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Journal of Child and Family Studies (2023) 32:2254–2270
https://doi.org/10.1007/s10826-022-02500-0
ORIGINAL PAPER
Does Poverty Affect Early Language in 2-year-old Children in
Germany?
Claudia Karwath 1●Manja Attig1●Jutta von Maurice1●Sabine Weinert2
Accepted: 25 November 2022 / Published online: 29 December 2022
© The Author(s) 2022, corrected publication 2023
Abstract
Previous studies reported negative effects of financial deprivation on child development during early childhood. As already
shown, child development, in particular language development, is associated with family background, e.g., educational level.
However, less is known about the impact of (restricted) financial resources on early language skills. Therefore, the present
study investigates whether family income, measured as a metric variable by net equivalence income, and poverty,
operationalized as income groups based on official income thresholds, impact vocabulary and grammar skills of 2-year-old
children even when taking the educational level of the mother as well as aspects of the home-learning environment (joint
picture book reading) and other relevant variables into account. Drawing on a German sample of N=1782, we found that
especially poverty is significantly associated with early language skills over and above maternal education and joint picture
book reading. Hence, our results indicate the relevance to consider the effect of (restricted) financial resources and especially
poverty on child development during early childhood additionally to other indicators of social background.
Keywords Income ●Poverty ●Early childhood ●Language ●Inequality ●Child development
Highlights
●Children that experienced poverty during the first year show comparatively lower early language skills even when
maternal education and joint picture book reading are considered.
●Using income groups defined by official income thresholds allow to more directly analyse the impact of poverty on
children.
●Especially poverty is found to affect child development even in a rich country with a strong social system such as
Germany.
Introduction
As recently emphasized by the Organisation for Economic
Co-operation and Development (OECD, 2018b) poverty
has a negative impact on all household members, however,
children are affected in particular. A large amount of
research, especially from the U.S., examined the detrimental
impact of poverty on child outcomes showing that children
living in poverty are negatively affected in their health,
cognitive, language, and socio-emotional development as
well as in their school achievement, and their later educa-
tional attainment compared to their peers who do not
experience poverty (for a brief overview: Brooks-Gunn &
Duncan, 1997; Dearing et al., 2006; Duncan et al., 2012).
Furthermore, poverty unfolds its effects across a broad age
range, namely from prenatal to adulthood, having the most
damaging effects during early childhood, preschool, and
early school years (Barajas et al., 2008). Additionally,
poverty is found to affect a child’s home-learning envir-
onment and parent-child interactions (e.g., Brooks-Gunn &
Duncan, 1997; Garrett et al., 1994; Hartas, 2011; Kalil
et al., 2016). According to the OECD, nearly 1 in 7 children
(0–17 years) are negatively affected by living in relative
income poverty, and in two-third of the OECD countries
*Claudia Karwath
claudia.karwath@lifbi.de
1Leibniz Institute for Educational Trajectories, Wilhelmsplatz 3,
96047 Bamberg, Germany
2University of Bamberg, Markusplatz 3, 96050 Bamberg, Germany
1234567890();,:
1234567890();,:
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child poverty even increased during the last years with a
large cross-country variation (OECD, 2018a,2018b).
However, though poverty is found to affect child
development negatively, the understanding of the con-
sequences of growing up in poverty for children during
early childhood remains limited (Barajas et al., 2008;
Schoon et al., 2010). Thus, analysing the timing of poverty
is of high relevance. Here, especially the first three years of
a child’s life are found to be important: In this time children
are highly dependent on their family. Experiences, knowl-
edge, nurture, and socialization of children are based on the
family they grow up with (for a brief overview: Hart &
Risley, 1995). Considering current research on early child
development, various studies report the association with
educational background or an overall construct of socio-
economic status, especially for early language development
(e.g., Hart & Risley, 1995,1999,2003; Hoff-Ginsberg,
2000; Linberg et al., 2020; Linberg et al., 2019; Linberg &
Wenz, 2017; Robertson, 1997; Weinert, 2010). At the age
from about 18 months onwards, children are found to differ
in their vocabulary when considering social background,
whereas the results are rather mixed for early grammar skills
(e.g., Fernald et al., 2013; Weinert & Ebert, 2013). Addi-
tionally, early language is also found to benefit by joint
activities with the child, such as joint picture book reading
(e.g., Attig & Weinert, 2019; Bus et al., 1995). However,
though an increased amount of studies examined the impact
of educational background or socioeconomic status as well
as with joint activities on language development, to our
knowledge only a few studies considered the association
with (restricted) financial resources, such as family income
or poverty, in particular with language development.
Therefore, this article analyses the impact of (restricted)
financial resources during the first years of a child’s life. In
particular, we focus on early language skills as child lan-
guage has been shown to impact later cognitive, educa-
tional, and socio-emotional development (e.g., Ebert,
2011,2015; Rose et al., 2018; Schuth et al., 2017) and are
particularly relevant to school success and later participation
in society (e.g., OECD, 2019). Moreover, early evolving
interindividual differences at the age of three, which covary
substantially with social background, proved to be rather
stable across preschool age with highly relevant implica-
tions for school learning (Weinert & Ebert, 2013). Further,
as most studies only consider vocabulary skills, we go
beyond that and additionally include early grammar skills.
As language development already starts during the first
months of a child’s life (Siegler et al., 2014), we focus on
the impact of family income, measured as a metric variable
by net equivalence income, and poverty, operationalized as
income groups based on official income thresholds, at the
age of about seven months and its effects on later language
skills at the age of two years. We also extend previous
research findings on child poverty using a German sample.
In doing so, we not only fill the lack of research on the
consequences of poverty on children growing up in German
(Laubstein et al., 2016), but additionally expand especially
the research on child poverty by focusing on a high-income
country with a well-established social system.
Child Poverty in Germany
The study of Corak et al. (2005) pointed out that while
children had a slightly lower chance of living in poverty
1
than the general population during the 1980s and early
1990s, this changed since then to the detriment of children.
The authors link the upwards trend of child poverty with
the unification of West and East Germany and thus the
related economic adjustments as well as the downgraded
situation of children from (recently arrived) non-citizen
households. Since 2008, the proportion of children living
in poverty in Germany has remained unchanged at about
15% (Statistisches Bundesamt/ Wissenschaftszentrum
Berlin für Sozialforschung/ Bundesinstitut für Bevölker-
ungsforschung, 2021). Consequently, each 6th child in
Germanyisgrowingupandlivinginpoverty.In2016,
about 14% of all children under 12 years and about 18% of
all children between 12 and 17 years experienced poverty
(Statistisches Bundesamt/ Wissenschaftszentrum Berlin
für Sozialforschung, 2018). Thus, some children are born
into a poor family and experience the negative effects of
poverty from early on. This is especially true for children
growing up with single parents, in households with
migration background, lower educated families, and
families with a higher number of children (Autorengruppe
Bildungsberichterstattung, 2020; Familienreport, 2020;
Statistisches Bundesamt/ Wissenschaftszentrum Berlin für
Sozialforschung, 2018; Statistisches Bundesamt/ Wis-
senschaftszentrum Berlin für Sozialforschung/ Bundesin-
stitut für Bevölkerungsforschung, 2021). These statistics
suggest, that even in Germany, which is one of the richest
countries and is known for its effective social system, child
poverty is a huge problem. For example, in the interna-
tional rating on child poverty by the OECD, Germany
ranks in the intermediate range with other countries
(OECD, 2018a) without such a strong social system (e.g.,
United Kingdom, Japan or New Zealand; for a brief
overview: Esping-Andersen, 1990).
1In the German context, mostly income poverty is used to describe
poverty. Here, usually, the official German poverty threshold of less
than 60% of the net equivalence income is used, which describes a
person as “at risk of poverty”(Statistisches Bundesamt, 2006). As this
category is not used in an international context, we label families
beyond this threshold as “poor”within this paper and by that deviate
from common German labels.
Journal of Child and Family Studies (2023) 32:2254–2270 2255
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However, the effect of poverty on the development and
education of children is hardly considered in German
research, although first studies on child poverty are dated
back to the 1980s. In particular, Laubstein et al. (2016)
addressed the state of research on child poverty in Germany
in their metastudy and summarized that, so far, no sys-
tematic and regular studies on the consequence and impact
of poverty on children exist. In fact, the insufficient state of
research on child poverty in the German context did not
change since the metastudy by Laubstein et al. (2016).
Until now, there is a lack of knowledge on the effects of
poverty for children born into and growing up in poverty in
Germany. Not least, since most research on poverty is
found in the U.S. and against the background that countries
deal differently with issues of poverty, for instance with
respect to social support and health systems, the under-
standing of the effects of poverty on child development in
different countries may be especially relevant. As docu-
mented by the study of Bradbury et al. (2018) generalizing
results across societies is not warranted without explicit
testing the effects of income. On the other hand, if effects
show up across different systems this may contribute sub-
stantially to our understanding of the factors that impact
early child development.
Theoretical Background
Children are dependent on other persons and thus experi-
encing poverty is associated with the economic circum-
stances within the family (Brooks-Gunn & Duncan, 1997;
Laubstein et al., 2016). Hence, especially the economic
model (Becker, 1981; Conger & Donnellan, 2007) and the
family stress model (Conger et al., 1992) are used to
describe and explain the effect of (family) poverty on the
development of children.
The economic model (Becker, 1981), also known as
family investment model, which “is rooted in economic
principles of investment and builds on the notion that
higher-SES compared with lower-SES parents have greater
access to financial (e.g., income), social (e.g., occupational
status) and human (e.g., education) capital”(Conger &
Donnellan, 2007, p. 179). The model assumes a dependence
between available resources within families and the devel-
opment of children and adolescents, proposing that families
with a greater amount of resources have the opportunity to
invest more in the development of their children and
therefore support their academic and social success as
compared to disadvantaged families. According to the
model, possible investments are learning materials within
the household, parental stimulation, standard of living (e.g.,
housing), and residing in good quality locations (Conger &
Donnellan, 2007). For example, families with lower income
may have a lower quality of housing (e.g., space per indi-
vidual), less healthy food, less quality of childcare and
schooling, and/or live more often in low-quality neigh-
bourhoods (e.g., with greater exposure to pollution and
violent crime). Furthermore, low-income families may be
restricted in providing their children with relevant resources
such as books or educational toys (Duncan & Magnuson,
2013; OECD, 2018a,2018b) resulting in a less stimulating
home-learning environment.
The second theoretical account considers a more psy-
chological and sociological point of view by referring to
family processes (Chase-Lansdale & Pittman, 2002). In this
context, the family stress model proposed by Conger et al.
(1992) is often mentioned: It assumes that economic hard-
ship can lead to economic pressure, which affects family
functioning and individual adjustment. In particular, the
model defines low income, high debts, and negative finan-
cial events (e.g., work instability) as indicators of economic
hardship and economic pressure as unmet material needs
(e.g., adequate food), the inability to pay bills, or make ends
meet as well as cutting back on necessary expenses (e.g.,
health insurance) (Conger & Donnellan, 2007). For exam-
ple, according to the model financial strain may lead to
stressful situations for parents, to parental depression, or
other forms of psychological parental distress. Conse-
quently, financial restrictions may negatively impact family
climate and parenting behaviour (e.g., lead to a more
punitive parenting style), limit parental interactions with
their children, disrupt family processes, may lead to a less
stimulating home-learning environment, or may cause
problems between work and childcare arrangements (Duncan
& Magnuson, 2013;OECD,2018a,2018b) and therefore
impact child development negatively.
As Dearing et al. (2006) outline, the environmental
context and the experiences of children are substantially
associated with cognitive, language, social-emotional, and
neurobiological development and thus poverty might
negatively affect most areas of development. Furthermore,
as poor children additionally face higher health risks (e.g.,
Chen et al., 2002), the authors refer to the combined effects
of poverty and poor health, both becoming risk factors for
developmental problems themselves. Moreover, especially
studies from the U.S. highlight that poverty seems to be
more harmful during early childhood than during later life
(Brooks-Gunn & Duncan, 1997; Dearing et al., 2006;
Duncan et al., 2012). However, relatively few studies focus
on the impact of poverty during a child’sfirst years (for a
summary see Barajas et al., 2008; Schoon et al., 2010).
Although studies focused on poverty experienced in early
childhood, these effects are mostly examined on outcomes
later in life such as achievement, health, behaviour, or
earnings in adulthood (e.g., Duncan & Brooks-Gunn, 1997;
Holzer et al., 1997). This also traces back to the fact that
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longitudinal studies most often address adolescents or
young adults as participants and thus cannot analyse the
effect of poverty during early childhood (e.g., see overview
by Brooks-Gunn et al., 2000).
Empirical Results on the Impact of Poverty
on Children’s Language Skills
Various studies have shown the association of social
background with language development starting at the age
of 18 months onwards (e.g., Fernald et al., 2013; Hart &
Risley, 1999; Weinert & Ebert, 2013). Children from
families with low socioeconomic status are found to show
lower levels of language skills than children from families
with a higher socioeconomic status (e.g., Hoff, 2006,2013;
Law et al., 2019). Here, especially vocabulary is found to be
associated with family background. One of the most cited
studies by Hart and Risley (1995) on social background and
language development points out that children growing up
in families with a low socioeconomic status have smaller
vocabularies compared to children from families with a
higher socioeconomic status. Though the results on the
effect of social background on language development are
quite robust for children at the age of three and above,
studies considering children under 3 years show contra-
dictory findings. For example, the study by Fernald et al.
(2013) reports differences in vocabulary related to social
background for children at 18 months and two years,
whereas Peyre et al. (2014) found an association between
parental education and language skills at the age of three but
not for vocabulary at the age of two. Additionally, Bus et al.
(1995) described in their meta-analysis on the frequencies
of book reading to preschoolers, that parent-preschool
reading is related to such outcomes as language growth. The
findings by Attig and Weinert (2019) pointed out that early
joint picture book reading predicts language skills already in
2-year-old children.
However, though the effect of social background and
joint activities on language development is well docu-
mented, comparatively little is known about the effect of
(restricted) financial resources, measured for example by
family income or poverty, on (early) language skills. Stu-
dies that focused on the effect of family income on lan-
guage processes early in life predominantly showed a
negative effect of low family income on early language
(e.g., Huttenlocher et al., 2010;McKeanetal.,2015;
Linberg & Wenz, 2017; Peyre et al., 2014;Weinert&
Ebert, 2013). Although family income is found to be
associated with early language outcomes, using a con-
tinuous variable of family income may underestimate the
adverse impact of a specific situation as, for example, the
experience of poverty can be, as the whole range of family
income does not consider non-linear associations. Fur-
thermore, although some studies used categorical variables
for family income, these vary between studies and, again,
do not measure the effect of specific, life-impacting con-
stellations as the experience of poverty.
Studies that focused directly on the effect of poverty on
early language outcomes report lower language skills for
poor children: The studies by Smith et al. (1997), Berger
et al. (2009) and Biedinger (2009) found lower vocabulary
in 3-to-5-year old children living in poverty and Law et al.
(2017) described a difference of 4.5 points in naming
vocabulary for poor versus non-poor children at the age of
11. Further, the study by Groos and Jehles (2015) reported
negative effects of poverty on language for children in
primary school age and found that daycare institutions with
a higher proportion of poor families affected children’s
German language skills negatively.
Furthermore, as especially language skills are relevant
for future cognitive, social, and educational development
(Weinert, 2022), it is an important theoretical question
whether different facets of language are affected to the same
extent. In fact, it has been argued that vocabulary is parti-
cularly prone to environmental stimulation while early
grammar is acquired even under rather restricted environ-
mental conditions (e.g., Vasilyeva & Waterfall, 2011)at
least in the very early phases of grammar acquisition. In
particular, nativist theories of language acquisition (e.g.,
Chomsky, 1988; Fodor, 1983; van der Lely & Pinker, 2014)
argue in favor of a rather robust acquisition of early
grammar while neuroconstructivist, usage-based, or con-
nectionist accounts emphasize the importance of the context
of language use (e.g., Elman et al., 1996; Karmiloff-Smith,
2015; Mareschal et al., 2007; Tomasello, 2003). Thus,
exploring the effects of (restricted) financial resources by
differentiating between the effect of family income and the
effect of poverty on vocabulary and grammar separately
also contributes to the discussion on language acquisition in
the context of social background.
The Present Study
To add to previous research, the present study focuses on
the effect of (restricted) financial resources on early child
language in Germany. Since financial resources can be
operationalized in different ways, we decided for two
operationalizations: First, we use family income as
measured by the metric variable of net equivalence income
and second, we defined income groups based on official
income thresholds to consider special financial situations as
poverty. By doing so, we are able to disentangle if different
facets of income can cause different results on early child
development. For early child language we consider both
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early vocabulary as well as early grammar skills and
explicitly include relevant predictive variables that have
been shown to impact children’s language skills such as
maternal education (e.g., Hart & Risley, 1995,1999,2003)
and joint book reading (Bus et al., 1995). Additionally, we
control for further variables, which are associated with
language skills, such as children’s gender and age (e.g.,
Zubrick et al., 2007) as well as number of siblings (e.g.,
Karwath et al., 2014). As mentioned above, especially
children from lower-educated families, with a higher num-
ber of siblings and growing up in a single-parent household
are found to experience poverty more often (e.g., Auto-
rengruppe Bildungsberichterstattung, 2020; Familienreport,
2020; Statistisches Bundesamt/ Wissenschaftszentrum
Berlin für Sozialforschung, 2018; Statistisches Bundesamt/
Wissenschaftszentrum Berlin für Sozialforschung/ Bunde-
sinstitut für Bevölkerungsforschung, 2021). As we are
interested in the effects of income in the early phases of a
child’s life, we additionally consider parental leave, which
in Germany normally is taken in the first year of a child’s
life and mostly reduces the amount of available income
(Federal Ministry for Family Affairs, Senior Citizens,
Women and Youth, 2021).
As for Germany studies addressing especially the effect
of poverty on children are still missing (Laubstein et al.,
2016) and there are hardly any studies (for exceptions see:
Berger et al., 2009; Biedinger, 2009; Law et al., 2017;
Smith et al., 1997) addressing the effect of poverty on early
language outcomes, this paper concentrates on the effect of
(restricted) financial resources with a special focus on
experiencing poverty in the early phases of a child’s life and
its effect on language in 2-year-old children. For this pur-
pose, the first research question of interest is: Do (restricted)
financial resources, in particular family income and living in
poverty, impact early language skills in 2-year-old children?
As vocabulary and grammar may be influenced by dif-
ferent factors (e.g., Vasilyeva & Waterfall, 2011) in the
early phases of language acquisition, the second research
question is: Are there different effects of family income and
poverty when considering vocabulary and grammar in
2-year-old children?
Method
Sample
The presented analyses are based on the Newborn Cohort
Study of the German National Educational Panel Study
(NEPS; Blossfeld et al., 2011;https://doi.org/10.5157/
NEPS:SC1:5.0.0). This study uses a representatively
drawn sample of infants born in Germany in 2012 with their
parents who were followed within a longitudinal design
from seven months onwards (Aßmann et al., 2015; Weinert
et al., 2016; Zinn et al., 2018). In the first wave, 3431
families agreed to take part in the longitudinal study.
To analyse the impact of families’social background on
early language skills, information on families’income and
education in the first year of life (6 to 8 months) were used
as well as measures of early child language at about 25 to
27 months. All assessments were conducted at the families’
homes by trained interviewers.
We limited our analyses to children growing up mono-
lingually (majority language German) for whom valid
information on the outcome measure (vocabulary and
grammar) at age 2 is available. Therefore, our analyses are
based on a sample of N=1782 cases.
We excluded families speaking other languages than
the majority language German at home due to several
reasons: Firstly, the data set does not include early child
language measures on other languages than the majority
language. Furthermore, if children grow up bi- or multi-
lingually, the results for these children are not comparable
to those growing up monolingually as we cannot consider
the language status in all languages. Not least, for this
group more detailed analyses would be necessary on
language input in the various languages spoken at home as
this is a major source of variance and may differ widely
across families.
Variables
Early child language at age 2
Children’s language skills were assessed via an extensive
language check-list (ELFRA-2; Grimm & Doil, 2006)filled
in by the parent (mostly the mother) in wave 3 when the
children were between 25 to 27 months old. The ELFRA-2
is a validated standardized and widely used instrument to
assess children’s early language skills (the instrument has
been constructed comparable to the MacArthur Commu-
nicative Development Inventories; CDI; Fenson et al.,
1993). It includes the assessment of productive vocabulary
(260 items), syntax (26 items), and morphology (11 items).
The instrument was developed for the assessment of early
language skills in German (Grimm & Doil, 2006) with
substantial and significant correlations to direct measures of
children’s language competence via language tests (corre-
lations between 0.45 and 0.85 e.g., with the SETK-2 or
RDLS-III; see Sachse et al., 2007a). Furthermore, the
ELFRA-2 shows good prognostic validity for later language
skills/language disorders (between 59% and 64% using
the RATZ-Index; Sachse et al., 2007b).
As we differentiate between vocabulary and grammar as
dependent variables in our analyses, we used z-standardized-
sum-scores for vocabulary and grammar respectively, the
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latter being averaged over syntax and morphology (inter-
correlation syntax-morphology: r=0.86, p<0.001).
Financial resources
To evaluate the effect of (restricted) financial resources, we
draw on the differentiation between family income and
income groups. We used income information based on the
self-report of the respondents on their family net income in
wave 1. First, for family income we used the net equiva-
lence income, which was generated by the modified
equivalence scale of the OECD, which assigns the value of
1 to the household head, the value of 0.5 to each other
person at age 14 or above, and the value of 0.3 to each child
under the age of 14 (OECD, 2013). Second, we divided the
sample into three income groups using the official national
thresholds for the year 2012 (Statistische Ämter 2020): (1)
Families with less than 60% of the median equivalence
income are defined as “poor”(for a brief overview: Statis-
tisches Bundesamt, 2006), (2) families having more than
150% of the median equivalence income are defined as
belonging to the “high income”group (for a brief overview:
Grabka & Frick, 2008; Lauterbach & Ströing, 2009), and
(3) families in between these two thresholds are defined as
having an “average income”(reference category).
Further indicators of family background
Mothers’education at wave 1 is considered as an additional
indicator of social background.
Mothers’education is grouped into a three-level scale (see
Linberg et al., 2019): (a) low education (no qualification to
intermediate secondary education without vocational qualifi-
cation), (b) intermediate education (intermediate secondary
education with vocational qualification to higher education
institution with vocational qualification, reference category),
and (c) high education (degree from university of applied
science to higher tertiary education: the completion of a tra-
ditional, academically orientated university education).
Table 1shows the correlations for maternal education with
family income and income groups. Maternal education and
both indicators of financial resources show a significant cor-
relation of medium size. Thus, maternal education and families’
income are measuring different aspects of social background
and are not replaceable with each other, suggesting to consider
these indicators independently from each other.
Home-learning environment
Joint picture book reading was considered as an indicator of
the home-learning environment. Parents were asked to
indicate the frequency of joint picture book reading with the
child on a five-level scale (1 =never, 5 =several times a
day) in wave 1.
Control variables
We also included two additional social background indi-
cators from wave 1 in our analyses: The variable single
parent (0 =no; 1 =yes) defined by a partner living in the
household as well as the self-reported information, if at least
one parent is in parental leave (0 =no; 1 =yes). Further-
more, we included the child’s age and gender (dichotomous
variable; 0 =boy, 1 =girl) as well as the number of sib-
lings in the household at wave 3 as further control variables.
Table 2shows the descriptive characteristics of the
sample.
Analytic strategy
To analyse the effect of financial resources on early lan-
guage competencies, we apply two analytic strategies:
Multiple regression analyses as well as path analyses using
structural equation modeling (SEM).
2
First, we use stepwise multiple regression analyses to test
for the effect of (restricted) financial resources on child
language while taking into account the other independent
variables (e.g., educational background). In our multiple
regression analyses, we use family income and income groups
(i.e., poverty and high income compared to average income)
on child vocabulary and grammar separately. Considering
first the analysis for family income, Models 1 to 4 address the
analysis for vocabulary and Models 5 to 8 for grammar.
Model 1 describes unconditional gaps by computing the
association between family income and child vocabulary
without controlling for other variables. Model 2 adds the
control variables gender, age, the number of siblings, single
parent, and parental leave. In Model 3, maternal education
was added to test whether the effect of family income
remains after controlling for maternal education and thus
whether family income has an additional independent effect
on vocabulary over and above maternal educational
Table 1 Correlations between maternal education and family income
as well as income groups (Spearman)
Family income Income groups
Maternal education 0.39*** 0.35***
The information is based on 15 multiple imputed datasets
Significance level: ***p< 0.001
Source: Newborn Cohort Study of the National Educational Panel
Study (https://doi.org/10.5157/NEPS:SC1:5.0.0)
2We decided to use both analyses in our study, because central
structure equation models are saturated, which is not the case for
regression models.
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background. The last model, Model 4, analyses the effect of
family income after additionally considering the joint
activity of picture book reading as an indicator of the home-
learning environment that proved to be particularly relevant
to language acquisition (Attig & Weinert, 2019; Bus et al.,
1995). The identical model structure is conducted for the
analyses for child grammar (Model 5 to 8) and the models
with income groups for child vocabulary and grammar.
Note, that theoretical assumptions suggest differential rela-
tionships of the family background indicators with early
child vocabulary and grammar.
Second, as child vocabulary and grammar are hypothe-
sised to be correlated, we additionally conducted an inte-
grated path analysis using a maximum likelihood estimator
to test for the assumed paths while considering all predictors
and control variables analysed in the stepwise regressions
as well as both measures of child language within one
model. Intercorrelations between variables, and particularly
between the two language measures, were included in the
models. Regarding the exogenous variables, we did not
consider correlations between the child’s age and gender
with family income or income groups, education, joint
picture book reading, number of siblings, single parent, and
parental leave as a relationship between these variables
cannot be assumed due to theoretical arguments. By not
considering all correlations for the exogenous variable, we
were able to estimate model fit indices (e.g., Chi-square,
RMSEA) and thus report the fit to our data. Including all
correlations between the exogenous variables, on the other
hand, resulted in saturated models. However, the presented
results of the path analyses are not affected, regardless of
whether all or only the correlations we allowed between
the exogenous variables are taken into account. Thus, we
are able to reanalyse the result of the regression analysis,
where a correlation between the two language measures is
not possible to consider. We concentrate on cross checking
the last model of vocabulary (Model 4) as well as grammar
(Model 8) for both family income and income groups. By
doing so, we can compare if the analyses of the multiple
regression models are in line with the path analyses results.
To address missing data, the multiple chained equations
(MICE package in R) were conducted to impute 15 datasets
(van Buuren, 2018) for all predictor variables for the
descriptives and the multiple regression models. All repor-
ted estimates are combined across the 15 imputed data sets
using Stata 16 in accordance with Rubin (1987). For the
path analyses we addressed missing data using full-
information maximum likelihood (FIML)
3
in Stata 16
(Acock, 2013). Table 2shows the amount of missing data
for each predictor variable. Only four variables of our
analysed sample show missing values, ranging from about
16% for net equivalence income and income groups, to less
than 1% for both maternal education and parental leave. All
variables are based on self-reports of the respondent per-
sons, the majority of whom was the mother
4
Hence, missing
values are due to non-response.
Table 2 Descriptive statistics
Mean/
Percent
SD Min Max Imputed
Language scores
(standardized)
Vocabulary 0.16 0.93 −2.12 1.81 0.00%
Grammar 0.14 0.92 −1.68 1.75 0.00%
Social
background
Family income 1,797.77 858.53 185.76 14,285.71 15.82%
Income groups 15.82%
Poor 8.11%
Average income 66.35%
High income 25.54%
Maternal
education
0.56%
Low 7.46%
Intermediate 46.74%
High 45.79%
Home-learning
environment
Picture book
reading
3.09 1.46 1 5 0.00%
Control variables
Child’s gender 0.00%
Girls 48.32%
Boys 51.68%
Child’s age 26.45 1.04 22.22 32.12 0.00%
Number of
siblings
0.73 0.80 0 6 0.00%
Single parent 0.00%
Yes 5.67%
No 94.33%
Parental leave 0.11%
Yes 85.13%
No 14.87%
N=1782. The information is based on 15 multiple imputation datasets
SD Standard Deviation. Min Minimum. Max Maximum
Source: Newborn Cohort Study of the National Educational Panel
Study (https://doi.org/10.5157/NEPS:SC1:5.0.0)
3No noteworthy differences in the results are found using the multiple
imputed estimation and the full-information maximum likelihood
(FIML) estimation.
4The respondent person was usually the mother, as mothers are best
able to provide information about pregnancy and the birth of the child.
In wave 1 99.66% and in wave 3 98.71% of the interviews were given
by the mother.
2260 Journal of Child and Family Studies (2023) 32:2254–2270
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Results
In the following, we report the unconditioned and condi-
tioned regression results as well as the results for the path
analyses separately for family income and income groups.
The Impact of Family Income on Early Child
Vocabulary and Grammar
Table 3presents the linear regression results for vocabulary
and grammar considering family income. Model 1 to Model
4 show the effect of family income on vocabulary and Model 5
to Model 8 the effect of family income on grammar.
Starting with vocabulary, Model 1 shows the uncondi-
tioned impact of the average family net equivalence income
on early vocabulary having a significant positive effect
(β=0.14, p< 0.001). Additionally, we see that only 2% of
the variance in a child’s vocabulary is explained by family
income. Although the family income effect decreases when
considering control variables (Model 2), maternal education
(Model 3), and joint picture book reading (Model 4) the
positive effect of family income remains significant (Model
4: β=0.05, p< 0.01). Especially Model 3 shows that
additionally controlling for maternal education not only
decreases the effect of family income, but also that maternal
education and family income both show significant effects
on early vocabulary at the age of two. When joint picture
book reading is additionally included in the model, the
effect of maternal education stays stable, while the effect of
family income is slightly reduced. Furthermore, joint pic-
ture book reading at the age of seven months is associated
with vocabulary size in 2-year-old children.
Table 3 Linear regression on the
effect of family income on
children’s vocabulary and
grammar
Vocabulary Grammar
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
ββ β β ββ β β
SES
Family income 0.14*** 0.11*** 0.06** 0.05** 0.13*** 0.09*** 0.05*0.04*
(0.03) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02)
Maternal education
(ref. intermediate)
Low −0.11***
−0.11***
−0.11***
−0.11***
(0.03) (0.03) (0.02) (0.02)
High 0.08*** 0.08** 0.07*** 0.07**
(0.02) (0.02) (0.02) (0.02)
Home-learning
environment
Picture book reading 0.12*** 0.12***
(0.02) (0.02)
Control variables
Child’s gender
(ref. boy)
Girls 0.13*** 0.13*** 0.12*** 0.14*** 0.14*** 0.14***
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02)
Child’s age 0.19*** 0.18*** 0.18*** 0.21*** 0.21*** 0.20***
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02)
Number of siblings −0.13*** −0.12*** −0.11*** −0.12*** −0.11*** −0.10***
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02)
Single parent (ref. no)
Yes −0.06** −0.04 −0.04 −0.06** −0.04 −0.04
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02)
Parental leave (ref. no)
Yes 0.02 0.01 0.01 0.01 −0.00 0.00
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02)
Adjusted R20.02*** 0.11*** 0.13*** 0.15*** 0.02*** 0.11*** 0.14*** 0.15***
ΔR² –0.09*** 0.02*** 0.02*** –0.09*** 0.01*** 0.01**
N=1782. The information is based on 15 multiple imputation datasets
βStandardized beta coefficients. R2Coefficient of determination. ΔR² Increase of the coefficient of
determination. Standard errors in parentheses
Significant levels are indicated as:*p< 0.05, **p< 0.01, ***p< 0.001
Source: Newborn Cohort Study of the National Educational Panel Study (https://doi.org/10.5157/NEPS:
SC1:5.0.0)
Journal of Child and Family Studies (2023) 32:2254–2270 2261
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Considering early grammar (Model 5 to Model 8), a
similar pattern of results is found: Family income (Model
5) shows an overall positive unconditional effect on
grammar at the age of two years (Model 1: β=0.13,
p< 0.001), explaining again 2% of the variance. The sig-
nificant positive effect of family income on early grammar
remains significant when controlling for all other relevant
factors (Model 8: β=0.04, p< 0.01). As already shown
for vocabulary, controlling for maternal education reduces
the effect of family income, which still remains significant
in addition to maternal education (Model 7). Although
joint picture book reading is found to decrease the effect of
family income slightly, again it still remains significant
(Model 8). Additionally and in line with the results for
vocabulary, joint picture book reading at the age of seven
months is found to have a significant positive effect on
early grammar skills.
In the next step, we considered child vocabulary and
grammar simultaneously as well as all other analysed
variables within a path analysis (Fig. 1). Using the path
analysis, we consider the correlation between early voca-
bulary and grammar, which is 0.58 (p< 0.001). In line with
the findings from the regression model, we again find an
effect of maternal education and joint picture book reading
on both language indicators. Furthermore, when consider-
ing all relevant variables simultaneously family income is
not related to child vocabulary (β=0.04, n.s.) or grammar
(β=0.03, n.s.) anymore. This is in contrast to the regres-
sion model, in which we found significant positive effects
of family income on vocabulary (β=0.05, p< 0.01) and
grammar (β=0.04, p< 0.05). The path analysis demon-
strates a good fit to the data (χ2(15) =20.77, n.s.;
RMSEA =0.015; CFI =0.998; TLI =0.997). The
explained variance for the path analysis is 14% for voca-
bulary and 15% for grammar and thus similar to the
regression models (15%). To sum up, our path analysis
underlines the findings for the effects of maternal education
and joint picture book reading on early vocabulary and
grammar from the multiple regression analyses, whereas we
do not find a significant (additional) effect of family income
on vocabulary and grammar in 2-year-old children, at least
when considering other relevant variables such as joint
picture book reading and the correlation between vocabu-
lary and grammar.
The impact of belonging to different income groups
on early child vocabulary and grammar
Splitting income into groups defined by income thresholds,
the picture of the effects of income becomes even more
pronounced: In Model 1 (Table 4), both poverty and high
income show a significant unconditional effect on vocabu-
lary. Our analyses indicate a negative unconditional effect
for children growing up in poverty (β=−0.15, p< 0.001),
Fig. 1 Path analysis with significant standardized paths on child
vocabulary and grammar (considering family income and frequency of
joint book reading). Intercorrelations between the variables vocabu-
lary, grammar, maternal education, joint picture book reading, number
of siblings, single parent and parental leave are considered. Source:
Newborn Cohort Study of the National Educational Panel Study
(https://doi.org/10.5157/NEPS:SC1:5.0.0)
2262 Journal of Child and Family Studies (2023) 32:2254–2270
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
whereas there is a positive unconditional effect for children
growing up in a high income family (β=0.10, p< 0.001).
Compared to family income, the amount of explained var-
iance by income groups is slightly higher (4%). Considering
all influential factors included in the analysis (Model 1 to
Model 4) only the negative effect of poverty remains:
Children that experienced poverty at the age of seven
months showed a less advanced vocabulary at the age of two
years (Model 4: β=−0.10, p< 0.001), whereas children
living in families with a higher net income did not benefit
Table 4 Linear regression on the
effect of income groups on
children’s vocabulary and
grammar
Vocabulary Grammar
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
ββββββββ
SES
Income groups (ref.
average income)
Poor −0.15*** −0.14*** −0.10*** −0.10** −0.12*** −0.10*** −0.06*−0.06*
(0.03) (0.03) (0.03) (0.03) (0.02) (0.02) (0.02) (0.02)
High income 0.10*** 0.07*** 0.04*0.04 0.09*** 0.06** 0.03 0.03
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02)
Maternal education
(ref. intermediate)
Low −0.09*** −0.08** −0.10*** −0.09***
(0.03) (0.03) (0.02) (0.02)
High 0.08*** 0.08*** 0.07** 0.07**
(0.02) (0.02) (0.02) (0.02)
Home-learning
environment
Picture book
reading
0.13*** 0.12***
(0.02) (0.02)
Control variables
Child’s gender
(ref. boy)
Girls 0.13*** 0.12*** 0.12*** 0.14*** 0.13*** 0.13***
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02)
Child’s age 0.19*** 0.19*** 0.18*** 0.21*** 0.21*** 0.20***
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02)
Number of siblings −0.12*** −0.12*** −0.11*** −0.11*** −0.11*** −0.10***
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02)
Single parent
(ref. no)
Yes −0.03 −0.02 −0.01 −0.04 −0.03 −0.03
(0.03) (0.03) (0.02) (0.02) (0.02) (0.02)
Parental leave
(ref. no)
Yes 0.02 0.01 0.01 0.00 −0.00 −0.00
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02)
Adjusted R20.04*** 0.12*** 0.14*** 0.15*** 0.03*** 0.12*** 0.14*** 0.15***
ΔR² –0.07*** 0.02*** 0.01*** –0.09*** 0.02*** 0.01**
N=1782. The information is based on 15 multiple imputation datasets
βStandardized beta coefficients. R2Coefficient of determination. ΔR² Increase of the coefficient of
determination. Standard errors in parentheses
Significant levels are indicated as: *p< 0.05, **p< 0.01, ***p< 0.001
Source: Newborn Cohort Study of the National Educational Panel Study (https://doi.org/10.5157/NEPS:
SC1:5.0.0)
Journal of Child and Family Studies (2023) 32:2254–2270 2263
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
compared to the average group (Model 4: β=0.04, n.s.).
However, controlling for maternal education decreases the
effect of income groups; yet, again both maternal education
and income groups show a significant effect on early
vocabulary at the age of two (Model 3). Considering addi-
tionally joint picture book reading (Model 4), the negative
effect of experiencing poverty at the age of seven months on
vocabulary at the age of two remains stable, while children
growing up in a high income family are not found to have a
significant advantage in early vocabulary. Additionally,
similar to the family income, our analysis indicates a sig-
nificant positive effect of joint picture book reading.
Considering early grammar (Table 4) a similar pattern of
results is found (Model 5 to Modell 8): Children that
experienced poverty in their first year of life show com-
paratively restricted grammar skills (Model 5: β=−0.12,
p< 0.001), whereas children growing up in a high income
family are found to have more advanced grammar skills
(Model 5: β=0.09, p< 0.001). This unconditional effect
explains 3% of the variance. Considering all predictive
variables (Model 6 to Model 8) the picture for early
grammar skills is similar to early vocabulary: Not only
maternal education and joint picture book reading are found
to have a significant impact on grammar at the age of two
years; our analyses additionally indicate a negative effect of
experiencing poverty during the first months of a child’s life
(Model 8: β=−0.06, p< 0.05), while there is no significant
effect for children of growing up in a high income family
(Model 8: β=−0.03, n.s.).
As for family income, we conducted an additional ana-
lysis using a path analysis for income groups. In contrast to
the analysis with family income, our path analysis confirms
the results in the regression models. Considering the effect
of maternal education, joint picture book reading as well as
all control variables (Fig. 2), we found no significant effect
of high income on vocabulary (β=0.02, n.s.) and grammar
(β=0.01, n.s.). However, the significant negative effect of
poverty on vocabulary (β=−0.11, p< 0.001) and grammar
(β=−0.06, p< 0.05) using a path analysis is similar to the
results in the regression analysis, showing that even the
consideration of all relevant variables as well as the corre-
lation of vocabulary and grammar (r=0.58, p< 0.001) does
not vanish the detrimental effect of poverty on early lan-
guage development. Additionally, we find an effect of
maternal education as well as of joint picture book reading
for both language measures. Again, the path analysis
demonstrates a good fit to the data (χ2(17) =26.26, n.s.;
RMSEA =0.017; CFI =0.996; TLI =0.995). The
explained variance for the path analysis is the same (15%)
as shown for the regression models (15%). In sum, our path
analysis underlines the findings from the multiple regression
analysis in Table 4on the effect of income groups, and here
especially the adverse effect of poverty, on early child
vocabulary and grammar.
Fig. 2 Path analysis with significant standardized paths on child
vocabulary and grammar (considering income groups and frequency of
joint book reading). Intercorrelations between the variables vocabu-
lary, grammar, maternal education, joint picture book reading, number
of siblings, single parent and parental leave are considered. Source:
Newborn Cohort Study of the National Educational Panel Study
(https://doi.org/10.5157/NEPS:SC1:5.0.0)
2264 Journal of Child and Family Studies (2023) 32:2254–2270
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Discussion
Child poverty is not only of high relevance across countries,
it is also shown in previous (international) studies to affect
children and their later life negatively (e.g., Brooks-Gunn &
Duncan, 1997; Barajas et al., 2008). However, as most
studies on poverty consider outcomes later in life (e.g.,
Duncan & Brooks-Grunn, 1997; Holzer et al., 1997),
comparatively less is known about outcomes during the first
years, although according to the economic model (Becker,
1981; Conger & Donnellan, 2007) and the family stress
model (Conger et al., 1992) children are assumed to be
affected in their early development growing up in poverty.
Not only are these children assumed to be provided with
less family resources (e.g., books or educational toys), they
are additionally assumed to grow up in a family environ-
ment experiencing economic pressure with negative effects
on parenting behaviour (e.g., punitive parenting style),
parental interaction with the child, or family process, thus
being disadvantageous for early child development (Duncan
& Magnuson, 2013; OECD, 2018a,2018b). In line with this
argument, various studies documented disparities in early
development according to the educational background of
the children starting at about 18 months onwards (e.g.,
Fernald et al., 2013; Hart & Risley, 1995,1999,2003;
Weinert & Ebert, 2013). Furthermore, especially German
research lacks to consider the effect of poverty on children
(Laubstein et al., 2016).
Thus, our study investigated the effect of (restricted)
financial resources during the first year of life considering
additionally the effect of maternal education and joint pic-
ture book reading on the language skills of 2-year-old
children. As financial resources can be operationalized in
different ways, we decided to use a metric form of income,
namely family income measured by net equivalence
income, as well as income groups defined by official
income thresholds, with which we are able to consider
special income situations such as poverty or high income.
Furthermore, as it has been argued that vocabulary is more
affected by social background than early grammar (e.g.,
Vasilyeva & Waterfall, 2011), we examined if family
income and poverty affect both language domains similarly.
To investigate the effect of (restricted) financial resources
on early language, we applied the following analysis strat-
egy: First, we conducted multiple regression models. Using
this stepwise approach, we were able to demonstrate the
extent to which the effect of family income and income
groups is still observable when other relevant variables for
early language development are considered. As we used
early vocabulary and grammar for language development
and as both language measures are correlated, we addi-
tionally considered path analyses to see if the results of the
regression analyses can be confirmed. The present paper,
therefore, addressed two questions: (1) whether (restricted)
financial resources, in particular family income and living in
poverty, impact early language skills in 2-year-old children
and (2) whether there are different effects of family income
and poverty when considering vocabulary and grammar in
2-year-old children.
Overall, our analyses go in line with current studies on
the effects of educational background (Hart & Risley,
1995,1999,2003; Linberg et al., 2019; Weinert & Ebert,
2013) and joint picture book reading (Attig & Weinert,
2019; Bus et al., 1995) on language development: Children
who, in the early phases of life, grow up in a family with a
higher educated mother and who are more often exposed to
joint picture book reading show more advanced language
skills at the age of two years. Here, maternal education as
well as joint picture book reading influences both language
domains, early vocabulary and early grammar. However,
our analyses additionally point out, that especially poverty
should be considered as an additional indicator of social
background on early child development. These results are
getting even more clear when considering path analyses.
Though our regression analyses show, that both family
income and poverty predicted later vocabulary and grammar
(even when considering many other influential variables),
this cannot be confirmed by the path analyses. In these
models, only the adverse effect of poverty remains sig-
nificant for both language measurements. Additionally, as
we present standardized coefficients, our results show the
high relevance of growing up in poverty on early language
development: Comparing the standardized coefficients of
the path analysis for joint picture book reading (β=0.13),
low (β=−0.08) and high education (β=0.08) as well as
poverty (β=−0.11), we see that joint picture book reading
and poverty have slightly higher effects on early vocabulary
than maternal education. For grammar, poverty (β=−0.06)
has the lowest effect, followed by high (β=−0.07) and low
education (β=0.09) as well as joint picture book reading
(β=0.12).
Our results indicate that early language skills of children
are associated with (restricted) financial resources. How-
ever, our study goes beyond the general effect of income
and refers to the special detrimental situation of growing up
in a poverty shaped household: Though we did not find a
significant effect of family income especially in the path
analysis when simultaneously considering maternal educa-
tion and joint picture book reading and allowing the cor-
relation of vocabulary and grammar, our results indicate that
children growing up in families with a higher income do not
seem to benefit in their early language skills, whereas
children experiencing poverty at the age of seven months
are found to have lower language skills being two years old.
These results are evident for both analysed language
domains: early vocabulary and early grammar skills.
Journal of Child and Family Studies (2023) 32:2254–2270 2265
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Taken as a whole, our analyses show the importance of
considering the effect of (restricted) financial resources and
especially poverty on early development, even when other
relevant variables such as maternal education and joint
picture book reading are considered. Furthermore, our
results indicate, that the explained variance for family
income and income groups is ranging between 2% and 4%.
Though the explained variance is relatively small, it shows
that early development is associated with (restricted)
financial resources. Moreover, comparing family income
and income groups, our analyses suggest, that using a
continuous variable of family income indeed underestimates
the adverse impact of poverty. In our analyses this is true for
both early vocabulary and early grammar skills. This result
not only suggests, that there is a poverty effect on early
language skills, but also points to the relevance that poverty
should be considered in future research on early childhood
additionally to other indicators of social background. This is
especially important, as our results suggest, that poverty
may affect different facets of language equally.
Additionally, our results go in line with existing national
(Biedinger, 2009; Groos & Jehles, 2015) and international
studies (Berger et al., 2009: Law et al., 2017; Smith et al.,
1997), by showing that even in Germany, being one of the
richest countries in the world and having a strong social
system, experiencing poverty in early childhood is asso-
ciated with language development. This leads to the
assumption that both theories, the family stress model
(Conger et al., 1992) and the economic model (Becker,
1981), which are used in international studies to explain the
effect of poverty on child development, may also be applied
to the German context. In specific, low family income or
poverty in German families may also affect family func-
tioning and individual adjustment (e.g., more punitive par-
enting style, limited parental interaction with child) as well
as available resources within the family (e.g., quality of
housing, books, or educational toys) thus having an impact
on child development from early on (Dearing et al., 2006).
Though our analyses found an association between poverty
and early language skill even when controlling for relevant
indicators such as maternal education and joint picture book
reading, future research in the German context should
consider the underlying mechanisms in more detail. This is
of relevance to ensure that the theoretical assumptions of the
family stress model (Conger et al., 1992) and the economic
model (Becker, 1981) can actually be applied to the German
context as well.
Our study has some limitations that should be taken into
account when interpreting our results. First of all, as the
assessments in the Newborn Cohort Study of the NEPS
measures language skills mainly in the majority language
German we could not consider other language(s) spoken
within the families. Thus, we limited our analyses to
children growing up in families only speaking the German
language. Hence, our results mirror the effect of financial
deprivation for this specific group without considering
children growing up with other language(s). Furthermore,
not considering children growing up bi- or multilingual, we
do not consider children with migration background in our
analyses, which are found to experience more often poverty
(Autorengruppe Bildungsbericht, 2018). However, con-
sidering only monolingual children, we underestimate
the amount of poor children in the analysed subsample.
Though currently about 15% children in Germany are
living in poverty (Statistisches Bundesamt/ Wissenschafts-
zentrum Berlin für Sozialforschung, 2018), the amount of
poor children in our analysed subsample is about 8%.
Considering that the amount of poor children for the rea-
lized sample of the Newborn Cohort Study in wave 1 is
about 15%, the small amount of children living in poverty
is traced back to not considering children with migration
background.
Moreover, as our analysed subsample has a small amount
of families living in poverty, we were not able to consider
longer periods of poverty over waves. To be more specific,
starting with a smaller amount of 8% of poor families in
wave 1, the proportion of these families still being poor in
wave 3, where we considered early language skills,
decreased by about a half, leading to an even smaller sample
size. However, though some (international) studies reported
negative effects of long lasting poverty on child develop-
ment (e.g., Brooks-Gunn & Duncan, 1997; Duncan et al.,
1998; Korenman et al., 1995; Schoon et al., 2012), future
research should not only consider the timing of poverty but
additionally the duration of children living in poverty from
early on. This is especially relevant for Germany, where we
have no studies considering the duration of child poverty
and its consequences.
Furthermore, all information used in our analyses is
given by the parents. This comes with some disadvantages:
For example, using self-reports of the respondents on their
families’net income is associated with a higher refusal rate,
which is due to the fact that questions about income are
shown to be very sensitive. Refusing or not answering
correctly this kind of question goes along with higher
missing rates, as we see in our data: the highest missing rate
relates to the information on income with 16%, whereas we
only have 1% for educational background and parental
leave. Furthermore, as described above, the used extensive
language check-list (ELFRA-2) is filled by the parents and
therefore may be associated with a bias. Klaiber (2007)
examined parents’response behaviour filling in the
ELFRA-2 and found, for example, that the instructions in
the ELFRA-2 leave parents some room for interpretation
when to tick the words (i.e., if the child uses the word
regularly or if the child has used the world only once) and
2266 Journal of Child and Family Studies (2023) 32:2254–2270
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
may lead to possible different ticking behaviour among
parents. However, Klaiber (2007) cannot confirm a different
ticking behaviour of parents according to their educational
background.
Finally, in our study we only considered income poverty.
Though Butterwege et al. (2008) argued, that income is the
main feature of poverty, different kinds of definitions of
poverty exist. However, especially international studies,
such as the study of Schenck-Fontaine and Panico (2019),
show that although income poverty is an important measure
it may not capture economic hardship on children suffi-
ciently. Though we considered an official and commonly
used threshold for poverty in Germany (Statistisches
Bundesamt, 2006), we only draw some light on the effects
of one dimension of poverty on child outcomes. To
understand the effect of poverty in a comprehensive way,
considering other dimensions of poverty (e.g., material
deprivation) is of high relevance.
Overall, our findings show that more research on the
consequences of poverty for early childhood is of high
relevance. Here, the timing of child poverty is of high
interest, as our results show that even the experience of
poverty at the age of seven months and so during the first
years of a child’s life are found to affect later language
development negatively. Additionally, as even in a rather
rich country with a strong social system like Germany our
results suggest that children are negatively affected by
poverty, more studies across different countries are of
importance to completely understand the impact of financial
deprivation on early child development.
Conclusion
Our results substantiate that social background impacts lan-
guage development from early on and that this holds true for
both vocabulary and grammar, although the latter has been
considered as less affected by environmental factors. Fur-
thermore, especially poverty affects children’s language
development even after controlling for maternal education and
joint picture book reading, showing that even in a country like
Germany economic hardship has an effect on young children.
Additionally, as we differentiated between family
income, measured as a metric variable by net equivalence
income, and income groups, defined by official income
thresholds, our results indicate that the effect of income is
non-linear. Hence, using income groups based on official
statistics (e.g., poverty), not only allows displaying non-
linear effects but furthermore being comparable with future
studies considering the effect of poverty.
In sum, more research is needed to determine the effect
of poverty especially on the developmental outcomes dur-
ing early childhood. Thereby, longitudinal research should
investigate long-term effects of poverty considering not
only different length of poverty but also their timing. This is
especially true for Germany, where about each 6th child is
living in poverty (Statistisches Bundesamt/ Wissenschafts-
zentrum Berlin für Sozialforschung/ Bundesinstitut für
Bevölkerungsforschung, 2021), but currently little is known
about the consequences of poverty on child development
in a rather rich country with a strong social system
(Laubstein et al., 2016).
Data availability
This paper uses data from the National Educational Panel
Study (NEPS): Starting Cohort 1–Newborns, https://doi.
org/10.5157/NEPS:SC1:5.0.0. From 2008 to 2013, NEPS
data were collected as part of the Framework Programme
for the Promotion of Empirical Educational Research fun-
ded by the German Federal Ministry of Education and
Research (BMBF). As of 2014, the NEPS survey is carried
out by the Leibniz Institute for Educational Trajectories
(LIfBi) at the University of Bamberg in cooperation with a
nationwide network.
Funding Open Access funding enabled and organized by Projekt
DEAL. This paper was developed as part of the work of the SEED
Consortium. SEED stands for Social InEquality and its Effects on
child Development: A study of birth cohorts in the UK, Germany
and the Netherlands (Grant # 462-16-030) and is part of the
Dynamics of Inequality Across the Lifecourse Programme of
the EU’s New Opportunities for Research Funding Agency
Co-operation in Europe (NORFACE) initiative. The consortium
members are: Manja Attig, Gwendolin Blossfeld, Marie-Christine
Franken, Wei Huang, Pauline Jansen, Claudia Karwath, Lisanne
Labuschagne, James Law (PI), Cristina McKean, Robert Rush,
Nathalie Tamayo Martinez, Hans-Günther Roßbach, Marc van der
Schroeff, Jutta von Maurice, Helen Wareham and Sabine Weinert.
This work was supported by the Deutsche Forschungsgemeinschaft
(DFG; Grant # WE1478/10-1).
Compliance with Ethical Standards
Conflict of Interest The authors declare no competing interests.
Ethical Statement The NEPS study is conducted under the supervision
of the German Federal Commissioner for Data Protection and Freedom
of Information (BfDI) and in coordination with the German Standing
Conference of the Ministers of Education and Cultural Affairs (KMK)
and –in the case of surveys at schools –the Educational Ministries of
the respective Federal States. All data collection procedures, instru-
ments and documents were checked by the data protection unit of the
Leibniz Institute for Educational Trajectories (LIfBi). The necessary
steps are taken to protect participants’confidentiality according to
national and international regulations of data security. Participation in
the NEPS study is voluntary and based on the informed consent of
participants. This consent to participate in the NEPS study can be
revoked at any time. All parents of the Newborn Cohort of the NEPS
give their agreement for participation and answering questions during
the assessments as well as written consent for participating in the
video-taped measures to each measurement point.
Journal of Child and Family Studies (2023) 32:2254–2270 2267
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References
Acock, A. C. (2013). Discovering Structural Equation Modeling
Using Stata. Texas: Stata Press.
Aßmann, C., Zinn, S., & Würbach, A. (2015). Sampling and
weighting the sample of the early childhood cohort of the
National Educational Panel Study (Technical Report on SUF
SC1 Version 2.0.0). Available at https://www.neps-data.de/Porta
ls/0/NEPS/Datenzentrum/Forschungsdaten/SC1/2-0-0/SC1-2-0-
0_Weighting.pdf.
Attig, M., & Weinert, S. (2019). Häusliche Lernumwelt und Spra-
cherwerb in den ersten Lebensjahren. Sprache-Stimme-Gehör,
43(2), 86–92. https://doi.org/10.1055/a-0851-9049.
Autorengruppe Bildungsbericht (2018). Bildung in Deutschland 2018.
Ein indikatorengestützter Bericht mit einer Analyse zu Wirkungen
und Erträgen von Bildung. Available at https://www.
bildungsbericht.de/de/bildungsberichte-seit-2006/bildungsbericht-
2018/pdf-bildungsbericht-2018/bildungsbericht-2018.pdf.
Autorengruppe Bildungsberichterstattung (2020). Bildung in
Deutschland 2020.Ein indikatorengestützter Bericht mit eine
Analyse zu Bildung in einer digitalisierten Welt. Available at
https://www.bildungsbericht.de/de/bildungsberichte-seit-2006/
bildungsbericht-2020/pdf-dateien-2020/bildungsbericht-2020-
barrierefrei.pdf.
Barajas, R. G., Philipsen, N., & Brooks-Gunn, J. (2008). Cognitive
and emotional outcomes for children in poverty. In D. R. Grane &
T. B. Heato (Eds.), Handbook of families and poverty (pp. 311-
333). Thousand Oaks, CA: SAGE.
Becker, G. (1981). A treatise on the family. Cambridge: Harvard
University Press.
Berger, L. M., Paxson, C., & Waldfogel, J. (2009). Income and child
development. Children and Youth Service Review,31(9),
978–989. https://doi.org/10.1016/j.childyouth.2009.04.013.
Biedinger, N. (2009). Kinderarmut in Deutschland: Der Einfluss von
relativer Einkommensarmut auf die kognitive, sprachliche und
behavioristische Entwicklung von 3- bis 4-jährigen Kindern.
Zeitschrift für Soziologie der Erziehung und Sozialisation,29(2),
197–214.
Blossfeld, H.-P., Roßbach, H.-G., & von Maurice, J. (Eds.). (2011).
Education as a lifelong process: The German National Educa-
tional Panel Study (NEPS) [Special Issue]. Zeitschrift für Erzie-
hungswissenschaft, 14.
Bradbury, B., Waldfogel, J., & Washbrook, E. (2018). Income-related
gaps in early child cognitive development: Why are they larger in
the United States than in the United Kingdom, Australia, and
Canada? Demography,56(1), 367–390. https://doi.org/10.1007/
s13524-018-0738-8.
Brooks-Gunn, J., Berlin, L. J., Leventhal, T., & Fuligni, A. S.
(2000). Depending on the kindness of strangers: Current
national data initials and developmental research. Child
Development,71(1), 257–268. https://doi.org/10.1111/1467-
8624.00141.
Brooks-Gunn, J., & Duncan, G. J. (1997). The effect of poverty on
children. The Future of Children,7(2), 55–71. https://doi.org/10.
2307/1602387.
Bus, A. G., van Ijzendoorn, M. H., & Pellegrini, A. D. (1995). Joint
book reading makes for success in learning to read: A meta-
analysis on intergenerational transmission of literacy. Review of
Educational Research,65(1), 1–21. https://doi.org/10.3102/
00346543065001001.
Butterwege, C., Klundt, M., & Belke-Zeng, M. (2008). Kinder-
armut in Ost- und Westdeutschland. Wiesbaden: Verlag für
Sozialwissenschaften.
Chase-Lansdale, P. L., & Pittman, L. (2002). Welfare reform and
parenting: Reasonable expectations. Future of Children,12(1),
167–185.
Chen, E., Matthews, K. A., & Boyce, W. T. (2002). Socioeconomic
differences in children’s health: How and why do these rela-
tionships change with age. Psychological Bulletin,128(2),
295–329. https://doi.org/10.1037/0033-2909.128.2.295.
Chomsky, N. (1988). Language and problems of knowledge: The
Managua Lectures. Cambridge, Massachusetts: MIT Press.
Conger, R. D., Conger, K. J., Elder, G. H., Lorenz, F. O., Simons, R.
L., & Whitbeck, L. B. (1992). A family process model of eco-
nomic hardship and adjustment of early adolescent boys. Child
Development,63(3), 526–541. https://doi.org/10.1111/j.1467-
8624.1992.tb01644.x.
Conger, R. D., & Donnellan, M. B. (2007). An interactionist per-
spective on the socioeconomic context of human development.
Annual Review of Psychology,58, 175–199. https://doi.org/10.
1146/annurev.psych.58.110405.085551.
Corak, M., Fertig, M., & Tamm, M. (2005). A portrait of child poverty
in Germany. Innocenti Working Paper. Available at https://www.
unicef-irc.org/publications/377-a-portrait-of-child-poverty-in-
germany.html.
Dearing, E., Berry, D., & Zaslow, M. (2006). Poverty during early
childhood. In K. McCartney & D. Phillips (Eds.), Blackwell
handbook of early childhood development, (pp. 399-423). Mal-
den, MA: Blackwell Publishing.
Duncan, G. J. & Brooks-Gunn, J. (Eds.) (1997). Consequences of
growing up poor. New York: Russel Sage Foundation.
Duncan, G. J., & Magnuson, K. (2013). The importance of poverty
early in childhood. Policy Quarterly,9(2), 12–17. https://doi.org/
10.1007/s11205-011-9867-9.
Duncan, G. J., Magnuson, K., Kalil, A., & Ziol-Guest, K. (2012).
The importance of early childhood poverty. Social Indicators
Research,108(1), 87–98. https://doi.org/10.1007/s11205-011-
9867-9.
Duncan, G. J., Yeung, W. J., Brooks-Gunn, J., & Smith, J. R. (1998).
How much does childhood poverty affect the life chances of
children. American Sociological Review,63(3), 406–423. https://
doi.org/10.2307/2657556.
Ebert, S. (2011). Was Kinder über die mentale Welt wissen –Die
Entwicklung von deklarativem Metagedächtnis aus der Sicht der
„Theory of Mind“. Hamburg: Dr. Kovac.
Ebert, S. (2015). Longitudinal relations between theory of mind and
metacognition and the impact of language. Journal of Cognition
and Development,16,559–586. https://doi.org/10.1080/
15248372.2014.926272.
Elman, J. L., Bates, E., Johnson, M. H., Karmiloff-Smith, A., Parisi,
D. & Plunkett, K. (1996). Rethinking innateness: Connection-
ism in a developmental framework. Cambridge, Massachusetts:
MIT Press.
2268 Journal of Child and Family Studies (2023) 32:2254–2270
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Esping-Andersen, G. (1990). Three worlds of welfare capitalism.
Princeton: Princeton University Press.
Familienreport (2020). Familie heute. Daten. Fakten. Trends.
Available at https://www.bmfsfj.de/resource/blob/163108/ceb1a
bd3901f50a0dc484d899881a223/familienreport-2020-familie-
heute-daten-fakten-trends-data.pdf.
Federal Ministry for Family Affairs, Senior Citizens, Women and
Youth (2021). Elternzeit [Parental leave]. Available at
https://www.bmfsfj.de/bmfsfj/themen/familie/familienleistungen/
elternzeit/elternzeit-73832
Fenson, I., Dale, P. S., Reznick, J. S., Thal, D., Bates, E., Hartung, J.,
Pethick, S., & Reilly, J. S. (1993). The MacArthur Commu-
nicative Development Inventories: User’s guide and technical
manual. San Diego, CA: Singular Publishing Group.
Fernald, A., Marchman, V. A., & Weisleder, A. (2013). SES differ-
ences in language processing skill and vocabulary are evident at
18 months. Developmental Science,16(2), 234–248. https://doi.
org/10.1111/desc.12019.
Fodor, J. A. (1983). Modularity of mind: An essay on faculty psy-
chology. Cambridge, Massachusetts: MIT Press.
Garrett, P., Ng’andu, N., & Ferron, J. (1994). Poverty experiences of
young children and the quality of their home environments. Child
Development,65(2), 331–345. https://doi.org/10.2307/1131387.
Grabka, M. G., & Frick, J. R. (2008). Wochenbericht. Schrumpfende
Mittelschicht –Anzeichen einer dauerhaften Polarisierung der
verfügbaren Einkommen? DIW Wochenbericht,75(10), 101–116.
https://www.diw.de/sixcms/detail.php?id=diw_01.c.451741.de
Grimm, H., & Doil, H. (2006). Die Elternfragebögen für die Frü-
herkennung von Risikokindern (ELFRA). Überarbeitete und
erweiterte Auflage. Göttingen: Hogrefe.
Groos, T., & Jehles, N. (2015). Der Einfluss von Armut auf die
Entwicklung von Kindern. Ergebnisse der Schu-
leingangsuntersuchung. Schriftreihe Arbeitspapiere wissenschaf-
tliche Begleitforschung “Kein Kind zurücklassen!”.Availableat
https://www.bertelsmann-stiftung.de/de/publikationen/publikation/
did/der-einfluss-von-armut-auf-die-entwicklung-von-kindern/.
Hart, B. & Risley, T. R. (1995). Meaningful differences in the
everyday experiences of young American children. Baltimore:
Paul H. Brookes Publishing Co.
Hart, B., & Risley, T. R. (1999). Social world of children learning to
talk. Baltimore: Paul H. Brooks Publishing Co.
Hart, B., & Risley, T. R. (2003). The early catastrophe: The 30 million
word gap. American Educator,27(1), 4–9.
Hartas, D. (2011). Famlies’social backgrounds matter: socio-
economic factors, home learning and young children’slan-
guage, literacy and social outcomes. British Educational
Research Journal,37(6), 893–914. https://doi.org/10.1080/
01411926.2010.506945.
Hoff, E. (2006). How social contexts support and shape language
development. Developmental Review,26(1), 55–88. https://doi.
org/10.1016/j.dr.2005.11.002.
Hoff, E. (2013). Interpreting the early language trajectories of children
from low-SES and language minority homes: implications for
closing achievement gaps. Developmental Psychology,49(1),
4–14. https://doi.org/10.1037/a0027238.
Hoff-Ginsberg, E. (2000). Soziale Umwelt und Sprachlernen. In H.
Grimm (Hrsg.), Enzyklopädie der Psychologie, Themenbereich
C, Serie III, Bd. 3, (S. 463-494). Göttingen: Hogrefe.
Holzer, H. J., Schanzenbach, D. W., Duncan, G. J., & Ludwig, J.
(1997). The economic costs of poverty in the United States:
Subsequent effects of children growing up poor. Institute for
Research on Poverty. Discussion Paper no. 1327-07. Available
at http://www.npc.umich.edu/publications/u/working_paper07-
04.pdf.
Huttenlocher, J., Waterfall, H., Vasilyeva, M., Vevea, J., & Hedges, L.
V. (2010). Sources of variability in children’s language growth.
Cognitive Psychology,61(4), 343–465. https://doi.org/10.1016/j.
cogpsych.2010.08.002.
Kalil, A., Ziol-Guest, K. M., Ryan, R. M., & Markowitz, A. J. (2016).
Changes in income-based gaps in parent activities with young
children from 1988 to 2012.AERA Open,2(3), 1–17.
Karmiloff-Smith, A. (2015). An alternative to domain-general or
domain-specific frameworks for theorizing about human evolu-
tion and ontogenesis.Neuroscience,19(2), 91–104. https://doi.
org/10.3934/Neuroscience.2015.2.91.
Karwath, C., Relikowski, I., & Schmitt, M. (2014). Sibling structure
and educational achievement: How do the number of siblings,
birth order, and birth spacing affect children’s vocabulary com-
petences? Journal of Family Research,26(3), 372–396. https://
doi.org/10.3224/zff.v26i3.18993.
Klaiber, S. (2007). Erprobung des ELFRA (Elternfragebogen für die
Füherkennung von Risikokindern): Probleme bei der Anwendung
des ELFRA-1 und des ELFRA-2. Available at https://edoc.ub.uni-
muenchen.de/6784/
Korenman, S., Miller, J. E., & Sjaastad, J. E. (1995). Long-term
poverty and child development in the United States: Results from
the NLSY. Children and Youth Services Review,17(1/2),
127–155. https://doi.org/10.1016/0190-7409(95)00006-X.
Laubstein, C., Holz, G., & Seddig, N. (2016). Armutsfolgen für Kinder
und Jugendliche.Erkenntnisse aus empirischen Studien in
Deutschland. Available at https://www.bertelsmann-stiftung.de/
fileadmin/files/BSt/Publikationen/GrauePublikationen/Studie_
WB_Armutsfolgen_fuer_Kinder_und_Jugendliche_2016.pdf.
Lauterbach, W. & Ströing, M. (2009). Wohlhabend, Reich und
Vermögend –Was heißt das eigentlich? In T. Druyen, W.
Lauterbach, & M. Grundmann (Hrsg.), Reichtum und Vermö-
gen. Zur gesellschaftlichen Bedeutung der Reichtums- und
Vermögensforschung, (S. 13-28). Wiesbaden: Verlag für
Sozialwissenschaften.
Law, J., Clegg, J., Rush, R., Roulstone, S., & Peters, T. J. (2019).
Association of proximal elements of social disadvantage with
children’s language development at 2 years: an analysis of data
from the Children in Focus (CiF) sample from the ALSPAC
birth cohort. International Journal of Language & Commu-
nication Disorders,54(3), 362–376. https://doi.org/10.1111/
1460-6984.12442.
Law, J., Rush, R., King, T., Westrupp, E., & Reilly, S. (2017). Early
home activities and oral language skills in middle childhood: A
quantile analysis. Child Development,89(1), 295–309. https://
doi.org/10.1111/cdev.12727.
Linberg, A., Attig, M., & Weinert, S. (2020). Disparities in vocabulary
of 2-year-old children by maternal education and the mediating
effect of language-stimulation interaction behavior. Journal for
Educational Research Online,12,12–35.
Linberg, T., & Wenz, S. E. (2017). Ausmaß und Verteilung sozioö-
konomischer und migrationsspezifischer Ungleichheiten im
Sprachstand fünfjähriger Kindergartenkinder. Journal for Edu-
cational Research Online,9(1), 77–98.
Linberg, T., Schneider, T., Waldfogel, J., & Wang, Y. (2019).
Socioeconomic status gaps in child cognitive development in
Germany and the United States. Social Science Research,79,
1–31. https://doi.org/10.1016/j.ssresearch.2018.11.002.
Mareschal, D., Johnson, M. H., Sirois, S., Spratling, M. W., Thomas,
M. S. C., & Westermann, G. (2007). Neuroconstructivism: How
the brain constructs cognition. Oxford: Oxford University Press.
McKean, C., Mensah, F. K., Eadie, P., Bavin, E. L., Bretherton, L.,
Cini, E., & Reilly, S. (2015). Levers for language growth:
Characteristics and predictors of language trajectories between 4
and 7 years. PLOS ONE,10(8), 1–21. https://doi.org/10.1371/
journal.pone.0134251.
OECD (2013). Framework of statistics on the distribution of household
income, consumption and wealth. Available at https://www.oecd.org/
Journal of Child and Family Studies (2023) 32:2254–2270 2269
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
statistics/framework-for-statistics-on-the-distribution-of-household-
income-consumption-and-wealth-9789264194830-en.htm.
OECD (2018a). Policy brief on child well-being.Poor children in rich
countries: Why we need policy action. Available at https://www.
oecd.org/els/family/Poor-children-in-rich-countries-Policy-brief-
2018.pdf.
OECD (2018b). Child poverty in the OECD: Trends, determinants and
policies to tackle it. OECD Social, Employment and Migration
Working Paper No. 218. Available at https://www.oecd-ilibrary.
org/employment/child-poverty-in-the-oecd_c69de229-en
OECD (2019). PISA 2018 results (Volume II): Where all students can
succeed. PISA, OECD, Publisihing, Paris. Available at
https://www.oecd-ilibrary.org/education/pisa-2018-results-
volume-ii_b5fd1b8f-en.
Peyre, H., Bernard, J. Y., Forhan, A., Charles, M.-A., De Agostini, M.,
Heude, B., & Ramus, F. (2014). Predicting changes in language
skills between 2 and 3 years in the EDEN mother-child cohort.
PeerJ,2(1), 1–16. https://doi.org/10.7717/peerj.335.
Robertson, K. L. (1997). Phonological awareness and reading
achievement of children from differing socioeconomic status
backgrounds. Dissertations and Master’s Theses (Campus
Access). Available at https://www.proquest.com/openview/
f7ccb996bcef017e34e1dc12255b8423/1?pqorigsite=gschola
r&cbl=18750&diss=y.
Rose, E., Weinert, S., & Ebert, S. (2018). The roles of receptive and
productive language in children’s socioemotional development.
Social Development,27(4), 777–792. https://doi.org/10.1111/
sode.12317.
Rubin, D. B. (1987). Multiple imputation for nonresponse in survey.
New York: J. Wiley & Sons.
Sachse, S., Anke, B., & Suchodoletz, W. V. (2007a). Früherkennung
von Sprachentwicklungsstörungen –ein Methodenvergleich.
Zeitschrift für Kinder- und Jugendpsychiatrie und Psychotherapie,
35(5), 323–331. https://doi.org/10.1024/1422-4917.35.5.323.
Sachse, S., Pecha, A., & Suchodoletz, W. V. (2007b). Früherkennung von
Sprachentwicklungsstörungen. Ist der ELFRA-2 für einen generellen
EinsatzbeiderU7zuempfehlen?Monatsschrift Kinderheilkunde,
155(2), 140–145. https://doi.org/10.1007/s00112-006-1314-7.
Schenck-Fontaine, A., & Panico, L. (2019). Many kinds of poverty:
Three dimensions of economic hardship, their combinations, and
children’s behavior problems. Demography,56(6), 2279–2305.
https://doi.org/10.1007/s13524-019-00833-y.
Schoon, I., Hope, S., Ross, A., & Duckworth, K. (2010). Family
hardship and children’s development: the early years. Long-
itudinal and Life Course Studies,1(3), 209–222. https://doi.org/
10.14301/llcs.v1i3.109.
Schoon, I., Jones, E., Cheng, H., & Maughan, B. (2012). Family
hardship, family instability and children’s cognitive development.
Journal of Epidemiology and Community Health,66(8),
716–722. https://doi.org/10.1136/jech.2010.121228.
Schuth, E., Köhne, J., & Weinert, S. (2017). The influence of academic
vocabulary knowledge on school performance. Learning & Instruc-
tion,49, 157–165. https://doi.org/10.1016/j.learninstruc.2017.01.005.
Siegler, R., DeLoache, J. S., Eisenberg, N., Saffran, J. R., & Leaper, C.
(2014). How children develop. New York: Worth Publishers.
Smith, J. R., Brooks-Gunn, J., & Klebanov, P. K. (1997). Con-
sequences of living in poverty for young children’s cognitive and
verbal ability and early school achievement. In G. J Duncan & J.
Brooks-Gunn (Eds), Consequences of growing up poor (p. 132-
189). New York: Russell Sage Foundation.
Statistische Ämter (2020). Armutsgefährdung. Statistische Ämter des
Bundes und des Landes. Gemeinsames Statistikportal.
Available at https://www.statistikportal.de/de/sbe/ergebnisse/
einkommensarmut-und-verteilung.
Statistisches Bundesamt (2006). Armut und Lebensbedingungen.
Ergebnisse aus LEBEN IN EUROPA für Deutschland 2005.
Available at http://ernaehrungsdenkwerkstatt.de/fileadmin/user_
upload/EDWText/TextElemente/SES/Armuts-Bericht_Leben_in_
Europa_-_2005_-.pdf.
Statistisches Bundesamt/ Wissenschaftszentrum Berlin für Sozial-
forschung (2018). Datenreport 2018. Ein Sozialbericht für die
Bundesrepublik Deutschland. Bonn: Bundeszentrale für poli-
tische Bildung. Available at https://www.destatis.de/DE/
Service/Statistik-Campus/Datenreport/Downloads/datenreport-
2018.html.
Statistisches Bundesamt/ Wissenschaftszentrum Berlin für Sozial-
forschung/ Bundesinstitut für Bevölkerungsforschung (2021).
Datenreport 2021. Ein Sozialbericht für die Bundesrepublik
Deutschland. Bonn: Bundeszentrale für politische Bildung.
Available at https://www.destatis.de/DE/Service/Statistik-Ca
mpus/Datenreport/Downloads/datenreport-2021.pdf;jsessionid=
F28D829A8FDAB74124E85C8D313C4644.live742?__blob=
publicationFile.
Tomasello, M. (2003). Constructing a language: A usage-based the-
ory of language acquisition. Cambridge, Massachusetts: Harvard
University Press.
van Buuren, S. (2018). Flexible imputation of missing data. Second
edition. Boca Raton: CRC Press.
van der Lely, J. K. J., & Pinker, S. (2014). The biological basis of
language: Insights from developmental grammatical impairments.
Trends in.Cognitive Science,18(11), 586–595. https://doi.org/10.
1016/j.tics.2014.07.001.
Vasilyeva, M., & Waterfall, H. (2011). Variability in language
development: Relation to socioeconomic status and environ-
mental input. In S. B. Neuman & D. K. Dickinson (Eds),
Handbook of early literacy research (p. 36-48). New York:
Guilford Press.
Weinert, S. (2010). Beziehungen zwischen Sprachentwicklung und
Gedächtnisentwicklung. In H.-P. Trolldeiner, W. Lenhard & P.
Marx (Hrsg.), Brennpunkte der Gedächtnisforschung: Entwick-
lungs- und pädagogisch-psychologische Perspektiven (S. 147-
170). Göttingen: Hogrefe.
Weinert, S. (2022). Language and cognition. In J. Law, S. Reily & C.
McKean (Eds.), Language Development: Individual differences
in a social context. Cambridge: Cambridge University Press.
Weinert, S., & Ebert, S. (2013). Spracherwerb im Vorschulalter:
Soziale Disparitäten und Einflussvariablen auf den Grammati-
kerwerb. Zeitschrift für Erziehungswissenschaft,16, 303–332.
https://doi.org/10.1007/s11618-013-0354-8.
Weinert, S., Linberg, A., Attig, M., Freund, J.-D., & Linberg, T.
(2016). Analyzing early child development, influential condi-
tions, and future impacts: Prospects of a German newborn cohort
study. International Journal of Child Care and Education Policy,
10(7), 1–20. https://doi.org/10.1186/s40723-016-0022-6.
Zinn, S., Würbach, A., Steinhauer, H. W., & Hammon, A. (2018).
Attrition and selectivity of the NEPS Starting Cohorts: An
overview of the past 8 years. NEPS Survey Paper No. 34.
Available at https://www.neps-data.de/Portals/0/Survey%20Pa
pers/SP_XXXIV.pdf.
Zubrick, S. R., Taylor, C. L., Rice, M. L., & Slegers, D. W. (2007).
Late language emergence at 24 months: An epidemiological
study of prevalence, predictors, and covariates. Journal of
Speech, Language, and Hearing Research,50, 1562–1592.
https://doi.org/10.1044/1092-4388(2007/106).
2270 Journal of Child and Family Studies (2023) 32:2254–2270
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