Research

Publications
  1. Huseynov, S., Kassas, B., Segovia, M. S., & Palma, M. A. (2018). “Incorporating biometric data in models of consumer choice.” Applied Economics, 1-18. (Link).
  2. Huseynov, S. and Palma, M.A. “Does California’s Low Carbon Fuel Standards reduce carbon dioxide emissions?” PloS one, 13(9): e0203167, September 2018. (Open Access Link)
  3. Ng, D., Aragon, L., and Huseynov, S. “Seek and you shall find: The role of exploitive and explorative search in a biotechnology firm’s patent claims” International Food and Agribusiness Management Review (Link)

Working Papers

Health versus the Economy Amid COVID-19: What Do People Value More? (with Marco A. Palma and Rodolfo M. Nayga Jr.)

Abstract: Public efforts to battle COVID-19 have been portrayed as a trade-off between health and the economy. We investigate how the U.S. general public prioritizes the health and the income dimensions amid COVID-19 using an incentivized instrument with real monetary consequences. Specifically, participants have to divide monetary contributions between two charitable organizations representing either the health or the income dimension. An overwhelming majority of participants supports both dimensions, with higher monetary contributions to the health dimension (56%) compared to income (44%), but the difference is not large. Only a small fraction of respondents contributes exclusively to the health (10%) or income (5%) dimensions. This finding is important since the course of COVID-19 will be shaped by the policies governments implement and how the general public reacts to these policies.

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Job Market Paper: Does the magnitude of relative calorie distance affect food consumption? (with Marco A. Palma and Ghufran Ahmad)

Abstract: Can the magnitude of the calorie distance between food items explain the contradictory findings in previous literature regarding the impact of calorie labeling laws? Our theoretical model suggests that the relative calorie difference between alternatives in food menus is a missing link important for understanding the impact of calorie labeling information on calorie intake and reconciling inconsistencies in previous findings. We implement laboratory and lab-in-the-field restaurant experiments where participants make incentivized food choices in binary menus. We exogenously manipulate the magnitude and saliency of the calorie distance between food alternatives. We find that providing accurate calorie information increases the likelihood of low-calorie choices by 3% and 10% in the lab and restaurant experiments, respectively. However, the menu-dependent calorie distance discounts the effect of information-provision. Our findings suggest that a 100-calorie increase in the calorie distance between the food alternatives reduces the probability of choosing the low-calorie alternative by 3%.

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Distributional Eects of Price Salience on Reservation Wages and Food Choices (with Marco Palma and Michelle Segovia).

Abstract: This article enriches the attribute salience literature in economics by providing compelling evidence that inducing price salience affects consumer expenditures and reservation wages. We use a laboratory experiment to show that high price salience reduces the likelihood of purchasing high quality low-calorie food items at a price premium. We also find that income is an important factor that moderates this effect. The low-income group demonstrates similar purchasing behaviors regardless of the price salience condition. In the absence of price salience in the decision environment, the high-income group is more likely to choose more expensive low-calorie foods. This effect vanishes when high-income consumers are exposed to environments with high price salience. Using a novel design, we find that inducing price salience reduces the reservation wage of high-income participants to perform a real effort task to offset the cost of their food expenditures. We conclude that the high-income group drives the variation in our outcome measures across experimental conditions, and they align their food choices and labor supply decisions with low-income subjects after being exposed to low and high price salience environments. Relative price changes between low- and high-calorie products yield significantly more healthy choices. A 20% price discount on the low-calorie alternative induces over 95% low-calorie selections.

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Food Decision-Making under Time Pressure (with Marco Palma).

Abstract: Does time pressure affect the cognitive process and subsequently food choices? We use the Drift Diffusion (DD) model and data from a well-controlled experiment to show that the cognitive process behind food choices is subject to significant changes under time pressure. Specifically, we find that subjects tend to accumulate less product information compared to the no time pressure condition. Under time pressure, they also spend less time encoding pre-decisional product stimuli and have more information accumulation speed to make food choices. However, faster decisions do not affect the consistency of food choices. Our post hoc analysis suggests that subjects manage to use acquired information more efficiently under time pressure. Particularly, with the same amount of accumulated information subjects are more likely to make consistent food choices under time pressure compared to the no time pressure condition. Overall, our results indicate that exposing consumers to less, but more crucial food information, may improve the efficiency and consistency of food choices.

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Predicting the Highway Costs Index with Machine Learning (with Luis Ribera and Marco Palma).

Abstract: Big data is increasingly attracting the attention of economists. New machine learning techniques can help to analyze unconventional data structures with a large number of variables relative to the number of observations. This new venue of research offers unique opportunities for analyzing previously untouched fields due to data limitations. Our study introduces a machine learning approach for modeling and forecasting highway construction cost changes. Our Lasso and Random Forest models have high predictive power, and it suggests that the application of machine learning techniques can improve the estimation of actual project costs for optimal allocation of public funds.

Work-in-progress
  • Convexity of Self-Control Cost (with Marco Palma and Alex Brown).
  • Conspicuity ranking and Reference Point (with Marco Palma, Ghufran Ahmad, and Rodolfo Nayga).
  • Pay Me To Choose Healthy? (with Marco Palma and Michelle Segovia) (Poster version). 
  • More Emotional Better Predictable (SAEA presentation link) (with Marco Palma).
  • Temptation and Food Choices (with Marco Palma and Ghufran Ahmad).