Measuring Poverty at the State Level

Abstract

This study implements the modern poverty measure for Minnesota using the American Community Survey (ACS) and simulates the potential effects of alternative safety net policies on poverty. The analysis uses the TRIM3 microsimulation model to correct for survey underreporting and to add information required for this poverty measure, including near-cash benefits, taxes and nondiscretionary expenses. The alternative simulations apply new program rules and behavioral assumptions to recalculate family resources and poverty. The results show the importance of the modern poverty measure for analyzing state policies and also highlight the numerous decisions and imputations required to implement the new measure.

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Introduction

Over the past several years, numerous states have initiated task forces and commissions focused on developing policies to reduce poverty.1 States’ recommendations frequently include expanding income supports such as the earned income tax credit (EITC), child care subsidies, nutritional assistance, and early childhood and postsecondary education investments. Some states focus solely on reducing child poverty, while others focus on options for reducing poverty more generally. States’ interest in reducing poverty stems from their recognition of the economic costs of poverty, especially the toll that poverty exerts on children.

Most state commissions quickly recognize the need for a benchmark that would allow them to track progress in reducing poverty and to test the effects of different policy proposals on poverty reduction. Such a benchmark would incorporate all components of family resources as well as an up-to-date measure of family needs in their state. The official measure of poverty used in the United States, based solely on cash income and a national measure of need set back in the 1950s (and subsequently adjusted by changes in prices), does not provide a good benchmark. For example, the official measure would not capture the effects of increasing child care subsidies or the effects of increasing the EITC for low-income families.

The measure of poverty recommended by the National Academy of Sciences (NAS) in 1995 meets states’ needs for a useful benchmark.2 The NAS measure includes all types of income, including that received in kind and through the tax system. The measure accounts for the effects of nondiscretionary work and out-of-pocket health expenses on net family income. The NAS measure also uses an updated measure of the cost of basic needs and captures variations by geographic area.

This report describes a model that implements the NAS measure of poverty at the state level. We use the American Community Survey (ACS) as the basic model input because this survey provides large, representative samples of the population in each state. We use the Transfer Income Model, Version 3 (TRIM3) for the model’s platform. The TRIM3 model includes procedures to impute in-kind resources and taxes, and it corrects for underreporting of benefits from key government assistance programs. Since TRIM3 uses state-specific program rules to simulate benefit programs and taxes, it provides an excellent platform for the remaining work required to estimate the NAS poverty measure using the ACS. The statistical model measures poverty at the state level for the base year and can estimate the likely effects of a variety of policies designed to reduce poverty.

This paper begins by describing the NAS poverty measure and how it differs from the official measure. The next section describes how we implement the NAS poverty measure. We describe some key features of the ACS, highlighting how it compares with the Current Population Survey—the Census survey used for the official poverty measure—and how we implement the NAS poverty measure using the ACS. The next section shows the results of the NAS poverty measure for Minnesota in 2006 compared with those using the official measure. We chose this state and year because we had previously estimated the NAS poverty measure in Minnesota using the Current Population Survey, and this provides an important benchmark against we could validate and compare the ACS results.3 Then we demonstrate how the model can be used to estimate the effects of alternative policies on poverty. We simulate several policies similar to those that Minnesota has considered. Our final section summarizes the benefits and challenges in implementing the NAS poverty using the ACS. The appendix provides more detail on model procedures, including results of the baseline simulations.

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