3

Measuring Child Poverty with the Supplemental Poverty Measure

Poverty measures compare resources available to families or households—such as pre- or post-tax cash income, the cash value of any in-kind benefits, and sometimes adjustments for certain expenses—to thresholds representing the dollar cost of obtaining a family’s or household’s bundle of basic material needs. This chapter reviews how poverty is measured in the United States and assesses the strengths and limitations of the most commonly used statistics for evaluating the impacts of the Earned Income Tax Credit (EITC) and the Child Tax Credit (CTC) under the American Rescue Plan Act of 2021 (ARPA) on child poverty (see Chapter 8 for a detailed discussion of these impacts) and for the potential impacts of alternative policy options to further reduce child poverty (see Chapter 9).

Per its statement of task, the committee used the Supplemental Poverty Measure (SPM), published by the U.S. Census Bureau, to estimate the resources available to households under various policy scenarios. This chapter explains why the SPM is more appropriate than the Official Poverty Measure (OPM) for evaluating the impact of the EITC and CTC policies in 2021 and the various alternative policy options the committee considered. The chapter acknowledges the limitations of both measures and considers the extent to which the estimates produced for this report can address those limitations. As detailed in Chapter 8 and Appendix H, the analysis relied on the Urban Institute’s Transfer Income Model version 3 (TRIM3), a microsimulation tool based on data from the Current Population Survey Annual Social and Economic Supplement (CPS ASEC). TRIM3 calculates income—including tax credits and transfers—for individuals in the CPS ASEC sample and uses those figures to estimate poverty. Microsimulation

models simulate the effects of policies on the income, earnings, and program participation decisions of individual households by applying detailed program rules and behavioral responses to survey data. By simulating poverty rates with and without the inclusion of the EITC and CTC, TRIM3 enables the impact of these credits on poverty to be assessed, thus directly linking SPM-based estimates to microsimulation results.

This chapter also discusses how quantitative analyses by other experts evaluating the impact of poverty-reducing policies—particularly the EITC and CTC—can inform the range of primary estimates commissioned for this report.

Finally, the statement of task directed the committee to assess the impact of the EITC and CTC policies in 2021 on poverty among all U.S. children in 2021, as well as among specific subpopulations (see Chapter 8). Subpopulations are defined by a range of characteristics, including parents’ employment and education levels, marital status, citizenship or nativity, children’s living arrangements, safety net participation, disability status, race or ethnicity, immigrant generation, family size, age, and geographic location—all measurable with the CPS ASEC using the SPM. Chapter 9 extends the analysis in Chapter 8 to consider the potential impacts of other policy options on the level and distribution of child poverty, also using the SPM. While poverty measures (including the SPM) provide valuable insights, they only capture a single resource and needs-based measure of well-being. Therefore, this chapter also briefly explores the limitations of poverty measures in reflecting the broader effects of policy on child and family well-being and discusses some other measures of well-being. Main messages from this chapter are highlighted in Box 3-1.

SUPPLEMENTAL POVERTY MEASURE

The SPM, a revised, experimental measure of poverty, was created by the Census Bureau and the Bureau of Labor Statistics in 2011. Subsequently, SPM estimates have been released annually.¹ Under the OPM, an individual is designated as living in poverty if their family’s total pre-tax cash income during the calendar year falls below the official poverty threshold, which varies by family size, age, and composition. For example, in 2023, the official poverty threshold for a family of four with two adults (both under age 65) and two children was $30,900. The concept of a threshold establishes a baseline dollar value intended to reflect the basic needs and expenses shaping the material well-being of families in the United States. OPM thresholds

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¹ The National Academies of Sciences, Engineering, and Medicine (National Academies; 2023) issued a more recent report suggesting further improvements to the SPM, which this chapter also draws from.

are based on a multiple of a food budget (estimated in 1963).² In contrast, the SPM establishes the threshold based on spending on a broader set of basic needs categories, including food but also clothing, shelter, utilities, and phone/internet. Crucially, the OPM concept of resources available to families is based on gross, before-tax money income, such as wages, pensions, and cash welfare, while the SPM includes the cash value of some in-kind benefits and subsidies, as well as tax credits, that can be used to meet expenditures on basic needs (National Academies, 2023). The SPM also deducts from resources some categories of expenditures, such as income and payroll taxes paid; medical, work, and child care expenses; and child

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² Original OPM poverty thresholds were based on the cost of the U.S. Department of Agriculture’s Economy Food Plan for a low-cost diet. These food budgets, which varied by family size, were scaled up to account for nonfood needs using a multiplier of 3 for families with three or more individuals and 3.7 for families of two. The threshold for individuals was specified as 80% of the threshold for two-person families (Orshansky, 1965). Fisher (2008) noted that the Economy Food Plan was chosen rather than a “low-cost” food plan because the former resulted in a 1964 poverty rate that matched the 20% poverty rate reported in the 1964 Economic Report of the President (also see Burkhauser et al., 2024). For additional details about the OPM, see Table C-1 in Appendix C.

support paid to other households. To create the published measure, the Census Bureau uses CPS ASEC data augmented with estimates from a tax model and other sources to measure resources and compares these resource estimates to thresholds computed by the Bureau of Labor Statistics.

Strengths of the SPM for Addressing the Statement of Task

Many of the shortcomings of the OPM and possible approaches for addressing these limitations were comprehensively laid out in a 1995 National Research Council report (National Research Council, 1995) and have been highlighted by many studies since then (Blank, 2011; Falk, 2023; Meyer & Sullivan, 2012). One of the most substantive flaws is that the OPM’s measure of family resources (i.e., pre-tax cash income) fails to capture resources provided to low-income families through refundable tax credits and in-kind transfer programs that provide assistance comparable to cash—programs that have expanded considerably since the OPM was created in the early 1960s (Bitler & Hoynes, 2010; Hardy, 2017; Moffitt, 2016; Ziliak, 2015). For example, pre-tax cash income does not include the CTC and the EITC (because these are post-tax income), nor does it include the Supplemental Nutrition Assistance Program (SNAP), housing subsidies, or other in-kind transfer programs. Additional flaws documented by the National Research Council (1995) and others include a possibly antiquated set of poverty thresholds developed in the 1960s; an outdated definition of the family that ignores the rise of nonmarried cohabiting partners and entirely excludes some children such as foster children; and a lack of accounting for differences in the cost of living across locations in the United States. Other issues addressed in the 1995 report include inadequate equivalence scales to accurately reflect varying levels of resources and needs for families of different types, failure to consider fixed costs of work, and the omission of medical needs and resources (e.g., health insurance) from the measure.

By addressing many of these concerns, the SPM improves upon the OPM, especially for assessing policy impacts (Creamer et al., 2022; Shrider, 2024; Shrider & Creamer, 2023; Shrider et al., 2021). In particular, the SPM relies on an expanded definition of resources that includes not only pre-tax cash income but also many in-kind transfers and is net of taxes, so it includes tax credits and taxes paid. These improvements relative to the OPM have made the SPM a more useful tool for assessing policy impacts—and it is increasingly relied upon and reported on as a preferred measure of income poverty in the United States.³ That said, a measure closely tied

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³ In the past, the Census Bureau released SPM estimates well after the release of OPM estimates. Since 2020, however, the Census Bureau has included SPM estimates in its annual official release of income and poverty estimates (e.g., Shrider, 2024).

to OPM thresholds is still used to set eligibility for participation in government programs, and the OPM is used for other programmatic government decisions (e.g., allocating funds across areas of the United States). The SPM also attempts to address many other concerns with the OPM. With regard to poverty units, the SPM broadens the OPM’s definition of the “family,” which historically has been anyone related to one another by blood, marriage, or adoption. The SPM treats cohabiting partners as equivalent to married partners with regards to an assumption of sharing resources, and it also makes some smaller changes such as including foster children and unrelated children under age 15 as members of the primary poverty unit in the household (rather than excluding them from the poverty universe entirely, as with the OPM).

As noted earlier, the SPM also attempts to modernize poverty thresholds, basing them off expenditures on a core basket of necessities, allowing them to vary geographically with cost and housing tenure (i.e., owning versus renting), and using different equivalence scales that account for varying needs of adults and children across the poverty unit. It is important to note that the decision about the poverty threshold in the SPM is partly a normative decision about what constitutes an adequate level of resources. For this reason, it is important to also consider alterative threshold levels, for example, half the SPM poverty threshold, 150% of the threshold, or twice the threshold, as discussed in Chapter 7. In addition to including in-kind and after-tax resources, the SPM also attempts to deal with other types of costs, such as medical, work, and child care costs, by deducting out-of-pocket expenses from resources.

Not all these changes are uniformly agreed upon, and since its creation, the SPM has been subject to continued consideration of improvements (e.g., see National Academies, 2023). Nevertheless, relative to the OPM, the SPM has become a more widely used measure of income poverty in recent years. Table C-1 in Appendix C lays out the key differences between the OPM and the SPM. See Burns and Fox (2021) for a detailed discussion of the methods for estimating the SPM.

Data and Measurement Limitations of the SPM

Several data and measurement challenges must be confronted when applying the SPM to policy analyses. These challenges arise due to limitations in the nature, quality, and availability of data from the CPS ASEC, which is used to estimate the SPM. Below, several challenges are discussed, along with the steps the committee took to address them.

Timing of Income Received, Especially Tax Credits

The SPM is an annual measure of poverty, comparing total resources across a calendar year to annual thresholds for that same year.⁴ Outside of ARPA, tax credits such as the EITC and CTC are calculated based on earnings in a given year (the accrual period) but are not paid to until taxes are filed, typically in the winter or spring of the following calendar year. A refundable tax credit, for example, may be owed to a household (or tax filer) based on their income in 2021, but would not be received by that household (or tax filer) until they file taxes in spring 2022. This is also true of income taxes paid when withholdings do not match actual taxes owed—deficits or surpluses are collected or paid in the following calendar year at tax time. The Census Bureau considers refundable tax credits like the EITC and CTC as income counted against the poverty threshold in the accrual period, despite such income typically being received in the following calendar year. If these credits do not change significantly from year to year and income remains relatively stable, the differences between treating tax credits as income in the year received rather than the year accrued might be small. However, the way these credits are treated when there are major changes in generosity or eligibility, as was the case in 2021, can have a large effect on the resulting measure of after-tax income.⁵ For this reason, this issue directly affects a key component of the committee’s statement of task: to measure the impact of the EITC and CTC policies in 2021 on child poverty.

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⁴ Relying on annual income may average out large fluctuations in income and resources available to families. For example, Morduch and Schneider (2017) documented insights from detailed information about high-frequency financial choices made by several hundred moderate- and low-income families over the course of a year, finding that many of these families had wide swings in income that did not always match up with the timing of expenses. Hardy et al. (2019) documented the state of “economic instability,” or frequently changing financial well-being, income, and/or earnings, that has increased over time due to changes in private income and the move to a more in-work means-tested safety net in the United States. These fluctuations may negatively impact children (e.g., Hill et al., 2013).

⁵ Under the CTC Policy in 2021, approximately half the credit was paid in advance of the 2022 tax filing season, through monthly payments from July to December 2021. For these advance payments, the credit was accrued and received in the same year.

Conceptually, treating taxes and credits as income in the year they were received, rather than when they accrued, more directly reflects resources available for consumption in a given year. However, if individuals anticipate the deficits or surpluses that will be collected or paid at tax time in the following calendar year, they can borrow or save in response. In that case, the way taxes and credits are treated may not substantively affect resources available for consumption. Thus, it is ultimately unclear when resources should be counted. Moreover, certain data limitations make it difficult to calculate taxes and credits in the year received. Namely, the CPS ASEC, which underlies official poverty statistics, includes only one year of income data per household surveyed. However, two years of income data are needed to fully account for the timing of taxes and tax credits paid and received. Taking 2021 as an example, one would need to know 2020 income to estimate tax credits received in spring 2021, and one would also need to know 2021 income from all other sources (e.g., earnings, SNAP) to calculate the 2021 poverty rate. Thus, using the CPS ASEC, treating taxes and credits as income in the year they were received requires making assumptions about income for the year prior to the survey reference year (Meyer et al., 2024). Chapter 8 describes alternative results for the period of receipt using TRIM3 and after making these requisite assumptions. In this analysis, the poverty effects of both the 2020 payments under the provisions of the pre-ARPA CTC received in 2021 and the advance CTC payments under ARPA—approximately half of the benefits that were increased under this act—were distributed from July through December 2021.

Reliance on Tax Modeling to Estimate Taxes Paid and Tax Credits Received

In addition to the challenges posed by the income accrual period versus income received period, estimating the effects of the EITC and CTC is challenging because receipt of these credits is fully modeled using the Census Bureau’s tax model in each year of the CPS ASEC. Rather than ask households about their receipt of tax credits (as well as their taxes paid), the Census Bureau takes reports of earnings, other income sources, and household relationships to estimate taxes paid (and tax credits received) using a tax model. The tax model thus “assigns” receipt of credits like the EITC and CTC based on observed (and sometimes imputed) values of inputs into the tax determination.

This procedure is not inherently better or worse than asking respondents about their after-tax income—indeed, self-reports of taxes paid or tax credits received might prove worse than those derived from a tax model. Nevertheless, the procedure entails a substantial possibility for error at the individual or household level, and possibly also at the population level. In

particular, reliance on a tax model involves assigning income and payroll taxes (including credits) to everyone who appears required (or eligible) to pay (or receive) them. As such, these totals, and their distributions, may differ from those actually received by households (e.g., Bee et al., 2023b; Jones & Ziliak, 2022; Meyer et al., 2020). Concomitantly, estimates of the effects of taxes (and tax credits like the EITC and CTC) may be biased, though the direction and magnitude of these biases is not fully known (see Chapter 4 for a closer examination of these biases). An additional limitation of this approach is that the Census Bureau’s tax model has imperfect information from which to determine eligibility for specific credits/levels of taxes owed. This information is imperfect both in its measurement of various income sources that determine taxes, and in terms of key inputs such as who actually filed taxes, who had benefits claimed on their behalf in those filings, and sociodemographic factors such as immigration status that affect tax credit eligibility. Bias in the modeling of taxes and credits could lead to bias in the committee’s estimates of the impact of these taxes and credits on child poverty.

One important consideration is that CPS ASEC data do not perfectly measure citizenship status, immigration status, or whether individuals have Social Security Numbers (SSNs) or Individual Taxpayer Identification Numbers (ITINs).⁶ Since eligibility for most government transfers depends on citizenship or on particular immigration statuses and/or having a SSN/ITIN, this means existing Census Bureau measures may overstate benefit eligibility for these groups and possibly overstate receipt. This is particularly important for the Hispanic subgroup, in which 57% of children live in immigrant families (Lee et al., 2024). Issues related to subgroup variation are discussed in greater detail in Appendix D.

Chapter 8 presents adjusted results from the Urban Institute’s TRIM3, which aims to reconcile some of these potential biases. However, it remains important to interpret SPM-based estimates of the credits’ effects with the limitations of tax modeling in mind.

Accounting for Complex and Dynamic Family Structures

A related, but distinct, limitation of the CPS ASEC for modeling tax credit assignment arises with complex and dynamic family structures. The respondents to the CPS ASEC survey are drawn from samples who

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⁶ ITINs are used for those reporting earnings to the Social Security Administration who do not qualify for SSNs due to their immigration status.

participate in the monthly CPS surveys in February, March, and April.⁷ While the CPS ASEC collects information about households’ sources and amounts of income in the previous calendar year, it only collects information about the individuals living in the household at the time of the interview. However, as noted in Chapter 2, eligibility for the CTC and the EITC for families with children is determined by when and whether adults with low incomes reside in a household during the previous calendar year and, importantly, whether and how many of their children live in that household during that calendar year. In the absence of such information for the previous year, the Census Bureau tax model must rely on the time-of-interview household composition to determine the tax filing units and credit eligibility of households in the CPS ASEC.

For many households, especially those with low earnings, composition in the time of interview does not accurately characterize the composition in the previous year. For example, separated or divorced parents may have children whose time is divided between households, and one parent may claim a child for tax purposes one year, while the other claims the child the next. Marital and cohabiting relationships and other living arrangements also may be fluid throughout the calendar year. Finally, some parents may have joint custody arrangements in which a child lives with another parent some of the time. Such arrangements are not captured in the CPS ASEC data and the errors associated with using time-of-interview measures are likely to affect the precision of estimates of the credits’ effects on child poverty, for the overall population and for subgroups, even if the income measures used to calculate the SPM are accurately measured. Currently, there is only limited evidence on the incidence of these and other forms of family composition mismeasurement in the CPS ASEC and their consequences for determining household poverty status. However, recent studies using linked surveys and administrative data have begun to fill this void. (See Appendix E for a summary of the evidence from linked CPS ASEC and Internal Revenue Service [IRS] data.)

Survey Quality and Accuracy of Reported Income

Additional challenges with measuring income poverty arise due to the quality of the survey data used to calculate it. Perhaps the most substantive challenge is that many income components are mismeasured in surveys, including the CPS ASEC (e.g., see survey of issues in Bound et al., 2001). Bollinger et al. (2019) documented that, especially at the top and

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⁷ Respondents from oversamples of certain demographic groups also are administered the ASEC survey questions. Also note that most of the respondents to the survey are from the March CPS sample.

bottom of the earnings distribution, survey respondents faced challenges with reporting their earnings accurately. For poverty measurement, there is particular concern with the understatement of components important for low-income families. A substantial number of studies (e.g., Meyer & Sulivan, 2003; Meyer et al., 2015; Wheaton & Giannarelli, 2000) documented that income from a variety of means-tested transfer programs and social insurance programs is significantly understated and this understatement has grown over time. This limitation affects the baseline poverty rate from which the changes to EITC and CTC under ARPA in 2021 are assessed and may complicate the estimate of both the absolute and relative magnitude of effects on child poverty.

Recent evidence indicates that underreporting is even more pronounced for some of the largest transfer programs during the pandemic—a period when there were extremely large fiscal responses that included changes to the eligibility and generosity of transfer programs such as Unemployment Insurance (UI). For example, administrative records indicate that $580 billion of UI benefits were distributed in 2020, but the weighted total reported in the CPS ASEC was only $218 billion (38% of the total), although some of this difference might be due to fraud in receipt of UI by individuals not reporting receipt in the CPS ASEC. Similarly, the CPS ASEC captured only half of all SNAP dollars (Meyer et al., 2024). Larrimore et al. (2023) used administrative tax data on UI paid, linked to the CPS ASEC, to show that UI benefits are significantly understated, particularly for low-income workers. For 2020, they found that this understatement led to a 2 percentage point overstatement of official poverty.

In part, this report’s use of TRIM3 for its analyses addresses concerns about underreporting of some income components, including SNAP and UI. TRIM3 uses demographic information to impute receipt of these sources for individuals or families who do not report receipt in the CPS ASEC. The model’s adjustments for understatement of these programs rely on information from administrative aggregates on the total dollar amount of benefits actually distributed, to determine how much to impute. (See Appendix H for details on how TRIM3 accounts for understatement of some programs.)

Other, more general, concerns arise about survey quality due to survey nonresponse during the pandemic. Potentially compounding the issue of underreporting, the pandemic itself seems to have brought new patterns of nonresponse to government surveys themselves, with Rothbaum and Bee (2021) documenting that respondents in the 2020 CPS ASEC (which measures poverty and income for calendar year 2019) were unusually well off on a number of measures of socioeconomic status, including income

and education.⁸ Though the Census Bureau released revised survey weights in attempt to account for some of these new patterns, it is only partially known whether these weights fully do so.

One solution for dealing with increasingly misreported or underreported survey data is to incorporate linked data from administrative sources on income and program participation. The authoring committee of the National Academies (2023) report recommended that the Census Bureau “expand the use of administrative data (income and program benefits) to improve estimates of resources [and] aggressively explore the strategy of using federal and state administrative records to improve models for imputation for item nonresponse, including nonreporting of receipt as well as amounts” (p. 8). One caveat with using these records for some purposes is that they are not necessarily available immediately.

Out-of-Scope and Poorly Captured Populations

Although not unique to the SPM, important low-income populations are either excluded or poorly represented in poverty statistics. For example, while the SPM includes foster children—a group excluded from the OPM—it does not cover individuals living in group quarters, such as homeless shelters. The SPM also excludes some individuals who are out of the scope of the CPS ASEC, such as incarcerated individuals and certain segments of the homeless population, including those living on the street (Meyer et al., 2023). Excluding these populations can affect both the baseline SPM child poverty rate and the effect of tax credits on poverty.

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⁸ Bee et al. (2023a) matched individuals and households in the survey data with data available (for respondents and nonrespondents) from administrative IRS, Social Security Administration, and 2010 Decennial Census data, and adjusted the weights for respondents, to capture nonrespondents based on income and demographics.

• item nonresponse). As currently implemented, the SPM considers tax liabilities, refunds, and credits in the year they were accrued rather than in the year they were received. No clear consensus exists among researchers on the most appropriate way to allocate taxes and credits and when to do so, but the chosen approach is more consequential during periods when tax policy and economic conditions change sharply year to year, such as during the pandemic. Thus, the fact that the SPM allocates taxes (and tax credits) based on the accrual year (which for the EITC and CTC is the calendar year earnings were received) and not during the time when refunds or taxes are received/paid, is consequential for understanding the timing of the EITC’s and CTC’s impacts on child poverty.
• The SPM relies on tax modeling to estimate taxes paid and credits received. Limitations in the implementation of the tax model are due to incomplete and/or incorrect information in the survey data, such as imperfect information on the composition of the tax unit; lack of knowledge on whether these tax units actually file; lack of detailed information on immigration status; and lack of detail on complex and dynamically changing household structures and tax filing units, and how these factors map onto tax filing and claiming. These issues are important for determining eligibility and receipt when estimating tax credits and liabilities and likely lead to errors in the measurement of the EITC’s and CTC’s impacts on child poverty.

Broader Conceptual Issues with Measuring Poverty and Policies’ Effects on Poverty

In addition to the data limitations discussed thus far, several broader conceptual issues exist related to the measurement of poverty that may have implications for estimating the effects of tax credits on poverty. Since the SPM’s inception, there have been calls to further refine and improve the measure. Indeed, a recent National Academies panel commissioned by the Census Bureau provided recommendations for further updating the SPM to enhance its accuracy, consistency, transparency, and feasibility (National Academies, 2023). The panel documented several areas for improvement, including the SPM’s treatment of health care, child care, and housing in estimating basic needs when calculating poverty. Although the SPM has been elevated in public and policy discourse in recent years relative to the OPM, some argue for alternative income-based measures. Some suggest a fully relative poverty measure more common in international contexts, whereby income poverty is measured against a fixed point in the income distribution

(e.g., half of median income; Brady, 2023), while others argue for an absolute measure (Burkhauser et al., 2024). Still others argue for alternatives to income-based poverty measures altogether, such as consumption-based measures (Armstrong et al., 2022; Meyer & Sullivan, 2012; Meyer et al., 2024) or multidimensional poverty measures (Alkire & Foster, 2011; Alkire et al., 2015), on the grounds that these alternatives may better reflect economic well-being according to economic theory, be less susceptible to understatement (in the case of consumption measures), or better capture individuals’ or households’ ability to “fully participate” in society (in the case of multidimensional measures). A full review and reconciliation of these differing approaches falls outside the scope of the committee’s charge. However, alternative poverty measures may offer new insights into the timing of the effects of the EITC and CTC policies in 2021 on child poverty. Further research using alternative measures would help clarify the full impact of the 2021 policy reforms.

CONCLUSION

As reviewed in this chapter, the committee used the SPM rather than the OPM to assess the impacts of the EITC and CTC policies in 2021 and the alternative options for both credits on child poverty, as directed by its statement of task. The SPM offers a more comprehensive measure of family resources because it accounts for after-tax income, refundable tax credits, and in-kind benefits—essential features that the OPM does not capture. Although the SPM has limitations, particularly regarding the timing of income receipt, reliance on tax modeling, treatment of complex family structures, and survey underreporting, it was judged more appropriate for evaluating the impact of the EITC and CTC policies in 2021. The Urban Institute’s TRIM3 was used to simulate resources and poverty outcomes more accurately, mitigating some—but not all—of the known data challenges. Overall, despite imperfections, the SPM provides a stronger foundation for assessing policy impacts on child poverty than older or alternative methods. Thus, the committee concludes that while continued refinement of the SPM is necessary, it remains the best available measure

for understanding the effects of the EITC and CTC policies in 2021 and the potential effects of some variations in configuration of these credits on child poverty. Nevertheless, it remains important to interpret results with caution given measurement challenges around taxes, credits, and family dynamics, especially during periods of rapid policy change.

The report turns next to a critical factor influencing those measurements: the extent to which eligible families actually received these tax credits. Understanding the patterns and barriers to take-up is essential for interpreting the real-world impact of the credits on child poverty, as incomplete participation among eligible families can alter the estimated effects of these policies.