Hello, I'm
XIBO WAN(万溪博)
I am a postdoc associate in the Department of Agricultural and Resource Economics in University of Connecticut. Before joining Uconn, I was a postdoc associate at the MIT Center for Energy and Environmental Policy Research. I received my Ph.D. in Economics from Iowa State University.
Welcome to My Space
My Research
My research primarily explores the economic applications of mobility data, employing a comprehensive approach that involves various methodologies such as econometric analysis, empirical IO, and machine learning. This multifaceted approach is applied to address significant questions in the realms of environmental and food fields.
Human mobility data has emerged as a transformative tool for applied economic research, offering unprecedented granularity in analyzing human behaviors and spatial dynamics. This paper reviews how large-scale mobility data can enhance economic analyses, highlighting its contributions to understanding travel behavior, labor markets, social interactions, and health outcomes. We discuss its advantages over traditional mobility data sources, which include real-time location information and fine spatial resolution, while addressing key empirical challenges such as measurement errors, sampling biases, and privacy concerns. Additionally, we examine how ma- chine learning and interdisciplinary approaches can enhance the usefulness of mobility data for applied economic research. By synthesizing previous studies and identifying future directions, our review provides a roadmap for leveraging human mobility data at scale to refine economic models and inform policy decisions, underscoring the potential of human mobility data to enhance empirical research across various economic research fields.
Human Mobility
developing economic methodologies for mobility data applications;
Food Access Disparity
grasping nutritional health disparities and refining the precision of food aid targeting;
Bringing the Neighborhood Farm to the Table: Can Farmers’ Markets Help Reduce Food Access Disparity?
with Cristina Connolly and Sandro Steinbach
Neighborhood disparities in food outlet access pose a significant concern for the US food system. Numerous attempts have been made in the past to address this concern, including farmer's markets. Farmers' markets offer potential benefits by providing direct access to fresh, locally sourced produce, however, the overall effectiveness of farmers’ markets in fully mitigating these disparities remains. Here, leveraging the cellphone mobility data on farmers' market visits nationwide, we quantify the extent of travel behavior to farmers' markets from 2019-2022. We link cell phone users with the socio-economic and food access characteristics of their residential areas to assess disparities in accessability and utilization of farmers’ markets across different communities. We note that low-access communities are less likely to visit farmer’s markets, travel longer distance, and experience elevated levels of income isolation, relative to their counterparts. To identify strategies that could improve market utilization, we developed a discrete choice model to analyze consumer preferences concerning farmers' market characteristics and to identify optimal locations for new farmers' markets in communities with limited access. Our results provide valuable insights for policy strategies designed to enhance food security in low food access communities.
Past research on health laws and policies such as ban on abortions focus on patients, however less attention has been given to how policies affect the broader healthcare utilization, including practitioners. Understanding the wider consequences on the healthcare infrastructure is essential for assessing the full impact of restrictive laws on women's health. The study utilizes individual-level mobile phone data from Veraset, capturing visit patterns to family care clinics among Texas residents from March 1, 2021, to February 28, 2022. K-means clustering was applied to categorize visitors into three groups: (1) night-shift family care practitioners, (2) patients, and (3) a mix of administrative staff and daytime family care practitioners. Statistical analyses were conducted to evaluate changes in clinic visits and travel behaviors pre- and post-SB 8. Our results suggest that the implementation of Texas SB 8 was associated with significant shifts in family care clinic utilization, with increased burden on practitioners and decreased patient engagement in certain populations.
Healthcare Utilization
evaluate the effect of healthcare policy and its consequences
Environmental (Dis)amenities
understanding individual behavior and preferences regarding environmental amenities and climate hazards;
Outdoor recreation plays a pivotal role in improving people’s physical and mental health, serving as a popular form of entertainment and a significant economic contributor. Limited access to these resources not only exacerbates health disparities but also deprives underserved areas of essential benefits like stress relief and community bonding, both of which are crucial for enhancing overall quality of life. This paper provides one of the first detailed analyses of water-based recreation at over 61,000 inland and coastal sites across the United States. We aim to explore disparities in recreational behavior across race, ethnicity, income, and socioeconomic status. Using Advan cellphone data from more than 70 million outdoor trips, representing 215,000 census block groups, we find that communities of color, rural areas, and socioeconomically disadvantaged groups are significantly underrepresented in water-based recreational visits. Despite living similar distances from recreational sites, these groups show notably different patterns in travel distance for water-based recreation. Additionally, we find Native Americans from underserved areas have to travel 3-5 times longer distances than other groups for water-based recreation. Our findings show that the extensive and frequent cellphone mobility data could reveal policy-relevant patterns especially those made by underserved Americans often overlooked in traditional household surveys.
Abstract
This paper examines the impact of fish consumption advisories on recreational behavior and visitor welfare using large-scale mobility data from Michigan. By integrating cellphone-based location data with a discrete choice modeling framework, we estimate the causal effects of advisories on recreation site selection and quantify the associated welfare benefits of disclosing risks. To address potential endogeneity, we employ an instrumental variable approach using the number of Toxic Release Inventory facilities in upstream watersheds. Our findings show that advisories significantly deter recreational visits, with an average visitor willing to pay approximately $72 per quarter to avoid sites under advisory. The deterrent effect is most pronounced during peak recreational seasons, particularly in the summer, when willingness to pay to avoid advisory sites rises to over $87 per quarter. We also find that full disclosure on advisories led to aggregated welfare benefits of $3.4 million annually, highlighting the broader recreational benefits of fish consumption advisories and emphasizing the need to account for these benefits when assessing the overall impact of advisories.
Abstract
Accurately estimating the welfare impacts of environmental changes in recreation demand modeling requires robust causal inference methods. However, a persistent challenge remains as the zero market share issue restricts the causal inference application to broader regions and longer periods. To address this, we integrate empirical Bayes posterior mean estimation into a two-step random coefficient logit model, ensuring that sites with low or zero visitation are properly incorporated without distorting demand estimates. We apply this framework to the 2021 Huntington Beach oil spill, using high-frequency cellphone data to track changes in beach visits. By combining the Synthetic Difference-in-Differences (SDID) approach with empirical Bayes-adjusted market shares, we isolate the causal effects of temporary beach closures on visitor welfare. Our findings reveal that Huntington Beach experienced the largest and most prolonged welfare loss, with an estimated aggregate loss of $1.03 million and weekly losses of $0.09 million persisting beyond the initial closure. In contrast, Newport Beach and Laguna Beach exhibited faster recoveries. This study advances recreation demand modeling by refining demand estimation for low-visit sites and strengthening causal inference techniques for environmental disruptions, ultimately providing a more reliable framework for assessing the economic costs of beach closures and other environmental policy interventions.
Abstract
While mobility data has emerged as a promising alternative for assessing the economic value of recreation, the extent to which challenges such as measurement error, privacy-accuracy trade-offs, sampling biases, and choice set definition impact economic valuation remains unknown. In this paper, we evaluate the impact of precision, privacy, and representation in cell phone data on the accuracy of recreation demand estimation by applying a random utility model to analyze recreation visits at Cape Cod beaches from 2019-2022. Specifically, we estimate the marginal willingness to pay (MWTP) for avoiding fecal bacteria contamination under various data-handling practices. We find an average MWTP of $8.92 per visit when using the proposed practices, such as refined visit definitions, sampling weights, and long-term choice sets. Deviations from these practices introduce significant biases: relaxing the minimum dwell time and applying differential privacy reduce MWTP by 57% and 65%, respectively, while short-term choice set definition inflates it by 10%. Our findings underscore the need for judicious data processing to ensure accurate non-market valuations in mobility data applications and highlight the trade-offs between data privacy and estimation accuracy. These results provide valuable insights into effectively utilizing mobility data for environmental valuation and policymaking.
Abstract
While a theoretically consistent measure of flood costs is welfare loss, existing estimates are primarily
based on asset losses. This paper leverages high tide flooding (HTF)—highly disruptive yet rarely
destructive small-scale coastal floods—to estimate the economic cost of floods. We find that on HTF
days, visits to places decrease by 5%. Further, each additional day of HTF in the past year reduces
rents by 0.25%, which we interpret, based on the hedonic model, as the marginal willingness-to-pay to
avoid disruptions from HTF. Using this parameter, we estimate a lower-bound annual economic cost of
Presidential Disaster Declaration floods at $4 billion.
Research
Beyond Mobility Data
In addition to utilizing cell phone data, my research also utilizes large datasets from sources like retail scanners, housing transactions, and customs data to evaluate policy effectiveness and derive behavioral insights crucial for environmental and food policy making.
Abstract
Political and economic tensions, which often jeopardize trade, are rising among the world’s major powers, and countries like China are more frequently using food-related trade actions to deal with deteriorating political relations. Using an event study approach, this paper investigates how importers respond to lasting political tensions by examining China’s seafood importers’ responses to the six-year Norway-China political tensions after Norway awarded Liu Xiaobo, a Chinese political dissident, a Nobel Peace Prize in 2010. Our results reveal firm-level responses at both the intensive and extensive margins. At the intensive margin, firms that imported Norwegian fresh salmon before the sanction saw a 20% persistent decline in their fresh salmon import value and an 80% decrease in the import share of Norwegian fresh salmon products over our study period. At the extensive margin, we find a trade diversion effect that firms imported fresh salmon from Norway to other countries and regions, but also a consistent "political hedging" effect three years after sanction with a 20% decline in the maximum import share from any particular country or region, even if not Norway.
Abstract
Environmental justice research has predominately focused on disparities in exposure to pollution, with comparatively less attention to inequities in access to and benefits derived from natural amenities. This study examines socioeconomic disparities in recreational preferences and welfare gains from water quality improvements using five waves of household data from the Iowa Lakes Valuation Project from 2004 to 2019. Applying a repeated random utility model, we find that low-socioeconomic-status households are consistently more sensitive to travel costs, while both low- and high-socioeconomic status (SES) groups exhibit positive marginal willingness to pay (MWTP) for water quality. The gap in MWTP follows a U-shaped trend over time, while disparities in non-marginal welfare gains show an inverse U-shape, peaking in 2009 and declining by 2019. Scenario analyses reveal that universal water quality improvements deliver larger recreational benefits to high-SES households, whereas site closures, such as the hypothetical closure of West Okoboji Lake, may impose greater welfare losses on low-SES households in some years. Models that ignore heterogeneous preferences underestimate both total welfare gains and distributional disparities. These findings underscore the need for equity-focused policies in both ex post program evaluations and ex ante site prioritization, ensuring inclusive access and more equitable distribution of environmental benefits.
Research
Ongoing Projects and Additional Works
Experience
Teaching, Services, and Skills
Teaching Experience
Teaching Assistant: Principles of Microeconomics (ISU, 2016-2019), Economics of Discrimination (ISU, 2017-2019), Intermediate Environmental and Resource Economics (ISU, 2018), Microeconomics Analysis I (ISU, 2019)
Guest lecturer: Principles of Microeconomics (Uconn, 2024)
Teaching certificate: Transforming Your Research Into Teaching (TYRIT) (ISU, 2020)
Services and Skills
Referee: National Science Foundation (NSF – SBIR/STTR), Journal of Regional Science (JRS), Canadian Journal of Agricultural Economics (CJAE), Journal of the Association of Environmental and Resource Economics (JAERE), Land Economics, Climatic Change
Campus and Department Services: Economics Graduate Student Association (President, 2018), Economics Graduate Student Seminar (Coordinator, 2018), China Center for Human Capital and Labor Market Research (President, 2014)
Skills: Python, Stata, Matlab, R, QGIS, SQL, Latex, Office, JavaScript
Contact Me
I Want To Hear From You
Please fill out the form on this section to contact with me. Or call between 9:00 a.m. and 8:00 p.m. ET, Monday through Friday
Address
Ratcliffe Hicks Arena, Unit 108, Storrs, CT 06269
xibo.wan@uconn.edu
