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Tip of the Iceberg: Tip Reporting at U.S. Restaurants, 2005-2018
November 2024
Working Paper Number:
CES-24-68
Tipping is a significant form of compensation for many restaurant jobs, but it is poorly measured and therefore not well understood. We combine several large administrative and survey datasets and document patterns in tip reporting that are consistent with systematic under-reporting of tip income. Our analysis indicates that although the vast majority of tipped workers do report earning some tips, the dollar value of tips is under-reported and is sensitive to reporting incentives. In total, we estimate that about eight billion in tips paid at full-service, single-location, restaurants were not captured in tax data annually over the period 2005-2018. Due to changes in payment methods and reporting incentives, tip reporting has increased over time. Our findings have implications for downstream measures dependent on accurate measures of compensation including poverty measurement among tipped restaurant workers.
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The Effect of Food Assistance Work Requirements on Labor Market Outcomes
September 2024
Working Paper Number:
CES-24-54
The Supplemental Nutrition Assistance Program (SNAP), formerly named the Food Stamp Program, has long been an integral part of the US social safety net. During US welfare reforms in the mid-1990s, SNAP eligibility became more restrictive with legislation citing a need to improve self-sufficiency of participating households. As a result, legislatures created two of these eligibility requirements: the General Work Requirement (GWR), which forces an adult to work to receive benefits, and the Able-Bodied Adult Without Dependents (ABAWD) work requirement, which requires certain adults to work a certain number of hours to receive benefits. Using restricted-access SNAP microdata from nine states, we exploit age cutoffs of the ABAWD work requirement and General Work Requirement (GWR) to estimate the effect of these policies on labor outcomes. We find that at the ABAWD age cutoff, there is no statistically significant evidence of a discontinuity across static and dynamic employment outcomes. At the GWR age cutoff, unemployed SNAP users and SNAP-eligible adults are on average more likely to leave the labor force than to continue to search for work.
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Grassroots Design Meets Grassroots Innovation: Rural Design Orientation and Firm Performance
March 2024
Working Paper Number:
CES-24-17
The study of grassroots design'applying structured, creative processes to the usability or aesthetics of a product without input from professional design consultancies'remains under investigated. If design comprises a mediation between people and technology whereby technologies are made more accessible or more likely to delight, then the process by which new grassroots inventions are transformed into innovations valued in markets cannot be fully understood. This paper uses U.S. data on the design orientation of respondents in the 2014 Rural Establishment Innovation Survey linked to longitudinal data on the same firms to examine the association between design, innovation, and employment and payroll growth. Findings from the research will inform questions to be investigated in the recently collected 2022 Annual Business Survey (ABS) that for the first time contains a Design module.
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There is Such Thing as a Free Lunch: School Meals, Stigma, and Student Discipline
July 2022
Working Paper Number:
CES-22-23R
The Community Eligibility Provision (CEP) allows high-poverty schools to offer free meals to all students regardless of household income. Conceptualizing universal meal provision as a strategy to alleviate stigma associated with school meals, we hypothesize that CEP implementation reduces the incidence of suspensions, particularly for students from low-income backgrounds and minoritized students. We link educational records for students enrolled in Oregon public schools between 2010 and 2017 with administrative data describing their families' household income and social safety net program participation. Difference-in-differences analyses indicate that CEP has protective effects on the probability of suspension for students in participating schools, particularly for students from low-income families, students who received free or reduced-price meals prior to CEP implementation, and Hispanic students.
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The Evolution of U.S. Retail Concentration
March 2022
Working Paper Number:
CES-22-07
Increases in national concentration have been a salient feature of industry dynamics in the U.S. and have contributed to concerns about increasing market power. Yet, local trends may be more informative about market power, particularly in the retail sector where consumers have traditionally shopped at nearby stores. We find that local concentration has increased almost in parallel with national concentration using novel Census data on product-level revenue for all U.S. retail stores. The increases in concentration are broad based, affecting most markets, products, and retail industries. We implement a new decomposition of the national Herfindahl Hirschman Index and show that despite similar trends, national and local concentration reflect different changes in the retail sector. The increase in national concentration comes from consumers in different markets increasingly buying from the same firms and does not reflect changes in local market power. We estimate a model of retail competition which links local concentration to markups. The model implies that the increase in local concentration explains one-third of the observed increase in markups.
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Neighborhood Income and Material Hardship in the United States
January 2022
Working Paper Number:
CES-22-01
U.S. households face a number of economic challenges that affect their well-being. In this analysis we focus on the extent to which neighborhood economic conditions contribute to hardship. Specifically, using data from the 2008 and 2014 Survey of Income and Program Participation panel surveys and logistic regression, we analyze the extent to which neighborhoods income levels affect the likelihood of experiencing seven types of hardships, including trouble paying bills, medical need, food insecurity, housing hardship, ownership of basic consumer durables, neighborhood problems, and fear of crime. We find strong bivariate relationships between neighborhood income and all hardships, but for most hardships these are explained by other household characteristics, such as household income and education. However, neighborhood income retains a strong association with two hardships in particular even when controlling for a variety of other household characteristics: neighborhood conditions (such as the presence of trash and litter) and fear of crime. Our study highlights the importance of examining multiple measures when assessing well-being, and our findings are consistent with the notion that collective socialization and community-level structural features affect the likelihood that households experience deleterious neighborhood conditions and a fear of crime.
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Small Business Pulse Survey Estimates by Owner Characteristics and Rural/Urban Designation
September 2021
Working Paper Number:
CES-21-24
In response to requests from policymakers for additional context for Small Business Pulse Survey (SBPS) measures of the impact of COVID-19 on small businesses, we researched developing estimates by owner characteristics and rural/urban locations. Leveraging geographic coding on the Business Register, we create estimates of the effect of the pandemic on small businesses by urban and rural designations. A more challenging exercise entails linking micro-level data from the SBPS with ownership data from the Annual Business Survey (ABS) to create estimates of the effect of the pandemic on small businesses by owner race, sex, ethnicity, and veteran status. Given important differences in survey design and concerns about nonresponse bias, we face significant challenges in producing estimates for owner demographics. We discuss our attempts to meet these challenges and provide discussion about caution that must be used in interpreting the results. The estimates produced for this paper are available for download. Reflecting the Census Bureau's commitment to scientific inquiry and transparency, the micro data from the SBPS will be available to qualified researchers on approved projects in the Federal Statistical Research Data Center network.
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Business Dynamics on American Indian Reservations: Evidence from Longitudinal Datasets
November 2020
Working Paper Number:
CES-20-38
We use confidential US Census Bureau data to analyze the difference in business establishment dynamics by geographic location on or off of American Indian reservations over the period of the Great Recession, and subsequent recovery (2007-2016). We geocoded U.S. Census Bureau's Longitudinal Business Database, a dataset with records of all employer business establishments in the U.S. for location in an American Indian Reservation and used it to examine whether there are differences in business establishment survival rates over time by virtue of their location. We find that business establishments located on American Indian reservations have higher survival rates than establishments located in comparable counties. These results are particularly strong for the education, arts and entertainment, wholesale and retail, and public administration industries. While we are not fully able to explain this result, it is consistent with the business establishments being positively selected with respect to survival given the large obstacles necessary to start a business on a reservation in the first place. Alternatively, there may be certain safeguards in a reservation economy that protect business establishments from external economic shocks.
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Reservation Nonemployer and Employer Establishments: Data from U.S. Census Longitudinal Business Databases
December 2018
Working Paper Number:
CES-18-50
The presence of businesses on American Indian reservations has been difficult to analyze due to limited data. Akee, Mykerezi, and Todd (AMT; 2017) geocoded confidential data from the U.S. Census Longitudinal Business Database to identify whether employer establishments were located on or off American Indian reservations and then compared federally recognized reservations and nearby county areas with respect to their per capita number of employers and jobs. We use their methods and the U.S. Census Integrated Longitudinal Business Database to develop parallel results for nonemployer establishments and for the combination of employer and nonemployer establishments. Similar to AMT's findings, we find that reservations and nearby county areas have a similar sectoral distribution of nonemployer and nonemployer-plus-employer establishments, but reservations have significantly fewer of them in nearly all sectors, especially when the area population is below 15,000. By contrast to AMT, the average size of reservation nonemployer establishments, as measured by revenue (instead of the jobs measure AMT used for employers), is smaller than the size of nonemployers in nearby county areas, and this is true in most industries as well. The most significant exception is in the retail sector. Geographic and demographic factors, such as population density and per capita income, statistically account for only a small portion of these differences. However, when we assume that nonemployer establishments create the equivalent of one job and use combined employer-plus-nonemployer jobs to measure establishment size, the employer job numbers dominate and we parallel AMT's finding that, due to large job counts in the Arts/Entertainment/Recreation and Public Administration sectors, reservations on average have slightly more jobs per resident than nearby county areas.
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An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices
August 2018
Working Paper Number:
CES-18-35
Statistical agencies face a dual mandate to publish accurate statistics while protecting respondent privacy. Increasing privacy protection requires decreased accuracy. Recognizing this as a resource allocation problem, we propose an economic solution: operate where the marginal cost of increasing privacy equals the marginal benefit. Our model of production, from computer science, assumes data are published using an efficient differentially private algorithm. Optimal choice weighs the demand for accurate statistics against the demand for privacy. Examples from U.S. statistical programs show how our framework can guide decision-making. Further progress requires a better understanding of willingness-to-pay for privacy and statistical accuracy.
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