Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net

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

Correcting for Misreporting of Government Benefits

Recent validation studies show that survey misreporting is pervasive and biases common analyses. Addressing this problem is further complicated, because validation data are usually convenience samples and access is restricted, making them more suitable to document than to solve the problem. I first use administrative SNAP records linked to survey data to evaluate corrections for misreporting that have been applied to survey data. Second, I develop a method that combines public use data with an estimated conditional distribution from the validation data. It does not require access to the validation data, is simple to implement and applicable to a wide range of econometric models. Using the validation data, I show that this method improves upon both the survey data and the other corrections, particularly for multivariate analyses. Some survey-based corrections also yield large error reductions, which makes them attractive alternatives when validation data do not exist. Finally, I examine whether estimates can be improved based on similar validation data, to mitigate that the population of interest is rarely validated. For SNAP, I provide evidence that extrapolation using the method developed here improves over survey data and corrections without validation data. Deviations from the geographic distribution of program spending are often reduced by a factor of 5 or more. The results suggest substantial differences in program effects, such as reducing the poverty rate by almost one percentage point more, a 75 percent increase over the survey estimate

What leads to measurement errors? Evidence from reports of program participation in three surveys

Measurement errors are often a large source of bias in survey data. Lack of knowledge of the determinants of such errors makes it difficult to reduce the extent of errors when collecting data and to assess the validity of analyses using the data. We study the determinants of reporting error using high quality administrative data on government transfers linked to three major U.S. surveys. Our results support several theories of misreporting: Errors are related to event recall, forward and backward telescoping, salience of receipt, the stigma of reporting participation in welfare programs and respondent's degree of cooperation with the survey overall. We provide evidence on how survey design choices affect reporting errors. Our findings help survey users to gauge the reliability of their data and to devise estimation strategies that can correct for systematic errors, such as instrumental variable approaches. Understanding survey errors allows researchers collecting survey data to reduce them by improving survey design. Our results indicate that survey design should take into account that higher response rates as well as collecting more detailed information may have negative effects on survey accuracy

Who Believes in Me? The Effect of Student-Teacher Demographic Match on Teacher Expectations

Teachers are an important source of information for traditionally disadvantaged students. However, little is known about how teachers form expectations and whether they are systematically biased. We investigate whether student-teacher demographic mismatch affects high school teachers’ expectations for students’ educational attainment. Using a student fixed effects strategy that exploits expectations data from two teachers per student, we find that non-black teachers of black students have significantly lower expectations than do black teachers. These effects are larger for black male students and math teachers. Our findings add to a growing literature on the role of limited information in perpetuating educational attainment gaps

Short-time work and related measures to mitigate the consequences of a (partial) economic shutdown

The objective of this document is to provide a basic foundation to think about the merits, alternatives and policy design choices of short-time work policies. The first section characterizes the motivation for short-term work and the types of costs that it can help to reduce or cause. The second section briefly overviews key policy alternatives and their merits, to lay out where short-time work has the potential to be useful, and what alternative tools can amend or replace it. This is followed by an overview of short-time work policies from the last recession and key lessons learned from that experience. The document closes with an overview of short-time work policies already enacted in response to the current economic situation. The main aim of this document is to draw general policy conclusions for the current situation in the Czech Republic based on the reviews and considerations in the first two sections. Section 3 will attempt to do so. Readers who are primarily interested in specific policies or those familiar with the literature on short-time work may want to go straight to Section 3

A simple method to estimate large fixed effects models applied to wage determinants and matching

Models with high dimensional sets of fixed effects are frequently used to examine, among others, linked employer-employee data, student outcomes and migration. Estimating these models is computationally difficult, so simplifying assumptions that are likely to cause bias are often invoked to make computation feasible and specification tests are rarely conducted. I present a simple method to estimate large two-way fixed effects (TWFE) and worker-firm match effect models without additional assumptions. It computes the exact OLS solution including estimates of the fixed effects and makes testing feasible even with multi-way clustered errors. An application using German linked employer-employee data illustrates the advantages: The data reject the assumptions of simpler estimators and omitting match effects biases estimates including the returns to experience and the gender wage gap. Specification tests detect both problems. The results suggest that firm fixed effects, not match effects, are the main channel through which job transitions drive wage dynamics, which underlines the importance of firm heterogeneity for labor market dynamics

Two Simple Methods to Improve Official Statistics for Small Subpopulations

Many important statistics are known from official records for the entire population, but have to be estimated for subpopulations. I describe two simple data combination methods that reduce the substantial sampling error of the commonly used direct survey estimates for small subpopulations. The first estimator incorporates information from repeated cross-sections, while the second estimator uses the knowledge of the statistic for the overall population to improve accuracy of the estimates for subpopulations. To evaluate the estimators, I compare the estimated number of female and elderly recipients of a government transfer program by county to the "true" number from administrative data on all recipients in New York. I find that even the simple estimators substantially improve survey error. Incorporating the statistic of interest for the overall population yields particularly large error reductions and can reduce non-sampling error

Income Support during the Great Recession: New Evidence from Linked Survey and Administrative Data

This project examines the poverty reducing and distributional effects of income support programs during the great recession. A new linked data set allows me to account for the pronounced program misreporting in the data used in previous evaluations. Most studies rely on survey reports that suffer from misreporting or on administrative records with limited income and demographic information. We combine the advantages of each data source by linking administrative records from multiple government programs to three large economic surveys. We will use this new and unique data set to analyze what share of the drop in income of disadvantaged households during the recession was replaced by safety net programs and how much the programs reduced poverty. We will also examine whether the programs interact to form an effective safety net or whether some households or demographic groups are missed

Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net

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. We link administrative data for food stamps, TANF, General Assistance, and subsidized housing from New York State to the CPS at the household level. Program receipt in the CPS is missed for over one-third of housing assistance recipients, over 40 percent of food stamp recipients and over 60 percent of TANF and General Assistance recipients. Dollars of benefits are also undercounted for reporting recipients, particularly for TANF, General Assistance and housing benefits. We find that the survey sharply understates the income of poor households. 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