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on Discrete Choice Models |
| By: | Davide Carpentiere; Angelo Petralia |
| Abstract: | We show that many models of choice can be alternatively represented as special cases of choice with limited attention (Masatlioglu, Nakajima, and Ozbay, 2012), and the properties of the unobserved attention filters that explain the observed choices are singled out. Moreover, for each specification, we infer information about the DM's attention and preference from irrational features of choice data. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.14879 |
| By: | Enoch H. Kang; Hema Yoganarasimhan; Lalit Jain |
| Abstract: | We study the problem of estimating Dynamic Discrete Choice (DDC) models, also known as offline Maximum Entropy-Regularized Inverse Reinforcement Learning (offline MaxEnt-IRL) in machine learning. The objective is to recover reward or $Q^*$ functions that govern agent behavior from offline behavior data. In this paper, we propose a globally convergent gradient-based method for solving these problems without the restrictive assumption of linearly parameterized rewards. The novelty of our approach lies in introducing the Empirical Risk Minimization (ERM) based IRL/DDC framework, which circumvents the need for explicit state transition probability estimation in the Bellman equation. Furthermore, our method is compatible with non-parametric estimation techniques such as neural networks. Therefore, the proposed method has the potential to be scaled to high-dimensional, infinite state spaces. A key theoretical insight underlying our approach is that the Bellman residual satisfies the Polyak-Lojasiewicz (PL) condition -- a property that, while weaker than strong convexity, is sufficient to ensure fast global convergence guarantees. Through a series of synthetic experiments, we demonstrate that our approach consistently outperforms benchmark methods and state-of-the-art alternatives. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.14131 |
| By: | Te Bao; John Duffy; Nobuyuki Hanaki |
| Abstract: | In the digital age, privacy in economic activities is increasingly threatened. In considering policies to address this threat, it is useful to gauge what value, if any, people attach to privacy in their economic activities: specifically, reputational concerns related to dishonest behavior. We assess individuals’ willingness to pay to avoid scrutiny of their potentially dishonest behavior in a simple coin flipping task, conducted in Japan, China, and the United States. Our findings reveal that people’s willingness to pay to “avoid the spotlight” is positive and economically sizable across all three countries and is largest in Japan. |
| Date: | 2024–04 |
| URL: | https://d.repec.org/n?u=RePEc:dpr:wpaper:1238rr |
| By: | Diem, Andrea (Swiss Coordination Centre for Research in Education); Gschwendt, Christian (University of Bern); Wolter, Stefan C. (University of Bern) |
| Abstract: | A university degree is a risky investment because of the non-negligible risk of having to drop out of university without graduating. However, the costs of this risk are controversial, as it is often argued that even an uncertified year of study has a value in the labor market. To determine this value causally, however, alternatives to studying must also be considered, which is done here with the help of a discrete choice experiment with a representative sample of over 2, 500 HR recruiters. The result is that dropping out of university with a major closely related to an advertised job leads to similar labor market outcomes as if someone had not studied at all. Without a direct link to a job, however, dropping out of university significantly reduces lifetime earnings. Furthermore, HR recruiters clearly prefer applicants who have used the years without studying for human capital accumulation in an alternative way, for example in the form of a traineeship. |
| Keywords: | dropouts, hiring decisions, discrete choice experiment, sheepskin effect, willingness to pay, tertiary education |
| JEL: | I26 J23 J24 J31 M51 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17693 |
| By: | Manzke, Leonie (Friedrich-Alexander University Erlangen-Nuremberg); O'Sullivan, Kevin; Tiefenbeck, Verena |
| Abstract: | Food choices profoundly impact population health and the environment. Related research often relies on self-reported data, which is prone to biases, compromising the accuracy and validity of conclusions about consumer behavior. There are few systematic validations of self-reported data with behavioral data, or examinations of predictors for their accuracy. Consequently, this study compares self-reported with observed food choices, by having participants (N = 290) complete a shopping task in a simulated online grocery store, followed immediately by shopping self-reports and a survey, therefore minimizing recall-related distortions to self-reports due to time delays. Nevertheless, on average, participants had reporting errors in 3.81 out of 29 categories, with accuracy as low as a mean of 44 % for categories with no cues provided. Reporting accuracy significantly increased to 78 % with image-based memory aids for specific product categories (e.g., apples), and to 89 % with text-based memory aids for general categories (e.g., vegetables). Contrary to expectations related to social desirability bias, processed foods, often perceived as unhealthy, were overreported. Regression analysis revealed mental load during shopping, deliberation time per item, and health-related identity as significant predictors of self-report accuracy, with mental load also predicting the accuracy of participants' estimates of the proportion of organic products in their shopping basket. Our findings show that even in conditions that minimize social desirability and recall limitations, substantial self-reporting errors persist. Accounting for mental load and product-specific biases is therefore necessary to enhance the validity of self-reports in food choice research. |
| Date: | 2025–01–15 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:bn6tg_v1 |
| By: | Kevin He; Ran Shorrer; Mengjia Xia |
| Abstract: | We conduct an incentivized laboratory experiment to study people's perception of generative artificial intelligence (GenAI) alignment in the context of economic decision-making. 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: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.14708 |
| By: | Buser, Thomas (University of Amsterdam); Sangi, Sahar (University of Amsterdam) |
| Abstract: | Competitive environments often leave room for "dirty" practices such as sabotage, retaliation, or dishonesty. We use an online experiment to document aggregate levels and individual differences in the willingness to engage in dirty competition and in the willingness to enter competitions where the opponent may play dirty. We then use the experimental data to validate a set of survey questions that capture willingness to engage in dirty competition above general willingness to compete. We elicit these questions in a representative survey panel and show that willingness to engage in dirty competition is a strong predictor of holding a management or supervisory position and of working in the private – versus the public – sector, but also of worse self-esteem, worse social relationships, and increased feelings of guilt and shame. Men, younger people, and lower-educated people are on average more willing to engage in dirty competition. |
| Keywords: | preferences, personaility, career choice |
| JEL: | D91 J24 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17676 |
| By: | Hasraddin Guliyev |
| Abstract: | The biastest command in Stata is a powerful and user-friendly tool designed to compare the coefficients of different regression models, enabling researchers to assess the robustness and consistency of their empirical findings. This command is particularly valuable for evaluating alternative modeling approaches, such as ordinary least squares versus robust regression, robust regression versus median regression, quantile regression across different quantiles, and fixed effects versus random effects models in panel data analysis. By providing both variable-specific and joint tests, biastest command offers a comprehensive framework for detecting bias or significant differences in model estimates, ensuring that researchers can make informed decisions about model selection and interpretation. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.15049 |
| By: | Julius Sch\"aper |
| Abstract: | This paper introduces R-OLS, an estimator for the average partial effect (APE) of a continuous treatment variable on an outcome variable in the presence of non-linear and non-additively separable confounding of unknown form. Identification of the APE is achieved by generalising Stein's Lemma (Stein, 1981), leveraging an exogenous error component in the treatment along with a flexible functional relationship between the treatment and the confounders. The identification results for R-OLS are used to characterize the properties of Double/Debiased Machine Learning (Chernozhukov et al., 2018), specifying the conditions under which the APE is estimated consistently. A novel decomposition of the ordinary least squares estimand provides intuition for these results. Monte Carlo simulations demonstrate that the proposed estimator outperforms existing methods, delivering accurate estimates of the true APE and exhibiting robustness to moderate violations of its underlying assumptions. The methodology is further illustrated through an empirical application to Fetzer (2019). |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.10301 |
| By: | Jibang Wu; Chenghao Yang; Simon Mahns; Chaoqi Wang; Hao Zhu; Fei Fang; Haifeng Xu |
| Abstract: | This paper develops an agentic framework that employs large language models (LLMs) to automate the generation of persuasive and grounded marketing content, using real estate listing descriptions as our focal application domain. Our method is designed to align the generated content with user preferences while highlighting useful factual attributes. This agent consists of three key modules: (1) Grounding Module, mimicking expert human behavior to predict marketable features; (2) Personalization Module, aligning content with user preferences; (3) Marketing Module, ensuring factual accuracy and the inclusion of localized features. We conduct systematic human-subject experiments in the domain of real estate marketing, with a focus group of potential house buyers. The results demonstrate that marketing descriptions generated by our approach are preferred over those written by human experts by a clear margin. Our findings suggest a promising LLM-based agentic framework to automate large-scale targeted marketing while ensuring responsible generation using only facts. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.16810 |