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| Comments: Final Report of the Interagency Technical Working Group on Evaluating Alternative Measures of Poverty |
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Laura Wheaton

FCSM 2021 Research and Policy Conference    

November 4, 2021

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My Comments

Race and ethnicity

Excluded populations

Consumption and Income Poverty

Health Coverage

Linked administrative data and rules-based approaches to assigning benefits and taxes in the survey data

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Informative discussions on-line at:

Brookings Institution and AEI:

https://www.brookings.edu/events/time-to-update-how-we-measure-poverty-evaluating-alternatives-measures-of-poverty/

Institute for Research on Poverty:

https://www.irp.wisc.edu/resource/poverty-measurement-for-the-next-generation-findings-from-the-interagency-technical-working-group-on-evaluating-alternative-measures-of-poverty/

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Consider Race and Ethnicity

ITWG report predates current surge in interest in racial equity

Measures will be called on and used to address equity issues

My recommendation:

Review new and existing measures with eye toward their strengths and limitations for different groups and ability to examine equity issues

Address data quality:

Sample size limitations (especially Consumer Expenditure Survey)

Higher survey undercoverage of Black and Hispanic people

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Address excluded populations

ITWG expressed need for more attention to populations excluded from poverty statistics: e.g., people who are homeless, incarcerated, or in the military

 Agreed, we would ideally know:

How does exclusion of these groups affect poverty rates?

Do changes over time in homelessness and incarceration affect poverty trends?

Do effects vary by race and ethnicity?

We also need to know:

How well do surveys cover secondary families in doubled-up households? 

Are people who move between friends and family members fully represented?

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Consider differing insights gained from consumption and income measures

Valuable to consider who is poor by both measures, or who is poor just by one, and reasons for differing results

Could poverty by one measure but not another be treated as a “warning flag”?

Important to keep subgroups at forefront in these comparisons

What does poverty by one measure and not by the other imply at different life stages?

What do differences imply when comparing poverty rates for racial and ethnic groups with differing levels of home ownership and wealth, geographic distribution, or other factors?

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Reconsider the approach to health coverage:

The value of health insurance should ideally depend on a person’s resources but the precise method to implement this should be the subject of further research. In the interim, the market value of health insurance (with values that do not depend on the health status of the recipient) should be capped at some share of total resources.

(ITWG page 29)

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Health coverage

My concern:

Although this recognizes health coverage is worth less than the equivalent amount of cash to a low-income person, it essentially treats health coverage as worth less to lower income people, because they are lower income.

My recommendation:

Build the need into the thresholds and count coverage toward filling that need

 e.g., Health Inclusive Poverty Measure (HIPM) produced by Dahlia Remler, Sanders Korenman, and colleagues

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Learn from the inconsistencies in rules-based and administrative data approaches

ITWG: Methods that combine administrative data with survey data are the preferred approach for adjusting survey data to correct for underreporting.

Administrative data research often treats linked administrative data as a “gold standard” to which rules-based approach should be compared.

However, rules-based approaches:

Are consistent with family and income characteristics as represented in the survey

Have been vetted by decades of research and multiple research teams

Differing results should not lead us to automatically reject rules-based approaches, but rather to ask “Why?”

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Example One: 

Research finds that ...(CPS) tax imputations introduce substantial error in overall taxes, as well as tax credits. Errors in both measures can be sharply introduced even with a limited set of administrative tax data (Meyer, Wu, Finley, Langetieg, Medalia, Payne, and Plumley 2020)

(ITWG page 43)

An important distinction: this study does not test the accuracy of the CPS tax calculator, but rather shows that taxes estimated on CPS income and family characteristics differ from taxes based on linked admin data for income, filing status, and dependency.

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Consistency is important:

To accurately measure available income, taxes must reflect the income included in resources for the poverty measure:

If taxes are from linked data, income should be too.

If a poverty measure uses survey income, taxes and tax credits should be calculated based on the survey income.

If family structure differs in tax data and survey data, we should understand:

To what extent is this noncompliance? What do we do about that?

To what extent is this survey error or recent household change?

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Example 2:

Shantz and Fox (2018)

Link SNAP administrative data to CPS ASEC data

Compare actual receipt and TRIM3 simulated receipt

Conclude that TRIM3 “over-assigns” SNAP to lowest income people

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Example 2 (continued):

Mathematica and the Urban Institute:

Consistently find fewer very low-income people eligible for SNAP in survey data than receive SNAP according to administrative data

Find fewer one adult plus child families eligible for SNAP than participate according to administrative data

As a result, TRIM3 assigns SNAP to most eligible one adult plus child families and very low-income households, yet still does not find enough.

Source: Wheaton, Wemmerus, Godfrey (WWG) (2021)

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It is important to understand the apparently contradictory results

WWG analyze linked SNAP and CPS data for 3-states and find:

Income imputation is much higher among linked SNAP cases found ineligible by TRIM3 than among those found eligible.

One adult plus child SNAP recipients and recipients with very low income are less likely to respond to the CPS ASEC and appear to be underrepresented.

Over 40 percent of one adult plus child cases do not appear to be one adult plus child in TRIM3 (case adult or kids are absent from the household; spouse or partner is included in the TRIM3 unit).

Do the findings hold in other states?

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In Summary:

Rules-based approach reflects taxes and benefits that are consistent with the income and household composition as observed in the  survey.

If linked-data approach obtains different results, we should understand why.

Solutions may differ, depending on the reason for the difference.

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In Summary (continued)

Avoid assigning a benefit from admin data that is inconsistent with income imputed to a survey respondent. 

Household composition changes and reporting errors may make an admin data benefit inconsistent with family income.

Income used to calculate taxes should be consistent with the income included in resources.

Consider how to handle noncompliant receipt.

Assess underrepresentation of subgroups in survey data and consider how best to compensate for this.

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Contact and References:

Contact: Laura Wheaton: lwheaton@urban.org

Meyer, Wu, Finley, Langetieg, Medalia, Payne, and Plumley (2020)

https://www.nber.org/system/files/chapters/c14441/c14441.pdf

Shantz and Fox (2018)

https://www.census.gov/content/dam/Census/library/working-papers/2018/demo/SEHSD-WP2018-30.pdf

Wheaton, Wemmerus, and Godfrey (2021) 

(not yet posted, available from Laura Wheaton)

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