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on Discrete Choice Models |
| By: | Michelle Sovinsky; Liana Jacobi; Alessandra Allocca; Tao Sun |
| Abstract: | As illicit substances move into the legal product space, substitution patterns with legal products become more salient. In particular, marijuana legalization may have implications for the use of other legal “sin” goods. We estimate a structural model of multi-product use of illegal and legal substances considering joint use, limited access to illicit products, and persistence in use. We focus on a young person’s choice to consume marijuana, alcohol or cigarettes (and possible combinations), and we find that sin goods are complements. Furthermore, our findings emphasize the necessity of accounting for joint consumption and access to obtain correct price sensitivity estimates. Post-legalization, youth marijuana use would increase from 25% to 37%. However, counterfactual results show that a combination of (reasonable) tax increases on all goods along with enforcement against illegal use can potentially revert use to pre-legalization levels. The earlier the tax increases are implemented the more effective they are at curbing future use. Our results inform the policy debate regarding the impact of marijuana legalization on the long-term use of sin goods. |
| Keywords: | complementarity, marijuana legalization, limited choice sets, data restrictions, discrete choice models |
| JEL: | C11 D12 L15 K42 H2 L66 C35 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2024_501&r=dcm |
| By: | Maria A. Maricheva (National Research University Higher School of Economics); Vadim A. Petrovsky (National Research University Higher School of Economics) |
| Abstract: | This work is devoted to the subjective value of money as a condition for the free choice of a person in the market for goods and services. Starting from the understanding of money as the universal equivalent of value, this paper emphasises the idea that money not only expresses (“measures”) value, but also represents a materialised possibility of having free choice of any commodity for a given value. It is assumed that the unfettered and multivariable nature of possible exchanges inherent in money has a special value for its holder. Thus, the hypothesis is formulated that money, as an embodiment of freedom of choice, symbolises something more than just the value of goods that can be purchased with that money. We refer to the hypothetical difference between the subjective value of money and the average price of goods that can be purchased with it as the "surplus value of money ( -«Delta»)". This phenomenon is experimentally fixed on the basis of the “Money - Commodity - Delta” methodology jointly developed by the authors. As such, in this work, the value of money as an instrument of free choice has been measured for the first time. Four groups of respondents were also identified according to their resolution of the "commodity or money" dilemma. The analysis of possible determinants of the surplus value of money may become a topic for further research. |
| Keywords: | money, psychological characteristics of money, freedom of choice |
| JEL: | Z |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:hig:wpaper:139psy2024&r=dcm |
| By: | Jeongbin Kim; Matthew Kovach; Kyu-Min Lee; Euncheol Shin; Hector Tzavellas |
| Abstract: | This paper explores the use of Large Language Models (LLMs) as decision aids, with a focus on their ability to learn preferences and provide personalized recommendations. To establish a baseline, we replicate standard economic experiments on choice under risk (Choi et al., 2007) with GPT, one of the most prominent LLMs, prompted to respond as (i) a human decision maker or (ii) a recommendation system for customers. With these baselines established, GPT is provided with a sample set of choices and prompted to make recommendations based on the provided data. From the data generated by GPT, we identify its (revealed) preferences and explore its ability to learn from data. Our analysis yields three results. First, GPT's choices are consistent with (expected) utility maximization theory. Second, GPT can align its recommendations with people's risk aversion, by recommending less risky portfolios to more risk-averse decision makers, highlighting GPT's potential as a personalized decision aid. Third, however, GPT demonstrates limited alignment when it comes to disappointment aversion. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.07345&r=dcm |
| By: | Lergetporer, Philipp (Technical University of Munich); Wedel, Katharina (Ifo Institute for Economic Research); Werner, Katharina (Ifo Institute for Economic Research) |
| Abstract: | We study how beliefs about the automatability of workers' occupation affect labor-market expectations and willingness to participate in further training. In our representative online survey, respondents on average underestimate the automation risk of their occupation, especially those in high-automatability occupations. Randomized information about their occupations' automatability increases respondents' concerns about their professional future, and expectations about future changes in their work environment. The information also increases willingness to participate in further training, especially among respondents in highly automatable occupation (+five percentage points). This uptick substantially narrows the gap in willingness to train between those in high- and low-automatability occupations. |
| Keywords: | automation, further training, labor-market expectations, survey experiment, information |
| JEL: | J24 O33 I29 D83 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16687&r=dcm |
| By: | Bo E. Honore |
| Abstract: | Amemiya (1973) proposed a ``consistent initial estimator'' for the parameters in a censored regression model with normal errors. This paper demonstrates that a similar approach can be used to construct moment conditions for fixed--effects versions of the model considered by Amemiya. This result suggests estimators for models that have not previously been considered. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.04803&r=dcm |
| By: | Jiayang Li; Qianni Wang; Liyang Feng; Jun Xie; Yu Marco Nie |
| Abstract: | The lack of a unique user equilibrium (UE) route flow in traffic assignment has posed a significant challenge to many transportation applications. The maximum-entropy principle, which advocates for the consistent selection of the most likely solution as a representative, is often used to address the challenge. Built on a recently proposed day-to-day (DTD) discrete-time dynamical model called cumulative logit (CULO), this study provides a new behavioral underpinning for the maximum-entropy UE (MEUE) route flow. It has been proven that CULO can reach a UE state without presuming travelers are perfectly rational. Here, we further establish that CULO always converges to the MEUE route flow if (i) travelers have zero prior information about routes and thus are forced to give all routes an equal choice probability, or (ii) all travelers gather information from the same source such that the so-called general proportionality condition is satisfied. Thus, CULO may be used as a practical solution algorithm for the MEUE problem. To put this idea into practice, we propose to eliminate the route enumeration requirement of the original CULO model through an iterative route discovery scheme. We also examine the discrete-time versions of four popular continuous-time dynamical models and compare them to CULO. The analysis shows that the replicator dynamic is the only one that has the potential to reach the MEUE solution with some regularity. The analytical results are confirmed through numerical experiments. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.08013&r=dcm |