Young Children in Deep Poverty:

Racial/Ethnic Disparities and Child Well-Being
Compared to Other Income Groups
Uyen Sophie Nguyen | Sheila Smith | Maribel R. Granja
October 2020

National Center for Children in Poverty
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The National Center for Children in Poverty (NCCP) is a non-partisan public policy research
center at Bank Street Graduate School of Education. Founded in 1989 with endowmentsfrom the Carnegie Corporation of New York and the Ford Foundation, NCCP is dedicatedto promoting the economic security, healthy development, and well-being of America’s
low-income children and families. Using research to inform policy and practice, the center
seeks to advance family-oriented solutions and strategic use of public resources at thestate and national levels to produce positive outcomes for the next generation.

Acknowledgments
This report was made possible by the generous support of The Alliance for Early Success.
The authors would like to thank Heather Koball, Suma Setty, Debra Rudick, and Molly Sirfor providing feedback on the draft, and Mona Rayachoti for design and production.

Suggested Citation
Nguyen, U.S., Smith, S., Granja, M.R. (2020). Young Children in Deep Poverty: Racial/Ethnic
Disparities and Child Well-Being Compared to Other Income Groups. New York: National
Center for Children in Poverty, Bank Street Graduate School of Education.
Copyright © 2020 by the National Center for Children in Poverty

National Center for Children in Poverty
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Introduction
Nine percent of young U.S. children live in deep poverty, with state rates ranging from 17 percent in Mississippi to
4 percent in Utah. The families of these children have incomes below 50 percent of the federal poverty line, or lessthan $10,289 for a family of one parent and two children.1 These figures, based on 2019 data, predate the COVID-19
pandemic, which likely drove more families with young children into poverty and deep poverty given the large increase in
unemployment related to workplace closures, lack of child care, and other pandemic conditions.2
While families in deep poverty may qualify for various forms of assistance, many experience severe financial hardship dueto the very limited support provided by public benefits. In 16 states, cash assistance in the form of TANF (Temporary Aid to
Needy Families) is provided to only 10 percent or fewer of families in poverty. Monthly TANF benefits vary across states,with 18 states providing less than $356 for a single parent family of three.3 Even SNAP (formerly known as food stamps),
a benefit credited with reducing child poverty by 28 percent, leaves families with unmet needs.4 A recent analysis showedthat the maximum SNAP benefit fell short of meeting monthly food costs by about $46 per family member.5 Althoughhousing is the largest portion of most families’ expenses, federal rental assistance is available to only 22 percent of lowincome families with children, and only six states supplement this support with housing assistance targeted to families.6
Both a lack of material resources and parental stress associated with poverty have been identified as key pathways toworse health, developmental, and school-related outcomes of poor children compared to their non-poor peers.7 Although
research focused specifically on young children in deep poverty is limited, the conditions of deep poverty suggest thatthese children may be at exceptionally high risk of poor outcomes. First, research has shown that poverty experienced
in early childhood is especially detrimental to children’s development.8 Second, deep poverty may lead to especiallyhigh levels of stress among parents struggling to meet basic needs, and stress is associated with less optimal parenting
behavior.9 Third, other factors associated with poverty and child well-being, such as poor birth outcomes and family social
isolation, may be more prevalent among families with very little or no income.
Understanding more about the early health and development of young children in deep poverty and related risk factors
can inform policies tailored to this group of vulnerable families. To date, most recommendations explicitly targeted
to reducing the number of families in deep poverty have focused mainly on policies that increase family income. The
National Academy of Sciences report, Roadmap to Reducing Child Poverty, examines two policy packages that meet thegoal of reducing both poverty and deep poverty by 50 percent. These packages include an increased minimum wage, achild allowance, and housing assistance.10 Based on an earlier examination of young children and families in deep poverty,
NCCP has recommended a mix of policies to increase family income and ensure immediate and longer-term supports forchildren’s healthy development in the family and in early care and education settings.11 This report presents new analyses
with more recent data that highlight the needs of young children and families in deep poverty, along with updated
recommendations.
The key sections of the report are: A description of methods
 Findings that show: Differences in health and development indicators across income groups Differences in family and community factors across income groups Racial/ethnic disparities in young children’s experience of deep poverty
 Summary Recommendations

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Methods
This brief used recent data from three nationally
representative data sets: the American Community Survey(ACS), the National Health and Nutrition Examination
Survey (NHANES), and the National Health Interview
Survey (NHIS).12 The ACS data were used to calculate thenumbers and percentages of children under age 9 at four
different income levels, nationally and state by state:





 eep poverty: Below 50 percent of the federalD
poverty line (FPL)Poverty: 50 percent to 99 percent of
the FPL
Low income: 100 percent to 199 percent of
the FPL
Non-poor: 200 percent or more of the FPL

The number and percentages of young children in different
racial/ethnic groups were also calculated using ACS data.
Five racial/ethnic groups included in the analysis werewhite, non-Hispanic/Latino; Black, non-Hispanic/Latino;
Hispanic/Latino; American Indian and Alaska Native; andAsian. The NHANES and NHIS data were used to show
the number and percentages of children with different
child health and developmental outcomes, as well as
parent and neighborhood characteristics, across income
groups. In order to obtain a sufficient sample size ofyoung children in deep poverty and ensure data reliability,
we combined two of the latest waves of data from the
NHANES and NHIS data sets. The final analytic samples
were drawn from 2015-2018 for NHANES and 20172018 for NHIS. Sample weights were also adjusted for the
combined datasets based on NHANES and NHIS analytic
guidelines.13 All data were weighted to ensure theirrepresentativeness in the U.S. population.
The analyses focused on children under the age of 9. The
data were based on parent reports for most measures.
Only child obesity and blood lead levels were directly
measured through physical examinations. All measures
were reported in percentages (for categorical variables)
and averages (for continuous variables). Logistic regression
was used to analyze the relationship between categoricaloutcomes and income levels, as well as to obtain odds
ratios for the likelihood of each categorical outcomeof interest.14 Simple linear regression was also run oncontinuous outcomes to assess the association between
these outcomes and income levels. Tests for differences
across all pairs of income groups were conducted andsignificant differences in values across groups are shown in
the figures.

Figure 1. U.S. Children Under Age 9 in Families at Different Income Levels

9%10%

Deep poverty (less than 50% FPL)
Poverty (50-99% FPL)
59%

22%

Low income (100-199% FPL)
Non-poor (200% or more FPL)

Source: National Center for Children in Poverty. (2020, August 10). Young child risk calculator. http://nccp.org/tools/risk/.
(Calculator uses ACS 1-Year Estimates - Public Use Microdata Sample 2018.)

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Figure 2. U.S. State Variation in Children Under Age 9 Living in Deep Poverty

WA

MT

ME

ND

MN

OR

VT
NH

ID

WI

SD

MA

NY

CT

MI

WY

RI

IA

NJ

PA

NE
NV

MD

OH
IN

IL

UT
CA

CO

WV
KS

MO

VA

DE

DC

KY
NC
TN

AZ

OK
NM

AR

SC

MS

TX

AL

GA

LA

FL
AK

HI

Source: NCCP Analysis of ACS 1-Year Estimates - Public Use Microdata Sample 2018

Percent of young children in deep poverty
4-6%

10-12%

7-9%

13% and above

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Findings
ACROSS STATE VARIATION IN RATES OFYOUNG CHILD DEEP POVERTY

Figure 3. Percent of U.S. Children Under Age 9 in Deep Poverty byRace/Ethnicity

As seen in Figure 2, there is large variation in rates ofyoung child deep poverty across the states. Four states(AL, LA, MS, NM) and DC have 13 percent or more ofchildren 0-9 living in deep poverty, 11 states (AR, KY, MI,
NC, OH, OK, RI, SC, SD, TN, WV) have rates of 10 to 12
percent. See Appendix A for all states’ percentages ofyoung children in deep poverty.
RACIAL/ETHNIC DIFFERENCES IN DEEP POVERTYAMONG YOUNG CHILDREN
Nationally, 5 percent of young white children and 3
percent of Asian American children live in deep poverty.
These percentages are more than double for other racial/ethnic groups of young children: 18 percent of Blackchildren, 15 percent of American Indian and Native
Alaskan children, and 11 percent of Hispanic/Latino
children under age 9 are in families earning less thanhalf of the federal poverty line. Large racial disparities
for rates of young child deep poverty exist in almost allstates. Three examples from states in different regionsof the U.S. illustrate the magnitude of these disparities.
In Connecticut, 3 percent of young white children, 14
percent of Hispanic/Latino children, and 35 percent ofAmerican Indian and Native Alaskan children are in deep
poverty. In Alabama, 7 percent of young white children
are in deep poverty, while the rate is 26 percent for Blackchildren. Ohio’s rates of deep poverty are 6 percent foryoung white children and 25 percent for young Blackchildren. Rates for young Asian American children varyconsiderably across states. For example, while 1 percent of
Asian American children in Texas live in deep poverty, therate is 18 percent for Asian American children in Utah. See
Appendix B for all state percentages by race/ethnicity.

18%
15%
11%

5%
3%

White,
nonHispanic/
LatinoBlack,
nonHispanic/
LatinoHispanic/
LatinoAmericanIndian and
NativeAmerican

Asian

Source: National Center for Children in Poverty. (2020, August 10).
Young child risk calculator. http://nccp.org/tools/risk/.
(Calculator uses ACS 1-Year Estimates - Public Use Microdata Sample
2018.)

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Health and Development of Young Children in Deep Poverty
LOW BIRTH WEIGHT
Low birth weight, especially when severe or coupled withpreterm birth, increases a child’s risks for health problems,and developmental delay and disability.15
 mong young children in deep poverty, 12.9
A
percent were born with low birth weight, thehighest rate among income groups; these children
are almost twice as likely to be born at low birthweight than non-poor children.

 ow birth weight affected 9.9 percent of poorL
children, 8.4 percent of low-income children, and7.9 percent of non-poor children.

PHYSICAL IMPAIRMENT
Physical impairment refers to a condition or healthproblem that limits the child’s ability to crawl, walk, run, orplay. Without effective interventions and environmental
supports, physical impairments can reduce young
children’s opportunities to learn through exploration andinteractions with others and harm their development.16
 mong young children in deep poverty, 3.3A
percent have a physical impairment, making them
about four times more likely to have this condition
than non-poor children.

 he percentages of young children with a physicalT
impairment are similar for the other income
groups at 1.1 percent for poor children, 1.2
percent for low-income children and 0.8 percent
for non-poor children.

INTELLECTUAL DISABILITY AND
DEVELOPMENTAL DELAY

Figure 4. Child Low Birth Weight by Poverty Status for Children Under Age 9

12.9%
9.9%

Deep Poverty* he prevalence of intellectual disabilities andTdevelopmental delays for young children indeep poverty is 9.4 percent, the highest among
the income groups. Nearly three times as many
children in deep poverty have an intellectuallydisability or developmental delay as non-poor
children.

7.9%

Low Income

Non-Poor*Young children in deep poverty are significantly more likely to be born at
low birth weight compared to non-poor children (odds ratio = 1.73; 95%
confidence interval [CI] = 1.13, 2.65; p = 0.0114).
Figure 5. Physical Impairment by Poverty Status for Children Under Age 9

3.3%

Deep Poverty*

1.1%

1.2%

Poverty

Low Income

0.8%

Non-Poor*Young children in deep poverty are significantly more likely to have a limiting
physical impairment or health problem compared to non-poor children (oddsratio = 4.22; 95% CI = 1.42, 12.56; p = 0.0207).
Figure 6. Diagnosed Intellectual Disability or Developmental Delay by
Poverty Status for Children Under Age 9

9.4%

An intellectual disability or developmental delay can bemild to severe. Even relatively mild developmental delays
can contribute to persistent or worsening problems related
to learning and school success.17


Poverty

8.4%

5.3%

5.0%
3.6%

Deep Poverty*

Poverty

Low Income

Non-Poor*Young children in deep poverty are significantly more likely to be diagnosed
with an intellectual disability or developmental delay compared to non-poor
children (odds ratio = 2.80; 95% CI = 1.59, 4.95; p = 0.0002).
Source: NCCP Analysis of Data From the National Health Interview
Survey (NHIS) 2017-2018
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National Center for Children in Poverty
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percent for low-income, and 3.6 for non-poor
children.

Figure 7. Early Intervention Participation by Poverty Status for Children
Ages 0 to 3

5.5%
4.6%

PARTICIPATION IN EARLY INTERVENTION (EI) AND
SPECIAL EDUCATIONChildren under age 9 may be eligible for Part C Early
Intervention (for children birth to 3), Part B Preschool
Special Education (for children ages 3 to 4), or K-12
Special Education (for school-age children). Children’s
receipt of EI or special education services indicates thatthey have a disability or developmental delay, although theeligibility criteria change across the different age groups
and vary by state. While participation reflects conditions
that can impede optimal development and learning,receipt of EI or special education services can also beviewed in a positive light since children are expected tobenefit from these programs.




2.5%

Deep
Poverty

Low Income

Non-Poor

Figure 8. Special Education Participation by Poverty Status for Children
Ages 3 to 9

13.4%

 bout 5.5 percent of children ages 0 to 3 in deepApoverty participate in the Part C EI program, arate higher but not significantly different than theother income groups. Participation is about 4.6
percent for children in poverty, 2.5 percent for
low-income children, and 2.3 percent for nonpoor children.
 or Special Education participation, the rate
Ffor children ages 3 to 9 in deep poverty is 13.4
percent, the highest percent among income
groups, but not significantly higher than non-poor
children. Rates are 7.7 percent for poor children,
10.2 percent for low-income children, and 8.3
percent for non-poor children.

Poverty

2.3%

10.2%
8.3%

7.7%

Deep
Poverty

Poverty

Low Income

Non-Poor

Source: NCCP Analysis of Data From the National Health Interview
Survey (NHIS) 2017-2018

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CHILD BEHAVIOR
The child behavior indicator reflects the degree to whicha child is well-behaved and follows directions basedon parent report. Young children’s behavior problems
and conflicts with the parent increase their risk of later
behavior and school difficulties.18 hildren ages 4 to 9 across income groups areCviewed by parents as generally well-behaved andcompliant with their parents’ requests (a score of 1
indicates parents “somewhat agree” that the childis well-behaved, and a score of 2 indicates they“certainly agree.”) he behavior score for children in deep poverty
Tis lowest (reflecting less positive behavior) at 1.6,
while scores are 1.7 for poor and low-income
children and 1.8 for non-poor children.

CHILD UNINSURED RATEChildren’s lack of health insurance can lead to lower useof preventive and illness-related health care. In turn, lesshealth care increases children’s risk of having untreatedhealth conditions and developmental delays that could
limit their learning and school success.19 ow-income children under age 9 have the highest
L
uninsured rate at 4.9 percent.

 he uninsured rates for young children at otherT
income levels are 4.8 percent for poor, 4.3 percent
for deeply poor, and 3.3 percent for non-poor
children.

Figure 9. Child Behavior Score by Poverty Status for Children Ages 4 to 9

1.6

Deep
Poverty*

1.7

1.7

1.8

Poverty

Low
Income**Non-Poor*Young children in deep poverty display fewer good behaviors than poor andnon-poor children (p= 0.0326 and p = 0.0013, respectively).
**Low-income children display fewer good behaviors than non-poor children(p < 0.0001).
Figure 10. Child Uninsured Rate by Poverty Status for Children Under
Age 9

4.3%

4.8%

4.9%

3.3%

Deep
Poverty

Poverty

Low
Income*Non-Poor*Low-income children are significantly more likely to be uninsured compared
to non-poor children (odds ratio = 1.53; 95% CI = 1.13, 2.09; p = 0.0106).
Source: NCCP Analysis of Data From the National Health Interview
Survey (NHIS) 2017-20187

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CHILD OBESITY

Figure 11. Child Obesity by Poverty Status for Children Under Age 9

Being obese is associated with poor health outcomes andpsychological problems throughout childhood and later inlife.20 he percent of young children with obesity isThighest among poor and low-income children(21.2 percent and 18.8 percent, respectively) andlowest among non-poor children (11.2 percent).

Early life exposure to lead is linked to low academic
achievement, decreased high school graduation rates, andincreased criminal activity.21



18.8%

15.6%
11.2%

 he percent of young children in deep poverty
Twith obesity is approximately 15.6 percent.

ELEVATED BLOOD LEAD LEVELS



21.2%

 he percent of young children with elevatedT
blood lead levels is highest among low-income
children (1.8 percent). Levels were not significantly
different across the income groups.

Deep
Poverty

Poverty*

Low
Income**Non-Poor*Poor children are significantly more likely than non-poor children to haveobesity (odds ratio = 2.13; 95% CI = 1.68, 2.69; p = 0.0001).
**Low-income children are significantly more likely than non-poor children tohave obesity (odds ratio = 1.84; 95% CI = 1.33, 2.54; p = 0.0004).
Figure 12. Elevated Blood Lead Levels by Poverty Status for Children
Under Age 9

1.8%

 nder 1 percent (0.6 percent) of children inUdeep poverty and poverty have high blood leadlevels, and 0.3 percent of non-poor children haveelevated blood lead levels.
0.6%

0.6%

Deep
Poverty

Poverty

0.3%

Low
IncomeNon-Poor

Source: NCCP Analysis of Data From the National Health and Nutrition
Examination Survey (NHANES) 2015-20188

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Parent Characteristics
OVERALL MENTAL HEALTH STATUS
Parental mental health conditions can have a negative
impact on children’s academic and social-emotional
outcomes.22 Parents were asked to report their experienceof a range of emotions (e.g., hopeless, nervous) they might
have experienced over the past 30 days.






 verall, the mental health scores of parentsO
reflected their infrequent report of negative
moods. A score of 0 reflect “never” having thefeelings in the past 30 days and a score of 1
indicated having them “sometimes.”
Parents of young children in deep poverty had
the highest mean mental health score (0.61),
reflecting their report of more negative emotions.
 cores decreased as income levels increased withS
a score of 0.57 for poor parents, 0.51 for lowincome parents, and 0.36 for non-poor parents.

SINGLE PARENT STATUS
Being a single parent increases the chance that thehousehold must rely on one parent’s income and increases
the parent’s share of home and caregiving responsibilities.


Figure 13. Average Overall Mental Health Scores of Parents With
Children Under Age 9 by Poverty Status

0.61

0.51
0.36

Deep
Poverty*

Poverty**

Low
Income***Non-Poor*Deeply poor parents reported worse mental health status than low-income
and non-poor parents (p=0.0089 and p<0.0001, respectively).
**Poor parents reported worse mental health status than low-income andnon-poor parents (p=0.0054 and p<0.0001, respectively).
***Low-income parents reported worse mental health status than non-poor
parents (p<0.0001).
Figure 14. Single Parent Families by Poverty Status

58.8%
43.4%

 8.8 percent of deeply poor parents are single.
5
They are significantly more likely to be single
parents compared to each of the other income
groups.
 3.4 percent of poor parents, 25.4 percent of
4
low-income parents, and 7.8 percent of non-poor
parents are single.

0.57

25.4%
7.8%
Deep
Poverty*

Poverty**

Low
Income***Non-Poor*Deeply poor parents are about two times more likely to be single parents
compared to poor parents (odds ratio = 1.86; 95% CI = 1.31, 2.65; p <0.0001), four times more likely compared to low-income parents (odds ratio =4.20; 95% CI = 3.00, 5.89; p = 0.0054), and 17 times more likely compared
to non-poor parents (odds ratio = 16.98; 95% CI = 12.48, 23.10;
p < 0.0001).
**Poor parents are about two times more likely to be single parents comparedto low-income parents (odds ratio = 2.26; 95% CI = 1.79, 2.85; p = 0.0054),
and nine times more likely compared to non-poor parents (odds ratio = 9.12;
95% CI = 7.22, 11.53; p <0.0001).
***Low-income parents are four times more likely to be single parents compared to non-poor parents (odds ratio = 4.04; 95% CI = 3.29, 4.96;
p < 0.0001).
Source: NCCP Analysis of Data From the National Health Interview
Survey (NHIS) 2017-20189

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PARENT EDUCATION ATTAINMENT

Figure 15. Parent Lack of High School Degree by Poverty Status

Parent education is a strong predictor of children’s
educational outcomes and parenting behavior that
influences these outcomes.23


 6.9 percent of parents in deep poverty lack a2high school degree; they are one-and-a-half times
less likely to have completed high school thanpoor parents, and 32 times less likely than
non-poor parents.

73.2%

 9.5 percent of poor parents, 9.7 percent of1
low-income, and 1.1 percent of non-poor parents
lack a high school diploma. Poor parents are alsosignificantly more likely to have at least a highschool diploma compared to deeply poor parents.

Deep
Poverty*

PARENT UNINSURED RATEParents’ lack of health insurance may limit their ability toseek care for conditions that interfere with parenting andemployment. In addition, the children of parents who lackpublic health insurance have been found to be less likely
to receive preventive health care than insured parents.24 oor parents and those in deep poverty areP
about six times more likely to be uninsured than
non-poor parents. 27.8 of poor parents and 26.0
percent of parents in deep poverty lack healthinsurance. The percentage of low-income parents
without insurance is also high, at 23.7 percent.

26.9%

80.5%

19.5%
Poverty**

90.3%

98.9%

9.7%

1.1%

Low
IncomeNon-Poor*Deeply poor parents are 1.5 times less likely to have a high school diploma or
above compared to poor parents (odds ratio = 1.52; 95% CI = 1.05, 2.20;
p <0. 0001), and about 32 times less likely compared to non-poor parents(odds ratio = 32.14; 95% CI = 20.07, 51.48; p < 0.0001).
**Poor parents are 21 times less likely to have a high school diploma or above
compared to non-poor parents (odds ratio = 21.18; 95% CI = 14.13, 31.74;
p < 0.0001).

Figure 16. Parent Uninsured Rates by Poverty Status

26.0%

27.8%
23.7%

5.7 percent of non-poor parents lack insurance.

5.7%

Deep
Poverty*

Poverty**

Low
Income***Non-Poor*Deeply poor parents are nearly six times more likely to be uninsured
compared to non-poor parents (odds ratio = 5.80; 95% CI = 4.11, 8.16;
p < 0.0001).
**Poor parents are six times more likely to be uninsured compared to
non-poor parents (odds ratio = 6.34; 95% CI = 4.85, 8.30; p < 0.0001).
***Low-income parents are five times more likely to be uninsured compared to
non-poor parents (odds ratio = 5.10; 95% CI = 4.14, 6.29; p < 0.0001).
Source: NCCP Analysis of Data From the National Health Interview
Survey (NHIS) 2017-201810

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PARENT UNEMPLOYMENTLong-term parent unemployment not only eliminates mostfamilies’ chief source of income, but also contributes toisolation and depression. It may also limit parents’ social
capital (i.e., networks of friends and colleagues), whichhelps them find work and increase their wages over time.25


Figure 17. Parent Unemployment by Poverty Status

31.3%
21.1%
16.0%

 arents in deep poverty have the highestP
unemployment rate at 31.3 percent; this indicatorreflects a parent’s unemployment over the past 12
months.
 he unemployment rates decrease to 21.1
T
percent for poor, 16.0 percent for low-income,and 9.0 percent for non-poor parents.

NEIGHBORHOOD SUPPORTParents’ residence in a neighborhood where they feel they
can count on people for assistance reflects their availablesocial support and potential practical assistance in times ofcrisis. Both experiences are likely to reduce parent stress.

9.0%

Deep
Poverty*

Poverty

Low
Income**Non-Poor*Deeply poor parents are significantly more likely to be unemployed compared
to non-poor parents (odds ratio = 4.58; 95% CI = 3.26, 6.42; p < 0.0001).

Figure 18. Parent Report of Neighborhood Support by Poverty Status arents in deep poverty had the lowest averagePscore, 2.7, indicating less confidence that they
can count on assistance from neighbors. (A scoreof “2” indicates the parent “somewhat disagrees,”“3” means “somewhat agrees,” and “4” means“definitely agree.”). Deeply poor parents reported
a significantly lower average level of agreementcompared to parents at other income levels.

2.7



 oor and low-income parents also reportedP
significantly lower levels of agreement that they
can count on neighbors than non-poor parents.
Scores were 2.9 for poor, 3.0 for low-income, and
3.4 for non-poor parents.

Deep
Poverty*

2.9

3.0

Poverty**

Low
Income***3.4

Non-Poor*Parents living in deep poverty reported a lower level of agreement that there
are people they can count on in the neighborhood, compared to those living
in poverty (p = 0.0491), low-income families (p = 0.0011), or non-poor ones
(p < 0.0001).
**Poor parents reported a lower level of agreement with the statement,
compared to non-poor parents (p < 0.0001).
***Low-income parents reported a lower level of agreement with thestatement compared to non-poor parents (p < 0.0001).
Source: NCCP Analysis of Data From the National Health and Nutrition
Examination Survey (NHANES) 2015-201811

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Summary
Among children under age nine, there is wide variation in rates of deep poverty across the states, ranging from 4 percent
to 17 percent. Large racial disparities exist nationally and within most states. In the U.S., 5 percent of young whitechildren and 3 percent of Asian American children live in deep poverty, while these percentages are more than double
for other racial/ethnic groups of young children: 18 percent of Black children, 15 percent of American Indian and Native
Alaskan children, and 11 percent of Hispanic/Latino children under age 9 are in families with incomes below 50 percentof the federal poverty line.
For all but a few of the indicators we compared across income groups, children in deep poverty were the most likely toexperience early conditions and circumstances that make them vulnerable to future health, development, and learning
problems. These indicators include low birth weight, a physical condition or health problem that limits activities, anintellectual disability or developmental delay, participation in early intervention or special education, and less positive
behavior. (As mentioned earlier, participation in early intervention and special education may also reduce risks posedby conditions that make children eligible for these programs.) The parents of young children in deep poverty were themost likely to have a mental health condition, to be a single parent, to lack a high school diploma, and to be unemployed.
These parents were also the least likely to report that they can count on people in their neighborhood for help when
they need it.
For some indicators, families in income groups other than deep poverty showed the greatest disadvantage. Young children
in poverty were the most likely to be obese. Low-income children were the most likely to lack health insurance, and theyalso had the highest prevalence of elevated blood lead levels, although percentages did not differ significantly acrossincome groups. Among parents, those in poverty were the most likely to lack health insurance.
Although the results suggest less optimal outcomes and family circumstances for poor and low-income children, therecommendations that follow focus on families with young children in deep poverty since indicators for this group suggest
exceptional risks to children’s development and life opportunities. These recommendations incorporate income support
policies targeting families in deep poverty into a two-generation approach that include investments in direct support
for parents’ health and mental health, child birth outcomes, and children’s development. A body of theory and researchsuggests that policies aimed at promoting work and higher family income alongside the provision of other supports forparent well-being and children’s development can offer benefits for children that are larger than policies that focus solely
on adult workforce development or children’s development.26 In addition, the recommendations call for policies that
directly promote the integration of these supports so that they provide maximum opportunities for families to thrive.

Recommendations
1. I ncrease the financial resources of families with young children in deep poverty through an expansion and reformof key public benefits. Two policy packages recommended by the National Academy of Sciences in Roadmap to
Reducing Child Poverty would reduce deep poverty among children by 50 percent. These include a balance ofwork incentives (e.g., raising the Earned Income Tax Credit and minimum wage) with policies not tied to work (e.g.,creating a Child Allowance; restoring eligibility for nonqualified legal immigrants for SNAP, TANF, Medicaid, and SSI;
and increasing SNAP benefits).27 Analysts at the Center on Budget and Policy have identified three policies in thesepackages—a child allowance, increase in SNAP benefits, and expansion of housing vouchers—that would result in thegreatest reduction in deep poverty among children.28
2. E
 xpand and implement Medicaid in states where this has not occurred in order to reach parents in deep poverty
who lack health insurance.28 Health insurance for parents helps them gain critical access to health care for physicaland mental health conditions that could reduce engagement in work and limit parenting capacities. Currently, 12
states have not expanded Medicaid and three have adopted but not implemented expansions of Medicaid.29
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3. I nvest in scaling programs that have shown promise for improving healthy pregnancy outcomes, including
reductions in low birth weight; prioritize participation of families in deep poverty. Programs with preliminary
evidence of efficacy include Centering Pregnancy, which provides social support and education about health andparenting in group neonatal visits and a model of prenatal care that offers parents a highly welcoming health caresetting and assistance with overcoming barriers to using prenatal care, such as a lack of child care or transportation.
In some states, these models are being covered by Medicaid.30
4. I nvest in expansions and quality improvement of programs with demonstrated capacity to provide strong supports
for a nurturing parent-child relationship and early learning and development; prioritize families in deep poverty
for participation. These include Early Head Start and MIECHV home visiting programs which already serve families
experiencing severe economic hardship.31 Child First, another model that targets children in families facing multiple
adverse circumstances, including deep poverty, has been found to produce large improvements in child development,
parenting, parent depression outcomes.32 This program coordinates evidence-based interventions (e.g., dyadic
parent-child treatment to strengthen impaired parent-child relationships, home-visiting, Early Head Start) andhelps parents obtain public benefits, housing assistance, and other critical supports. In addition, expansion of highquality pre-kindergarten programs, and intentional efforts to include children in deep poverty, is important in lightof research suggesting that the learning and development of children at lower income levels is greater when they
participate in classrooms with children from more affluent families.33 Locating high-quality pre-kindergarten programsin communities that can attract families at different income levels could help programs achieve economically diverse
classrooms.
5. E stablish dedicated federal and state funding to enable child development-focused programs (e.g., high-quality
early care and education and home-visiting programs) to effectively incorporate adult education and work training
services for families; prioritize engagement of families in deep poverty in two-generation programs tailored totheir needs. Currently, there is a lack of public funding that child development and parenting programs could use toincrease their capacity to become two-generation programs through partnerships with adult education and workforce
development programs. Funding to support effective partnerships could be targeted to help them incorporate
promising components based on available research and theory.34 These include supports for ensuring quality in thechild development and adult work-related programs in the partnerships (e.g., ECE teacher training and coaching,
workforce training with stackable credentials), parent coaches and flexible funding to help families overcome
barriers to participation in multiple program activities, and support for aligning services, including collocated orwell-coordinated services. Several researchers have recently proposed public investment in the implementation andevaluation of two-generation Head Start programs with several of these features.35
6. A
 ddress racial/ethnic disparities in young children’s experience of deep poverty on multiple fronts. Achievinga large overall reduction in deep poverty among families with young children (who are disproportionately Black,
Hispanic/Latino, and American Indian and Alaska Native (AI/AN)) by advancing the policies recommended here, canhelp reduce racial/ethnic disparities in deep poverty. In addition, quality improvement in early care and education(ECE) programs along with outreach to ensure greater participation of very poor Black, Hispanic/Latino, and AI/AN
families in high-quality programs is essential in light of evidence that poor families and Black families participate lessthan white families in high-quality ECE programs.36 In addition, research points to less positive treatment of Black,
Hispanic/Latino, and AI/AN clients in human services (e.g., public assistance offices), leading to less assistance being
provided or benefits being cut off.37 This research highlights the need for monitoring of benefits’ receipt and sanctions
in TANF and other human services programs that could show racial and ethnic disparities in client treatment andprompt the testing of interventions to change the staff behavior driving these disparities.

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Appendix A
Number and Percent of Young Children in Deep Poverty by State, 2018

State

Number ofchildren underage 9 in deep
poverty

Alabama

67,838

AlaskaPercent ofchildren underage 9 in deep
povertyState

Number ofchildren underage 9 in deep
poverty

Percent ofchildren underage 9 in deep
poverty

13%

Montana

6,876

6%

4,600

5%

Nebraska

10,606

4%

Arizona

63,921

8%

Nevada23,128

7%

Arkansas

39,67812%

New Hampshire

8,457

7%

California

324,652

7%

New Jersey

58,1906%

Colorado

29,858

5%

New Mexico

32,67815%

Connecticut

23,646

7%

New York

176,680

9%Delaware

7,903

8%

North Carolina

105,425

10%

District of Columbia

10,522

14%

175,265

9%North Dakota

5,912

6%

FloridaGeorgia

109,7729%

Ohio

120,37710%

Hawaii12,8809%

Oklahoma

54,26412%

Idaho

15,0097%

Oregon29,737

7%

Illinois

94,066

7%

Pennsylvania

102,4738%

Indiana

57,397

8%

Rhode Island

10,31110%

Iowa

21,205

6%

South Carolina

55,01110%

Kansas21,837

6%

South Dakota

10,46210%

Kentucky

56,766

12%

Tennessee

86,145

12%

Louisiana

71,841

13%

Texas

338,956

9%

Maine

6,146

5%

Utah

17,6964%Maryland

35,516

5%

Vermont

2,671

5%

Massachusetts

36,574

6%

Virginia

65,326

7%

Michigan

104,773

10%

Washington

44,9695%Minnesota

32,8255%West Virginia

19,143

11%

Mississippi

56,50917%

Wisconsin

39,761

7%

Missouri

53,8188%Wyoming

4,531

7%

Source: NCCP analysis of ACS 1-Year Estimates - Public Use Microdata Sample 201814

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Appendix B
Number and Percentage of Young Children in Deep Poverty by Race/Ethnicity, 2018
NUMBER OF CHILDREN UNDER AGE 9 IN DEEP POVERTY
White, nonHispanic/Latino

Black, nonHispanic/Latino

Hispanic/Latino

National

886,827

831,956

963,338

49,911

57,695

Alabama

20,79039,358

5,800

106

-

Alaska

1,177

-

-

2,631

184

Arizona

13,853

3,167

35,422

8,483

472

Arkansas

15,593

16,416

5,487

446

-

California

46,00539,315

207,502925

15,084

Colorado

10,4572,439

14,326127

671

Connecticut

5,735

3,740

12,239134815

Delaware

1,940

2,916

2,146

-

148

District of Columbia

0

9,303

1,219

-

-

Florida43,322

58,172

61,917132

1,999

Georgia

24,79755,432

20,992458

526

Hawaii

1,574

-

3,567

-

166

Idaho

7,475

-

6,980

394

-

Illinois

27,46332,90527,143-

1,645

Indiana

26,023

17,278

10,711

118

793

Iowa

13,318

1,619

3,312

270

746

Kansas

7,002

4,678

7,018

-

737

Kentucky

37,841

8,991

4,221

223

-

Louisiana

17,52447,060

3,618

-

105

Maine

5,320

153

207

133

-

Maryland

7,972

15,341

7,252

-

2,108

Massachusetts

10,716

3,914

17,676-944

Michigan

45,27941,666

10,575328

1,518

Minnesota

10,246

12,301

4,364

1,981

1,549

Mississippi

15,21835,888

1,755

622

582

Missouri

27,616

16,316

5,288

106

0

Montana

4,579

-

140

1,462

-

Nebraska

3,830

2,154

3,043

951

-

Nevada

4,797

4,503

11,701

266

543

New Hampshire

5,235

801

1,228

-

312

New Jersey

12,377

14,34826,403-

2,473

New Mexico

4,511

437

21,165

5,851

261

New York

52,92942,89961,526788

8,747

North Carolina

28,893

43,592

23,497

1,043

409

North Dakota

2,171

1,466

241

1,172

170

State/National

American Indianand Alaska Native

Asian

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NUMBER OF CHILDREN UNDER AGE 9 IN DEEP POVERTY
White, nonHispanic/Latino

Black, nonHispanic/Latino

Ohio

51,29942,404

12,703-525

Oklahoma

21,606

11,458

9,095

5,336

145

Oregon17,211

280

7,700

685

540

Pennsylvania

43,103

22,463

26,787-1,845

Rhode Island

3,168

1,155

4,692

-

361

South Carolina

13,592

29,156

7,569

656

261

South Dakota

2,486

-

1,444

5,323

-

Tennessee

33,544

31,611

15,044

-

506

Texas

47,757

66,344

214,626

122

2,076

Utah

10,170

1,057

3,680

441

951

Vermont

2,475

-

-

-

-

Virginia

22,34328,544

7,621

-

867

Washington

16,784

4,775

17,066873

2,143West Virginia

16,059

1,259

325

-

-

Wisconsin

18,747

12,882

3,697

295

-

Wyoming

2,905

-

1,477

149

-

State/National

Hispanic/Latino

American Indianand Alaska Native

Asian

PERCENTAGE OF CHILDREN UNDER 9 IN DEEP POVERTY
White, nonHispanic/Latino

Black, nonHispanic/Latino

Hispanic/Latino

American Indianand Alaska Native

Asian

National

5%

18%

11%

15%

3%

Alabama

7%

26%

14%

18%

-

Alaska

3%

-

-

12%

4%

Arizona

5%

9%

10%

22%

3%

Arkansas

7%

27%

14%

21%

-

California

4%

18%

9%

12%

3%

Colorado

3%

10%

8%

4%

4%

Connecticut

3%

11%

14%

35%

5%

Delaware

4%

12%

14%

-

4%

District of Columbia

0%

27%

10%

-

-

Florida

5%

15%

10%

10%

5%

Georgia

5%

14%

11%

30%

1%

Hawaii

7%

-

11%

-

1%

Idaho

5%

-

18%

14%

-

Illinois

4%

17%

8%

-

2%

Indiana

5%

21%

13%

26%

5%

Iowa

5%

12%

9%

23%

9%

Kansas

3%

20%

11%

-

10%

Kentucky

10%

23%

13%

32%

-

State/National

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PERCENTAGE OF CHILDREN UNDER 9 IN DEEP POVERTY
White, nonHispanic/Latino

Black, nonHispanic/Latino

Hispanic/Latino

American Indianand Alaska Native

AsianLouisiana

6%

25%

8%

-

2%

Maine

5%

5%

10%

27%

-

Maryland

3%

8%

6%

-

6%

Massachusetts

3%

7%

13%

-

2%

Michigan

7%

25%

12%

7%

5%

Minnesota

2%

17%

8%

25%

5%

Mississippi

9%

27%

12%

47%

22%

Missouri

6%

18%

13%

10%

0%

Montana

5%

-

2%

14%

-

Nebraska

2%

20%

7%

45%

-

Nevada

4%

14%

9%

8%

4%

New Hampshire

5%

34%

15%

-

7%

New Jersey

3%

11%

10%

-

3%

New Mexico

9%

15%

16%

27%

12%

New York

6%

16%

12%

15%

6%

North Carolina

5%

19%

13%

11%

1%

North Dakota

3%

39%

6%

26%

10%

Ohio

6%

25%

16%

-

2%

Oklahoma

9%

31%

11%

12%

2%

Oregon

7%

3%

8%

16%

3%

Pennsylvania

5%

14%

17%

-

4%

Rhode Island

6%

17%

16%

-

11%

South Carolina

5%

19%

15%

43%

4%

South Dakota

3%

-

21%

39%

-

Tennessee

7%

24%

20%

-

5%

Texas

4%

16%

12%

2%

1%

Utah

3%

16%

5%

14%

18%

Vermont

5%

-

-

-

-

Virginia

5%

17%

6%

-

2%

Washington

4%

13%

10%

9%

4%

West Virginia

11%

22%

11%

-

-

Wisconsin

4%

24%

5%

6%

-

Wyoming

6%

-

17%

7%

-

State/National

Source: NCCP analysis of ACS 1-Year Estimates - Public Use Microdata Sample 2018
Note: Data with small cell sizes were not displayed due to their unreliability.

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Appendix C
DEFINITIONS OF OUTCOME VARIABLES
Child low birth weight (under 9 years)
A child was considered to have a low birth weight if his/her birth weight was 5.5 lbs. or less.
Physical impairment (under 9 years)
Parents reported whether their child had an impairment
or health problem that limited their child’s ability to crawl,
walk, run or play.
Intellectual disability and developmental delay(under 9 years)
Parents reported whether their child had been diagnosed
with an intellectual disability or developmental delay by
their doctor before.
Participation in early intervention and special education(under 9 years)
Parents reported whether their child received early intervention services (children 0 to 3) or special education(children 3 to 9).
Child behavior (ages 4 to 9)
Parents reported on the degree to which their child was
well-behaved and did what was requested. The 3-point
Likert response scale was: Not true (coded as 0), Somewhat true (coded as 1), and Certainly true (coded as 2).
Child uninsured rate (under 9 years)
Parents identified whether their child received no healthinsurance coverage of any type (i.e., private health insurance, Medicare, Medicaid, State Children’s Health Insurance Program (CHIP), a State-sponsored health plan, othergovernment programs, or military health plan).
Child obesity (under 9 years)
Children were weighed and measured to determine theirbody mass index (BMI). Based on their BMI scores, children were placed into one of the four categories: underweight, normal weight, overweight, and obese.
Elevated blood lead levels (under 9 years)
A sample of each child’s blood was taken and used todetermine the child’s blood lead level. The detection limit
used by the CDC is 5 g/dL; lead content levels in the blood
higher than the limit were coded as “elevated.”
Parent overall mental health status
This outcome was a composite variable and combinedparents’ responses to the following questions:
During the past 30 days, how often did you feel1) …that everything was an effort?; 2) …hopeless?;3) …nervous?; 4) …restless/fidgety?; 5) …so sad nothing
cheers you up?; 6) …worthless?
Parents’ responses were on a 5-point Likert scale: Noneof the time (coded as 0), A little of the time (coded as 1),
Some of the time (coded as 2), Most of the time (coded as3), and All of the time (coded as 4).
Single parent status
Parents were asked to identify their family household
structure. A parent was coded as “single” if s/he was oneof the following scenarios: Mother and biological or non-biological child(ren) only Father and biological or non-biological child(ren) only All other single-adult and child(ren) families
Parent education attainment
Parents reported their highest level of education attainment in their families. Parental education levels included
two groups:
Less than high school Less than/equal to 8th grade
 9-12th grade, no high school diploma
High school and above GED recipient High school graduate
 Some college, no degree
 AA degree, technical or vocational
 AA degree, academic program Bachelor’s degree (BA, BS, AB, BBA) Master’s, professional, or doctoral degree

Parent uninsured rate
Parents reported whether they had no health insurance coverage of any type (i.e., private health insurance,
Medicare, Medicaid, a State-sponsored health plan, othergovernment programs, or military health plan).
Parent unemployment
Parents reported their job status in the past week and past
12 months. Parents were coded as unemployed if they did
not have any job last week and in the past 12 months.
Neighborhood support
Parents responded to the following question: How muchdo you agree that there are people you can count on inthis neighborhood?
Responses were based on a 4-point Likert scale: Definitely
disagree (coded as 1), Somewhat disagree (coded as 2),
Somewhat agree (coded as 3), and Definitely agree (codedas 4).
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REFERENCES
1S
 ee the poverty thresholds for 2019 by size of family and number ofrelated children under 18 years issued by U.S. Census Bureau.

2Trading Economics. (2020, September 21). United States
unemployment rate. https://tradingeconomics.com/united-states/
unemployment-rate3F loyd, I. (2020, March 4). Boost TANF to reduce deep poverty amongchildren. Center on Budget and Policy Priorities.
https://www.cbpp.org/blog/boost-tanf-to-reduce-deep-povertyamong-children.

4

 heaton, L., & Tran, V. (2018). The antipoverty effects of SNAP.
W
Urban Institute. https://www.urban.org/research/publication/
antipoverty-effects-supplemental-nutrition-assistance-program.

5

Waxman, E., Gundersen, C., & Thompson, M. (2018). How far do
SNAP benefits fall short of covering the cost of a meal? Urban Institute.
https://www.urban.org/research/publication/how-far-do-snapbenefits-fall-short-covering-cost-meal.

6R ice, D., Schmit, S., & Matthews, H. (2019). Child care and housing:
Big expenses with too little help available. Center on Budget andPolicy Priorities. https://www.cbpp.org/research/housing/childcare-and-housing-big-expenses-with-too-little-help-available

Centers for Disease Control and Prevention (CDC). National Centerfor Health Statistics (NCHS). (2020). 2017-2018 National HealthInterview Survey (NHIS) Data. U.S. Department of Health andHuman Services. https://www.cdc.gov/nchs/nhis/about_nhis.htm
13 ational Health and Nutrition Examination Survey. (2018).
N
NHANES: Analytic guidelines, 2011-2014 and 2015-2016.
https://wwwn.cdc.gov/nchs/data/nhanes/analyticguidelines/11-16analytic-guidelines.pdf
National Health Interview Survey. (n.d.). User note–Sampling weights.
https://nhis.ipums.org/nhis/userNotes_weights.shtml

14

 perandei, S. (2014). Understanding logistic regression analysis.
S
Biochemia Medica, 24(1), 12–18. https://doi.org/10.11613/
BM.2014.003

15

 eyes, L., & Mañalich, R. (2005). Long-term consequences of low
Rbirth weight. Kidney International, (97), S107–S111. https://doi.
org/10.1111/j.1523-1755.2005.09718.x
Negrato, C. A., & Gomes, M. B. (2013). Low birth weight: Causes
and consequences. Diabetology & Metabolic Syndrome, 5. https://doi.
org/10.1186/1758-5996-5-49
Lowe, J., Erickson, S. J., MacLean, P., Duvall, S. W., Ohls, R. K., &Duncan, A. F. (2014). Associations between maternal scaffolding
and executive functioning in 3 and 4 year olds born very low birthweight and normal birth weight. Early Human Development, 90(10),587-593. https://doi.org/10.1016/j.earlhumdev.2014.07.009

Wheaton, L., & Tran, V. (2018). The antipoverty effects of
SNAP. Urban Institute. https://www.urban.org/sites/default/
files/2018/02/14/the_antipoverty_effects_of_snap.pdf
National Center for Children in Poverty. (2020, September 15).
United States Early Childhood Profile. https://www.nccp.org/
ecprofile/?state=US

Schieve, L. A., Tian, L. H., Rankin, K., Kogan, M. D., Yeargin-Allsopp,
M., Visser, S., & Rosenberg, D. (2016). Population impact ofpreterm birth and low birth weight on developmental disabilities
in US children. Annals of Epidemiology, 26(4), 267-274. https://doi.
org/10.1016/j.annepidem.2016.02.012

7N ational Academies of Sciences, Engineering, and Medicine. (2019).
A roadmap to reducing child poverty. The National Academies Press.
https://doi.org/10.17226/25246

16

8

D
 uncan, G. J., Ziol-Guest, K. M., & Kalil, A. (2010). Early-childhood
poverty and adult attainment, behavior, and health. Child
Development, 81(1), 306-325. https://doi.org/10.1111/j.14678624.2009.01396.x

 alisano, R. J., Chiarello, L. A., King, G. A., Novak, I., Stoner, T.,P& Fiss, A. (2012). Participation-based therapy for children withphysical disabilities. Disability and Rehabilitation, 34(12), 1041-1052.
https://doi.org/10.3109/09638288.2011.628740

17

Larney, R. (2002). The relationship between early language delay
and later difficulties in literacy. Early Child Development and Care,
172(2), 183-193.

Edmunds, C. (2020). Academic failure and the role of early life
course economic deprivation. Children and Youth Services Review,
108, 104528. https://doi.org/10.1016/j.childyouth.2019.104528
9B lair, C., & Raver, C. C. (2016). Poverty, stress, and brain
development: New directions for prevention and intervention.
Academic Pediatrics, 16(3), S30–S36. https://doi.org/10.1016/j.
acap.2016.01.010

10

National
Academies of Sciences, Engineering, and Medicine. (2019).
A roadmap to reducing child poverty. The National Academies Press.
https://doi.org/10.17226/25246

11E kono, M., Yang, J., & Smith, S. (2016). Young children in deep
poverty. New York: National Center for Children in Poverty, Mailman
School of Public Health, Columbia University. https://www.nccp.
org/wp-content/uploads/2016/01/text_1133.pdf

12U .S. Census Bureau. (2019). 2018 American Community Survey
1-year Public Use Microdata Samples. https://data.census.gov/
cedsci/Centers for Disease Control and Prevention (CDC). National
Center for Health Statistics (NCHS). (2020). 2015-2018 National
Health and Nutrition Examination Survey (NHANES) Data. U.S.
Department of Health and Human Services. https://wwwn.cdc.gov/
nchs/nhanes/default.aspx

Garon-Carrier, G., Boivin, M., Lemelin, J. P., Kovas, Y., Parent,
S., Séguin, J. R., ... & Dionne, G. (2018). Early developmental
trajectories of number knowledge and math achievement from4 to 10 years: Low-persistent profile and early-life predictors.
Journal of School Psychology, 68, 84-98. https://doi.org/10.1016/j.
jsp.2018.02.004
Piek, J. P., Dawson, L., Smith, L. M., & Gasson, N. (2008). The roleof early fine and gross motor development on later motor andcognitive ability. Human Movement Science, 27(5), 668-681.
https://doi.org/10.1016/j.humov.2007.11.002
18

Mäntymaa, M., Puura, K., Luoma, I., Vihtonen, V., Salmelin, R. K., &Tamminen, T. (2009). Child’s behaviour in mother–child interaction
predicts later emotional and behavioural problems. Infant and Child
Development: An International Journal of Research and Practice,
18(5), 455-467. https://doi.org/10.1002/icd.633
Bornstein, M. H., Hahn, C. S., & Haynes, O. M. (2010). Social
competence, externalizing, and internalizing behavioral adjustment
from early childhood through early adolescence: Developmental
cascades. Development and Psychopathology, 22(4), 717.
http://doi.org/10.1017/S0954579410000416

19

National Center for Children in Poverty
Bank Street Graduate School of Educa on

19

Cohodes,
S.R., Grossman, D.S., Kleiner, S.A., & Lovenheim,
M.F. (2016). The effect of child health insurance access onschooling: Evidence from public insurance expansions. Journalof Human Resources, 51(3), 727-759. https://www.muse.jhu.edu/
article/629236
Stella, M. Y., Bellamy, H. A., Kogan, M. D., Dunbar, J. L., Schwalberg,
R. H., & Schuster, M. A. (2002). Factors that influence receipt
of recommended preventive pediatric health and dental care.
Pediatrics, 110(6), e73-e73.

27

 ational Academies of Sciences, Engineering, and Medicine. (2019).
N
A roadmap to reducing child poverty. The National Academies Press.
https://doi.org/10.17226/25246

28

Trisi, D., & Saenz, M. (2020). Policy brief: Deep poverty amongchildren rose in TANF’s first decade, then fell as other programs
strengthened. Center on Budget and Policy Priorities. https://www.
cbpp.org/sites/default/files/atoms/files/2-27-20pov2.pdf

29

Kaiser Family Foundation. (2020, August 17). Status of state
Medicaid expansion decisions: Interactive map. https://www.kff.
org/medicaid/issue-brief/status-of-state-medicaid-expansiondecisions-interactive-map/

Flores, G., Lin, H., Walker, C., Lee, M., Currie, J. M., Allgeyer, R.,... & Massey, K. (2017). The health and healthcare impact ofproviding insurance coverage to uninsured children: A prospective
observational study. BMC public health, 17(1), 553.
20

Pulgarón,
E. R. (2013). Childhood obesity: A review of increased riskfor physical and psychological comorbidities. Clinical Therapeutics,
35(1), A18–A32. https://doi.org/10.1016/j.clinthera.2012.12.014
Cote, A. T., Harris, K. C., Panagiotopoulos, C., Sandor, G. G.,& Devlin, A. M. (2013). Childhood obesity and cardiovascular
dysfunction. Journal of the American College of Cardiology, 62(15),1309–1319. https://doi.org/10.1016/j.jacc.2013.07.042

21

Jusko, T. A., Henderson, C. R., Lanphear, B. P., Cory-Slechta, D. A.,
Parsons, P. J., & Canfield, R. L. (2008). Blood lead concentrations< 10 μg/dL and child intelligence at 6 years of age. Environmental
Health Perspectives, 116(2), 243–248. https://doi.org/10.1289/
ehp.10424
Muennig, P. (2009). The social costs of childhood lead exposurein the post–lead regulation era. Archives of Pediatrics & Adolescent
Medicine, 163(9), 844-849. https://doi.org/10.7916/D8XG9RJX

22Abbott, M., & Reilly, A. (2019). The role of social capital in
supporting economic mobility. Office of the Assistant Secretaryfor Planning and Evaluation, Department of Health and Human
Services. https://aspe.hhs.gov/system/files/aspe-files/261791/
socialcapitalsupportingeconomicmobility.pdf

Dahlen,
H. M. (2016). The impact of maternal depression
on child academic and socioemotional outcomes. Economicsof Education Review, 52, 77-90. https://doi.org/10.1016/j.
econedurev.2016.01.006
Fitzsimons, E., Goodman, A., Kelly, E., & Smith, J. P. (2017). Poverty
dynamics and parental mental health: Determinants of childhood
mental health in the UK. Social Science & Medicine, 175, 43-51.
https://doi.org/10.1016/j.socscimed.2016.12.040

23

Davis-Kean,
P. E. (2005). The influence of parent education andfamily income on child achievement: the indirect role of parental
expectations and the home environment. Journal of Family
Psychology, 19(2), 294.

24

Venkataramani, M., Pollack, C. E., & Roberts, E. T. (2017). Spillover
effects of adult Medicaid expansions on children’s use of preventive
services. Pediatrics, 140(6). https://doi.org/10.1542/peds.20170953

30

Novoa, C. (2020). Ensuring healthy births through prenatal
support: Innovations from three models. Center for American
Progress. https://cdn.americanprogress.org/content/
uploads/2020/01/30074701/Prenatal-Support-Models-1.
pdf?_ga=2.136667191.644537461.16003744991524745188.1600374499

31

Health Resources and Services Administration. (2020). The
Maternal, Infant, and Early Childhood Home Visiting Program:
Partnering with parents to help children succeed. https://mchb.hrsa.
gov/sites/default/files/mchb/MaternalChildHealthInitiatives/
HomeVisiting/pdf/programbrief.pdf

32

For information on Child First and its impacts, see
https://www.childfirst.org/about-us

33

Reid, J. L., & Ready, D. D. (2013). High-quality preschool: The
socioeconomic composition of preschool classrooms and children’s
learning. Early Education & Development, 24(8), 1082-1111. https://doi.org/10.1080/10409289.2012.757519

34

Chase-Lansdale, P. L., & Brooks-Gunn, J. (2014). Two-generation
programs in the twenty-first century. The Future of Children, 13-39.

35

Sommer, T. E., Sabol, T. J., Chor, E., Schneider, W., Chase-Lansdale,
P. L., Brooks-Gunn, J., ... & Yoshikawa, H. (2018). A two-generation
human capital approach to anti-poverty policy. RSF: The Russell Sage
Foundation Journal of the Social Sciences, 4(3), 118-143.
https://doi.org/10.7758/rsf.2018.4.3.07

36

 guiar, A. L., & Aguiar, C. (2020). Classroom composition andAquality in early childhood education: A systematic review. Children
and Youth Services Review, 115, 105086. https://doi.org/10.1016/j.
childyouth.2020.105086
Lee, K. (2019). Impact of Head Start quality on children’sdevelopmental outcomes. Social Work in Public Health, 34(3), 239250. https://doi.org/10.1080/19371918.2019.1576566

Committee on the Consequences of Uninsurance, Institute ofMedicine. (2002). Health insurance is a family matter. The National
Academy Press. https://doi.org/10.17226/10503
25

Institute for Work and Health. (2009). Unemployment and mental
health. https://www.iwh.on.ca/sites/iwh/files/iwh/reports/iwh_
issue_briefing_mental_health_2009.pdf

26

 ommer, T. E., Sabol, T. J., Chor, E., Schneider, W., Chase-Lansdale,S
P. L., Brooks-Gunn, J., ... & Yoshikawa, H. (2018). A two-generation
human capital approach to anti-poverty policy. RSF: The Russell Sage
Foundation Journal of the Social Sciences, 4(3), 118-143. https://doi.
org/10.7758/rsf.2018.4.3.07

37

McDaniel, M., Woods, T., Pratt, E., & Simms, M. (2017). Identifying
racial and ethnic disparities in the human services: A conceptual
framework and literature review. Office of Planning, Research,and Evaluation, Administration for Children and Families, U.S.
Department of Health and Human Services. https://www.acf.
hhs.gov/sites/default/files/opre/identifying_racial_and_ethnic_
disparities_b508.pdf

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