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Report
Estimating Demand Shocks from Foot Traffic: A Big-Data Approach
This study leverages high-frequency foot-traffic data from SafeGraph to estimate demand shocks in customer-facing establishments across New York City’s retail, service, and health sectors. Recognizing that variations in foot traffic can arise from both unpredictable demand shocks and firm-driven strategies to attract customers, we present a theoretical framework that isolates establishment-level demand fluctuations from firm-level strategic choices. Implementing this empirically, we employ an unsupervised machine learning approach to classify establishments into distinct categories that are ...
Working Paper
Texas Service Sector Outlook Survey: Survey Methodology, Performance and Forecast Accuracy
The Texas Service Sector Outlook Survey (TSSOS) is a monthly survey of service sector and retail firms in Texas conducted by the Federal Reserve Bank of Dallas. TSSOS indexes provide timely information about activity in the Texas private service sector, which makes up the bulk of the state economy. The survey provides invaluable information on regional economic conditions—information that the Dallas Fed president and economists use in the formulation of monetary policy and informing the public. This paper describes the survey methodology and analyzes the explanatory and predictive power of ...
Report
Information and anti-American attitudes
This paper investigates how attitudes toward the United States are affected by the provision of information. We generate a panel of attitudes in urban Pakistan, in which respondents are randomly exposed to fact-based statements describing the United States in either a positive or negative light. Anti-American sentiment is high and heterogenous in our sample at the baseline, and systematically correlated with intended behavior, such as intended migration. We find that revised attitudes are, on average, significantly different from baseline attitudes: attitudes are revised upward (downward) ...
Report
Occupational Licensing of Uber Drivers
We examine whether occupational licensing improves service quality and safety using trip-level Uber data that include driver ratings and telematics-based measures of driving behavior. Exploiting quasi-random assignment from proximity-based dispatch, we compare trips served by licensed and unlicensed drivers in two settings: a cross-border comparison between New York City and New Jersey, and a deregulation event in Houston. Across settings and specifications, including instrumental variable estimates, we find no consistent evidence that licensing improves consumer outcomes. In Houston, ...