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on Discrete Choice Models |
| By: | Lewandowski, Piotr; Lipowska, Katarzyna; Smoter, Mateusz |
| Abstract: | We study preferences for remote work using a large-scale discrete choice study with 10, 000 workers and 1, 500 employers in Poland. Workers value remote work more than employers. On average, workers are willing to sacrifice 2.9% of earnings for remote work, with hybrid work from home (WFH) for 2-3 days (5.1%) preferred over 5 days (0.6%). Employers expect a 21.0% wage cut from remote workers. This 18 pp gap between employers' and workers' valuations reflects employers' concerns over productivity loss (14 pp) and effort to manage remote workers (4 pp). Only 25-36% of employers with positive perceptions of remote work productivity show valuations of remote work that align with workers' willingness to pay for it. |
| Keywords: | Working from home, remote work, discrete choice experiment, willingness to pay |
| JEL: | J21 J31 J81 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:rwirep:1026&r=dcm |
| By: | Jens Abildtrup (BETA - Bureau d'Économie Théorique et Appliquée - AgroParisTech - UNISTRA - Université de Strasbourg - Université de Haute-Alsace (UHA) - Université de Haute-Alsace (UHA) Mulhouse - Colmar - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Jette Bredahl Jacobsen (IFRO - Institute of Food and Resource Economics [Copenhagen] - Faculty of Science [Copenhagen] - UCPH - University of Copenhagen = Københavns Universitet); Suzanne Elizabeth Vedel (Tech & Environm Adm, Dept Analyt, Copenhagen); Udo Mantau (INFRO Informationssysteme Rohstoffe, Celle,); Robert Mavsar (EFI - European Forest Institute); Davide Pettenella (TeSAF - Department of Land, Environment, Agriculture and Forestry - Unipd - Università degli Studi di Padova = University of Padua); Irina Prokofieva (EFI - European Forest Institute); Florian Schubert (INTEND Geoinformat GmbH, Kassel); Anne Stenger (BETA - Bureau d'Économie Théorique et Appliquée - AgroParisTech - UNISTRA - Université de Strasbourg - Université de Haute-Alsace (UHA) - Université de Haute-Alsace (UHA) Mulhouse - Colmar - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Elsa Varela (Forest Sci & Technol Ctr Catalonia, Lleida); Enrico Vidale (TeSAF - Department of Land, Environment, Agriculture and Forestry - Unipd - Università degli Studi di Padova = University of Padua); Bo Jellesmark Thorsen (IFRO - Institute of Food and Resource Economics [Copenhagen] - Faculty of Science [Copenhagen] - UCPH - University of Copenhagen = Københavns Universitet) |
| Abstract: | Policies mitigating climate change provide a global public good but are also likely to imply local co-benefits where implemented. This may affect citizens' preferences for what policy to implement as well as where to implement it. This aspect remains understudied despite its relevance for international climate negotiations, national policies, and the development of voluntary carbon credit markets. The results of a discrete choice experiment show that citizens in five countries (Denmark, France, Germany, Italy and Spain) have quite similar mean willingness to pay for carbon emission reductions and agree on the ranking of policies targeting different sectors. Specifically, policies targeting renewable energy use, are preferred over policies targeting industrial energy efficiency or carbon sequestration and biomass production in forests. Applying follow-up questions shows that concerns over co-benefits, notably air pollution, is linked to preferences for implementation in the home country. In the absence of co-benefits, citizens are indifferent or prefer policies implemented in other countries. |
| Keywords: | Carbon emissions, Cobenefits, Willingness to pay, Choice experiment, Crosscountry study, Policy acceptability |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04132398&r=dcm |
| By: | Yuki Oyama |
| Abstract: | This study performs an attribute-level analysis of the global and local path preferences of network travelers. To this end, a reward decomposition approach is proposed and integrated into a link-based recursive (Markovian) path choice model. The approach decomposes the instantaneous reward function associated with each state-action pair into the global utility, a function of attributes globally perceived from anywhere in the network, and the local utility, a function of attributes that are only locally perceived from the current state. Only the global utility then enters the value function of each state, representing the future expected utility toward the destination. This global-local path choice model with decomposed reward functions allows us to analyze to what extent and which attributes affect the global and local path choices of agents. Moreover, unlike most adaptive path choice models, the proposed model can be estimated based on revealed path observations (without the information of plans) and as efficiently as deterministic recursive path choice models. The model was applied to the real pedestrian path choice observations in an urban street network where the green view index was extracted as a visual street quality from Google Street View images. The result revealed that pedestrians locally perceive and react to the visual street quality, rather than they have the pre-trip global perception on it. Furthermore, the simulation results using the estimated models suggested the importance of location selection of interventions when policy-related attributes are only locally perceived by travelers. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2307.08646&r=dcm |
| By: | Amandeep Singh; Ye Liu; Hema Yoganarasimhan |
| Abstract: | Choice Modeling is at the core of many economics, operations, and marketing problems. In this paper, we propose a fundamental characterization of choice functions that encompasses a wide variety of extant choice models. We demonstrate how nonparametric estimators like neural nets can easily approximate such functionals and overcome the curse of dimensionality that is inherent in the non-parametric estimation of choice functions. We demonstrate through extensive simulations that our proposed functionals can flexibly capture underlying consumer behavior in a completely data-driven fashion and outperform traditional parametric models. As demand settings often exhibit endogenous features, we extend our framework to incorporate estimation under endogenous features. Further, we also describe a formal inference procedure to construct valid confidence intervals on objects of interest like price elasticity. Finally, to assess the practical applicability of our estimator, we utilize a real-world dataset from S. Berry, Levinsohn, and Pakes (1995). Our empirical analysis confirms that the estimator generates realistic and comparable own- and cross-price elasticities that are consistent with the observations reported in the existing literature. |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2307.07090&r=dcm |