nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2023‒09‒04
twelve papers chosen by
Edoardo Marcucci, Università degli studi Roma Tre

  1. Identification in Dynamic Binary Choice Models By Gary Chamberlain
  2. Identification in a Binary Choice Panel Data Model with a Predetermined Covariate By Stéphane Bonhomme; Kevin Dano; Bryan S. Graham
  3. Panel Data Models with Time-Varying Latent Group Structures By Yiren Wang; Peter C B Phillips; Liangjun Su
  4. What predicts willingness to participate in a follow-up panel study among respondents to a national web/mail survey? By Saw, Htay-Wah; West, Brady; Couper, Mick P.; Axinn, William G.
  5. Subjective Expected Utility and Psychological Gambles By Gianluca Cassese
  6. The Social Meaning of Mobile Money: Willingness to Pay with Mobile Money in Bangladesh By Jean N. Lee; Jonathan Morduch; Saravana Ravindran; Abu S. Shonchoy
  7. Determinants of climate change perception and behaviour of European households By Horbach, Jens
  8. Consistent estimation of finite mixtures: An application to latent group panel structures By Raphaël Langevin
  9. Combining Large Numbers of Density Predictions with Bayesian Predictive Synthesis By Tony Chernis
  10. Impact of depenalization on drugs deaths in England and Wales. An instrumental variable approach By Alessandra Foresta; Andrew Pickering
  11. Should we trust web-scraped data? By Jens Foerderer
  12. Strategic Ignorance and Perceived Control By Balietti, Anca; Budjan, Angelika; Eymess, Tillmann; Soldà, Alice

  1. By: Gary Chamberlain
    Abstract: This paper studies identification in a binary choice panel data model with choice probabilities depending on a lagged outcome, additional observed regressors and an unobserved unit-specific effect. It is shown that with two consecutive periods of data identification is not possible (in a neighborhood of zero), even in the logistic case.
    Date: 2023–07–26
  2. By: Stéphane Bonhomme; Kevin Dano; Bryan S. Graham
    Abstract: We study identification in a binary choice panel data model with a single predetermined binary covariate (i.e., a covariate sequentially exogenous conditional on lagged outcomes and covariates). The choice model is indexed by a scalar parameter θ, whereas the distribution of unit-specific heterogeneity, as well as the feedback process that maps lagged outcomes into future covariate realizations, are left unrestricted. We provide a simple condition under which θ is never point-identified, no matter the number of time periods available. This condition is satisfied in most models, including the logit one. We also characterize the identified set of θ and show how to compute it using linear programming techniques. While θ is not generally point-identified, its identified set is informative in the examples we analyze numerically, suggesting that meaningful learning about θ may be possible even in short panels with feedback. As a complement, we report calculations of identified sets for an average partial effect, and find informative sets in this case as well.
    Date: 2023–07–26
  3. By: Yiren Wang; Peter C B Phillips; Liangjun Su
    Abstract: This paper considers a linear panel model with interactive fixed effects and unobserved individual and time heterogeneities that are captured by some latent group structures and an unknown structural break, respectively. To enhance realism the model may have different numbers of groups and/or different group memberships before and after the break. With the preliminary nuclear-norm-regularized estimation followed by row- and column-wise linear regressions, we estimate the break point based on the idea of binary segmentation and the latent group structures together with the number of groups before and after the break by sequential testing K-means algorithm simultaneously. It is shown that the break point, the number of groups and the group memberships can each be estimated correctly with probability approaching one. Asymptotic distributions of the estimators of the slope coefficients are established. Monte Carlo simulations demonstrate excellent finite sample performance for the proposed estimation algorithm. An empirical application to real house price data across 377 Metropolitan Statistical Areas in the US from 1975 to 2014 suggests the presence both of structural breaks and of changes in group membership.
    Date: 2023–07
  4. By: Saw, Htay-Wah; West, Brady; Couper, Mick P.; Axinn, William G.
    Abstract: The American Family Health Study (AFHS) collected family health and fertility data from a national probability sample of persons aged 18–49 between September 2021 and May 2022, using web and mail exclusively. In July 2022, we surveyed AFHS respondents and gauged their willingness to become part of a national web panel that would create novel longitudinal data on these topics. We focus on predictors of willingness to participate, identifying the potential selection bias that this type of approach may introduce. We found that efforts of this type to create a national web panel may introduce potential selection bias in estimates based on the panel respondents, with individuals having higher socioeconomic status being more cooperative. Thus, alternative recruitment strategies and re-weighting of the subsample may be needed to further reduce selection bias. We present methodological implications of our results, limitations of our approach, and suggestions for further research on this topic.
    Date: 2023–07–27
  5. By: Gianluca Cassese
    Abstract: We obtain an elementary characterization of expected utility based on a representation of choice in terms of psychological gambles, which requires no assumption other than coherence between ex-ante and ex-post preferences. Weaker version of coherence are associated with various attitudes towards complexity and lead to a characterization of minimax or Choquet expected utility.
    Date: 2023–07
  6. By: Jean N. Lee (World Bank); Jonathan Morduch (Robert F. Wagner Graduate School of Public Service, New York University); Saravana Ravindran (Lee Kuan Yew School of Public Policy, National University of Singapore); Abu S. Shonchoy (Department of Economics, Florida International University)
    Abstract: Mobile money has spread globally, introducing new payment technologies and reducing dependence on cash. Using mobile money can affect spending decisions and how people perceive money itself. Behavioral household finance shows that people are often more willing to spend when using less tangible forms of money like debit and credit cards than when spending in cash. We test whether a similar positive “payment effect†holds for mobile money. In contrast, we find a consistently lower willingness to spend in Bangladesh, where mobile money is now widespread. We draw on surveys embedded within an experiment that allows us to control for the relationships between senders and receivers of mobile money. The findings are consistent with mobile money being earmarked or labeled for particular uses. For rural households, who typically receive remittances from relatives working in the city, for example, mobile money often comes with expectations of how the money should be spent. Spending with cash, in contrast, tends to be more fungible. In urban areas, where the sample is largely comprised of remittance-senders, payment effects are substantially smaller.
    Keywords: payment effect, digital finance, willingness to pay, social meaning of money, earmarks
    JEL: O15 G41 G50 D91 D14
    Date: 2023–08
  7. By: Horbach, Jens
    Abstract: The success of climate change measures is highly dependent on household behaviour as one of the most important emission sources of carbon dioxide. Private heating, electricity consumption or private transport are important key levers to reduce households' impacts on climate change. The paper analyses the determinants of climate change related attitudes and activities based on econometric estimations of European survey data. The results show that personal factors such as female gender, qualification and a high income are positively correlated to green behaviour. Persons having difficulties to pay their bills show a lower probability of buying local, climatefriendly products, but a bad economic situation is not a barrier for green attitudes. The results for the political orientation show that politically left and middle oriented persons are more likely for supporting climate change related actions.
    Keywords: Climate change, green household behaviour, European data, multivariate probit model
    JEL: C25 D12 D91 Q01
    Date: 2023
  8. By: Raphaël Langevin (McGill University)
    Abstract: In this presentation, I show that maximizing the likelihood of a mixture of a finite number of parametric densities leads to inconsistent estimates under weak regularity conditions. The size of the asymptotic bias is positively correlated with the overall degree of overlap between the densities within the mixture. In contrast, I show that slight modifications in the classification expectation-maximization (CEM) algorithm—the likelihood generalization of the K-means algorithm—produce consistent estimates of all parameters in the mixture, and I derive the asymptotic distribution of the proposed estimation procedure. I confirm the inconsistency of MLE procedures, such as the expectation-maximization (EM) algorithm, using numerical experiments with simple Gaussian mixture models. Simulation results show that the proposed estimation strategy generally outperforms the EM algorithm when estimating latent group panel structures with unrestricted group membership across units and over time. I also compare the finite-sample performance of each estimation strategy using a mixture of two-part models to predict individual healthcare expenditures from health administrative data. Estimation results show that the proposed consistent CEM approach leads to smaller prediction errors than models estimated with the EM algorithm, with a reduction of more than 40% in the out-of-sample prediction error compared with the standard, single-component, two-part model. The proposed estimation procedure thus represents a useful tool when both homogeneity of the parameters and constant group membership are assumed not to hold in panel-data analysis.
    Date: 2023–07–29
  9. By: Tony Chernis
    Abstract: Bayesian predictive synthesis is a flexible method of combining density predictions. The flexibility comes from the ability to choose an arbitrary synthesis function to combine predictions. I study the choice of synthesis function when combining large numbers of predictions—a common occurrence in macroeconomics. Estimating combination weights with many predictions is difficult, so I consider shrinkage priors and factor modelling techniques to address this problem. The dense weights of factor modelling provide an interesting contrast with the sparse weights implied by shrinkage priors. I find that the sparse weights of shrinkage priors perform well across exercises.
    Keywords: Econometric and statistical methods
    JEL: C11 C52 C53 E37
    Date: 2023–08
  10. By: Alessandra Foresta; Andrew Pickering
    Abstract: This article investigates the role of drug depenalization on drug related deaths in England and Wales. We use an instrumental variable approach, based on Police and Crime Commissioners elections and voters left-wing preferences corresponds a decrease in drug-related arrests. The IV results indicate that a decrease in our instrumented variables generates an increase in deaths related to drug poisoning/drug misuse. Specically, to a decrease of 1% in our instrumented variables corresponds to an increase between 0.04% and 0.07% in the drug poisoning/misuse deaths ratio. We replicate our analysis using different definitions of political preferences, lag specications, dependent and independent variables and the findings are similar.
    Date: 2023
  11. By: Jens Foerderer
    Abstract: The increasing adoption of econometric and machine-learning approaches by empirical researchers has led to a widespread use of one data collection method: web scraping. Web scraping refers to the use of automated computer programs to access websites and download their content. The key argument of this paper is that na\"ive web scraping procedures can lead to sampling bias in the collected data. This article describes three sources of sampling bias in web-scraped data. More specifically, sampling bias emerges from web content being volatile (i.e., being subject to change), personalized (i.e., presented in response to request characteristics), and unindexed (i.e., abundance of a population register). In a series of examples, I illustrate the prevalence and magnitude of sampling bias. To support researchers and reviewers, this paper provides recommendations on anticipating, detecting, and overcoming sampling bias in web-scraped data.
    Date: 2023–08
  12. By: Balietti, Anca; Budjan, Angelika; Eymess, Tillmann; Soldà, Alice
    Abstract: Information can trigger unpleasant emotions. As a result, individuals might be tempted to willfully ignore it. We experimentally investigate whether increasing perceived control can mitigate strategic ignorance. Participants from India were presented with a choice to receive information about the health risk associated with air pollution and later asked to recall it. We find that perceived control leads to a substantial improvement in information retention. Moreover, perceived control mostly benefits optimists, who show both a reduction in information avoidance and an increase in information retention. This latter result is confirmed with a US sample. A theoretical framework rationalizes these findings.
    Keywords: air pollution; information avoidance; information retention; perceived control; motivated cognition; Luftverschmutzung
    Date: 2023–08–18

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