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on Discrete Choice Models |
| By: | Schouwer, Thimo De; Gsottbauer, Elisabeth; Kesternich, Iris; Schumacher, Heiner |
| Abstract: | Work meaning can be an important driver of labor supply. Since, by definition, work meaning is associated with benefits for others, it also has an important fairness dimension. In a theoretical model, we show that workers’ willingness to pay for work meaning can be positive or negative, depending on the relative strength of fairness concerns and meaning preferences. To examine the importance of these behavioral motives for labor supply, we conduct a survey experiment with representative samples from The Netherlands and Germany in which we vary within-subject the benefits that a job creates for others. We find that only a minority of workers are actually willing to sacrifice wage for work meaning. The average willingness to pay for work meaning is positive, but substantially lower than the willingness to pay for job flexibility. There is a strong negative relationship between fairness concerns and willingness to pay for work meaning. Thus, individuals who prioritize fairness are less likely to accept lower wages for meaningful work. |
| Keywords: | work meaning; labor supply; fairness preferences |
| JEL: | C90 M52 |
| Date: | 2025–12–31 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:129675 |
| By: | Nathalie Picard; André de Palma |
| Abstract: | This chapter explores residential location models through a comprehensive review of the literature, key facts, theoretical frameworks, estimation methods, and simulation techniques. It focuses on the factors driving residential segregation using a standard individual discrete choice model, specifically a nested logit framework. This model incorporates household preferences for local amenities, dwelling types, and homeownership. The analysis is extended by introducing borrowing constraints that restrict some households' ability to purchase property. To illustrate, the framework is applied to the Paris region. By relaxing borrowing constraints, we simulate a hypothetical redistribution of socio-demographic characteristics across the region and demonstrate how this tool can be employed for policy analysis. A comparison of actual and simulated distributions reveals that easing credit constraints encourages households to relocate farther from the city center. However, if only poor households benefit, they are less likely to integrate with wealthier households, thereby intensifying segregation. This finding highlights those policies designed to support low-income households might inadvertently increase segregation citywide, necessitating careful re-evaluation. |
| Keywords: | Housing choice, financial constraints, borrowing, segregation, suburban areas, urban sprawl, endogenous choice sets. |
| JEL: | R21 R23 R31 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ulp:sbbeta:2025-33 |
| By: | Kevin He (University of Pennsylvania); Ran Shorrer (Pennsylvania State University); Mengjia Xia (University of Pennsylvania) |
| Abstract: | We conduct an incentivized laboratory experiment to study people’s perception of generative artificial intelligence (GenAI) alignment in the context of economic decisionmaking. Using a panel of economic problems spanning the domains of risk, time preference, social preference, and strategic interactions, we ask human subjects to make choices for themselves and to predict the choices made by GenAI on behalf of a human user. We find that people overestimate the degree of alignment between GenAI’s choices and human choices. In every problem, human subjects’ average prediction about GenAI’s choice is substantially closer to the average human-subject choice than it is to the GenAI choice. At the individual level, different subjects’ predictions about GenAI’s choice in a given problem are highly correlated with their own choices in the same problem. We explore the implications of people overestimating GenAI alignment in a simple theoretical model. |
| Date: | 2025–04–06 |
| URL: | https://d.repec.org/n?u=RePEc:pen:papers:25-019 |
| By: | Enrico Moretti; Harrison Wheeler |
| Abstract: | More than 42 million Americans are exposed to medium or high levels of traffic noise. Despite its potentially large toll and unequal distribution, the economic costs, incidence, and policy implications of traffic noise have received limited attention in economics. We quantify the aggregate economic burden of this externality and its distribution across demographic groups by estimating homebuyers' willingness to pay for quieter environments. Using quasi-experimental variation from the construction of noise barriers, we find that reduced traffic noise exposure leads to significant increases in house prices, implying that buyers are willing to pay a substantial premium for each decibel of noise reduction. In the five years before construction, we detect no differential pre-trends in prices between treated and control properties. Following construction, we observe an immediate and largely permanent 6.8% increase in prices within 100 meters, with smaller gains at greater distances. Information on each barrier's noise attenuation allows us to recover the willingness to pay per decibel of traffic noise. We calculate the aggregate economic cost of traffic noise at $110 billion nationwide. The economic burden is disproportionately borne by lower income and minority households, suggesting that the externality is regressive. The cost varies widely across cities, reflecting differences in noise levels, property values and population density. Based on our estimates, the socially efficient Pigouvian tax amounts to $974 per vehicle. A broad shift to electric vehicles -- which are quieter than traditional vehicles -- could yield noise reduction benefits of $77.3 billion, concentrated among low-income families in dense urban areas. |
| JEL: | H0 I14 R0 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34298 |
| By: | Tommaso Bondi; Daniel Csaba; Evan Friedman; Salvatore Nunnari |
| Abstract: | Several behavioral models assume that choice over multi-attribute goods is systematically affected by the ranges of attribute values. Two recurring principles in this literature are contrast, whereby attributes with larger ranges attract attention and are therefore overweighted, and normalization, whereby attributes with larger ranges are underweighted as fixed differences appear smaller against a larger range. These principles lead to divergent predictions, and yet, both contrast-based and normalization-based models have found strong empirical support, albeit in different contexts and with different experimental designs. The question remains: when does one effect emerge over the other? We experimentally test a unifying explanation: normalization dominates in simple choices, while contrast dominates in complex choices. We conduct an experiment with real-effort tasks in which we manipulate attribute ranges in both simple and complex choices. We find that, indeed, contrast dominates as the number of attributes increases. We also find that contrast emerges with cognitive load induced by time pressure. |
| Keywords: | multi-attribute choice, range effects, focusing, relative thinking, salience, bottom-up attention, context dependence, complexity, experiment |
| JEL: | C91 D91 D12 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12175 |
| By: | Dimitris Georgarakos; Geoff Kenny; Justus Meyer; Maarten van Rooij |
| Abstract: | Global temperatures are rising at an alarming pace and public awareness of climate change is increasing, yet little is known about how these developments affect consumer expectations. We address this gap by conducting a series of experiments within a large-scale, population-representative survey of euro area consumers. We randomly assign consumers to hypothetical global temperature change scenarios, after which we elicit their expectations for inflation and key macroeconomic indicators under these conditions. We find that a 0.5°C rise in global temperatures leads to a 0.65 percentage point increase in five-year-ahead inflation expectations, with effects particularly pronounced among consumers with greater awareness of climate change. Additionally, respondents expect adverse impacts of global warming on economic growth, employment, public debt, tax burdens, and their well-being. Despite these pessimistic expectations, many consumers demonstrate limited willingness to pay for mitigating further temperature increases. Instead, they place primary responsibility for climate action on governments. Our findings underscore the interplay between climate change and economic expectations, highlighting the potential implications for monetary and fiscal policy in a warming world. |
| Keywords: | Climate change; Global Warming; Consumer expectations; Randomized Control Trial (RCT); Consumer Expectations Survey (CES) |
| JEL: | D12 E31 E52 H31 Q54 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:dnb:dnbwpp:843 |
| By: | Karlsson, Niklas (Insper - Instituto de Ensino e Pesquisa); Lunander, Anders (Örebro University School of Business) |
| Abstract: | In most multiple-choice tests using confidence based marking (CBM), a discrete certainty scale is applied, often with three or four probability intervals of equal length. In this paper we derive a continuous certainty scale for CBM which we think circumvents the alleged complexity that would be inherent in a continuous scale. In our approach, the examinee, given a correct answer, is awarded the same number of points as her reported degree of confidence that her chosen alternative is the correct answer, i.e., the examinee’s uncertainty is directly reflected in terms of the number of points achieved if the answer is correct. We test our continuous scoring scheme in an examination in basic statistics at our university. The results indicate that most students are quite good at assessing their confidence levels, but students tend on average to overrate their confidence for high levels of stated confidence and underrate their confidence for low levels of stated confidence. |
| Keywords: | Multiple choice questions; confidence-based marking; scoring function |
| JEL: | A22 C12 |
| Date: | 2025–10–01 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:oruesi:2025_011 |
| By: | Müge Süer (IWH Halle); Michel Tolksdorf (TU Berlin); Vincent Meisner (HU Berlin); Sokol Tominaj (TU Berlin) |
| Abstract: | Contrary to classical theory, we provide experimental evidence that preference reports in a strategy-proof school-choice mechanism systematically depend on beliefs. We employ a "hard-easy gap" to exogenously vary students' beliefs about their priority rank. As predicted, underconfidence induces more manipulation and thus more justified envy than overconfidence. The effect of priority information on justified envy crucially depends on the initial beliefs and the real priority ranks: while top students always gain, non-top students lose from this information. In total, correcting overconfidence/underconfidence increases/decreases justified envy. Finally, we confirm that additionally providing information on school availability through a dynamic implementation of the mechanism reduces justified envy compared to priority information alone. |
| Keywords: | market design; school choice; overconfidence; strategy-proofness; information; |
| JEL: | C92 D47 |
| Date: | 2025–09–25 |
| URL: | https://d.repec.org/n?u=RePEc:rco:dpaper:546 |
| By: | Monica Bonacina (Fondazione Eni Enrico Mattei, Università degli Studi di Milano); Mert Demir (Fondazione Eni Enrico Mattei); Antonio Sileo (Fondazione Eni Enrico Mattei, GREEN – Università Bocconi); Angela Zanoni (Fondazione Eni Enrico Mattei, Università di Roma La Sapienza, Research Institute for Sustainable Economic Growth – National Research Council) |
| Abstract: | The transition to a zero-emission vehicle fleet represents a pivotal element of Europe’s decarbonization strategy, with Italy’s participation being particularly significant given the size of its automotive market. This study investigates the potential for battery electric cars (BEVs) to drive decarbonization of Italy’s passenger vehicle fleet, focusing on the feasibility of targets set in the National Integrated Plan for Energy and Climate (PNIEC). Leveraging artificial neural networks, we integrate macroeconomic indicators, market-specific variables, and policy instruments to predict fleet dynamics and identify key factors influencing BEV adoption. We forecast that while BEV registrations will continue growing through 2030, the growth rate is projected to decelerate, presenting challenges for meeting ambitious policy targets. Our feature importance analysis demonstrates that BEV adoption is driven by an interconnected set of economic, infrastructural, and behavioral factors. Specifically, our model highlights that hybrid vehicle registrations and the vehicle purchase index exert the strongest influence on BEV registrations, suggesting that policy interventions should prioritize these areas to maximize impact. By offering data-driven insights and methodological innovations, our findings contribute to more effective policy design for accelerating sustainable mobility adoption while accounting for market realities and consumer behavior. |
| Keywords: | sustainable mobility, electric vehicle, neural networks, shap interpretation |
| JEL: | N74 Q55 Q58 R40 C45 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:fem:femwpa:2025.16 |