|
on Discrete Choice Models |
| By: | Njideka Aguome; Nonso Ewurum; Phenyo Mpolokang; Fidelis Emoh |
| Abstract: | This study employs a discrete choice analysis to investigate homeowners' risk-time preferences for energy efficiency upgrades, considering both upfront costs and future savings. The study's objectives are to understand trade-offs homeowners are willing to make between upfront costs and expected savings for energy efficiency upgrades and to identify the tipping point where homeowners switch preferences between discounted long-term savings and higher upfront costs amongst household demographic characteristics. A quantitative methodology is employed to achieve these objectives, utilising discrete choice analysis. This methodology allows for examining homeowners' preferences by presenting them with various hypothetical scenarios that include different combinations of upfront costs and expected savings for energy efficiency upgrades. Using a dataset of 461 homeowners in Nigeria, we estimate latent class, multinomial logit models, and conjoint analysis to analyse the preference heterogeneity in the population. Our results provide a comprehensive understanding of homeowners' risk-time preferences for energy efficiency upgrades, and the tipping point where they switch preferences between discounted long-term savings and higher upfront costs. The findings elicit insights that direct policymakers to tailor their interventions consistent with demographic variances towards effectively incentivising energy efficiency upgrades. |
| Keywords: | discrete choice experiment; Energy Efficiency; household preferences; smart homes; sustainable transition |
| JEL: | R3 |
| Date: | 2024–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:afr:wpaper:2024-035 |
| By: | Njideka Aguome; Nonso Ewurum; Phenyo Mpolokang; Fidelis Emoh |
| Abstract: | This study employs a discrete choice analysis to investigate homeowners' risk-time preferences for energy efficiency upgrades, considering both upfront costs and future savings. The study's objectives are to understand trade-offs homeowners are willing to make between upfront costs and expected savings for energy efficiency upgrades and to identify the tipping point where homeowners switch preferences between discounted long-term savings and higher upfront costs amongst household demographic characteristics. A quantitative methodology is employed to achieve these objectives, utilising discrete choice analysis. This methodology allows for examining homeowners' preferences by presenting them with various hypothetical scenarios that include different combinations of upfront costs and expected savings for energy efficiency upgrades. Using a dataset of 461 homeowners in Nigeria, we estimate latent class, multinomial logit models, and conjoint analysis to analyse the preference heterogeneity in the population. Our results provide a comprehensive understanding of homeowners' risk-time preferences for energy efficiency upgrades, and the tipping point where they switch preferences between discounted long-term savings and higher upfront costs. The findings elicit insights that direct policymakers to tailor their interventions consistent with demographic variances towards effectively incentivising energy efficiency upgrades. |
| Keywords: | discrete choice experiment; Energy Efficiency; household preferences; smart homes; sustainable transition |
| JEL: | R3 |
| Date: | 2024–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:afr:wpaper:afres2024-035 |
| By: | Donald S. Kenkel; Alan D. Mathios; Grace N. Phillips; Revathy Suryanarayana; Hua Wang; Sen Zeng |
| Abstract: | Heated tobacco products (HTPs), a harm reducing cigarette alternative, gained popularity over the past decade and appear to have contributed significantly to the reduction of smoking in Japan. While the increased popularity of HTPs suggests a consumer preference for cigarette alternatives, there is a limited understanding of how consumers choose between different tobacco products. Understanding consumer choice is especially salient given the evolving policy landscape and proposals to increase HTP taxes. This study uses a large discrete choice experiment to examine the decision-making processes of smokers in Japan when choosing between cigarettes, HTPs, and quitting. We assess the influence of various product attributes such as prices, flavors, nicotine content, and warning messages on these choices. The findings reveal that prices and flavors significantly influence smokers' preferences. Specifically, higher HTP prices tend to drive smokers back to combustible cigarettes and discourage them from choosing to quit. Additionally, there is some evidence that consumers prefer HTPs with a wide variety of flavors. Meanwhile, hypothetical policy simulations that change warning messages or nicotine content do not affect consumers' choices. |
| JEL: | I12 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33301 |
| By: | Niousha Bagheri; Milad Ghasri; Michael Barlow |
| Abstract: | This paper introduces a framework for capturing stochasticity of choice probabilities in neural networks, derived from and fully consistent with the Random Utility Maximization (RUM) theory, referred to as RUM-NN. Neural network models show remarkable performance compared with statistical models; however, they are often criticized for their lack of transparency and interoperability. The proposed RUM-NN is introduced in both linear and nonlinear structures. The linear RUM-NN retains the interpretability and identifiability of traditional econometric discrete choice models while using neural network-based estimation techniques. The nonlinear RUM-NN extends the model's flexibility and predictive capabilities to capture nonlinear relationships between variables within utility functions. Additionally, the RUM-NN allows for the implementation of various parametric distributions for unobserved error components in the utility function and captures correlations among error terms. The performance of RUM-NN in parameter recovery and prediction accuracy is rigorously evaluated using synthetic datasets through Monte Carlo experiments. Additionally, RUM-NN is evaluated on the Swissmetro and the London Passenger Mode Choice (LPMC) datasets with different sets of distribution assumptions for the error component. The results demonstrate that RUM-NN under a linear utility structure and IID Gumbel error terms can replicate the performance of the Multinomial Logit (MNL) model, but relaxing those constraints leads to superior performance for both Swissmetro and LPMC datasets. By introducing a novel estimation approach aligned with statistical theories, this study empowers econometricians to harness the advantages of neural network models. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.05221 |
| By: | Zhentong Lu; Kenichi Shimizu |
| Abstract: | We propose a new approach to estimating the random coefficient logit demand model for differentiated products when the vector of market-product level shocks is sparse. Assuming sparsity, we establish nonparametric identification of the distribution of random coefficients and demand shocks under mild conditions. Then we develop a Bayesian procedure, which exploits the sparsity structure using shrinkage priors, to conduct inference about the model parameters and counterfactual quantities. Comparing to the standard BLP (Berry, Levinsohn, & Pakes, 1995) method, our approach does not require demand inversion or instrumental variables (IVs), thus provides a compelling alternative when IVs are not available or their validity is questionable. Monte Carlo simulations validate our theoretical findings and demonstrate the effectiveness of our approach, while empirical applications reveal evidence of sparse demand shocks in well-known datasets. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.02381 |
| By: | Lu, Zhentong (Bank of Canada); Shimizu, Kenichi (University of Alberta, Department of Economics) |
| Abstract: | We propose a new approach to estimating the random coefficient logit demand model for differentiated products when the vector of market-product level shocks is sparse. Assuming sparsity, we establish nonparametric identification of the distribution of random coefficients and demand shocks under mild conditions. Then we develop a Bayesian procedure, which exploits the sparsity structure using shrinkage priors, to conduct inference about the model parameters and counterfactual quantities. Comparing to the standard BLP (Berry, Levinsohn, & Pakes, 1995) method, our approach does not require demand inversion or instrumental variables (IVs), thus provides a compelling alternative when IVs are not available or their validity is questionable. Monte Carlo simulations validate our theoretical findings and demonstrate the effectiveness of our approach, while empirical applications reveal evidence of sparse demand shocks in well-known datasets. |
| Keywords: | Demand Estimation; Sparsity; Bayesian Inference; Shrinkage Prior |
| JEL: | C10 C30 D10 L00 |
| Date: | 2025–01–16 |
| URL: | https://d.repec.org/n?u=RePEc:ris:albaec:2025_001 |
| By: | H. Spencer Banzhaf |
| Abstract: | The Random Utility Model (RUM) is a workhorse model for valuing new products or changes in public goods. But RUMs have been faulted along two lines. First, for including idiosyncratic errors that imply unreasonably high values for new alternatives and unrealistic substitution patterns. Second, for involving strong restrictions on functional forms for utility. This paper shows how, instead, starting with a revealed preference framework, one can partially identify nonparametrically the answers to policy questions about discrete alternatives. When the Generalized Axiom of Revealed Preference (GARP) is satisfied, the approach weakly identifies a pure characteristics model. When GARP is violated, it recasts the RUM errors as departures from GARP (critical cost efficiency), to be minimized using a minimum-distance criterion. This perspective provides an alternative avenue for nonparametric identification of discrete choice models. The paper illustrates the approach by estimating bounds on the values of ecological improvements in the Southern Appalachian Mountains using survey data. |
| JEL: | C14 C25 C61 D12 Q51 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33225 |
| By: | Thompson, Bethan; Akaichi, Faical; Toma, Luiza |
| Abstract: | Single-use disposable cups are a significant contributor to plastic waste due to their widespread use and limited recyclability. Policymakers worldwide are implementing measures to reduce their consumption and encourage reusable alternatives. This study evaluates the impact of regulatory measures (charges and discounts), persuasion (environmental information prompts), and consumer motivations (using Protection Motivation Theory) on preferences for single-use, refillable, and returnable cups. Using discrete choice experiments with a nationally representative sample, we find that a charge of 25–30 pence is required to reduce single-use cup selection by 50%, whereas a discount of at least 70 pence achieves a similar effect. Information prompts have minimal influence on choices, while environmentally motivated consumer segments demonstrate greater responsiveness to discounts. These findings provide actionable, evidence-based insights for policymakers and industry stakeholders, supporting the design of effective interventions to accelerate the transition from single-use to reusable systems. |
| Date: | 2024–11–26 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:e2da7 |
| By: | Lingbo Huang; Jun Zhang |
| Abstract: | Lotteries are commonly employed in school choice to fairly resolve priority ties; however, current practices leave students uninformed about their lottery outcomes when submitting preferences. This paper advocates for revealing lottery results prior to preference submission. When preference lists are constrained in length, revealing lotteries can reduce uncertainties and enable informed decision-making regarding the selection of schools to rank. Through three stylized models, we demonstrate the benefits of lottery revelation in resolving conflicting preferences, equalizing opportunities among students with varying outside options, and alleviating the neighborhood school bias. Our findings are further supported by a laboratory experiment. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.04243 |
| By: | Sullivan, Nikki; Breslav, Alexander; Doré, Samyukta; Bachman, Matthew; Huettel, Scott A. |
| Abstract: | Defaults are pervasive in consumer choice. Here, laboratory experiments that used eye tracking were combined with cognitive modeling to pinpoint the influence of defaults in the decision process, along with naturalistic experiments with large pre-registered samples to test the limits of defaults on consumer choices. Contrary to previous assumptions, in simple binary choices, default options did not potentiate rapid heuristic-based decisions but instead altered processes of attention and valuation. Model comparison indicated that defaults received a positive boost in value – a golden halo – that was large enough to increase hedonic choices when the default was hedonic, but had limited effects for utilitarian defaults or for when defaults were incongruent with background goals. The findings illustrate and quantify the mechanisms through which default options shape subsequent decisions in simple choices. Further, boundary conditions for when defaults can and cannot be used to nudge consumer choice are established. |
| Keywords: | choice architecture; default options; eye tracking; case modeling |
| JEL: | L81 |
| Date: | 2024–10–09 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:126086 |