Research

Publications

Huseynov, S., Kassas, B., Segovia, M. S., & Palma, M. A. (2018). “Incorporating biometric data in models of consumer choice.” Applied Economics, 1-18. (Link).

Abstract: The use of neuro-physiological data in models of consumer choice is gaining popularity. This article presents some of the benefits of using psycho-physiological data in analyzing consumer valuation and choice. Eye-tracking, facial expressions, and electroencephalography (EEG) data were used to construct three non-conventional choice models, namely, eye-tracking, emotion and brain model. The predictive performance of the non-conventional models was compared to a baseline model, which was based entirely on conventional data. While the emotion and brain models proved to be as good as conventional data in explaining and predicting consumer choice, the eye-tracking model generated superior predictions. Moreover, we document a significant increase in predictive power when biometric data from different sources were combined into a mixed model. Finally, we utilize a machine learning technique to sparse the data and enhance out-of-sample prediction, thus showcasing the compatibility of biometric data with well-established statistical and econometric methods.

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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)

Abstract: The Low Carbon Fuel Standards (LCFS) represents a new policy approach designed to reduce carbon dioxide emissions by applying standards to all stages of motor fuel production. We use the synthetic control and difference-in-differences econometric methods, and Lasso machine learning to analyze the effect of the LCFS on emissions in California’s transportation sector. The three different techniques provide robust evidence that the LCFS reduced carbon dioxide emissions in California’s transportation sector by around 10%. Furthermore, our calculations show that improved air quality, due to the application of the LCFS, may have benefited California in the magnitude of hundreds of millions of dollars through an increase in worker’s productivity.

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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 (In press)

Abstract: Patents are widely recognized to provide legal protections to a firm’s inventions. However, such protections are dependent upon claims that delineate the exclusive rights of the patent. This study examines theoretically and empirically the role of exploitive and explorative search on a firm’s patent claims in the biotechnology industry. We argue that firms are subject to ‘boundedly rational’ behaviors where firms are unable to cite their patent’s prior art and therefore are unable to identify with their patent’s novel claims. A firm’s exploitive and explorative search is offered as a solution to overcoming such bounded rationality. We argue and find that a biotechnology firm’s exploitive and explorative search has an inverted u-shaped relationship to a firm’s patent claims. A key contribution of this study is that a firm’s citation behavior is not only attributed to strategic and legal motivations, but also to behavioral explanations.

Working Papers

Job Market Paper: Does the magnitude of relative calorie distance affect food consumption? (with Marco 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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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.

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The Economics of Petro-Authoritarianism: Post-Soviet Transitions and Democratization (with Gubad Ibadoglu and Rashad Sadigov).

Abstract: The sharp decline in oil prices in the second half of 2014 drew attention to petro-authoritarian regimes. Crackdowns on civil societies across oil-rich nations of the post-Soviet space, and especially the aggressive behavior of Russian government, restored academic interest on the oil-hinders-democracy hypothesis of Ross (2001). However, some recent studies challenge the hypothesis and suggest that previous articles on the topic might suffer from endogeneity and not control the heterogeneous initial institutional quality. To address these issues, we use instrumental variable approach and employ the Synthetic Control Method (SCM) to test the impact of oil income on democracy in post-Soviet countries. The analyzed sample enables us to control the initial institutional quality and treat oil resources as a quasi-random assignment. Our empirical findings suggest that oil still impedes democracy.

Work-in-progress
  1. Pay Me To Choose Healthy? (with Marco Palma and Michelle Segovia) (Poster version).
  2. More Emotional Better Predictable (SAEA presentation link) (with Marco Palma).
  3. Temptation and Food Choices (with Marco Palma and Ghufran Ahmad).