber), coinciding with growing need for transportation and sheltering resources in evacuations. To understand influencers on sharing willingness in evacuations, we employed a multi-modeling approach across four sharing scenarios using three model types: 1) four binary logit models that capture each scenario separately; 2) a multi-choice latent class choice model (LCCM) that jointly estimates multiple scenarios via latent classes; and 3) a portfolio choice model (PCM) that estimates dimensional dependency. We tested our approach by employing online survey data from 2017 Hurricane Irma evacuees (n=368). The multi-model approach uncovered behavioral nuances undetectable with a single model. First, the multi-choice LCCM and PCM models uncovered scenario correlation, specifically willingness to share for both transportation scenarios and both sheltering scenarios. Second, the multi-choice LCCM found three classes – transportation sharers, adverse sharers, and interested sharers. Transportation sharers were more likely to be female, lower-income, and residents of Southwest Florida compared to adverse sharers. Interested sharers were more likely to be male, long-time residents, and higher-income compared to adverse sharers. Third, families with children were unwilling to share regardless of the model, while spare capacity (i.e., seatbelts, spare beds) had a positive but somewhat insignificant influence on sharing. Fourth, experienced home sharers were more willing to share shelter in the binary logit and PCM models. We suggest that local agencies consider holistic sharing mechanisms across resource types and time (i.e., before, during, and after a hurricane evacuation). |
| Keywords: | Engineering, Joint Choice Modeling, Multi-Choice Latent Class Choice Model, Portfolio Choice Model, Hurricane Evacuations, Sharing Economy, Shared Mobility |
| Date: | 2021–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsrrp:qt70g3c3nk&r=all |
| By: | Nikita Gusarov (GAEL - Laboratoire d'Economie Appliquée de Grenoble - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes); Amirreza Talebijamalabad (Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes); Iragaël Joly (GAEL - Laboratoire d'Economie Appliquée de Grenoble - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes) |
| Abstract: | This work is a cross-disciplinary study of econometrics and machine learning (ML) models applied to consumer choice preference modelling. To bridge the interdisciplinary gap, a simulation and theorytesting framework is proposed. It incorporates all essential steps from hypothetical setting generation to the comparison of various performance metrics. The flexibility of the framework in theory-testing and models comparison over economics and statistical indicators is illustrated based on the work of Michaud, Llerena and Joly (2012). Two datasets are generated using the predefined utility functions simulating the presence of homogeneous and heterogeneous individual preferences for alternatives' attributes. Then, three models issued from econometrics and ML disciplines are estimated and compared. The study demonstrates the proposed methodological approach's efficiency, successfully capturing the differences between the models issued from different fields given the homogeneous or heterogeneous consumer preferences. |
| Keywords: | Discrete choice models,Neural network analysis,Performance comparison,Heterogeneous preferences |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03019739&r=all |
| By: | Aveek Bhattacharya |
| Abstract: | Governments in several countries have sought to increase choice in public services. Proponents claim the value of such choice is both instrumental (it improves outcomes) and intrinsic (choice is valuable in itself). Yet while the instrumental benefits of such measures are strongly contested, the supposed intrinsic value of public service choice is both normatively and empirically underexplored. This paper draws on the philosophical and psychological literature on the costs and benefits of choice to identify why and under what circumstances choice in public services might have intrinsic value (or indeed, disvalue). Through this process, it develop a framework of empirical questions that can be used to analyse the intrinsic (dis)value of particular choice reforms. |
| Keywords: | choice, intrinsic value, quasi-markets |
| JEL: | I0 I31 I38 |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:cep:sticas:/220&r=all |
| By: | Fernando V. Ferreira; Maisy Wong |
| Abstract: | This paper presents a new framework to estimate preferences for neighborhoods in the presence of individual imperfect information about every amenity in each neighborhood. We estimate the model with data from a new neighborhood choice program that provided information about market rents and same-school network, and collected neighborhood rankings for the same individual before and after receiving information. We find that switchers - who change rankings after the information intervention - increase network shares by 1.46 percentage points and decrease rents by $430. This variation from the panel data of individual rankings is critical to produce a latent quality index that addresses biases arising from imperfect information. Estimates from the neighborhood sorting model reveal a strong negative marginal utility of rents, and a positive marginal willingness to pay of $123 per month to live in a neighborhood with a larger network. Finally, information also influenced residential choices after graduation. |
| JEL: | C1 J60 R0 |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:28165&r=all |
| By: | Sponagel, Christian; Back, Hans; Angenendt, Elisabeth; Bahrs, Enno |
| Abstract: | Impacts on nature and landscape in Germany must be compensated for in accordance with the Federal Nature Conservation Act. Farmers can participate by voluntarily applying appropriate measures on their land. We used a geodata-based model to analyse environmental compensation measures on arable land from an economic perspective on the example of the Stuttgart Region, a metropolitan area where construction activities and their compensation are huge, exemplary for many European metropolises. In order to estimate a possible realistic potential, the willingness to accept for compensation measures previously determined in a discrete choice experiment with farmers in the Stuttgart region was integrated into the model. The analysis compares the economic viability of current agricultural use with the income generated from the sale of so called ecopoints by supply curve. The results show wide variation in ecopoint potential in spatial terms. The implementation of compensation measures is not economically reasonable, depending on the legal security provided by a land register entry at a price of less than 1.00 € per ecopoint in the Stuttgart city district. In contrast, measures can be implemented economically and on a large scale in surrounding districts for less than 0.60 €, regardless of legal protection. The optimal type of compensation measure from an economic point of view depends on type and land is also important. The model and its results can provide important information for decision-makers in politics, landscape planning and nature conservation. |
| Keywords: | Agribusiness, Agricultural and Food Policy, Agricultural Finance |
| Date: | 2020–09–25 |
| URL: | http://d.repec.org/n?u=RePEc:ags:haaepa:308132&r=all |
| By: | Sponagel, Christian; Back, Hans; Angenendt, Elisabeth; Bahrs, Enno |
| Abstract: | Impacts on nature and landscape in Germany must be compensated for in accordance with the Federal Nature Conservation Act. Farmers can participate by voluntarily applying appropriate measures on their land. We used a geodata-based model to analyse environmental compensation measures on arable land from an economic perspective on the example of the Stuttgart Region, a metropolitan area where construction activities and their compensation are huge, exemplary for many European metropolises. In order to estimate a possible realistic potential, the willingness to accept for compensation measures previously determined in a discrete choice experiment with farmers in the Stuttgart region was integrated into the model. The analysis compares the economic viability of current agricultural use with the income generated from the sale of so called ecopoints by supply curve. The results show wide variation in ecopoint potential in spatial terms. The implementation of compensation measures is not economically reasonable, depending on the legal security provided by a land register entry at a price of less than 1.00 € per ecopoint in the Stuttgart city district. In contrast, measures can be implemented economically and on a large scale in surrounding districts for less than 0.60 €, regardless of legal protection. The optimal type of compensation measure from an economic point of view depends on type and land is also important. The model and its results can provide important information for decision-makers in politics, landscape planning and nature conservation. |
| Keywords: | Agribusiness, Agricultural and Food Policy, Agricultural Finance |
| Date: | 2020–09–25 |
| URL: | http://d.repec.org/n?u=RePEc:ags:haaewp:308132&r=all |