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Economics of Education Review
j o u r n a l h o m e p a g e :w w w . e l s e v i e r . c o m / l o c a t e / e c o n e d u r e vBenefits of early childhood interventions across the world: (Under)
Investing in the very young
Milagros Nores
∗, W. Steven Barnett
National Institute for Early Education Research, Rutgers, The State University of New Jersey, 120 Albany Street, Suite 500, New Brunswick, NJ 08901, United States
a r t i c l e i n f o
Article history: Received 13 August 2009 Accepted 9 September 2009 JEL classification: I21 J24 Keywords: Early childhood Nutrition Stimulation Effect size International policy Program effectivenessa b s t r a c t
This paper reviews the international (non-U.S.) evidence on the benefits of early childhood interventions. A total of 38 contrasts of 30 interventions in 23 countries were analyzed. It focuses on studies applying a quasi-experimental or random assignment. Studies were coded according to: the type of intervention (cash transfer, nutritional, educational or mixed); sample size; study design and duration; country; target group (infants, prekinder-garten); subpopulations of interventions; and dosage of intervention. Cohen’sDeffect sizes were calculated for four outcomes: cognitive gains; behavioral change; health gains; and amount of schooling. We find children from different context and countries receive substan-tial cognitive, behavioral, health and schooling benefits from early childhood interventions. The benefits are sustained over time. Interventions that have an educational or stimulation component evidenced the largest cognitive effects.
© 2009 Elsevier Ltd. All rights reserved.
1. Introduction
The last several decades have seen a growing inter-est in public invinter-estments in children at early ages around the globe (Choi, 2004; Kirp, 2007; OECD, 2006). A pri-mary source of this interest is growing knowledge and awareness of the importance of environmental influences on development, particularly, but not only on cognitive development, during the early years (Cunha, Heckman,
Lochner, & Masterov, 2006). Information on the loss of
potential intellectual development in these years, stunt-ing, and early emergence of gaps between more and less advantaged groups have pointed to the importance of tar-geting children in the first 5 years of life to increase later developmental and educational outcomes. Although there is increasing agreement about the importance of interven-ing to improve early development, there is less agreement
∗ Corresponding author. Tel.: +1 732 932 4350x224; fax: +1 732 932 4360.
E-mail address:[email protected](M. Nores).
about the most effective and efficient ways to improve early development. In the case of developing, low-income coun-tries there has been particular interest in combining edu-cation with other interventions that prevent malnutrition and stunting given the irreversibility of early nutritional insufficiencies (Agüero, Carter, & Woolard, 2006).
Across countries, early childhood education and care providers differ tremendously. Preschool programs may focus on one or several of the following aspects of chil-dren’s growth and development: physical growth and health, mental health, nutrition, language and cognition, and social and emotional development. These programs may take place in formal, informal and non-formal set-tings, and they can be center-based, formal preschools, parent/community-based arrangements, or home-based arrangements. In addition, impacts on maternal employ-ment are important for many programs as these (at least potentially) generate income that might enhance early childhood development and in some countries policy emphasis has been on child care with employment and child development viewed as joint products. Coverage has
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increased dramatically in the developed world over the last three decades, but progress in the developing world has been uneven and slow (UNESCO, 2006). Much of the growth in funding for programs has been due to expan-sions by the public sector, yet child care continues to be mostly privately provided and is often provided by care-givers with little or no formal preparation and low levels of education. Particular interventions, such as those that will be reviewed in this paper, vary in a number of aspects, including intensity, staff qualifications, quality of services, and, not unexpectedly, effectiveness.
A central question for policymakers in the current con-text is what types of programs are cost-effective, and, in particular, whether to provide interventions with a single focus on child care services only, education, nutri-tion or health care, or some combinanutri-tion of these, either through multiple programs or a single multi-purpose pro-gram (UNESCO, 2006). Answering this question can be quite involved as it is to be expected that effectiveness will vary with the broader conditions in which children live and develop, including such circumstances as the avail-ability and quality of later schooling. A growing body of research provides relevant information for policymakers. It is important to use the research not only to obtain evidence regarding whether (early childhood education and devel-opment) ECD programs have impacts, but also the extent to which child outcomes vary with the intensity and duration of ECD investments. In other words, in addition to asking “what kinds of preschool intervention, if any, are effec-tive, attention should [be] given to the question of what amount of treatment yields what amount of gain” (McKay,
Sinisterra, McKay, Gomez, & Lloreda, 1978).
Research from the United States going back many years has provided evidence that intensive, high-quality early childhood interventions have direct and persistent effects on cognitive and non-cognitive development (Barnett, 2008; Blau & Currie, 2005; Camilli, Vargas, Ryan, & Barnett, in press; Heckman & Masterov, 2007; Temple & Reynolds, 2007). Important short- and long-term effects across var-ious dimensions of child development have been found in multiple randomized trials with interventions as varied as part-day preschool education at ages 3 and 4, full-day edu-cational child care birth to age 5, and home visitation begin-ning prenatally. Income supplements and comprehensive services programs have had relatively disappointing results for the most part, though some suggest that modest gains in achievement follow from even small increases in income
(Barnett, 2002; Dahl & Lochner, 2008; Duncan, 2005; Lucas,
McIntosh, Petticrew, Roberts, & Shiell, 2008).
In the international arena, there is a consensus on early childhood interventions having developmental benefits in early childhood (Engle et al., 2007; Vargas-Barón, 2009;
Vegas & Santiba ˜nez, 2008). However, the studies that are
the basis for this consensus vary in method, population, type of intervention (e.g., nutrition, education, parenting education, income supplementation, countrywide or localized1), and type of outcome measured (anthropomet-1Although we do not differentiate between countrywide and local or
targeted programs in this study, there is enormous variation, even within-countries in program characteristics depending on this dimension.
ric, cognitive, behavioral, school readiness and progress,
inter alia), with some outcomes being short-term and some long-term. Traditional reviews have not gone beyond the general consensus to tackle the difficult problem of trying to understand what else might be learned from the variation in methods, interventions, national contexts, and outcomes. Meta-analysis provides a means to sum-marize studies’ outcomes on a common scale in ways that may help us understand more about how best to design intervention policies and programs. To our knowledge, no meta-analyses of early intervention have been conducted for studies done outside the United States, and there is a lack of analyses that consider the effects of research design, context, services provided (stimulation, nutrition, care, preschool, cash transfer), duration, age of intervention, and other key elements of early intervention program design.
This paper reviews the evidence from outside the United States and Canada in such a way as to examine the studies along comparable dimensions and with outcomes trans-lated into a comparable scale. We summarize research on short- and long-term effects of a wide range of early childhood interventions. We group results from these international studies into four outcome domains: cogni-tion, behavior, health, and amount of schooling. The review examines outcomes from 38 contrasts which employed rigorous quasi-experimental or randomized designs to evaluate the effects of 30 interventions in 23 countries. To compare contrasts we constructed a detailed dataset of the outcomes containing information on outcomes and study characteristics. Outcomes were converted to effect sizes (Cohen’sD) providing a single scale for use in summarizing the results. This dataset includes estimated effects across types of intervention (nutrition, nutrition and education or stimulation, cash transfers), and along different domains of a child’s development (cognitive, behavioral, health and schooling), and differentiates outcomes based on the time of the follow-up beyond the end of the intervention and several other study characteristics.
Through meta-analysis we explore and estimate the impact of early childhood interventions in cognitive, behavioral, health and schooling domains, and assess how the characteristics of the intervention and the target popu-lation are associated with such impacts. We find moderate benefits across all four domains and evidence that effects were sustained over the longer run. We observe that edu-cational or mixed interventions (with eduedu-cational, care or stimulation components) have the largest cognitive effects compared to cash transfers or solely nutritional interven-tions and smaller effect sizes for higher quality design (e.g. randomized).
2. Methodology
We collected information on interventions in devel-oped and developing countries through computer-aided searches and reviews of reference lists in the studies identified. Year of intervention did not matter for inclu-sion and interventions were mostly published in early childhood or nutrition journals, but publication in a peer-review journal was not a requirement for
inclu-sion, and the broader “grey” literature was searched to reduce potential effects of publication bias.2However, we required that all studies employ either experimental or more rigorous quasi-experimental methods to evaluate program effects on child development (Shadish, Cook, &
Campbell, 2002). In particular, studies were either
ran-domized trials or they estimated intervention effects using one of these state-of-the-art quasi-experimental meth-ods: difference-in-difference, propensity score matching, instrumental variables, or another simultaneous equations estimation technique. Most studies provided a contrast or a group of contrasts in which intervention effects were esti-mated as differences between treatment(s) and a control group. In the case of propensity score matching designs or difference-in-difference designs, contrasts were con-structed rather than provided by contrasting “naturally” occurring comparison and treatment groups.
Interventions covered countries in Europe, Africa, Asia, Central and South America. We reviewed more than one publication on each intervention where available. Often this was necessary because different publications pre-sented results from the same study at different points in time, enabling us to track short- and long-term effects. In addition, publications sometimes focused on particular dimensions of child development in reporting outcomes, and it was common to find that separate publications reported on cognitive and physical outcomes for different audiences.
We coded contrasts in studies that contrasted more than one contrast group (e.g. stimulation and supplementation, stimulation only, supplementation only, in contrast to a control group) separately. A total of 38 contrasts from the 30 studies (seeAppendix Afor publications), covering 24 countries in Europe, Asia, Africa, Central and South Amer-ica are covered by this review and meta-analysis. Of the 38 contrasts, 23 were studied with randomized designs, 4 were studied with a propensity score matching design (two of these had an imbedded difference-in-difference design), 2 were studied with a difference-in-difference design, another 3 interventions were studied with instrumental variables (IV) or two-stage least squares (2SLS) designs, and 6 were studied using other approaches (bivariate probit estimations or combinations of other approaches).
For each effect size within a contrast we calculate Cohen’s Dusing Thalheimer and Cook’s (2002) method which takes into account sample sizes for treatment and control groups whenever means and standard deviations of control and treatment groups,t-tests,F-tests, or mean and standard errors of control and treatment groups were reported. If these were not reported, we were not able to generate a Cohen statistic and did not include the effect. We encountered this problem with two studies and several effects within studies where other effects were included.3 We included four studies with probability effect sizes and flagged these with dummy codes in the meta-analyses. In
2JSTOR, ScienceDirect, PubMed, EBSCOhost, AJCN, AJP, BMJ search
among others.
3This is the case whenF-tests comparing more than two groups are
reported.
studies that evaluated various model specifications for the same outcome, we averaged results across specifications within outcomes. We include all estimates of effects on outcomes for which we could calculate a Cohen’sDfrom the statistics reported.
Outcomes were coded as belonging to one of four outcome categories: cognition (e.g., vocabulary, language, literacy and mathematics), behavior (e.g. self-regulation, play, aggression, hyperactivity), health (e.g., height, weight, nutritional status, motor skills), or schooling (e.g., school attendance, years of schooling). Moreover, depending on the age of intervention and the follow-ups, we coded out-comes as short- and long-term. Short-term was defined as up to age 7 (roughly the end of the early childhood period and the beginning of compulsory formal schooling in many countries). Long-term was defined as ages 7 and above. Since different follow-ups for the same interventions were reported separately (immediate versus long-term out-comes) we coded these individually.
In addition, when a vulnerable subpopulation group was reported, we coded results for this subgroup sepa-rately so that we could compare results for more highly disadvantaged populations across the different studies. We also coded studies on the type of intervention (cash trans-fer, nutritional, educational or mixed), sample size of the control and treatment groups, the country of intervention, the age group targeted by the intervention (infant/toddler, preschool age, or both), and duration.
Of the interventions, eight were early education inter-ventions, five were child care interinter-ventions, five were nutrition interventions, four were both nutrition and edu-cation interventions, two were nutrition and child care interventions, one was an early education and child care intervention, and six were cash transfer interventions.4 In terms of contrasts, these translate into six cash trans-fer contrasts, six nutrition-only contrasts, and 26 mixed contrasts (stimulation, care or education only and or with nutritional components).
Sample sizes varied between 63 and 29,817 (mean 1743, SD 4076). The age of participation varied from prenatal to age 7. A total of 17 contrasts targeted children under the age of 36 months (defined as infant and toddler interven-tions) exclusively. Another 14 contrasts targeted children between ages 3 and 7. Seven contrasts targeted children in both age groups. The duration of interventions varied between 6 months and 6.5 years.
Cognitive outcomes are an amalgam of IQ measures and related tests such as: the Denver Developmental Screening Test (Frankenburg, Dodds, Archer, Shapiro, &
Bresnick, 1992); Griffiths Mental Developmental Scales
(Griffiths, 1970); theTest de Vocabulario e Imágenes Peabody
(Spanish version of the Peabody Picture Vocabulary Test;
Dunn, Padilla, Lugo, & Dunn, 1986); the Raven’s
Pro-gressive Matrices (Raven, Raven, & Court, 1958); other
4Although early education and child care are to some extent joint
prod-ucts, but to the extent to which the intent was to provide a curriculum that was cognitively stimulating for the child or to provide a safe substitute for parental care while the mother worked or just home stimulation, these were differentiated.
vocabulary measures; expressive and receptive language tests; achievement tests including the Woodcock–Johnson
(Woodcock, McGrew, & Mather, 2001); and tests of
math-ematics knowledge and skills, literacy; and measures of long-term and short-term memory. There are important differences in what is measured by these tests, and collaps-ing the distinctions results in the loss of some information. Gains on some measures do not imply gains on all, though it is highly unlikely that positive effects in any aspect of this domain would be accompanied by negative effects on another cognitive outcome.
Social and emotional outcomes were coded across an extremely wide range of measures. These included: play, cooperation, self-regulation, hyperactivity, confor-mity, sociability, anxiety, depression, attention deficit disorder, delinquency, socialized aggression, schizotypal personality disorder, the Griffiths Personal-Social subscale
(Griffiths, 1970), the Behavior Problems Index (Zill, 1990),
the Denver Behavior Rating Scale (Frankenburg et al., 1992), the Adaptive Social Behavior Inventory (Hogan,
Scott, & Bauer, 1992) and the Rohner Personality
Assess-ment Questionnaire (Rohner & Khaleque, 2005). Again, this should be considered a broad domain within which there are important differences, and it should not be assumed that positive impacts on one area within the domain will be accompanied by positive impacts on the others. It is pos-sible for an intervention to have both positive and negative impacts within this domain.
Health encompasses all anthropometric outcomes and in most studies reflects a concern to assess the impacts of an intervention on nutrition and its consequences for devel-opment. These are weight, height, and their standardized equivalents (WAZ, orz-scores for weight-for-age and HAZ, orz-scores for height-for-age), arm circumference, head circumference, skin fold thickness and Body Mass Index (BMI). Closely related to these outcomes are assessments of stunting, underweight and malnourishment, which are measured on the basis of the United States average HAZ and WAZ. In addition, this domain also encompasses indi-cators of fine and gross motor development, as measured by such instruments as the Bayley Psychomotor Develop-ment Index (Bayley, 1993), Denver Fine Motor and Gross Motor Scales (Frankenburg et al., 1992) and McCarthy Leg Motor Scale (McCarthy, 1972).
Finally, we categorized as schooling outcomes those effects that are directly related to school progress. Out-comes in this domain include the likelihood of pre-primary enrollment, age of enrollment, late enrollment, first grade repetition, overall repetition, dropping out, seventh grade placement (in the German educational system), highest grade attained, attainment at age 9, grade completion by age 13, school suspension and expulsion, and school atten-dance. A few of these were measured as probabilities and we were not able to translate them into Cohen’sDeffect sizes; we report them as probability effect sizes, as dis-cussed earlier.
The ways in which we categorized effect sizes allow us to look at whether there is a difference between short- and long-term effects by type of outcome and type of inter-vention. In the literature from the United States much has been made of the tendency of cognitive effects to decline
over the long-term from their initial levels, while for other types of outcomes this does not appear to be true (Camilli
et al., in press). In addition, it is possible that larger
cogni-tive effects might persist as the result of earlier and longer intervention or from specific types of interventions (e.g., those combining education and nutrition).
We describe studies differentiating along all aspects mentioned above. In addition, we estimate the effects on outcomes of a country’s level of economic development, the age group of the target group, duration, type of inter-vention (mixed versus nutrition or cash transfer), design (randomized or propensity score matching versus others), time of evaluation of effect (long-term versus short-term), and controlling for whether the effect is a probability model (for those that were not converted into Cohen effects). In the next section, we first present the results of simple descriptive statistics and univariate analyses for all stud-ies and subgroups of studstud-ies. We then present the results of a maximum-likelihood random effects estimator (MLE) in a multivariate analysis of the determinants of effects sizes overall and in each of the four domains (cognition, behavior, health and schooling).
3. Results
3.1. Effect sizes by selected design and population characteristics
Table 1summarizes the basic findings for each of the
four outcome domains across all of the interventions and follow-up periods. The overall mean Cohen effect size across the 38 contrasts is of 0.29 (SD 0.28). The mean aver-age effect size for cognitive outcomes is 0.31 (SD 0.21). The mean effect size for the other outcomes is similar: behavior is 0.27 (SD 0.24), health is 0.31 (SD 0.41); and the mean for schooling outcomes is 0.27 (SD 0.31).5 Cog-nition and behavior domains have much smaller standard deviations than the other two, indicating more heterogene-ity in effects across studies for outcomes within the health and schooling domains. It should also be noted that school-ing outcomes by their nature tend to be longer term than other measures as the former cannot be measured dur-ing the preschool period. Many schooldur-ing outcomes are cumulative or become evident only after a number of years (e.g., grade repetition, educational attainment, and drop out). The greater diversity of outcomes summarized under “schooling” may explain the higher degree of dispersion of results compared to other types of outcomes.
Assessing mean effects by intervention type (cash trans-fer, nutrition-only or nutrition and care or education component) we observe that cash transfer interventions have a mean effect of 0.29 (SD 0.46), nutrition interventions have a mean effect of 0.25 (SD 0.19) and mixed interven-tions evidence a similar effect to cash transfers with half the variation (ES 0.30, SD 0.26). Moreover, by disaggregat-ing into types of outcomes by intervention type, we observe that studies of mixed interventions have a larger average effect on three of the four dimensions (nutritional
Table 1
Summary statistics across child development dimensions.
Ncontrast Neffects Mean SD Minimum Maximum
All 38 280 0.294 0.285 −0.400 2.850 Cognition 26 118 0.310 0.206 −0.050 1.430 Behavior 13 65 0.266 0.237 −0.400 0.990 Health 21 68 0.306 0.410 −0.391 2.850 Schooling 9 29 0.266 0.309 0.016 1.250 Intervention type Short-term 35 174 0.301 0.329 −0.193 2.850 Long-term 12 106 0.284 0.191 −0.400 0.710 Cash transfer 6 39 0.292 0.461 0.000 2.850 Cognition 14 0.170 0.064 0.080 0.300 Behavior 3 0.207 0.159 0.100 0.390 Health 22 0.382 0.601 0.000 2.850 Schooling – – – – – Nutrition 6 47 0.248 0.186 0.000 0.750 Cognition 20 0.255 0.155 0.056 0.515 Behavior 1 0.460 – 0.460 0.460 Health 12 0.375 0.232 0.000 0.750 Schooling 14 0.113 0.059 0.035 0.270 Mixed 26 194 0.306 0.257 −0.400 1.430 Cognition 84 0.347 0.219 −0.050 1.430 Behavior 61 0.266 0.241 −0.400 0.990 Health 34 0.232 0.287 −0.391 1.210 Schooling 15 0.409 0.378 0.016 1.250 Duration Low (<1 year) 5 36 0.196 0.284 −0.193 1.250 Medium (1–3 years) 11 115 0.312 0.201 −0.400 0.800 High (>3 years) 22 129 0.306 0.339 0.000 2.850 Age group Infants/toddlers 17 130 0.339 0.259 −0.400 1.430 Pre-k 14 105 0.285 0.207 −0.110 1.250 Both 7 45 0.187 0.444 −0.193 2.850 Study type Randomized 23 206 0.277 0.269 −0.400 2.850 Propensity score 4 26 0.127 0.154 −0.193 0.470 Other 11 48 0.459 0.328 0.016 1.430 Development level Low/middle 15 97 0.255 0.279 −0.193 1.430 Upper middle/high 23 183 0.315 0.286 −0.400 2.850
Note: Appendix A lists studies included by country.
tions have a larger average impact on the health dimension and these only have one contrast in the behavioral dimen-sion).
Table 1 also summarizes effect sizes by differences
in duration of intervention and age group intervened (at entry). There is a slight apparent advantage in interven-tions that last between 1 and 3 years or more in comparison (ES 0.30–0.31) to less than a year (ES 0.20) in terms of average effect, although effects as presented do not dis-aggregate between short- and long-term effects; therefore we do not know if greater duration yields longer term effects. For age groups (infants/toddlers, pre-K or both) we observe a slightly greater average effect for interventions that targeted infants/toddlers (ES 0.34) and pre-K children (ES 0.28) than for interventions that targeted children in both age ranges at the same time from the start (ES 0.19).
Lastly, Table 1also presents Cohen’sDseparately for studies with a randomized design and those using other designs. Studies with a randomized design have smaller effect sizes (ES 0.28) compared to other studies (ES 0.46,
SD 0.29) except for propensity score matching designs (ES 0.13, SD 0.15) which have even smaller effect sizes.
The last section ofTable 1summarizes effect according to the World Bank classification of economies.6 Average effect sizes for low-income and middle-low-income coun-tries appear to be lower (ES 0.25, SD 0.28) than average effect sizes for middle and middle-high income countries (ES 0.31, SD 0.28).
Fig. 1displays the distribution of effect sizes for each
type of child development outcome. A kernel distribution has been drawn alongside the histograms as a smooth estimator of the distribution function. The distribution of effect sizes for all domains indicates a positive central ten-dency with a positive skew indicating relatively fewer large effect sizes. The schooling domain also has a relatively long right tail. Apparent differences in the distributions
6World Bank List of Economies. July 2006. The World Bank. http://www.iqla.org/joining/World-Bank Classification-List 2009.pdf.
Fig. 1.Distribution of outcomes across child development dimensions.
across outcome domains may to some extent reflect the greater attention to cognitive outcomes and the larger sam-ple of those outcomes over all other types of outcomes. The smaller numbers of outcomes in the behavioral and school domains likely reflects a lesser interest in the for-mer and the greater difficulty and expense of measuring the latter. Again we note that schooling outcomes tend to be measured later than others.7
Cognitive outcomes by study and for different follow-up points in time, subpopulations, and amounts of treat-ment (where identified) within each study are displayed
in Fig. 2, so as to observe the distribution of outcomes
along types of interventions and follow-ups. Each bar rep-resents within-study aggregates of effects. We differentiate through shading three types of intervention: (1) nutrition, (2) cash transfers, and (3) prekindergarten education or early stimulation or a mix of educational or early stimula-tion and nutristimula-tion combined. As is apparent fromFig. 2that summarizes the mean cognitive effect per study, studies that utilize cash transfers (N= 6,n= 14) as the intervention have the smallest average effect sizes on cognitive out-comes (0.17). Nutritional interventions (N= 6,n= 20) have an intermediate average effect size (0.25). Most interven-tions have an educational component (N= 26,n= 84) and these early childhood interventions evidenced the highest average effects sizes on cognitive outcomes (0.35).
Average cognitive effect sizes in the short-term (typ-ically measured at the end of the intervention or a year after) are only slightly larger than long-term effects which are most often measured during the years of primary or
7Dropping the outliers observed in cognition, health and schooling the
corresponding means are 0.30 (SD 0.18), 0.27 (SD 0.27) and 0.23 (SD 0.25).
secondary education. These are 0.30 (N= 25,n= 59) and 0.32 (N= 12,n= 59), respectively. There are two studies that measured effects into adulthood. Several studies report short-term cognitive effect sizes larger than long-term effects.
We replicate the analysis for average effect sizes for studies of behavioral development (social and emotional). Only one nutritional intervention (imbedded within a larger study in which one group received supplementation only and another received supplementation and stimula-tion) measures this dimension and shows an effect size of 0.46. Two of the cash transfer interventions evaluated effects on behavior and they present an average effect size of 0.21 (n= 3). The early educational interventions (N= 15) yield effects sizes that average 0.27 (n= 61) and vary by an order of magnitude from−0.04 to 0.99.
There is no evident effect of time on the estimated impacts of early childhood interventions on behavior. Aver-age effects sizes for short-term outcomes are of 0.26 (n= 40), and for long-term outcomes of 0.27 (n= 25). Cognitive and behavioral outcomes are highly corre-lated regardless of being short-term (0.79) or long-term (0.68).
Results from the fourteen studies that measure health outcomes are displayed inFig. 3. Of these, seven inter-ventions are cash transfers (or results were reported for a subgroup) with an average effect size of 0.38 (n= 22). On the other hand, only one is uniquely a nutritional study, and there are two studies in which a treatment group is a nutri-tional supplement alone. The average effect size for these interventions is 0.37 (n= 12). The other 13 studies evaluate the effects of interventions that combined nutritional and educational or child care components. The mean effect size across those 13 studies is 0.23 (n= 34).
Fig. 2.Distribution of cognitive outcomes by study and types of interventions.
Table 2
Maximum-likelihood random effects estimation of program and target population characteristics on effect sizes.
Variables All Cognition Behav Health Schooling
Low/middle-low-income country −0.127**(0.059) 0.054 (0.057) 0.069 (0.117) −0.133 (0.134) 0.199 (0.156)
Mixed intervention −0.019 (0.047) 0.116**(0.046) −0.196 (0.126) −0.109 (0.101) −0.132 (0.110)
Infants/toddlers 0.099 (0.075) 0.120 (0.085) 0.396***(0.135) −0.087 (0.135) −0.003 (0.056)
Pre-K child −0.032 (0.080) −0.024 (0.086) 0.262*(0.150) −0.212 (0.180) 0.023 (0.066)
Dosage total −0.018 (0.038) 0.044 (0.040) −0.036 (0.056) 0.134*(0.072) −0.487***(0.072)
Study type: randomized −0.114 (0.071) −0.182***(0.070) −0.329***(0.099) 0.085 (0.227) −0.221**(0.112)
Study type: propensity score matching −0.199*(0.111) −0.355**(0.154) −0.236 (0.183) −0.026 (0.242)
Long-term follow-up −0.036 (0.049) 0.042 (0.047) 0.096 (0.075) −0.246*(0.128) 0.267***(0.072)
Probability estimation 0.245***(0.094) 0.129 (0.098) 0.122 (0.129) 0.445 (0.300) −0.117 (0.124)
Type effect: behavioral 0.021 (0.075) Type effect: cognition 0.061 (0.067) Type effect: health 0.014 (0.077)
Observations 280 118 65 68 28
Study code 30 20 13 16 8
Notes: Standard errors in parentheses.
***p< 0.01. **p< 0.05. *p< 0.1.
When distinguishing between short- and longer term health effects, we find evidence of reduced health effects over time across studies and within the two studies that report longitudinal outcomes; that is, the Jamaica study and the Bogotá study (Colombia III). Effect sizes for health are on average 0.36 (N= 17,n= 50) in the short-term and 0.15 (N= 7,n= 18) in the long-term.
Finally, the schooling domain has the smallest number of studies evaluating the effects of interventions. Although there were no cash transfer interventions that reported school progress outcomes, results are displayed for five nutrition-only interventions and seven mixed interven-tions. These average effect sizes are 0.11 (n= 14) and 0.41 (n= 15), respectively, indicating much larger effects when education or stimulation was a component of the intervention.
Differentiating short- and long-term outcomes across 12 interventions we find, as in the other domains, no evident trend over time. Studies in Jamaica and Uruguay provide evaluations of later ages that evidence large effects on schooling outcomes. On average, effect sizes over time across these studies are of 0.25 (n= 24) and 0.41 (n= 4) in the short and long run, respectively. There are only three studies that are long-term by our definition.
3.2. Random effects estimation
To assess the independent effects of program and population characteristics on effect sizes, we estimated equations for the influence on child outcomes of a country’s economic development level, the age group of the target group, program duration, type of intervention (mixed in contrast to nutrition or cash transfer), evaluation design (randomized or propensity score matching versus others) and time of follow-up (long-term versus short-term). In addition, given that for a few studies we could not convert effects into Cohen effects given that these were probability models reported as marginal probabilities, we control for whether the reported effect is such a model. These analyses provide inferential statistics that indicate whether differ-ences observed in the descriptive statistics presented above
are robust to controls for multiple influences and are larger than might be observed purely by chance (p< .05).
We estimate these equations using a maximum-likelihood random effects estimator (MLE) for the full sample and the four domains (cognition, behavior, health, and schooling), with random effects at the study level.8 This type of estimation allows for unconditional general-izations about relations between program and population characteristics and child outcomes (Hedges & Vevea, 1998). Random effects estimation assumes that study level vari-ation may be partly random, such that effects within a study (and all its contrasts) are not independent. Conse-quently, we do not estimate individual effects, but estimate the effects of underlying design and population character-istics.
Results from the random effects analyses are presented
inTable 2. For the overall sample, we find significantly
smaller average effect sizes for interventions in low-income countries. Context matters. We find no overall effect of intervention characteristics. However, for cogni-tive effects only, mixed interventions have significantly larger effects than other types of interventions (nutrition and cash transfers). Behavioral effects were significantly larger for interventions that begin with infant/toddlers or with preschoolers than for interventions that begin with children across the full age range. While dosage is asso-ciated with larger health effects, a longer intervention is associated with a much smaller effect on schooling, an unexpected finding. Randomized and propensity score matching evaluations tend to have significantly smaller effect sizes than other types of evaluations (omitted group). Marginal probability effect sizes are on average higher than Cohen effect sizes for the overall sample so it is important to control for this and to keep this in mind when comparing
8We attempted to estimate multilevel maximum restricted likelihood
estimators (REML) differentiating between study level variables (dura-tion, design, development level, type of intervention and age group) and effect size level variables (long-term, type of outcome, type of effect). However this type of model did not converge for subsamples of dimen-sions of outcome given the numbers of observations.
results across studies. For cognition and behavioral effects time was not of importance, while longer follow-up was associated with smaller effects on health and larger effects on schooling.
4. Conclusions and discussion
A total of 56 studies reporting the effects of 30 inter-ventions (and 38 contrasts) in 23 countries in Europe, Asia, Africa, Central and South America are analyzed. This review includes quasi-experimental and randomized studies. We coded studies on the type of intervention (cash transfer, nutritional, educational or mixed), the sample size of the control and treatment group, the study design, the country of intervention, subpopulations of interventions, follow-ups, whether it targeted infants, pre-K age children or both, and dosage. We calculate Cohen’sDeffect sizes for all out-comes reported and conduct meta-analyses for all types of effects. Average effect sizes are positive for all four broad domains of child development and of moderate size com-pared to the results of large scale educational and social interventions generally, about 0.26–0.39 for the different outcomes. Meta-analyses show a relation between mixed interventions and cognition, a reduced effect size of higher quality interventions (randomized and propensity score matching), a positive relationship between targeting by age and health outcomes, and an overall lower effect of outcomes in lower income economies.
The findings of this study are broadly consistent with the results of similar reviews for research conducted in the United States, including a recent meta-analysis of 123 early education studies conducted since 1960 that evidences the importance of moderators such as program design, as well as the absence of significance of time for some dimensions
(Camilli et al., in press). We find children from different
contexts and countries receive substantive benefits across all dimensions, and that interventions providing direct care or education to be more effective particularly in terms of cognition. Also, effects are sustained over the long run in studies that evaluate effects at older ages, and interven-tions appear to benefit children more in the behavioral dimension (maybe because of differences in the content of the intervention) when they target one particular group (infant/toddlers or pre-k children, slightly more the for-mer) than when they are universal since the beginning.
However, there are some differences from the findings in the larger literature for the United States (Camilli et al., in
press). Overall U.S. effect sizes are somewhat smaller, and
declined from the short-term to the long-term in the cog-nitive domain, but not for behavioral/social and schooling outcomes. If U.S. estimates are limited to studies with bet-ter research designs, as we did in this meta-analysis, effect sizes are more comparable. The estimated cognitive effects still decline over time: 0.69 for immediate impact; 0.35 at ages 5–10, and 0.28 beyond age 10. The smaller sample size in our review may be insufficient to detect differences in effect sizes during the early childhood years and beyond. The finding that effects increased with length of follow-up in our study might be explained by the fact that the measures typically were cumulative and effects on school dropout may take several or even many years to become
fully apparent. The finding that effects decreased over time for health is more puzzling.
It is not unexpected that context matters, but the finding that effects were smaller in less economically developed countries is puzzling, particularly as this result appears to be concentrated in effects on health. Possibly this is because there is a threshold that must be crossed to improve the outcomes measured and it is more difficult to cross when the economic level is low. Possibly it is because intervention effects depend on other supports in the environment that are less likely to be present in less developed economies. Clearly, this is a topic worthy of fur-ther research.
The findings with regard to timing and duration of interventions were also unexpected. Beginning with either infants or preschoolers was better than beginning with both, and this may suggest that it is some other characteris-tic of an intervention rather than age at start that produces this finding. The duration of intervention had positive effects on health outcomes, but negative effects on school-ing. The positive effect is expected, but the negative effect makes little sense. Again, it may be that this is because longer interventions studied for effects on schooling were systematically less intensive or offered lower quality edu-cation because of a cost trade-off between intensity and duration. Further research into this topic could be quite useful.
Given the number of studies it is difficult to determine if effect sizes are larger for interventions with more disad-vantaged children within the different dimensions, though the overall sample and some of the studies reviewed show partial evidence this might be the case and that would be consistent with evidence from the larger U.S. literature
(Barnett & Belfield, 2006). This might be particularly true
where outcomes of interest focus on problems such as mal-nourishment stunting, school failure and drop out, much of which the population in developed countries may not be experiencing, rather than focusing on learning growth generally where there is no ceiling.
Interventions that were either educational or mixed (e.g. stimulation and nutrition, care and nutrition, pre-K, pre-K and nutrition) evidenced the largest statistically significant effect on cognition, in comparison to interven-tions that were cash transfers or solely nutritional. The combination of education and nutritional assistance seems to be more powerful for improving child development over nutritional assistance alone. However, the indepen-dent contributions of each aspect of the intervention and the potential for synergistic effects have not been well-researched. This is broadly consistent with the results of research from the United States, Canada, and other highly economically developed nations.
Overall, our findings indicate that program design matters, but that there is a lack of clarity about what dimen-sions matter how much and for what reasons. In addition, the results raise questions about potential trade-offs in program design due to the costs of interventions. Unfortu-nately, cost appears to have been greatly neglected here, as it is in studies of education and human development gen-erally. Early intervention studies would contribute more to policymaking if the costs of the interventions (or the
interventions implied by comparisons where they are not explicit interventions) were estimated as well as the effects on child development.
Extrapolating from the results reviewed here to poli-cies for a broad range of countries where millions do not have access to early education, child care is of poor quality, children are malnourished, and grade repetition and school drop outs are high, is complicated by the vast differences in social and economic conditions across countries. Nev-ertheless, the potential for large economic benefits from improvements in child development outcomes is large nearly everywhere, and all of the major types of early child-hood interventions had substantial average effect sizes across a diverse sample of programs and countries. This is consistent with theories of human development and the economics of the family that point to externalities, low levels of parental education, and economic constraints on family investments in young children as factors that result
in suboptimal investments in young children that can be addressed by public programs (Haveman & Wolfe, 1994;
Heckman & Masterov, 2007). In the United States such
programs have been estimated to have a present value amounting to tens and even hundreds of thousands of dollars per child (Barnett, 2006). If investments in early childhood development could be directed to the more opti-mally designed interventions among those studied, there could be high returns in nations with much less developed economies as well. One of the most obvious findings of our review is that there are view relatively few high-quality studies of early intervention outside the United States. Research seeking to provide greater specificity regard-ing the design and implementation of interventions to replicate and improve upon those found to be effective would seem to have a high potential payoff for coun-tries with less developed, as well as more developed economies.
Appendix A. List of studies by country
No. Country Reference
1 Argentina Berlinski, S., & Galiani, S. (2007). The effect of a large expansion of pre-primary school facilities on preschool attendance and maternal employment.Labour Economics,14(3), 665–680.
2 Bangladesh Aboud, F. E. (2006). Evaluation of an early childhood preschool program in rural Bangladesh.Early Childhood Research Quarterly,21(1), 46–60.
3 Aboud, F. E. (2007). Evaluation of an early childhood parenting programme in rural Bangladesh.Journal of Health Population and Nutrition,25(1), 3.
4 Hamadani, J. D., Huda, S. N., Khatun, F., & Grantham-McGregor, S. M. (2006). Psychosocial stimulation improves the development of undernourished children in rural Bangladesh.Journal of Nutrition,136(10), 2645.
5 Bolivia Behrman, J. R., Cheng, Y., & Todd, P. E. (2004). Evaluating preschool programs when length of exposure to the program varies: A nonparametric approach.Review of Economics and Statistics,86(1), 108–132.
6 Colombia McKay, H., Sinisterra, L., McKay, A., Gomez, H., & Lloreda, P. (1978). Improving cognitive ability in chronically deprived children.Science,200(4339), 270–278.
7 Perez-Escamilla, R., & Pollitt, E. (1995). Growth improvements in children above 3 years of age: The Cali study.Journal of Nutrition,125(4), 885.
8 Attanasio, O., Syed, M., & Vera-Hernandez, M. (2004). Early evaluation of a nutrition and education programme in Colombia. The Institute for Fiscal Studies, Briefing Note,44(11).
9 Attanasio, O., Battistin, E., Fitzsimmons, E., Mesnard, A., & Vera-Hernandez, M. (2005).How effective are conditional cash transfers? Evidence from Colombia: Briefing Note 54. London: The Institute for Fiscal Studies.
10 Attanasio, O., Gómez, L. C., Heredia, P., & Hernández, M. V. (2005).The short-term impact of a conditional cash subsidy on child health and nutrition in Colombia: Report Summary: Familias 03. London: The Institute of Fiscal Studies.
11 Super, C. M., Herrera, M. G., & Mora, J. O. (1990). Long-term effects of food supplementation and psychosocial intervention on the physical growth of Colombian infants at risk of malnutrition.Child Development,61(1), 29. 12 Waber, D. P., Vuori-Christiansen, L., Ortiz, N., Clement, J. R., Christiansen, N. E., Mora, J. O., et al. (1981). Nutritional
supplementation, maternal education, and cognitive development of infants at risk of malnutrition.American Journal of Clinical Nutrition,34, 807–813.
13 Overholt, C., Sellers, S. G., Mora, J. O., Paredes, B., Herrera, M. G. (1982). The effects of nutritional supplementation on the diets of low-income families at risk of malnutrition.American Journal of Clinical Nutrition,36, 1153–1161. 14 Ecuador Schady, N. R., & Paxson, C. H. (2007). Does money matter?: The effects of cash transfers on child health and
development in rural Ecuador.World Bank Policy Research Working Paper No. 4226. Washington, DC: World Bank Development Research Group.
15 England Sammons, P., Sylva, K., Melhuish, E. C., Siraj-Blatchford, I., Taggart, B., Barreau, S., et al. (2007). Influences on children’s development and progress in key stage 2: Social/behavioral outcomes in year 5 (No. DCSF-RR007). London: Institute of Education, University of London.
16 Sammons, P., Sylva, K., Melhuish, E. C., Siraj-Blatchford, I., Taggart, B., Grabbe, Y., et al. (2007).Influences on children’s development and progress in key stage 2: Cognitive outcomes in year 5 (No. DCSF-RR828). London: Institute of Education, University of London.
17 Sammons, P., Elliot, K., Sylva, K., Melhuish, E., Siraj-Blatchford, I., & Taggart, B. (2004). The Impact of pre-school on young children’s cognitive attainments at entry to reception.British Educational Research Journal,30(5), 691–712. 18 Germany Spiess, C. K., Büchel, F., & Wagner, G. G. (2003). Children’s school placement in Germany: Does kindergarten
attendance matter?Early Childhood Research Quarterly,18(2), 255–270.
19 Guatemala Behrman, J. R., Hoddinott, J., Maluccio, J. A., Soler-Hampejsek, E., Behrman, E. L., Martorell, R., et al. (2008). What determines adult cognitive skills? Impacts of pre-school, school-years and post-school experiences in Guatemala.
IFPRI Discussion Paper 00826. Washington, D.C.: International Food Policy Research Institute.
20 Hoddinott, J., Maluccio, J. A., Behrman, J. R., Flores, R., & Martorell, R. (2008). Effect of a nutrition intervention during early childhood on economic productivity in Guatemalan adults.The Lancet,371(9610), 411–416.
Appendix A (Continued)
21 Maluccio, J. A., Hoddinott, J., Behrman, J. R., Martorell, R., Quisumbing, A. R., & Stein, A. D. (2003). The impact of nutrition during early childhood on education among Guatemalan adults.PIER Working Paper Archive. Philadelphia, PA: Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
22 Stein, A. D., Barnhart, H. X., Wang, M., Hoshen, M. B., Ologoudou, K., Ramakrishnan, U., et al. (2004). Comparison of linear growth patterns in the first three years of life across two generations in Guatemala.Pediatrics,113(3), 270–275. 23 Li, H., Barnhart, H. X., Stein, A. D., & Martorell, R. (2003). Effects of early childhood supplementation on the educational
achievement of women.Pediatrics,112(5), 1156–1162.
24 India Arora, S., Bharti, S., & Sharma, S. (2007). Comparative study of cognitive development of ICDS and non-ICDS children (3–6 years).Journal of Human Ecology,22(3), 201–204.
25 Indonesia Pollitt, E., Watkins, W. E., & Husaini, M. A. (1997). Three-month nutritional supplementation in Indonesian infants and toddlers benefits memory function 8 years later.American Journal of Clinical Nutrition,66, 1357–1363.
26 Northern Ireland Melhuish, E., Quinn, L., Hanna, K., Sylva, K., Sammons, P., Siraj-Blatchford, I., et al. (2004).The effective pre-school provision in Northern Ireland (EPPNI) project: Summary report 1998–2004. Bangor, Ireland: Department of Education, Northern Ireland Statistics and Research Agency.
27 Melhuish, E., Quinn, L., Sylva, K., Sammons, P., Siraj-Blatchford, I., Taggart, B., et al. (2002).The effective pre-school provision in Northern Ireland (EPPNI). Pre-school experience and social/behavioural development at the start of primary school. Belfast: Stranmillis Press.
28 Melhuish, E., Quinn, L., Sylva, K., Sammons, P., Siraj-Blatchford, I., Taggart, B., et al. (2002).The effective pre-school provision in Northern Ireland (EPPNI). Pre-school experience and cognitive development at the start of primary school. Belfast: Stranmillis Press.
29 Quinn, L., Melhuish, E., Hanna, K., Sylva, K., Siraj-Blatchford, I., Sammons, P., et al. (2006).The effective pre-school provision in Northern Ireland (EPPNI). Pre-school experience and literacy and numeracy development at the end of the key stage 1. Belfast: Stranmillis Press.
30 Jamaica Gardner, J. M. M., Grantham-McGregor, S. M., Himes, J., & Chang, S. (1999). Behaviour and development of stunted and nonstunted Jamaican children.Journal of Child Psychology and Psychiatry,40(5), 819–827.
31 Gardner, J. M. M., Grantham-McGregor, S. M., Chang, S. M., Himes, J. H., & Powell, C. A. (1995). Activity and behavioral development in stunted and nonstunted children and response to nutritional supplementation.Child development,
66(6), 1785–1797.
32 Chang, S. M., Walker, S. P., Grantham-McGregor, S., & Powell, C. A. (2002). Early childhood stunting and later behaviour and school achievement.Journal of Child Psychology and Psychiatry,43(6), 775–783.
33 Walker, S. P., Chang, S. M., Powell, C. A., & Grantham-McGregor, S. M. (2005). Effects of early childhood psychosocial stimulation and nutritional supplementation on cognition and education in growth-stunted Jamaican children: Prospective Cohort Study.The Lancet,366(9499), 1804–1807.
34 Walker, S. P., Chang, S. M., Powell, C. A., Simonoff, E., & Grantham-McGregor, S. M. (2006). Effects of psychosocial stimulation and dietary supplementation in early childhood on psychosocial functioning in late adolescence: Follow-up of randomized controlled trial.British Medical Journal,333(7566), 472.
35 Grantham-McGregor, S. M., Walker, S. P., Chang, S. M., & Powell, C. A. (1997). Effects of early childhood
supplementation with and without stimulation on later development in stunted Jamaican children.American Journal of Clinical Nutrition,66(2), 247–253.
36 Mauritius Raine, A., Mellingen, K., Liu, J., Venables, P., & Mednick, S. A. (2003). Effects of environmental enrichment at ages 3–5 years on schizotypal personality and antisocial behavior at ages 17 and 23 years.American Journal of Psychiatry,160(9), 1627.
37 Raine, A., Venables, P. H., Dalais, C., Mellingen, K., Reynolds, C., & Mednick, S. A. (2001). Early educational and health enrichment at age 3–5 years is associated with increased autonomic and central nervous system arousal and orienting at age 11 years: Evidence from the Mauritius child health project.Psychophysiology,38(2), 254–266.
38 Liu, J., Raine, A., & Venables, P. H. (2006). Malnutrition at age 3 and externalizing behavior problems at ages 8, 11, and 17 years: 1–8.Year Book of Psychiatry & Applied Mental Health, 9–10.
39 Mexico Fernald, L. C. H., Gertler, P. J., & Neufeld, L. M. (2008). Role of cash in conditional cash transfer programmes for child health, growth, and development: An analysis of Mexico’s Oportunidades.The Lancet,371(9615), 828–837. 40 Leroy, J. L., Garcia-Guerra, A., Garcia, R., Dominguez, C., Rivera, J., & Neufeld, L. M. (2008). The Oportunidades Program
increases the linear growth of children enrolled at young ages in urban Mexico.Journal of Nutrition,138(4), 793. 41 Nicaragua Macours, K., Schady, N., & Vakis, R. (2008). Cash transfers, behavioral changes, and cognitive development in early
childhood: Evidence from a randomized experiment.Policy Research Working Paper 4759. Washington, D.C.: The World Bank.
42 Philippines Armecin, G., Behrman, J., Duazo, P., Ghuman, S., Gultiano, S., & King, E., et al. (2006). Early childhood development through an integrated program: Evidence from the Philippines.World Bank Policy Research Working Paper 3922. Washington, D.C.: The World Bank.
43 Ghuman, S., Behrman, J., & Gultiano, S. (2006). Children’s nutrition, school quality, and primary school enrollment in the Philippines.Working Paper Series Vol. 2006-24. Kitakyushu: The International Centre for the Study of East Asian Development.
44 Daniels, M. C., & Adair, L. S. (2004). Growth in young Filipino children predicts schooling trajectories through high school.Journal of Nutrition,134(6), 1439–1446.
45 Glewwe, P., Jacoby, H. G., & King, E. M. (2001). Early childhood nutrition and academic achievement: A longitudinal analysis.Journal of Public Economics,81(3), 345–368.
46 Scotland Woolfson, L., & King, J. (2008).Evaluation of the extended pre-school provision for vulnerable two year olds pilot programme. Final report. Edinburgh: Scottish Government Social Research.
47 South Africa Cooper, P. J., Landman, M., Tomlinson, M., Molteno, C., Swartz, L., & Murray, L. (2002). Impact of a mother–infant intervention in an indigent peri-urban South African context pilot study.British Journal of Psychiatry,180, 76–81. 48 Agüero, J., Carter, M., & Woolard, I. (2006).The impact of unconditional cash transfers on nutrition: The South African child
support grant. Unpublished manuscript, University of California at Riverside.
49 Sub-Saharan Africa Hadley, C., Tegegn, A., Tessema, F., Makkonen, A., & Galea, S. (2008). Maternal and paternal anxiety-mood disorders and children’s social, motor, and cognitive development in Sub-Saharan Africa.Annals of Human Biology,35(3), 259–275.
Appendix A (Continued)
50 Turkey Kagitcibasi, C., Sunar, D., & Bekman, S. (2001). Long-term effects of early intervention: Turkish low-income mothers and children.Journal of Applied Developmental Psychology,22(4), 333–361.
51 Kagitcibasi, C., Bekman, S., & Goksel, A. (1995). A multipurpose model of nonformal education: The Mother–Child Education Programme.Coordinators’ Notebook,17, 24–32.
52 Uganda Alderman, H. (2007). Improving nutrition through community growth promotion: Longitudinal study of the Nutrition and Early Child Development Program in Uganda.World Development,35(8), 1376–1389.
53 Britto, P. R., Engle, P., & Alderman, H. (2007). Early intervention and caregiving: Evidence from the Uganda Nutrition and Child Development Program.Child Health and Education,1, 112–133.
54 Uruguay Berlinski, S., Galiani, S., & Manacorda, M. (2008). Giving children a better start: Preschool attendance and school-age profiles.Journal of Public Economics,92(5–6), 1416–1440.
55 Vietnam Watanabe, K., Flores, R., Fujiwara, J., & Tran, L. T. H. (2005). Early childhood development interventions and cognitive development of young children in rural Vietnam 1.Journal of Nutrition,135(8), 1918–1925.
56 Madagascar Galasso, E., & Yau, J. (2006). Learning through monitoring: Lessons from a large-scale nutrition program in Madagascar. Washington D.C.: The World Bank.
Appendix B. Supplementary data
Supplementary data associated with this article can be found, in the online version, atdoi:10.1016/j.econedurev.
2009.09.001.
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