Many educational policies hinge on the valid measurement of student economic disadvantage at the school level. Measures based on free and reduced-price lunch enrollment are used widely. However, recent research raises questions about their reliability, particularly following the introduction of universal free lunch in certain schools and districts. Using unique data linking the universe of students in Oregon public schools to IRS tax records and other data housed at the U.S. Census Bureau, we provide the first examination of how well different measures capture school economic disadvantage. We find that, in Oregon, direct certification provides the best widely-available measure, both over time and across the distribution of school economic disadvantage. By contrast, neighborhood-based measures consistently perform relatively poorly.
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Potential Bias When Using Administrative Data to Measure the Family Income of School-Aged Children
January 2025
Working Paper Number:
CES-25-03
Researchers and practitioners increasingly rely on administrative data sources to measure family income. However, administrative data sources are often incomplete in their coverage of the population, giving rise to potential bias in family income measures, particularly if coverage deficiencies are not well understood. We focus on the school-aged child population, due to its particular import to research and policy, and because of the unique challenges of linking children to family income information. We find that two of the most significant administrative sources of family income information that permit linking of children and parents'IRS Form 1040 and SNAP participation records'usefully complement each other, potentially reducing coverage bias when used together. In a case study considering how best to measure economic disadvantage rates in the public school student population, we demonstrate the sensitivity of family income statistics to assumptions about individuals who do not appear in administrative data sources.
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Capturing More Than Poverty: School Free and Reduced-Price Lunch Data and Household Income
December 2017
Working Paper Number:
carra-2017-09
Educational researchers often use National School Lunch Program (NSLP) data as a proxy for student poverty. Under NSLP policy, students whose household income is less than 130 percent of the poverty line qualify for free lunch and students whose household income is between 130 percent and 185 percent of the poverty line qualify for reduced-price lunch. Linking school administrative records for all 8th graders in a California public school district to household-level IRS income tax data, we examine how well NSLP data capture student disadvantage. We find both that there is substantial disadvantage in household income not captured by NSLP category data, and that NSLP categories capture disadvantage on test scores above and beyond household income.
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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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Peer Income Exposure Across the Income Distribution
February 2025
Working Paper Number:
CES-25-16
Children from families across the income distribution attend public schools, making schools and classrooms potential sites for interaction between more- and less-affluent children. However, limited information exists regarding the extent of economic integration in these contexts. We merge educational administrative data from Oregon with measures of family income derived from IRS records to document student exposure to economically diverse school and classroom peers. Our findings indicate that affluent children in public schools are relatively isolated from their less affluent peers, while low- and middle-income students experience relatively even peer income distributions. Students from families in the top percentile of the income distribution attend schools where 20 percent of their peers, on average, come from the top five income percentiles. A large majority of the differences in peer exposure that we observe arise from the sorting of students across schools; sorting across classrooms within schools plays a substantially smaller role.
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School Discipline and Racial Disparities in Early Adulthood
June 2021
Working Paper Number:
CES-21-14
Despite interest in the role of school discipline in the creation of racial inequality, previous research has been unable to identify how students who receive suspensions in school differ from unsuspended classmates on key young adult outcomes. We utilize novel data to document the links between high school discipline and important young adult outcomes related to criminal justice contact, social safety net program participation, post-secondary education, and the labor market. We show that the link between school discipline and young adult outcomes tends to be stronger for Black students than for White students, and that inequality in exposure to school discipline accounts for approximately 30 percent of the Black-White disparities in young adult criminal justice outcomes and SNAP receipt.
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Errors in Survey Reporting and Imputation and Their Effects on Estimates of Food Stamp Program Participation
April 2011
Working Paper Number:
CES-11-14
Benefit receipt in major household surveys is often underreported. This misreporting leads to biased estimates of the economic circumstances of disadvantaged populations, program takeup, and the distributional effects of government programs, and other program effects. We use administrative data on Food Stamp Program (FSP) participation matched to American Community Survey (ACS) and Current Population Survey (CPS) household data. We show that nearly thirty-five percent of true recipient households do not report receipt in the ACS and fifty percent do not report receipt in the CPS. Misreporting, both false negatives and false positives, varies with individual characteristics, leading to complicated biases in FSP analyses. We then directly examine the determinants of program receipt using our combined administrative and survey data. The combined data allow us to examine accurate participation using individual characteristics missing in administrative data. Our results differ from conventional estimates using only survey data, as such estimates understate participation by single parents, non-whites, low income households, and other groups. To evaluate the use of Census Bureau imputed ACS and CPS data, we also examine whether our estimates using survey data alone are closer to those using the accurate combined data when imputed survey observations are excluded. Interestingly, excluding the imputed observations leads to worse ACS estimates, but has less effect on the CPS estimates.
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School Accountability and Residential Location Patterns: Evaluating the Unintended Consequences of No Child Left Behind
January 2017
Working Paper Number:
CES-17-28
The 2002 to 2015 No Child Left Behind (NCLB) Act is often considered the most significant federal intervention into education in the United States since 1965 with the passage of the Elementary and Secondary Education Act. There is growing evidence that holding schools accountable is leading to some improved educational outcomes for students. There is in contrast very little work examining whether these sweeping reforms have unintended consequences for the communities which these schools are serving. As school attendance, particularly at the elementary school level, is closely tied to one's residence, placing sanctions on a school could have negative repercussions for neighborhoods if it provides new information on school failure. In contrast, if these sanctions also bring new resources, including financial resources or school choice, they could spark additional demand within a neighborhood. Through the use of restricted access census data, which includes local housing values, rents and individual residential choices in combination with the use of a boundary discontinuity identification strategy, this paper seeks to examine how failure to meet Adequate Yearly Progress (AYP), the key enforcement mechanism of NCLB, is shaping local housing markets and residential choices in five diverse urban school districts: New York, Los Angeles, Philadelphia, Detroit and Tucson.
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Virtual Charter Students Have Worse Labor Market Outcomes as Young Adults
June 2023
Working Paper Number:
CES-23-32
Virtual charter schools are increasingly popular, yet there is no research on the long-term outcomes of virtual charter students. We link statewide education records from Oregon with earnings information from IRS records housed at the U.S. Census Bureau to provide evidence on how virtual charter students fare as young adults. Virtual charter students have substantially worse high school graduation rates, college enrollment rates, bachelor's degree attainment, employment rates, and earnings than students in traditional public schools. Although there is growing demand for virtual charter schools, our results suggest that students who enroll in virtual charters may face negative long-term consequences.
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Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net
October 2015
Working Paper Number:
CES-15-35
We examine the consequences of underreporting of transfer programs in household survey data for several prototypical analyses of low-income populations. We focus on the Current Population Survey (CPS), the source of official poverty and inequality statistics, but provide evidence that our qualitative conclusions are likely to apply to other surveys. We link administrative data for food stamps, TANF, General Assistance, and subsidized housing from New York State to the CPS at the individual level. Program receipt in the CPS is missed for over one-third of housing assistance recipients, 40 percent of food stamp recipients and 60 percent of TANF and General Assistance recipients. Dollars of benefits are also undercounted for reporting recipients, particularly for TANF, General Assistance and housing assistance. We find that the survey data sharply understate the income of poor households, as conjectured in past work by one of the authors. Underreporting in the survey data also greatly understates the effects of anti-poverty programs and changes our understanding of program targeting, often making it seem that welfare programs are less targeted to both the very poorest and middle income households than they are. Using the combined data rather than survey data alone, the poverty reducing effect of all programs together is nearly doubled while the effect of housing assistance is tripled. We also re-examine the coverage of the safety net, specifically the share of people without work or program receipt. Using the administrative measures of program receipt rather than the survey ones often reduces the share of single mothers falling through the safety net by one-half or more.
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Mobility, Opportunity, and Volatility Statistics (MOVS):
Infrastructure Files and Public Use Data
April 2024
Working Paper Number:
CES-24-23
Federal statistical agencies and policymakers have identified a need for integrated systems of household and personal income statistics. This interest marks a recognition that aggregated measures of income, such as GDP or average income growth, tell an incomplete story that may conceal large gaps in well-being between different types of individuals and families. Until recently, longitudinal income data that are rich enough to calculate detailed income statistics and include demographic characteristics, such as race and ethnicity, have not been available. The Mobility, Opportunity, and Volatility Statistics project (MOVS) fills this gap in comprehensive income statistics. Using linked demographic and tax records on the population of U.S. working-age adults, the MOVS project defines households and calculates household income, applying an equivalence scale to create a personal income concept, and then traces the progress of individuals' incomes over time. We then output a set of intermediate statistics by race-ethnicity group, sex, year, base-year state of residence, and base-year income decile. We select the intermediate statistics most useful in developing more complex intragenerational income mobility measures, such as transition matrices, income growth curves, and variance-based volatility statistics. We provide these intermediate statistics as part of a publicly released data tool with downloadable flat files and accompanying documentation. This paper describes the data build process and the output files, including a brief analysis highlighting the structure and content of our main statistics.
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