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on Discrete Choice Models |
| By: | Laurent Ott; Mehdi Farsi; Sylvain weber |
| Abstract: | This paper investigates public opinion on the Swiss CO2 levy and its 2020 revision by using a discrete choice experiment answered by a sample of 586 respondents living in Switzerland. The experiment is designed to elicit citizen preferences among various taxation attributes and is followed by a referendum voting experiment on various CO2 levy proposals. Based on latent class modeling approaches, we find that the population is composed by two distinct but relatively preference profiles: Environmentalists and Neutrals. Respondents belonging to the first group tend to favor higher carbon tax rates and a redistribution of proceeds benefiting low-income individuals, whereas those in the second group prefer lower rates and a uniform redistribution of proceeds across all taxpayers. Findings from the voting experiment point to a general support among the Environmentalists, but an uncertain approval from the Neutral group. |
| Keywords: | Carbon tax, preference heterogeneity, public opinion, latent class, discrete choice experiment. |
| JEL: | C25 D72 D78 H23 Q48 Q54 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:irn:wpaper:20-11&r=all |
| By: | Julia Bronnmann (Department of Sociology, Environmental and Business Economics, University of Southern Denmark); Veronika Liebelt (German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, 04103 Leipzig, Germany and Leipzig University Department of Economics, Grimmaische Str. 12, 04109 Leipzig, Germany); Fabian Marder (German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, 04103 Leipzig, Germany and Leipzig University Department of Economics, Grimmaische Str. 12, 04109 Leipzig, Germany); Jasper Meya (German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, 04103 Leipzig, Germany and Leipzig University Department of Economics, Grimmaische Str. 12, 04109 Leipzig, Germany); Martin Quaas (German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, 04103 Leipzig, Germany and Leipzig University Department of Economics, Grimmaische Str. 12, 04109 Leipzig, Germany) |
| Abstract: | The wide range of benefits for humans and biodiversity conservation provided by urban green spaces (UGS) are receiving substantial attention in relation to urban planning and management. However, little is known about to which extent people value the naturalness and biodiversity of urban green spaces. We study how citizens value the naturalness of and the walking distance to their closest UGS in 22 major German cities. For this purpose, we develop a unique measurement scale for the naturalness of UGS, which is embedded in an online survey and in a discrete choice experiment. Results of Mixed Logit estimates and willingness to pay values indicate clear preferences regarding the naturalness of urban green space. For our national representative sample, we elicit a mean marginal WTP for the naturalness of UGS of € 2.31 per month with a standard error of € 0.12. Moreover, the results show that WTP varies between cities. These figures underline the importance of biodiversity in urban areas and can inform urban planning. Acknowledgements: We gratefully acknowledge the support of iDiv funded by the German Research Foundation (DFG– FZT 118, 202548816). |
| Keywords: | Biodiversity, discrete choice experiments, non-market valuation, urban green space, willingness-to-pay |
| JEL: | C81 H41 Q51 Q57 R21 R58 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:sdk:wpaper:sebe5&r=all |
| By: | Gusarov, N.; Talebijmalabad, A.; Joly, I. |
| 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;PERFORMANCE COMPARISON;HETEREGENEOUS PREFERENCES |
| JEL: | C25 C45 C52 C80 C90 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:gbl:wpaper:2020-12&r=all |
| By: | Anders Dugstad; Kristine Grimsrud (Statistics Norway); Gorm Kipperberg; Henrik Lindhjem; Ståle Navrud |
| Abstract: | Sensitivity to scope in nonmarket valuation refers to the property that people are willing to pay more for a higher quality or quantity of a nonmarket public good. Establishing significant scope sensitivity has been an important check of validity and a point of contention for decades in stated preference (SP) research, primarily on contingent valuation. Recently, researchers have begun to differentiate between statistical and economic significance. This paper contributes to this line of research by studying the significance of scope effects in discrete choice experiments (DCE) using the scope elasticity of willingness-to-pay (WTP) concept. We first formalize the scope elasticity concept in a DCE context and relate it to economic significance. Next, we review a selection of DCE studies from different fields and derive their implied scope elasticity estimates. We observe that scope tests as validity checks are uncommon in the DCE literature. Most studies assume unitary elastic scope sensitivities by employing linear functional forms, and when more flexible specifications are employed, the tendency is towards inelastic scope sensitivity. Then, we apply the scope elasticity concept to primary DCE data on people’s preference for expanding the production of renewable energy in Norway. We find that all scope elasticity estimates are statistically significant and vary between 0.18 and 0.46, depending on attribute analyzed, model specification, geographic subsample, and unit of measurement chosen for a key attribute. While there is no strict, universally applicable benchmark for determining the economic significance of scope impacts, we deem these estimates to be of an adequate and plausible order of magnitude. Implications of the results for future DCE research are provided. |
| Keywords: | Discrete choice experiments; scope sensitivity; willingness-to-pay; scope elasticity; economic significance |
| JEL: | D01 Q51 Q57 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:ssb:dispap:942&r=all |
| By: | Chakraborty, Debapriya; Bunch, David S.; Lee, Jae Hyun; Tal, Gil |
| Abstract: | Government agencies, utilities, automakers, and charging network companies are increasingly investing in charging infrastructure to encourage the adoption of plug-in electric vehicles (PEVs), which include both battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). Public infrastructure is particularly important for those without access to home charging and for vehicles with driving range limitations. However, it is difficult to quantify the optimal number and location of public chargers needed for a growing number of PEVs. Finding the answer will depend on a mix of behavioral and economic factors that drive charging demand. Much is at stake. Too little infrastructure could cause congestion at the chargers and inhibit the adoption and use of PEVs, while developing more infrastructure than is needed would create unnecessary costs. For example, Level 2 public chargers can cost up to 15 times more than Level 2 at-home chargers. Researchers at UC Davis analyzed the choice of charging infrastructure of more than 3,000 PEV commuters who had access to home, work, and public locations to understand the importance of various factors driving demand for charging infrastructure at the three locations. Key factors include the cost of charging, driver characteristics, accessibility of charging infrastructure, and vehicle characteristics. |
| Keywords: | Engineering |
| Date: | 2020–11–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt7xq3s2br&r=all |
| By: | David T. Frazier; Eric Renault; Lina Zhang; Xueyan Zhao |
| Abstract: | We study the impact of weak identification in discrete choice models, and provide insights into the determinants of identification strength in these models. Using these insights, we propose a novel test that can consistently detect weak identification in many commonly applied discrete choice models, such as probit, logit, and many of their extensions. Furthermore, we demonstrate that if the null hypothesis that identification is weak can be rejected, Wald-based inference can be carried out using standard formulas and critical values. A Monte Carlo analysis compares our proposed testing approach against commonly applied weak identification tests. The results simultaneously demonstrate the good performance of our approach and the fundamental failure of conventionally applied, i.e., linear, weak identification tests in this context. We compare our testing approach to those commonly applied in the literature within two empirical examples: married women labor force participation, and US food aid and civil conflicts. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.06753&r=all |
| By: | Kanika Rana (ICSSR Research Fellow, Madras School of Economics, Chennai); Brinda Viswanathan (Professor, Madras School of Economics) |
| Abstract: | In developing countries, the economically disempowered, borrow from multiple sources and also have multiple borrowings notwithstanding that some may be unable to access any form of credit. To ensure a greater amount of financial inclusion, it becomes necessary to understand what determines the choice between alternative loan source combinations while taking into account that borrowers may have distinct characteristics from non-borrowers. Access to formal credit sources, are elusive for the disadvantaged due to different demand and supply side perceptions. Microfinance institutions (MFI) play an intermediate role having some attributes of the informal network and some similar to formal institutions. This study uses an observational data set for 2011-12 to analyse the role of socio-economic-demographic characteristics in the household‟s choice for different types of loan sources. In particular, the extensive nature of data allows us to study the mediating role played by MFI through its linkages with formal and informal sources. The results of Multinomial Probit with Heckman selection, to account for onborrowing households, reveal that where institutional sources are still a preferred option for the relatively advantaged section of the population, presence of microfinance loans in combination with other loan sources has contributed in ensuring greater equity in credit access to all. However, women headed households or dalit households with lesser opportunities of networking are less likely to take credit from formal sources |
| Keywords: | Household credit and sources, formal and informal institution, microfinance institutions, multinomial probit, Heckman selection |
| JEL: | C35 E51 G21 |
| URL: | http://d.repec.org/n?u=RePEc:mad:wpaper:2019-181&r=all |