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on Discrete Choice Models |
| By: | Maria A. Cattaneo; Christian Gschwendt; Stefan C. Wolter |
| Abstract: | Advances in technology have always reshaped labor markets, increasing demand for highly skilled workers and automating human labor in many areas, leading to job creation but also losses. However, emerging AI innovations like ChatGPT may reduce labor demand in occupations previously considered "safe" from automation. While initial studies suggest that individuals adjust their educational and career choices to mitigate automation risk, the subjective monetary value of reduced automation risk is unknown. This study quantifies this value by assessing individuals' preferences for occupations for a hypothetical child in a discrete-choice experiment with almost 6'000 participants. The results show that survey respondents' willingness to accept lower wages for an occupation with a lower exposure to automation of 10 percentage points is substantial and amounts to almost 20 percent of an annual gross wage. Although the preferences are quite homogeneous, there are still some significant differences in willingness to pay between groups, with men, younger people, those with higher levels of education, and those with a higher risk tolerance showing a lower willingness to pay. |
| Keywords: | Artificial intelligence, automation, willingness to pay, survey experiment |
| JEL: | J24 O33 |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:iso:educat:0213&r=dcm |
| By: | Fanny Le Gloux (SMART - Structures et Marché Agricoles, Ressources et Territoires - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Rennes Angers - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement); Carole Ropars-Collet (SMART - Structures et Marché Agricoles, Ressources et Territoires - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Rennes Angers - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement); Alice Issanchou (SMART - Structures et Marché Agricoles, Ressources et Territoires - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Rennes Angers - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement); Pierre Dupraz (SMART - Structures et Marché Agricoles, Ressources et Territoires - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Rennes Angers - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement) |
| Abstract: | Designing incentives for agri-environmental public good provision with threshold effects calls for payment mechanisms favouring critical mass participation and continuity of commitments at the landscape scale. We conducted a choice experiment to test the acceptability of a bonus in a scheme for improving river water quality in France. We introduce a sponsorship bonus each time the farmer convinces a peer into entering the scheme, which can be combined with a collective result bonus per hectare if the river reaches a higher step on the water quality scale. We consider the involvement of local financers could increase the willingness to pay beyond opportunity costs and income foregone and propose higher levels of payment than agri-environmental schemes. Results suggest a sponsorship bonus on its own is cost-effective. We characterize respondents' heterogeneity and identify three groups based on choice patterns: (i) "pro-environment individualists", (ii) "management change averse" farmers, and (iii) "pro-incentive" farmers. |
| Keywords: | Water quality, Choice experiment, Collective action, Mixed logit model, Latent class model |
| Date: | 2024–02–06 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04523614&r=dcm |
| By: | Tiziana Assenza (Toulouse School of Economics, University of Toulouse Capitole, IAST); Alberto Cardaci (Goethe University, Frankfurt. E-mail: albertocardaci@gmail.com); Stefanie J. Huber (Department of Economics, University of Bonn; ECONtribute Research Cluster) |
| Abstract: | This paper investigates and quantifies citizens’ susceptibility to fake news and assesses, using a randomized control trial, the effectiveness of a policy intervention to raise awareness. We find that the average citizen lacks proficiency in identifying fake news and harbors an inflated perception of his/her ability to differentiate between true and fake news content. Increasing awareness by providing information about personal susceptibility to fall for fake news causally adjusts individuals’ beliefs about their fake news detection ability. Most importantly, we show that the simple intervention of informing citizens about their personal susceptibility to fall for fake news causally increases their willingness to pay for the fact-checking service. |
| Keywords: | Fake news, misinformation, disinformation, fact checking, information provision experiments, belief updating, willingness to pay |
| JEL: | C83 D83 D84 D91 |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:ajk:ajkdps:290&r=dcm |
| By: | Passmore, Reid; Watkins, Kari E; Guensler, Randall |
| Abstract: | Planners and engineers need to know how to assess the impacts of proposed cycling infrastructure projects, so that projects that have the greatest potential impact on the actual and perceived cycling safety are selected over those that would be less effective. Planners also need to be able to communicate these impacts to decision-makers and the public. This research addresses these problems using the BikewaySim cycling shortest path model. BikewaySim uses link impedance functions to account for link attributes (e.g., presence of a bike lane, steep gradients, the number of lanes) and find the least impedance path for any origin-destination pair. In this project, BikewaySim was used to assess the impacts of using time-only and time with attribute impedances, as well as two proposed cycling infrastructure projects, on 28, 392 potential trips for a study area in Atlanta, Georgia. These impacts were visualized through bikesheds, individual routing, and betweenness centrality. Two metrics, percent detour and change in impedance, were also calculated. Results demonstrate that BikewaySim can effectively visualize potential improvements of cycling infrastructure and has additional applications for trip planning. An expanded study area was also used to demonstrate bike + transit mode routing for four study area locations. Visualizations examine the accessibility to TAZs, travel time, and the utilized transit modes for each location. Compared to the walk + transit mode, the bike + transit mode provided greater access to other TAZs and reached them in a shorter amount of time. The locations near the center of the transit network where many routes converge offered the greatest accessibility for both the bike + transit and walk + transit modes. The difference in accessibility was greatest for locations near fewer transit routes. This research demonstrated how BikewaySim can be used to both examine the current cycling network and show changes in accessibility likely to result from new infrastructure. Both BikewaySim and TransitSim are open-source Python based tools that will be made available for practitioners to use in bicycle network planning. Future research will focus on calibrating link impedance functions with revealed preference data (cycling GPS traces) and survey response data (surveys on user preference for cycling infrastructure). View the NCST Project Webpage |
| Keywords: | Engineering, Social and Behavioral Sciences, bicycle networks, shortest path routing, bicycle route choice, bicycle facility preference, first and last mile travel |
| Date: | 2024–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsdav:qt7pt4d1tk&r=dcm |
| By: | Timothy Hunt |
| Abstract: | I analyse strategic interaction between adult siblings in the provision of care to an elderly parent by estimating a dynamic discrete-choice game in which siblings make location, work and care choices. I find that the opportunity for strategic play exacerbates gender differences in caring responsibilities as sons in particular strategically shirk providing care as they believe their sibling is relatively likely to provide care in their absence. Counterfactual experiments show that if siblings instead took care, location and work choices independently then the gender care gap would be around 14% smaller. Also, I find that unobserved preference differences between sons and daughters are far more important in driving the gender care gap than observed differences in wages. |
| Date: | 2024–02–29 |
| URL: | http://d.repec.org/n?u=RePEc:oxf:wpaper:1042&r=dcm |
| By: | Brooke, Sian; Rao, Aliya |
| Abstract: | Online labour markets (OLMs) are a vital source of income for globally diverse and dispersed freelancers. Despite their promise of neutrality, OLMs are known to perpetuate hiring discrimination, vested in how OLMs are designed and what kinds of interactions they enable between freelancers and hirers. In this study, we go beyond understanding mechanisms of hiring discrimination in OLMs, to identifying platform design features that can minimise hiring discrimination. To do so, we draw on a methodology guided by the design justice ethos. Drawing on a survey on UK-based freelancers and interviews with a purposefully drawn sub-sample, we collaboratively identify five platform design interventions to minimise hiring discrimination in OLMs: community composition, identity-signalling flairs, text only reviews, union membership, and an antidiscrimination prompt. The core of our study is an innovative experiment conducted on a purpose-built, mock OLM, Mock-Freelancer.com. On this mock OLM, we experimentally test mechanisms of discrimination, including how these mechanisms fare under the five altered platform design interventions through a discrete-choice experiment. We find that both community and flairs were important in encouraging the hiring of women and non-White freelancers. We also establish that anonymity universally disadvantages freelancers. We conclude with recommendations to design OLMs that minimise labour market discrimination. |
| Keywords: | future of work; gender; design justice; ethnicity; platforms; affordances; STICERD; SRG21\210549; REF fund |
| JEL: | R14 J01 |
| Date: | 2024–03–30 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:122152&r=dcm |
| By: | Li, Jing Jing; Shi, Chengchun; Li, Lexin; Collins, Anne G.E. |
| Abstract: | Computational cognitive modeling is an important tool for understanding the processes supporting human and animal decision-making. Choice data in decision-making tasks are inherently noisy, and separating noise from signal can improve the quality of computational modeling. Common approaches to model decision noise often assume constant levels of noise or exploration throughout learning (e.g., the ϵ-softmax policy). However, this assumption is not guaranteed to hold – for example, a subject might disengage and lapse into an inattentive phase for a series of trials in the middle of otherwise low-noise performance. Here, we introduce a new, computationally inexpensive method to dynamically estimate the levels of noise fluctuations in choice behavior, under a model assumption that the agent can transition between two discrete latent states (e.g., fully engaged and random). Using simulations, we show that modeling noise levels dynamically instead of statically can substantially improve model fit and parameter estimation, especially in the presence of long periods of noisy behavior, such as prolonged lapses of attention. We further demonstrate the empirical benefits of dynamic noise estimation at the individual and group levels by validating it on four published datasets featuring diverse populations, tasks, and models. Based on the theoretical and empirical evaluation of the method reported in the current work, we expect that dynamic noise estimation will improve modeling in many decision-making paradigms over the static noise estimation method currently used in the modeling literature, while keeping additional model complexity and assumptions minimal. |
| Keywords: | attention; cognitive modeling; decision noise; decision-making; hidden Markov model; lapses; reinforcement learning; task-engagement; 1R01MH119383 |
| JEL: | C1 |
| Date: | 2024–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:122383&r=dcm |
| By: | Enzo Brox (SEW, University of St.Gallen); Riccardo Di Francesco (DEF, University of Rome "Tor Vergata") |
| Abstract: | The fear of social stigma and discrimination leads many individuals worldwide to hesitate in openly disclosing their sexual orientation. Due to the large costs of concealing identity, it is crucial to understand the extent of anti-LGB sentiments and reactions to coming out. However, disclosing one’s sexual orientation is a personal choice, complicating data access and introducing endogeneity issues. This paper tackles these challenges by using an innovative data source from a popular online video game together with a natural experiment. We exploit exogenous variation in the identity of a playable character to identify the effects of disclosure on players’ revealed preferences for that character. Leveraging detailed daily data, we monitor players’ preferences for the character across diverse regions globally and employ synthetic control methods to isolate the effect of the disclosure on players’ preferences. Our findings reveal a substantial and persistent negative impact of coming out. To strengthen the plausibility of social stigma as the primary explanation for the estimated effects, we systematically address and eliminate several alternative game-related channels. |
| Keywords: | LGB economics, social stigma, concealable stigma |
| JEL: | J15 J71 K38 |
| Date: | 2024–04–16 |
| URL: | http://d.repec.org/n?u=RePEc:rtv:ceisrp:572&r=dcm |
| By: | Daniele Caliari; Henrik Petri |
| Abstract: | We show that the set of aggregate choices of a population of rational decision-makers - random utility models (RUMs) - can be represented by a population of irrational ones if, and only if, their preferences are sufficiently uncorrelated. We call this representation: Irrational RUM. We then show that almost all RUMs can be represented by a population in which at least some decision-makers are irrational and that under specific conditions their irrational behavior is unconstrained. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.10208&r=dcm |