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Theories of Child Development and Their Impact on Early Childhood Education and Care

• Published: 29 October 2021
• Volume 51 , pages 15–30, ( 2023 )

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• Olivia N. Saracho   ORCID: orcid.org/0000-0003-4108-7790 1  

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Developmental theorists use their research to generate philosophies on children’s development. They organize and interpret data based on a scheme to develop their theory. A theory refers to a systematic statement of principles related to observed phenomena and their relationship to each other. A theory of child development looks at the children's growth and behavior and interprets it. It suggests elements in the child's genetic makeup and the environmental conditions that influence development and behavior and how these elements are related. Many developmental theories offer insights about how the performance of individuals is stimulated, sustained, directed, and encouraged. Psychologists have established several developmental theories. Many different competing theories exist, some dealing with only limited domains of development, and are continuously revised. This article describes the developmental theories and their founders who have had the greatest influence on the fields of child development, early childhood education, and care. The following sections discuss some influences on the individuals’ development, such as theories, theorists, theoretical conceptions, and specific principles. It focuses on five theories that have had the most impact: maturationist, constructivist, behavioral, psychoanalytic, and ecological. Each theory offers interpretations on the meaning of children's development and behavior. Although the theories are clustered collectively into schools of thought, they differ within each school.

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The author is grateful to Mary Jalongo for her expert editing and her keen eye for the smallest details.

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Saracho, O.N. Theories of Child Development and Their Impact on Early Childhood Education and Care. Early Childhood Educ J 51 , 15–30 (2023). https://doi.org/10.1007/s10643-021-01271-5

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• Published: 07 October 2016

Analyzing early child development, influential conditions, and future impacts: prospects of a German newborn cohort study

• Sabine Weinert 1 ,
• Anja Linberg 2 ,
• Manja Attig 3 ,
• Jan-David Freund 1 &
• Tobias Linberg 3  

International Journal of Child Care and Education Policy volume  10 , Article number:  7 ( 2016 ) Cite this article

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The paper provides an overview of a German cohort study of newborns which includes a representative sample of about 3500 infants and their mothers. The aims, challenges, and solutions concerning the large-scale assessment of early child capacities and skills as well as the measurements of learning environments that impact early developmental progress are presented and discussed. First, a brief overview of the German regulations related to early child education and care (ECEC) and parental leave as well as the study design are outlined. Then, the assessments of domain-specific and domain-general cognitive and socio-emotional indicators of early child functioning and development are described and the assessments of structural, orientational, and process quality of the children’s learning environment at home and in child care are presented. Special attention is given to direct assessments and their reliability and validity; in addition, some selected results on social disparities are reported and the prospects of data analyses are discussed.

Early childhood and early child education are an important basis for later development, educational performance, and pathways as well as for lifelong learning and well-being. This important claim has been made repeatedly (Caspi et al. 2003 ; Noble et al. 2007 ), and even critical phases of development have been suggested (e.g., Mayberry et al. 2002 ). Nevertheless and despite the existence of quite a few longitudinal studies addressing this issue, empirical evidence concerning effective conditions, differential child progress, and how the early phases of life impact future development and prospects is still rare.

From an educational and political point of view, it is alarming that various studies have documented profound disparities in child development according to family background when children are merely 3 years of age (Brooks-Gunn and Duncan 1997 ; Dubowy et al. 2008 ; Hart and Risley 1995 , 1999 ; Weinert et al. 2010 ). Even in the first year of life, very early roots of social disparities have been demonstrated which increased substantially over the next few years (Halle et al. 2009 ). In addition, some studies show a high stability of interindividual differences and social disparities from age three onward across preschool (Weinert and Ebert 2013 ; Weinert et al. 2010 ) and school age (Law et al. 2014 ). Notably, the stability of individual differences in children’s test performance has been shown to be even more pronounced in educationally dependent domains of development, like language and factual knowledge, than in more domain-general and less culture-dependent facets of children’s cognitive functioning, as indicated by non-verbal intelligence test scores (Weinert et al. 2010 ).

Drawing on a bioecological model of development (Bronfenbrenner and Morris 2006 ), developmental progress and child education are influenced from early on by the interaction between (developing) child characteristics, skills, and competencies and the quality of structural and process characteristics of the learning environment at the child’s home (Bakermans-Kranenburg and van Ijzendoorn 2011 ; Bradley and Corwyn 2002 ; Ebert et al. 2013 ; Weinert et al. 2012 ) as well as in child care (Anders et al. 2013 ). Longitudinal studies shed light on these interactions and how they impact later development and education, which is of great importance for gaining a better understanding of the underlying processes and influential conditions. It is important to note that the form and organization of the various learning environments are affected by state regulations, which differ between countries, resulting in different support systems, offers and regulations for parents from child birth until her/his formal school enrolment (Waldfogel 2001 ).

Regulations in Germany

Maternity leave regulations in Germany prescribe a period of 14 weeks for maternity leave which is divided into two phases: 6 weeks before and 8 weeks after birth. Mothers receive maternity pay from public funds in addition to their employer’s contribution which amounts to 100 % of their former income. After this period, parents are offered various options for taking parental leave until the child’s third birthday. Specifically, parents may interrupt their employment to provide child care and are legally protected from dismissal during this 3-year period; parents also receive parental pay during their parental leave (substitution of income) amounting to two-thirds of her/his prior salary (ranging from € 300.- up to € 1800.-) for a maximum period of 14 months.

Governments also support families through child care policies. The German early child education and care (ECEC) system covers institutional care and education before and alongside elementary and secondary school. Since 1993 children from age of three onward have had a legal right to institutional child care which is primarily organized by local communities and welfare organizations providing care to mainly age-mixed groups at centers with varying opening hours (Linberg et al. 2013 ). However, during the last decade, there has been growing demand for ECEC for children under the age of three that led to the enactment of laws on the demand-driven expansion of child care (“Tagesbetreuungsausbaugesetz TAG”) and the expansion of child care infrastructure for infants and children (“Kinderförderungsgesetz KiföG”) in 2005 and 2008, respectively. Additionally, the legal right to institutional ECEC was expanded in 2013 to include 1-year-old children and political leaders from local, state, and federal levels agreed to provide enough places for 35 % of the children.

Accordingly, the actual use of child care for young children under the age of three has rapidly changed during recent years: Within 8 years (2005–2013), the child care rates for the under 3-year olds increased from 7 to 23 % in the Western states of Germany and from 36 to 47 % in the Eastern states, which have their own distinct tradition and infrastructure concerning early care and education (Kreyenfeld and Krapf 2016 ). In 2015, the nation-wide care rate amounted to 32.9 % with mean values of 28.2 % for the Western and 51.9 % for the Eastern states (Statistisches Bundesamt 2016 ).

However, despite rising rates of early education, a child’s family still is the first and often only environment for developmental processes during the first years of life. Thus, there is a substantial need for analyzing the decision mechanisms as well as the effects of the various options available for early child care.

To summarize, longitudinal studies that provide a basis for analyzing the conditions which significantly contribute to early developmental progress are of great importance for the individual child as well as for society. These studies produce relevant knowledge on how children’s abilities, skills, and competencies develop based on individual resources and conditions; how learning opportunities influence their development in different contexts; how disparities emerge early in life; and how all this impacts educational careers, lifelong learning, well-being, and participation in society.

The German National Educational Panel Study

(NEPS) Footnote 1 has been set up to substantially contribute to these issues (Blossfeld et al. 2011 ). The idea of a multicohort panel study was brought up by the German Federal Ministry of Education and Research (BMBF). A nation-wide interdisciplinary scientific network of researchers was established to develop this idea further and to prepare a proposal for a longitudinal representative large-scale educational study to investigate, monitor, and compare competence development and educational processes in Germany. In light of the specific challenges associated with sampling and measurement of early child characteristics, a newborn cohort study was not initially included in the main NEPS program, but was planned to be conducted as an associated add-on project. However, the study was incorporated into the NEPS study design on behalf of the international evaluation committee organized by the German Research Foundation (DFG) for two main reasons: the growing research on the importance of early child development and education and the rapid changes taking place in early child care, including new social policies being implemented in Germany (see above).

The NEPS is carried out by a network of excellence. It features a longitudinal multicohort sequence design and comprises more than 60,000 target persons as well as 40,000 context persons. In particular, the NEPS design encompasses six longitudinal panel studies conducted simultaneously, which cover a wide range of ages and educational stages. NEPS data are disseminated in a user-friendly way to the scientific community. According to the sensitivity of data, the access is given by a web download, a remote access solution, or on-site in a secure environment. All data are documented in English and are available for use by national and international researchers. In addition to providing substantial analyses of the data themself, it can be used as a benchmark for intervention research, international comparison, and for evaluating issues such as the differences and changes in the use of institutional child care.

At the moment, more than 1100 researchers from more than 700 projects are drawing on the NEPS data already published. The data are used for research in a variety of scientific disciplines and also for educational monitoring—especially, the indicator-based National Report for Education. In order to facilitate access to results for a wide range of professions interested in education—including policy, administration, and practice—scientific papers with important conclusions and empirical evidence are currently summarized by the Leibniz Institute for Educational Trajectories (LIfBi) for public communication and information beyond science and are distributed via the NEPS webpage. Moreover, results are regularly fed back to these groups by presentations and newsletters.

The present paper provides an overview of the NEPS newborn cohort study and its analytic potential. First, the design of the study will be presented with a special emphasis on the aims, challenges, and solutions for the assessment of child characteristics and learning environments. We will then report a few selected results (a) concerning the validity and reliability of the measures used and (b) on early social disparities.

Design of the newborn cohort study of the NEPS: a brief overview

Like all other cohort studies of the NEPS, the cohort study of newborns addresses five research perspectives (Blossfeld et al. 2011 ). Drawing on a theoretical framework, various domain-specific as well as domain-general indicators of early child capacities, characteristics, and developments are assessed as well as measures of structural and process characteristics of their (different) learning environments and their social, occupational, and educational family background. In addition, there is a special focus on families with a migration background, on educational decisions (e.g., concerning child care), and—especially in the newborn cohort study—on patterns of coparenting and child care arrangements. By combining direct observational measures, interview data, and questionnaires, the newborn cohort study allows for in-depth analyses of developmental progress and influential conditions that affect the development of educationally relevant competencies and the stability or changes of interindividual differences. Therefore, it provides insight into the mechanisms through which social disparities emerge, change, and impact children’s future prospects and returns to education.

Sampling strategy

To ensure a representative sample, a two-stage procedure was implemented: 84 German municipalities were used as primary sampling units, explicitly stratified according to three strata of urbanization (via the number of inhabitants; see Aßmann et al. 2015 ). Within these municipalities, addresses were sampled and divided into two birth tranches (infants born between February and April 2012 and between May and June 2012) in order to guarantee a small age range for the infant sample. Starting from a gross sample of about 8500 families, a total of about 3500 families (response rate 41 %) took part in the first assessment wave. In the second wave, the realized sample still included about 2850 families (panel stability 83 %).

Assessment waves and data collection

During the very early phases of child development, three successive assessment waves were carried out when children were on average 7 months (wave 1), 17 months (wave 2), and 26 months of age (wave 3). In the first and third wave video-taped observations and computer-assisted personal interviews (CAPI) were conducted at the family’s home for the entire sample. In the second wave, families were surveyed by computer-assisted telephone interviews (CATI), while video-taped observational measures at the child’s home were only assessed in half of the sample (subsample approx. 1500) in accordance with the study’s design. After wave 3 (i.e., from age two onward) children and their context persons were and will be surveyed every year. Data are collected by trained interviewers. Mothers are the primary respondents, as they can provide valid information about conditions and feelings during and after their pregnancy. Each assessment wave is preceded by a longitudinal pilot study, which runs 1 year before the main study is conducted, to test all instruments and procedures.

Measuring early child characteristics: aims, challenges, and solutions

The assessment of a child’s capacities, characteristics, and early development is pivotal for analyzing the effects of environmental conditions and the impact of early child development and education on later development, educational achievement, career, and life satisfaction or other outcomes and returns. In particular, measuring child characteristics is essential to the modeling of intra-individual progress and changes in interindividual differences, including the emergence of social disparities in various domains of development across childhood. At the same time, it is crucial for analyzing the mechanisms of change, the effects of learning environments and opportunities, and their interactions with the individual capacities and characteristics of the children, while taking the risk or protecting factors of the individual child and his/her environment into account, as well as for controlling for basic interindividual differences if necessary.

However, measuring early child characteristics is a major challenge for longitudinal studies, especially large-scale studies. This is due to various issues and questions, such as which aspects and indicators of early child development should be assessed, how should they be measured, and how can the standardization and validity of measurements be ensured in large-scale assessments of very young children.

Early child development: domain-specific challenges for the child

Developmental psychology has convincingly documented for a long time that neither the development of children nor the development of infants is a homogeneous endeavor. Since the time of Piaget’s ( 1970 ) overarching stage theory of development, it has been empirically demonstrated that development is domain-specific, i.e., demands, prerequisites, effective environmental stimulations differ according to the developmental domain under study (e.g., the acquisition of language, of mathematical competencies, of competencies in natural science, or of an intuitive psychology) (Karmiloff-Smith 1999 ). Even in infancy domain-specific precursors of e.g., mathematical and psychological knowledge and competencies are observable (Goswami 2008 ). Determining how educationally relevant competencies emerge from the interplay of these domain-specific precursors and domain-general basic capacities of the child (like basic reasoning abilities, speed of information processing, or executive functions including cognitive flexibility, inhibition, working memory) on the one hand and of the environmental conditions in the family and in child care on the other is an important issue to be addressed by educational studies. It is important to note that (interindividual differences in) basic capacities also change with age and environmental conditions, although not to the same extent as culture- and education-dependent competencies, and that stimulation of and progress in one developmental domain may enhance, hinder, or compensate for those in other domains.

General NEPS framework for assessing competencies

Within the NEPS, a general framework for assessing educationally relevant abilities and competencies has been developed (Weinert et al. 2011 ). Specifically, the assessments include (a) domain-general cognitive abilities/capacities captured by the constructs of “fluid intelligence” (Cattell 1971 ) or “cognitive mechanics” (Baltes et al. 2006 ); these refer to performance differences in speed of basic cognitive processes, the capacity of working memory, and the ability to apply deductive or analogical thinking in new situations (Brunner et al. 2014 ); (b) domain-specific cognitive competencies, e.g., language competencies, mathematical competencies, and natural science competencies are to be assessed longitudinally and as coherently as possible; and not least (c) meta-competencies, including self-regulation (in the cognitive, behavioral, and emotional domain) and socio-emotional competencies are to be measured (see Weinert et al. 2011 for an elaborated rational of the assessments).

Selecting and measuring relevant and predictive indicators of early child development: a challenge for research

As already mentioned, even in infancy and early childhood, there is no overall indicator for children’s capacities and development. Considering the fact that there are thousands of studies into infant competencies, the indicators have to be carefully selected—not least because of the limited study time and other constraints associated with large-scale assessments, especially those concerning infants and young children who cannot be tested in group settings and whose attentional capacities are still limited. Within the NEPS, the selection draws on the general framework outlined above, including domain-general basic capacities, domain-specific precursors and early roots of language and mathematics as well as indicators of socio-emotional development and early self-regulation.

However, deciding on how to measure these early child characteristics and developments is a major challenge for theoretically sound educational large-scale assessments. Just relying on parents’ reports is problematic since the parents’ judgements might be affected, for example, by their (different) knowledge of child development, by possible restrictions/differences in how they observe the child, and by their particular cultural and individual beliefs and biases. In addition, major aspects of domain-general and domain-specific cognitive functioning and development are not easily observable and need sophisticated assessment methods developed in infancy research.

If newborn cohort studies took direct measures into consideration in addition to interviews and questionnaires, they often relied on the Bayley Scales of Infant Development (Bayley 2006 ; Schlesiger et al. 2011 for a brief overview). However, the NEPS feasibility and pilot studies revealed that the standardized administration of test items (using an educationally sound selection of items) turned out to be highly error-prone for trained interviewers who are usually experts in administering interviews but not tests. In addition, the sensorimotor indicators of developmental status measured by the Bayley Scales have been shown to be rather instable across situations (Attig et al. 2015 ) and infancy (McCall et al. 1977 ) and were hardly predictive for later cognitive functioning (e.g., Fagan and Singer 1983 ). Therefore, an indicator of basic information processing abilities was introduced within the NEPS newborn cohort study which has predominantly been used in baby lab studies, namely, the children’s visual attention and speed of habituation within a habituation–dishabituation paradigm. Within this paradigm, the child’s visual attention and the decrease of her/his visual attention when being presented with a series of identical or categorically similar stimuli are used as indicators of the child’s ability to build up a cognitive representation of a stimulus or a stimulus category (Pahnke 2007 ; Sokolov 1990 ). In addition, a new stimulus (or a stimulus from a new category) is presented in the dishabituation phase of the paradigm and a new increase of the child’s visual attention is interpreted as a signal of her/his ability to distinguish stimuli or categories presented during the two phases of the paradigm and to show a preference toward new information. These measures have been shown to be highly predictive of later intelligence scores or other indicators of cognition and language (Bornstein and Sigman 1986 ; Fagan and Singer 1983 ; Kavšek 2004 ). Thus, this paradigm was used to assess early domain-general information processing/categorization abilities; it was also used to measure early precursors of numeracy and word learning (see Table  1 ). To assure standardization and reliability, pictures were presented on a computer screen and the child’s looking behavior (look at/away from the respective stimulus) was video-taped (as were all other direct measures) and coded afterward on a 30 frames per second basis. A third direct indicator of early child characteristics relevant to learning and education is her/his interactional behavior (cognitive, behavioral, and socio-emotional aspects) in mother–child interaction (see “ Assessment of mother–child interaction: direct measurement of the home-learning environment and of the child’s characteristics in mother–child interaction ” section). Table  1 summarizes the measurements of child characteristics and development assessed in the first three waves of the NEPS newborn cohort study.

In addition to direct assessment, mothers were asked (see Table  1 ) about the child’s skills and development as well as about the child’s health. The questions on the child’s skills and development cover items on cognition (e.g., means-end task and object categorization), communicative gesture (e.g., to draw someone’s attention, negation/headshaking), gross and fine motor skills (e.g., climbing up steps, stacking of toy blocks) as well as language (e.g., size of productive vocabulary, comprehension of short instructions). A short version of the Infant Behavior Questionnaire (IBQ-R, Gartstein and Rothbart 2003 ) was used to assess facets of the child’s temperament, specifically orienting/regulatory capacity (items like “if you sing or speak to <target child’s name>, how often does she/he calm down instantly?”) and negative affectivity (items like “when <target child’s name> can’t have what she/he wants, how often does she/he get angry?”) (Bayer et al. 2015 ). In wave 3, a German language checklist and, for bilingual children, an additional Turkish or Russian language checklist (versions of the well-known MacArthur Communicative Development Inventory (CDI); Fenson et al. 1993 ) was introduced.

Measuring learning environments: aims, challenges, and solutions

Likewise, measuring learning environments that impact child development is an important challenge for longitudinal large-scale educational studies. As suggested by bioecological theories (Bronfenbrenner and Morris 2006 ), it is not enough to just focus on the home-learning environment; the use and features of non-parental care and other learning environments like parent–child programs, which 55 % of the children in the newborn cohort study experience in their first year of life, should also be assessed. Moreover, it is not sufficient to only measure quantitative structural characteristics, since domain-general and domain-specific qualitative aspects have been shown to be especially important (e.g., Anders et al. 2012 ; Sylva et al. 2006 ); however, indispensable direct observational measurements are hard to obtain in large-scale studies. It is important to note that the meaningfulness of the specific features/aspects assessed for characterizing the different learning environments and the constraints of the measurements have a large impact on the validity of subsequent analyses and conclusions.

General framework of the NEPS

To deal with these issues coherently across cohorts, the measurement of important characteristics of learning environments draws on a general framework which subdivides three different dimensions: Structural quality , which refers to relatively persistent general conditions; orientational quality , like values, norms, and attitudes of an actor; and process quality , which refers to the interaction of the individual with her/his learning environment (Bäumer et al. 2011 ).

Selection and measurement of indicators

For the assessment of the process quality of the home-learning environment as the central learning environment in the very early years, the NEPS newborn cohort study relies on both interviews/questionnaires and direct observations (see below).

In addition, as approx. 24 % of the children of the newborns’ cohort sample were using supplementary non-parental care settings in wave 2, the dimensions specified above were also surveyed in these child care settings using self-administered drop-off questionnaires for center-based ECEC as well as for child minders. Because the NEPS has to rely on survey data, the validity of the quality of non-parental care settings gained from the questionnaire is tested by conducting a sub-study, which compares observational methods with the questionnaire used in the NEPS study. The questionnaire covers structural characteristics as well as process characteristics (see Table  2 for examples).

Besides external day care, the newborn cohort study of the NEPS places a strong emphasis on the home-learning environment—especially in very early childhood—as it is of central importance for later development (NICHD 1998 ). Large-scale longitudinal studies mostly focus on the structural aspects of the home-learning environment to account for variability in infants’ and toddlers’ cognitive and social skills (Halle et al. 2009 ; Hillemeier et al. 2009 ). However, process variables account for additional variance in both social and cognitive child outcomes and may even mediate the effect of structural characteristics (Flöter et al. 2013 ; NICHD 1998 ). Therefore, the assessment of the home-learning environment is not only limited to measuring structural aspects like sociodemographics, but also includes orientations (see Table  3 ); in particular, special emphasis is given to the assessment of processes . Mothers are asked about issues, such as joint activities and their language use at home and the quality of these interactions is also assessed by means of videotaping mother–child interactions during the first three assessment waves (see Table  3 ; “ Assessment of mother–child interaction: direct measurement of the home-learning environment and of the child’s characteristics in mother-child interaction ” section).

Assessment of mother–child interaction: direct measurement of the home-learning environment and of the child’s characteristics in mother–child interaction

On the one hand, the assessment of mother–child interactions as a dyadic process allows a deeper look into maternal interaction behavior as a crucial characteristic of the home-learning environment; on the other hand, it captures additional information about the relevant characteristics of the child.

The quality of maternal interaction behavior has been shown to impact a child’s language (Nozadi et al. 2013 ; Tamis-LeMonda et al. 2001 ), cognitive (NICHD 1998 ; Pearson et al. 2011 ), and socio-emotional development (Bigelow et al. 2010 ; Meins et al. 2001 ). High-quality maternal interaction behavior in very early childhood is mostly described as interaction behavior that provides the child with emotional support in terms of sensitivity, which is defined as a prompt, warm, and contingent reaction to the child’s needs and signals (Ainsworth et al. 1974 ). But stimulating interaction behavior in the sense of scaffolding behavior (Wood 1989 ) is also regarded as high-quality maternal behavior, even in early childhood.

However, maternal interaction behavior cannot be considered separately from the child’s behavior, as interaction is a dyadic process in which both partners’ behavior refers to each other in a reciprocal way. It is well acknowledged that children play an active role in the dyadic interaction process from the very beginning, initiating interactions (van den Bloom and Hoeksma 1994 ) and influencing their occurrence and appearance (Lloyd and Masur 2014 ). Additionally, the child’s temperament (e.g., fear, excitement, protesting, and crying) can become effective in an interaction (Mayer 2013 ).

Accordingly, the NEPS newborn cohort study assesses maternal as well as filial interaction behavior via observation. The mother–child interactions are videotaped in the family home and are rated afterward by trained coders. The interaction itself takes place in a semi-standardized play situation in which the mother and the child play with a standardized toy set (Sommer et al. 2016 ). The play situation is adapted to the different age-related requirements: In the first wave, the mother–child interaction is videotaped for 5 min in which toys from the NEPS toy set are provided. In waves 2 and 3, the mother and child are observed while carrying out a three-bag procedure in which the mother and child played for 10 min with toys from three different bags in a set order (NICHD 2005 ).

Maternal as well as filial interaction behavior is assessed using a macro analytic rating system whereby various interactional characteristics are evaluated on five-point-rating scales with qualitatively specified graduations ([EKIE]; Sommer and Mann 2015 ). The assessment of maternal behavior covers emotional supportive interaction behavior (like sensitivity to distress and non-distress, positive regard for the child, emotionality) and stimulating interaction behavior, including a common rating for language and play stimulation in the first two waves and differentiating language and mathematical stimulation in wave 3 when children were 2 years of age (see Table  3 ). The mother’s intrusiveness, detachment, and negative regard of the child were also rated. The coding of the child’s behavior and emotions focuses on the child’s mood, activity level, social interest in the mother, and sustained attention to objects.

Some selected results

NEPS data are disseminated among the scientific community for analysis and provide an important basis for substantive longitudinal and comparative research. In particular, the various measurements of child characteristics and the detailed measures of the home-learning environment, including the observation of mother–child interactions, enable in-depth analyses to be conducted. In the first section, the results on the reliability and validity of these direct measures and information on the underlying constructs are given, while the second section contains an analysis of early social disparities in the mother’s behavior and child’s development. In addition to using the data from the newborn cohort study (wave 1), Footnote 2 we also draw on the data obtained from the “ViVA project,” Footnote 3 which aims to validate the NEPS measures as one of its objectives.

Reliability and validity of measures of mother–child interaction

Assessing interactions in a large-scale assessment is challenging with regard to validity and reliability of the measurements and ratings. In the NEPS newborn cohort study, these challenges were solved quite successfully: Weighted inter-rater reliability ranged from 84 to 100 % and the ecologic validity of the observed maternal interaction behavior seems to be high, as the data from the ViVA project show that interaction behavior assessed in the semi-structured play situation is comparable to maternal interaction behavior in other situations, i.e., natural feeding and diapering situations (Friedman test comparing differences between interaction situations: χ 2  = 0.74, p  = 0.69; Intra-Class-Correlations of maternal interaction behavior in different situations: ICC  = 0.68, p  < 0.001; n  = 23–30; Vogel et al. 2015 ).

Assessing the quality of the mother’s interaction behavior is a core construct of the home-learning environment in the first waves of the newborn cohort study and focuses on socio-emotional aspects as well as on stimulation. Although the assessed indicators address different aspects of maternal interaction behavior, some of them are related to each other (see Table  4 ). It is worth noting that aspects, like intrusiveness, detachment, or negative regard, are not simply the negative end of the more or less pronounced positive dimensions.

From a theoretical point of view, high-quality interaction behavior includes both sensitivity and stimulation behavior. To test the assumption that a rather broad composite indicator of quality of interaction behavior is not only theoretically but also empirically meaningful, a confirmatory factor analysis was conducted (see Fig.  1 ). Items in the socio-emotional domain ( sensitivity to non - distress , Footnote 4 positive regard , and emotionality ) as well as stimulation loaded substantially on quality of interaction behavior (all standardized coefficients above 0.45). Positive regard (0.69) and stimulation (0.77) contributed the most to this factor. Internal consistency was high (Cronbach's α = 0.80).

Results from confirmatory factor analysis for the latent variable Q uality of interaction behavior (Linberg et al. 2016 ). N  = 2190; Chi 2 (2) = 16.05, p  < .000; RMSEA  = 0.06; CFI  = .99; based on all German-speaking mother–child interactions in wave 1

One should note, however, that this broad measure of the quality of interaction behavior is only slightly, albeit significantly, related to other aspects of the home-learning environment which were assessed via the parents interview: This includes issues, like the overall amount of joint activities with the child ( r  = 0.13, p  < 0.000) and special activities (joint picture book reading, r  = 0.13, p  < 0.000; joint construction play, r  = 0.07, p  < 0.000; and talking to the child, r  = 0.07, p  < 0.000).

Reliability and validity of measures of early child characteristics

Given the sample size and household setting, the available data on child characteristics provide a rather detailed insight into the early stages of development, especially with respect to early cognitive capacities and child temperament, which are both measured by multiple indicators. As expected, the first results revealed that these multiple assessment approaches refer to different facets of early child development.

The mother’s report on the child’s temperament deals with the reactions of the child to stressful situations and her/his susceptibility to calming related behavior. In line with previous evidence, this is hardly related to the indicators of child’s temperament, which were assessed in a fairly relaxed mother–child interaction situation ( r  = 0.05, p  < 0.05; Freund and Weinert 2015 ). At the same time, there is evidence supporting the validity and reliability of these measurements. In the ViVA validation study, the information from the questionnaire has been shown to represent the complete subscales of the IBQ-R from which the items were selected ( r  = 0.51 for negative affectivity/0.70 for orienting/regulatory capacity, p  < 0.01; Bayer et al. 2015 ). In addition, it is correlated with the children’s reactions to stress-inducing maternal behavior in a still-face-paradigm where the mother is instructed not to react to her child’s signals ( r  = 0.34–0.43, p  < 0.05; Freund and Weinert 2015 ).

Likewise this can be shown for the assessments of early cognitive capacities/competencies. In the ViVA study, the items on sensorimotor development (assessment of developmental status) were highly correlated with the complete cognition and motor subscales of the Bayley Scales, respectively ( r  = 0.48–0.63, p  < 0.01; Attig et al. 2015 ). Hence the data on sensorimotor development as well as the data on basic information processing abilities (habituation–dishabituation paradigm; 85 % of the videos codable; non-completion of child <1 %; inter-coder reliability in wave 1: κ = 0.91) both rely on scientifically well-established and successfully applied assessments. Nevertheless, they are hardly correlated with each other and thus seem to cover different aspects of early development ( r  = 0.06/0.14, p  < 0.05; Weinert et al. 2016 ).

Although the findings always have to be considered within the context in which the assessments were made (e.g., short version/time), the validity of the various measurements of child characteristics and maternal interaction behavior seems to be apparent.

Early roots of social disparities in child development

The data of the NEPS newborn cohort study allow for an analysis of early social disparities with respect to both early child characteristics and their mother’s interaction behavior. Analyses of data from the first assessment wave when children were 6–8 months of age are in accordance with a bioecological model of child development (Weinert et al. 2016 ). As hypothesized, the mother’s interaction behavior in the video-taped mother–child interaction situation varied significantly according to her educational background. With regard to the broad concept of quality of interaction behavior described above, the mother’s education accounted—even in these early phases of child development—for 4 % ( p  < 0.001) of the variance within the German subgroup of participants. However, as expected we did not find substantial disparities in child characteristics in early childhood, like basic information processing abilities (habituation–dishabituation paradigm), developmental status (sensorimotor scale), or socio-emotional child characteristics coded during mother–child interaction. Interestingly, some early roots of social disparities were observed in child’s characteristics, such as sustained attention to objects and activity level in mother–child interaction. Notably, as predicted, mother–child interaction turned out to be a mutual endeavor: Interactional characteristics of the child (especially the child’s mood, her/his social interest, and continuing sustained attention to objects) and the child’s temperament (orienting/regulatory capacity) accounted for 29 % ( p  < 0.001) of the differences in the overall quality of the mother’s interaction behavior, over and above the control variables (age, sex) and socio-economic conditions (equivalized family income, education of mother, living in partnership) (Weinert et al. 2016 ). Of course, it is still an open question whether the differences observed between children result from former or actual differences in the mother’s behavior or whether the differences in child characteristics and behavior are effective in eliciting their mother’s behavior. In fact, the interrelation between mother and child behavior may vary according to other factors, e.g., additional protective or risk factors (Freund et al. 2016 ). Future findings from the NEPS cohort study of newborns will contribute to explaining how social disparities (suspected at age two and beyond) emerge, how they change over time, which mechanisms contribute to their emergence, and how they impact future development and education.

Prospects and conclusions

Insights and conclusions from longitudinal studies and analyses on the conditions which influence early developmental progress, the emergence of disparities, and their impacts are relevant to educational facilities and social policy and thus to the individual child as well as to society. The present paper focused on the first waves of a large-scale German cohort study of newborns. The various measures will help to better understand the stabilities, changes, and effects of qualitative and quantitative characteristics that early learning environments and other influential conditions have. They also illustrate how the very early outcomes of infant development act as a basis for future development. The child’s development will be measured by testing the development of mathematical, language, and early natural science competencies. Domain-general cognitive abilities will also be assessed (i.e., non-verbal categorization, delay of gratification, verbal memory, and executive functions) along with indicators of socio-emotional development (subscales of the Strength and Difficulties Questionnaire (SDQ), Goodman 1997 ), temperament (subscales of the Children’s Behavior Questionnaire (CBQ), Rothbart et al. 2001 ), and personality (BigFive; short version of the Five Factor Questionnaire for Children (FFFK); Asendorpf and van Aken 2003 ). Learning environments will be measured by interviews and questionnaires which draw on the general framework described above and will be supplemented with assessments of different facets of parenting style. To ensure standardization and reduce administration errors, all tests are carried out on tablet computers in child-oriented, playful settings.

It is worth noting that the kindergarten cohort of the NEPS, which started in 2010, also assessed comparable measures from age five onward. Here a sample of about 3000 children (institutional sample from 279 ECEC centers and 720 groups) was included. Despite differences between cohort designs (e.g., individual vs. institutional sample; child assessments at the children’s home vs. in preschool; playful test administration with vs. without tablet computers; CAPI vs. CATI interviews of the parents) the two cohort studies allow for comparisons while at the same time being characterized by partially complementary strengths and weaknesses (e.g., more elaborate information on home-learning environment vs. on institutional characteristics; extensive assessment of early roots vs. extensive assessment of further development). Among other things, this allows for an in-depth analysis of the interrelation between variations as well as an analysis of the constancies and changes in learning environments and child development, and it also relays important information concerning relevant aspects of early education and how it impacts development, educational career, and future prospects.

A better understanding of the relevant factors and conditions influencing early child development and learning together with their impact on children’s future development, educational success, and well-being is of special importance for ECEC policy. Longitudinal studies are needed because they allow analyses of the mechanism and processes of change in these decisive variables. While in cross-sectional studies causal effects cannot be inferred, longitudinal studies—especially those that enable complex group-specific growth-curve modeling and the modeling of intra-individual change—combined with experimental and quasi-experimental comparisons not only contribute significantly to gaining deeper insights into developmental and educational processes and the conditions influencing them but can also answer important questions relevant to ECEC policy such as how does early compared to late entry to institutional care impact later development in various cognitive and non-cognitive domains? Is early institutional care especially valuable (and to what extent) for different subgroups of children/families (e.g., disadvantaged families, children/families with specific risk factors, children with a migration background, refugees, multilingual children, e.g., children learning German as an (early) second or third language)? What are the determinants of the quality of home-learning environment and its effects on child development and education? What are specific risk (or protective) factors and is it possible to compensate for (or to draw on) them?

Obviously, even longitudinal studies will not deliver straightforward conclusions for ECEC policy. However, they provide an important and essential basis for evidence-based policy by informing about relevant conditions of early child education and how they impact later development (e.g., successful future development, educational drawbacks or opportunities in the social, socio-emotional, and cognitive domain). In fact, it has been suggested that high-quality early education is of special importance from a psychological, an educational, a sociological, and an economic perspective and thus is of significant relevance not only to the individual but also to society as a whole (Heckman 2013 ; Sylva et al. 2011 ). NEPS data are especially helpful when it comes to gaining a better understanding of the development of competencies and decisive conditions over the life course—the samples are carefully drawn, the validity of data is high, and longitudinal data are available in a user-friendly form for analyses and even for international comparisons.

From 2008 to 2013, NEPS data were collected as part of the Framework Program for the Promotion of Empirical Educational Research funded by the German Federal Ministry of Education and Research (BMBF). As of 2014, NEPS is carried out by the Leibniz Institute for Educational Trajectories (LIfBi) at the University of Bamberg, Germany, in cooperation with a nation-wide network.

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“Video-based Validity Analyses of Measures of Early Childhood Competencies and Home Learning Environment” (ViVA)—project funded by the German Research Foundation (DFG; grant to S. Weinert) within the priority program 1646.

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Authors’ contributions

SW conceptualized and drafted the overall manuscript, sequence alignment, and revisions. In addition she cooperatively conceived the design and assessments of the studies, in particular the assessment of early child competencies, and the analyses of social disparities. AL especially drafted the part on the learning environments and the assessment of mother-child interaction; she conducted the data analyses on mother-child interaction and supported the analyses on ecologic validity of mother-child-interaction. MA contributed to the description of the overall design and did the analyses on early roots of social disparities. She is also involved in the conceptualization and coordination of data assessment of the infant cohort study. TL drafted the part on regulations in Germany and contributed to the description of the assessment of learning environments. He is also involved in the conceptualization of the assessment of this data. JDF did the analyses on the reliability and validity of measures of early child characteristics; he drafted this part and cooperatively planned and conducted the validation study. All authors were involved in the sequence alignment and revisions, and approved the final manuscript. All authors read and approved the final manuscript.

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Weinert, S., Linberg, A., Attig, M. et al. Analyzing early child development, influential conditions, and future impacts: prospects of a German newborn cohort study. ICEP 10 , 7 (2016). https://doi.org/10.1186/s40723-016-0022-6

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Barriers to high-quality ece, steps to improve quality ece, recommendations for pediatricians, recommendations for community-level actions, recommendations for national- and state-level actions, lead author, council on early childhood executive committee, 2015–2016, quality early education and child care from birth to kindergarten.

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Elaine A. Donoghue , COUNCIL ON EARLY CHILDHOOD , Dina Lieser , Beth DelConte , Elaine Donoghue , Marian Earls , Danette Glassy , Alan Mendelsohn , Terri McFadden , Seth Scholer , Jennifer Takagishi , Douglas Vanderbilt , P. Gail Williams; Quality Early Education and Child Care From Birth to Kindergarten. Pediatrics August 2017; 140 (2): e20171488. 10.1542/peds.2017-1488

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High-quality early education and child care for young children improves physical and cognitive outcomes for the children and can result in enhanced school readiness. Preschool education can be viewed as an investment (especially for at-risk children), and studies show a positive return on that investment. Barriers to high-quality early childhood education include inadequate funding and staff education as well as variable regulation and enforcement. Steps that have been taken to improve the quality of early education and child care include creating multidisciplinary, evidence-based child care practice standards; establishing state quality rating and improvement systems; improving federal and state regulations; providing child care health consultation; as well as initiating other innovative partnerships. Pediatricians have a role in promoting quality early education and child care for all children not only in the medical home but also at the community, state, and national levels.

Children’s early experiences are all educational, whether they are at home, with extended family and friends, or in early education and child care settings. Those educational experiences can be positive or negative. At present, more than half of children less than 5 years old regularly attend some type of out-of-home child care or early childhood program, 1 and their experiences in these settings will affect their future lives. 1 The arrangements families make for their children can vary dramatically, including care by parents and relatives, center-based child care, family child care provided in a caregiver’s home, care provided in a child’s own home by nannies or baby-sitters, or a combination of these types of care. 1 , – 3 How a family chooses this care is influenced by family values, affordability, and availability. 2 , 4 For many families, high-quality child care is not available or affordable. 2 , 4 This policy statement outlines the importance of quality child care and what pediatricians can do to help children get care in high-quality early childhood education (ECE) settings.

When care is consistent, developmentally appropriate, and emotionally supportive, and the environment is healthy and safe, there is a positive effect on children and their families. 5 , – 14 Children who are exposed to poor-quality environments (whether at home or outside the home) are more likely to have unmet socioemotional needs and be less prepared for school demands. 5 , – 14 Behavioral problems in ECE can lead to preschool expulsion with cascading negative consequences. Each year, 5000 children are expelled from ECE settings, which is a rate 3 times higher than that of their school-aged counterparts. 15 When behavioral health consultation is available to preschool teachers, the rate of reported expulsions is half that of the control population. 15 , 16  

Early education does not exist in a silo; learning begins at birth and occurs in all environments. Early brain and child development research unequivocally demonstrates that human development is powerfully affected by contextual surroundings and experiences. 17 , – 19 A child’s day-to-day experiences affect the structural and functional development of his or her brain, including his or her intelligence and personality. 17 , – 19 Children begin to learn to regulate their emotions, solve problems, express their feelings, and organize their experiences at an early age and then use those skills when they arrive at school. 19 The American Academy of Pediatrics (AAP) has recognized the importance of early brain and child development by making it a strategic priority.

Research of high-quality, intensive ECE programs for low-income children confirm lasting positive effects such as improved cognitive and social abilities (including better math and language skills than control groups). 5 , – 14 The indicators of high-quality ECE have been studied and are summarized in Table 1 .

Domains of Health and Safety Quality in ECE

Adapted from Stepping Stones 20  

There are different staff-to-child ratios for small-family homes, large-family homes, and centers. Ratios are also based on the ages of the children. Specific staff-to-child ratios are described in standard (1.1.1.2). 21  

Many families have no quality child care options in their immediate communities. 2 The positive effects from high-quality programs and the negative effects from poor-quality programs are magnified in children from disadvantaged situations or with special needs, and yet, these children are least likely to have access to quality early education and child care. 2 , 4 , 22 , 23 Barriers to high-quality ECE include inadequate funding and staff education as well as inconsistent regulation and enforcement. 15 Funding on the federal, state, and local levels (even when combined with parental fees) often does not provide adequate financial support to ensure proper training, reasonable compensation, or career advancement opportunities for the early education workforce. 2 , – 4 , 22 , – 25 Adequate compensation of early education providers promotes quality by recruiting and retaining trained staff and their directors. Young children, especially infants and toddlers, need stable, positive relationships with their caregivers to thrive, and staff retention helps maintain those strong relationships. 19 Budget restrictions also limit the number of children who can be served. 22 As of 2012, 23 states had wait lists for their child care subsidy programs, and many areas have wait lists for Head Start programs. 4 Finally, budget restrictions may limit a program’s ability to hire child care health consultants. ECE settings rarely have health professionals like school nurses despite the fact that the children served are younger, less able to express their symptoms, and are prone to more frequent infectious illnesses. 26 Some states require child care health consultants to visit infant and toddler programs regularly.

State regulations of ECE programs vary dramatically because of an absence of national regulation, and this contributes to variation in ECE quality. Family child care settings have different regulations than center-based care, and some forms of child care are exempt from regulation. 23 , 25 , 27 The variability in regulation, staff screening, staff training, and the availability of supports such as child care health consultation contribute to a wide variation in quality. Even when regulations are present, enforcement varies, and only 44 states conduct annual health and safety inspections. 23 , 25  

The definition of quality in ECE is becoming more evidence based as newer, validated measures become available. State licensing standards have been the traditional benchmarks, but they set a minimum standard that is typically considerably less than the recommendations of health and safety experts. 20 , 21 , 23 , 25 , 27 , 28 National organizations including the AAP, the American Public Health Association, and the National Association for the Education of Young Children have developed standards and voluntary systems of accreditation that are often more robust than state licensing regulations. The publication Caring for Our Children, Third Edition 21 includes evidence-based practice standards for nutrition, safety, hygiene, staff-to-child ratios, and numerous other subjects that have been shown to improve the quality of child care. 29 , 30  

The quality rating and improvement system (QRIS) is a method of quality improvement that is being implemented in >75% of states. 25 QRISs use research-based, measurable standards to define quality levels, which are often denoted by a star rating system. QRISs often use incentives (such as staff scholarships, tiered reimbursement for child care subsidies, and technical assistance and/or professional development) as strategies to improve ECE quality. Unfortunately, the QRIS does not always include key health and safety standards. Those who are responsible for implementing QRISs would benefit from input from pediatricians, who are familiar with health issues and with the challenges of translating research into practice. Child care resource and referral agencies are available nationwide, and they serve as regional resources for information about quality child care. They often also serve as a resource for QRIS implementation; however, most child care resource and referral agencies do not have adequate funding to hire early childhood health consultants as part of that technical assistance.

Improving access to child care health consultation is another way to positively affect the health and safety of children in ECE. Child care health consultants are health professionals who are trained to provide technical assistance and develop policies about health issues, such as medication administration, infection control, immunization, and injury prevention. 31 Child care health consultants also can provide developmental, hearing, oral health, and vision screenings and provide assistance with integrating children with special health care needs into ECE settings. 29 , 32 , 33  

The opportunities to use ECE programs to teach healthy habits (including healthy food choices, increased physical activity, and oral health practices) should not be overlooked. These messages can then be shared with families. Health screening services (such as vision and dental testing) also can be provided.

Innovative strategies to promote access to quality care and education also include state initiatives to promote cross-disciplinary teams (such as Early Childhood Advisory Councils), public-private funding partnerships, and universal preschool programs.

Ask families what child care arrangements they have made for their children, and educate them about the importance of high-quality child care. Resources include brochures (listed in Resources); checklists of quality, which can be accessed at www.aap.org/healthychildcare ; and referrals to local child care resources and referral agencies, which can be found at www.childcareaware.org .

Become educated about high-quality child care through the resources on the Healthy Child Care America Web site ( www.healthychildcare.org ), in Caring for Our Children , 21 and others (see Resources).

Be a medical home by participating in the 3-way collaboration with families and ECE professionals. The medical home concept of comprehensive, coordinated care is particularly critical for children with special health care needs. Three-way communication among the pediatricians, families, and ECEs can facilitate shared knowledge of the unique child care needs of children with special needs and foster implementation of child care policies and practices to meet those needs. 32 , 33 These activities are likely to improve access to ECE for these patients. Detailed care plans written in lay language assist in this collaboration. Medical team-based or time-based coding and billing may provide support for these efforts.

Advise families and early educators when children are having behavioral problems in ECE and are at risk for expulsion. Explain the triggers for behavior problems and recommend behavioral health resources as needed. 16 Some states have behavioral health resources available for young children through an Early Childhood Mental Health Consultation program. Read the AAP policy statement and technical report on toxic stress 19 and learn about the resources that are available through each state’s early care and education system.

Discuss the importance of guidelines on safe sleep, immunization, safe medication administration, infection control, healthy diet and physical activity, oral health, medical home access, and other health topics with local child care centers. Share resources such as Caring for Our Children , 21   Bright Futures , and the Healthy Child Care Web site ( www.healthychildcare.org ).

Become a child care health consultant or support your local child care health consultant nurses. Consider conducting a health and safety assessment in a local child care program by using a national health and safety checklist ( www.ucsfchildcarehealth.org ).

Educate policy makers about the science that supports the benefits of quality early child care and education and, conversely, the lost opportunities and setbacks that result from poor-quality care. 15 , 24  

Close the gaps between state regulations and the quality standards outlined in Caring for Our Children by encouraging strong state regulation and enforcement. Each AAP chapter has a legislative group that can help target these public policy makers with visits and letters. Nearly every AAP chapter also has an Early Childhood Champion, a pediatrician who is familiar with the early education and child care needs in that chapter and has knowledge about local resources to assist your efforts. Find your Early Childhood Champion at www.aap.org/coec .

Support a QRIS in your state if one is being implemented, and encourage robust child health and safety standards based on Caring for Our Children .

Advocate for improved funding for child care health consultation.

Encourage training of ECE professionals on health and safety topics, such as medication administration and safe sleep practices for infants. Consider providing training that uses the Healthy Futures curriculum provided on the Healthy Child Care Web site ( www.healthychildcare.org ).

Advocate and encourage expanded access to high-quality ECE through funding, such as expanded Child Care Developmental Block grants or Head Start funding. Reach out to legislators on the national and state levels to make the case for investing in quality early education as a good business, education, and social investment that has shown a strong return on investment. Encourage pediatric representation on state Early Childhood Advisory Councils or similar state groups to make the case to state officials personally.

American Academy of Pediatrics. Choosing Child Care: What’s Best for Your Family [Pamphlet]. Elk Grove Village, IL: American Academy of Pediatrics; 2002. Available through the AAP publications department: 800/433-9016 or at www.aap.org

American Academy of Pediatrics. The Pediatrician’s Role in Promoting Health and Safety in Child Care. Elk Grove Village, IL: American Academy of Pediatrics; 2001. Available at: www.healthychildcare.org

Child Care Aware, National Association of Child Care Resource and Referral Agencies (NACCRRA). Is this the right place for my child? 38 research-based indicators of quality child care. Available at: http://childcareaware.org/resources/printable-materials/

Child Care Aware, National Association of Child Care Resource and Referral Agencies (NACCRRA). Quality child care matters for infants and toddlers. Available at: http://childcareaware.org/families/choosing-quality-child-care

Child Care Resource and Referral Agencies, local referral agencies that can assist families in finding quality, affordable programs. Available at: http://childcareaware.org/families/choosing-quality-child-care/selecting-a-child-care-program/

Head Start. Early childhood learning and knowledge center. Available at: http://eclkc.ohs.acf.hhs.gov/hslc/tta-system/health

Healthy Child Care America. Federally funded and housed at the AAP, this Web site has many resources for health and ECE professionals. Available at: www.healthychildcare.org

National Association for the Education of Young Children. Developmentally Appropriate Practice in Early Childhood Programs Serving Children from Birth through Age 8. 3rd ed. Washington, DC: National Association for the Education of Young Children (NAEYC); 2009. Available at: www.naeyc.org/files/naeyc/file/positions/PSDAP.pdf

National Resource Center for Health and Safety in Child Care and Early Education. Available at: www.nrckids.org

Zero to Three. Early Experiences Matter Policy Guide. Washington, DC: Zero to Three; 2009. Available at: https://www.zerotothree.org/resources/119-early-experiences-matter-policy-guide

Zero to Three. Matching Your Infant’s and Toddler’s Style to the Right Child Care Setting. Washington, DC: Zero to Three; 2001. Available at: https://www.zerotothree.org/resources/86-matching-your-infant-s-or-toddler-s-style-to-the-right-child-care-setting

American Academy of Pediatrics

early childhood education

quality rating and improvement system

Dr Donoghue updated the previous policy statement and revised that original document by adding references, updating the wording, and adding new sections based on updates from the field. The document went through several layers of review, and Dr Donoghue was responsible for responding to those comments.

This document is copyrighted and is property of the American Academy of Pediatrics and its Board of Directors. All authors have filed conflict of interest statements with the American Academy of Pediatrics. Any conflicts have been resolved through a process approved by the Board of Directors. The American Academy of Pediatrics has neither solicited nor accepted any commercial involvement in the development of the content of this publication.

Policy statements from the American Academy of Pediatrics benefit from expertise and resources of liaisons and internal (AAP) and external reviewers. However, policy statements from the American Academy of Pediatrics may not reflect the views of the liaisons or the organizations or government agencies that they represent.

The guidance in this statement does not indicate an exclusive course of treatment or serve as a standard of medical care. Variations, taking into account individual circumstances, may be appropriate.

All policy statements from the American Academy of Pediatrics automatically expire 5 years after publication unless reaffirmed, revised, or retired at or before that time.

FUNDING: No external funding.

Elaine A. Donoghue, MD, FAAP

Jill Sells, MD, FAAP, Chairperson

Beth DelConte, MD, FAAP

Elaine Donoghue, MD, FAAP

Marian Earls, MD, FAAP

Danette Glassy, MD, FAAP

Alan Mendelsohn, MD, FAAP

Terri McFadden, MD, FAAP

Seth Scholer, MD, FAAP

Jennifer Takagishi, MD, FAAP

Douglas Vanderbilt, MD, FAAP

P. Gail Williams, MD, FAAP

Claire Lerner, LCSW, Zero to Three

Barbara U. Hamilton, MA, Maternal and Child Health Bureau

David Willis, MD, FAAP, Maternal and Child Health Bureau

Lynette Fraga, PhD, Child Care Aware

Abbey Alkon, RN, PNP, PhD, National Association of Pediatric Nurse Practitioners

Laurel Hoffmann, MD, AAP Section on Medical Students, Residents, and Fellows in Training

Charlotte O. Zia, MPH, CHES

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Step 2: How Does Early Child Development Happen?

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• : 8 Things to Remember about Child Development
• : InBrief: The Science of Resilience

Step 3: What Can We Do to Support Child Development?

• : From Best Practices to Breakthrough Impacts
• : 3 Principles to Improve Outcomes

The science of early brain development can inform investments in early childhood. These basic concepts, established over decades of neuroscience and behavioral research, help illustrate why child development—particularly from birth to five years—is a foundation for a prosperous and sustainable society.

Brains are built over time, from the bottom up.

The basic architecture of the brain is constructed through an ongoing process that begins before birth and continues into adulthood. Early experiences affect the quality of that architecture by establishing either a sturdy or a fragile foundation for all of the learning, health and behavior that follow. In the first few years of life, more than 1 million new neural connections are formed every second . After this period of rapid proliferation, connections are reduced through a process called pruning, so that brain circuits become more efficient. Sensory pathways like those for basic vision and hearing are the first to develop, followed by early language skills and higher cognitive functions. Connections proliferate and prune in a prescribed order, with later, more complex brain circuits built upon earlier, simpler circuits.

The interactive influences of genes and experience shape the developing brain.

Scientists now know a major ingredient in this developmental process is the “ serve and return ” relationship between children and their parents and other caregivers in the family or community. Young children naturally reach out for interaction through babbling, facial expressions, and gestures, and adults respond with the same kind of vocalizing and gesturing back at them. In the absence of such responses—or if the responses are unreliable or inappropriate—the brain’s architecture does not form as expected, which can lead to disparities in learning and behavior.

The brain’s capacity for change decreases with age.

The brain is most flexible, or “plastic,” early in life to accommodate a wide range of environments and interactions, but as the maturing brain becomes more specialized to assume more complex functions, it is less capable of reorganizing and adapting to new or unexpected challenges. For example, by the first year, the parts of the brain that differentiate sound are becoming specialized to the language the baby has been exposed to; at the same time, the brain is already starting to lose the ability to recognize different sounds found in other languages. Although the “windows” for language learning and other skills remain open, these brain circuits become increasingly difficult to alter over time. Early plasticity means it’s easier and more effective to influence a baby’s developing brain architecture than to rewire parts of its circuitry in the adult years.

Cognitive, emotional, and social capacities are inextricably intertwined throughout the life course.

The brain is a highly interrelated organ, and its multiple functions operate in a richly coordinated fashion. Emotional well-being and social competence provide a strong foundation for emerging cognitive abilities, and together they are the bricks and mortar that comprise the foundation of human development. The emotional and physical health, social skills, and cognitive-linguistic capacities that emerge in the early years are all important prerequisites for success in school and later in the workplace and community.

Toxic stress damages developing brain architecture, which can lead to lifelong problems in learning, behavior, and physical and mental health.

Scientists now know that chronic, unrelenting stress in early childhood, caused by extreme poverty, repeated abuse, or severe maternal depression, for example, can be toxic to the developing brain. While positive stress (moderate, short-lived physiological responses to uncomfortable experiences) is an important and necessary aspect of healthy development, toxic stress is the strong, unrelieved activation of the body’s stress management system. In the absence of the buffering protection of adult support, toxic stress becomes built into the body by processes that shape the architecture of the developing brain.

Policy Implications

• The basic principles of neuroscience indicate that early preventive intervention will be more efficient and produce more favorable outcomes than remediation later in life.
• A balanced approach to emotional, social, cognitive, and language development will best prepare all children for success in school and later in the workplace and community.
• Supportive relationships and positive learning experiences begin at home but can also be provided through a range of services with proven effectiveness factors. Babies’ brains require stable, caring, interactive relationships with adults — any way or any place they can be provided will benefit healthy brain development.
• Science clearly demonstrates that, in situations where toxic stress is likely, intervening as early as possible is critical to achieving the best outcomes. For children experiencing toxic stress, specialized early interventions are needed to target the cause of the stress and protect the child from its consequences.

Suggested citation: Center on the Developing Child (2007). The Science of Early Childhood Development (InBrief). Retrieved from www.developingchild.harvard.edu .

Related Topics: toxic stress , brain architecture , serve and return

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Child Development in a Changing World: Risks and Opportunities

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Jo Boyden, Stefan Dercon, Abhijeet Singh, Child Development in a Changing World: Risks and Opportunities, The World Bank Research Observer , Volume 30, Issue 2, August 2015, Pages 193–219, https://doi.org/10.1093/wbro/lku009

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This review explores current understandings of child development and the consequences for children of risk exposure in low- and middle-income countries by integrating empirical evidence from development economics with insights from allied social science disciplines. It provides a holistic perspective that highlights the synergies between children's developmental domains, drawing particular attention to dimensions such as self-efficacy, self-esteem and aspirations, which have had only limited treatment in the economics literature to date, especially in developing countries. It concludes that there is strong evidence of dynamic relationships between risk factors in early childhood and later outcomes across multiple developmental domains, emphasizing the heightened effect of shocks to the care environment and the cumulative effect of multiple shocks. It also concludes that risk is distributed unevenly, with children who are both in poverty and disadvantaged socially according to, for example, their ethnicity bearing the greatest burden; within a household, gender, birth order and other factors mean that some suffer disproportionately from shortfalls and incomplete protection. However, this review finds that low endowments in early childhood can be at least partially compensated for through improved environments and investments in later childhood, emphasizing the resilience of some children. The review goes on to explore the impact on children of dramatic socio-economic changes that have occurred in recent years with rapid growth across most developing countries. It highlights four key forces for change—fall in absolute poverty, increased access to services, changing household incentives for investing in children, and changing social and cultural values—and stresses the ambiguous effects on the welfare of children and their long-term prospects. In so doing, the review aims to consolidate emerging evidence on how risks and opportunities for child development may have changed in these dynamic contexts.

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• DOI: 10.5964/ijpr.9715
• Corpus ID: 270839426

Interpersonal trust: Its relevance for developing positive emotions and social skills during childhood

• L. Oros , Sonia Noemí Chemisquy , Jael Vargas-Rubilar
• Published in Interpersona : An… 28 June 2024

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• Martin Butwell 2
• 1. Ravensbourne University London, London, United Kingdom
• 2. Terapia, Middlesex University London, London, United Kingdom
• Received: 10 March 2024 Revised: 21 May 2024 Accepted: 14 June 2024 Published: 26 June 2024
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• integrative child psychotherapy ,
• evidence-based practice ,
• common core ,
• unified approach

Citation: Tracey Cockerton Tattersall, Nadja Rolli, Martin Butwell. Integrative child psychotherapy: discussion of a common core and unified theory approach[J]. AIMS Medical Science, 2024, 11(2): 181-209. doi: 10.3934/medsci.2024015

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The physical environment and child development: An international review

Kim t. ferguson.

a Sarah Lawrence College

Rochelle C. Cassells

b Department of Human Development, Cornell University

Jack W. MacAllister

Gary w. evans.

c Department of Environmental Design and Analysis, Cornell University

d Bronfenbrenner Center for Translational Research, Cornell University

A growing body of research in the United States and Western Europe documents significant effects of the physical environment (toxins, pollutants, noise, crowding, chaos, housing, school and neighborhood quality) on children and adolescents’ cognitive and socioemotional development. Much less is known about these relations in other contexts, particularly the global South. We thus briefly review the evidence for relations between child development and the physical environment in Western contexts, and discuss some of the known mechanisms behind these relations. We then provide a more extensive review of the research to date outside of Western contexts, with a specific emphasis on research in the global South. Where the research is limited, we highlight relevant data documenting the physical environment conditions experienced by children, and make recommendations for future work. In these recommendations, we highlight the limitations of employing research methodologies developed in Western contexts ( Ferguson & Lee, 2013 ). Finally, we propose a holistic, multidisciplinary and multilevel approach based on Bronfenbrenner’s (1979) bioecological model to better understand and reduce the aversive effects of multiple environmental risk factors on the cognitive and socioemotional development of children across the globe.

The majority of the world’s children live in the global South (countries with a low to medium Human Development Index score, including Africa, Central and Latin America, and most of Asia), yet nearly all of the research on relations between the physical environments experienced by children and their cognitive and socioemotional development has taken place within North America and Western Europe. The purpose of this review is to call attention to this important gap in the literature and to introduce readers to emerging scholarship on children’s environments in the global South. We do not cover work on the physical environment and children’s physical health because this literature is extensive (cf., Wigle, 2003 ). We do, however, discuss physiological indicators of stress in explaining relations between components of the physical environment (e.g., crowding, noise) and children’s development.

We organize our review into a discussion of the impacts of toxins and pollutants (heavy metals, pesticides, air and water pollution), noise, crowding, chaos, housing quality, school and childcare quality, and neighborhood quality on the cognitive and socioemotional development of children and adolescents across the globe. For each of these commonly studied physical environment factors, we briefly review what is currently known in Western (North American and Western European) contexts and, where appropriate, discuss some of the known mechanisms linking each factor and children’s development. We also identify when the evidence is especially strong for particular influences. We provide a more extensive review of the research to date outside of Western contexts, with a specific emphasis on research in the global South. As we do so, we discuss the strength of the evidence for each influencing factor and, where there are gaps in the extant research, we briefly discuss what we do know (including available statistics) and make recommendations for future work. In these recommendations, we pay particular attention to the limitations of employing the same research methodologies and predicting similar results in previously under-studied contexts ( Ferguson & Lee, 2013 ; Nsamenang, 1992 ; 2004 ). We close with a call for a holistic, multidisciplinary and multilevel approach to investigate the impacts of the physical environment on child and adolescent development that employs an extension of Bronfenbrenner’s bioecological model ( Bronfenbrenner, 1979 ; Bronfenbrenner & Evans, 2000 ; Ferguson & Lee, 2013 ; Ferguson, Kim, Dunn, & Evans, 2009 ) as a theoretical framework.

Toxins and pollutants

Needleman et al. (1979) documented the impacts of lead exposure on young (grade school) children’s IQ and externalizing behaviors. Since then, many studies have shown that lead significantly impacts the cognitive functioning of children and adolescents in the United States and Western Europe, even when controlling for socioeconomic status (SES) and other confounding factors ( Evans, 2006 ; Hubbs-Tait, Nation, Krebs, & Bellinger, 2005 ; Koger, Schetteler, & Weiss, 2005 , Surkan et al., 2007 ; Wigle, 2003 ). More recently, a significant body of research has documented the effects of prenatal and childhood exposure to lead on children’s current and prospective developmental functioning in middle-income, newly industrial countries such as China ( Wang, Xu, Zhang, & Wang, 1989 ; Shen et al., 1998 ; Tang et al., 2008 ), India ( Ahamad et al., 2007 ; Patel, Mamtani, Thakre, & Kulkarni, 2006 ), the Philippines ( Solon et al., 2008 ) and Malaysia ( Zailina, Junidah, Josephine, & Jamal, 2008 ). Similar impacts have been documented in Egypt ( Mostafa, El-Shahawi, & Mokhtar, 2009 ), Mexico ( Acosta-Saavegra, 2011 ; Hu et al., 2006 ; Kordas et al., 2006 ), Peru ( Vega-Dienstmaier et al., 2006 ) and Bolivia ( Ruiz-Castell et al., 2012 ). Importantly, lead levels in these and other countries in the global South are still high and largely unregulated ( Karrari et al., 2012 ; Shen et al., 1998 ; Tong, von Schirnding, & Prapamontol, 2000 ; Walker et al., 2007 ). In fact, it is estimated that around 40% of children living in economically developing countries have elevated blood lead levels ( Walker et al., 2007 ). In addition, some of the most recent work across the globe has found that even very low levels of lead exposure can be toxic to infants and young children ( Canfield et al., 2003 ; Lanphear, Dietrich, Auinger, & Cox, 2000 ; Lanphear et al., 2005 ; Patel et al., 2006 ; Ruiz-Castell et al., 2012 ; Zailina et al., 2008 ).

An important characteristic of many toxins is that even after emissions are eliminated (e.g., removal of lead from gasoline and paint, the banning of the pesticide dichlorodiphenyltrichloroethane – DDT – in North America), they remain in the ecosystem for a very long time ( Meyer, Brown, & Falk, 2008 ). There are several pathways that enable this to occur. Heavy metals settle into the ground, and so lead is still found in the soil and in older houses that were painted prior to the banning of lead over three decades ago in the US. Lead used to be incorporated in plumbing (e.g., sodder) and thus can potentially leach into water supplies. Many toys and other common household products used to be made with lead, a practice that unfortunately continues today in China, for example ( Meyer et al., 2008 ). Another pathway that is perhaps more insidious is the cross-generational transmission of toxins. Some toxins are lipophilic, which means they can be stored in body fat. Thus prior exposure to some toxins, even preconception, can eventually affect the developing organism ( Hubbs-Tait et al., 2005 ; Koger et al., 2005 ). Finally, even when children themselves are not exposed to toxins, they may be susceptible to indirect exposure via parental exposures (Bouchard et al., 2011). A common example of this is from agricultural workers who absorb pesticides into their skin and/or their clothes ( Koger et al., 2005 ). Tragically, child laborers in many parts of the world remain in direct contact with toxins in agricultural, construction, and manufacturing sectors.

Numerous studies in North America document a dose-response function between body lead level burdens and IQ reductions. These findings have been replicated and demonstrated in prospective research designs, and hold true even when statistical controls rule out alternative explanations such as social class ( Evans, 2006 ; Hubbs-Tait et al., 2005 ; Koger et al., 2005 ). For example, Canfield et al. (2003) found that, after controlling for socioeconomic status and other demographic variables, three- to five-year-olds’ blood lead levels were significantly negatively associated with IQ, even at levels of exposure below the US regulated 10 μg/dl level. Teachers also report more attentional problems among children who have been exposed to lead ( Evans, 2006 ), and at least one North American study uncovered lead related deficits in attention, reaction time, and visual-motor integration among elementary school aged children ( Chiodo et al., 2004 ).

Estimates of developmental impacts of toxins such as lead may underestimate effects because of genetic differences in vulnerability. As an illustration, Nigg et al. (2008) showed that blood lead levels among 8- to 17-year-old American children were weakly associated overall with hyperactivity and impulsivity. However, these symptoms were significantly more accentuated in the subset of youth with abnormalities in a catecholamine receptor gene.

In the global South, most of the work to date has considered the impacts of lead on general cognitive functioning. Mostafa et al. (2009) showed that nearly half (43%) of a middle-class sample of 6- to 12-year-olds in Cairo had blood lead levels at or above the US Centers for Disease Control and Prevention limit of 10 μg/dl. A large proportion (37%) of these children were diagnosed with cognitive dysfunction. The most significant independent predictor of cognitive dysfunction was a blood lead level at or above 10 μg/dl. Similar to research in other contexts, a 1-μg/dl increase in blood lead level was associated with a two-point decline in IQ. Similarly, in a study of 6.5- to 8.5-year-old urban Malaysian children, Zailina et al. (2008) found that blood lead levels, statistically controlling for parents’ education, household income, and other family demographic factors, predicted children’s cognitive functioning. Studies in China across a wide age range have documented similar effects (Ling et al., 1989; Shen et al., 1998 ; Tang et al., 2012 ), as have recent studies of 6- to 8.5-year-old children in Peru (Vega-Dienstmaier et al., 2005) and Ecuador ( Counter, Buchanan, & Ortega, 2008 ).

The evidence for long-term effects of lead on children’s cognitive functioning following prenatal exposure is equally strong. Most recently, Yorifuji et al. (2011) found that, after controlling for SES and other potential covariates, 7- and 14-year-old children living in the Faroe Islands exposed to high levels of lead had deficits in short-term memory and attention compared to children exposed to lower levels of lead. Hu et al. (2006) found that maternal blood lead levels in the first trimester, but not in the second or third trimester, predicted 12- and 24-month-old Mexican infants’ general cognitive functioning (Mental Development Index, MDI). These effects were large; a 1-SD increase in first-trimester maternal plasma lead level was associated with a 3.5-point decrease on the MDI. Shen et al. (1998) similarly found that, after statistically controlling for confounding factors such as family SES and parental exposure to lead at work, 3-, 6-, and 12-month-old Shanghai infants with high umbilical cord lead levels received significantly lower MDI scores than those with lower lead levels. Current blood lead levels were not associated with MDI scores. In contrast, Solon et al. (2008) found that 6- to 30-month-old Filipino infants’ current blood lead levels significantly predicted their MDI scores. And Patel and colleagues (2006) found that the cord blood lead levels of neonates living in Nagpur, India, significantly predicted autonomic stability and abnormal reflexes. In addition, amongst infants with cord blood lead levels of 5–10 μg/dL, lead levels significantly predicted arousal state regulation, motor functioning and autonomic stability. These findings suggest that lead exposure has important effects on early motor functioning, even at very low levels.

Much less work on behavioral toxins has examined potential adverse socioemotional consequences. However, in Needleman et al.’s (1979) classic Boston school children study of lead and IQ, teachers, blind to the pupil’s lead dentine levels, rated children with higher lead burdens with more overt classroom behaviors indicative of behavioral problems such as inhibitory control. Eleven years later, these same children had higher rates of juvenile delinquency ( Needleman, Schell, Bellinger, Leviton, & Allred, 1990 ). Several other studies have shown linkages between early lead exposure and impulsivity, aggression, and hyperactivity in children ( Chandramouli, Steer, Ellis, & Emond, 2009 ; Chiodo et al., 2004 ; Evans, 2006 ; Hubbs-Tait et al., 2005 ).

The research on associations between lead exposure and children’s socioemotional functioning outside of the Western world is even more limited. However, in an early study of the impacts of prenatal lead exposure on both cognitive and socioemotional functioning at ages 2, 4 and 7 years in Kosovo, Factor-Livak et al. (1999) found that children’s behavior problems were associated with blood lead levels. Similarly, Bao et al. (2009) found that levels of lead and zinc in 7- to 16-year-old Chinese children’s hair samples predicted their behavioral functioning. And, in an intervention study in which 6- to 8-year-old children living close to a metal foundry in Torreón, Mexico, were given iron, zinc, both or placebo nutrition supplements over a period of 6 months, Kordas et al. (2006) found that blood lead levels were positively associated with passive off-task behaviors within classroom settings and negatively associated with activity levels during recess.

The impacts of exposure to mercury on children’s cognitive functioning are well documented. Low-level maternal mercury exposure damages infant sensorimotor functioning ( Mckeown-Eyssen, Ruedy, & Neims, 1983 ) and 6-year-old children’s IQ scores and language development ( Kjellstrom, Kennedy, Wallis, & Mantell, 1989 ). In addition, high-level maternal mercury exposure in Japan ( Matsumoto, Koya, & Takeuchi, 1965 ; Takeuchi, 1968 ) and Iraq ( Cox et al., 1989 ; Cox, Marsh, Myers, & Clarkson, 1995 ; Marsh et al., 1980 ) has been reported to adversely affect cognitive and physical prenatal and neonatal development. Two major longitudinal projects, one in the Seychelles (e.g., Myers et al., 2009 ; Stokes-Riner et al., 2011 ) and one in the Faroe Islands (e.g., Debes et al., 2006 ), have documented the adverse impacts of prenatal exposure to mercury from maternal consumption of seafood on young children’s cognitive functioning. Little work has documented impacts on socioemotional functioning, suggesting that further work in this area is needed.

In the Seychelles Child Development Study, maternal and child methylmercury (MeHg) levels, children’s cognitive and behavioral development, and various demographic factors have been assessed at the ages of 6, 19, 29, 66 and 107 months ( Myers et al., 2009 ) following an assessment of prenatal MeHg exposure. In the Faroe Islands study, postnatal MeHg exposure and children’s cognitive and behavioral functioning have been measured at ages 1, 7 and 14 years ( Debes et al., 2006 ), following an assessment of prenatal levels. In both studies, significant relations between prenatal and current MeHg and children’s early motor development and later cognitive functioning have been found, although the results are more consistent in the Faroe Islands study ( Myers et al., 2009 ; Stokes-Riner et al., 2011 ). These differences may have resulted from differential sources of MeHg (primarily fish in the Seychelles; primarily pilot whale meat in the Faroe Islands), as well as lower levels of aquatic food consumption in the Seychelles. Nevertheless, together these projects suggest that young children’s motor and cognitive development, and language, attention and memory in particular, are compromised following prenatal exposure to methylmercury.

Polychlorinated biphenyls (PCBs)

Prenatal exposure to polychlorinated biphenyls (PCBs), which are used in the manufacture of vinyl and other plastic compounds, has been linked with children’s cognitive and socioemotional functioning ( Evans, 2006 ; Lai et al., 2002 ; Ribas-Fito, Sala, Kogevinas, & Sunyer, 2001 ; Williams & Ross, 2007 ). In contrast, postnatal exposure appears to have few effects, except in the case of severe poisoning ( Ribas-Fito et al., 2001 ). These compounds have been banned in most high-income countries, but they continue to persist in environments across the globe, particularly as they tend to bioaccumulate in fish and other animals ( Faroon, Keith, Smith-Simon, & De Rosa, 2003 ; WHO, 2010 ).

A series of studies at two different American sites indicate that prenatal PCB exposure due to fish ingestion from polluted lakes has consistent adverse effects on neonatal developmental status (especially hyporesponsiveness) and memory among preschool and elementary school aged children ( Evans, 2006 ). In a more recent set of studies among Native American adolescents, Newman and colleagues (2006 ; 2009 ) found that PCB body burden was associated with memory impairments and poorer comprehension/reasoning. This replicates some prior work with preadolescents ( Evans, 2006 ). An important and sobering aspect of these recent data is that, although indigenous populations in both the global North and South are frequently exposed to higher levels of toxins than are other populations, the Native American youths’ levels of PCBs were well within the “normal” range found in American children. Most research on PCBs and development has focused on highly exposed populations.

No known research has investigated the impacts of PCB exposure on the cognitive functioning of children living in the global South, and in fact levels of exposure are also largely unknown ( Faroon et al., 2003 ; WHO, 2010 ). However, presumably the effects would be consistent with those reported in other contexts. This was found to be the case in a longitudinal study assessing Taiwanese children’s cognitive and behavioral development every year through age 12 following prenatal exposure to PCBs in contaminated cooking oil ( Lai et al., 2002 ). In comparison to matched unexposed children, children exposed to PCBs had long-term deficits in IQ.

Much less is known about PCB exposure and various aspects of socioemotional development. There may be problems with executive functioning such as attentional control ( Evans, 2006 ; Hubbs-Tait et al., 2005 ; Koger et al., 2005 ). And Lai et al. (2002) found that Taiwanese children exposed to high levels of PCBs prenatally exhibited a greater number of externalizing and internalizing symptoms than did matched unexposed children.

One other developmental aspect of toxin exposure and children’s maturation worth mentioning is that lower SES contexts appear to accentuate the harmful impacts of toxins on children’s development ( Evans, 2006 ). This might occur for several reasons, including chronic stress, levels of cognitive stimulation in the home, co-occurrence of other toxin exposures, co-occurrence of other risk factors, and, for older children, poorer quality school environments.

Research on the developmental impacts of direct residential pesticide exposure or indirect prenatal or occupational exposure (on the skin or clothing of exposed caregivers) is somewhat limited. However, there is an extensive research literature documenting severe impacts of pesticide exposure on both rats and in vitro models of the mammalian brain (see, e.g., Aldrige, Meyer, Seidler, & Slotkin, 2005 ; Jameson, Seidler, & Slotkin, 2007 ). Since pesticides are neurotoxic agents, they may well have serious effects on the developing brain. Indeed, in a recent review, Jurewicz and Hanke (2008) conclude that there is good evidence for the impact of various pesticides on motor functioning (abnormal reflexes) in the newborn human and both motor and cognitive functioning (particularly reaction times, attention and short-term memory) on children. We also know that the developing fetus and young children have lower levels of the detoxifying enzymes that may deactivate organophosphate compounds in adults ( Furlong et al., 2006 ). This suggests that the effects of agricultural pesticides on children may be particularly problematic.

Dichlorodiphenyltrichloroethane (DDT) and related organochlorine compounds used as pesticides have been largely phased out in the US and Europe ( Rohlman et al., 2005 ). Thus their impacts on children’s developmental functioning in these contexts are understudied. However, a longitudinal study in the early 1990s in the United States found that prenatal dichlorodiphenyldichloroethylene (DDE) exposure impacted motor functioning at 18 and 24 months, but did not impact cognitive development at ages 3, 4 and 5 years ( Jurewicz & Hanke, 2008 ). Two more recent studies in Spain ( Ribas-Fito et al., 2003 ; 2006 ) and one in the United States ( Eskenazi et al., 2006 ), however, using similar assessment tools, did find significant relationships between cord blood and maternal serum levels of DDE, DDT and related compounds on the cognitive and psychomotor functioning of both infants and young children.

More contemporary organophosphate pesticides may similarly impact reflexes in infants ( Jurewicz & Hanke, 2008 ), reaction times in early childhood ( Rohlman et al., 2005 ), and infant and early childhood psychomotor development ( Jurewicz & Hanke, 2008 ; Rauh et al., 2006 ; Ruckart, Kakolewski, Bove, & Kaye, 2004 ). There is also some evidence for effects on specific cognitive skills, particularly short-term memory and attention ( Jurewicz & Hanke, 2008 ; Lizardi, O’Rourke, & Morris, 2008 ; Rauh et al., 2006 ; Ruckart et al., 2004 ). In addition, these effects appear to persist over time: Rauh et al. (2006) found that low-income, urban minority children in New York City who were exposed to high levels of the insecticide chlorpyrifos were more likely than other children to have delays in their overall cognitive and motor development at 12, 24 and 36 months, and were also more likely to exhibit attention problems.

DDT and DDE are currently commonly used in the global South ( Jurewicz & Hankel, 2008 ; Mishra & Sharma, 2011 ), yet there is almost no research documenting the impacts of these compounds on children’s developmental functioning. However, a longitudinal study of infant cognitive and psychomotor functioning following prenatal exposure to DDE in Mexico found that maternal serum levels during the first trimester were negatively associated with infants’ motor development at 1, 3, 6 and 12 months of age (Torres-Sanchez et al., 2007). Similarly, Grandjean et al. (2006) and Harari et al. (2010) found that prenatal exposure to pesticides adversely impacted Ecuadorian children’s cognitive functioning at ages 6–9 years. Children’s current exposure was negatively associated with reaction times, but not with other cognitive measures. Likewise, Guilette et al. (1998) found that Mexican 4- and 5-year-olds’ prenatal and current exposure to pesticides delayed their motor development and some aspects of cognitive functioning. In a study using a similar design, comparing children living in rural areas with high pesticide use to those residing in low pesticide use areas, 4- to 5-year-old Indian children showed a similar profile (Kuruganti, 2005). And Rodríguez (2012) found that 7- to 9-year-old children of Nicaraguan agricultural workers who were exposed to a variety of pesticides prenatally had deficits in working memory, verbal comprehension, and overall IQ. Eckerman et al. (2007) demonstrated similar impacts on 10- to 18-year-old Brazilian children’s memory and attention resulting from current exposure. Thus there is some evidence that prenatal exposure may be particularly problematic, but that later exposure may also impact some aspects of children’s cognitive development. In addition, there is good evidence for high levels of prenatal and childhood exposure to both organochlorine and organophosphate compounds in low- and middle-income countries, including India ( Mathews, Reis, & Iacopino, 2003 ; Mishra & Sharma, 2011 ), Kazakhstan ( Zetterström, 2003 ), Ghana ( Mull & Kirkhorn, 2005 ), Nigeria ( Okafor, 2010 ) and Egypt ( Kishk, Gaber, & Abd-Allah, 2004 ). In Ecuador, Corriols and Aragón (2010) estimated that there have been 18,516 cases of acute pesticide poisonings between 1995 and 2006 among children aged 5–14 years, based on the 2069 reported cases. Many of these were due to occupational exposure, which is in fact a primary mode of exposure for young children working in agricultural settings in the global South ( Dorman, 2008 ).

The research documenting effects of pesticide exposure on children’s socioemotional development is limited, and the findings are mixed ( Ruckart et al., 2004 ). Rodríguez (2012) , however, found that ADHD symptoms were more common amongst pesticide-exposed girls, but not boys, in a sample of 7- to 9-year-old Nicaraguan children. These findings make sense, given other results documenting the impacts of pesticide exposure on children’s attention processes. Clearly, more research on the impacts of pesticide exposure on the socioemotional functioning of young children is warranted.

Air pollution

With ongoing rapid industrialization and urban growth, poor air quality is a serious concern in much of the global South, as well as in newly industrial countries in general ( Bartlett, Hart, Satterthwaite, de la Barra, & Missair, 1999 ). Here we discuss work in both the global North and South documenting the impacts of exposure to air pollution, primarily resulting from proximity to industrial plants and to air and road traffic, on children’s cognitive and socioemotional development.

Among the most common pollutants to be studied for its effect on cognition is nitrogen dioxide (NO 2 ), a toxicant produced by fossil fuel combustion and thus closely linked to road traffic as well as gas stoves. In Quanzhou, China, exposure to traffic-related pollution was found to be associated with poor performance on neurobehavioral tests ( Wang et al., 2009 ). Similarly, Dutch children exposed to high levels of NO 2 at home were found to score lower on memory evaluations, while no similar correlation was found between NO 2 exposure at school and cognitive outcomes ( van Kempen et al., 2012 ). A related study of children living near London’s Heathrow airport, however, found no association between exposure to NO 2 and cognitive performance in nine- to ten-year-olds ( Clark et al., 2012 ).

In other work on air pollution, prenatal exposure to environmental tobacco smoke was negatively associated with cognitive performance at age two in African American and Dominican children in New York City ( Rauh et al., 2004 ). Within the same populations, exposure to high levels of airborne polycyclic aromatic hydrocarbons (PAH) (largely from road traffic fuel combustion) was associated with lower cognitive scores and moderate developmental delay at age three ( Perera et al., 2006 ), and lower IQ scores at age five ( Perera et al., 2009 ). Similarly, exposure to PAHs was significantly associated with lower non-verbal IQ scores among five-year-olds in Poland ( Edwards et al., 2010 ). In China, children living within proximity of a coal-fueled power plant were found to have higher cord PAH levels than those in both the New York City and Poland studies, and these levels were associated with a greater risk of delay in motor development and language abilities at age two ( Tang et al., 2008 ). There are also potentially prolonged consequences of overexposure to PAHs. Noting that children highly exposed to PAHs were 2.89 times as likely to have lower MDI scores than unexposed children at the age of three, Perera et al. (2006) suggested that greater exposure to such high levels of pollution could adversely affect language, reading and math abilities later on.

Changes in brain structure as a result of exposure to high levels of air pollution have been proposed as a possible explanation for resulting cognitive defects. In Mexico City, urban air pollution was found to be associated with prefrontal white matter hyperintense lesions in both children and dogs; these lesions are believed to be associated with poor cognitive outcomes ( Calderón-Garcidueñas et al., 2008 ). Calderón-Garcidueñas and colleagues found that 56.5% of children living in highly polluted Mexico City possessed such lesions, in comparison to just 7.6% of children living in Polotitlan, an area with lower levels of pollution. The former also performed more poorly on psychometric tests. However, seven- and eight-year-olds in Mexico City exposed to high pollution levels generally scored lower in evaluations of short-term memory, attention and learning ability than those in Polotitlan, whether they possessed such lesions or not ( Calderón-Garcidueñas et al., 2011 ). Thus, as Calderón-Garcidueñas and Torres-Jardon (2012) note, exposure to high levels of air pollution is just one aspect of the environmental inequalities experienced by children from lower socioeconomic backgrounds in both the global North and South ( Evans, 2004 ).

Against the backdrop of such settings as New York City, Mexico City, and the rapidly growing cities of China, the majority of the literature on the subject seems to suggest that the relation between air pollution and developmental outcomes is one largely tied to industrialization and urbanization. A notable exception is Munroe and Gauvain’s (2012) investigation of the association between indoor open-fire cooking—a common practice in the global South—and cognition in four communities: Garifuna in Belize, Logoli in Kenya, Newar in Nepal, and Samoans in American Samoa. A moderate negative correlation between indoor open-fire cooking and block building performance, memory, pattern recognition and structured play was found.

Water pollution, sanitation and access

Many families in the global South have limited access to clean water and sanitation facilities ( Bartlett, 1999 ; Bartlett et al., 1999 ; Walker et al., 2007 ). This section will outline the effects of water quality (specifically pollution and sanitation) on children’s cognitive and socioemotional development in the global North and South.

The most common water pollutant studied in relation to children’s development is arsenic. Rosado et al. (2007) found that amongst 6- to 8-year-old children attending school near a smelter complex in Torreón, Mexico, those with higher concentrations of urinary arsenic performed worse on several measures of cognitive and language development than did children with lower concentrations. This relationship was not impacted by lead exposure, demographics, or nutritional factors, although lower SES children had higher levels of urinary arsenic. Likewise Tsai et al. (2003) found that young Taiwanese adolescents exposed to arsenic in well water had lower scores than unexposed adolescents on cognitive assessments of memory and attention switching, even after controlling for education and gender. And in a study of 9.5- to 10.5-year-old children using tubewells in Bangladesh, Wasserman et al. (2004) found that water arsenic levels were associated with poorer cognitive functioning. Asadullah and Chaudhury (2011) similarly found that eighth grade children exposed to arsenic-contaminated tubewells in rural Bangladesh had lower mathematics scores than those not exposed, even when controlling for schooling history, prior arsenic exposure, and parental factors. Wang et al. (2007) likewise found that rural Chinese eight- to twelve-year-olds living close to wells with high levels of arsenic received lower IQ scores than those who did not, although it should be noted that this relationship was only documented for children with high levels of exposure, and sociodemographic factors were not controlled for.

High manganese levels in the public water system may also impact children’s behavior, as documented by Bouchard et al. (2007) in a study of 6- to 15-year-old children’s behavioral functioning in Canada. After controlling for potential confounding variables (age, sex and income), they found that hair manganese was significantly associated with hyperactivity and oppositional behavior, as measured by teachers’ report. Interestingly, the positive relationship between hair manganese and hyperactivity was greater for older children (above 11 years old).

Research suggests that a lack of proper water sanitation and waste management exposes many children to water-borne diseases. For example, Copeland et al. (2009) found that 30% of households in Brazilian shantytowns had fecal contaminated drinking water. Besides their health effects, water-borne diseases also have adverse developmental consequences for children. Guerrant and colleagues (1999) explored the relationship between diarrheal illness (a common water-borne disease) early in childhood and the cognitive functioning of 6.5- to 9-year-old children living in a Brazilian shantytown. A significant negative correlation was found between children’s cognitive functioning and early childhood diarrhea (see also Niehaus et al., 2002 for similar results). And Lima et al. (2004) found that the availability of garbage collection and access to a toilet partially explained differences in cognitive and psychomotor performance of low-income 12-month-olds living in northeast Brazil. Likewise, in an investigation of the environmental conditions (including poor access to drinking water, an inconsistent electricity supply and inadequate sewage drains) impacting 7- to 8-year-old children’s cognitive development in war-torn Baghdad City, Ghazi and colleagues (2012) found that below average water quality (as reported by parents) was associated with lower IQ scores, and that access to services (including water quality, electricity supply and access to grocery stores) independently predicted IQ, after adjusting for parent education and income.

In addition to direct impacts on cognitive functioning, diarrhea and intestinal parasites resulting from bacteria-contaminated water (often from sewage) contribute towards malnutrition and stunting, both of which impact children’s IQ and school performance, and may also contribute towards behavioral problems ( Bartlett, 2003 ). These associations may result as early malnutrition and exposure to environmental toxins and stress can alter both brain structure and function, thus leading to long-term changes in cognitive and socioemotional functioning ( Grantham-McGregor et al., 2007 ). In addition, both illness and malnutrition may lead to increased absences from school and attention problems when in school. Further, access to water may impact school attendance directly, particularly for girls in the global South, who frequently have to walk long distances to collect clean water ( Bartlett, 2003 ). Finally, it is worth noting that global climate change is likely to affect access to clean water for millions of low-income families in the global South, particularly in Africa and parts of Asia, in the next 20 years ( Bartlett, 2008 ).

A recent article suggests that contaminated drinking water in childhood may have lasting effects. Aschengrau et al. (2011) conducted a retrospective study of children from eight towns in the US who were exposed to water contaminated with tetrachloroethane (PCE, a solvent used in dry cleaning) during the prenatal period and/or early childhood. They found that, after controlling for parental SES and other potential covariates, highly exposed individuals had higher rates of cigarette, alcohol, and other drug use in adolescence and early adulthood.

Numerous studies in high-income countries reveal that chronic noise exposure early in childhood interferes with reading acquisition ( Evans, 2006 ). Although most studies are cross-sectional with statistical controls for SES, several studies have demonstrated a dose-response function. Adverse impacts on reading have also been replicated in prospective longitudinal studies with the introduction of a new major noise source such as an airport, as well as in experiments with noise attenuation interventions. Children in higher elementary school grades suffer greater adverse reading outcomes; this has been attributed to longer duration of exposure ( Evans & Hygge, 2007 ) but might also reflect greater awareness of noise (Dockrell & Shield, 2004). Some studies have also shown worse reading outcomes for children exposed to noise at home and school, bolstering the duration of exposure explanation. Children with poorer cognitive skills also appear more vulnerable to the induction of reading deficits from noise exposure ( Evans, 2006 ; Dockrell & Shield, 2006 ).

Several cognitive deficits reliably associated with noise exposure are candidate mechanisms for the well-documented noise – reading link. Long-term memory is adversely affected by noise, and attentional strategies are altered by noise exposure ( Evans, 2006 ). Interestingly, a few studies have also shown linkages between chronic noise exposure and deficits in auditory discrimination (e.g., phoneme perception), a critical aspect of speech perception ( Evans, 2006 ; Evans & Hygge, 2007 ). Speech perception is a major building block of reading acquisition. Finally, emerging work in neuroscience indicates potentially detrimental noise effects on brain speech function and structure ( Kujala & Brattico, 2009 ).

Chronic noise exposure, similar to many of the environmental conditions described herein, is not only aversive but also uncontrollable and sometimes unpredictable as well. Repeated exposures to uncontrollable as well as unpredictable events can undermine human motivation ( Cohen, Evans, Stokols & Krantz, 1986 ), thus impacting the persistence and effort needed (amongst other things) for academic achievement. The first human studies of learned helplessness employed uncontrollable noise as the induction stimulus ( Hiroto, 1974 ; Krantz, Glass & Schneider, 1974 ). Since then, many studies have shown that uncontrollable noise exposure can cause learned helplessness ( Evans & Stecker, 2004 ).

The bioecological perspective ( Bronfenbrenner & Morris, 1998 ) suggests a complementary set of processes that might also be related to noise and reading acquisition. Noise might alter caregiving behaviors salient to reading acquisition. We know, for example, that teachers in high noise impact schools alter their teaching methods and also complain about interruption and fatigue ( Evans, 2006 ). It is conceivable that parents might talk less to their children, be less responsive to children’s verbalizations, and not read aloud as much to their children in high noise settings.

Research on the relation between noise and children’s cognitive development outside of the United States and Europe is extremely limited. However, what evidence there is suggests that noise levels impact children in varying contexts similarly. Seabi, Goldschagg, and Cockcroft (2010) found that 9- to 13-year-old South African children attending a public school in a high aircraft noise area had poorer reading comprehension and reduced visual attention in comparison to a matched group of children attending a public school with typical levels of noise exposure. No differences in working memory were found, however. Clearly, further work in the global South is desperately needed, particularly as there is some evidence to suggest that noise levels might be significantly higher than in higher-income countries. For example, in a recent comparison of quiet versus noisy public schools in urban India, Lepore, Shejwal, Kim and Evans (2010) recorded a decibel level of 85 dBA. Since decibels is a logarithmic scale, and about 45 dBA is considered appropriate, this is very loud.

Outside of the global South, Hiramatsu and colleagues (2004) found deficits in long-term but not short-term memory among 8- to 11-year-olds residing proximate to a large air force base in Okinawa, Japan compared to their peers living in quiet areas. Similarly, Karsdorf and Klappach (1968) found that secondary school aged children attending noisy schools (proximate to road traffic) in Halle, former East Germany, had more focused attention problems compared to their peers in relatively quiet secondary schools. Finally, recent work in Belgrade, Serbia indicates that chronic residential noise exposure from road traffic can interfere with executive functioning, but only among elementary school aged boys ( Belojevic et al., 2012 ).

Evidence from both laboratory and field studies in North America and Western Europe shows that noise exposure is stressful, creating irritation and annoyance and elevating cardiovascular indicators of stress such as blood pressure and neuroendocrine stress hormones (e.g., cortisol) ( Evans, 2006 ; Paunovic et al., 2011). In most of these studies, resting physiological stress measures were taken under quiet conditions. Thus the indications of elevated stress are in relation to chronic noise exposure. There are more studies of aircraft relative to street traffic noise, with evidence for the former having stronger physiological impacts than the latter ( Evans, 2006 ). However, Babisch et al. (2009) found that a nationally representative sample of 8- to 14-year-old German children whose bedrooms faced a high traffic street had higher blood pressure than those with a bedroom facing a low traffic street. These relations were independent of various sociodemographic factors.

Studies in Slovakia ( Regecova & Kellcrova, 1995 ) and Serbia ( Belojevic et al., 2008 ; Paunovic et al., 2009 ) also revealed adverse impacts of road traffic noise on children’s blood pressure, even after statistically controlling for variables such as maternal education. Nine- to 13-year-old children residing near airports in Russia in the mid 1960s had higher blood pressure than their peers in quiet areas ( Karagodina, Soldatkin, Vinokur, & Klimukhin, 1969 ). In a study also conducted in the mid 1960s in former East Germany, Karsdorf and Klappach (1968) found that secondary school children attending urban schools located proximate to busier streets with higher noise levels had significantly higher resting blood pressure. Finally, Wu et al. (1993) found that, amongst 7- to 12-year-old Taiwanese children attending schools in high road traffic noise areas of Taipei, those with typical hearing had significantly higher blood pressure than those who were deaf.

Data are mixed on chronic noise exposure and children’s socioemotional development. Prospective, longitudinal data show that German elementary school children report lower levels of psychological well being with increases in noise exposure from aircraft ( Bullinger et al., 1999 ). A cross-sectional Austrian study of traffic noise reported a dose-response function between noise levels and teacher ratings of psychological well being among elementary school children if the child had biological risk factors such as prematurity or low birth weight ( Lercher et al., 2002 ). Two different cross-sectional studies of European school children have uncovered relations between aircraft noise exposure and elevated symptoms of hyperactivity ( Haines, Stansfeld, Brentnall et al., 2001 ; Stansfeld et al., 2009 ; but see Haines, Stansfeld, Job et al., 2001 ). None of these European studies found a link between noise levels and general, overall indices of psychological well being. Finally, Ristovska et al. (2004) compared several measures of mental health among 4 th grade children in Macedonian schools varying in traffic noise exposure. Children in the noisier schools had decreased social skills and more oppositional behaviors but were similar in levels of anxiety compared to their peers attending quieter schools. Recall also that, as indicated above, several studies have shown a link between chronic noise exposure and elevated learned helplessness among children ( Evans, 2006 ).

The most consistent crowding metric with human consequences is people per room. Indices of external density such as people per census tract typically yield no associations with human behavior ( Evans, 2006 ). Studies that have teased apart residential density from family size find the former rather than the latter to be the more critical variable. Although many believe there are differences in tolerance for crowding across different cultural contexts, the cognitive and behavioral development of children living in as diverse contexts as the United States, India, Thailand, Egypt, Hong Kong, South Africa, and Jamaica indicates similar developmental correlates of crowding in both residential and school settings ( Evans, 2006 ; Liddell & Kruger, 1987 ; 1989 ; Wachs & Corapci, 2003 ).

It is important to note that children in the global South, relative to North America and Western Europe, tend to live in more crowded home environments. For example, Evans, Lepore, Shejwal and Palsane (1998) found that densities (people per room) among primarily working class Indian families ranged from .67 to 5 persons/room, with a mean of 1.81. The US Census considers > 1 person/room to be crowded.

Significant research across multiple contexts documents the impacts of crowding on general school achievement and IQ, reading comprehension, and object spatial relations ( Evans, 2006 ). In a study of low-SES rural Eygptian 3- to 6-month-olds, Rahmanifar et al. (1993) found that infants in more crowded households were more lethargic and drowsy, conditions associated with delayed development. In their examination of 12-month-old children of recent Haitian immigrants to the US, Widmayer and colleagues (1990) found similarly that residential crowding was linked to delays in psychomotor, but not cognitive development. These associations may result from disruptions of children’s exploration, play and engagement with both objects and people in their immediate environments (Heft, 1985; Liddell & Kruger, 1987 ; 1989 ).

Crowding in educational environments has also been linked to more off-task time ( Kantrowitz & Evans, 2004 ; Krantz, 1974 ). For example, Liddell and Kruger (1987) found that levels of crowding within a crowded urban South African childcare center were negatively associated with 32- to 64-month-old children’s levels of cooperative play and positively associated with the percentage of time spent unoccupied. In a follow-up study, they found that children from more crowded homes spent less time engaged in play with objects, more time unoccupied, and more time as onlookers ( Liddell & Kruger, 1989 ). Similarly, in an investigation of relationships between the home environment and Egyptian toddlers’ adaptive behavior, Wachs et al. (1993) found that 24- to 29-month-olds’ simultaneous involvement with persons and objects in their environment was negatively correlated with density.

Residential crowding can also disrupt parent-child interactions ( Evans, 2006 ; Wachs & Corapci, 2003 ). In more crowded homes, parents talk less with their infants and toddlers ( Wachs et al., 1993 ) and use less complicated vocabulary and sentence structures with their toddlers ( Evans, Maxwell, & Hart, 1999 ). Not surprisingly, in an investigation of the influences of parental SES on South African children’s outcomes, Goduka et al. (1992) found that crowding predicted 5- to 6-year-old vocabulary scores. Children’s physical development and quantitative skills were also adversely associated with household crowding.

Evans et al. (1998) showed that some of the adverse effects of residential crowding, statistically controlling for SES, on Indian elementary school children’s academic achievement were mediated by heightened family conflict. Another variable that may help account for the link between household crowding and diminished academic achievement is inadequate space to do homework. In a study of low-income families living in apartments in Singapore, Hassan (1976) found an inverse relationship between apartment square footage and school performance among children. More crowded apartments also had inadequate privacy for students to study. The latter relation was also reported among secondary school pupils living in apartments in Hong Kong ( Mitchell, 1971 ). These effects of crowding on children’s cognitive functioning have similarly been reported in North America and Western Europe ( Evans, 2006 ), with consistent differences found for standardized achievement scores in grade school children. Moreover, the adverse associations uncovered between residential density and diminished academic achievement continue through secondary school, independent of family SES ( Evans, 2006 ). In addition, in an instrumental variable analysis of national data in France, Goux and Maurin (2005) showed that the probability of having to repeat a grade among 15-year-olds was strongly linked to overcrowding in the household.

Crowded home and school environments significantly impact the behavior and socioemotional functioning of both children and their parents ( Evans, 2006 ; Wachs & Corapci, 2003 ). For example, Ani and Grantham-McGregor (1998) found that crowding independently predicted Nigerian elementary school boys’ levels of aggressive behavior in school. Parental perceptions of residential crowding were inversely associated with positive social behavior amongst 3- to 35-month-old Burundian refugee children living in the United States ( McAteer, 2012 ). Interestingly, in a study of feeding practices in Jamaican primary schools, Grantham-McGregor, Chang, Walker, and Powell (1998) found that the negative impacts of classroom crowding on children’s behavior were exacerbated by poor nutrition.

One of the effects of high-density living may be greater difficulty monitoring and regulating children’s behaviors. Less parental monitoring is a well-documented predictor of behavioral conduct disorders, including juvenile delinquency. Parents in both Singapore (Hassan, 1976) and Hong Kong ( Mitchell, 1971 ) noted greater difficulties monitoring their children as a function of household crowding, and in the former case this appeared to contribute to greater juvenile delinquency rates.

Greater family conflict and tension have been reported among crowded Indian and Thai families ( Evans et al., 1998 ; Fuller et al., 1993 ), and a number of studies in low-income countries have documented positive associations between household crowding and physical punishment of children ( Afifi, El-Lawindi, Ahmed, & Basily, 2003 ; Youssef et al., 1998 ; Gage & Silvestre, 2010 ; Sumba & Bwibo, 1993 ; Vega-Lopez et al., 2008 ). In a survey of parenting values conducted in 34 low- and middle-income countries around the globe, Cappa and Kahn (2011) documented a relatively consistent link between household crowding and maternal endorsement of the need for physical punishment in child rearing.

In high-income countries both children and parents report more strained, negative familial interactions in high-density homes ( Evans, 2006 ), as well as instances of elevated punitive parenting practices. Children in more crowded preschools and elementary schools also evidence more aggressive behaviors towards their classmates ( Evans, 2006 ). One of the factors believed to drive part of the crowding – aggression link is conflict over scarce resources such as toys ( Evans, 2006 ).

One of the ways in which crowded family members appear to cope with crowding is to socially withdraw from one another, which can have the unintended consequence of diminishing socially supportive relationships ( Evans et al., 2001 ). A number of studies, including some with random assignment, have shown that crowded children tend to be more socially withdrawn ( Evans, 2006 ). Parents in more crowded homes are also typically less responsive to their children ( Evans, 2006 ).

Given greater social withdrawal among children in high-density homes and lower levels of parental responsiveness in similar situations, some investigators have explored whether crowding might also be linked to psychological distress among children. As indicated above, there is already evidence of elevated rates of aggression, withdrawal, and behavioral conduct disorders such as juvenile delinquency. A small number of studies in North America and Europe have shown that children in more crowded homes have higher levels of psychological distress ( Evans, 2006 ). They are also more susceptible to learned helplessness ( Evans, 2006 ; Evans & Stecker, 2004 ). This effect has been produced in a laboratory experiment on crowding and persistence on puzzles, and at least two field studies showed a dose-response function between residential density and learned helplessness ( Evans, 2006 ).

In a study of 10- to 12-year-old Indian children, Evans et al. (1998) showed that residential density was inversely related to teacher ratings of behavioral adjustment at school, and elevated conflict and lower levels of social support within the family. SES was included as a statistical control. For girls but not boys, density was also related to learned helplessness. Family conflict partially mediated these relationships. The authors also found that resting blood pressure was elevated among more crowded boys, but not girls. This matches several studies indicating elevated indices of physiological stress among children living in more crowded homes or attending more crowded schools/childcare ( Evans, 2006 ).

Household chaos

Research on children’s environments focuses on the intensity of exposures, largely ignoring temporal issues such as duration and stochasticity. The paucity of research on duration of exposure is unfortunate, particularly in thinking about the maturation of developing processes over time. This section brings attention to another largely unexamined property of children’s environments – their degree of structure and predictability. One of Urie Bronfenbrenner’s fundamental contributions to child development was the insight that proximal processes, the exchanges of energy between the developing child and the persons and objects in their immediate settings, need to occur on a regular, sustained basis in order to be effective ( Bronfenbrenner & Morris, 1998 ). Bronfenbrenner also argued that proximal processes need to be reciprocal between the child and her surroundings and become progressively more complex as she matures. Settings that are unpredictable and unstructured may destabilize children’s development because they interfere with effective proximal processes ( Bronfenbrenner & Evans, 2000 ; Bronfenbrenner & Morris, 1998 ). This thinking has led to emerging interest in chaos and children’s development ( Evans & Wachs, 2010 ; Fiese, 2006 ). Most studies use parental or observer ratings of levels of structures and routines coupled with indications of noise, crowding, and various other interruptions of household activities to evaluate levels of chaos. Evans and Wachs (2010) , in a recent volume on chaos and child development, provide an in-depth discussion of the measurement of chaos.

Chaos has been linked, primarily in cross-sectional studies in North America, to academic achievement and socioemotional development, including behavioral conduct difficulties and symptoms of internalization (e.g., depression, anxiety) ( Ackerman & Brown, 2010 ; Fiese & Winter, 2010 ). Chaos has also been linked to deficits in self-regulation and learned helplessness ( Brody, Flor, & Gibson, 1999 ; Evans, Marcynyszyn, Gentile, & Salpekar, 2005 ) and comprehension of social cues ( Dumas et al., 2005 ).

Although the majority of the work on chaos and child development has been conducted in Western contexts ( Wachs & Corapci, 2003 ; Weisner, 2010 ), a recent study by Shamama-tus-Sabah, Gilani, and Wachs (2011) found that levels of chaos in the homes of 8- to 11-year-old Pakistani children uniquely predicted internalizing and externalizing behavioral problems and lower levels of adaptive behavior, as rated by both mothers and teachers. No relations between chaos and cognitive development were found. Using the same data set, Shamama-tus-Sabah and Gilani (2010) also found that home chaos predicted children’s conduct problems. Clearly, further work in low-income countries is warranted, particularly as at least some components of chaotic environments (specifically the interruption of daily routines, and thus children’s proximal processes) likely impact children growing up in the global South in similar ways to their American and European counterparts ( Wachs & Corapci, 2003 ; Weisner, 2010 ).

Residential mobility

Poverty, substandard housing, and slum dwellings without security of legal tenure often lead to excessive residential mobility. Reliable housing is critical for children’s security and stability, and is essential if families are to establish daily routines ( Bartlett et al., 1999 ). High levels of residential mobility in North America are associated with poorer psychological adjustment, less socially supportive peer relationships, and deficits in academic achievement ( Adam, 2004 ; Jelleyman & Spencer, 2008 ; Oishi, 2010 ). In addition, students and teachers in classes with high levels of mobility face considerable challenges because of the instability of their members. Early childhood residential instability can also influence developmental trajectories. Adolescents with more frequent moves tend to have diminished social networks and hold comparatively less central positions therein ( South & Haynie, 2004 ), and are vulnerable to earlier onset of sexual activity ( South, Haynie, & Bose, 2005 ). Bures (2003) , using a nationally representative sample of middle-aged American adults found that more frequent moves during childhood were associated with poorer mental health and more strained social relationships in midlife, independent of race, income, and education.

In the global South, residential mobility is high, particularly for low-income families living in urban areas ( Bartlett et al., 1999 ), who frequently face forced evictions ( Chatterjee, 2007 ). Although little work in the global South has directly evaluated the impacts of high residential mobility on children’s cognitive and socioemotional functioning, it is likely that high mobility disrupts proximal processes ( Bronfenbrenner & Evans, 2000 ). Further, children whose families are evicted from their homes in a violent manner may experience trauma. For example, Dizon and Quijano (1997) have documented the impact of violent forced evictions in Manila on young children’s emotional functioning, noting that many children report recurring nightmares and/or become withdrawn.

An extensive body of international research, much of it employing adapted versions of the HOME scale ( Bradley & Caldwell, 1980 ), has documented the impacts of the quality of the home environment on children’s cognitive and socioemotional development ( Bradley & Corwyn, 2005 ; Evans, Wells, & Moch, 2003 ; Iltus, 2007 ). The HOME scale and its variants, however, primarily consist of indices of parent-child interactions, with fewer items focused on the physical environment. Furthermore, most studies with the HOME do not look at the impacts of individual physical environment items on children’s developmental outcomes. Wachs and colleagues’ Purdue Home Stimulation Inventory (PHSI, Wachs, Francis, & McQuiston, 1979 ) provides more detailed information about the quality of the physical environment experienced by children, but it has not been employed as widely. In addition, although the HOME has been widely used in various cultural contexts, the scale as a whole, and the physical environment items in particular, may not adequately assess the full range of physical affordances offered by housing for children, particularly in the global South ( Hayes, 1997 ; Iltus, 2007 ; Ngorosho, 2010). In this section, we focus on what is currently known regarding the effects of housing type, physical housing quality, and the availability of resources for children, such as books and toys in the home.

Housing type

Research on housing type in more affluent countries has focused primarily on the potential developmental implications of high-rise housing. There is a long history of popular discourse about the allegedly harmful effects of living on the upper floors of large buildings on children’s development. These concerns are rooted in the association of large, multistory housing blocks with crime in public housing in the US, and with well-documented associations between building scale and crime ( Newman, 1972 ; Taylor & Harrell, 1996 ). However, although a few studies in high-income countries have shown an association between children’s academic achievement and residence in high-rise compared to low-rise buildings, there are also several non-replications of these relations ( Evans, 2006 ; Evans et al., 2003 ). One study showed that the effects held only for boys, which could also explain the mixed set of findings since most studies have not investigated gender differences in response to high-rise housing ( Saegert, 1982 ).

Several studies in high-income countries have found that children and youth in high-rise buildings manifest greater levels of behavioral conduct disorders (e.g., delinquency, aggression) ( Evans, 2006 ; Evans et al., 2003 ). In an investigation of relationships between high-rise dwelling and Japanese children’s behavior, Oda, Taniguchi, Wen, and Higurashi (1989) found that infants living on lower floors received higher scores on independent behaviors (such as greeting and potty training) than did those living on higher floors. However, these differences were not significant for kindergartners. These findings largely mirror those in Western contexts ( Evans, 2006 ; Evans et al., 2003 ). In addition, although children’s outcomes were not measured, Levi, Ekblad, Changhui, and Yuequin (1991) found that parents living in high-rise apartments in Beijing showed anxiety regarding the lack of easily monitored play spaces for children. In a study of families living in high- versus low-rise apartments in Israel, Churchman and Ginsberg (1984) similarly found that the outdoor play behavior of 4- to 5-year-old children living in high-rises was more restricted than that of other children, although it should be noted that these effects were not found at other ages (within the range of 2–13 years).

In the global South, housing type is inextricably connected to housing quality. There is little research investigating the impacts of housing type alone. Further, the variations in housing type are somewhat different from those in the global North, with high-rise dwellings being uncommon. However, there is some evidence that a high percentage of families, particularly low-income families in urban areas, live in informal housing, and that such housing often lacks basic amenities such as access to clean water ( Bartlett et al., 1999 ; Hall & Lobina, 2006 ). The implications of an unclean water supply have already been discussed above. In addition, informal housing is typically unstable, and children living in such areas frequently face eviction and therefore frequent residential mobility ( Bartlett et al., 1999 ), the implications of which have already been discussed. In addition, children living in informal housing may be more vulnerable to injury, and are more likely to be exposed to toxins from industrial waste. And children who are homeless or who live in informal housing may be less likely to attend school, as they lack a formal address ( Wegelin & Borgman, 1995 ). For example, a recent survey in Delhi found that only 54.5% of children in slums enrolled in school, as compared to 90% across the city as a whole ( Aggarwal & Chugh, 2003 ). For those in school, homelessness has significant impacts on school performance and socioemotional well being ( Hicks-Coolick et al., 2003 ; Neil & Fopp, 1992 ). There is also some evidence that children’s self-esteem is negatively impacted by residence in slum dwellings and other informal settlements ( Kruger, 2002 ).

In addition to direct effects, housing type may interact with other physical characteristics of children’s early environments to influence human development. Delays in cognitive development associated with residential density among preschool children are attenuated if children have access to a room where they can spend time alone ( Wachs & Gruen, 1982 ). Negative self- and teacher-ratings of Austrian primary school children’s psychological well being in more crowded homes are exacerbated by residence in multi-family complexes in comparison to living in either single family or small row family housing units ( Evans, Lercher & Kofler, 2002 ).

Housing quality

With ongoing urbanization, the number of families living in substandard housing in the global South is only likely to increase ( Chawla, 2002 ; Meng & Hall, 2006 ). In addition, there is some evidence that indigenous populations in Australia, for example, are disproportionately exposed to substandard housing ( Dockery et al., 2010 ). Yet most research to date on housing quality and children’s development has been conducted in the US and Europe ( Bradley & Putnick, 2012 ; Evans, 2006 ; Leventhal & Newman, 2010 ). There is a desperate need for further work in this area.

A small number of studies in North America and Europe have examined housing quality and cognitive development. A few, including a large national British cohort, reveal that, independent of SES, children living in substandard housing have lower academic competency ( Evans, 2006 , Evans et al., 2003 ). These effects are amplified by duration of exposure to substandard housing ( Douglas, 1964 ), and one study showed that when families moved into better housing, elementary school performance improved ( Wilner, Walkley, Pinkerton, & Tayback, 1962 ). Dunifon, Duncan and Brooks-Gunn (2004) , using a US national data set, also showed that residential clutter during childhood predicted adult educational attainment.

A number of cross-sectional studies in North America and Europe show that children living in substandard housing suffer from greater psychological distress ( Evans, 2006 ; Evans et al., 2003 ). Nearly all of these studies incorporate statistical controls for SES, and the effects replicate in longitudinal studies examining changes in housing quality (cf., Blackman & Harvey, 2001 ). Learned helplessness is also greater among children living in substandard housing, with statistical controls for SES ( Evans, Saltzman & Cooperman, 2001 ), and two studies reveal elevated physiological stress among low-income children inhabiting poorer quality housing. In a cross-sectional study, low-income primary school children living in substandard housing coupled with noise and crowding had higher levels of overnight stress hormones (e.g., cortisol) ( Evans & Marcynyszyn, 2004 ). In a second, longitudinal study, low-SES children residing in lower quality housing had elevated cortisol over their first four years of life (Blair et al., 2011). Differences were already present at 7 months of age.

An important conceptual limitation of North American and European research is the rather limited range of variation in housing quality. Because of building codes and general levels of affluence, “bad” housing in these contexts is a lot better than most of the housing found in the global South. Note that, unlike the potential problem of unaccounted for confoundings in cross-sectional research that might lead us to over-estimate the impacts of housing quality on children’s development, the truncated range in housing quality leads to the opposite estimation bias.

A high percentage of children growing up in the global South live in substandard housing ( Bradley & Putnick, 2012 ; Govender et al., 2010) constructed with inferior building materials, leaking pipes, and cracks or holes in the walls and ceilings ( Chaudhuri, 2004 ). In 2002 it was estimated that more than half the housing units in Zimbabwe, 52.6%, were considered semi-permanent dwellings ( United Nations Statistics Division, 2012 ). In 2010, 30.6% of the housing units in Mexico did not posses basic amenities such as bathrooms, kitchens, and piped water within the household.

Substandard living conditions lead to higher levels of exposure to lead and other toxins, air pollutants and pests ( Govender et al., 2011 ). In addition, poor quality housing, and particularly unsafe dwellings, place additional stress on low-income parents already facing multiple stressors (Evans & English, 2002). This may result in parental fatigue and thus reduce caregivers’ capacity to be warm and responsive ( Bartlett et al., 1999 ; Bradley & Putnick, 2012 ; Evans et al., 2003 ; Leventhal & Newman, 2010 ). Furthermore, in unsafe home environments parents and other caregivers may constrain children’s play and other activities, so as to reduce the risk of injury ( Bartlett et al., 1999 ; Bradley & Putnick, 2012 ; Evans et al., 2003 ; Ferguson, 2002). Such constraints are not unfounded: Dal Santo et al. (2004) found that preschoolers’ estimated risk of unintentional injury is almost four times greater for a child living in a household needing repair. In rural sub-Saharan African contexts, limited space renders household items like kerosene easily accessible for children, and open fires for heating and cooking pose a serious injury risk ( Munro et al., 2006 ). Play constraints in particular likely have important implications for children’s cognitive and socioemotional development, given the importance of play for healthy development ( Bartlett, 1999 ; Milteer et al., 2012 ).

Research on direct impacts of housing quality on children’s cognitive and socioemotional development in the global South is very limited. However, in one study Ferguson (2008) found that the quality of Malawian orphanages appears to be associated with infants’ cognitive functioning. Space and furnishings (e.g., room arrangement, displays for children) predicted children’s cognitive outcomes. This effect may partially be explained by the fact that the provision of separate, soft, cozy areas for children may both offer comfort and help regulate social interaction. Such processes may help counter some of the negative effects of crowding and institutionalization on children. In addition, separate, enclosed areas with comfortable furnishings provide a more homey, and less institutional, setting for young children ( Evans, 2006 ; Greenman, 1988 ; Olds, 2001 ; Sanoff, 1995 ).

Given the limited work directly linking housing quality to children’s developmental outcomes in the global South, further research in this area is desperately needed. One useful data source may be the Multiple Indicator Cluster Survey (MICS), an international household survey that has been implemented across a large number of countries in the global South.

Resources for children

Another aspect of the physical environment that may influence young children’s development is the availability of learning materials ( Bradley & Corwyn, 2005 ; Bradley & Putnick, 2012 ). However, the availability of such materials is seldom disentangled from parent-child interactions in the literature. Nevertheless, there is a strong relation between income and the provision of both stimulating materials and experiences for young children from birth through adolescence (e.g., Bradley, Burchinal & Casey, 2001 ; Evans, 2004 ; McLoyd, 1998 ). And, several studies have shown that cognitive enrichment in the home mediates much of the co-variation between parental income and child cognitive development ( Duncan, Brooks-Gunn, & Klebanov, 1994 ; Linver, Brooks-Gunn, & Koben, 2002 ; Smith, Brooks-Gunn, & Klebanov, 1997 ). Access to other material resources such as electricity, a radio, a television, a telephone and transportation may also impact children’s cognitive development in particular ( Bradley & Putnick, 2012 ).

There is much debate over what constitutes appropriate learning materials in the home, particularly cross- culturally ( Bornstein et al., 2012 ; Bradley & Putnick, 2012 ; Ferguson, 2008 ). Nevertheless, the UNICEF-developed MICS, which has been adopted for use in evaluating factors contributing towards the well being of women and children by a large number of governments worldwide, includes items evaluating the number of books, the number of children’s books, and the availability of various types of homemade and store-bought toys and other play materials. There is some evidence that such materials are rarely available in the global South and in rural areas of newly industrial countries such as India, Thailand and China ( Bradley & Putnick, 2012 ). The availability of other material resources in the home is likewise limited.

A retrospective evaluation of developmental impacts of the availability of learning materials and material resources associated with modernity (writing tablets, books, electricity, piped water, a radio, a television, and a transportation vehicle) on children’s cognitive development at ages 3, 5, 7 and 9 years in Belize, Kenya, Nepal and American Samoa was conducted by Gauvain and Munroe (2009) . Access to these resources was positively correlated with children’s general cognitive functioning, perspective taking, and levels of exploratory play. Similarly, Hamadani et al. (2010) found that, after controlling for socioeconomic variables, the variety of play materials and the availability of magazines and newspapers in rural Bangladeshi homes independently predicted 18-month-olds’ cognitive development. And, in Ferguson’s (2008) investigation of relations between the quality of the physical environments of Malawian institutions and infants’ developmental functioning, access to learning materials independently predicted infants’ language and socioemotional development.

Schools and childcare

Unfortunately, continual innovation in the design of schools and classrooms throughout the world is typically not based on evidence, instead reflecting current trends in architecture and design ( Lackney, 2005 ). Much of instructional facility innovation at present is driven by the infusion of information technology into learning environments. Although this practice has some potential benefits, we simply do not know how to train teachers and designers in the use and configuration of learning environments to take advantage of the affordances offered by information technology in schools. This explosion of learning technologies in the West inevitably will be transported to the global South. Yet evidence to date from low-income countries indicates no clear impacts of exposure to computers and other related technologies on children’s academic achievement ( Glewwe, Hanushek, Humpage, & Ravina, 2011 ; Riddell, 2008 ).

There is a significant body of research investigating the impacts of school quality on children’s school achievement ( Evans, 2006 ; Glewwe et al., 2011 ; Irwin, Siddiqi & Hertzman, 2007 ; Riddell, 2008 ). However, as is true for the work on home environments, little research has specifically investigated the impacts of the physical environment of schools on children’s developmental outcomes, particularly in the global South. Most research in the US and Europe on the physical characteristics of educational settings has focused on open versus traditional plan configurations ( Evans, 2006 ). Because this issue has tangential relevance at best to children throughout most of the world, we focus here instead on school and classroom size; the quality of building infrastructure (structural quality, lighting, and indoor climate, and access to electricity, water and sanitation); and access to basic resources (classroom furniture, blackboards, books, computers, laboratories and libraries), as these have the clearest documented impact on children’s school achievement in the global South ( Glewwe et al., 2011 ; Riddell, 2008 ).

School and classroom size

There is a large body of research on school and classroom size. Because nearly all of this work has been conducted within the US and Western Europe, we do not know what happens when much larger scale schools or bigger classrooms occur. Although there is some variation across regions, primary school pupil-teacher ratios (PTRs) in the global South are typically much higher than those in the global North. For example, compare PTRs of 81:1 (Central African Republic), 76:1 (Malawi), 61;1 (Chad) and 58:1 (Rwanda) to 18:1 (UK), 14:1 (US) and 13:1 (Germany) ( World Bank, 2012 ). Notably, though, PTRs in East Asia and the Pacific (average: 17.9:1) and Latin America (22:1) are much lower than in South Asia (40:1) and sub-Saharan Africa (42.5:1).

Students in smaller schools in the US and Western Europe perform slightly better on standardized tests and feel more connected to their school ( Evans, 2006 ). There is some evidence that the benefits of smaller school size are greater for low-income children, and for children in lower grades ( Woessmann & West, 2006 ). Similarly, classroom size research yields a relatively consistent picture of small, adverse effects on children in both high- and low-income countries with increasing size ( Blatchford, 2003 ; Ehrenberg, Brewer, Gamoran, & Willms, 2001 ; Woessmann & West, 2006 ). For example, in an investigation of linkages between school physical quality and rural Kenyan first grade children’s cognitive functioning and behavior, Daley et al. (2005) found that the number of students per classroom predicted levels of off-task behavior and teachers’ ratings of general behavioral functioning. There is also some evidence that smaller classrooms support more student- as opposed to teacher-directed learning and, similar to school size, are associated with more socially supportive settings ( Blatchford, 2003 ; NICHD Early Child Care Research Network, 2004 ).

It is worth noting that both school and classroom size are confounded with crowding. Work on household size and density shows that the critical variable is density, not family size ( Evans, 2006 ). Insufficient work exists to tease apart school/class size from crowding.

Physical quality

A surprisingly large number of school spaces for American children are in disrepair. In a 2000 survey of school principals in 32 countries in both the global North and South, nearly 30% of US principals noted that the quality of their school’s buildings and grounds impacted student learning, and almost 40% noted the same for available instructional space ( Ahlehfeld, 2007 ). Estimates were much higher for the majority of other participating countries, including the United Kingdom, Norway, Turkey, Uruguay and the Slovak Republic. In the global South, the majority of rural schools in particular have inadequate building facilities, including a lack of finished flooring ( Glewwe et al., 2011 ; Riddell, 2008 ). In many countries, half to two thirds of schools lack electricity, water, and basic sanitation facilities ( UNICEF, 2010 ). For example, the 2005 UNESCO EFA Global Monitoring Report found that just 39% of classrooms in Senegal had sanitation facilities, and even fewer (33%) had access to drinking water.

One important limitation in most work on educational settings and student achievement, however, is over-reliance on school professionals’ ratings of building quality. Since teachers and administrators are well aware of children’s achievement profiles in their own schools and are themselves likely affected by building quality, the potential for spurious associations in this measurement approach is considerable. However, assessments of building quality conducted by independent raters (e.g., structural engineers) have also been consistently associated with standardized test scores ( Evans, 2006 ). Further strengthening these conclusions are several studies comparing performance before and after building improvements ( Evans, 2006 ). In two recent studies utilizing the New York City school facilities building quality database, Duran-Narucki (2008) showed that the significant association between these expert rating measures of school building quality and academic achievement in elementary school children was largely mediated by attendance. Moreover children in New York City primary schools with higher rates of student mobility suffer even worse achievement outcomes as a function of substandard school facilities ( Evans, Yoo, & Sipple, 2010 ).

Given that nearly all of the research on school facility quality and student performance emanates from wealthy countries where the range of school quality is truncated, this is an area of particular importance to examine in the global South where the range of quality is considerably broader. And, in fact, improvements in the physical structure of schools in the global South do appear to positively impact students’ test scores ( Glewwe et al., 2011 ). However, the research to date in this area is very tentative, and typically the schools being compared have multiple factors that differ in quality, making it difficult to clearly identify individual influences on children’s outcomes.

In a recent meta-analysis of the research to date on the impact of school quality, including both physical and psychosocial factors, on children’s school achievement in low-income countries, Glewwe et al. (2011) found that there appears to be good evidence for the impact of access to electricity on children’s educational outcomes. And, in their investigation of the relations between school physical quality and rural Kenyan first grade children’s cognitive functioning and behavior, Daley et al. (2005) found that the availability of natural light (in schools without electricity) predicted students’ test scores. In high-income countries, where lighting is typically sufficient, research has focused more on potential benefits of exposure to natural light. Although the work on natural light exposure and children’s health and performance is limited, some rigorous work suggesting the potential importance of natural light for young children has been conducted in Sweden ( Küller & Lindsten, 1992 ). These investigators found evidence for the importance of sufficient natural light exposure for primary school children’s well being during periods of the year when daylight hours are limited.

In North America, upper respiratory infections, asthma and allergies are the most common cause of primary school absenteeism and have been routinely linked to exposure to mold and other allergens as well as ambient pollutants inside both schools and children’s homes ( EPA, 2003 ). Poorly maintained heating and ventilation systems as well as low levels of indoor:outdoor air exchange exacerbate these adverse indoor climate impacts on children ( Evans, 2006 ). Although work in this area in the global South is limited, similar impacts of poor quality ventilation and heating would be expected.

Consistent with the bioecological perspective ( Bronfenbrenner & Morris, 1998 ), in addition to focusing on the direct effects of school setting physical conditions on children themselves, it is important to keep in mind that substandard working conditions influence labor satisfaction and retention, and the same holds true for teachers. Several studies have shown that poor quality school physical conditions adversely influence teacher satisfaction and retention ( Buckley et al., 2004 ).

In the global South, there is some evidence that access to basic resources in school environments, such as a sufficient number of desks, tables and chairs; access to blackboards; access to textbooks and other books; and the availability of a school library all impact children’s school achievement ( Glewwe et al., 2011 ; Riddell, 2008 ). However, frequently these physical environment factors are correlated with each other and with other physical and psychosocial factors such as class size, building quality and teacher training, and so it can be difficult to clearly identify key factors impacting child outcomes. In addition, the mechanism explaining learning outcomes is somewhat unclear; perhaps the availability of these resources partly signals a commitment on the part of the school administration and relevant local and national government agencies to quality education ( Glewwe et al., 2011 ). Nevertheless, a number of carefully controlled studies across multiple contexts document the importance of having a desk, chair and textbook per student. For example, in their investigation of the relations between school physical quality and rural Kenyan first grade children’s cognitive functioning and behavior, Daley et al. (2005) found that the number of books per student independently predicted standardized test scores.

In preschool and childcare settings across the global South, there is a growing interest in improving the quality of both physical and psychosocial environments for children ( Engle et al., 2007 ; Hyde & Kabiru, 2003 ; Irwin et al., 2007 ; Myers, 1992 ; van der Gaag & Tan, 1998 ). And, indeed, the most commonly used assessment of the quality of childcare environments, the Early Childhood Environment Rating Scale (ECERS, Harms, Clifford & Cryer, 1998 ), includes two rating scales that assess children’s interactions with the physical environment: Space and Furnishings and Activities (which includes both the availability of learning materials and their use). However, although a significant body of research in the United States indicates an association between childcare quality and children’s cognitive and socioemotional outcomes (e.g., Sylva et al., 2006 ), there is little research that considers the impact of the physical environment directly.

There is almost no work documenting the impact of the quality of childcare environments on children’s developmental outcomes in the global South. However, as part of a preschool intervention program in rural Bangladesh, Moore, Akhter and Aboud (2008) implemented a series of changes, including increasing the availability of learning materials for reading and mathematical problem-solving. They found that preschool scores on the Activities subsection of the ECERS-R increased, and that children’s cognitive outcomes and school readiness improved. However, it should be noted that the Activities subscale does not separate the availability of learning materials from their use. In addition, many researchers in the global South debate the applicability of the ECERS-R in evaluating childcare and preschool quality in non-Western contexts ( Aboud, 2006 ; Moore et al., 2008 ).

Neighborhood quality

Sadly, most children growing up in the global South live in neighborhoods of poor physical quality ( Bartlett, 1999 ; Chawla, 2002 ; Hardoy, Mitlin, & Satterthwaite, 2001 ). Physical characteristics of these environments include high levels of air and water pollutants; nonexistent or inadequate collection of household waste; poor drainage; poor sanitation; proximity to busy street traffic; and limited or absent access to childhood resources such as open green space, grocery stores, schools and hospitals and play space (e.g., Bartlett, 1999 ; Bartlett et al., 1999 ; Chawla, 2002 ; Hardoy et al., 2001 ; Kruger & Chawla, 2002 ). Many of these neighborhoods are also unsafe because of high traffic volumes and limited street lighting (e.g., Bartlett et al., 1999 ; Kruger, 2002 ; Kruger & Chawla, 2002 ). However, the research linking children’s cognitive and socioemotional development to neighborhood physical conditions, beyond those already discussed (exposure to toxins, air and water pollution, sanitation, and high mobility) is very limited. The situation is similar in high-income countries. There is a large literature on neighborhood quality and human health and well being ( Diez-Roux & Mair, 2010 ) and more specifically child development ( Leventhal & Brooks-Gunn, 2000 ), but this work is bereft of considerations of the physical environment of neighborhoods. In nearly all of the extant research, neighborhood quality is defined by the socioeconomic profile of the population. Two areas of neighborhood physical environment that are receiving considerable attention because of the obesity epidemic are access to places for physical activity and proximity to healthy food sources. This work, although still in its early stages, indicates that both of these neighborhood characteristics are related to obesity in children and are much more likely to be wanting in low-SES neighborhoods ( Diez-Roux & Mair, 2010 ; Evans, Wells, & Schamberg, 2010 ).

UNESCO’s Growing Up in Cities ( Chawla, 2002 ) provides some interesting insights into children’s experiences in neighborhood environments in Argentina ( Cosco & Moore, 2002 ), India ( Bannerjee & Driscoll, 2002 ) and South Africa ( Kruger, 2002 ). In all three contexts, children aged 10–15 years reported a keen awareness of the physical quality of their neighborhood environments, noting specific aspects of these environments (e.g., high traffic, litter, poor sanitation, a lack of open green spaces) that limited play opportunities. Similar data have been found among Australian primary school children ( Homel & Burns, 1989 ). Perhaps most salient in children’s narratives across these and the other contexts studied (Australia, the United Kingdom, the United States, Norway, Poland, South Africa) was the importance of access to green play spaces. Other work in low-income countries has similarly documented the importance of play spaces and access to natural settings for children (e.g., Bartlett et al., 1999 ). However, little work has specifically investigated the impacts of natural settings on the cognitive and socioemotional development of children in the global South.

Neighborhood physical quality

Parents rated their 9- to 12-year-old children in two Canadian cities as higher in psychological distress if the neighborhood was rated by trained observers as lower in physical quality ( Gifford & Lacombe, 2006 ). Both longitudinal and cross-sectional studies ( Diez Roux & Mair, 2010 ) show that neighborhood upkeep influences adults’ psychological distress. To illustrate the potential power of neighborhood physical quality on adult mental health, adjusting for income, race and neighborhood poverty, New York City adults living in poor quality neighborhoods were more than 30% more likely to suffer from depression in the past six months compared to adults residing in better physical quality neighborhoods (Galea et al., 2006). Psychological distress in adults is a central risk factor for healthy parenting.

Close proximity to street traffic caused Zurich parents to restrict children’s outdoor play activities, which in turn was associated with diminished social and motor skills among preschoolers ( Hüttenmoser, 1995 ). High levels of street traffic have also been associated with less social interaction among neighbors in San Francisco neighborhoods ( Appleyard & Lintell, 1972 ).

Natural settings

As has been discussed above, the majority of research on the impacts of access to the natural environment on children’s well being has taken place in the US and Europe. Parallel to findings in North America and Western Europe ( Evans, 2006 ), children across the global South prefer natural areas and engage in more complex levels of play in such settings ( Bannerjee & Driscoll, 2002 ; Bartlett, 1999 ; Bartlett et al., 1999 ; Chawla, 2002 ; Cosco & Moore, 2002 ; Kruger, 2002 ; Kruger & Chawla, 2002 ). Given the potential for access to natural play spaces to mitigate some of the impacts of poor quality physical environments on low-income children’s cognitive and socioemotional development, further work in this area is warranted. A few North American studies suggest that children’s executive functioning may be enhanced by access to nearby natural outdoor play spaces ( Evans, 2006 ), and a meta-analysis revealed that the greening of school yards across multiple sites in North America and Western Europe has been associated with improved academic performance and better psychological well being among pupils ( Bell & Dyment, 2008 ).

Evaluations of outdoor nature experiences such as Outward Bound in high-income countries reveal consistent, positive associations with psychological well being ( Hattie et al., 1997 ). Part of the apparent psychological benefits of access to outdoor play areas is likely related to enhanced physical activity, which has been consistently linked in both children and adults to proximate, outdoor recreational spaces ( Evans et al., 2010 ). In a recent WHO study of approximately 1200 6- to 18-year-olds residing in eight European cities, the well-documented, inverse relation between household income and childhood obesity was explained, in part, by proximity to open green space. Children from wealthier households had greater access to open green spaces, which in turn was linked to higher levels of physical activity. The latter largely accounted for the inverse, household income – body mass index correlation ( Evans et al., 2012 ).

Adults living in Los Angeles neighborhoods with more parks, independent of SES characteristics, perceived greater collective efficacy, an index reflecting greater social cohesion and social control ( Cohen, Inagami, & Finch, 2008 ). There are also several studies showing that adults’ physiological stress responses to aversive stimuli are attenuated by natural surroundings ( Evans, 2003 ). Thus some of the benefits of nearby nature for children may also operate via their parents. One study also revealed that children’s psychological reactions to stressful life events were attenuated by proximity to outdoor nature ( Wells & Evans, 2003 ).

Conclusions and future directions

As can be seen upon reviewing the current state of the evidence on the physical environment and child development, very little work has documented the impacts of environmental conditions on the development of children growing up in the global South and other low-income countries. This is unfortunate for many reasons. Foremost, the majority of the world’s children grow up outside of the affluent countries where most of the work has transpired. In fact, Bornstein and colleagues (2012) argue that less than 10% of developmental science research has studied communities that account for 90% of the world’s population.

What we do know suggests that the physical environment experienced by children impacts their cognitive and socioemotional development across the lifespan, from the prenatal period through adulthood. The development of interventions to improve the physical environments experienced by children across the globe is thus warranted. Interventions would also offer tremendous research opportunities to examine how environmental improvements can change developmental trajectories. This would also help address perhaps the major methodological weakness in most work on children and the physical environment: potential selection bias. Comparisons between children living in different environmental conditions nearly always face the alternative explanation that some individual characteristic rather than environmental conditions might be the root cause of developmental changes. Another critical reason for studying children in the global South and elsewhere outside of high-income countries is the severely restricted range of environmental conditions typically monitored in research on child settings in North America and Western Europe. Essentially every single environmental factor reviewed herein exists in a substantially greater range in low-income countries. Thus not only is 90% of the research on children and the environment from samples of less than 10% of children, the same goes for the environmental side of the equation. We know a reasonable amount about how variability within the top 10 or 20% of conditions matters. We know almost nothing about how variability from the top to the bottom 10% of environmental conditions affects children.

With these caveats in mind, the evidence to date documents adverse impacts of individual environmental risk factors, particularly environmental toxins and pollutants, on children’s cognitive development. However, the impacts on socioemotional functioning are less certain. In addition, the documented evidence for impacts of noise, crowding and chaos on the cognitive and socioemotional development of children growing up in the global South is tentative at best. And, across the globe, the impacts of individual aspects of the physical environment of housing, schools and neighborhoods are unclear, primarily because multiple factors tend to be correlated. This is especially true for low-income families, underfunded schools and poor neighborhoods in both the global North and South, where poverty is frequently associated with multiple environmental risks ( Evans, 2004 ; Ferguson et al., 2009 ). It is also important to recognize that when cumulative, environmental insults have been studied, they typically reveal worse outcomes than singular environmental risks ( De Fur et al., 2007 ; Evans, Li & Whipple, in press ). Furthermore, for low-income children, the confluence of deteriorating physical conditions along with inadequate psychosocial conditions is a primary, underlying pathway that helps account for the ill effects of poverty on child development ( Evans & Kim, 2013 ).

In order to better understand the effects of multiple environmental risk factors on children’s cognitive and socioemotional development, a holistic, multidisciplinary and multilevel approach that encompasses the complex interactions between biological, physical, and psychosocial factors impacting children’s developmental outcomes is needed. Such an understanding will allow us to more effectively intervene in children’s actual lived environments. In other work ( Ferguson & Lee, 2013 ), we have proposed a bioecocultural framework that integrates key components of Bronfenbrenner’s bioecological model ( Bronfenbrenner & Evans, 2000 ; Bronfenbrenner & Morris, 1998 ) with Nsamenang and colleagues’ ecocultural approach (e.g., Nsamenang, 1992 ; Nsamenang & Dawes, 1998 ), and Li’s (2003) cross-level dynamic biocultural coconstructivist paradigm (see also Boivin & Giordani, 2009 ). We thus focus here on outlining key steps involved in utilizing this framework to better understand and address the impacts of the physical environment on the cognitive and socioemotional development of children living in multiple contexts.

Developing and implementing a bioecocultural framework

The first step in developing and implementing a bioecocultural framework is to identify what is known and what is not yet known about the impacts of individual and intersecting environmental factors on children’s development. The present review, in conjunction with Evans’ (2006) earlier review that focused on Western contexts, does just that. We summarize the evidence to date below, while at the same time considering when the methodologies employed in related work are appropriate for filling in the gaps in the research literature, and when they are not. When they are not, it is important to identify what is currently known in a particular context, for example identifying relevant country-level statistics and databases. In addition, new tools for assessing children’s development in varying cultural contexts might be needed ( Ferguson & Lee, 2013 ; Nsamenang, 1992 ). Second, key factors, what public health researchers call “leverage points”, influencing children’s developmental outcomes should be identified (see Ferguson et al., 2009 ). Where possible, those leverage points most susceptible to change should be noted. Third, all of this information can be incorporated into an overarching bioecocultural framework, as outlined above, that identifies all known and hypothesized factors influencing a particular developmental outcome (e.g., literacy), key leverage points, known interacting influences between factors and, when possible, the mechanisms behind the relations between each factor and children’s development. Once this is done, interdisciplinary, international research teams should develop and implement a collaborative research program to test the model, with a specific focus on filling in the gaps in the research literature in understudied contexts, namely the global South. In doing this work, the intimate involvement of individuals, communities, local and national governmental agencies and researchers living in each context studied is essential ( Dawes & Donald, 2000 ; Weisner, 2010 ). In fact, ideally relevant individuals and communities should be involved in every stage outlined above. This will ensure that similarities and differences between contexts are adequately considered. Finally, in collaboration with all of these important constituents, key leverage points can be confirmed and leveraged in implementing a holistic program of reform that will effectively address current environmental inequalities, so as to ensure healthy developmental outcomes for all children.

Phase 1: Identifying influencing factors

Conceptually, given their direct impact on children’s biological systems, it is likely that environmental toxins and pollutants (specifically lead, mercury, PCBs, various pesticides, NO 2 , polycyclic aromatic hydrocarbons, environmental tobacco smoke, arsenic, manganese, and tetrachloroethane) impact the cognitive and socioemotional development of children living in different contexts similarly. The limited evidence to date indicates that this is the case. Further work on factors impacting socioemotional development is warranted, however, especially in the global South. Similarly, despite differences in adults’ perceptions of crowding and chaos, the evidence we have reviewed here suggests that factors contributing to chaos, including noise and crowding, likely impact children and adults across the globe in similar ways. However, given the limited work in this area, particularly in considering socioemotional development, these predictions need to be tested more thoroughly in low-income countries.

In terms of home and school environments, adequate building quality seems essential, but determining what this should entail in differing contexts is challenging. Home, classroom and school designs that reduce chaos may be particularly important. In addition, adequate lighting and comfortable climatic conditions (temperature, indoor air quality) are important for effective learning in school environments. Finally, the availability of key material and learning resources in both home and school environments appears to be particularly important for cognitive development, but the specific resources needed in differing contexts is unclear. Further work in this area is needed. Likewise, although it is clear that children growing up in low-income neighborhoods in both the global North and South encounter numerous disadvantages that impact their cognitive and socioemotional development, little is currently known regarding the specific components of the physical environment of neighborhoods impacting these developmental outcomes. Neighborhood physical quality is the most understudied aspect of the environmental characteristics discussed herein.

An important caveat at this point is that the majority of the work discussed in this review, with a few notable exceptions (discussed throughout), employs environmental and outcome measures developed in the West. Yet the specific components of the physical environment impacting child development in the global South may differ from those in the global North, as we have noted throughout. In addition, different cultural contexts, values and beliefs in the global South may mean that although, for example, there are documented impacts of lead on Egyptian children’s IQ scores, this aspect of children’s development may be less important than socioemotional competency in this context. Thus a consideration of what engenders competence within particular cultural contexts is essential ( Ferguson & Lee, 2013 ; Weisner, 2010 ).

The development of culturally appropriate assessments of both environmental quality and children’s developmental outcomes in the global South is sorely needed. This could, and should, go hand-in-hand with an evaluation of the effectiveness of a larger bioecocultural model in capturing the multiple environmental factors impacting children’s specific developmental outcomes in particular contexts, so as to provide a good test for the effectiveness of these methodologies in each context ( Ferguson, 2008 ; Ferguson & Lee, 2013 ; Nsamenang, 1992 ). The questionnaires developed for the MICS, an international household survey, may be a useful beginning. The involvement of key stakeholders living within each context studied will also be essential in this process. UNESCO’s Growing up in Cities project ( Chawla, 2002 ) provides a nice illustration of a participatory process in which research questions and assessment tools were developed jointly by researchers and community stakeholders.

Phase 2: Identifying leverage points and mechanisms

Bronfenbrenner noted that proximal processes, the exchanges of energy between the developing child and the persons and objects in her immediate settings, are the “engines of development” ( Bronfenbrenner & Morris, 1998 ; Bronfenbrenner & Evans, 2000 ). In order for these processes to be effective, they need to occur on a regular, sustained basis and become increasingly complex as the child matures. Given this, a starting point for identifying key leverage points is the identification of environmental factors that clearly interrupt proximal processes for children. Factors that contribute towards chaos, including noise, crowding, and residential mobility (partially instantiated by informal housing facilities), are likely candidates here, as they are likely to interfere with effective proximal processes ( Bronfenbrenner & Evans, 2000 ; Evans & Wachs, 2010 ). As we have discussed above, housing and school design may also contribute towards chaos, particularly when a large number of people live or study in a small number of open plan rooms. In addition, schools and neighborhoods characterized by high residential instability may contribute towards chaos at the macro level.

We have noted above that one of the unintended consequences of various coping strategies for dealing with crowding, noise, and chaos may be deteriorations in socially supportive relationships and less responsive parenting. The design of spaces, not simply the presence of stressors like chaos, can also influence interpersonal relationships, thus affording or inhibiting ease of interpersonal interactions. For example, are typical travel routes likely to lead to unplanned, impromptu interactions? Are there spaces that people feel comfortable spending time in such as cafes and common facilities (e.g., a communal laundry area, community play spaces)?

In addition to proximal processes such as parent-child interactions (e.g., responsiveness, monitoring), several other candidate mechanisms are worthy of further examination both in the global North and South. One of the common qualities of many of the suboptimal physical settings children encounter is their uncontrollability. We need more examination of mastery, self-efficacy and other control-related processes in relation to the environment and children’s development. Some of the ways in which physical settings can influence mastery include: uncontrollable stressors such as noise and crowding; highly unpredictable and variable conditions such as chaos; the degree of inflexibility and regimentation of settings such as school; the scale and manipulability of settings for children; and design and planning features that afford crime, such as undifferentiated spaces lacking in ownership and defensibility.

Considerable work shows that time spent in nature and other restorative spaces can help counteract cognitive fatigue and stress engendered by the fast-paced, multitasking demands of modern life, increasingly common throughout the world, regardless of economic development ( Kaplan & Kaplan, 1989 ). Fascination or the experience of involuntary attention (e.g., curiosity) is not the sole purview of natural elements but can include human-made objects and spaces that attract and hold attention effortlessly (e.g., people-watching in a plaza, gazing at a fountain, meandering through a museum or good bookstore, or enjoying street entertainment).

Stressors such as crowding, noise, traffic, and chaos can directly strain physical and psychological systems, but they also have the ability to alter regulatory processes such as coping and executive functioning ( Evans & Kim, 2013 ). Thus another area worthy of further scrutiny in considering children’s environments is the role of coping and self-regulatory processes. When children and their parents encounter various suboptimal environments, they often adapt strategies, be they behavioral, cognitive, or both, to right the balance between environmental demands and human comfort and well being. These adjustments and adaptations to the environment, in and of themselves, can lead to developmental changes. For example, parents who cope with too much unwanted social interaction by withdrawing from their children are likely to be less responsive.

The impact of the environment on adult caregivers is a particularly important underlying process to consider. Parents in crowded homes are typically less responsive and less patient ( Evans et al., 2001 ). Teachers in noisy schools report more fatigue and frustration, and observations of noisy schools show substantial reductions in teaching time ( Evans & Hygge, 2007 ). The stress and anxiety engendered by knowledge of toxic exposures or parental struggles with substandard housing are bound to translate into less than ideal parent-child interactions. Interestingly, such parent-child interactions may in fact modify children’s gene expression without altering the nucleotide sequence, as recent work in epigenetics has demonstrated ( Meaney, 2010 ).

Phase 3: Identifying and addressing key inequalities and opportunities

As we have discussed above, the final step involves interdisciplinary, international research teams both filling in the gaps in the research literature in understudied contexts and implementing interventions to improve children’s developmental functioning. One key leverage point for both cognitive and socioemotional development that might be further studied and then addressed is chaos. Implementing interventions within children’s home, school and neighborhood environments that reduce chaos and/or moderate its impacts on children may be a particularly effective way to improve children’s developmental outcomes. Interventions could include building sound barriers to block out aircraft and traffic noise, relocating homes and schools further from busy highways and airports, and redesigning open plan homes and classrooms to include quiet, secluded spaces for children.

One of the ways in which chaos has a particularly insidious effect is in its interruption of play, a key proximal process for young children’s cognitive and socioemotional development ( Bartlett, 1999 ; Milteer et al., 2012 ). Unsafe housing, school and neighborhood settings also disrupt play, as was coherently argued by the children living in as diverse contexts as Argentina, India, South Africa, Australia, the United Kingdom, the United States, Norway and Poland involved in UNESCO’s Growing Up in Cities project ( Chawla, 2002 ). Low-income urban children may be at particular risk for interruption of play processes ( Chawla, 2002 ; Milteer et al., 2012 ), and these same children frequently encounter multiple environmental risk factors in their home, school and neighborhood environments ( Bartlett et al., 1999 ; Evans, 2004 ). Thus building safe, green play spaces for low-income and other children across the global North and South will likely have a particularly positive impact on their cognitive and socioemotional development. In addition, the implementation of Community Adventure Play Experiences ( CDI, 2012 ), that is, temporary play spaces within children’s own communities that engage them in interactive play with recycled materials, may be a particularly low-cost and sustainable approach to increasing opportunities for child play in low-resource settings. In low-income and highly mobile communities, these may provide a good alternative to constructing new playgrounds, and have the added advantage that they can take place both indoors and outdoors. Such spaces may also provide common ground for community members to have greater social interaction, forming networks of relationships.

The physical environments experienced by children have important impacts on their cognitive and socioemotional development. Yet the work to date documenting these impacts in the global South is limited. We thus call for the development of a holistic, multidisciplinary and multilevel approach, based on Bronfenbrenner’s bioecological model, to the investigation of the impacts of the physical environment on child and adolescent development. Such work should be led by an interdisciplinary, international team of researchers in collaboration with local and national government agencies and community members, including the children themselves. This approach will allow us to more effectively intervene in the actual lived environments of both high- and low-income children across the globe.

Acknowledgments

This research was supported in part by grants from the W.T. Grant Foundation and the John D. and Catherine T. MacArthur Foundation. Special thanks to Jane Gorski for her help in locating and organizing reference materials and to Sheridan Bartlett for advice on this paper.

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Climate Change and Education: Building Momentum through a Shared Research Agenda

Recommended.

The intersection of education and climate change raises two pivotal questions: What impact is climate change having on education? And what role can or should education play in the face of imminent climate change? On the first, it is clear that education delivery (especially its quality and equity) is being affected by climate change, but we need to know more about the specific impacts (where, how, on who) and how to mitigate them. Better and more targeted research is needed to ensure all children continue to receive education as the effects of climate change intensify. On the second, education could play a role in building resilience among populations in the face of climate change, but we have very limited evidence for this and how it might work.

Here, we—a group of researchers, activists, donors, and policymakers—have come together to outline a research agenda to protect children’s education in the face of a deepening climate crisis. We aim to align the academic, policy and practitioner community around this set of shared research goals to better understand the impacts of climate change on education and the role education could play in driving a more resilient future generation.

Figure 1. Building blocks for a research agenda on climate change and education

1. Advance knowledge of the impacts of climate change on learning and wellbeing

Acute climate shocks and long-term climate change, as well as environmental crises (like wildfires or earthquakes ), are having a negative impact on learning. Rising temperatures have adverse effects on students' wellbeing and academic performance. Floods, droughts, and cyclones affect families’ income and their ability to invest in their children’s education, as well as children's l ong-term cognitive developmen t. Higher pollution levels from increased greenhouse emissions or natural and manmade emergencies (like wildfires and conflict) reduce executive function and academic ability. All this raises the likelihood of children leaving school early , including for marriage or work. When climate shocks lead to additional domestic work, the burden invariably falls on girls, further reducing available time to study. The loss of life, injury , food insecurity , material losses and forced displacement which can result from an extreme disaster have profound impacts on children’s mental health and ability to return to schooling . The adverse effects of climate shocks can transmit from mother’s to their children, with intergenerational impacts on children’s long-term skills development. Exposure to an extreme weather event when children are young has compound impacts on wellbeing , educational attainment and economic opportunity in the future. Repeat exposure to shocks worsens these outcomes, especially when schools close for long periods, but can also incentivise families to desire more education for their children. Recent research has shown that children born in LMICs in recent years will face significantly more environmental disasters throughout their lifetimes compared with older cohorts. And disruptions from extreme events are chipping away at hard won gains in education access and learning outcomes.

Despite this growing body of evidence, there are large knowledge gaps about the direct and indirect impacts of climate change on young people's lives. Are children most affected through household-level impact (like increased poverty and ill health) and subsequent challenges like malnutrition? Or are the inhospitable teaching and learning conditions caused by slow-onset climate change or climate shocks a more serious driver of impact? Across which pathways are these impacts most profound, and which policy levers are most effective? How can we project the long-term impacts of exposure to climate shocks and incremental climate change, across generations?

Figure 2. Potential pathways of impact on children’s learning and well-being from climate change

These interactions are complex and require a systematic approach for effective practice, research and policy in these areas. While we are not starting from zero, we lack rigorous evidence in upper- and middle-income settings where research is relatively easier to conduct. For the contexts where the need is greatest and more urgent–for example, across low-income countries and fragile and conflict affected states–robust research and evidence is nearly non-existent. This needs to change.

An important starting point for this research agenda is to acknowledge that the impacts of climate change on vulnerable children, families, and communities do not exist in isolation from the disadvantages of intergenerational poverty, nutrition deficits, health system and education access disparities, displacement, conflict, and structural and systemic inequalities. Research should examine how these intersecting challenges impact children's access to education, learning outcomes, mental and physical well-being, and overall development, and what can be done to better support coordinated cross-sectoral adaptation and mitigation strategies (including through social protection programmes ). Research should disaggregate data and evidence by gender, wealth, ability, and other relevant identities of marginalisation. Longitudinal research would help us to understand changing trends over time and the impacts of specific climate shocks in different countries and social contexts.

2. Increase evidence on interventions in the most climate-vulnerable contexts and how these can be institutionalised

We need more evidence on which interventions can support teachers, learners, schools and education systems when faced with immediate and incremental climate change, and how to pay for them. This includes testing both large scale solutions as well as smaller scale programmes to inform the discussion on innovative programming that could be taken to scale. What can education systems do to respond to the changing climate in a way that maximises quality learning? What resources are needed to prepare to implement these responses for affected populations promptly? It is important to understand what works at scale in the most climate-vulnerable contexts, as well as where finance for effective programmes will come from.

CGD’s preliminary work shows that, in South Asia, schools often close in the event of shocks, rather than implementing other measures that might mitigate impacts. Physical damage to school buildings is becoming more common during climate shocks, and schools are often repurposed as emergency shelters during crises, both of which result in delays in children returning to school. Schools that are in poorer and more vulnerable communities face the harshest consequences and longest disruptions from a shock. The promise of remote learning has yet to be realised , particularly in poorer contexts where school systems still struggle to provide technological pedagogies and tools equitably. CGD research also reveals that most immediate support and adaptation efforts, such as temporary learning centres and catch-up programmes, are typically implemented by non-state actors (such as civil society organisations or international NGOs), even in areas where climate shocks are common. Governments should be prepared with a suite of responses that have been tested at scale in their contexts, and can be reliably adapted when faced with emergencies.

We don't have enough knowledge of what works at a small scale, so we need innovation and testing of promising actions to better inform the design of large-scale actions. Research should aim to identify which interventions would be best suited and how to build education systems, school, and teacher capacity to deliver these interventions, especially in settings that often experience climate shocks. Of course, we cannot ask governments to wait until we have all the evidence, so we have to start thinking of large-scale actions now (even with the lack of evidence at small scale and in controlled settings).

A related knowledge gap is how much preparing to implement these interventions would cost and who should pay. Education is yet to have a seat at the table in global climate finance negotiations. Education budgets are already constrained, with many competing priorities. The $700 million climate loss and damage fund , committed to at COP28, does not even mention education. This might be because, for starters, we don’t know how much it costs to help schools adapt to climate change at scale. Recent efforts to remedy this have run into issues with insufficient evidence base or country-specific data. Improved understanding of the needs of systems to build resilience for continued education delivery will enable access to the required funding from international and domestic climate finance pools. This includes exploring the flexibility, adaptability, and efficacy of administrative structures in facilitating timely responses to climate shocks and slow onset climate change. Data to help target efforts and investment, including predictive analysis on areas most affected by a shock, would also streamline these claims. Second, guidance on returns from disaster risk reduction (DRR) investment for climate change doesn’t currently include education. Recent efforts to bridge this gap through investments in education from climate financing and associated support to governments are a step in the right direction, and should be underpinned by evaluation and evidence to make the findings translatable and ensure funding is used for programmes that provide actual benefits to all people in the communities served.

3. Increase the evidence base around the use of education as a tool for climate action

A range of actors within the global education sector are prioritising the other key part of the two way relationship between education and climate change: enabling and empower young people to be better prepared for climate change, and to lead climate change initiatives through greater understanding and acquired “green skills” that could potentially transform communities from the grassroots. The United Nations Framework Convention on Climate Change Article 6 represents a commitment to this agenda through teacher training and introducing climate change curricula across education levels, and recently a new indicator has been proposed to track the inclusion of related content in curricula. Efforts to mainstream “c limate smart education systems ” i.e. education systems that are more resilient, inclusive, and climate-conscious, are gaining momentum. This includes both increasing children’s scientific knowledge of climate change through the formal curriculum and fostering behaviour change to influence climate action and awareness. Some emphasis has also been placed on creating young climate leaders/advocates, but this unfairly burdens children and risks their efforts being tokenised in systems not engineered to accommodate their opinions.

However, these efforts lack rigorous evaluation and evidence of impact on either adaptation or mitigation efforts. There is also little coherent differentiation in existing frameworks between skills acquisition for green livelihoods and behavioural change that supports climate action. Higher levels of education are correlated with higher levels of emissions, which makes the mitigation link appear even more tenuous. So, what are environmental and climate education’s most effective entry points for real impact and for which outcomes? Which elements of this framework are most central to effective resilience and response? We should learn more about whether and how these interventions work, so that efforts to tackle climate change within and beyond education don’t burden children in poor countries with a problem not of their making.

There is some evidence we can draw on. Curricular change is extremely difficult to implement , especially for the subject of climate change, which depends on teachers ’ own knowledge of climate change , prevailing misconceptions among students , and their parents’ opinions, all of which affect both delivery and take-up. These dependencies may differ widely based on geographical context and existing pedagogical approaches . But research on the effective introduction of climate change curricula is lacking, especially in developing countries, and ought to be explored further. Further, evidence is still lacking on not only the impact of introducing climate change curricula but also on the mechanisms through which this impact is achieved. While some argue that climate change education can empower and enable children to be powerful agents of change , others find that attitudes and behaviours of children are unaffected by current instructional approaches to climate change education. There is very little existing evidence on the effectiveness of these approaches, and so we suggest this is an area where rigorous research could be most useful.

Examples of initiatives promoting green skills for climate change adaptation

• The ReWired Summit at COP28 celebrated the launch of the Green Rising initiative to support and empower the grassroots mobilisation of young people through the three pillars of volunteerism, advocacy and skills, jobs and entrepreneurship.
• UNESCO’s Greening Education Partnership emphasises equipping young learners with the skills to tackle climate change and promote sustainable development
• The UK’s Climate Change Education Partnership details plans to include climate change education in curricula to enhance young people’s understanding of climate change and effective actions to combat climate change.
• African Union’s Climate Change and Resilient Development Strategy and Action Plan (2022–2032) names formal and informal climate change literacy as a priority intervention and suggests action
• According to the Report on the 2022 United Nations Transforming Education Summit , over 80 countries plan to include climate education in their national curricula.
• Green Generation is an educational model for schools that combines Save the Children's expertise in education and WWF's knowledge in conservation and environmental sustainability to engage children in environmental learning and projects.

Increasing educational attainment makes students, especially girls , more resilient and adaptive to shocks. Generating credible evidence of the real pathways through which green education can transform future generations’ livelihood and wellbeing outcomes can inform the direction and implementation of these initiatives, while ensuring that other education priorities are not left behind.

4. Prioritise climate-vulnerable country contributors

Researchers from climate-vulnerable countries and populations possess first-hand knowledge of the immediate challenges and nuances related to climate change and education in their settings. Yet these researchers face persistent funding and opportunity barriers. Fostering collaboration between researchers in climate-vulnerable countries with those in the global north, creating networks with indigenous researchers across the global north and south, and south-south collaboration is crucial to ensure that research is embedded in specific local and country context. This would improve the relevance of research questions and outcomes, enrich the understanding of climate impacts and ensure that context-specific evidence reaches a broader audience.

There are examples of this type of collaboration in the climate sector that those of us working on education should learn from. Nationally Determined Contributions (NDCs ) are a way to ensure that developing country partners exercise ownership of climate change adaptation and mitigation strategies and integrate it into their own national action and finance plans. During COP28 the “ Teachers for the Planet '' initiative showcased outstanding teacher-led climate education solutions from over 60 countries. It is important that this collaborative approach also be adopted in research on education and climate change. As well as being the right thing to do, collaboration with local actors improves the likelihood of evidence influencing national policies and informing local programming.

Research should focus on identifying existing networks of research-and-policy partnerships that have been successful at sparking system-level change relevant for the education sector. Rigorous data collection, monitoring, and evaluation methodologies would be integrated in ongoing and emerging climate strategies to provide empirical evidence of the impact of interventions on schooling and learning in the short and long term. Emphasis would also be placed on assessing the effectiveness of interventions aimed at enhancing the capacity of stakeholders (students, teachers, parents, and administrators) to understand, respond to, and mitigate the effects of climate change.

Forging the path forward

At COP28, the latest Global Tipping Points report noted society’s proximity to a domino of devastating tipping points that could severely damage whole ecosystems, “with societal impacts including mass displacement, political instability and financial collapse”. Even in the best-case scenario, the frequency and severity of climate shocks is predicted to worsen, with the poorest communities often the most vulnerable. Without targeted action, the climate crisis is likely to further exacerbate educational inequalities, leaving many children from disadvantaged backgrounds behind, just as we witnessed from the COVID-19 pandemic. Looking forward to COP29, efforts are underway to centre education in climate change dialogues and provide a platform for exploring the challenges and opportunities for the education sector.

We hope that this research agenda can align actors behind a core set of research priorities to guide the education sector’s role in climate change dialogues. Education must be part of the response to climate change and cross-sectoral collaboration is key to responding to the climate crisis. It is critical to ensuring that we invest in policies and interventions that are both best for children and best for the planet.

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Directorate for Education and Skills

The Education and Skills Directorate is one of twelve substantive departments of the OECD and provides policy analysis and advice on education to help individuals and nations to identify and develop the knowledge and skills that drive better jobs and better lives, generate prosperity and promote social inclusion.

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The OECD Directorate for Education and Skills seeks to help individuals and nations to identify and develop the knowledge, skills and values that drive better jobs and better lives, generate prosperity and promote social inclusion. It assists OECD countries and partner economies in designing and managing their education and skills systems, and in implementing reforms, so that citizens can develop the knowledge, skills, attitudes, and values they need throughout their lives.

Andreas Schleicher

Director Directorate for Education and Skills

Yuri Belfali

Head Early Childhood and Schools Division

Paulo Santiago

Head Policy Advice and Implementation Division

Tia Loukkola

Head Innovation and Measuring Progress Division

How we work

The work of the Directorate for Education and Skills is overseen by four bodies, each with its own mandate, membership, and programme of work and budget, to help deliver work under the overall governance of the OECD Council:

• The Education Policy Committee, which also provides strategic oversight of our work
• The Centre for Educational Research and Innovation Governing Board (CERI) 
• The Programme for International Student Assessment Governing Board (PISA)
• The Programme for Teaching and Learning International Survey Governing Board (TALIS)
• The Board of Participating Countries for the Programme for the International Assessment of Adult Competencies (PIAAC) is overseen by both the Education Policy Committee and the Employment, Labour and Social Affairs Committee.

What we are working on

The best way for education systems to improve is to learn what works from each other. We deploy large scale surveys and reviews, designing common methodological and analytical frameworks for utmost comparability of empirical evidence from different education systems. We collect data about nearly all aspects of countries’ education systems from key policies, teacher practises, adult proficiency, and early childhood learning and well-being to how 15-year-olds perform in mathematics and what their attitudes are about global issues like climate change.

• The International Early Learning and Child Well-Being Study
• OECD Survey on Social and Emotional Skills
• Survey of Adult Skills
• The OECD Teaching and Learning International Survey
•    Education at a Glance
•   The Education Policy Outlook
•   PISA Global Crisis Module
•   Global Teaching Insights
• Explore by country
• Explore by topic
• Review policies    
•   PISA for schools

Assisting countries with policy development and implementation

We help countries answer important questions facing education policy makers and practitioners alike: how to identify and develop the right skills and turn them into better jobs and better lives; how best to allocate resources in education to support social and economic development; and how to offer everyone the chance to make the most of their abilities at every age and stage of life OECD and partner countries look to our expertise to review their education and skills systems, and assist them in developing and implementing policies to improve them. We conduct reviews ranging from those on individual national education policy to comparative educational policy and thematic peer-analysis. We review and support the development of higher education systems with analysis on resource use and labour market relevance. All of these provide in-depth analyses and advice that draw on OECD data resources, national policy documents and research, and field-based interviewing by OECD review teams. Comparative thematics, covering areas such as ECEC in a digital world, diversity, equity and inclusion in education, teacher policy and transitions in upper secondary education, are based on a common conceptual framework and methodology developed with advice from a group of national experts.

Through tailored implementation support the directorate offers countries assistance in implementing policy, from curriculum reform to helping schools become effective learning organisations. It also brings countries and stakeholders together in a variety of fora to exchange ideas, an important step in the policymaking process.  

Pivoting to tomorrow

What knowledge, skills, attitudes and values will students need in a swiftly evolving world? We develop long-term “leading-edge” thinking that looks beyond the current state of education to what it can become. These multiple-scenario analyses nourish our ground-breaking Education 2030 work on curriculum. They inform international debate and inspire policy processes to shape the future of education. The one certainty about the future of education is that it will be a digital one though we cannot know to what degree. In staying ahead of the EdTech curve, the directorate advises countries on the fast-changing potential of digital tools like robotics, blockchain and artificial intelligence, and how they can be integrated and used to equitably boost teaching, learning and administrative performance. The digitalisation of education is just one of the many strategic foresight areas the OECD’s Centre for Educational Research and Innovation (CERI) focuses on. Its exploration of best practices flagged by international comparisons helps countries move towards the frontiers of education.

Programmes of work

• Education and Skills Policy Programme The OECD’s programme on education and skills policy support policymakers in their efforts to achieve high-quality lifelong learning, which in turn contributes to personal development, sustainable economic growth, and social cohesion. Learn more
• CERI The Centre for Educational Research and Innovation (CERI) provides and promotes international comparative research, innovation and key indicators, explores forward-looking and innovative approaches to education and learning, and facilitates bridges between educational research, innovation and policy development. Learn more
• INES The OECD Indicators of Education Systems (INES) programme seeks to gauge the performance of national education systems through internationally comparable data. Learn more
• PISA PISA is the OECD's Programme for International Student Assessment. PISA measures 15-year-olds’ ability to use their reading, mathematics and science knowledge and skills to meet real-life challenges. Learn more
• PIAAC The Survey of Adult Skills, a product of the PIAAC, measures adults’ proficiency in literacy, numeracy and the ability to solve problems in technology-rich environments. Learn more
• TALIS TALIS - the Teaching and Learning International Survey - is the world's largest international survey about teachers and school leaders. Learn more
• Survey on Social and Emotional Skills (SSES) The OECD Survey on Social and Emotional Skills is an international survey that identifies and assesses the conditions and practices that foster or hinder the development of social and emotional skills for 10- and 15-year-old students. Learn more
• Early Childhood Education and Care The Early Childhood Education and Care (ECEC) programme conducts analysis and develops new data to support countries in reviewing and improving their early childhood services and systems. Learn more
• Higher Education Policy The Higher Education Policy Programme carries out analysis on a wide range of higher education systems and policies Learn more

Directorate outputs

Policy and working papers

More facts, key findings and policy recommendations

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Related policy issues

• Education access, participation, and progression
• Education economic and social outcomes
• Education equity
• Education evaluation and quality assurance
• Education financing
• Education leadership
• Education organisation and governance
• Future of education and skills
• Learning environment
• Teachers and educators
• Student performance (PISA)

Get in touch

Contact us: edu.contact@oecd.org