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Household surveys, one of the main innovations in social science research of the last century, are threatened by declining accuracy due to reduced cooperation of respondents. While many indicators of survey quality have steadily declined in recent decades, the literature has largely emphasized rising nonresponse rates rather than other potentially more important dimensions to the problem. We divide the problem into rising rates of nonresponse, imputation, and measurement error, documenting the rise in each of these threats to survey quality over the past three decades. A fundamental problem in assessing biases due to these problems in surveys is the lack of a benchmark or measure of truth, leading us to focus on the accuracy of the reporting of government transfers. We provide evidence from aggregate measures of transfer reporting as well as linked microdata. We discuss the relative importance of misreporting of program receipt and conditional amounts of benefits received, as well as some of the conjectured reasons for declining cooperation and for survey errors. We end by discussing ways to reduce the impact of the problem including the increased use of administrative data and the possibilities for combining administrative and survey data.

We examine changes in consumption and income inequality between 2000 and 2011. During the most recent recession, unemployment rose and asset values declined sharply. We investigate how the recession affected inequality while addressing concerns about underreporting in consumption data. Income inequality rose throughout the period from 2000 to 2011. The 90/10 ratio was 19 percent higher at the end of this period than at the beginning. In contrast, consumption inequality rose during the first half of this period but then fell after 2005. By 2011, the 90/10 ratio for consumption was slightly lower than it was in 2000.

This paper addresses the economic impact of the COVID-19 pandemic by providing timely and accurate information on the impact of the current pandemic on income and poverty to inform the targeting of resources to those most affected and assess the success of current efforts. We construct new measures of the income distribution and poverty with a lag of only a few weeks using high-frequency data from the Basic Monthly Current Population Survey (CPS), which collects income information for a large, representative sample of US families. Because the family income data for this project are rarely used, we validate this timely measure of income by comparing historical estimates that rely on these data to estimates from data on income and consumption that have been used much more broadly. Our results indicate that at the start of the pandemic, government policy effectively countered its effects on incomes, leading poverty to fall and low percentiles of income to rise across a range of demographic groups and geographies. Simulations that rely on the detailed CPS data and that closely match total government payments made show that the entire decline in poverty that we find can be accounted for by the rise in government assistance, including unemployment insurance benefits and the Economic Impact Payments. Our simulations further indicate that of those losing employment the vast majority received unemployment insurance, though this was less true early on in the pandemic, and receipt was uneven across the states, with some states not reaching a large share of their out of work residents. Updated results during the pandemic for a subset of the tables in this article can be found at povertymeasurement.org.

We investigate well-being changes for single mother headed families targeted by recent tax and welfare reforms. Measured income changes sharply differ from consumption changes. We examine disaggregated consumption, time use, and health insurance coverage. Increases in housing and transportation spending mostly account for the rise in consumption in the bottom quintiles. We find modest improvement in housing quality, but the evidence is less strong at the very bottom. The consumption of nonmarket time for those in the bottom half of the consumption distribution falls sharply, indicating a loss in utility for those families if nonmarket time is valued above $3 per hour. (JEL D12, I31, I32, J12, J16)

We discuss poverty measurement, focusing on two alternatives to the current official measure: consumption poverty, and the Census Bureau's new Supplemental Poverty Measure (SPM) that was released for the first time last year. The SPM has advantages over the official poverty measure, including a more defensible adjustment for family size and composition, an expanded definition of the family unit that includes cohabitors, and a definition of income that is conceptually closer to resources available for consumption. The SPM's definition of income, though conceptually broader than pre-tax money income, is difficult to implement given available data and their accuracy. Furthermore, income data do not capture consumption out of savings and tangible assets such as houses and cars. A consumption-based measure has similar advantages but fewer disadvantages. We compare those added to and dropped from the poverty rolls by the alternative measures relative to the current official measure. We find that the SPM adds to poverty individuals who are more likely to be college graduates, own a home and a car, live in a larger housing unit, have air conditioning, health insurance, and substantial assets, and have other more favorable characteristics than those who are dropped from poverty. Meanwhile, we find that a consumption measure compared to the official measure or the SPM adds to the poverty rolls individuals who are more disadvantaged than those who are dropped. We decompose the differences between the SPM and official poverty and find that the most problematic aspect of the SPM is the subtraction of medical out-of-pocket expenses from SPM income. Also, because the SPM poverty thresholds change in an odd way over time, it will be hard to determine if changes in poverty are due to changes in income or changes in thresholds. Our results present strong evidence that a consumption-based poverty measure is preferable to both the official income-based poverty measure and to the Supplemental Poverty Measure for determining who are the most disadvantaged.

We evaluate consumption and income measures of the material well-being of the poor. We begin with conceptual and pragmatic reasons that favor income or consumption. Then, we empirically examine the quality of standard data by studying measurement error and under-reporting, and by comparing microdata from standard surveys to administrative microdata and aggregates. We also compare low reports of income and consumption to other measures of hardship and well-being. The closer link between consumption and well-being and its better measurement favors the use of consumption when setting benefits and evaluating transfer programs. However, income retains its convenience for determining program eligibility.

Despite the prevalence of temporary financial assistance programs for those facing imminent homelessness, there is little evidence of their impact. Using data from Chicago from 2010 to 2012 (n = 4448), we demonstrate that the volatile nature of funding availability leads to good-as-random variation in the allocation of resources to individuals seeking assistance. To estimate impacts, we compare families that call when funds are available with those who call when they are not. We find that those calling when funding is available are 76% less likely to enter a homeless shelter. The per-person cost of averting homelessness through financial assistance is estimated as $10,300 and would be much less with better targeting of benefits to lower-income callers. The estimated benefits, not including many health benefits, exceed $20,000.

Human service nonprofits are a major provider of social services in this country, spending billions of dollars each year implementing programs to improve outcomes for their clients. Unfortunately, these programs are typically not rigorously evaluated to determine whether they are having their intended effect. Many obstacles make it challenging to rigorously evaluate services provided by these nonprofits, including evaluation costs, limited access to data, and small sample sizes, but these obstacles are surmountable. Policymakers could accelerate the pace and quality of evidence building by providing more resources for impact evaluations, streamlining and standardizing access to key administrative data, and expanding support for the replication of effective programs. Better evidence of what works for human service nonprofits will ultimately mean more effective programs at the national level.