Research
Publications
Deniers and Compliers: Unpacking the Heterogeneous Effectiveness of U.S. Stay-at-Home Mandates During the COVID-19 Pandemic
Abstract: While existing work shows COVID-19 stay-at-home (SAH) policies decreased mobility on average, we lack evidence regarding heterogeneity in policy effectiveness across US counties. To uncover potential heterogeneity, we implement a novel two-stage approach. First, we employ recent advances in synthetic control estimation and inference to obtain and bound both time-average and county-specific treatment effects. Next, we use a local linear forest to explain observed heterogeneity using county-level predictors. This procedure reveals substantial variation in policy effectiveness and identifies predictors of policy effectiveness which can be used to target future SAH policies.
Closing the Equity–Revenue Gap: Pricing Pathways for Unmetered Urban Water Utilities in the Global South
Abstract: Urban water utilities across South Asia grapple with persistent financial shortfalls and significant inequities in water affordability, with limited tools for reform in largely unmetered systems. In this study, we evaluate non-volumetric pricing strategies and service reforms capable of improving utility revenues while reducing the disproportionate financial burden of water expenditures borne by lower-income piped households. Using detailed household-level survey data and property value assessments from Karachi, Pakistan, we estimate a logistic regression model of bill payment behavior and train a local linear regression forest to model household reliance on expensive alternative water sources. Next, we build a simulation framework using these models to estimate how utility revenues and household water expenditures change for a range of tariff and service reforms, and how these reforms reshape inequality in water expenditures. Simulation results for the current tariff system indicate that coupling full bill distribution with improved service quality could increase utility revenues by approximately 80% by leveraging latent willingness to pay among currently-unbilled households. Considering two additional tariff systems—one that prioritizes reducing regressivity of the existing tariff system and another that maximizes expected utility revenues—yields evidence of a revenue-equity tradeoff: shifting to property value-indexed tariff yields improved equity but reduced utility revenues under both service scenarios. Uniform tariff increases aimed solely at maximizing revenues worsen regressivity, lower payment compliance, and raise affordability concerns. Our findings demonstrate that equity-focused tariff design combined with administrative and service improvements could enable greater financial sustainability and distributional equity in unmetered urban water systems.
Heterogeneous Consumer Responses to Added Sugar Disclosures: Evidence from the U.S. Nutrition Facts Panel Update
Abstract: In recent years, health and nutrition policies that spur behavioral change through the provision of information have risen in popularity, in part due to their relatively lower implementation costs. This study investigates the heterogeneous impact of the Nutrition Facts Panel (NFP) update, specif- ically focusing on the disclosure of added sugars on carbonated soft drink (CSD) consumption in the United States. Using NielsenIQ household panel data covering 9,989 U.S. households between 2017 and 2020, we first estimate a difference-in-differences framework before and after label im- plementation as a baseline analysis. We find that the policy led to significant reductions in CSD purchases on average. We then use causal forest methods to examine heterogeneous treatment ef- fects and show that the response varies systematically with household demographic and attitudinal characteristics. Lower-income and less-educated consumers exhibited stronger reductions, while higher-income, health-conscious households, who already prioritized healthier attributes, were less influenced. Consumers’ attitudes towards different food attributes also play a critical role. Counter- factual simulations suggest that increasing consumer emphasis on health-conscious food attributes enhances responsiveness among the least responsive households but has a more mixed impact on high-consumption households. These findings underscore the importance of considering consumer heterogeneity in policy design and suggest that labeling policies could be complemented by tar- geted interventions to maximize their effectiveness.
Are we #stayinghome to Flatten the Curve?
Abstract: The recent spread of COVID-19 across the U.S. led to concerted efforts by states to ``flatten the curve" through the adoption of stay-at-home mandates that encourage individuals to reduce travel and maintain social distance. Combining data on changes in travel activity with COVID-19 health outcomes and state policy adoption timing, we characterize nationwide changes in mobility patterns and isolate the portion attributable to statewide mandates. We find evidence of dramatic nationwide declines in mobility prior to adoption of any statewide mandates. Once states adopt a mandate, we estimate further mandate-induced declines between 2.1 and 7.0 percentage points across methods that account for states' differences in travel behavior prior to policy adoption. In addition, we investigate the effects of stay-at-home mandates on changes in COVID-19 health outcomes while controlling for pre-trends and observed pre-treatment mobility patterns. We estimate mandate-induced declines between 0.13 and 0.17 in deaths (5.6 to 6.0 in hospitalizations) per 100 thousand across methods. Across 43 adopting states, this represents 23,366-30,144 fewer deaths (and roughly one million averted hospitalizations) for the months of March and April - which indicates that death rates could have been 42-54% higher had states not adopted statewide policies. We further find evidence that changes in mobility patterns prior to adoption of statewide policies also played a role in reducing COVID-19 mortality and morbidity. Adding in averted deaths due to pre-mandate social distancing behavior, we estimate a total of 48-71,000 averted deaths from COVID-19 for the two-month period. Given that the actual COVID-19 death toll for March and April was 55,922, our estimates suggest that deaths would have been 1.86-2.27 times what they were absent any stay-at-home mandates during this period. These estimates represent a lower bound on the health impacts of stay-at-home policies, as they do not account for spillovers or undercounting of COVID-19 mortality. Our findings indicate that early behavior changes and later statewide policies reduced death rates and helped attenuate the negative consequences of COVID-19. Further, our findings of substantial reductions in mobility prior to state-level policies convey important policy implications for re-opening.
Tree-based Matching on Structural Equation Model Parameters
Abstract: Understanding causal effects of a treatment is often of interest in the social sciences. When treatments cannot be randomly assigned, researchers must ensure that treated and untreated participants are balanced on covariates before estimating treatment effects. Conventional practices are useful in matching such that treated and untreated participants have similar average values on their covariates. However, situations arise in which a researcher may instead want to match on model parameters. We propose an algorithm, Causal Mplus Trees, which uses decision trees to match on structural equation model parameters and estimates conditional average treatment effects in each node. We provide a proof of concept using two small simulation studies and demonstrate its application using COVID-19 data.
Clustering Life Course to Understand the Heterogeneous Effects of Life Events, Gender, and Generation on Habitual Travel Modes
Abstract: Daily transportation mode choice is largely habitual, but transitions between life events may disrupt travel habits and can shift choices between alternative transportation modes. Although much is known about general mode switches following life event transitions, less is understood about differences that may exist between subpopulations, especially from a long-term perspective. Understanding these differences will help planners and policymakers introduce more targeted policy interventions to promote sustainable transportation modes and inform longer-term predictions. Extending beyond existing literature, we use data collected from a retrospective survey to investigate the effects of life course events on mode use situated within different long-term life trajectory contexts. We apply a machine-learning method called joint social sequence clustering to define five distinct and interpretable cohorts based on trajectory patterns in family and career domains over their life courses. We use these patterns as an innovative contextual system to investigate (1) the heterogeneous effects of life events on travel mode use and (2) further differentiation between gender and generation groups in these life event effects. We find that events occurring relatively early in life are more strongly associated with changes in mode-use behavior, and that mode use can also be affected by the relative order of events. This timing and order effect can have lasting impacts on mode use aggregated over entire life cycles: members of our “Have-it-alls” cohort-who finish their education, start working, partner up, and have children early in life-ramp up car use at each event, resulting in the highest rate of car use occurring the earliest among all the cohorts. Women drive more when having children primarily when their family formation and career formation are intertwined early in life, and younger generations rely relatively more on car use during familial events when their careers have a later start.
Describing the Users: Understanding Adoption of and Interest in Shared, Electrified, and Automated Transportation in the San Francisco Bay Area
Abstract: Emerging technologies and services stand poised to transform the transportation system, with large implications for energy use and mobility. The degree and speed of these impacts depend largely on who adopts these innovations and how quickly. Leveraging data from a novel survey of San Francisco Bay Area residents, we analyze adoption patterns for shared mobility, electrified vehicle technologies, and vehicle automation. We find that ride-hailing and adaptive cruise control have penetrated the market more extensively than have electrified vehicles or car-sharing services. Over half of respondents have adopted or expressed interest in adopting all levels of vehicle automation. Overall, there is substantial potential for market growth for the technologies and services we analyzed. Using county fixed effects regressions, we investigate which individual and location-level factors correlate to adoption and interest. We find that, although higher-income people are disproportionately represented among current adopters of most new technologies and services, low to middle-income people are just as likely to have adopted pooled ride-hailing. Younger generations have high interest in automated and electrified vehicles relative to their current adoption of these technologies, suggesting that young people could contribute substantially to future market growth—as they are doing for ride-hailing. We find no evidence that longer commutes present a barrier to plug-in electric vehicle adoption. Finally, women are less likely than men to adopt and/or be interested in adopting most new transportation technologies, with the exception of ride-hailing; designing or marketing technologies with women’s preferences in mind could contribute to future market expansion.
Research Papers
How Much More Fluid is Urban Water Demand in the Long Run? Evidence and Mechanisms from California
Abstract: I estimate short- and long-run urban water demand using administrative data from a major Northern California water district. Leveraging variation in prices across the district, I find a short-run marginal price elasticity of -0.13. Next, leveraging persistent price discontinuities across arbitrary intra-district boundaries, I find long-run elasticities of -0.24 to -0.32, with finer geographic controls yielding bigger values. Investments in water-efficient technologies partly explain why long-run demand is more elastic. These are the first causal estimates comparing long-run vs. short-run demand for households in the same water district, and are valuable to guide both utility pricing and conservation policy.
Culpable Consumption: Residential Response to Price and Non-Price Water Conservation Measures
Abstract: When reducing resource consumption is necessary during temporary scarcity, what policy instrument is best to maximize conservation? Using unique variation in price and behavioral policy instruments, I separately identify residential water users' responses to price and non-price drought policy instruments. Using water use data for 300,000 single family homes in the San Francisco Bay Area before, during, and after the 2015-2016 drought emergency, I first characterize differences in and changes to consumption patterns for both typical and high-usage patterns. Next, I take advantage of cross-sectional variation in prices across the utility district to obtain causal estimates of medium/long-run price elasticities prior to drought and show how price-responsiveness for both typical and high-usage households changes during extreme drought. I employ a pseudo-instrument approach coupled with flexible lagged consumption distribution controls to address simultaneity and mean reversion concerns. Nesting behavioral instrument event studies into the water demand model, I find that public shaming induced the greatest short-run reductions in water use even after households were exposed to moral suasion. Moral suasion on its own was the next most effective, with roughly half the conservation response. Finally, I show how the necessary price increases to match would benearly impossible in current policy contexts.
In Progress
Food for Thought: How Food Banks Mitigate the SNAP Benefit Cycle
Abstract: Many recent works have documented the existence of the “SNAP Benefit Cycle” in which a non-negligible portion of enrolled households exhaust their benefits early in the benefit month. Lack of benefits late in the month carries large consequences, with negative impacts ranging from reduced energy intake and nutritional content, to increased likelihood of hypoglycemia and pregnancy-related emergency room admissions, along with lowered test performance and increased disciplinary action for students. These effects diminish or are otherwise nonexistent for SNAP households that exhibit consumption smoothing. The ability for low-income households to complement resources from federal poverty programs with a local public good has major implications for household welfare and the value of food bank networks. This paper investigates the extent to which SNAP participants in California utilize food bank resources to augment consumption throughout the benefit month.
A Reputation For the Good Stuff: User Feedback Signaling and the Deep Web Market Silk Road
Abstract: Despite complete user anonymity, asymmetrical information, and incomplete enforcement mechanisms, the deep web market Silk Road facilitated approximately $200 million in illegal drug sales in 34 months. This study tests how the site’s reputation system facilitated successful transactions and how user feedback functioned as the primary signal of seller quality in the absence of formal contract enforcement. Using novel data from the site on marijuana, amphetamine, and meth transactions, listings, and vendors, I find strong evidence that consumers engaged with the site’s reputation system and relied on both item and seller-level information. Hedonic regressions provide evidence of a “bad news” learning environment, estimating a 3 to 11% price discount for negative item reviews. Seller ratings are found to act as an effective proxy for permanent seller characteristics, and named trains act as a primary source of quality (and price) differentiation for marijuana. I find no evidence of price penalties or signal heterogeneity for new sellers. This study is the first to shed light on the value of reputation on the deep web’s largest marketplace, yielding new insight into the mechanisms modern markets use to overcome social distance and prevent market failure.