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on Discrete Choice Models |
| By: | Leonardo Brogi; Gianni Betti; Francesca Gagliardi |
| Abstract: | The aim of this paper is to test the questionnaire developed for a survey that will be conducted in the framework of the PNRR Agritech project. It is a consumer survey to gather information on willingness to pay, preferences and sensory perceptions of sustainable products. A pilot survey was conducted using a web-based questionnaire designed to evaluate and analyse these topics. The questionnaire responses reflect consumers' views on sustainability issues, as they are asked to assign ratings to the sustainability attributes of generic products and to choose between specific supply chain products, such as wine, meat and packaging, in order to explore environmental and social sustainability attributes. The data collected is summarized in frequency tables and charts and can be used to develop statistical and econometric models. Indeed, a logit model and a structural equation model (SEM) using the PLS-PM algorithm were constructed to examine consumer willingness to pay for sustainability attributes in agri-food products, based on constructs of environmental, social and economic sustainability. |
| Keywords: | agrifood chain; sustainability; willingness-to-pay |
| JEL: | D91 C21 Q18 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:usi:wpaper:917 |
| By: | David P. Brown; Lucija Muehlenbachs |
| Abstract: | To avoid electric-infrastructure-induced wildfires, millions of Californians had their power cut for hours to days at a time. We show that rooftop solar-plus-battery-storage systems increased in zip codes with the longest power outages. Rooftop solar panels alone will not help a household avert outages, but a solar-plus-battery-storage system will. Using this fact, we obtain a revealed-preference estimate of the willingness to pay for electricity reliability, the Value of Lost Load, a key parameter for electricity market design. Our estimate, with an average of $4, 980/MWh, suggests California’s wildfire-prevention outages resulted in losses from foregone consumption of $406 million to residential electricity consumers. |
| Keywords: | batteries, reliability, averting expenditures, power outages, Value of Lost Load |
| JEL: | Q40 Q54 Q58 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11377 |
| By: | Max R. P. Grossmann |
| Abstract: | When is autonomy granted to a decision-maker based on their knowledge, and if no autonomy is granted, what form will the intervention take? A parsimonious theory shows how policymakers can exploit decision-maker mistakes and use them as a justification for intervention. In two experiments, policymakers ("Choice Architects") can intervene in a choice faced by a decision-maker. We vary the amount of knowledge decision-makers possess about the choice. Full decision-maker knowledge causes more than a 60% reduction in intervention rates. Beliefs have a small, robust correlation with interventions on the intensive margin. Choice Architects disproportionately prefer to have decision-makers make informed decisions. Interveners are less likely to provide information. As theory predicts, the same applies to Choice Architects who believe that decision-maker mistakes align with their own preference. When Choice Architects are informed about the decision-maker's preference, this information is used to determine the imposed option. However, Choice Architects employ their own preference to a similar extent. A riskless option is causally more likely to be imposed, being correlated with but conceptually distinct from Choice Architects' own preference. This is a qualification to what has been termed "projective paternalism." |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.20970 |
| By: | Li, Jiangtao (Singapore Management University); Tang, Rui (Department of Economics, Hong Kong University of Science and Technology); Zhang, Mu (Department of Economics, University of Michigan) |
| Abstract: | We present a model of associative networks that captures how a decision maker expands her consideration set through mental associations between alternatives. This model serves as a tool to understand the influence of association on decision making. As a proof of concept, we characterize this model within a random attention framework and demonstrate that all the relevant parameters are uniquely identifiable. Notably, in a novel choice domain where not all observable alternatives are available, the presence of unavailable alternatives can affect the choice frequencies of other alternatives through association. |
| Keywords: | associative network; random attention; consideration set; random choice; availability and observability |
| JEL: | D01 D91 |
| Date: | 2024–09–16 |
| URL: | https://d.repec.org/n?u=RePEc:ris:smuesw:2024_009 |
| By: | Helen Hayes;Priscila Radu;David Mott;Chris Skedgel |
| Keywords: | Understanding societal preferences for priority by disease severity in England & Wales |
| JEL: | I1 |
| Date: | 2024–11–14 |
| URL: | https://d.repec.org/n?u=RePEc:ohe:conres:002495 |
| By: | Debopam Bhattacharya; Ekaterina Oparina; Qianya Xu |
| Abstract: | We analyze demand settings where heterogeneous consumers maximize utility for product attributes subject to a nonlinear budget constraint. We develop nonparametric methods for welfare-analysis of interventions that change the constraint. Two new findings are Roy's identity for smooth, nonlinear budgets, which yields a Partial Differential Equation system, and a Slutsky-like symmetry condition for demand. Under scalar unobserved heterogeneity and single-crossing preferences, the coefficient functions in the PDEs are nonparametrically identified, and under symmetry, lead to path-independent, money-metric welfare. We illustrate our methods with welfare evaluation of a hypothetical change in relationship between property rent and neighborhood school-quality using British microdata. |
| Keywords: | hedonic model, nonlinear budget, nonparametric identification, welfare, compensating/equivalent variation, partial differential equation, Slutsky symmetry, Roy’s Identity, Path Independence. |
| Date: | 2024–11–08 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2050 |
| By: | Xavier Gabaix; Thomas Graeber |
| Abstract: | We propose a theory of the complexity of economic decisions. Leveraging a macroeconomic framework of production functions, we conceptualize the mind as a cognitive economy, where a task's complexity is determined by its composition of cognitive operations. Complexity emerges as the inverse of the total factor productivity of thinking about a task. It increases in the number of importance-weighted components and decreases in the degree to which the effect of one or few components on the optimal action dominates. Higher complexity generates larger decision errors and behavioral attenuation to variation in problem parameters. The model applies both to continuous and discrete choice. We develop a theory-guided experimental methodology for measuring subjective perceptions of complexity that is simple and portable. A series of experiments test and confirm the central predictions of our model for perceptions of complexity, behavioral attenuation, and decision errors. We provide a template for applying the framework to core economic decision domains, and then develop several applications including the complexity of static consumption choice with one or several interacting goods, consumption over time, the tax system, forecasting, and discrete choice between goods. |
| JEL: | C91 D03 D11 D14 D90 E03 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33109 |
| By: | Hayri Alper Arslan; Brantly Callaway; Tong Li |
| Abstract: | Motivated by studying the effects of marriage prospects on students' college major choices, this paper develops a new econometric test for analyzing the effects of an unobservable factor in a setting where this factor potentially influences both agents' decisions and a binary outcome variable. Our test is built upon a flexible copula-based estimation procedure and leverages the ordered nature of latent utilities of the polychotomous choice model. Using the proposed method, we demonstrate that marriage prospects significantly influence the college major choices of college graduates participating in the National Longitudinal Study of Youth (97) Survey. Furthermore, we validate the robustness of our findings with alternative tests that use stated marriage expectation measures from our data, thereby demonstrating the applicability and validity of our testing procedure in real-life scenarios. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.19947 |
| By: | Facundo Arga\~naraz; Juan Carlos Escanciano |
| Abstract: | Models with Conditional Moment Restrictions (CMRs) are popular in economics. These models involve finite and infinite dimensional parameters. The infinite dimensional components include conditional expectations, conditional choice probabilities, or policy functions, which might be flexibly estimated using Machine Learning tools. This paper presents a characterization of locally debiased moments for regular models defined by general semiparametric CMRs with possibly different conditioning variables. These moments are appealing as they are known to be less affected by first-step bias. Additionally, we study their existence and relevance. Such results apply to a broad class of smooth functionals of finite and infinite dimensional parameters that do not necessarily appear in the CMRs. As a leading application of our theory, we characterize debiased machine learning for settings of treatment effects with endogeneity, giving necessary and sufficient conditions. We present a large class of relevant debiased moments in this context. We then propose the Compliance Machine Learning Estimator (CML), based on a practically convenient orthogonal relevant moment. We show that the resulting estimand can be written as a convex combination of conditional local average treatment effects (LATE). Altogether, CML enjoys three appealing properties in the LATE framework: (1) local robustness to first-stage estimation, (2) an estimand that can be identified under a minimal relevance condition, and (3) a meaningful causal interpretation. Our numerical experimentation shows satisfactory relative performance of such an estimator. Finally, we revisit the Oregon Health Insurance Experiment, analyzed by Finkelstein et al. (2012). We find that the use of machine learning and CML suggest larger positive effects on health care utilization than previously determined. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.23785 |