HB Ch: Larrimore, Mortenson & Splinter (2022)

Chapter 12: "Presence and Persistence of Poverty in US Tax Data", in the book "Measuring Distribution and Mobility of Income and Wealth" (2022).
Seems to be an (exact?) copy of the NBER working paper by the same team from 2020. Since this is a 'published' version, this page is based on the book chapter.
The data section is written a bit chaotically. They could've written a more systematic overview of the differences between ACS, CPS, and IRS data, the adjustments they make, and the implications of these adjustments.
== Summary ==
=== 1. Introduction ===
This chapter produces new estimates of the level and persistence of poverty in the US between 2007 and 2018 using a new population-level panel of HH-level incomes in tax record data.
They estimate: HH-level incomes, income distributions, and poverty levels in each year, and additionally use the panel nature of these data to study the persistence of poverty and the characteristics of individuals experiencing short-term versus persistent poverty.
* Compare the levels and trends in cross-sectional poverty in the tax data---both pretax and after-tax---with the Census bureau's OPM (based on the CPS): levels and trends differ.
* Estimate poverty persistence, as the #years a person is in poverty, conditional on being in poverty in a base year.
* Estimate the share of people experience poverty at some point over a multiyear period.
They frame it as building on the PSID and SIPP literature, e.g. Bane & Ellwood (1986), and Stevens (1999). Papers using US tax data to measure income volatility (Auten & Gee 2009; Larrimore, Mortenson, & Splinter 2016; Splinter 2021), or persistence of program receipt (Dowd & Horowitz 2011). "To our knowledge, this chapter is the first study to consider poverty rates or the persistence of poverty directly in US tax data." 
Benefits compared to this survey-based literature: surveys suffer from (1) measurement error in income, and (2) non-representative attrition. Admin tax records do not suffer from these issues as: (1.a) nearly all individuals are listed on tax returns or have income sent tot the IRS on their behalf, (1.b) since there is a penalty for misreporting income this is frequently considered to provide more comprehensive reporting, and (2) does not suffer from survey attrition bias. Downside: lack important demographic information such as race and ethnicity.I think this could be added through linking with the Census? IRS-Census links exist already e.g. OI, right? 
Alternatively, most people at some point have pictures taken for official documentation. These must be stored somewhere. If there is a way to access them, it would probably be quite doable to write an ML algorithm to classify race/ethnicity.
Summary of findings:
* Overall, results on poverty dynamics are consistent with panel-survey findings. 
* The levels and trends of poverty rates differ from the CPS: observe more stability in poverty rates using pretax income for 2007-2018, in part due to underreporting of UI benefits in CPS during the great recession.
* A large share experiences poverty at some point: (1) 10% are in poverty in any single year based on after-tax income, (2) 40% spent at least one year in poverty over the 12-year period, and (3) for those in poverty at any point in 2007-2018, the average duration is about three years/25% of the time.
* Substantial persistence: for people in poverty in 2007, (1) 57% remain in poverty the next year, (2) 28% were in poverty ten years later (not cntsly), (3) 33% were poor for at least 6/12 years and (4) 9% were poor for at least 10/12 years.
=== 2. Data and Methods ===
==== 2.1 Data ====
Use a 1% sample from the Tax HH Data, which are built from IRS tax records by Larrimore, Mortenson & Splinter (2021) for tax year 2010. This paper extends these data to cover the 12-year period from 2007 through 2018. Prior to 2007, address data are less complete in IRS data, which limits their ability to extend the panel to earlier years. The Tax Household Data (THHD): 
* Start with the universe of IRS tax data for individual taxpayers. This includes annual tax returns (e.g. Form 1040) and information return forms that are submitted to the IRS by third parties (e.g. W-2, 1099-SSA, and 1099-INT for various income sources, and 1098-T for college attendance). 
* Link individuals residing at the same address based on the address listed on these forms. By doing so, treats all individuals living together as a joint economic unit.
* While almost 15% of adults do not appear on annual tax returns, the IRS also received information returns. The include address and income data from almost all major taxable sources if income for both filers and non-filers. (...) "Consequently, tax data can capture nearly the entire US population and observe incomes from taxable (and some nontaxable) sources even among non-filers". Yet, they also incorporate and adjustment for people who have zero incomes in the data.
* Aggregate tax data to the HH level
* Incorporate non-filers using income data from their information returns (such as W-2 and 1099 series)
* Since the IRS receives annual tax records for almost all adults, these data provide a naturally occurring population-level panel.
*
Comparing individual population to the CPS and ACS: (see Table 12.1)
* ACS includes institutionalized populations, those on military bases, and those living in group quarters.Census Bureau: group quarters. Includes military bases, or institutionalized. Overall about 3% of the US population.Based on the address information, can't we figure out relatively easily today for many of these what the situation is? E.g. if we look at google maps we can see the building type and whether there is some sort of entity at a particular address. I don't know how much this changes, but 8 million people doesn't sound like nothing.
Second-order concern probably, but seems like a very viable area to make some progress on if building a dataset for more repeated/general use. Hence, it is their preferred comparison for overall population.
* CPS data used for the OPM exclude institutionalized and military base populations. Hence, the CPS estimates are smaller than the ACS. The CPS will miss homeless populations, du e to the survey's HH-based sampling frame. The Dept. of Housing and Urban Development estimates the homeless population of 2007-2018 to be between 550,000 and 647,000 (~0.2% of the population).
* IRS data will miss those who have no reportable income and no activity generating an information return. Individuals with zero income do not have tax reporting requirements and will not appear on a tax return, this means the IRS data likely understates the zero-income population. To correct for this, they add the difference between the #people in HHs with zero incomes in the CPS and IRS data. Note: this is what 
* To follow the OMP data approach to exclude institutional and group quarters populations, they exclude 8 million people each year who live in HH's with 11 or more individuals. 
Comparing number of HHs:As shown in figure 12.1, the number of IRS tax units is about 50% higher than the number of ACS and CPS HHs. This reflects that if Phil, Matt, Wesley and I live together we were being counted as four different IRS tax units. In the IRS HHs they construct here, (and I think what they do for the CPS/ACS) the four of us would be counted as single HH. This seems incorrect for purposes of poverty. How big of a difference does this distinction make? My guess would be quite a big as people living together are often those with lower incomes; they live together precisely because they have lower incomes. there are about 2% more HHs compared to the ACS and 4% less compared to the CPS.
==== 2.2 Income and Sharing Unit Differences from the Official Poverty Measure ====
There are three major income measurement differences between the CPS and IRS annual income data, that prevent replicating the OPM using IRS data: (1) transfer income: in CPS and lack of coverage in IRS data, (2) tax credits: lack of coverage in CPs but is in the tax data, and (3) private retirement income: limited coverage in CPS but is in the tax data. 
Using the IRS data to estimate poverty trends, they:
# Consider pretax income: all taxable income plus nontaxable income observable on IRS forms (e.g. SSI, Disability insurance, and tax-exempt interest). This is narrower than the OPM income definition due to missing some transfer income.
# For main estimates, focus on after-tax income: this deducts federal income and payroll taxes, and includes tax credits such as the EITC and the CTC. Note: these tax credits are conditional on earned income requirements and represent some of the largest antipoverty programs in the US. 
The other difference from the OPM income definition is the choice of sharing unit and the equivalence scale. The OPM uses a family unit concept, which includes all people "related by birth, marriage, or adoption and residing together". In the tax data, this information is not available and instead they aggregate tot he HH level, including all those in the HH even if not related by blood or marriage. This usually combines cohabiting partners and roommates (if not claimed as dependents) into the same sharing unit. Note: HH is common in the literature, also used by Bane & Ellwood (1986), Stevens (1999), etc, and the sharing unit in international poverty estimates (e.g. OECD). 
=== 3. Results ===
==== 3.1 Annual Poverty Rates ====
==== 3.2 Poverty Persistence in the IRS Data ====
==== 3.3 Multiyear Frequency of Poverty in IRS Data ====
=== 4. Conclusion ===
== Questions ==
* Based on how they define an 'economic unit' (all individuals living together), these estimates might be understanding poverty? Multiple people living together who are not naturally considered an economic unit, are especially younger people and those with lower incomes. Especially, considering that people often choose to live together out of economic necessity, because they can't afford to live by themselves.