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"We examine the relationship between the business cycle and poverty for the period from 1960 to 2008 using income data from the Current Population Survey and consumption data from the Consumer Expenditure Survey. This new evidence on the relationship between macroeconomic conditions and poverty is of particular interest given recent changes in anti-poverty policies that have placed greater emphasis on participation in the labor market and in-kind transfers. We look beyond official poverty, examining alternative income poverty and consumption poverty, which have conceptual and empirical advantages as measures of the well-being of the poor. We find that both income and consumption poverty are sensitive to macroeconomic conditions. A one percentage point increase in unemployment is associated with an increase in the after-tax income poverty rate of 0.9 to 1.1 percentage points in the long-run, and an increase in the consumption poverty rate of 0.3 to 1.2 percentage points in the long-run. The evidence on whether income is more responsive to the business cycle than consumption is mixed. Income poverty does appear to be more responsive using national level variation, but consumption poverty is often more responsive to unemployment when using regional variation. Low percentiles of both income and consumption are sensitive to macroeconomic conditions, and in most cases low percentiles of income appear to be more responsive than low percentiles of consumption"--National Bureau of Economic Research web site

Contents -- Contributors -- Acknowledgments -- Introduction // Bruce D. Meyer and Douglas Holtz-Eakin -- Part I. The History of the Earned Income Tax Credit -- Chapter 1. The Collision of Tax and Welfare Politics: The Political History of the Earned Income Tax Credit // Dennis J. Ventry, Jr. -- Part II. Work and Marriage Incentives -- Chapter 2. Making Single Mothers Work: Recent Tax and Welfare Policy and Its Effects // Bruce D. Meyer and Dan T. Rosenbaum

In this paper, I first summarize how the US Earned Income Tax Credit (EITC) operates and describe the characteristics of recipients. I then discuss empirical work on the effects of the EITC on poverty and income distribution, and its effects on labor supply. Next, I discuss a few policy concerns about the EITC: possible negative effects on hours of work and marriage, and problems of compliance with the tax system. I then briefly discuss some possible reforms to the structure of the current EITC.

Survey error is known to be pervasive and to bias even simple, but important estimates of means, rates, and totals, such as poverty statistics and the unemployment rate. To summarize and analyze the extent, sources, and consequences of survey error, we define empirical counterparts of key components of the Total Survey Error Framework that can be estimated using data combination. Specifically, we estimate total survey error and decompose it into three high level sources of error: representation error, item non-response error and measurement error. We further decompose these sources into lower level sources such as a failure to report a positive amount and errors in amounts conditional on reporting a positive value. For error in dollars paid by two large government transfer programs, we use administrative records on the universe of program payments in New York State linked to three major household surveys to estimate the error components we define. We find that total survey error is large and varies in its size and composition, but measurement error is always by far the largest source of error. Our application shows that data combination makes it possible to routinely measure total survey error and its components. The results allow survey producers to assess error reduction strategies and survey users to mitigate the consequences of survey errors or gauge the reliability of their conclusions.

We describe methods of combining administrative and survey data to improve the measurement of income. We begin by decomposing the total survey error in the mean of survey reports of dollars received from a government transfer program. We decompose this error into three parts, generalized coverage error (which combines coverage and unit non-response error and any error from weighting), item non-response or imputation error, and measurement error. We then discuss these three sources of error in turn and how linked administrative and survey data can assess and reduce each of these sources. We then illustrate the potential of linked data by showing how using linked administrative variables improves the measurement of income and poverty in the Current Population Survey, focusing on the substitution of administrative for survey data for three government transfer programs. Finally, we discuss how one can examine the accuracy of the underlying links used in the combined data.