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IIM Indore

We develop a two-part high-dimensional Bayesian modelling approach to analyse the relationship between saving behaviour and health. In contrast to the existing literature, our approach allows different data-generating processes for the decision to save and the amount saved, and therefore unveils a more detailed picture of the relationship between financial behaviour and health than previous work. We explore different measures of saving, including monthly saving behaviour and the stock of financial assets held. Further, we exploit British panel data, which includes an extensive range of biomarkers. Our second contribution lies in comparing the effects of these objective measures of health with commonly used self-assessed health measures. We find that health is a significant determinant of saving behaviour and financial asset holding, and that biomarker measures have differential impacts on saving behaviour compared to self-reported health measures.

World University root node

Operation Management and Quantitative Techniques

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The European Journal of Finance

Saving behaviour and health: A high-dimensional Bayesian analysis of British panel data

In this paper, we employ a unique tax experiment and dataset in a highly salient tax rate environment to examine consumer response to complex and uncertain tax reforms. Tax reforms raise some fundamental questions in public finance: How does consumption respond to tax change? How is the tax burden distributed among the stakeholders? More importantly, are these answers conditional on whether the tax regime (and not the tax rate) is salient? We find that unpredictability of tax policy changes induces imperfectly rational consumption response. The tax regime complexity does not allow clear tracing of price change despite full information about tax rate changes and so, consumers also respond to notional price changes. This notional response is based on inaccurate beliefs about the impact of visible tax changes on real commodity prices and thus, consumers hoard even those goods that experience a price fall post reform. We further analyse commodity pricing to see how businesses respond and provide evidence of skewness in pricing response post the reform. The burden of tax increase is passed through more than the benefit of tax decrease. Both these aspects, irrational hoarding and an asymmetric transfer of tax benefit to final consumers, are welfare reducing. This is important from a policy perspective since it highlights issues created by complex tax policies.

SSRN Electronic Journal

(Ir)Rational Consumer Responses: Effect of Uncertain Tax Changes

Measuring the consumption response to a tax regime has been a central issue in the optimal design of fiscal policy. This study leverages a large-scale natural experiment in India, the 2017 Goods-Service-Tax (GST) Reform, to quantify its impact on suppliers’ pricing strategy and consumer spending. Using scanner data on purchasing behavior of a panel of households and a triple difference design, we find a spending reduction in quantities of 0.23 percent, on average, following one percentage point increase in the GST tax. Tax elasticity is not uniform across product categories and is highest for goods in the fresh and fashion class (2.1 and 1.7, respectively). Consumers exhibit intertemporal substitution by preponing spending during the announcement period and substitute consumption across products. We also find evidence of asymmetry for tax pass-through to consumers. The tax is shifted at a lower rate in the case of tax rise, but when the tax is reduced, the elasticity of the tax exclusive price is significantly smaller than -1, resulting in a higher final price and a lower level of spending.

SSRN

Tax-Pass-through, Pricing Strategy and Consumer Spending Dynamics: The Indian GST Experience

The goal of this research is to study jointly physician prescription decisions and patient drug request behaviours. We have adopted a binary logit model and a multinomial logit model to study patient drug request data with excessive zero requests and a multinomial logit model to capture physician prescription decisions. These models are further joined by a flexible non‐parametric multivariate distribution for their random effects. We also adopt an analytically consistent expression for interaction effects in our non‐linear and joint modelling framework. We apply our model to a unique physician panel data set from the erectile dysfunction category. Our key empirical findings include that the triggering of drug requests by direct‐to‐consumer advertising (DTCA) is complex with category level DTCA reducing patients’ probabilities of making drug requests and drug‐specific DTCA driving drug requests for the drug advertised, patients’ characteristics may play a role in both the influence of DTCA on drug requests and the influence of patients’ requests on physicians’ prescription decisions, patients’ drug requests have a positive effect on physicians’ prescription decisions and patients can be consistent with physicians in choosing a drug based on their diagnosis levels and some unobserved factors, and there are significant correlations between physician level random effects that drive both patients’ drug requests and physicians’ prescription decisions, which validate the joint modelling approach.

Journal of the Royal Statistical Society. Series A: Statistics in Society

Ask your doctor whether this product is right for you: a Bayesian joint model for patient drug requests and physician prescriptions

Using big datasets from four diverse sources, of both suppliers and facilitators or recipients of digital payment platforms, we conduct a comprehensive analysis of the impact of unexpected demonetization in India on payment digitization. The drying-up of cash due to demonetization leads to a significant increase in the level of payment digitization and the rise applies to both traditional and non-traditional digital payment modes, including card and e-wallet. The usage pattern persists even during the re-monetization period when cash makes a comeback in the economy, indicating the success of demonetization in enabling new digitization technologies to make inroads into consumer payment habits. Heterogeneity tests reveals that regions that are underdeveloped economically, lag in financial inclusion and technology infrastructure and have a population with more conservative demographic characteristics experience a weaker digitization response.

Demonetization and digitization

Commonly used methods of evaluating the impact of marketing communications during political elections struggle to account for respondents&rsquo; exposures to these communications due to the problems associated with recall bias. In addition, they completely fail to account for the impact of mediated or earned communications, such as newspaper articles or television news, that are typically not within the control of the advertising party, nor are they effectively able to monitor consumers&rsquo; perceptual responses over time. This study based on a new data collection technique using cell-phone text messaging (called real-time experience tracking or RET) offers the potential to address these weaknesses. We propose an RET-based model of the impact of communications and apply it to a unique choice situation: voting behavior during the 2010 UK general election, which was dominated by three political parties. We develop a Bayesian zero-inflated dynamic multinomial choice model that enables the joint modeling of: the interplay and dynamics associated with the individual voter's choice intentions over time, actual vote, and the heterogeneity in the exposure to marketing communications over time. Results reveal the differential impact over time of paid and earned media, demonstrate a synergy between the two, and show the particular importance of exposure valence and not just frequency, contrary to the predominant practitioner emphasis on share-of-voice metrics. Results also suggest that while earned media have a reducing impact on voting intentions as the final choice approaches, their valence continues to influence the final vote: a difference between drivers of intentions and behavior that implies that exposure valence remains critically important close to the final brand choice. Supplementary materials for this article are available online.

Journal of the American Statistical Association

Tracking the Impact of Media on Voter Choice in Real Time: A Bayesian Dynamic Joint Model

The goal of this research is to jointly study physician prescription decisions and patient drug request behaviors. We have adopted a binary logit model and a multinomial logit (MNL) model to study patient drug request data with excessive zero requests and a MNL model to capture physician prescription decisions. These models are further joined by a flexible nonparametric multivariate distribution for their random effects. We also adopt an analytically consistent expression for interaction effects in our non-linear and joint modeling framework. We apply our model to a unique physician panel data set from the Erectile Dysfunction category. Our key empirical findings include the following: (1) the triggering of drug requests by DTCA is complex with category level DTCA reducing patients' probabilities of making drug requests and drug specific DTCA driving drug requests for the advertised drug; (2) patient characteristics may play a role in both the impact of DTCA on drug requests and the impact of patient requests on physician prescription decisions; (3) patient drug requests have a positive impact on physician prescription decisions and patients can be consistent with physicians in choosing a drug based on their diagnosis levels and some unobserved factors; (4) there are significant correlations among physician-level random effects that drive both patient drug requests and physician prescription decisions, which validates the joint modeling approach.

Journal of Royal Statistical Society: Series A

Ask Your Doctor If This Product Is Right for You: A Bayesian Joint Model for Patient Drug Requests and Physician Prescriptions

We develop a dynamic zero-inflated model to analyse the number of hospital admissions within an aging population, which allows for the considerable number of zero hospital admissions at the individual level and occurrence dependence. In addition, certain health conditions may lead to groups of individuals having similar hospital admission rates. We analyse the US Health and Retirement Survey, which includes selfassessed health (SAH), which can be predictive of hospital admissions. Our modelling framework embeds a dynamic hierarchical matrix stick-breaking process to flexibly characterize this dynamic group structure allowing individuals to belong to different SAH groups at different points in time.

Sheffield economic research paper series

A Semiparametric Bayesian Approach to a New Dynamic Zero-Inflated Model

The goal of this research is to study physicians' prescription decisions and patients' drug request behaviors jointly. We have developed a new zero-inflated multinomial (ZiMNL) choice model to study patient drug request data with excessive zero requests and a standard multinomial logit (MNL) model to capture physician prescriptions decisions. The two models are joined by a flexible nonparametric multivariate distribution for their random effects. We also adopt an analytically consistent expression for the interaction effect in our non-linear and joint modeling framework. We apply our model to a unique physician panel data set from the Erectile Dysfunction category. Our key empirical findings include the following: (1) the triggering of drug requests by DTCA is complicated with category level DTCA reducing patients' probability of making drug requests and drug specific DTCA driving drug requests for the advertised drug; (2) patient characteristics may play a role in the impact of DTCA on drug requests and the impact of patient requests on physicians' prescription decisions; (3) patient drug requests have a significant impact on physicians' prescription decisions and patients can be consistent with physicians in choosing a drug based on patient diagnosis level and some unobserved factors; (4) there are significant correlations among physician-level random effects that drive both patients' drug requests and physicians' prescription decisions, which validates the joint modeling approach.

WORKING PAPER

Ask your doctor if this product is right for you: a bayesian zero-in ated multinomial joint model for patient drug requests and physician prescriptions

Statistics in Medicine

Discussion on 'Joint modeling of survival and longitudinal non-survival data' by Gould et al.

In survival analysis, or in reliability study, an investigator is often interested in the assessment of a specific risk in the presence of other risk factors. It is well known as the competing risks problem in statistical literature. Moreover, censoring is inevitable in any life testing or reliability study. In this paper, we consider a very general censoring scheme, namely a progressive censoring scheme. It is further assumed that the lifetime distribution of the individual causes are independent and Weibull-distributed with the same shape parameters but different scale parameters. We obtain the maximum likelihood and approximate maximum likelihood estimates of the unknown parameters. We also compute the observed Fisher information matrix using the missing information principles, and use them to compute the asymptotic confidence intervals. Monte Carlo simulations are performed to compare the performances of the different methods, and one data set is analyzed for illustrative purposes. We also discuss different optimality criteria, and selected optimal progressive censoring plans are presented. © 2009 Elsevier B.V. All rights reserved.