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on Discrete Choice Models |
| By: | Haoge Chang; Yusuke Narita; Kota Saito |
| Abstract: | We obtain a necessary and sufficient condition under which parametric random-coefficient discrete choice models can approximate the choice behavior generated by nonparametric random utility models. The condition turns out to be very simple and tractable. For the case under which the condition is not satisfied (and hence, where some stochastic choice data are generated by a random utility model that cannot be approximated), we provide algorithms to measure the approximation errors. After applying our theoretical results and the algorithm to real data, we found that the approximation errors can be large in practice. |
| Date: | 2022–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2205.01882&r= |
| By: | John K. Dagsvik (Statistics Norway) |
| Abstract: | Consumers often face choice settings in which alternatives are discrete. Examples include choices between variants of differentiated products, modes of urban transportation, residential locations, etc. In this paper compensated price elasticities and a corresponding(aggregate) Slutsky equation for discrete choice models are derived. A remarkable feature of compensated price elasticities in the discrete case is that they usually are not symmetric, as compensated elasticities with respect to a price increase versus a price decrease may be different. Finally, compensated marginal price effects and elasticities are derived for selected examples. |
| Keywords: | Compensated choice; Discrete/continuous choice; Slutsky equation; Marginal compensated effects |
| JEL: | C25 C43 D11 |
| Date: | 2022–05 |
| URL: | http://d.repec.org/n?u=RePEc:ssb:dispap:978&r= |
| By: | Inderst, Roman; Thomas, Stefan |
| Abstract: | The failure to fully internalize externalities from production and consumption, including on future generations, is supposed to be at the core of the perceived failure to ensure (ecological) sustainability within the realm of antitrust enforcement. As policymakers put increasing pressure on competition agencies to account for sustainability in their enforcement practice, it becomes pivotal to define whether and, if so, how such externalities can be incorporated into competition analysis. Rather than positing that sustainability should constitute a goal in itself, we explore how sustainability can be incorporated within a consumer welfare analysis. Our paper makes a key distinction between what we term an individualistic and a collective consumer welfare analysis. Within an individualistic consumer welfare analysis, consumers’ willingness-to-pay is measured ceteris paribus, holding other consumers’ choices fixed. We explore how, e.g., through contingent valuation and conjoint analysis, consumers’ appreciation of sustainability benefits and with it the reduction of externalities on others can be elicited. Specifically, we discuss how the context-sensitivity of extracted willingness-to-pay provides both challenges and opportunities for antitrust enforcement in the context of sustainability measures. In a collective consumer welfare analysis, consumers may express their willingness-to-pay also for the choices of others and, thereby, also for the reduction of externalities on themselves. Borrowing from environmental and resource economics, we also discuss more indirect ways of incorporating such externalities. And we critically assess the possibility of “laundering” consumers’ sustainability preferences in the light of supposed biases and cognitive limitations. |
| Keywords: | sustainability,externalities,willingness-to-pay |
| JEL: | A13 K21 K32 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:253668&r= |
| By: | Nikhil Agarwal; Paulo J. Somaini |
| Abstract: | Consumer choices are constrained in many markets due to either supply-side rationing or information frictions. Examples include matching markets for schools and colleges; entry-level labor markets; limited brand awareness and inattention in consumer markets; and selective admissions to healthcare services. Accounting for these choice constraints is essential for estimating consumer demand. We use a general random utility model for consumer preferences that allows for endogenous characteristics and a reduced-form choice-set formation rule that can be derived from models of the examples described above. The choice-sets can be arbitrarily correlated with preferences. We study non-parametric identification of this model, propose an estimator, and apply these methods to study admissions in the market for kidney dialysis in California. Our results establish identification of the model using two sets of instruments, one that only affects consumer preferences and the other that only affects choice sets. Moreover, these instruments are necessary for identification. We find that dialysis facilities are less likely to admit new patients when they have higher than normal caseload and that patients are more likely to travel further when nearby facilities have high caseloads. Finally, we estimate consumers' preferences and facilities' rationing rules using a Gibbs sampler. |
| JEL: | C50 I11 L0 |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:29993&r= |
| By: | Fukushige, Tatsuya; Fitch, Dillon T.; Mohiuddin, Hossain; Andersen, Hayden; Jenn, Alan |
| Abstract: | Micromobility services (e.g., conventional and electric bikeshare programs and electric scootershare programs) hold great potential for reducing vehicle miles traveled and greenhouse gas emissions if these services are used as substitutes for car travel and/or to access public transit. But estimating these environmental effects is challenging, as it requires measuring changes in human behavior—that is, the choice of what transportation mode to use. While many cities collect various micromobility usage metrics to regulate services, these metrics are not sufficient for calculating the sustainability benefits of these services. |
| Keywords: | Engineering |
| Date: | 2022–05–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt72v218gn&r= |
| By: | Kirby Nielsen; Luca Rigotti |
| Abstract: | We design an experiment to detect incomplete preferences in a domain of monetary gambles with subjective uncertainty. To do this, we use subjects' choices to estimate their preferences, and pay them based on their estimated preferences rather than the choices they make. We find 39\% of subjects express incompleteness. We provide evidence on the extent of incompleteness and the nature of gambles that are incomparable, and demonstrate how incompleteness is distinct from indifference. Incompleteness remains at approximately the same levels for individuals with certain beliefs and in an environment with objective uncertainty, suggesting that most incompleteness can be attributed to imprecise tastes rather than imprecise beliefs. We compare these choices to an environment where we force individuals to decide, as in standard experiments. Forced choice leads to more inconsistencies in preferences. |
| Date: | 2022–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2205.08584&r= |
| By: | Edith Elkind; Martin Lackner; Dominik Peters |
| Abstract: | Social choice becomes easier on restricted preference domains such as single-peaked, single-crossing, and Euclidean preferences. Many impossibility theorems disappear, the structure makes it easier to reason about preferences, and computational problems can be solved more efficiently. In this survey, we give a thorough overview of many classic and modern restricted preference domains and explore their properties and applications. We do this from the viewpoint of computational social choice, letting computational problems drive our interest, but we include a comprehensive discussion of the economics and social choice literatures as well. Particular focus areas of our survey include algorithms for recognizing whether preferences belong to a particular preference domain, and algorithms for winner determination of voting rules that are hard to compute if preferences are unrestricted. |
| Date: | 2022–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2205.09092&r= |
| By: | Zhan Gao; M. Hashem Pesaran |
| Abstract: | This paper proposes a linear categorical random coefficient model, in which the random coefficients follow parametric categorical distributions. The distributional parameters are identified based on a linear recurrence structure of moments of the random coefficients. A Generalized Method of Moments estimator is proposed, and its finite sample properties are examined using Monte Carlo simulations. The utility of the proposed method is illustrated by estimating the distribution of returns to education in the U.S. by gender and educational levels. We find that rising heterogeneity between educational groups is mainly due to the increasing returns to education for those with postsecondary education, whereas within group heterogeneity has been rising mostly in the case of individuals with high school or less education. |
| Keywords: | random coefficient models, categorical distribution, return to education |
| JEL: | C01 C21 C13 C46 J30 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_9714&r= |
| By: | Toru Kitagawa; Sokbae Lee; Chen Qiu |
| Abstract: | The literature on treatment choice focuses on the mean of welfare regret. Ignoring other features of the regret distribution, however, can lead to an undesirable rule that suffers from a high chance of welfare loss due to sampling uncertainty. We propose to minimize the mean of a nonlinear transformation of welfare regret. This paradigm shift alters optimal rules drastically. We show that for a wide class of nonlinear criteria, admissible rules are fractional. Focusing on mean square regret, we derive the closed-form probabilities of randomization for finite-sample Bayes and minimax optimal rules when data are normal with known variance. The minimax optimal rule is a simple logit based on the sample mean and agrees with the posterior probability for positive treatment effect under the least favorable prior. The Bayes optimal rule with an uninformative prior is different but produces quantitatively comparable mean square regret. We extend these results to limit experiments and discuss our findings through sample size calculations. |
| Date: | 2022–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2205.08586&r= |
| By: | Kurani, Ken; Sanguinetti, Angela |
| Abstract: | Electrifying ridehailing services provided by transportation network companies (TNCs) can reduce climate-altering emissions and air pollution and provide cost savings on fuel and maintenance to TNC drivers. Policy levers have emerged to nudge the industry in this direction. California’s Senate Bill 1014 establishes a “clean miles standard” requiring an increasing percentage of ride-hailing services be provided by zero-emissions vehicles such as plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs)—together referred to as plug-in vehicles (PEVs). This can be achieved by increasing the number of TNC drivers using BEVs and PHEVs, and by increasing the electric miles PHEV drivers travel. |
| Keywords: | Engineering |
| Date: | 2022–05–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt6bv833zm&r= |
| By: | Kurani, Ken; Sanguinetti, Angela |
| Abstract: | Electrifying ridehailing services provided by transportation network companies (TNCs) can reduce greenhouse gas emissions and air pollution while providing fuel and maintenance cost savings to TNC drivers. Policy levers have emerged to nudge the industry in this direction. California’s Senate Bill 1014 establishes a “clean miles standard” requiring an increasing percentage of ride-hailing services be provided by zero-emissions vehicles, such as plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs)—together referred to as plug-in vehicles (PEVs). In spring 2019, researchers at UC Davis surveyed 732 TNC drivers in the US who already use a PEV, to understand their use and charging of their PEV. This is the second in a series of briefs highlighting the results of the survey. There is limited understanding of how drivers’ charging practices affect the potential benefits of electrifying TNCs. This research identifies segments of TNC-PEV drivers based on their vehicle charging practices (i.e., location, level, and time of day) to inform infrastructure planning. |
| Keywords: | Engineering |
| Date: | 2022–05–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt1pz2w3pp&r= |
| By: | Sanguinetti, Angela; Kurani, Ken |
| Abstract: | Electrifying ridehailing services provided by transportation network companies (TNCs) such as Uber and Lyft can reduce greenhouse gas emissions and air pollution and provide cost savings on fuel and maintenance to TNC drivers. Policy levers have emerged to nudge the industry in this direction. California’s Senate Bill 1014 establishes a “clean miles standard” requiring that an increasing percentage of ridehailing services be provided by zero-emissions vehicles such as plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs)—together referred to as plug-in electric vehicles (PEVs). Because TNC drivers operate their personal vehicles, government and industry must accelerate PEV adoption among TNC drivers to achieve this goal. |
| Keywords: | Engineering |
| Date: | 2022–05–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt1x85q7tj&r= |