Keith Chen's Research at UCLA: A Multifaceted Exploration of Human and Economic Behavior
Keith Chen is a distinguished Professor of Behavioral Economics at UCLA's Anderson School of Management, holding the Bing and Alice Liu Yang Endowed Term Chair in Management and Innovation. His research transcends traditional disciplinary boundaries, employing innovative methodologies to investigate complex issues at the intersection of economics, psychology, and biology. Chen's work often leverages large-scale datasets and unconventional approaches to shed light on various aspects of human and economic behavior.
Early Research: Exploring the Roots of Economic Behavior
Chen's early research ventured into areas outside the traditional scope of economics. One notable project examined the potential long-term effects of prison conditions on the lives of former inmates. Furthermore, he explored the evolutionary origins of economic behavior, demonstrating that monkeys exhibit many of the same cognitive biases as humans. This suggests that certain fundamental biases may be deeply rooted in our evolutionary history. His work "How basic are behavioral biases? Evidence from capuchin monkey trading behavior" (2006) is a key publication in this area.
Language and Economic Decision-Making
A significant portion of Chen's research investigates the influence of language on economic choices. His work explores the hypothesis that languages that do not grammatically distinguish between present and future events (weak-FTR languages) may lead their speakers to adopt more future-oriented behaviors.
His research indicates that speakers of weak-FTR languages tend to save more, accumulate greater retirement wealth, smoke less, have lower rates of obesity, and enjoy better long-term health. These findings hold true across different regions of the world and even when comparing demographically similar individuals within the same country. Chen's earlier study demonstrated a correlation between languages that grammatically mark future events and their speakers' propensity to save. The implication is that languages which grammatically distinguish the present and the future may bias their speakers to distinguish them psychologically, leading to less future-oriented decision making. His 2013 paper, "The Effect of Language on Economic Behavior: Evidence from Savings Rates, Health Behaviors, and Retirement Assets," published in the American Economic Review, is a seminal work in this field.
Chen has also addressed the potential for spurious correlations due to the relatedness of languages (Galton's problem). By applying stricter tests for relatedness, he found that the statistical correlation between language and economic behavior weakens but remains reasonably robust under several tests. He argues that claims of synchronic patterns between cultural variables should be tested for spurious correlations.
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Leveraging Smartphone Data: Unveiling Real-World Behaviors
More recently, Professor Chen's research has focused on analyzing large, anonymized digital trace datasets obtained from smartphone location information. This unique data source provides precise insights into the movements and behaviors of millions of individuals over time, enabling him to study a wide range of issues.
Face-to-Face Interactions and Knowledge Flows
One area of investigation involves the returns to face-to-face interactions in driving agglomeration. Using smartphone geolocation data, Chen measures meetings between workers at different establishments in Silicon Valley. He explores the relationship between these meetings and citations among the firms involved to understand how knowledge flows result from such interactions. To isolate the causal impacts of face-to-face meetings, he uses meetings between workers in adjacent firms belonging to unconnected industries as an instrument.
The Gig Economy and Flexible Work
Chen's research delves into the dynamics of the gig economy, particularly the Uber platform. He examines how dynamic pricing, such as "surge" pricing, influences labor supply. Contrary to findings in some earlier studies, Chen's research indicates that Uber partners drive more when earnings are high, flexibly adjusting to drive more at high surge times. A discontinuity design confirms the causal effect of surge pricing on increasing the supply of rides.
His work also investigates the effects of pay flexibility on labor supply, focusing on the option for gig-economy workers to be paid immediately after work (Instant Pay). A randomized controlled trial at Uber revealed that flexible pay substantially increased drivers' work time, particularly when they were further away from the end of their counterfactual weekly pay cycle, consistent with present-biased preferences. The paper "The value of flexible work: Evidence from Uber drivers" (2019) published in the Journal of Political Economy, co-authored with Chevalier and Rossi, provides detailed insights into this topic.
Furthermore, Chen has studied how drivers utilize real-time flexibility in setting their work schedules and adjusting them throughout the day. His findings suggest that Uber drivers benefit significantly from this real-time flexibility, earning more than twice the surplus they would in less flexible arrangements. If required to supply labor inflexibly at prevailing wages, they would also reduce their hours supplied by more than two-thirds.
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COVID-19 Spread and Community Connections
Chen's research has also addressed critical public health issues, such as the spread of COVID-19. By mapping the movement of prison staff using smartphone location data and publicly available employment data, he has examined how California communities are connected to nearby prisons. Leveraging an exogenous prisoner transfer-induced COVID-19 outbreak at San Quentin state prison as a quasi-experiment, Chen measured the unidirectional spread of the disease from the prison to surrounding communities.
The study found that zip codes connected to San Quentin via staff movement experienced significantly higher rates of new COVID-19 cases compared to unconnected zip codes with similar pre-transfer characteristics. This research highlights the role of staff movements in spreading community infections.
Racial Disparities in Voting and Policing
Chen's work has also shed light on racial disparities in various societal contexts. He has examined voting wait times, revealing that residents of entirely-black neighborhoods waited significantly longer to vote compared to those in entirely-white neighborhoods, even within the same states and counties.
In the realm of policing, Chen's research, using anonymized smartphone data, maps the neighborhood movement of police officers across major American cities. The findings indicate that police spend more time in neighborhoods with higher proportions of Black residents, even after controlling for socioeconomic factors and crime rates.
Impact of Political Polarization on Family Ties
In a study that garnered significant attention, Chen explored the impact of political polarization on close family ties. Using anonymized smartphone-location data and precinct-level voting data, he found that Thanksgiving dinners attended by opposing-party precinct residents were significantly shorter than those attended by same-party residents. This effect was particularly pronounced in areas with heavy political advertising during the 2016 election. The findings were published in Science in 2018 in the article "The effect of partisanship and political advertising on close family ties".
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Other notable publications
Chen's other notable publications include:
- "Do harsher prison conditions reduce recidivism? A discontinuity-based approach" (2007) in American Law and Economics Review.
- "How choice affects and reflects preferences: Revisiting the free-choice paradigm" (2010) in Journal of Personality and Social Psychology.
Consulting and Teaching
In addition to his academic research, Professor Chen advises numerous companies on topics related to behavioral economics, business strategy, and dynamic pricing. At UCLA Anderson School of Management, he teaches the MSBA core course in competitive analytics and Ph.D. courses.
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