The Effects of Income on the Economic Wellbeing of Families with Low Incomes — NBER Working Paper No. 30533
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NBER Working Paper No. 30533, The Effects of Income on the Economic Wellbeing of Families with Low Incomes: Evidence from the 2021 Expanded Child Tax Credit, by Natasha Pilkauskas, Katherine Michelmore, Nicole Kovski and H. Luke Shaefer, dated October 2022. Using a parameterized difference-in-differences approach and data from a national sample of families who receive SNAP, the paper studies the monthly Child Tax Credit payments made from July to December 2021. The authors report a 17% decline in material hardships and a 32% decline in food insecurity associated with a $500 monthly credit, some evidence of reduced medical hardship, and no effects on labor supply. The paper also reviews the history of the credit and closes with robustness tables reporting effects of a $100 increase in the CTC.
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NBER WORKING PAPER SERIES
THE EFFECTS OF INCOME ON THE ECONOMIC WELLBEING
OF FAMILIES WITH LOW INCOMES:
EVIDENCE FROM THE 2021 EXPANDED CHILD TAX CREDIT
Natasha Pilkauskas
Katherine Michelmore
Nicole Kovski
H. Luke Shaefer
Working Paper 30533
http://www.nber.org/papers/w30533
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
October 2022
The authors thank Propel for access to their Providers data. We thank Samiul Jubaed for his
excellent data assistance. We also thank the Charles and Lynn Schusterman Family
Philanthropies and the Washington Center for Equitable Growth for their generous support of our
research. The views expressed herein are those of the authors and do not necessarily reflect the
views of the National Bureau of Economic Research.
NBER working papers are circulated for discussion and comment purposes. They have not been
peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies
official NBER publications.
© 2022 by Natasha Pilkauskas, Katherine Michelmore, Nicole Kovski, and H. Luke Shaefer. All
rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without
explicit permission provided that full credit, including © notice, is given to the source.
The Effects of Income on the Economic Wellbeing of Families with Low Incomes: Evidence
from the 2021 Expanded Child Tax Credit
Natasha Pilkauskas, Katherine Michelmore, Nicole Kovski, and H. Luke Shaefer
NBER Working Paper No. 30533
October 2022
JEL No. H20,I3,I30,I31,I38,J20
ABSTRACT
We examine the effects of an unconditional cash transfer on the economic wellbeing (material
hardship, ability to meet needs, money on hand, use of friends and family for assistance, and
employment) of families and children with very low incomes. We use a parameterized difference-
in-differences approach to study the impact of the 2021 temporary expansion of the Child Tax
Credit (CTC), which provided monthly, unconditional cash payments to families with children
from July to December 2021. The 2021 monthly CTC reduced the number of hardships families
experienced, and in particular their food insecurity. We find some evidence that the credit
reduced medical hardships, reduced reliance on friends and family for food, and improved
respondents’ ability to pay utility bills. We also find no effects on any labor supply measures.
Analyses that examine differences by racial/ethnic groups show that the effects are somewhat
stronger for Black families than for Hispanic and White families, but the differences are not large.
Natasha Pilkauskas Nicole Kovski
University of Michigan University of Michigan
npilkaus@umich.edu Gerald R. Ford School of Public Policy
nkovski@umich.edu
Katherine Michelmore
Gerald R. Ford School of Public Policy H. Luke Shaefer
University of Michigan Gerald R Ford School of Public Policy
735 S State St University of Michigan
Ann Arbor, MI 48109 735 S State St
and NBER Ann Arbor, MI 48109
kmichelm@umich.edu lshaefer@umich.edu
THE 2021 CTC AND ECONOMIC WELLBEING
Income, or lack thereof, is associated with many short-and long-term outcomes for
families and children (e.g., Duncan and Brooks-Gunn, 1997). Although many studies document
the positive effects of income on wellbeing (e.g., Akee et al., 2010), the causal effect of
unconditional cash transfers on material hardship in the U.S. is not well understood. Studies of
income support policies that are coupled with employment (like the Earned Income Tax Credit
[EITC]) or public assistance programs that target particular hardships (like Supplemental
Nutrition Assistance Program [SNAP]/Food Stamps), find positive effects of these policies on
material wellbeing (e.g., Kondratjeva et al., 2022; McKernan et al., 2021). But new evidence
from three studies of one-time unconditional cash transfers (Jacob et al., 2022; Jaroszewicz et al.,
2022; Pilkauskas et al., 2022) and one study of a recurring cash transfer (Gennetian et al., 2022)
found little to no effect of cash transfers on material wellbeing. In this study, we add to the
literature on the causal effects of income on material hardship in the U.S. by studying the effects
of a recurring unconditional cash transfer – the monthly 2021 Child Tax Credit (CTC). We also
extend the nascent evidence base on the effects of the 2021 CTC (Parolin et al., 2021; Shafer et
al., 2021; Ananat et al., 2022; Hamilton et al., 2022) by focusing on a national sample of families
with very low incomes – those who are often under-represented in national samples – and by
examining a wide range of material hardship measures and indicators of economic wellbeing.
Heeding growing calls to create a universal child allowance or child benefit (National
Academies of Sciences, Engineering, and Medicine ,2019; Garfinkel et al., 2016; Shaefer et al.,
2018; Center on Poverty and Social Policy, 2021; Collyer et al., 2019), in March of 2021,
Congress passed a one-year expansion of the Child Tax Credit (CTC) as part of the American
Rescue Plan Act. The 2021 CTC expansion increased the size of the benefit, made the credit
fully refundable, and extended eligibility to those with little or no earnings. Additionally, rather
than distributing the entire CTC at tax time, half of the benefit was delivered in monthly
installments from July 2021 to December 2021 reaching roughly 61 million children (Internal
Revenue Service [IRS], 2021). These reforms made 26 million children eligible for a larger
credit; 6 million of whom were entirely ineligible before the reform (Goldin and Michelmore,
2022). Thus, the 2021 CTC provided millions of children in families with low incomes with
additional, or entirely new, income support. Our study focuses on these families, those at the
lowest end of the income distribution, who were most likely to have gained new income support
from the 2021 CTC. This group is of particular policy importance since these families faced
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THE 2021 CTC AND ECONOMIC WELLBEING
pronounced economic challenges during the COVID-19 pandemic and have also been the focus
of debates about whether the 2021 reform reduced parents’ incentives to participate in the labor
force (e.g., Corinth et al., 2021).
To study the effects of the monthly 2021 CTC on the economic wellbeing (material
hardship, ability to meet needs, money on hand, use of friends and family for assistance, labor
supply) of families with low incomes, we employ a parameterized difference-in-differences
approach. We use data from a national sample of families who receive SNAP, collected in
partnership with Propel, the administrators of a mobile application that assists families in
managing their SNAP benefits. We consider heterogeneity by household income, as those with
no or very low income may have been impacted more strongly by the 2021 CTC and have been
historically ineligible for the CTC. Additionally, research suggests that cash transfers have
bigger impacts on wellbeing when they represent a larger proportion of household income
(Haushofer and Shapiro, 2016; Haushofer et al., 2020; Pilkauskas et al., 2022). We also
investigate heterogeneity by race/ethnicity, as there are important intersections between tax
policy and racial inequality (e.g., Brown, 2021). Approximately half of Black and Hispanic
children have also historically been excluded from receiving the full CTC, compared with one
quarter of White children (Goldin and Michelmore, 2022). Thus, the 2021 CTC may have had
larger effects on the economic wellbeing of Black and Hispanic children.
Our results suggest that the 2021 CTC expansion led to a significant reduction in the
number of material hardships experienced by families with low incomes (a 17% decline
associated with a $500 monthly credit), primarily driven by declines in food insecurity (32%
decline associated with a $500 monthly credit). We also find some evidence that the expansion
led to reductions in medical hardship, reduced inability to pay utility bills, and reduced the need
to rely on friends and family for food. We find no evidence that the unconditional cash benefit
affected labor supply, suggesting the 2021 monthly CTC did not lead to significant declines in
employment among families with low incomes. We also find no evidence of heterogeneity by
income and suggestive evidence of larger effects for Black families, but these differences were
not large.
Our study speaks to an important policy debate about the design and delivery of cash
transfers to families with low incomes. Unconditional cash transfers are currently a topic of
considerable political and policy attention, with important questions raised about whether
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monthly, universal transfers are the right mechanism by which to reduce poverty, as opposed to
lump sum or conditional cash transfers. The findings from this study suggest that monthly
unconditional cash transfers to families with low incomes reduce some material hardships, but
not all.
BACKGROUND
The Child Tax Credit
The Child Tax Credit (CTC) was first introduced in 1997 as a $400, non-refundable
credit for each child under the age of 17. The CTC is not indexed to inflation, but in 1998, the
credit was increased to $500, and in 2001 it was increased to $1,000 per child. The credit was
originally designed to offset taxes owed by parents, but in 2001, the credit was made partially
refundable for families with earnings over $10,000. Making the credit partially refundable meant
that even households without tax liability could still claim part of the credit. In 2009, the
earnings minimum was reduced to $3,000 (temporarily and later permanently; Crandall-Hollick,
2018). In 2017, the credit was expanded to $2,000 per child, with the refundable portion
increased to $1,400 per child and the earnings minimum reduced to $2,500.
In March of 2021, Congress passed a one-year expansion to the CTC that fundamentally
altered the structure of the credit. First, the benefit was made fully refundable, and therefore,
available to families regardless of tax liability. Second, the size of the credit increased for all
children, but especially so for younger children: $3,600 per child under the age of 6 and $3,000
per child aged 6 to 17. Third, the earnings minimum was removed, extending eligibility to
households with little or no earnings. Last, eligible households1 received the first half of the
credit in monthly installments over a six month period (July-December, 2021), and the remaining
half of the credit in a lump-sum with their 2021 tax refunds, in early 2022. Though there were
discussions of making these reforms permanent, Congress failed to pass a permanent reform, and
thus the credit reverted back to pre-2021 law beginning in January of 2022. Our study focuses on
1 The full credit was available to households earning up to $112,000 for single/$150,000 for married households.
The credit then phased out to $2000 per household until it phased out again. Households with incomes over
$240,000 for single ($440,000 for married) were not eligible for the credit. For example, a married household could
get the full credit if their income was below $150,000. If they earn between $150,000 and $182,000 the credit phases
out to a maximum of $2000 per child. Once their income reached $400,000, the credit again phased out until
$439,999 at which point they no longer received any credit.
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the 2021 expanded CTC, and in particular the effects of monthly payments made to families with
children in the second half of 2021.
The first payments for the 2021 CTC were distributed in July 2021; however, there were
some implementation challenges. In some cases, payments were delayed (IRS, 2021), and survey
evidence suggests that many eligible families did not receive the monthly payments (Pilkauskas
and Cooney, 2021; Parolin et al., 2021). In theory, families who filed taxes in either 2020 or
2019 were automatically eligible to receive the monthly credit. Families with little or no earnings
and those with the lowest levels of education are least likely to file taxes, and were most likely to
report not receiving the monthly payments in 2021 (Pilkauskas and Michelmore, 2021).
Furthermore, intra-year changes in children’s living or custody arrangements, which is common
among economically vulnerable families, might have reduced CTC claims due to tax filing
complexity. For instance, if a child was living in a different tax filing unit in 2021 than in 2020,
the payments would have gone to the 2020 unit unless the family filed this change on the IRS
online portal. Nearly 60% of households with low incomes face ambiguity in tax filing for the
EITC due to family complexity (such as custody or residency issues), challenges that families
claiming the CTC also face (Michelmore and Pilkauskas, 2022), suggesting that this issue could
be widespread.
With implementation challenges and no guarantee of payments past December, parents’
behavioral responses to the CTC may have differed than if the policy were in place for longer or
payments more consistently reached all eligible families. The expansion was implemented during
a global pandemic, when the federal government put forth a robust policy response (e.g.,
stimulus payments, expanded SNAP benefits, extended Unemployment Insurance, and eviction
moratoria) to help families avoid extreme hardships. A challenge of identifying the effect of the
CTC is that many of these programs were ending just as monthly CTC payments were first
disbursed. Furthermore, many children also returned to in-person school in August and
September 2021 after more than a year of virtual schooling for most students – just two months
into the monthly CTC payments. Lastly, inflation increased dramatically during the last several
months of 2021, likely dampening some of the effects of the CTC. Thus, the findings we present
here are only suggestive of impacts on economic wellbeing that might follow a full scale child
benefit program, were it longer lasting or implemented during a time of less uncertainty and
change.
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Prior Literature
The literature on income support and economic wellbeing is extensive. Here we focus on
the U.S., although many studies have examined the effects of cash transfers on economic
wellbeing outside the U.S. context (see Bastagli et al., 2016 for a review). In the U.S., income
support policy is frequently in-kind, such as SNAP, Medicaid, or housing assistance (Edin and
Shaefer, 2015). These policies, which target particular forms of consumption, have been found to
improve economic wellbeing, and in particular the hardships they target (i.e., SNAP reduces
food insecurity; Shaefer and Gutierrez, 2013; McKernan et al., 2021; Schmidt et al., 2016).
Social safety net policies that provide cash transfers are also linked with improved outcomes. A
recent study of a cash transfer that is conditioned on work (or looking for work), the Temporary
Assistance for Needy Families (TANF), found that TANF improved food insecurity but also
reduced labor supply, potentially as a result of either income support, or the program’s marginal
tax rates that reduced benefits with added earnings (Freedman and Kim 2022, see also Shaefer at
al. 2020 for impacts on food insecurity). Two recent studies found that declines in cash
assistance through TANF were linked to rising homelessness and housing instability in public
schools (Parolin, 2021; Shaefer et al., 2020). Studies of earning supplements, like the Earned
Income Tax Credit (EITC) are also linked with improved economic outcomes (i.e., Barr et al.,
2022; Hoynes and Patel, 2017; Michelmore and Pilkauskas, 2021) and improved material
hardship. The EITC reduces some housing hardships (household crowding, cost burdens,
doubling up; Pilkauskas and Michelmore, 2019), medical hardship (Kondratjeva et al., 2021) and
food insecurity (Kondratjeva et al., 2022; Batra and Hamad, 2021), but does not seem to impact
ability to pay bills, buy prescriptions, or make housing payments (Kondratjeva et al. 2021, 2022).
However, the EITC is conditioned on work making it is difficult to disentangle the effect of
employment from the cash transfer itself.
The effects of cash on economic wellbeing have also been studied outside the social
policy context. For example, one-time cash transfers reduced homelessness when the cash was
provided for housing (Evans et al., 2016). On the other hand, three recent studies find few effects
of one-time cash transfers on financial and material wellbeing of families with low incomes
(Jacob et al., 2022; Jaroszewicz et al., 2022; Pilkauskas et al., 2022). However, Pilkauskas and
colleagues found that a one-time $1,000 cash transfer did improve material hardship outcomes
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(ability to pay bills, utilities, rent, food insecurity, and medical hardship) for those with
extremely low incomes (below $500 a month in household earnings). Descriptive studies find
that stimulus payments made during the pandemic reduced food insufficiency and improved
financial stability (Cooney and Shaefer, 2021) and also increased consumption (Chetty et al.,
2020).
Recurring cash transfers, akin to the 2021 CTC, may have different impacts on material
wellbeing than one-time cash transfers. Studies of the Casino payments (Akee et al., 2010; Wolfe
et al., 2012), Negative Income Tax experiments (Maynard and Murnane, 1979), and Alaska
Permanent Fund (Amorim, 2021) have found positive effects on some measures of wellbeing
(like health, education or consumption), but surprisingly few studies examine the impact on
material hardship. One recent randomized controlled trial (Baby’s First Years) provided monthly
benefits of approximately $333 to low-income families with children in their first year of life and
examined material hardship outcomes finding no impacts of the transfer on food insecurity,
housing quality, or economic stress, although this study did find positive effects of the cash on
other outcomes including child investments and parenting time (Gennetian et al., 2022). Thus,
the findings from these new randomized control trials raise questions about the ability of
unconditional cash transfers to reduce material hardships.
Prior research has also examined the impact of recurring cash transfers on labor supply in
the U.S. The Negative Income Tax experiments (which also had a marginal tax rate) found some
reductions in labor supply (Levine et al., 2005), whereas studies of the Alaska Permanent Fund –
a program with no marginal tax rate – find no effects on employment (Jones and Marinescu,
2022). The Baby’s First Years study also found no detectable effects on mothers labor supply
when the child was one (Gennetian et al., 2022). Differences across studies are likely driven by
heterogeneity in treatment size, timing and targeting.
Child benefits, which are regular cash transfers to families with children similar to the
2021 CTC, are common in high income countries outside the U.S. (Shaefer et al., 2018; Van
Lancker and Mechelen, 2015). The Canadian child benefit is linked with improved outcomes for
children and families (Milligan and Stabile, 2011), increased spending on basic needs like food,
shelter, and transportation (Jones et al., 2019), no effect on the labor supply of single mothers
(Baker et al., 2021), but reduced employment among married mothers (Schirle, 2015). The
implementation of the Austrian child benefit increased labor supply, especially for women
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(Christl et al., 2020). Although findings from other countries are informative, the social welfare
states in Canada and western Europe are quite different from that of the U.S., making it difficult
to generalize these findings to the U.S. context.
A handful of studies have directly examined the effects of the 2021 CTC, suggesting that
the monthly payments improved families’ economic wellbeing (see Curran, 2021 for a review of
descriptive studies of the 2021 CTC). A number of studies have estimated the effects of the 2021
CTC on child poverty, all suggesting reductions in poverty to varying degrees (Columbia
University Center for Poverty and Social Policy [CPSP], 2021; Acs and Werner, 2021; Corinth et
al., 2022; Bastian, 2022). Other descriptive studies suggest that the CTC reduced food
insufficiency (Perez-Lopez, 2021; Rapid EC, 2021; Karpman et al., 2022). These findings have
been corroborated in studies using more causal methodological approaches (Hamilton et al.,
2022; Parolin et al., 2022; Shafer et al., 2022). Hamilton and colleagues (2022) find that families
were more likely to be able to pay for an emergency expense, but they find few effects on other
hardships like paying utilities, bills, housing, needed medical treatments or debt. Three studies
also find that the monthly CTC cash payments had no effect on parental employment (Ananat et
al., 2022; Hamilton et al., 2022; Lourie et al., 2021).
We build on these earlier studies by studying the effects of the 2021 monthly cash
payment on a broad array of hardship measures in a national sample of families with very low
incomes, many of whom gained access to the CTC or received a larger credit as a result of the
2021 expansion. Based on prior research and theory, we expect that the monthly credit should
reduce material hardships. We also anticipate material hardships that are less severe or
potentially sensitive to small amounts of money (e.g., food insecurity or a bill hardship) will be
affected by the 2021 CTC, whereas material hardships that are more severe or might require
larger transfers or longer durations of a transfer to alleviate (e.g., severe housing hardships, like
eviction) may not be affected. We also expect that families will be less likely to engage in
hardship avoidance tactics (e.g., relying on friends or family for material or financial assistance)
in response to the monthly credit, given reduced need to rely on social networks to avoid
hardship. Lastly, we do not expect to find impacts on parents’ employment given prior research.
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METHOD
Data
Data come from a national monthly survey conducted in partnership with Propel, the
creators of Providers (formerly Fresh EBT) a free mobile application that helps over 5 million
families manage their SNAP benefits. Propel advertises this application on social media, but
most users find out about it through word of mouth, either through friends, or their SNAP
caseworkers. In any given month, roughly one-quarter of all SNAP beneficiaries in the U.S. use
Providers to track their monthly benefits; thus, Providers has a very wide reach and includes
users from all 50 states.
Every month, Propel invites a random sample of users of the Providers app to participate
in a survey on a range of topics related to household financial stability. They are not
compensated for their time, but each survey only takes about 11 minutes to complete.
Approximately 4,000-6,000 users respond to the survey each month, roughly half of whom live
in households with children under the age of 18.
Following passage of the expanded CTC law, we partnered with Propel to add questions
about the CTC to their monthly survey. These questions were first fielded in June 2021. Because
the monthly survey is administered on the 1st-14th day of each month, we observed participants
for two months before CTC payments were first delivered on the 15th of July 2021. Beginning in
August 2021, we asked participants about their CTC receipt in the previous month (e.g., in
August, participants were asked about CTC receipt in July). Our analyses focus on surveys
conducted between June 2021 and January 2022 (participants were asked about the final
December 2021 payment in January 2022). This period includes two pre-CTC observations and
six post-CTC observations.
We restrict our analyses to households with at least one child under the age of 18. (As we
describe in greater detail in the robustness checks section, in extensions, we also conducted a
number of analyses including households without children as a comparison group.) We excluded
households living in the U.S territories since they were not eligible for the 2021 monthly CTC
payments. To assess the representativeness of the Providers respondents, we compared the
characteristics of parents in our sample to a nationally representative sample of households with
children from the 2019 American Community Survey (ACS). In Appendix Table 1 we show the
ACS estimates for households with children both among households that reported receiving
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SNAP in the previous year and among households below the poverty line (100% of poverty),
groups that we expect to be similar to the Providers respondents. Although there are some minor
differences, we find that the parents in the Providers data look very similar in terms of their basic
demographic characteristics to parents living in poverty in the ACS, as well as the average ACS
parent who received SNAP. Thus, the Providers data appear to be representative of households
with children below the poverty line.
In Table 1, we provide descriptive statistics on households with children in our sample
showing the overall average as well as statistics for the families in the two pre-period
observations (pre-CTC) as compared to the sample for the six post periods (post-CTC). In
general we find that the sample is similar across the two periods. Thirty-five percent of
respondents identified as Black, 35% as White, and 21% as Hispanic. Nearly all parents in our
study are female (94%) and only 30% live with a partner or spouse. The families in our study are
very economically disadvantaged: 85% receive SNAP and 31% were unemployed in the prior
month. In addition, 22% reported having no household monthly earnings in the previous month
and another 15% earned less than $500. Extrapolating from the monthly household earnings to
the annual level, 88% of households in our study earn less than $24,000 per year, and the mean is
approximately $10,000.
Measures
Material Hardship
Material hardship is a consumption-based indicator of economic wellbeing that has been
linked with poorer outcomes for both children (e.g., Zilanawala and Pilkauskas, 2012) and adults
(Heflin and Iceland, 2009), suggesting that reductions in hardship should improve family
wellbeing. Our main measure of material hardship is a 14-item index of hardships that includes
food, medical, housing, utility, transportation, and bill hardships (see Appendix 1 for a detailed
discussion of these measures; α=0.71). For each index, we sum across the items and standardize
(M=0, SD=1). We also examine specific types of material hardship since studies show that both
the predictors and consequences of hardship can vary across hardship domains (Heflin et al.,
2009). Each question related to material hardship asks respondents to indicate whether they have
experienced the given material hardship in the past 30 days.
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First, we consider food insecurity with a 4-item index that includes both the 2-item food
insecurity scale derived from the USDA’s food security scale (Hager et al., 2010) and questions
about skipping meals or eating less (α=0.71). Second, we examine medical hardship with a 2-
item index about missed visits to the doctor or dentist due to cost (α=0.61). Third, we consider
severe housing hardship with a 4-item index (α=0.65) that includes homelessness, shelter use,
evictions and lack of stable housing (e.g., living in a motel, car or shelter). We refer to this
measure as severe housing hardship because it does not include less severe degrees of housing
insecurity, such as the inability to pay rent or staying in someone else’s home. Fourth, we
examine two items related to utility hardship. Here we do not construct a scale since the two
survey items of interest had a very low Cronbach’s alpha (α=0.29) when combined into an index,
suggesting poor internal consistency. Thus, we examine each item separately as a binary
measure, one for having utilities cut off and another for utility bill hardship (not paying one’s full
utility bill). Fifth, we examine a single-item question that asks respondents if they “decided not
to pay a bill” as a general measure of bill hardship. Unfortunately, this question does not indicate
why the respondent decided not to pay their bill (e.g., due to financial constraints), and thus has
some ambiguity in interpretation. Lastly, we study a single transportation insecurity item that
asks respondents if they missed an appointment, skipped going somewhere, or missed work due
to lack of transportation. Transportation insecurity is rarely considered as a measure of material
hardship, but recent work suggests it is a commonly experienced hardship (Murphy et al., 2022).
Additional measures of economic wellbeing
We examine several other measures indicating overall economic wellbeing. The first is a
broad measure of having things one typically needs. Respondents are asked “Do you have
everything you typically need in your home right now? Like food, household products, and
cleaning supplies”, and can respond by indicating 1) they have everything, 2) most things, 3)
some things, 4) running low on most things, or 5) they do not have most things they need. We
create a binary indicator taking on the value of one if the respondent reports having everything or
most things; all others are coded as zero. The second measure asks respondents about cash on
hand. Respondents were asked “About how much money do you have in total right now (not
including food stamps)?” in a six-item response category ranging from less than $25 to $1,000 or
more. The final measure asks respondents how long the money they have on hand will last in
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days, in a six-item response category ranging from 1-2 days to two weeks or more. For both of
these questions about money on hand, we set the response to the midpoint of the range and treat
the outcomes as continuous.
Hardship Avoidance
Hardship avoidance is a set of behaviors that individuals undertake to avoid the
experience of hardship. We examine three measures of hardship avoidance that all refer to
experiences “during the last 30 days.” First, we examine if respondents relied on friends or
families for meals. Second, we study if respondents reported visiting a food pantry. Last, we
construct an indicator for borrowing money from friends/family. We chose not to construct an
index with these items as their internal reliability was relatively low (α=0.41).
Labor Force Participation
Our final set of outcomes relate to parents’ employment. Specifically, we construct
measures of the respondent’s current labor force participation (whether the respondent is either
employed or seeking employment versus not actively seeking employment). We also separately
examine whether respondents are employed, employed full-time or employed part-time.
Empirical Strategy
We use a parameterized difference-in-differences approach to study the effect of the CTC
on economic wellbeing. Because of endogeneity concerns, whereby differences in CTC receipt
and benefit size are correlated with other household characteristics that may be associated with
economic wellbeing, we do not use household self-reports of CTC benefits. Instead, following
many prior studies (e.g., Currie and Gruber, 1996; Michelmore and Pilkauskas, 2021), we use a
simulated measure of household benefits based on policy changes. We predict monthly CTC
benefits based on the number and ages of children in the household. Specifically, beginning with
the August 2021 survey, which asks about CTC benefit receipt in July, we calculate the monthly
household CTC benefit as the sum of $300 multiplied by the number of children under age six,
and $250 multiplied by the number of children aged six to seventeen. Respondents in surveys
prior to the rollout of the monthly CTC benefit are assigned $0 for their monthly CTC benefit,
regardless of the number and ages of children residing in the household. Variation in this
measure stems from the timing of CTC payments (before and after the implementation of the
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CTC expansion), and benefit size as a function of the number and ages of children (under 6
years/6-17 years) in the household.
We then estimate reduced-form models of the following form:
(1) 𝑌𝑖𝑡 = 𝛽0 + 𝛽1 𝐶𝑇𝐶𝑖𝑡𝑐 + 𝛽2 𝑋𝑖𝑡 + 𝛼𝑡 + 𝛿𝑠 + 𝛾𝑠𝑡 + 𝜃𝑐 + 𝜀𝑖𝑡
Where Yit is the outcome of interest (e.g. employment, economic well-being, etc.) for individual i
in month t. CTC is the predicted monthly benefit for a family with number of children (by age) c.
The parameter of interest is 𝛽1 , which represents the effect of a $100 increase in predicted CTC
benefits on the economic well-being indicator of interest. X is a vector of demographic
characteristics (age, race/ethnicity, gender, state, education, urbanicity, and partnership status)
that may be correlated with the outcome of interest. We also include month fixed effects (𝛼𝑡 ) to
address concerns that other policies occurring over this time frame may contribute to our effects,
such as the expiration of the eviction moratorium in the fall of 2021. State fixed effects (𝛿𝑠 )
control for state level characteristics that might affect economic outcomes and also correlate with
CTC receipt. 𝛾𝑠𝑡 is a set of state-by-month policy variables. Namely, we include controls at the
state-month level for the presence of (1) Supplemental Nutrition Assistance Emergency
Allotments – waivers to continue providing maximum SNAP assistance to eligible households;
(2) extended Pandemic Electronic Benefit Transfers (P-EBT) – additional food assistance for
school-aged children;2 and (3) extended federal Unemployment Insurance. We create a set of
binary indicators equal to 1 when the policy is operative in a particular state-month.3
In separate model specifications, we also control for either 1) household size fixed effects
or 2) number-of-children fixed effects (𝜃𝑐 is a term representing either variable). These two
variables are correlated with CTC benefit size and likely with economic wellbeing outcomes, and
thus would lead to omitted variable bias in estimating the impact of the CTC on hardship without
their inclusion in the models. Since benefits are determined based on the number of children in
the household, household size is correlated with, but does not directly determine CTC benefits.
Models that include household size fixed effects allow for hardship to vary according to how
2 Data on these programs comes from the USDA Food and Nutrition Service
(https://www.fns.usda.gov/programs/fns-disaster-assistance/fns-responds-covid-19/snap-covid-19-waivers).
3 Data on unemployment benefits comes from the Century Foundation
(https://tcf.org/content/report/7-5-million-workers-face-devastating-unemployment-benefits-cliff-labor-day/).
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many people live in the household, and are identified by comparing households of the same size,
but different number and age composition of the children residing in the household.
Controlling for number of child fixed effects directly addresses the concern that any
effect of the CTC we observe is driven by underlying differences in the prevalence of hardship
according to the number of children residing in the household, rather than the CTC itself. With
number of child fixed effects in the model, variation is generated by comparing households with
the same number of children before and after the CTC expansion, but different age compositions
of their children. However, because number of children directly determines CTC benefit amount,
models that adjust for number-of-children fixed effects also absorb the vast majority of the
variation in our predicted CTC measure, reducing our statistical power. Together, month and
number-of-children fixed effects explain 81% of the variation in the predicted CTC, 4 while
month fixed effects along with household size fixed effects explain 60% of the variation. We
present results from both specifications (those with household size fixed effects, and those with
the number of child fixed effects) and discuss any differences we observe across these different
specifications.
Equation 1 represents our reduced-form, intent-to-treat analysis (ITT, i.e., the effect on
the full treatment group regardless of CTC receipt). However, because not all families with
children received the monthly CTC payment (as shown in Table 2, 66% of respondents with
children in our sample reported receiving last month’s CTC payment), we also estimate a
treatment-on-the-treated response (the local average treatment effect, LATE). 5 To do this, we use
our measure of predicted monthly CTC benefits to instrument for self-reported monthly CTC
benefits. This approach is similar to that represented by Equation 1), but we estimate a two-stage
least squares regression model (2SLS), in which the first stage regresses self-reported CTC
benefits (the endogenous variable) on our predicted measure of CTC benefits (the exogenous
variable).6
4 We tested the inclusion of a linear specification of the number of children (and of household size) and it made no
difference to the percent of the variation explained nor to the results.
5 Note, unlike Parolin et al. (2022), we do not estimate the “extra CTC” provided to these families during the 2021
CTC as many were not eligible for the credit in 2020. We also do not take into account any lump-sum payment these
families would receive during tax filing, as tax filing season is outside of our window of observation.
6
We also ran an extension where we calculated the predicted monthly CTC benefit based on the number and ages of
the children in the household, but set it to zero if the respondent did not report receiving the benefit in that month
and find a similar pattern of results (using a 2SLS analysis where we use the predicted amount received adjusted for
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The 2SLS method essentially rescales our ITT estimates to those who reported receipt of
monthly CTC benefits. We use this strategy rather than directly relying on self-reported monthly
CTC benefits because receipt and credit size are endogenous to family characteristics (see
Pilkauskas and Michelmore 2021). In particular, self-reported CTC benefits are positively
correlated with income and educational attainment. Income and educational attainment are
negatively correlated with hardship, so we would expect that a naïve regression of material
hardship on self-reported CTC benefits would generate a negative relationship. In fact, in
Appendix Table 2 we present these “naïve” regressions, which largely confirm this hypothesis.
In addition to the parameterized difference-in-differences analyses for all families with
children, we also study heterogeneity in CTC effects on economic wellbeing by 1) race/ethnicity
and 2) monthly household income. We focus on household earnings in the last month because it
provides a recent proxy for income. Last month’s earnings are a good measure of current
economic resources for families with very low incomes, who make up the majority of our
sample, because their rates of earnings volatility are high. We divided the sample into
households with monthly earnings above and below the median of $500. Our null findings on
employment, as detailed further below, reduce concerns that monthly earnings are endogenous to
CTC receipt.
In Table 2, we show the mean values of our outcomes measures, CTC receipt, and CTC
value for the full sample, by race/ethnicity, and by household earnings. Rates of CTC receipt
were similar between Black respondents (69%) and White respondents (68%). However, rates of
CTC receipt were much lower among Hispanic respondents (61%) despite similar levels of tax
filing (Pilkauskas and Michelmore, 2021). Differences in CTC receipt by earnings are even more
stark, 58% of respondents with less than $500 in earnings received the CTC as compared to 73%
of those with $500 or greater. Among respondents who reported receiving the CTC, the average
value is close to $500, whereas the average imputed value (based on the number of children in
the household) is higher and closer to $700. Although as noted, using the imputed value or self-
reported value results in similar findings, we think this divergence between received amount and
imputed amount may reflect implementation challenges (like changes in custody).
receipt rather than amount received). The findings from this analysis are similar to those presented here although the
point estimates were slightly smaller.
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RESULTS
How do families with low incomes spend their monthly benefits?
We first present some descriptive evidence on how families with low incomes spent their
2021 monthly CTC benefits in Table 3. Between August, 2021 and January, 2022, we included
an open-ended question in our survey asking families how they spent the credit, which we then
coded allowing multiple responses per respondent. We categorized the responses into three broad
groups: bills and living expenses, child-related expenses, and other expenses.
Paying bills was by far the most common response. Each month, approximately three-
quarters of families reported using the credit to pay bills. Families also indicated that they spent
the credit on other living expenses, such as paying the rent or mortgage (approximately 9-12%
each month), food (7-8%), gas (4%), and other household necessities such as toiletries and
cleaning supplies (5%). Many respondents also reported spending the credit on child-related
expenses and these responses varied over the course of the year. In July and August, around the
time that children were returning to school, about 25% of families said they spent the money on
school supplies or school clothes and uniforms for their children. In contrast, very few families
mentioned school supplies in November and December, whereas in November and December far
more parents reported using the credit for holiday gifts for their children. Each month, about
10% of respondents indicated they spent the money on other child necessities such as diapers and
wipes, and 5-7% reported using the money to pay for child care. Finally, each month, a small
fraction of families reported putting the credit in savings accounts (1-2%). Given the very low
income of the respondents in our sample, it is not surprising that only a small percentage of
families said they saved the credit.
From this open-ended question, it is clear that the vast majority of families spent the
credit on immediate living expenses and child-related expenses, while very few were able to save
the credit for future expenses. Given the patterns reported here – that families reported spending
the money on immediate expenses – we expect to find reductions in material hardships among
these families as well.
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Does the expanded CTC improve the economic wellbeing of households with low incomes?
Material Hardship. We next examine the effect of the CTC on a set of material hardship
measures (see Table 4). We present both the Intent-to-Treat (ITT) and the Local Average
Treatment Effect (LATE) results for our two models: 1) including household size fixed effects
and 2) including number-of-children fixed effects. In Appendix Table 3, we show how the
inclusion of various control measures affect the results. In general, our estimates are not sensitive
to the inclusion of other controls beyond those controlling for household size or number of
children.7 As expected, the ITT and LATE results are similar in terms of direction of the point
estimates and statistical significance; however, the LATE estimates suggest larger effects on
economic wellbeing as they apply to those who reported receiving the CTC. Here we focus our
discussion on the LATE estimates.
We find that each additional $100 in monthly CTC payments significantly decreases the
number of material hardships experienced by respondents by 0.033 to 0.042 standard deviations
(SDs) across the two models. Recall, the average CTC among those who received it was close to
$500; thus, if we multiply these estimates by five it suggests that overall hardship was reduced
by 0.17-0.20 SDs. This suggests that the average monthly CTC benefit reduced the total number
of hardships by roughly 17%. When we examine the particular types of hardship, we find that
food hardship significantly declined in response to an additional $100 in monthly CTC benefits
by about 0.06 SDs (or closer to 0.30 SDs for a $500 CTC, a reduction of about 32%). We
similarly find that medical hardships declined in response to monthly CTC payments, by about
0.02-0.03 SDs; however, the effect was not significant once we controlled for number of child
fixed effects, although the point estimate was similar in magnitude and direction.
In terms of utility hardships, we find that an additional $100 in monthly CTC payments
significantly increased the likelihood of having a utility cut off in Model 1, but the effect was
substantially attenuated and no longer significant after controlling for number of child fixed
effects in Model 2. The impact of the CTC on the inability to pay the full utility bill is negative
across the models, but only marginally significant, providing suggestive evidence that the CTC
increased the likelihood of families paying their full utility bill (1 to 1.4 percentage points). We
7 We also tested whether excluding a control for race/ethnicity changed the results and found it did not.
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find no significant effects on severe housing hardships, bill hardships more generally, nor
transportation hardship. As we discussed earlier, in some ways it is not surprising to find null
effects on housing hardships, since our measures are fairly extreme housing hardships (e.g.
eviction, homelessness), which might be difficult to alleviate with a modest monthly benefit
provided over a short duration of time.
Additional Measures of Economic Wellbeing. In Table 5, we turn to results for needs
being met, money on hand, and hardship avoidance techniques. Here we present the LATE
estimates for simplicity. Each additional $100 in monthly CTC payments significantly increases
the amount of money parents have on hand by about $5 (on a mean of $126, or about 4%), but
once we include the number of children fixed effects the point estimate is rendered insignificant
and becomes negative. We find no effect of the CTC on respondents reporting that they have
their needs met nor on the amount of time that the money they have in their bank account will
last. These null results may be surprising, but recall that respondents take the monthly surveys
between two weeks and one month since receiving their last CTC payment. Since these monthly
benefits make up a large portion of household monthly budgets, it is not surprising that
respondents spend their benefits very soon after receiving them.
In terms of hardship avoidance techniques, we see some evidence of a reduction in
relying on friends or family for food (a 1-2 percentage point reduction). This estimate is no
longer significant once we control for number of child fixed effects; however, the point estimate
is still negative and about one percentage point. We find no effects of the CTC on visiting a food
pantry nor borrowing money from friends or family.
Employment. One of the main concerns raised by opponents of the 2021 reform to the
Child Tax Credit was that it would decrease the incentive to work. Since the reform removed the
earnings requirement to obtain the credit, some researchers hypothesized that a substantial
number of parents, unmarried mothers with low incomes in particular, would stop working in
response to the reform (Corinth et al., 2021). In Table 6, we provide evidence that this is not the
case, at least with the 2021 CTC reform. We find no evidence that the monthly CTC benefits led
to a reduction in employment or labor force participation in the six months during which the
benefits were distributed. In our estimates that include number of child fixed effects (Model 2),
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there is some suggestive evidence of shifts from full-time employment to part-time employment
– we find a 1.1 percentage point decrease in full-time employment and a 0.7 percentage point
increase in part-time employment – but none of these estimates are significant at conventional
levels (and the part-time point estimate is not consistent across models). Taken at face value, our
point estimates in Model 2 imply a 0.3 percentage point decline in labor force participation,
which is an implied elasticity of about 0.05 with respect to after-tax monthly household income.8
Given our standard errors, we can rule out declines in labor force participation larger than 2.1
percentage points (an elasticity of 0.25) associated with a $100 monthly benefit. In sum, we do
not find evidence that the monthly, unconditional cash benefits reduced work in any meaningful
way.
Are there heterogeneous treatment effects by race/ethnicity?
Previous research has shown differences in CTC receipt by race/ethnicity (Pilkauskas and
Cooney, 2021; Parolin et al., 2022), and historically, Black and Hispanic children have been less
likely to be eligible for the full CTC (Goldin and Michelmore, 2022). Though our entire sample
is economically disadvantaged, and therefore unlikely to have been eligible for the full CTC
regardless of race, we still may expect to find differential impacts of the 2021 monthly credit by
race and ethnic background. In Table 7, we present results for the effects of the CTC by
racial/ethnic groups, again focusing on the LATE estimates. We generally find more pronounced
effects of the 2021 monthly CTC payments on various hardship items for Black respondents.
However, it is important to note that almost all point estimates have overlapping confidence
intervals across racial/ethnic groups.
For the overall material hardship index, we find that each additional $100 in monthly
CTC payments significantly reduces the number of material hardships for Black respondents, but
less so for White and Hispanic respondents. As in the full sample, monthly payments were
significantly associated with reductions in food hardship for all three groups in Model 1. This
association only remains significant in Model 2 for Black respondents (although estimates
remain negatively signed for both White and Hispanic respondents). Similarly, the effect of the
8
The average monthly income in our sample is about $833. A $100 increase in monthly earnings represents a 12%
increase household income; a 0.3 percentage point decline in labor force participation amounts to a 0.4% decline in
labor supply.
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CTC on medical hardship is only significant for Black respondents. Point estimates are similar
for Hispanic respondents but less precise and not statistically significant, potentially due to
sample size limitations. We again find the perplexing result that the CTC increased utility cut
offs, but these results dissipate with the inclusion of number of children fixed effects. We also
find that monthly CTC payments reduced the likelihood that Black respondents were unable to
pay their full utility bill, but not for White or Hispanic respondents.
In Appendix Table 4, we show the results for basic needs, cash on hand, hardship
avoidance and employment outcomes by race/ethnicity. We continue to find that monthly CTC
payments reduce reliance on friends/family for food across racial/ethnic groups, though these
results are not consistently robust to the inclusion of number of child fixed effects. We also find
some evidence that CTC payments increase the likelihood of Black families reporting having all
or most of their needs met (by 2.8 percentage points, or 10%), but this effect is only marginally
significant. We find no significant impact of CTC payments on the other outcomes (employment,
cash on hand, etc.).
Are there heterogeneous treatment effects by monthly household earnings?
Although respondents in our study, on average, have very low earnings, many studies
have found that the effects of public programs are larger for lower income groups. Additionally,
families with the lowest incomes, those below $2,500 annually, have historically been
completely ineligible for the CTC. The 2021 reform therefore marked a larger gain in benefits
for the lowest income families in our sample relative to those with slightly higher earnings. To
test this hypothesis, in Table 8, we show the LATE estimates for the material hardship items (and
present the findings for hardship avoidance and employment in Appendix Table 5) by monthly
household earnings (below $500 and $500 or greater). We find no evidence that monthly
earnings moderates the effect of the CTC. Although significance varies across particular
estimates, in general the estimates are of similar magnitude and in the same direction across the
two groups.
Robustness checks
Comparing to families without children. Our main analyses focused on households with
children under the age of 18, exploiting variation in the generosity of CTC benefits by number
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and ages of children in households before and after 2021 CTC rule changes. In a supplemental
analysis (shown in Appendix Table 6), we also conducted a traditional difference-in-differences
analysis, where we compared changes in economic wellbeing following implementation of the
2021 CTC between treated (households with children under 18) and untreated (households
without children under 18) groups. We conducted these analyses measuring the CTC in two
ways: 1) with a pre-post indicator interacted with an indicator for the presence of children in the
household and 2) with predicted CTC amounts. 9 In general, many of the findings discussed thus
far are insignificant in these models, though we continue to find evidence of reductions in food
hardship in models that use families without children as a comparison group and take into
account the generosity of the monthly CTC benefits. For several reasons, discussed in detail
below, we do not believe these models appropriately capture the impact of the monthly CTC
benefits on material hardship, and thus our preferred estimates exclude families without children
from the comparison group.
We are reluctant to rely on models that include families without children in the
comparison group because we do not believe that they serve as a good counterfactual for how
material hardship would have trended in the absence of the monthly CTC benefits. The
traditional difference-in-differences approach relies on the assumption that trends among
households without children serve as a valid counterfactual for what would have occurred among
households with children if not for the monthly CTC payments (i.e., parallel trends assumption).
To test this assumption, we visually inspected trends in our outcomes over the months leading up
to the monthly CTC disbursement for households with children vs. households without children.
Plotted trends were parallel across the two groups for some outcomes, but not others and the
levels of hardship were often very different. To further assess baseline differences, we also
compared demographic characteristics between households with and without children and noted
several significant differences. Households without children were more likely to be male, older,
and White, but less likely to live in an urban area or to be employed.
9
As shown in Appendix Table 2, the results of the pre-post difference-in-difference estimates differ somewhat from
the continuous exposure ($) estimates of the CTC when we use childless households as our comparison group. We
are unsure why this is the case. It may be because childless households are a poor comparison group; however, we
find that the continuous exposure estimates are more similar (point in the same direction but of a different
magnitude) to our main specification that relies on households with children.
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Another central assumption of the difference-in-differences approach is that no other
events or policy changes differentially affected treatment and control groups over the study
period. This assumption might be too strong when comparing families with children to those
without children since policy responses to COVID-19 (e.g, eviction moratoria, rounds of
stimulus payments) likely impacted these two groups differently. As noted earlier, we controlled
for a set of far-reaching policy initiatives during the pandemic, but other potential confounders
remain. In particular, disbursement of the first CTC payments coincides with the start of the
school year and the return to in-person schooling for many families with children. Month fixed
effects can account for some time-varying factors, but only those that uniformly affected
households with and without children. Therefore, due to concerns about differential trends by
household type, in concert with large demographic differences, we determined that the
households without children in our sample served as a poor counterfactual.
Testing different time periods. Because of some implementation issues, fewer families
received the CTC payment in July 2021 than in later months. To test whether the inclusion of the
August survey (asking about the July CTC and economic wellbeing in the previous months)
changed the results, we dropped that month in an analysis shown in Appendix Table 7. Dropping
the August survey had little to no impact on the results. Similarly, we tested the exclusion of the
January 2022 survey (asking about the December 2021 credit), as responses may have been
affected by the fact that families were aware that the credit was not being renewed, or by the
large surge in the COVID-19 omicron variant during this time. Again as can be seen in Appendix
Table 7, excluding January had little impact on the point estimates.
Testing the inclusion of additional controls. Although the COVID-19 pandemic was
occurring throughout the study period, we tested whether including a state-specific monthly
COVID count affected the results. As shown in Appendix Table 7, this control did nothing to the
point estimates. Families received the monthly CTC payment on the 15th of each month. Because
our surveys ran from the 1st to the 14th of the following month it is possible that families reported
higher levels of hardship if they took the survey later in the survey window. To test for this
possibility we added a control to account for the timing of their survey response. This did
nothing to change the findings. We also tested the inclusion of an interaction of the number of
days since the last benefit would have been received, but found no significant interactions, likely
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because the vast majority of respondents took the survey in the first few days of the survey
period so there is little variation in this measure.
CONCLUSION
A few recent studies of the effectiveness of unconditional cash transfers in the United
States in reducing material hardship have yielded mixed results. Our study adds to this evidence
base by examining the impact of a monthly unconditional cash transfer to families with low
incomes. Specifically, we examine the effect of the monthly 2021 CTC on a broad set of
economic wellbeing indicators. We use a national sample of families who live in poverty in the
U.S., a population that has previously been excluded from the full value of the CTC prior to the
2021 reform. This population was also disproportionately impacted by the COVID-19 pandemic
(in terms of job losses and disease burden) making the effects of the policy on this population
especially important to study. Lastly, policy debates around the extension of the CTC have
focused on poor households, as some politicians have concerns about providing cash to families
without work requirements.
We find that the 2021 CTC reduced the overall number of hardships by about 17% for the
average family, who received $500 per month. Similarly, the number of food hardships declined
by 32%. These findings are robust to a number of different model specifications, including
number of child fixed effects, and a number of state-month controls for other pandemic-era
policies in place during the same time period. The food insecurity findings are in keeping with
other studies in nationally representative samples (e.g., Parolin et al., 2021). We also find
suggestive evidence that the monthly CTC reduced medical hardship. Though the medical
hardship effect was no longer significant with the inclusion of number of children fixed effects,
the point estimates were similar in magnitude to the models with household size fixed effects. In
effect sizes, a $500 CTC was associated with a 0.155 SD reduction in medical hardships (16%
decline). We also find suggestive evidence that respondents were more likely to pay their full
utility bill (5 percentage points more likely with a $500 credit, or about 9-13%); the point
estimates were nearly identical across models, but only marginally significant.
Three results were not entirely robust across models; thus, we interpret them with more
caution. First, in the model with household size fixed effects, we find that the CTC reduces
reliance on friends and family for food by about 10 percentage points (for a $500 CTC), a
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THE 2021 CTC AND ECONOMIC WELLBEING
reduction of 63%. However, the model with number of children fixed effects is much smaller
and not significant (though continues to be negatively signed), suggesting a smaller effect size –
a reduction of about 22%. Thus, we believe there is suggestive evidence of reduced reliance on
friends/family for food, but we interpret this finding with caution.
Second, we find that families report having more money on hand (about $30 more for
$500 in CTC, or a 24% increase) in response to the monthly CTC, but this finding is not robust
to the inclusion of number of child fixed effects. Including that control flips the sign, reduces the
estimate eight fold and renders it insignificant. Given the inconsistency across models, we do not
feel we have sufficient evidence to confirm or reject this finding. Lastly, we observe a perplexing
finding – that the CTC increases the likelihood of having a utility cut off by about 7.5 percentage
points (for $500 in CTC) an increase of 68% (only 11% of the sample experiences a utility cut
off). Here when we turn to the model with number of children fixed effects, the point estimate
remains positively signed but is far smaller and insignificant. Taken at face value, the estimate
with number of child fixed effects would suggest an increase in utility shut offs of about 18%.
This finding is especially surprising given that we find evidence that families expressed less
difficulty paying their full utility bill. Those estimates, while only marginally significant, suggest
a 9-13% reduction in inability to pay utility bills with a $500 CTC. Utility cut offs are far rarer
events (11%) than being unable to pay the full utility bill (53%). Thus, because of the conflicting
evidence, and the lack of robustness across model specifications, we do not have sufficient
evidence to draw conclusions about the effects of the CTC on utility cut offs or payments.
We find no significant effects of the CTC on employment, which is consistent with other
studies that use nationally representative samples (Ananat et al., 2022; Lourie et al., 2021;
Hamilton et al., 2022). We examined overall employment, full-time employment, part-time
employment and general labor force participation and found no significant effects for any
outcome in either model. That we find no effects on employment in this population should
provide some reassurance to policy makers who are concerned that individuals with very low
incomes may leave the labor force, or reduce their labor supply as a result of the CTC.
Prior studies have shown that Hispanic and Black families have disproportionately been
excluded from the CTC due to earnings minimums and the phase in structure of the CTC prior to
the 2021 reform (Goldin and Michelmore, 2022; Collyer et al., 2019). Thus, the 2021 CTC
provided these families with new support relative to White families, who have historically had
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higher eligibility rates. However, it is worth noting that our study sample consists of families
with very low incomes; thus, most families in our study were likely excluded from the CTC prior
to 2021, regardless of race or ethnic background. We find some evidence that the 2021 CTC
helped reduce hardship among Black families more than White or Hispanic families, but the
differences were not large (there were overlapping confidence intervals).
When we examined differences by income, we found little to no evidence that monthly
earnings moderated the effect of the 2021 CTC. We hypothesized that the CTC would have a
larger impact on those with lower earnings given prior studies (e.g., Pilkauskas et al., 2022).
However, it is also notable that among parents whose household earnings were less than $500
per month, only 58% reported getting the CTC. In comparison, among those in the top half of the
distribution ($500+ earnings/month), nearly 73% reported getting the credit. Thus, more work
that considers heterogeneous treatment effects by income is needed, as we may not have
sufficient power to detect differences by income.
This study provides evidence of the potential effects of a monthly, unconditional cash
transfer to families and children with low incomes in the U.S. Our results suggest that a monthly
unconditional cash transfer improves the material wellbeing of families with low incomes, but
has little to no effect on hardship avoidance, having needs met, or employment. The findings
from our sample of households with low incomes are consistent with other recent studies
(Parolin et al., 2022; Hamilton et al., 2022), providing yet more evidence that regular
unconditional cash transfers may be effective at reducing material hardships among lower
income populations and little to no effect on employment.
We should use caution when extrapolating from the findings in this study. First, the 2021
CTC expansion was short lived and it was unclear whether the expansion would continue beyond
six months. Thus, behavioral changes observed here might be different than if the expansions
were more permanent. Second, families experienced some problems with receipt (e.g., payments
were mailed instead of put in direct deposit) and many reported not knowing why they did not
receive the credit, despite filing taxes (Pilkauskas and Michelmore, 2021). Thus, families could
not depend on it and likely did not change spending patterns in the same way as they might have
were it longer lived or more certain. Third, this expansion occurred during an unprecedented
crisis, the COVID-19 pandemic. The federal government took many measures to ensure families
were buffered from the potential economic fallout in the form of expanded access to social
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THE 2021 CTC AND ECONOMIC WELLBEING
services (like SNAP) and through stimulus checks. In our analyses, we control for a number of
federal programs that expired (unemployment insurance, pandemic EBT, SNAP). The inclusion
of these policy level variables in our analyses did little to nothing to change the point estimates,
suggesting these were not driving the effects we observe. However, we cannot account for all
policy changes that might have affected families of different sizes during this time period. Lastly,
inflation started to rise during this time period (although increases in inflation were more
pronounced after the end of the 2021 monthly credit); increased prices for goods and services
may have also impacted the effect of the 2021 CTC on material wellbeing.
Despite these limitations, we found that the monthly 2021 CTC reduced the experience of
material hardship, and in particular food hardships, in ways that were likely meaningful to
families. We know from other research that material hardships are associated with poorer
outcomes for children (e.g., Zilanawala and Pilkauskas, 2012); thus, it is likely that in the longer
term these types of policies would have real impacts on children’s wellbeing. Unlike recent
studies of unconditional cash transfers to families with low incomes during the pandemic
(Gennetian et al., 2022; Jacob et al., 2022; Jaroszewicz et al., 2022; Pilkauskas et al., 2022), we
find that the monthly unconditional cash transfer did improve material hardship. Questions
remain about why different studies and different approaches to providing unconditional cash
transfers yield different results, some of which may be answered by the ongoing experimental
studies (e.g., Baby’s First Years, Open Research). But the findings here suggest that regular cash
transfers to households with very low incomes, even in the short-term, help reduce their
experience of hardships.
26
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THE 2021 CTC AND ECONOMIC WELLBEING
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Pilkauskas, Michelmore, Kovski & Shaefer
Table 1: Sample Descriptive Statistics, Households with Children, Before and After the CTC Expansion
Average Pre-CTC Post-CTC
Age
18-24 0.07 0.07 0.07
25-34 0.40 0.38 0.41
35-44 0.35 0.36 0.35
45-54 0.12 0.13 0.11
55+ 0.05 0.07 0.05
Race/Ethnicity
Black 0.35 0.31 0.37
White 0.35 0.38 0.34
Hispanic 0.21 0.22 0.21
Other 0.09 0.10 0.09
Education
Less than High School 0.23 0.22 0.24
High School 0.39 0.37 0.40
Some College 0.27 0.28 0.26
Associate Degree + 0.10 0.12 0.10
Female 0.94 0.94 0.94
Urbanicity
Urban 0.46 0.44 0.47
Rural 0.32 0.32 0.32
Suburban 0.22 0.24 0.21
Household structure
Partner/spouse 0.30 0.33 0.29
Household size 4.30 4.26 4.32
(SD) 1.64 1.65 1.64
Number of kids
1 0.25 0.26 0.24
2 0.31 0.32 0.31
3 0.21 0.21 0.21
4 0.12 0.12 0.13
5+ 0.10 0.09 0.11
Receive food stamps 0.85 0.85 0.85
Employment status
Employed 0.40 0.37 0.41
Unemployed 0.31 0.31 0.31
Out of labor force 0.29 0.32 0.29
Monthly household earnings
No earnings 0.22 0.23 0.21
<$500 0.15 0.13 0.16
$500-$999 0.19 0.18 0.19
$1000-$1,999 0.25 0.26 0.25
$2000+ 0.12 0.12 0.11
N 20,545 5,265 15,280
Note: Data come from the providers study, June 2021- January 2022. Sample is restricted to households
with at least one child under the age of 18.
Table 2: Means on the Child Tax Credit and Outcomes, by Race/Ethnicity and by Household
Earnings
Monthly household
All Race/Ethnicity earnings
Black White Hispanic <$500 $500+
CTC (August - January)
% received CTC 65.9 68.9 67.5 61.2 58.0 72.9
Reported CTC $$ (all) 325 338 334 303 271 373
(SD) (314) (305) (319) (314) (298) (319)
Reported CTC $$ (among recievers) 494 490 495 495 467 512
(SD) (257) (246) (266) (257) (248) (262)
Simulated CTC $$ 712 743 644 744 702 717
(SD) (425) (436) (373) (440) (433) (412)
Outcomes
Hardship
Hardship index (14-item scale) 2.98 3.06 3.20 2.58 3.32 2.87
(SD) (2.39) (2.37) (2.53) (2.14) (2.54) (2.28)
Food hardship (4-item scale) 1.12 1.09 1.30 0.90 1.25 1.08
(SD) (1.31) (1.28) (1.40) (1.17) (1.36) (1.29)
Medical hardship (2-item scale) 0.40 0.34 0.46 0.41 0.38 0.42
(SD) (0.68) (0.64) (0.71) (0.69) (0.67) (0.69)
Severe housing hardship (4-item scale) 0.17 0.21 0.15 0.14 0.26 0.12
(SD) (0.57) (0.64) (0.53) (0.49) (0.70) (0.47)
Utilities cut off 0.11 0.15 0.09 0.09 0.14 0.10
Unable to pay full utility bill 0.53 0.58 0.51 0.52 0.59 0.51
Bill hardship 0.39 0.41 0.41 0.33 0.38 0.41
Transportation hardship 0.26 0.29 0.27 0.18 0.32 0.22
Needs/Cash on hand
Have needs met 0.35 0.29 0.37 0.37 0.28 0.37
Total $ on hand ($) 125.93 110.33 118.28 147.05 87.90 145.74
Time $ will last (days) 3.65 3.37 3.64 3.84 3.26 3.78
Hardship avoidance
Relied on friends/family for food 0.16 0.18 0.18 0.12 0.21 0.14
Visited food pantry 0.18 0.15 0.22 0.16 0.20 0.17
Borrow from friends or family 0.54 0.60 0.52 0.52 0.61 0.52
Employment
Any employment 0.40 0.40 0.37 0.43 0.17 0.55
Full-time work 0.21 0.22 0.21 0.20 0.05 0.32
Part-time work 0.19 0.19 0.16 0.23 0.12 0.24
Labor force participation 0.71 0.77 0.62 0.74 0.63 0.77
Note: Data come from the providers study, June 2021- January 2022. Sample is restricted to
households with at least one child under the age of 18.
Table 3: How Did You Use the CTC?
Month July August September October November December Average
Bills and living expenses
Paid bills 72 75 75 75 79 74 75
Paid rent/mortgage/for housing 9 9 9 12 9 9 9
Paid of loans/debts 5 4 4 4 4 3 4
Bought food 5 7 8 8 8 8 7
Bought household necessities (toiletries, cleaning supplies etc) 4 5 5 6 7 5 5
Paid for gas or car expenses 2 5 5 5 5 4 4
Child related expenses
Bought school supplies 11 14 6 2 2 1 6
Bought school clothes/uniforms 8 10 5 2 1 4
Bought child necessities (diapers, wipes, other) 8 8 12 9 13 10 10
Bought child clothes 6 4 6 9 9 5 7
Paid for child care 4 6 5 5 7 7 6
Other
Put money in savings 2 2 2 1 1 1 2
Other 4 4 3 2 2 0 3
Clothes (not specified kid/adult) 2 3 2 2 1 2
Holiday/birthday gifts 1 1 3 12 3
N 1,151 2,208 1,354 1,982 1,778 1,228 9,701
Note: Categories are not mutually exclusive (people could select multiple responses). Sample is restricted to parents who received the CTC and
who provided a response. Percents may sum to more than 100%.
Table 4: Effect of the Child Tax Credit on Material Hardship, Intent-to-Treat Estimates and Local Average
Treatment Effects, in $100
ITT LATE
(1) (2) (1) (2)
a
Hardship index (14-item scale) -0.007 ** -0.008 * -0.033 ** -0.042 *
(0.002) (0.003) (0.012) (0.019)
a
Food hardship (4-item scale) -0.013 ** -0.010 ** -0.065 ** -0.055 **
(0.002) (0.003) (0.012) (0.019)
Medical hardship (2-item scale) a -0.006 ** -0.003 -0.031 ** -0.019
(0.002) (0.003) (0.012) (0.019)
b
Severe housing hardship (4-item scale) 0.003 0.000 0.014 0.002
(0.002) (0.003) (0.012) (0.019)
b
Utilities cut off 0.003 ** 0.001 0.015 ** 0.004
(0.001) (0.001) (0.004) (0.006)
Unable to pay full utility bill b -0.002 + -0.002 -0.010 + -0.014
(0.001) (0.002) (0.006) (0.010)
Bill hardship a 0.001 -0.002 0.007 -0.009
(0.001) (0.002) (0.006) (0.009)
Transportation hardship a 0.002 0.001 0.008 0.005
(0.001) (0.002) (0.005) (0.009)
Household size fixed effects x x
Number of children fixed effects x x
Note: Coefficients represent the effect of a $100 increase in the CTC. Intent-to-treat (ITT) estimates. Local
average treatment effects (LATE) obtained by instrumenting for CTC amount received as reported by the
respondent. SE's in parentheses. Scales are in standard deviation units (M=0; SD=1). Sample restricted to
households with at least one child under the age of 18. All models include all demographic controls (age,
gender, education, race/ethnicity, partnered, urbanicity), state fixed effects, month fixed effects, and contextual
(SNAP/PEBT/UI) controls. Model 1 includes household size fixed effects; Model 2 includes number of
children fixed effects.
a
N= 19,154
b
N=20,545
+p < .10;*p < .05; **p < .01
Table 5 : The Effects of the Child Tax Credit on Basic Needs, Cash on
Hand, and Hardship Avoidance, Local Average Treatment Effects
LATE
(1) (2)
Needs/Cash on hand
Have needs met 0.005 0.003
(0.006) (0.009)
Total $ on hand ($) 5.664 * -0.692
(2.611) (4.230)
Time $ will last (days) 0.028 0.061
(0.043) (0.070)
Hardship avoidance
Relied on friends/family for food -0.020 ** -0.007
(0.004) (0.007)
Visited food pantry -0.002 -0.002
(0.005) (0.007)
Borrow from friends or family a -0.001 0.013
(0.006) (0.010)
Household size fixed effects x
Number of children fixed effects x
N 19,154
Note: Coefficients represent the effect of a $100 increase in the CTC. Local
average treatment effects (LATE) obtained by instrumenting for CTC receipt
as reported by the respondent. SE's in parentheses. Sample restricted to
households with at least one child under the age of 18. All models include all
demographic controls (age, gender, education, race/ethnicity, partnered,
urbanicity), state fixed effects, month fixed effects, and contextual
(SNAP/PEBT/UI) controls. Model 1 includes household size fixed effects;
Model 2 includes number of children fixed effects.
a
Sample for this outcome is slightly larger (19,327)
+p < .10;*p < .05; **p < .01
Table 6: The Effects of the Child Tax Credit on Employment,
Local Average Treatment Effects
LATE
(1) (2)
Any employment -0.009 -0.004
(0.006) (0.009)
Full-time work -0.002 -0.011
(0.005) (0.008)
Part-time work -0.007 0.007
(0.005) (0.008)
Labor force participation -0.005 -0.003
(0.005) (0.009)
Household size fixed effects x
Number of children fixed effects x
N 20,545
Note: Coefficients represent the effect of a $100 increase in the
CTC. Local average treatment effects (LATE) obtained by
instrumenting for CTC receipt as reported by the respondent. SE's
in parentheses. Sample restricted to households with at least one
child under the age of 18. All models include all demographic
controls (age, gender, education, race/ethnicity, partnered,
urbanicity), state fixed effects, month fixed effects, and contextual
(SNAP/PEBT/UI) controls. Model 1 includes household size
fixed effects; Model 2 includes number of children fixed effects.
+p < .10;*p < .05; **p < .01
Table 7: Effect of the Child Tax Credit on Material Hardship, Local Average Treatment Effects, by Race/Ethnicity
Black White Hispanic
(1) (2) (1) (2) (1) (2)
Hardship index (14-item scale) -0.064 ** -0.096 ** -0.031 + -0.016 -0.014 0.033
(0.023) (0.037) (0.017) (0.024) (0.032) (0.08)
Food hardship (4-item scale) -0.071 ** -0.088 * -0.057 ** -0.023 -0.067 * -0.014
(0.02) (0.036) (0.017) (0.025) (0.032) (0.078)
Medical hardship (2-item scale) -0.043 * -0.039 -0.011 -0.007 -0.062 + -0.025
(0.021) (0.034) (0.016) (0.024) (0.036) (0.087)
Severe housing hardship (4-item scale) -0.013 -0.059 -0.022 -0.008 0.082 * 0.104
(0.026) (0.042) (0.015) (0.022) (0.033) (0.081)
Utilities cut off 0.016 + 0.005 0.013 ** 0.005 0.020 * 0.029
(0.008) (0.013) (0.005) (0.007) (0.011) (0.026)
Unable to pay full utility bill -0.032 ** -0.044 * -0.003 0.001 -0.008 -0.047
(0.011) (0.018) (0.008) (0.012) (0.018) (0.045)
Bill hardship -0.012 -0.024 0.011 -0.01 0.020 0.041
(0.011) (0.018) (0.008) (0.011) (0.017) (0.042)
Transportation hardship 0.004 0.007 -0.002 0.005 0.018 0.032
(0.01) (0.017) (0.007) (0.01) (0.014) (0.035)
Household size fixed effects x x x
Number of children fixed effects x x x
N 6,430 6,146 3,985
Note: Coefficients represent the effect of a $100 increase in the CTC. Local average treatment effects (LATE) obtained by
instrumenting for CTC receipt as reported by the respondent.SE's in parentheses. Scales are in standard deviation units (M=0;
SD=1). Sample restricted to households with at least one child under the age of 18. All models include all demographic controls
(age, gender, education, partnered, urbanicity), state fixed effects, month fixed effects, and contextual (SNAP/PEBT/UI)
controls. Model 1 includes household size fixed effects; Model 2 includes number of children fixed effects.
+p < .10;*p < .05; **p < .01
Table 8: Effect of the Child Tax Credit on Material Hardship, Local Average Treatment Effects, by
Monthly Earnings
$500 or less More than $500
(1) (2) (1) (2)
Hardship index (14-item scale) -0.039 -0.063 -0.034 ** -0.035 +
(0.028) (0.045) (0.013) (0.020)
Food hardship (4-item scale) -0.073 ** -0.074 + -0.062 ** -0.050 *
(0.027) (0.044) (0.013) (0.020)
Medical hardship (2-item scale) -0.035 -0.011 -0.028 * -0.013
(0.025) (0.041) (0.013) (0.021)
Severe ousing hardship (4-item scale) 0.013 -0.035 0.001 -0.011
(0.033) (0.053) (0.011) (0.017)
Utilities cut off 0.028 ** 0.013 0.007 + -0.001
(0.009) (0.015) (0.004) (0.006)
Unable to pay full utility bill -0.030 * -0.018 -0.002 -0.013
(0.013) (0.021) (0.007) (0.010)
Bill hardship 0.011 -0.017 0.006 0.001
(0.013) (0.021) (0.007) (0.010)
Transportation hardship 0.01 -0.005 0.006 0.010
(0.012) (0.02) (0.006) (0.009)
Household size fixed effects x x
Number of children fixed effects x x
N 7,040 10,390
Note: Coefficients represent the effect of a $100 increase in the CTC. Local average treatment effects
(LATE) obtained by instrumenting for CTC receipt as reported by the respondent.SE's in parentheses.
Scales are in standard deviation units (M=0; SD=1). Sample restricted to households with at least one
child under the age of 18. All models include all demographic controls (age, gender, education, partnered,
urbanicity), state fixed effects, month fixed effects, and contextual (SNAP/PEBT/UI) controls. Model 1
includes household size fixed effects; Model 2 includes number of children fixed effects.
+p < .10;*p < .05; **p < .01
Appendix 1 – Additional Information on Measures
This appendix provides additional information on the coding of the measures of economic
wellbeing.
Measures of Material Hardship
Food Insecurity
Our measure of food insecurity relies on four items. First is the 2-item food insecurity scale
(Hager et al., 2010) that is a well-validated instrument that is derived from the 18-item USDA
food insecurity scale. Asking about the last 30 days we asked respondents if they “worried
whether your food would run out before you got money to buy more” and if “the food you
bought just didn’t last and you didn’t have money to get more”. Second, respondents were asked
if they had “skipped meals” or “eaten less” in the last 30 days. Because these questions did not
specify that these behaviors were undertaken as a result of a lack of money, we were unsure if
we should include them in our index. To explore this issue we examined the Cronbach’s alpha of
the 2-item food insecurity scale (=0.62) and compared it to the alpha that included the two
additional food related items. We found that including the additional two food items increased
the alpha to =0.71. Thus, we decided to keep all 4 items in our measure of food insecurity.
Items were summed and then standardized (M=0, SD=1). In extensions we tested the robustness
of our findings to just using the 2-item food insecurity index and found they were robust.
Medical Hardship
Medical hardship is assessed with two questions that are commonly assessed in the material
hardship literature whether in the last 30 days the respond had: “Not visited the doctor when you
needed to because of the cost” or “Not visited the dentist when you needed because of the cost”
(=0.61). These measures were selected as they are frequently used in the material hardship
literature (e.g., Rodems and Shaefer, 2020; Pilkauskas, Campbell and Wimer 2017). Items are
summed and then standardized.
Severe Housing Hardship
Inability to pay rent or mortgage is a common measure of material hardship. Unfortunately the
available survey questions asking about rent, do not allow us to create a good measure of rental
hardship. We do, however, have four good indicators of more severe housing hardships. The first
measure is whether the respondent was evicted by a landlord or court order in the last 30 days,
the second assesses shelter use – “have you slept or stayed in a shelter at least one night in the
past month”. A third indicator relies on a question that asks respondents about their current living
situation that gets at homelessness. If a respondent says they do not currently have a home, we
code them as homeless. The final measure asks respondents “Do you currently live in a house,
apartment or other stable housing? Answer “no” if you currently live in a motel, car or shelter.”
We code respondents as not having stable housing if they say they say no to this question. We
create an index summing across these four measures (=0.65) and then we standardize the
measure.
Pilkauskas, Michelmore, Kovski & Shaefer 1
CTC and Economic Wellbeing
Utility Hardship
Another common measure of material hardship is that of utility hardship, although how this
measure is constructed varies across studies. Some focus on all utilities, others consider the
ability to pay a utility bill a bill hardship, others consider this a measure of financial precarity. In
this study, we look at two utility hardship questions independently. The first is “In the last 30
days, did you pay the full amount of your utility bills (e.g., water, gas, oil, electric)?”. The
second question asks if the respondent had their utilities shut off in the last 30 days. We
considered putting these measures together in an index but the Cronbach’s alpha was only 0.29,
suggesting they did not belong together in an index. Additionally shut offs are rare (about 11%)
whereas not paying a utility bill is common (53%). We also considered combining inability to
pay the utility bill with the measure of bill hardship but again found a low alpha (=0.44).
Bill hardships
We consider a single item – “Did you decide not to pay a bill”. Unlike more common measures
of material hardship, this item is somewhat unusual as it says “decide not to pay” versus “did not
pay”. We examine this outcome as bill hardship is a common material hardship but are unsure
how this measure might compare with other studies of similar outcomes.
Transportation hardship
Lastly, we examine a single item measure of transportation hardship with a question that was
adapted from the Transportation Security Index (Murphy et al. 2021). This question asks
respondents (about the last 30 days) if they “missed an appointment, skipped going somewhere,
or missed work because you didn’t have a way to get there”. Transportation hardship is rarely
included in measures of material hardship but new work suggests that 24% of adults experience
transportation insecurity, with rates that are closer to 50% among lower income households
(Murphy et al., 2022); thus, we added this measure to our suite of hardship indicators. However,
we note, this one-item measure likely does not fully capture the likelihood of experiencing
transportation insecurity.
Material Hardship Scale
Finally, we construct an index using all 14 indicators of material hardship (=0.71). This
measure is summed and standardized (M=0, SD=1).
Additional Measures of Economic Wellbeing
Have Needs Met
To assess how well families were doing economically, we used a measure that Propel created to
consider whether families were able to meet their basic needs: “Do you have everything you
typically need in your home right now? Like food, household products, and cleaning supplies”.
Respondents can indicate 1) they have everything, 2) most things, 3) some things, 4) running low
on most things, or 5) they do not have most things they need. We create a binary indicator taking
Pilkauskas, Michelmore, Kovski & Shaefer 2
CTC and Economic Wellbeing
on the value of one if the respondent reports having everything or most things; all others are
coded as zero.
Cash on Hand
To assess whether the CTC changed the amount of cash families had on hand, we used a
question that asked “About how much money do you have in total right now (not including food
stamps)? This question was asked as a categorical question with responses ranging from $25 to
$1,000 or more. To make this variable into a continuous measure we coded them with mid-
points, with the top code being $1,000.
How Long Will Cash Last?
After asking respondents how much cash they had on hand, a follow up question asked about
how long that money might last. The six-item responses ranged from 1-2 days to two weeks or
more. We converted the weeks into days and assigned respondents the mid-point (e.g., 1.5 days
for 1-2 days). The top code was 14 days for those who reported two or more weeks.
Hardship Avoidance
Rely on Friends and Family for Food
One way in which families might avoid experiencing food insecurity is through relying on
friends and family for food. This measure was a single item question that asked respondents if
they had to rely on friends or family for meals in the last 30 days. We coded this as a dummy
variable (yes/no).
Food Pantry Use
Respondents were asked if they had visited a food pantry in the last 30 days. We consider this a
hardship avoidance technique as those who got food from a food pantry may have been less
likely to experience food insecurity – or may have been able to put some of their money towards
other living expenses. We create a dummy variable where 1= yes the respondent went to a food
pantry.
Borrow Money from Friends/Family
Our final measure of hardship avoidance is relying on friends and family for money. This
measure was derived from two variables, first was a question that asked respondents if they
“borrowed money or used credit to cover their expenses? This could be from family, friends,
churches, GoFundMe, etc.” If respondents said they had gotten money, they were then asked
“How did you borrow or use credit?” and respondents could say “friend/family” as well as a
number of additional options. If respondents said friends/family then they were coded as one on
this variable.
We did not combine these three measures into an index as their internal reliability was relatively
low (α=0.41).
Pilkauskas, Michelmore, Kovski & Shaefer 3
CTC and Economic Wellbeing
Labor Force Participation
We rely on a measure that asked respondents about their current labor force from a question that
asked “How would you describe your employment status currently?”. Respondents could answer
that they worked full-time, part-time, not working but looking for work, not working and not
looking for work, not able to work, or retired. We categorized respondents who were working
full-time or part-time or looking for work as those who were in the labor force and the rest were
considered out of the labor force. Those who were not working but looking for work were
considered unemployed.
Reference
Murphy, Alexandra K., Alix Gould-Werth, and Jamie Griffin. 2021. “Validating the Sixteen-Item Transportation
Security Index in a Nationally Representative Sample: A Confirmatory Factor Analysis.” Survey Practice 14
(1). https://doi.org/10.29115/SP-2021-0011.
Murphy, A., McDonald-Lopez, K., Pilkauskas, N.V. & Gould-Werth, A. 2022. The prevalence of transportation
insecurity.
Pilkauskas, Michelmore, Kovski & Shaefer 4
Appendix Table 1: Comparing Providers Survey Data to the American Community
Survey Data - Households with Children
ACS 2019 Providers
Households below
SNAP recipients 100% of poverty
Age
18-24 6 7 7
25-34 31 33 33
35-44 32 33 31
45-54 17 16 16
55+ 14 10 13
Household structure
Household size 4.29 4.03 4.03
Number of kids 2.21 2.24 2.53
Partner/spouse 32 27 30
Race/Ethnicity
Black 27 25 32
White 38 38 38
Hispanic 28 30 20
Other 7 8 9
Education
<High school 19 22 25
High school 45 44 40
Some college 27 24 25
College or more 10 10 10
Female 71 74 94
Receive food stamps 100 56 85
Notes: All samples are restricted to households with at least one coresident child under
the age of 18. SNAP recipients = those who reported receiving Supplemental Nutrition
Assistance Program in the last 12 months. Poor = households with income below 100%
of poverty using the Census Bureau's poverty thresholds.
Data: ACS= American Community Survey 2019. Sample is restricted to the reference
person. Providers sample is June 2021- January 2022.
Appendix Table 2: Association between own Child Tax Credit Receipt and Amount and Material
Hardship
Self-reported CTC receipt Self-reported CTC $$ (in
(0/1) $100's)
(1) (2) (1) (2)
a
Hardship index (14-item scale) -0.098 ** -0.096 ** -0.019 ** -0.018 **
(0.018) (0.018) (0.003) (0.003)
a
Food hardship (4-item scale) -0.085 ** -0.08 ** -0.019 ** -0.016 **
(0.018) (0.018) (0.003) (0.003)
a
Medical hardship (2-item scale) -0.022 -0.02 -0.005 * -0.004
(0.017) (0.017) (0.003) (0.003)
Severe housing hardship (4-item scale) b
-0.148 ** -0.149 ** -0.021 ** -0.023 **
(0.017) (0.017) (0.003) (0.003)
Utilities cut off b -0.021 ** -0.022 ** -0.003 ** -0.004 **
(0.005) (0.005) (0.001) (0.001)
Unable to pay bill b -0.018 * -0.017 * -0.002 + -0.002
(0.008) (0.009) (0.001) (0.001)
Bill hardship a 0.055 ** 0.054 ** 0.006 ** 0.006 **
(0.009) (0.009) (0.001) (0.001)
Transportation hardship a -0.036 ** -0.036 ** -0.005 ** -0.005 **
(0.008) (0.008) (0.001) (0.001)
Household size fixed effects x x
Number of children fixed effects x x
Note: SE's in parentheses. Scales are in standard deviation units (M=0; SD=1). Sample is restricted to
households with at least one child under the age of 18. All models include all demographic controls (age,
gender, education, race/ethnicity, partnered, urbanicity), state fixed effects, month fixed effects, and
contextual (SNAP/PEBT/UI) controls. Model 1 includes household size fixed effects; Model 2 includes
number of children fixed effects.
+p < .10;*p < .05; **p < .01
a
N= 19,154
b
N=20,545
Appendix Table 3: Testing the Addition of Controls - Intent-to-Treat and Local Average Treatment Effects
ITT LATE
a
Hardship index (14-item scale) -0.002 -0.002 -0.007 ** -0.008 * -0.007 -0.007 -0.033 ** -0.042 *
(0.002) (0.002) (0.002) (0.003) (0.008) (0.008) (0.012) (0.019)
Food hardship (4-item scale) a -0.013 ** -0.013 ** -0.013 ** -0.01 ** -0.054 ** -0.054 ** -0.065 ** -0.055 **
(0.002) (0.002) (0.002) (0.003) (0.008) (0.008) (0.012) (0.019)
Medical hardship (2-item scale) a -0.003 -0.003 -0.006 ** -0.003 -0.011 -0.011 -0.031 ** -0.019
(0.002) (0.002) (0.002) (0.003) (0.008) (0.008) (0.012) (0.019)
Severe housing hardship (4-item scale) b 0.002 0.002 0.003 0.000 0.009 0.009 0.014 0.002
(0.002) (0.002) (0.002) (0.003) (0.008) (0.008) (0.012) (0.019)
Utilities cut off b 0.005 ** 0.005 ** 0.003 ** 0.001 0.02 ** 0.02 ** 0.015 ** 0.004
(0.001) (0.001) (0.001) (0.001) (0.003) (0.003) (0.004) (0.006)
Unable to pay bill b 0.003 ** 0.003 ** -0.002 + -0.002 0.013 ** 0.013 ** -0.010 + -0.014
(0.001) (0.001) (0.001) (0.002) (0.004) (0.004) (0.006) (0.01)
Bill hardship a 0.003 ** 0.003 ** 0.001 -0.002 0.011 ** 0.011 ** 0.007 -0.009
(0.001) (0.001) (0.001) (0.002) (0.004) (0.004) (0.006) (0.009)
Transportation hardship a 0.003 ** 0.003 ** 0.002 0.001 0.013 ** 0.013 ** 0.008 0.005
(0.001) (0.001) (0.001) (0.002) (0.004) (0.004) (0.005) (0.009)
Demographic controls (age, gender,
race/ethnicity, education, partnered,
x x x x x x x x
urbanicity), month fixed effects + state fixed
effects
Contextual controls (SNAP/PEBT/UI) x x x x x x
Household size fixed effects x x
Number of children fixed effects x x
Note: Coefficients represent the effect of a $100 increase in the CTC. Intent-to-treat (ITT) estimates. Local average treatment effects (LATE) obtained
by instrumenting for CTC receipt as reported by the respondent. SE's in parentheses. Scales are in standard deviation units (M=0; SD=1). Sample
restricted to households with at least one child under the age of 18.
a
N= 19,154
b
N=20,545
+p < .10;*p < .05; **p < .01
Appendix Table 4: The Effects of the Child Tax Credit on Basic Needs, Cash on Hand, Hardship Avoidance, and Employment,
Local Average Treatment Effects, by Race/Ethnicity
Black White Hispanic
(1) (2) (1) (2) (1) (2)
Needs/Cash on hand
Have needs met 0.020 + 0.028 + 0.007 -0.010 -0.012 -0.013
(0.010) (0.017) (0.008) (0.011) (0.017) (0.043)
Total $ on hand ($) 4.732 6.595 4.074 -11.118 * 12.483 21.133
(4.531) (7.291) (3.388) (5.053) (8.167) (20.461)
Time $ will last (days) 0.022 0.125 0.036 -0.108 0.034 0.366
(0.078) (0.126) (0.057) (0.084) (0.131) (0.332)
Hardship avoidance
Relied on friends/family for food -0.025 ** -0.008 -0.016 ** 0.004 -0.032 ** -0.031
(0.009) (0.014) (0.006) (0.009) (0.012) (0.029)
Visited food pantry -0.002 0.002 0.004 0.002 -0.004 -0.019
(0.008) (0.013) (0.007) (0.01) (0.013) (0.033)
Borrow from friends or family 0.011 0.022 -0.010 0.000 -0.024 0.029
(0.011) (0.018) (0.008) (0.012) (0.018) (0.045)
Employment
Any employment -0.018 -0.013 -0.007 -0.011 -0.019 -0.016
(0.011) (0.018) (0.008) (0.011) (0.018) (0.044)
Full-time work -0.010 -0.015 0.002 -0.010 -0.018 -0.058
(0.009) (0.015) (0.006) (0.009) (0.014) (0.039)
Part-time work -0.008 0.002 -0.010 0.000 -0.001 0.042
(0.009) (0.014) (0.006) (0.009) (0.015) (0.039)
Labor force participation -0.022 * -0.026 + -0.010 -0.004 0.019 0.057
(0.010) (0.015) (0.008) (0.011) (0.016) (0.04)
Household size fixed effects x x x
Number of children fixed effects x x x
6430 6146 3985
Note: Coefficients represent the effect of a $100 increase in the CTC. Local average treatment effects (LATE) obtained by
instrumenting for CTC receipt as reported by the respondent.SE's in parentheses. Scales are in standard deviation units (M=0;
SD=1). Sample restricted to households with at least one child under the age of 18. All models include all demographic controls
(age, gender, partnered, education, urbanicity), state fixed effects, month fixed effects, and contextual (SNAP/PEBT/UI)
controls. Model 1 includes household size fixed effects; Model 2 includes number of children fixed effects.
+p < .10;*p < .05; **p < .01
Appendix Table 5: Effect of the Child Tax Credit on Material Hardship, Local Average
Treatment Effects, by Monthly Earnings
Less than $500 More than $500
(1) (2) (1) (2)
Needs/Cash on hand
Have needs met -0.018 -0.001 0.016 * 0.003
(0.012) (0.019) (0.007) (0.01)
Total $ on hand ($) 3.2135 -3.652 6.304 * -0.068
(4.752) (7.711) (3.129) (4.82)
Time $ will last (days) -0.077 -0.024 0.046 0.014
(0.090) (0.145) (0.049) (0.076)
Hardship avoidance
Relied on friends/family for food -0.043 ** -0.026 -0.011 * 0.001
(0.011) (0.017) (0.005) (0.007)
Visited food pantry 0.007 -0.006 -0.007 -0.004
(0.011) (0.017) (0.005) (0.008)
Borrow from friends or family 0.003 0.012 -0.004 0.014
(0.013) (0.021) (0.007) (0.01)
Employment
Any employment -0.007 0.008 -0.010 -0.011
(0.010) (0.016) (0.007) (0.010)
Full-time work -0.003 0.002 -0.006 -0.018 +
(0.006) (0.009) (0.006) (0.010)
Part-time work -0.003 0.007 -0.004 0.008
(0.009) (0.014) (0.006) (0.009)
Labor force participation 0.006 0.023 -0.006 -0.005
(0.012) (0.020) (0.006) (0.009)
Household size fixed effects x x
Number of children fixed effects x x
N 7040 10390
Note: Coefficients represent the effect of a $100 increase in the CTC. Local average treatement
effects (LATE) obtained by instrumenting for CTC receipt as reported by the respondent.SE's in
parentheses. Scales are in standard deviation units (M=0; SD=1). Sample restricted to households
with at least one child under the age of 18. All models include all demographic controls (age,
gender, education, race/ethnicity, partnered, urbanicity), state fixed effects, month fixed effects,
and contextual (SNAP/PEBT/UI) controls. Model 1 includes household size fixed effects; Model
2 includes number of children fixed effects.
+p < .10;*p < .05; **p < .01
Appendix Table 6: Pre-Post Difference-in-Differences and Continuous/Parameterized Difference-in-Differences - Households without Children as the
Comparison Group. Effects on Material Hardship
Pre-post Continuous (parameterized/reduced form)
ITT LATE ITT LATE
(1) (2) (1) (2) (1) (2) (1) (2)
a
Hardship index (14-item scale) 0.110 ** 0.111 ** 0.164 ** 0.165 ** -0.002 -0.001 -0.007 -0.006
(0.040) (0.040) (0.060) (0.060) (0.002) (0.003) (0.008) (0.012)
a
Food hardship (4-item scale) 0.057 0.065 0.086 0.097 -0.007 ** -0.005 + -0.028 ** -0.019 +
(0.040) (0.040) (0.060) (0.060) (0.002) (0.003) (0.008) (0.012)
Medical hardship (2-item scale) a 0.062 0.064 0.093 0.096 -0.001 0.000 -0.004 -0.002
(0.040) (0.040) (0.059) (0.060) (0.002) (0.003) (0.008) (0.012)
b
Severe housing hardship (4-item scale) 0.035 0.033 0.052 0.049 -0.005 * 0.001 -0.022 * 0.005
(0.040) (0.040) (0.060) (0.060) (0.002) (0.003) (0.008) (0.012)
b
Utilities cut off 0.004 0.002 0.006 0.002 0.003 ** 0.001 0.012 ** 0.002
(0.012) (0.012) (0.018) (0.018) (0.001) (0.001) (0.002) (0.004)
Unable to pay bill b 0.018 0.014 0.027 0.021 0.000 -0.001 -0.002 -0.005
(0.020) (0.020) (0.030) (0.030) (0.001) (0.001) (0.004) (0.006)
a
Bill hardship 0.031 + 0.031 + 0.046 + 0.046 + 0.003 ** 0.000 0.013 ** 0.001
(0.019) (0.019) (0.028) (0.028) (0.001) (0.001) (0.004) (0.005)
Transportation hardship a 0.069 ** 0.069 ** 0.104 ** 0.103 ** 0.004 ** 0.003 ** 0.014 ** 0.014 **
(0.018) (0.018) (0.026) (0.026) (0.001) (0.001) (0.003) (0.005)
Household size fixed effects x x x x
Number of children fixed effects x x x x
Note: SE's in parentheses. Scales are in standard deviation units (M=0; SD=1). Sample includes households with and without children (excludes
individuals who live alone). All models include all demographic controls (age, gender, education, race/ethnicity, partnered, urbanicity), state fixed effects,
month fixed effects, and contextual (SNAP/PEBT/UI) controls. Model 1 includes household size fixed effects; Model 2 includes number of children fixed
effects.
+p < .10;*p < .05; **p < .01
a
N= 30,265
b
N= 32,615
Appendix Table 7: Effect of the Child Tax Credit on Material Hardship, Intent-to-Treat Estimates and Local Average Treatment Effects, in $100;
Robustness Checks
Excluding August Excluding January Controlling for covid rates Controlling for the date
(1) (2) (1) (2) (1) (2) (1) (2)
a
Hardship index (14-item scale) -0.035 ** -0.041 * -0.033 * -0.037 -0.033 ** -0.042 * -0.033 ** -0.043 *
(0.012) (0.019) (0.013) (0.020) (0.012) (0.019) (0.012) (0.019)
Food hardship (4-item scale) a -0.067 ** -0.055 ** -0.068 ** -0.051 * -0.065 ** -0.055 ** -0.065 ** -0.055 **
(0.013) (0.019) (0.013) (0.020) (0.012) (0.019) (0.012) (0.019)
Medical hardship (2-item scale) a -0.031 * -0.018 -0.031 * -0.020 -0.031 ** -0.020 -0.031 ** -0.020
(0.012) (0.019) (0.013) (0.019) (0.012) (0.019) (0.012) (0.019)
Severe housing hardship (4-item scale) b
0.014 -0.001 0.017 0.003 0.014 0.002 0.015 0.002
(0.012) (0.019) (0.013) (0.020) (0.012) (0.019) (0.012) (0.019)
Utilities cut off b 0.013 ** 0.003 0.015 ** 0.005 0.015 ** 0.004 0.015 ** 0.003
(0.004) (0.006) (0.004) (0.006) (0.004) (0.006) (0.004) (0.006)
Unable to pay full utility bill b -0.010 -0.012 -0.007 -0.008 -0.010 + -0.014 -0.010 + -0.014
(0.006) (0.009) (0.006) (0.010) (0.006) (0.010) (0.006) (0.010)
a
Bill hardship 0.007 -0.007 0.007 -0.007 0.007 -0.009 0.007 -0.009
(0.006) (0.009) (0.006) (0.010) (0.006) (0.009) (0.006) (0.009)
Transportation hardship a 0.009 0.007 0.009 0.005 0.008 0.005 0.009 + 0.005
(0.005) (0.008) (0.006) (0.009) (0.005) (0.009) (0.005) (0.009)
Household size fixed effects x x x x
Number of children fixed effects x x x x
Note: Coefficients represent the effect of a $100 increase in the CTC. Local average treatment effects (LATE) obtained by instrumenting for CTC
receipt as reported by the respondent.SE's in parentheses. Scales are in standard deviation units (M=0; SD=1). Sample restricted to households with
at least one child under the age of 18. All models include all demographic controls (age, gender, education, race/ethnicity, partnered, urbanicity), state
fixed effects, month fixed effects, and contextual (SNAP/PEBT/UI) controls. Model 1 includes household size fixed effects; Model 2 includes
number of children fixed effects.
+p < .10;*p < .05; **p < .01
a
Excluding August: N= 17414; Excluding January: N=17411; Covid control: N=19154; Date control: N=19154
b
Excluding August: N=18709; Excluding January: N=18674; Covid control: N=20545; Date control: N=20545
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