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on Discrete Choice Models |
| By: | Ryan Alyamani; Dimitris Pappelis; Maria Kamargianni (King Abdullah Petroleum Studies and Research Center) |
| Abstract: | This study aims to contribute to the literature by shedding light on consumers’ acceptance of electric vehicles (EVs) in Riyadh and their potential response to adoption incentives. A stated preference experiment (SPE) was developed and then incorporated into an online stated preference survey targeting adult residents of Riyadh to collect 703 responses. Accordingly, a mixed logit model was constructed, complemented by other survey insights to derive the final findings of this paper. |
| Keywords: | Agent based modeling, Electric Vehicles, Autometrics |
| Date: | 2023–10–23 |
| URL: | http://d.repec.org/n?u=RePEc:prc:dpaper:ks--2023-dp20&r=dcm |
| By: | Laetitia Tuffery; Soukaina Anougmar; Basak Bayramoglu; Carmen Cantuarias; Maia David |
| Abstract: | Cities concentrate almost 60% of the world's population. Worldwide, urban populations are highly vulnerable to climate change. Urban green spaces and related ecosystem services help increase inhabitants’ quality of life and well-being and mitigate the impacts of climate change. However, in terms of urban planning, green spaces can raise a dilemma by reducing the space available for vehicle traffic and parking. In this paper, we focus on green spaces around the tram network in the Lyon metropolitan area, France, to assess the social demand for the greening of the urban transport infrastructure, using a Discrete Choice Experiment (DCE). The survey was conducted in 2022 with 500 inhabitants. Our results show that respondents are in favor of urban greening due to its capacity to reduce air temperature and increase biodiversity. However, they are, on average, against a high reduction of the available space for traffic and parking, because of urban greening development. Outcomes also demonstrate a high heterogeneity in inhabitants’ preferences partly driven by their sensitivity and commitment to the environment. |
| Keywords: | Choice experiment; Transport infrastructure; urban greening; Urban traffic |
| JEL: | R3 |
| Date: | 2023–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2023_67&r=dcm |
| By: | Oguzhan Celebi |
| Abstract: | I study the relationship between diversity preferences and the choice rules implemented by institutions, with a particular focus on the affirmative action policies. I characterize the choice rules that can be rationalized by diversity preferences and demonstrate that the recently rescinded affirmative action mechanism used to allocate government positions in India cannot be rationalized. I show that if institutions evaluate diversity without considering intersectionality of identities, their choices cannot satisfy the crucial substitutes condition. I characterize choice rules that satisfy the substitutes condition and are rationalizable by preferences that are separable in diversity and match quality domains. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.14442&r=dcm |
| By: | Jorge A. Rivero |
| Abstract: | This paper extends the linear grouped fixed effects (GFE) panel model to allow for heteroskedasticity from a discrete latent group variable. Key features of GFE are preserved, such as individuals belonging to one of a finite number of groups and group membership is unrestricted and estimated. Ignoring group heteroskedasticity may lead to poor classification, which is detrimental to finite sample bias and standard errors of estimators. I introduce the "weighted grouped fixed effects" (WGFE) estimator that minimizes a weighted average of group sum of squared residuals. I establish $\sqrt{NT}$-consistency and normality under a concept of group separation based on second moments. A test of group homoskedasticity is discussed. A fast computation procedure is provided. Simulations show that WGFE outperforms alternatives that exclude second moment information. I demonstrate this approach by considering the link between income and democracy and the effect of unionization on earnings. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.14068&r=dcm |
| By: | Anett Wins; Marcelo Del Cajias |
| Abstract: | Modern location analysis evaluates location attractiveness almost in real time, combining the knowledge of local real estate experts and artificial intelligence. In this paper we develop an algorithm – The Amenities Magnet algorithm – that measures and benchmarks the attractiveness of locations based on the urban amenities’ footprint of the surrounding area, grouped according to relevance for residential purposes and taking distance information from Google and OpenStreetMap into account. As cities are continuously evolving, benchmarking locations’ amenity-wise change of attractiveness over time helps to detect upswing areas and thus supports investment decisions. According to the 15-minute city concept, the welfare of residents is proportional to the amenities accessible within a short walk or bike ride. Measuring individual scorings for the seven basic living needs results in a more detailed, disaggregated location assessment. Based on these insights, an advanced machine learning (ML) algorithm under the Gradient Boosting framework (XGBoost) is adapted to model residential rental prices for the region Greater Manchester, United Kingdom, and achieves an improved predictive power. To extract interpretable results and quantify the contribution of certain amenities to rental prices eXplainable Artificial Intelligence (XAI) methods are used. Tenants' willingness to pay (WTP) for accessibility to amenities varies by type. In Manchester tram stops, bars, schools and the proximity to the city center in particular emerged as relevant value drivers. Even if the results of the case study are not generally applicable, the methodology can be transferred to any market in order to reveal regional patterns. |
| Keywords: | Amenities Magnet algorithm; location analysis; residential rental pricing; XGBoost |
| JEL: | R3 |
| Date: | 2023–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2023_102&r=dcm |