About

Marlon Boarnet is a faculty member at USC Price with a focus on public policy, urban planning, and real estate development. His work encompasses research in areas such as economic development, environment, governance, housing, security, nonprofit management, and transportation. Boarnet contributes to the academic community through his involvement in research centers including the Center for Sustainable Solutions, the Center for Health Financing, Policy and Management, and the Lusk Center for Real Estate, among others. His expertise supports the development of policies and strategies aimed at improving urban environments and public welfare, and he is actively engaged in teaching and mentoring students in these fields.

Research topics

• Economics
• Demographic economics
• Business
• Engineering
• Political Science
• Computer Science
• Mathematics
• Statistics
• Physics
• Econometrics
• Medicine
• Transport engineering

Selected publications

• The effect of COVID-19 slow streets on dockless scooter travel: a quasi-experiment

  Transportation Research Part A Policy and Practice · 2026-05-07

  articleOpen access1st author

  We used the implementation of slow streets during COVID-19 as a quasi-experiment to understand how street treatments that prioritize non-car travel influenced dockless scooter trips. We gathered data on slow streets implementation from Los Angeles, Oakland, Portland, and San Francisco and identified control group, non-implemented slow streets candidates in three cities (excluding Portland). We examined the impact of slow streets on dockless scooter trips using a before-after, experimental-control group design. We implemented differences-in-differences and panel regressions to analyze the effect of slow streets on scooter trips, finding a robust pattern of statistically significant, positive associations between slow streets implementation and dockless trips. The treatment effect is generally in the range of a 7% to 94% increase in dockless trips. The results indicate potential for infrastructure treatments that slow car travel and prioritize non-car modes to increase dockless scooter trip-making. We discuss the findings and suggest avenues for future research.

  PublisherDOI

• Deepening Megaregional Interrelatedness Through Migration: The Case of the Northern California Megaregion

  Growth and Change · 2024-12-21

  articleSenior author

  ABSTRACT The increasing connectedness between neighboring metropolitan areas anchored by global economic centers highlights the relevance of the megaregional scale for government and governance. Yet, there is a lack of data to examine the expansion of megaregions and understand prevalent challenges to coordination. We use data from the Census Bureau and the California Franchise Tax Board (FTB) to analyze migration patterns within the Northern California Megaregion that combines the San Francisco Bay Area and California Central Valley and highlight different trends underlying regional expansion. We find that people are leaving Bay Area zip codes at the edge of the urbanized area where population growth is robust, migration rates lower, job accessibility is low, rents are nearly as high as the more central locations, and home values are lower, making it difficult to move elsewhere within the Bay Area. Moves into the Central Valley are divided between the suburbs of the main urban centers and isolated towns leading to fragmented growth that increases stress on transportation infrastructure and worsens spatial inequality in the region.

  PublisherDOI

• Smart Cities and Transportation: Governance and Institutional Frameworks

  Springer tracts on transportation and traffic · 2024-01-01

  book-chapter
  PublisherDOI

• Expo Line Study Dataset

  TIB Data Manager · 2024-01-01

  datasetOpen access1st authorCorresponding
  PublisherDOI

• Effects of new transit lines on commuting: Evidence from restricted-use Census Bureau microdata

  Applied Geography · 2024-01-13 · 5 citations

  articleSenior author
  PublisherDOI

• Who Drives Neighborhood Income Growth? An Analysis of New Versus Long-Term Residents in the Northern California Megaregion

  Journal of Planning Education and Research · 2024-09-06 · 1 citations

  articleSenior author

  Increases in neighborhood income are often attributed to the in-migration of higher income people. We use individual tax records from 1994 to 2015 to show a more nuanced finding. New residents generally have lower incomes than existing residents when they first move into a zip code. However, in-movers have steeper income growth and catch up with original residents within ten years. This pattern is mediated by turnover and generally short tenures for younger people, highlighting the importance of selective migration in combination with incumbents’ income growth in differentiating high- and low-growth zip codes.

  PublisherDOI

• Monetary cost, time cost, and mode choice: Transit and ridehailing in California

  Transportation Research Part D Transport and Environment · 2024-03-22 · 5 citations

  article1st authorCorresponding
  PublisherDOI

• Measuring the impact of COVID-19 policies on local commute traffic: Evidence from mobile data in Northern California

  Travel Behaviour and Society · 2023 · 8 citations

  • Business
  • Demographic economics
  • Economics
  PublisherDOI

• Replication Data for: Slow Streets and Dockless Travel: Using a Natural Experiment for Insight into the Role of Supportive Infrastructure on Non- Motorized Travel

  Harvard Dataverse · 2023-03-09

  datasetOpen access1st authorCorresponding

  In the early stages of the COVID-19 pandemic, cities across the globe converted street space to non-automobile uses. We study four of these slow street programs in the U.S., in Los Angeles, Portland, Oakland, and San Francisco. In each city, we use the slow streets (implemented in late spring to early fall 2020) as a treatment, and compare those slow streets to non-implemented control groups. Our dependent variable is counts of dockless scooter trips passing a mid-block screenline for time periods both before and after slow street implementation. We obtained those dockless scooter counts from historical data provided by Lime, a dockless scooter provider in each of our study cities. We use two methodological approaches: differences-in-differences (DID) and panel regression analysis with block fixed effects. For the DID analysis, we use networks of candidate slow streets that were not implemented as the control group. Such control networks were available in Los Angeles, Oakland, and San Francisco. For the panel analysis, we use slow street segments implemented later in our study period as control segments for earlier implemented slow street segments, including fixed effects for blocks and for time periods in the panel regressions. We find statistically significant associations between increased dockless scooter trips and slow street implementation in each study city, using both DID and panel analyses. The associations are robust to different specifications. We calculate the magnitude of the slow street treatment effect by dividing the estimated treatment effect by a 2019 baseline of dockless trip counts. In the DID analysis, we find that slow street implementation increased dockless scooter trip counts by from 54.78% to 74.5% relative to a 2019 (before slow streets) baseline. In the panel analysis, the increase in dockless trip counts on slow streets ranged from 10.77% to 16.75% relative to a 2019 baseline.

  PublisherDOI

• Replication Data for: New Open-Source Analyses of Transit Job Access and Transit Ridership

  Harvard Dataverse · 2022-04-19

  datasetOpen access1st authorCorresponding

  This research project examines the link between job access and stop/station level transit ridership. Job access, following recent literature, is measured as the number of jobs that can be reached within a 30-minute transit travel time, including transfers and walk time to access jobs once exiting a transit station. Cumulative opportunity job access measures of this sort – i.e. the number of jobs that can be reached within 30 minutes – have become common in the recent access literature, and those measures have often focused on access via transit. Yet there have been few studies that examine the link between transit job access and transit ridership, and of those none that examine the link at a station or stop level. We use station and stop level ridership data for the Los Angeles Metro bus and rail system and the BART rail system in the San Francisco Bay Area. We calculate transit job access as jobs that can be reached within 30 minutes, using the Remix software tool. Regression analysis of 1,000 randomly selected Los Angeles bus stops reveals a robust relationship between stop-level ridership and job access. The association between transit job access and bus stop ridership (embarkations and disembarkations at the stop) is statistically significant. Converting that association into an elasticity, if the number of jobs accessible within 30-minutes were to increase by 1 percent, on average stop-level ridership would increase between 0.6 to 0.8 percent. The same association, with similar magnitudes, exists for Metro rail stations and BART rail stations, but due the smaller sample sizes, those relationships are not statistically significant when control variables are added to the regression. Our findings show that job access is closely related to ridership at the bus stop level, suggesting transit agencies can increase job access by increasing bus frequency, reducing transfers, siting lines that connect job concentrations to residents, and by improving bus stop/rail station access/egress times.

  PublisherDOI

Frequent coauthors

• Kristen Day

  21 shared

• Mariela Alfonzo

  19 shared

• Randall Crane

  17 shared

• Seva Rodnyansky

  Occidental College

  16 shared

• Douglas Houston

  15 shared

• Raphael W. Bostic

  Federal Reserve Bank of Atlanta

  12 shared

• Wei Li

  12 shared

• Craig L. Anderson

  HEC Paris

  12 shared

Education

• Ph.D., Urban Planning

  University of California, Los Angeles

  1995

• M.A., Urban Planning

  University of California, Los Angeles

  1992

• B.A., Urban Studies

  University of California, Los Angeles

  1989