metadata: authors: 0: author: Holla,Alaka
1: author: Bendini,Maria Magdalena
2: author: Dinarte Diaz,Lelys Ileana
3: author: Trako,Iva
count: World
docna: 0: docna: Is Investment in Preprimary Education Too
            Low ? Lessons from (Quasi) Experimental Evidence across Countries
docty: Policy Research Working Paper
subtopic: Educational Sciences,Health Care Services Industry,Effective Schools and Teachers,Educational Institutions & Facilities,Education For All,Education for Development (superceded),Educational Populations
teratopic: Industry,Education
abstracts: cdata!: A large body of evidence suggests
            sizeable improvements in cognitive and social-emotional
            skills and subsequent educational attainment following
            preprimary education interventions as well as increases in
            earnings later in life. Yet, while the world has nearly
            reached universal primary education, coverage of early
            childhood education is still low in many countries. This
            study uses a novel global dataset of effect sizes from more
            than 50 studies conducted in 19 countries to examine
            measures of school participation, cognitive skills,
            social-emotional skills, and behavior, both during and after
            preprimary ages. Estimates from meta-regression analysis
            suggest both strong demand for preprimary services when
            offered and significant improvements in children’s cognitive
            skills (0.15 sd) and their executive functioning,
            social-emotional learning, and behavior (0.12 sd) during the
            pre-primary period. Moreover, our meta-analytic results
            indicate statistically significant persistent advantages
            (0.07 sd) in each type of skill beyond the preprimary
            period. Pooled heterogeneous treatment effects also suggest
            higher gains for disadvantaged children. Lastly,
            cost-benefit analysis using studies from low- and
            middle-income countries implies benefit-to-cost ratios
            ranging between 1.7 and 14.2, suggesting high returns to
            preprimary investments even in contexts with limited state capacity.
repnme: repnme: Is Investment in Preprimary Education Too
            Low ? Lessons from (Quasi) Experimental Evidence across Countries
display_title: Is Investment in Preprimary
            Education Too Low Lessons from (Quasi) Experimental Evidence
            across Countries
listing_relative_url: /research/2021/06/33238388/investment-preprimary-education-too-low-lessons-quasi-experimental-evidence-across-countries
topicv3: Teachers Management m1326119 1523,Education, Skills Development and Labor
            Market m1326135 1484,Education m1327048 470,Soft Skills m1327287 2137,Health m1504661 3480
publishtoextweb_dt: 2021-06-30T00:00:00Z
dois: 10.1596/1813-9450-9723
historic_topic: Industry,Education
origu: DECRG: Human Development (DECHD)
no_of_pages: 103
author_keys: 0: author_key: 1048923
1: author_key: 390194
2: author_key: 1668999
3: author_key: 1630031
model_extractions: Policy Research Working Paper 9723 Is Investment in Preprimary Education Too Low? Lessons from (Quasi) Experimental Evidence across Countries _Alaka Holla Magdalena Bendini Lelys Dinarte Iva Trako_ Development Research Group Education Global Practice & Development Impact Evaluation Group June 2021
Policy Research Working Paper 9723

# **Abstract**

A large body of evidence suggests sizeable improvements in cognitive and social-emotional skills and subsequent educational attainment following preprimary education interventions as well as increases in earnings later in life. Yet, while the world has nearly reached universal primary education, coverage of early childhood education is still low in many countries. This study uses a novel global dataset of - effect sizes from more than 50 studies conducted in 19 coun tries to examine measures of school participation, cognitive skills, social-emotional skills, and behavior, both during and after preprimary ages. Estimates from meta-regression analysis suggest both strong demand for preprimary services when offered and significant improvements in children’s cognitive skills (0.15 sd) and their executive functioning, social-emotional learning, and behavior (0.12 sd) during the pre-primary period. Moreover, our meta-analytic results indicate statistically significant persistent advantages (0.07 sd) in each type of skill beyond the preprimary period. Pooled heterogeneous treatment effects also suggest higher gains for disadvantaged children. Lastly, cost-benefit analysis using studies from low- and middle-income countries implies benefit-to-cost ratios ranging between 1.7 and 14.2, suggesting high returns to preprimary investments even in contexts with limited state capacity.

This paper is a product of the Development Research Group, the Education Global Practice, and the Development Impact Evaluation Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aholla@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Is investment in preprimary education too low? Lessons from more than 50 (quasi) experimental studies around the world<sup>*</sup>

Alaka Holla<sup>†</sup> Magdalena Bendini<sup>‡</sup> Lelys Dinarte-Diaz<sup>§</sup> Iva Trako<sup>**</sup> Originally published in the Policy Research Working Paper Series on _June 2021._ This version is updated on _March 2022._ To obtain the originally published version, please email prwp@worldbank.org.

**Keywords:** Preprimary education, preschool, meta-regression, cognitive and non-cognitive skills **JEL Classification:** I20, I26, I28

> * We would like to thank Kathleen Beegle, David Evans, Emanuela Galasso, Amer Hasan, Marcus Holmlund, Katherine King, Arianna Legovini, Florencia Lopez Boo, Dana McCoy, David McKenzie, Emma Näslund-Hadley, Berk Ozler, Sharon Wolf, and Hirokazu Yoshikawa for their valuable comments and suggestions. We are also grateful to Steffanny Romero Esteban, Yilin Pan, Luiza Cardoso de Andrade for their assistance in the meta-regression and benefit-cost calculations. This research benefitted from funding from the World Bank’s Strategic Impact Evaluation Fund. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> † Senior Economist. Human Development Vice Presidency. The World Bank. email: aholla@worldbank.org

> ‡ Senior Economist. Education Global Practice. The World Bank. email: mbendini@worldbank.org

> § Economist. Development Research Group. The World Bank. email: ldinartediaz@worldbank.org

> ** Economist. Development Impact Evaluation Group (DIME). The World Bank. email: itrako@worldbank.org
# **1. Introduction**

Studies in high-income countries have shown preprimary education provided to children between the ages of 3 and 6 has high returns both in small-scale interventions like the Perry Preschool Project (Heckman et al., 2010) and larger-scale government programs like childcare expansions in Norway (Havnes & Mogstad, 2011) and the Head Start program in the United States (Carneiro & Ginja, 2014; Kline & Walters, 2016; Bailey et al., 2020b). Studies have also estimated gains in cognitive and social emotional skills following modifications to curricula and pedagogy in existing preprimary classrooms (Barnett et al, 2008 and Clements et al, 2013) or changes in teacher quality (Araujo et al, 2016). In a comparative welfare analysis of 133 policies in the United States, Hendren and Sprung-Keyser (2020) find that investments in health and education for low-income children pay for themselves in the long-run.

Recent global evidence also shows a strong positive association between participation in preprimary education and child development measures, and this relationship is even stronger in low- and middle-income countries (McCoy et al, 2021). Evaluations in low- and middle-income countries also demonstrate sizeable improvements in skills and subsequent educational attainment following preschool interventions that expand access to preprimary education or improve the quality of existing programs (Berlinski et al., 2008; Martinez et al., 2017; Ganimian et al., 2021; Dean & Jayachandran, 2020). Yet, while the world has nearly reached universal primary education, coverage of early childhood education is not universal in many high and middleincome countries and falls below 20 percent on average in low-income countries (UIS, 2018).

Determining whether preprimary education investments are falling short needs to factor in both the benefits as well as the costs of such programs. To date, there has been no global metaanalytical study that aggregates estimates of causal impact for preprimary education or consistently calculates benefit-cost ratios of preprimary interventions in low- and middle-income countries. This review uses a multidisciplinary body of experimental and quasi-experimental evidence on preprimary education to estimate (i) the effect of expanding access or improving quality of preprimary education, separately for studies in high-income countries and studies in low- and middle-income countries, and (ii) benefit-cost ratios based on studies in low- and middle-income countries that report the per-child costs of the interventions under evaluation. Drawing on 798 effect sizes from more than 50 studies conducted in 19 countries, we examine 2
measures of school participation, cognitive skills, social-emotional skills, and behavior, both during and after preprimary ages.

As Elango et al (2016) argue, evaluating preprimary programs solely on their potential to improve cognitive skills risks overlooking important drivers of the long-term impacts observed in adulthood, such as social-emotional skills (Heckman, Pinto, and Savelyev, 2013). For this study, we extracted all outcomes related to school participation and progression and skill formation from studies in our sample. School participation impacts of expanding access to preprimary education not only provide an indication of the strength of an evaluation’s first stage, but they also reflect demand for the services under evaluation, which may be low if parents do not value the services more than their status quo arrangements, which might include care in the home or private daycare (Bouguen et al., 2018).

During and after the preprimary years studies tend to focus on cognitive skills (such as literacy and numeracy) and noncognitive skills (such perseverance, attention, and motivation). There is little uniformity, however, in how studies measure these skills. Moreover, most studies tend to report estimates for multiple measures of the same type of skill (for example, they may use two different instruments to measure early literacy). To handle this heterogeneity and to exploit all the information in studies that estimate effects using multiple outcome measures with varying levels of precision, we standardize the outcomes we extracted from studies following Lipsey and Wilson (2001). Then, we use meta-regression methods to aggregate evidence across studies and estimate an average effect size for each class of outcomes, effectively weighting studyspecific average treatment effects by their precision. Specifically, we use robust variance metaregression, which adjusts the standard error of the aggregate average effect size for potential dependence among outcomes coming from the same study (Hedges, et al., 2010; Tanner‐Smith & Tipton, 2014; Tanner-Smith, et al., 2016; Tipton, 2015; Tipton et al., 2019).

Although we rely on our own judgment to screen studies for inclusion in our sample, metaregression offers a way to assess whether an intervention (or class of interventions) works across contexts that does not require the analyst to engage in subjective weighting of positive, negative, and statistically insignificant effects as is done in more narrative reviews. It also does not require the analyst to engage in heuristics like “vote counting,” which tallies the number of positive, negative, statistically significant, and statistically insignificant results but ignores information contained in the size and precision of estimated effects.

3
The average effect sizes we estimate for outcomes related to school participation and progression and children’s skills during the preprimary years and beyond suggest high returns to increasing investment in preprimary education and to targeting disadvantaged populations for investment. When experimentally offered access to preprimary education, children are on average 1.4 standard deviations (sd) more likely to participate in the preprimary program than their control group counterparts, suggesting strong demand for services. This is approximately equivalent to a 30 percentage point increase in preprimary school participation. During the preprimary period, children’s cognitive skills (in language, literacy, math, and other general skills) improve by an average of 0.15 sd, with larger improvements in literacy (0.22 sd) and math (0.22 sd) compared with language (0.10 sd) and general skills/indices capturing multiple domains (0.08 sd). During this period, children’s non-cognitive skills (their executive functions, socialemotional skills, and behavior) improve by an average of 0.12 sd. Effects are similar (and statistically indistinguishable) across high- and low- & middle-income countries.

The average effects pool effects from studies examining impacts relative to different counterfactual scenarios. Sometimes the intervention under evaluation introduced preprimary education where the only other option was care in the home; sometimes the intervention expanded the set of facility-based options or attempted to improve the quality of existing services. We find larger average effects among evaluations assessing interventions that aimed to improve the quality of existing preprimary education services than among evaluations of expansions in services. The average effect on cognitive skills, for example, was approximately 0.2 sd among evaluations of quality improvements compared to 0.1 sd among evaluations of expansions.

Our estimates of average effect sizes also reflect average effects across small-scale pilots implemented by non-governmental organizations and nationally scaled government programs; they also average effects across contexts where teachers with formal qualifications teach small class sizes for a full day and contexts where teachers with only secondary education and a couple of weeks of training manage large classes for half a day. Insufficient reporting of program implementation features and contexts, as well as the sample size of studies, preclude testing for heterogeneous average effects along these dimensions.

A focus solely on impacts observed during the preprimary period could underestimate the full benefits of improving skills during the early childhood. Investments in preprimary education could lead to improvements that extend beyond the pre-primary period (Kline & Walters, 2016) 4
– even if some impacts may fade out during primary school only to materialize later in life (Chetty et al, 2011; Elango et al, 2016). Only a subset of studies in our sample had longitudinal designs, but this more limited sample shows persistence of these skill advantages beyond the preprimary period, with significant average gains of 0.07 sd in both cognitive and non-cognitive skills. These smaller effects highlight an advantage of meta-analyses: they can offer greater statistical power to detect impacts than individual studies, particularly small-sample studies with longitudinal designs. An even smaller subset of studies, all in high-income contexts, can track children into adulthood, and while some individual studies do find large and statistically significant advantages in both adult health and labor market outcomes (for example, Carneiro and Ginja, 2014; and Bailey et al., _forthcoming_ ), we lack sufficient statistical power to estimate average effects with precision.

To consistently estimate longer-term returns for a subset of studies from low- and middleincome countries that report unit costs for the interventions under evaluation, we combine studyspecific estimates of impacts on skills and unit costs with data on average wages and real wage growth and estimates from the literature on the extent to which improvements in childhood skills translate into earnings. Our most conservative estimates suggest benefit-to-cost ratios above 1, ranging from 1.7 to 14.2. Less conservative estimates, where we assume a discount rate and return to cognitive skills for low- and middle-income countries similar to values typically used in economic evaluations of programs in high-income countries, suggest benefit-to-cost ratios ranging from 3.5 to 103.5. Both sets of ranges can be considered lower bounds for the returns to preprimary investments as they focus on benefits solely related to the future earnings of children. They do not consider any “fiscal externalities” in the sense of Hendren and Sprung-Keyser (2020), such as the increases in tax revenues and decreases in transfer payments (public assistance) that might accompany increases in children’s lifetime earnings. Our estimates of the rate of return also ignore any contemporaneous benefits of preprimary education that might arise from increases in female labor force participation (Evans et al., 2021).

While very few studies permit a comparison of effects across subpopulations, the limited set of studies that report disaggregated effects is sufficient to detect statistically meaningful differences between populations of high and low socioeconomic status. Children from more disadvantaged backgrounds who are exposed to the preprimary interventions evaluated in the sample show significantly higher responses when we pool outcomes.

5
Our results are consistent with other reviews of subsets of the preprimary experimental and quasi-experimental literature that find significant positive impacts on cognitive skills during early childhood (Nores & Barnett, 2010; Duncan & Magnuson, 2013; Yoshikawa et al., 2013; McCoy et al., 2017; van Huizen & Plantenga, 2018) and larger impacts for disadvantaged children (Yoshikawa et al., 2013; van Huizen & Plantenga, 2018) by using narrative, vote-tallying, and meta-analytic techniques to aggregate evidence. Our review is unique in that we limit our focus to center-based preprimary education programs (rather than more holistic packages of services that intervene at earlier stages and often include intensive, home-based coaching for parents).

In contrast to other studies and reviews that document fade-out (Yoshikawa et al., 2013; Duncan & Magnuson, 2013), we find persistence of an advantage in cognitive skills after the preprimary period, although it is possible that individual studies (and therefore more narrative or vote-tallying reviews) lack sufficient statistical power to detect effects as small as 0.07 sd. Persistence in an advantage in executive function and social-emotional skills, however, is consistent with the hypothesized mechanism behind “sleeper effects,” or gains in health and earnings observed in adulthood when test score advantages dissipate or disappear after the preprimary period (Heckman et al., 2010; Bailey et al., 2020a; Cascio & Schanzenbach, 2014).

Finally, our results, along with current coverage rates, suggest that levels of preprimary investment may be suboptimal, particularly in low- and middle-income countries. When offered services, families do elect to send their children to preprimary education centers. The significant average effects for cognitive and social-emotional skills and the lack of any differences across high and low- and middle-income countries show that preprimary education does provide better conditions for the development of skills than children’s counterfactual options of remaining at home or in informal care settings, even in contexts with low state capacity. This improvement in skills could help counter the inequities in skill development that children bring with them when they enter primary school (Fernald et al., 2011; Naudeau et al., 2011; Duncan and Magnuson, 2013; Schady et al., 2014). The high benefit-to-cost ratios indicate high returns to preprimary investments, and the persistence of impacts beyond the preprimary period suggests that investments in preschool can play a role in improving the effectiveness of primary education (Johnson & Jackson, 2019), possibly by enhancing executive functions and social-emotional skills that help children learn (Cunha and Heckman, 2007) or by reducing heterogeneity in the 6
classroom and thus making it easier to teach foundational skills (Banerjee et al., 2007; Dupas et al., 2011).

The rest of this paper is organized as follows. The next section (Section 2) provides background, describing skill development during the preprimary period, theories on when preprimary education should improve skills and well-being, and preprimary coverage and quality in low- and middle-income countries. Section 3 describes the methods we used to search for studies, screen them for inclusion, and extract data on effect sizes. This section also describes the studies, interventions, and outcomes included in the analysis, as well as the methods we use to aggregate evidence across studies and to test our research questions related to the demand for preprimary education, skill improvements during the preprimary years, the persistence of any advantages beyond the preprimary years, and the returns to preprimary investments. Section 4 presents the main results and those by socioeconomic status. Section 5 discusses the implications of these results for preprimary investments and concludes. Various appendixes provide more detailed information on our methods and studies and present study-specific estimates of the standardized average treatment effects that we use in our main analyses.

# **2. Background and context**

## **Learning during the preprimary years**

Research in neuroscience, psychology, and developmental cognitive science has established that, due to higher brain malleability at younger ages, children learn fast in their first six years of life compared to later stages of development (Shonkoff et al., 2000; Knudsen, 2004). They rapidly acquire skills in areas of knowledge that include numbers (DeWind, 2019) and language (Wang et al., 2020; Yuan & Fisher, 2009) as well as social interactions (Hamlin et al., 2013; TamisLeMonda et al., 2008; Tomasello, et al., 2005). Before primary school, children also develop skills including executive functions (inhibition, working memory, and cognitive flexibility) that allow them to “learn to learn,” manage emotions, and relate to others (Diamond, 2013; Zelazo et al., 2003). As a result, most research on the development of children during the preprimary period measures skills in “domains” of development that include cognitive skills (like language, literacy, and numeracy), executive functioning, and social-emotional skills (Fernald et al., 2017).

Evidence from neuroscience and developmental psychology also demonstrates that ‐ exposure to learning opportunities promotes children’s innate learning abilities (Jara Ettinger et 7
al., 2016; Wang et al., 2020). Learning appears to be sequential and cumulative as early, more basic skills provide the basis to master later and more complex skills (Knudsen et al., 2006; Spelke & Shutts, forthcoming). Learning is also interdependent; learning in specific domains promotes learning in other domains and results in complex skills such as literacy (Dehaene 2010; Bailey et al., 2020a). Cunha and Heckman (2007) argue that due to the cumulative and interdependent nature of learning, the establishment of sound foundational skills early in life can lead to a virtuous cycle of skill acquisition as children develop.

Whether preprimary education facilitates this process in practice, however, depends on the quality of the learning environment provided through pre-primary education and how it compares to what children would have experienced without formal preprimary education—both during and after the pre-primary period. If children receive more cognitive and psychosocial stimulation from their interactions with caregivers in the home or informal care settings (for example, the homes of relatives or friends) than informal pre-primary education, then an expansion of formal services is unlikely to improve average skill development and may even set children back. That is what researchers suggest happened when Canada’s province of Quebec extended subsidized coverage of childcare to less needy families (Baker et al., 2008; Baker et al., 2015). If, on the other hand, the learning environment offered through pre-primary education services provides more stimulation to children than their counterfactual situation, then coverage expansions or quality improvements will likely enhance skill development (Cascio, 2015; Cascio & Schanzenbach, 2014).

Similarly, if available preprimary education substitutes for other services that children would otherwise access, then investments in preprimary education may not translate into improvements in children’s skills. For example, in Denmark, children who had already benefited as infants and toddlers from a nurse home visiting program exhibited smaller gains in adulthood on an index of human capital from an expansion in preprimary education than children who did not have the home visiting program (Rossin-Slater & Wust, 2020). Similarly, a preprimary school construction program in Cambodia induced parents to switch their underage children out of formal primary school and into the new community-based preprimary schools (Bouguen, et al., 8
2018).<sup>1</sup> The federal Head Start program in the United States also induced switching out of private preprimary schools (Kline & Walters, 2016).

The inadequacy of stimulation in their counterfactual environment and lower access to substitute services may partially explain why children from more disadvantaged backgrounds tend to benefit more from preprimary education (Cascio and Schanzenbach, 2014; Currie, 2001).

# **Preprimary education in low- and middle-income countries**

According to UNESCO’s Institute of Statistics, preprimary enrollment has increased considerably around the world over the last two decades, from an average of 30 percent of children in 2000 to 50 percent in 2018.<sup>2</sup> Access to preprimary education in low- and middleincome countries is still low, with 19 percent of preprimary-aged children in low-income countries enrolled (UIS, 2018), a coverage rate less than half of what was observed in high-income countries fifty years ago. Beyond these averages, household survey data using UNICEF’s Multiple Indicator Cluster Surveys (MICS) suggest substantial variation in preprimary enrollment across and within countries that is associated with socioeconomic status, with the largest differences in enrollment in the poorest countries (Figure 1). This unequal access to preprimary education can exacerbate learning inequalities, as children in families from lower socioeconomic groups tend to also have limited learning opportunities and stimulation at home and in their communities _(_ McCoy et al., 2018) (Figure 2).

Domestic financing for preprimary education has increased over the past decade, amounting to 6.6 percent of domestic education budgets globally. Low-income countries allocate substantially less, with less than 2 percent of their education budgets going toward preprimary education (UIS, 2018). In these countries, standards and quality assurance systems are often nonexistent or under-resourced and learning spaces often do not meet minimum safety and

> 1 In contrast to the practice of “red-shirting” in higher-income countries, in many low-income countries, parents try to enroll their children in primary school before the children are age eligible.

> 2 UIS use a variety of data sources to measure enrolment: administrative data from schools or household survey data on enrolment; population censuses and surveys for population estimates by single year of age (if using administrative data on enrolment); administrative data from ministries of education on the structure (entrance age and duration) of early childhood education. Data on provision are limited but suggest that around 38 percent of countries offer some free preprimary education, with most of these countries offering between one and three years of service. There is also wide variation of private provision across regions, ranging from 7 percent in Europe and Central Asia to 72 percent in the Middle East and North Africa (UIS, 2018).

9
sanitation conditions (World Bank, 2013; UNICEF, 2019). Due to an acute shortage of preprimary teachers and staff, child-teacher ratios remain high even when enrollment rates are low (UNICEF, 2019) _._ The preprimary education workforce also has lower renumeration and higher attrition rates than what is found among the workforce in primary education (UIS, 2018).

Thus, on the one hand, the low levels of stimulation occurring at home and very limited access to preprimary education in low-income countries suggests that offered preprimary education services must exceed a low bar in terms of cognitive stimulation and psychosocial support to promote children’s skill development and learning. On the other hand, countries with limited state capacity to provide and regulate school quality may struggle to meet even this low bar.

# **3. Methods**

To assess the average effects of preprimary education interventions, we conducted a global systematic review of the experimental and quasi-experimental literature focused on preprimary education. We then used meta-regression methods to aggregate evidence across studies. This section describes our process for selecting studies to use in our analyses and for extracting “raw data” from the studies. We also lay out our empirical specifications and describe how we standardize extracted coefficients across studies and map study-specific outcomes into broader categories that we can use in meta-regressions.

## **Data**

To identify studies to review, we followed standard practice for systematic reviews and proceeded in three iterative stages: (i) search and application of inclusion criteria, (ii) screening (two rounds), and (iii) data extraction. Figure 3 summarizes the entire process and indicates the number of studies that remained in the review sample after each stage.

### **3.1.1 Search strategy and selection criteria**

To start, we conducted a systematic search for studies using relevant keywords and terms in several search engines and databases, as well as known portfolios of experimental and quasiexperimental studies, such as the portfolio of the World Bank’s Strategic Impact Evaluation 10
Fund.<sup>3</sup> Table 1 Panel A lists the databases and specific search terms we used. In this phase, we looked for studies published before 2021 that stated an aim to estimate the effects of preprimary interventions. Keywords related to experimental and quasi-experimental evaluation designs effectively limit our attention to studies published after 2007.

In addition to database searches, we found studies through two other ways. We searched bibliographies of papers found through the database search. We also contacted experts and researchers who have frequently published on the impacts of preprimary programs in peerreviewed journals and asked for other relevant published studies to incorporate in our review.

This search process yielded a total of 270 studies, with 183 studies from database searches and 87 additional studies from other search methods. These studies included systematic reviews, experimental and quasi-experimental studies, and studies that did not employ experimental or quasi-experimental methods (for example, cost analysis, qualitative reports, etc.).

Table 1 Panel B describes the inclusion criteria we used to identify studies for review. We sought to include experimental and quasi-experimental studies of preprimary programs that provided group-based childcare in a center setting with a developmental or educational focus for children between the ages of 3 and 6 years. This definition of preprimary education encompasses the provision of formal preprimary, community-based preprimary, kindergarten, pre-k, and daycare with an educational component, as well as interventions that make preprimary education more affordable (such as subsidies) or increase its quality (such as teacher training, the introduction of a new curriculum or pedagogical approach, and provision of materials). We did not include programs that also provided services outside the preprimary period, such as childcare during infancy or classroom quality improvements after kindergarten, as it would be impossible to distinguish the impacts of interventions during the preprimary years from those of interventions that spanned a longer period. This restriction ruled out seminal studies of programs like the Abecedarian Project (Campbell et al., 2014; Conti et al., 2016) and Project STAR (Chetty

> 3 The Strategic Impact Evaluation Fund supports research that estimates the effects of programs and policies to improve education, health, access to quality water and sanitation, and early childhood development in low- and middle-income countries. See <u>here</u> for more information on the fund and a list of evaluations in its portfolio.

11
et al., 2011; Krueger, 1999), as well as large-scale expansions in childcare that also covered infants (Baker et al., 2008; Baker et al., 2015; Bernal, 2019; Bernal and Ramirez, 2019).<sup>4</sup> For our purposes, experimental (randomized controlled trials or RCTs) or quasiexperimental designs had to isolate the causal impact of preprimary programs or policies on children’s outcomes. Children’s outcomes had to include either a measure of school participation or progression, or a measure of children’s skills or development. This measurement could take place either when children were still in preprimary education or after they had progressed to higher levels of education. We also extracted outcomes observed when the beneficiaries of preprimary programs were adults, although as we will discuss in later sections, the number of outcomes and studies reporting wellbeing in adulthood was too small to estimate average effects with precision.

Included studies also had to be published in peer-reviewed journal articles or in a formal working paper series, such as the working papers of the National Bureau of Economic Research or the Policy Research Working Paper series of the World Bank. We included technical reports only if they included a suggested formal institutional citation.<sup>5</sup>

# **3.1.2 Screening**

We then screened these studies to verify that they met our inclusion criteria, focusing on the credible estimation of causal impacts of preprimary interventions and the inclusion of outcomes that captured preprimary coverage and/or children’s skills or wellbeing.

Figure 3 describes the screening process. We first eliminated studies based solely on a review of their citations, abstracts, and introductions. Of the 270 studies identified in the initial search, we excluded 111 studies during this stage because (i) they were not published in peerreviewed journals or did not appear as part of a working paper series; (ii) they were not experimental or quasi-experimental studies; (iii) they assessed early childhood interventions that targeted children outside of the 3 to 6 year age range, (iv) they measured outcomes unrelated to

> 4 We did include a subset of effects from Baker et al., (2008) – specifically those restricted to 4-year-olds in the post-period survey – who were unlikely to have access to subsidized care before age 3 since the childcare expansion started with older children.

> 5 One implication of not including unpublished studies is the potential for publication bias—that is, a bias toward positive and significant results. On the other hand, Brodeur et al. (2020) find that randomized control trials exhibit less of this bias than methods like instrumental variables and differences-indifferences, and more than 70 percent of the studies we eventually included were randomized control trials.

12
learning and well-being for affected children at some point in their life cycle; (v) they were duplicates (for example, working-paper versions of published studies); or (vi) they had no relevance to our research questions. This first stage of screening left us with a total of 159 studies.

In the second stage of screening, at least two authors of the current study reviewed each identified study, reading the full text and assessing features meant to proxy for study quality, such as the use of an evaluation design that would generate causal impacts (RCT, regression discontinuity design, differences-in-differences estimation, matching, or an instrumental variable strategy) and the presentation of information on issues that could compromise evaluation design.<sup>6</sup> Specifically, to be included in our final sample, studies needed to (i) isolate the impact of the program using some sort of comparison group; (ii) present sufficient evidence that the experimental groups in an RCT were balanced on a set of characteristics prior to the intervention; (iii) report sample attrition and compliance to the treatment assignment for the case of RCTs; (iv) experience less than 30 percent attrition in either the treatment or comparison group between the initiation of treatment and measurement; (v) report the precision of the estimated effect size by providing either standard errors, confidence intervals, t-statistics, standard deviations, sample size and/or p-values; and (vi) use well-known or established outcome variables and tools to measure them. For issues like attrition and compliance, we could not exclude studies based on how authors addressed them, as approaches vary across disciplines.<sup>7</sup> Some studies omitted key design and estimation details such as balance checks and the presentation of the exact precision of estimated impacts. When related analyses already published in earlier work provided this information, we kept the study in the review if it met our other criteria.

Taking all screening criteria into account, each reviewer assigned studies to two categories—those to include in the review and those to exclude. When reviewers’ ratings matched, a study was either automatically included or removed from consideration. All four authors discussed ratings discrepancies to arrive at a conclusion. Of the 159 studies that

> 6 We did not include studies that used sibling fixed effects to isolate the impact of a program on children’s outcomes, which excluded studies of the Head Start program such as Garces et al. (2002) and Deming et al., (2009).

> 7 For example, papers published by economists typically document attrition, note whether it is differential across treatment and comparison groups, and address it by calculating bounds for average treatment effects using the methods of either Lee (2009) or Horowitz and Manski (2000). Papers written by developmental psychologists, however, tend to address sample attrition through multiple imputation methods and then calculate average treatment effects.

13
underwent this more intense screening, we excluded 105 studies largely because they failed to credibly identify causal impacts or to report sufficient information for us to calculate an effect size or its precision. Thus, this second phase of screening yielded a final sample of 54 studies that moved to the next phase of data extraction.

# **3.1.3 Data extraction**

We extracted three levels of information from each study. _Studies_ are defined as independent publications concerning preprimary programs. _Interventions_ refer to the studies’ different experimental arms (in an RCT) or implied treatment and comparison groups (in a quasiexperimental study). For example, a study might provide one group of participants with a preprimary program, a second group of participants with the preprimary program and an accompanying program targeting parents, and a third group with no service. During data extraction, we treated these as three different intervention groups or contrasts. _Outcomes_ refer to the estimated coefficients corresponding to the average treatment effects we extracted for each outcome for each intervention. The resulting dataset includes 54 studies, 141 interventions or contrasts, and 798 outcomes.

# _3.1.3.1_ **_Studies_**

Figure 4 maps the locations of studies, demonstrating coverage across 19 different countries with a concentration of research in the United States. Table 2 presents characteristics of the studies in our sample.<sup>8</sup> We see that 85 percent of the studies we reviewed appeared in peerreviewed journals, and around half appeared in journals that publish research in the field of Economics. More than 70 percent of studies were RCTs. Close to half evaluated a program that expanded coverage of preprimary education, while the other half focused on programs that aimed to improve the quality of existing preprimary education services.

# **_3.1.3.2 Interventions_**

We also extracted information about the evaluated interventions. Table 3 summarizes characteristics of the treatment arms of the included studies for our full sample and separately for high-income countries and low- and middle-income countries.<sup>9</sup>

> 8 Appendix Table 1 presents additional details of each study, including intervention components and evaluation design

> 9 Appendix Table 2 presents additional characteristics of the programs evaluated in each country.

14
We assigned interventions to _at least_ one of 12 intervention categories.<sup>10</sup> Around 60 percent of programs included a component focused on teachers’ professional development, with more of a focus on this kind of component in low- and middle-income countries. This is not surprising as many interventions that aim to improve the quality of preprimary education—such as a change in curriculum or pedagogy—will require teachers to receive some training prior to program implementation. Similarly, offering a new service will also initially entail training. Close to half of all programs provided subsidized or free access in studies from high-income countries, compared to only 3 percent among studies from low- and middle-income countries. In both contexts, around 70 percent aimed to target a disadvantaged population.

These interventions include both small-scale pilots implemented by non-governmental organizations (NGOs) and nationally scaled government programs. Daycare programs with education components represented very few evaluated programs; formal preschool was the dominant program in both high-income and low- and middle-income contexts, although community preschool accounted for more than 40 percent of programs in low- and middleincome contexts. While a majority of programs in both contexts took place in publicly managed schools, a sizeable fraction had a combination of public and private management. A higher fraction of programs in studies from high-income countries took place in classrooms with a teacher who was formally qualified to teach preprimary students than in studies from low- and middle-income countries, where teachers were formally qualified to teach in preprimary settings in only half of the studies in the sample.

# **_3.1.3.3 Outcomes_**

We focused on outcomes related to school participation and child development, extracting average treatment effects and their precision for each intervention (Table 4). When available, we also captured outcomes of teachers, such as classroom practice or self-reported measures of wellbeing. We extracted a total of 798 outcomes at the child and teacher levels. In one study that relied on a very large administrative dataset with a panel structure (Rossin-Slater & Wust, 2020), outcomes were aggregated to combinations of geographic unit and birth cohort. Sample sizes

> 10 These categories included (i) teacher professional development, (ii) subsidized or free access, (iii) change in curriculum, (iv) change in pedagogy, (v) provision of materials, (vi) provision of new staff, (vii) provision of health and nutrition services, (viii) preschool construction, (ix) parental engagement, (x) community outreach, (xi) preschool day extension, and (xii) teacher payments.

15
associated with extracted outcomes ranged from 123 to 22,480,000 individuals, with a median of 2,449.

Measures of precision collected from the studies included standard errors, confidence intervals, and p-values. When only stars or other symbols indicated precision in a table or chart and the study provided no other metric of precision, we assumed p-values corresponding to the highest p-value of the interval indicated by the symbol.<sup>11</sup> If a paper presented no standard measure of precision, we extracted information on the sample sizes and standard deviations of the outcome for the treatment and control groups to calculate the standard error of the average treatment effect.

For each outcome, we included both average treatment effects measured on the full study sample and those measured separately for different socioeconomic groups and different age groups, as these tend to be the characteristics of children used to target preprimary programs when universal coverage is not an option.<sup>12</sup> We aimed to extract intention-to-treat estimates of impact, although we extracted local average treatment effects or treatment-on-the-treated effects when intention-to-treat effects were unavailable or less relevant.<sup>13</sup> Three-quarters of extracted outcomes were not disaggregated by socioeconomic status. Appendix Table 3 lists the different subpopulations for which we extracted separate coefficients plus a mapping of these subpopulations to a more aggregate indicator meant to capture high and low socioeconomic status.

We classified outcomes according to the timing of their measurement, tagging them as occurring during the preprimary period (below age 6), during post-preprimary education (age 6 to 18), or during adulthood (after age 18). The majority (63 percent) of extracted outcomes were measured when children were still in the preprimary period, while 37 percent, all from highincome countries, were measured afterwards.

> 11 For example, if ** indicated p<0.05, we assumed a p-value of 0.04. We could not use the exact threshold of 0.05 as that led us to misclassify effects that were statistically significant in their respective studies as insignificant when standardized.

> 12 For two studies (Carneiro & Ginja, 2014; Heckman et al., 2010), we had to extract effects disaggregated by gender as full sample estimates were not reported.

> 13 For example, in Ganimian et al. (2021), the evaluated intervention entailed an improvement in existing services. Thus, while all children living around the preprimary education center could access the services, only those regularly attending the services would be exposed to the treatment.

16
We scored each outcome as _positive_ or _negative_ depending on whether increases represented improvements or reductions in welfare. We also mapped the specific outcomes measured in each study (for example, receptive vocabulary using the Peabody Picture Vocabulary Test) to more aggregate domains that could be used in our subsequent analysis (for example, language), following standard domains observed in the early childhood education literature (Fernald et al., 2017). Specifically, we classified all outcomes that measured attendance, enrollment, years of education, and schooling attainment into a single category meant to capture _school participation and progression_ .

We mapped all outcomes that measured children’s skills and knowledge about specific content areas (for example, mathematics, language, and literacy), as well as dispositions and skills that help children to think about and understand the world around them (for example, general intellectual ability) into one of the following categories: _literacy, language, math,_ and _general_ cognition. In our analyses, we classify these domains as cognitive skills.

We classified constructs such as attention, inhibition, and working memory as _executive functions;_ outcomes such as social cognition, social competence, and emotional regulation as _social-emotional skills_ ; and reported or observed measures of aggression, internalizing and externalizing behavior problems, conduct, and disciplinary actions, as well as incarceration as _behaviors_ .<sup>_14_</sup> We refer to executive functioning, social-emotional skills, and behaviors as noncognitive skills.

_Health outcomes_ encompassed _physical health_ and _motor development._ Lastly, we classified teachers’ responses in the classroom, such as the extent of emotional and pedagogical support they provide their students, as _teacher practices_ .

One-to-one mapping of outcomes to domains was not always straightforward since some measures used in studies were composite scores that included items from more than one related domain – for example, both language and literacy or both social-emotional skills and behavior. To address such difficulties with classification and to manage potential multiple inference problems that could arise as the number of outcome categories gets larger, we focus on three main categories: school participation and progression, cognitive skills, and non-cognitive skills. We

> 14 Appendix 1 describes the contents of each skill category in more detail.

17
report effects on teacher practices and health outcomes in an appendix. Though the sample sizes for these outcomes are adequate, far fewer studies include outcomes from these categories.

When a study estimated treatment effects using more than one measure for a particular domain of development (for example, the authors used both a measure of separation anxiety and a measure of physical aggression and opposition to assess social-emotional skills), we extracted coefficients for each mode of measurement, unless one measure represented only the aggregation of all the other measures. We omitted effect sizes for outcomes where reported measurement did not conform to accepted practice—for example, asking about nonacute, nonchronic illnesses with a 12-month (instead of 2–4 week) recall period—and for outcomes with very low response rates.<sup>15</sup> We also omitted outcomes when it was not clear if an increase in the measured outcome represented an improvement or decrement to well-being. Take, for example, outcomes such as grade retention in kindergarten or body mass index. Grade retention in kindergarten could occur when a child’s learning suffers in the classroom (a bad outcome) or when teachers become more attuned to children’s readiness to progress to first grade (a good outcome). An increase in body mass index for an underweight child in low-income countries would represent progress, but a decrease in body mass index for an overweight poor child in high-income countries would also be considered an improvement.

Not all studies measured outcomes covering all domains of development, nor did studies routinely measure outcomes like teachers’ behavior in the classroom. Thus, sample sizes and the composition of studies vary by aggregated outcome.

# **Analytical strategy**

We aggregate all extracted outcomes to measure an average treatment effect across studies, using meta-regression methods that account for the potential correlation among outcomes measured from the same study and that penalize precision appropriately when the sample size of either studies or outcomes is low. We also combine our estimates of the average effects of preprimary interventions with information on the costs of these interventions and their estimated rates of return to estimate benefit-to-cost ratios.

> 15 We also did not include outcomes for all reported age groups in Berlinski et al. (2008), as primary school enrollment rates suggested little room for improvement for some ages. For this study, we selected age groups that matched with the age composition of other studies in our sample that reported similar outcomes.

18
# **3.2.1 Robust variance meta-regression**

Many reviews of evidence, particularly those focused on low- and middle-income countries, do not estimate an average effect across studies or incorporate the uncertainty associated with estimated effects in their assessments of the benefits of a certain intervention or class of interventions. For example, authors may plot effect sizes in a bar chart and use their expert judgement to determine whether an intervention does or does not improve outcomes (Kremer & Holla, 2009; Evans & Popova, 2016; Evans & Yuan, 2021). Others may employ a vote counting method, tallying the number of positive and significant coefficients. These methods, however, do not fully consider the fact that estimated coefficients come with confidence intervals of different sizes that inform us about the precision of estimated impacts. That is, even if two estimated coefficients are both significantly different from zero, they are likely to have different levels of precision. Moreover, when a literature does include many statistically insignificant effects, we might worry about inter-rater reliability when human judgment adjudicates whether the average impact of an intervention appears positive (or negative) overall.

The education, health, and psychology literatures try to solve these issues through metaregression, where estimated coefficients yj for studies _j_ = 1, ... J are the units of observation and the goal of the analysis is to estimate the average effect size, β0, in a model such as where ej is the study-level residual, uj is a study-level random effect, and β0 and any other coefficients in vector β associated with covariates in vector xj are estimated through weighted least squares, with weights coming from the (inverse of) standard errors sj associated with each coefficient yj (Tanner-Smith et al., 2016; Tipton et al., 2019).<sup>16</sup> The precision of the average effect size β0 is measured under the assumption that all estimated coefficients included in the regression are independent draws from the potential distribution of outcomes.

> 16 Some meta-analyses are not only interested in measuring the average effect β0 across studies but also (i) the effects β of any study characteristics xj that might determine the size of a study’s average effect and (ii) heterogeneity in effects observed across studies that is not due to sampling variation. Meager (2019), for example, uses hierarchical linear modeling to jointly estimate both this sampling variance and heterogeneity for studies of microcredit interventions to assess external validity of estimates and the extent to which we can generalize treatment effects from one context to another. Vivalt (2020) has a similar goal for a larger set of randomized controlled trials with multiple objectives.

19
Since we had no reason a priori to select one outcome over another when studies reported effects using multiple measures, we extracted all outcomes. Thus, this assumption of independence is unlikely to hold. We could have selected one effect size per study or averaged all effects within a study to create a single synthetic effect size, as in Baird et al. (2014) or Hidrobo et al. (2018). However, this could result in loss of information, as outcomes within a study are rarely perfectly correlated (and sometimes not correlated), and we would like to use as many extracted effect sizes as possible.

To deal with the potential dependence among outcomes measuring the same outcome category extracted from the same study, we use robust variance meta-regression (Hedges et al., ‐ 2010; Tanner Smith & Tipton, 2014; Tanner-Smith et al., 2016; Tipton, 2015). This method of metaregression uses a working model of the structure of dependence of outcomes within a study (that is, the variance-covariance structure) and does not require assumptions on the exact distribution of effect size estimates, unlike approaches such as hierarchical linear modeling that nest estimated effects within clusters. The robust variance regression method also provides unbiased estimates of the variance for the average effect size across studies even with sample sizes as low as 10 studies (Tanner-Smith et al., 2016) and adjusts the robust variance estimation (RVE) estimator and the degrees of freedom when the number of studies is small (Tipton, 2015). In particular, we use a working model of the variance-covariance structure characterized by correlated effects, assuming that study average effect size varies across studies, the effect sizes within studies are equally correlated (that is, they all have the same intracluster correlation), and this correlation arises from sampling error when multiple outcome measures are collected on the same units.<sup>17</sup> Thus, we modify Equation (1) to account for multiple outcomes coming from a single study in Equation (2),

> 17 We use the _robumeta_ command in Stata developed by Hedberg (2011) and the default assumption about the correlation among outcomes of the same study (that is, ρ= 0.80). Another option for the working model of the variance-covariance structure is the hierarchical effects model, in which the observed effect size estimates are nested within studies that are nested within clusters, where clusters may correspond to countries, research groups, or interventions (when there are multiple studies drawn from the same intervention). We have no reason to believe that our country-income groups correspond to clusters, and while we do have some studies focused on a single intervention (for example, the Head Start experiment in the United States), this is not our dominant driver of potential dependence among outcomes. We follow Tanner-Smith and Tipton (2014) and use the model of variance that likely describes most of our data when both correlated effects and hierarchical effects may describe the dependence of outcomes of our sample of estimated coefficients.

20
where yijk is the effect size _i_ in study _j_ of outcome category _k_ ; βk is the average effect size of outcome category _k_ ; uj is the study-level random effect such that var(uj) captures the betweenstudy variance component; and εij is the residual for effect size _i_ in study _j_ .

For the benefits of preprimary education to outweigh its costs, families must send their children to preprimary programs when they are made available and these programs must, at a minimum, improve children’s skills relative to their counterfactual situations. We use Equation (2) and measures of school participation in the preprimary years to assess demand for preprimary education. We use measures of children’s knowledge and skills plus teacher practices measured during the preprimary years to assess whether preprimary services can be delivered with sufficient quality to provide children a better learning environment than they would otherwise receive. In both cases, we test whether β<sup>k</sup> > 0.

Finally, to test whether certain subsamples benefit more from preprimary interventions, we modify Equation (2) to add a covariate Dij, which indicates if effect size _i_ in study _j_ was measured for a disadvantaged population, and we limit our sample to the set of effect sizes separately estimated for subpopulations defined by their socioeconomic status. Because so few studies reported effects for different subpopulations, we group the outcomes yijk into two main aggregate categories of outcomes: school participation and progression, and skills (cognitive and noncognitive). For the pooled heterogeneity analysis, we regress Equation (3) as follows: where yijz is the effect size _i_ in study _j_ of the aggregated outcome category _z_ and βz is the average effect size of the aggregated outcome category _z._ Our estimated coefficient of interest in this case is δ̂, which indicates if the average effect size of preprimary interventions differs for disadvantaged populations relative to populations that are less disadvantaged.

# **3.2.2 Standardization of outcomes**

Even within the outcome categories that we created, studies in our sample used different measures, each with their own scales, to assess school participation and progression, children’s skills and behavior, the responses of teachers, and well-being in adulthood. Of all extracted outcomes, 62.6 percent were reported in terms of standard deviations by the researchers. Thus, 21
before we could use treatment effects as dependent variables in our meta-regression, we needed to standardize the remaining outcomes, using other information we extracted from each study, such as sample sizes, standard errors, and the means of the treatment and control groups at follow-up. Appendix 2 details our process for standardizing both continuous and binary outcomes and for imputing values when information (like the separate sample sizes of the treatment and control group) was missing. We were able to standardize an additional 30.5 percent of all outcomes.<sup>18</sup> Sometimes information required for standardization was missing – for example, separate sample sizes for the treatment and control groups or the mean of the control group at endline for each sub-population when heterogeneous treatment effects are presented. To address these issues, we relied on information provided in the text or appendices of papers as much as possible to approximate sample allocations across treatment and control groups and assumed an even allocation when papers provided no guidance. We used control means of the full sample to represent the means for each sub-population when this information was missing. Given that disadvantaged sub-populations tend to be minorities and given that their average outcomes tend to be lower than less disadvantaged sub-populations, this type of imputation should bias us against finding significant differences between children coming from high socio-economic backgrounds and those coming frow low socioeconomic backgrounds.

# **3.2.3 Economic evaluation**

Very few studies in general can track children into adulthood to assess the full impact of preprimary education and thus appropriately capture the benefits in a benefit-to-cost ratio. Moreover, no study from a low- or middle-income country in our sample follows children into adulthood. To estimate a benefit-to-cost ratio for these countries, we need to infer what eventual benefits might be from the short-term effects that we can observe. For the subset of studies in lowand middle-income countries that do report significant improvements in children’s cognitive skills or social-emotional skills as well as sufficient information to infer per-child costs for the duration of the evaluated intervention, we follow the strategy of Galasso and Wagstaff (2018) and

> 18 A total of 6.9 percent of extracted outcomes were not standardized because some inputs for the standardization process were not reported.

22
Ganimian et al. (2021) to project what benefits might be and assess whether these projected benefits exceed reported costs.

For each study, we gather information about the country’s labor force participation, average wages, and real wage growth to calculate the total expected lifetime earnings of a child.<sup>19</sup> We assume children will work between the ages of 22 and 65, which should be a conservative assumption for the number of active years in the labor market, given that labor market participation tends to start much earlier than age 22 in low- and middle-income countries. Because most studies reported program costs in US dollars, we first convert wage data into US dollars using official exchange rates from the International Financial Statistics of the International Monetary Fund.<sup>20</sup> We discount this lifetime stream of earnings to the year that preprimary program costs were incurred, using two different discount rates – 3 percent and 5 percent. While 3 percent reflects the standard in the literature on economic evaluation in high-income countries, some researchers have argued that the higher economic growth rates in low- and middle-income countries would make rates as high as 5 percent more appropriate (Haacker et al, 2020).

Next, we rescale the present value of earnings to account for the benefits of preprimary interventions, combining study-specific estimates of the improvement in cognitive skills following a preprimary intervention with estimates from the literature on the extent to which improvements in cognitive skills observed during the preprimary period translate into improvements in earnings. To be conservative, we select each study’s lowest estimated treatment effect that is significantly different from zero. To translate average effects on cognitive skills into increases in earnings, Kline and Walters (2016) use a value of 13 percent per standard deviation increase in cognitive skills in their study of the Head Start program in the United States, while Galasso and Wagstaff (2018), focusing on low-income countries, use a value of 4.3 percent. Thus, for example, we can multiply the present value of earnings by (0.13 x study-specific average

> 19 We omit this data collection and calculation for Ganimian et al., (2021) which directly reports benefit to cost ratios. Because of lack of information from the general population on monthly wages in Mozambique, we also cannot calculate benefit-to-cost ratios corresponding to Martinez et al., (2017) which found significant impacts on children’s cognitive skills from an expansion of community preschools and did report per child costs.

> 20 As studies did not indicate whether they had used official exchange rates or exchange rates adjusted for purchasing power parity to convert program costs to US dollars, we assume they used official exchange rates and therefore use official exchange rates to convert wages to US dollars.

23
treatment effect expressed in standard deviations) to calculate the lifetime gain in earnings from an increase in cognitive skills.

When studies also include effect sizes for social-emotional outcomes, we again extracted the lowest significant average treatment effect and augmented the estimate of individual gains in earnings with the gains implied by improvements in social-emotional skills, again using estimates from the literature of the returns to social-emotional skills (Belfield et al., 2015).

These estimates of benefit-to-cost ratios, even when using a discount rate of 3 percent and the higher value of the labor market returns to cognitive skills, should be considered conservative, as they solely capture earnings benefits accruing to the individual child. They do not capture any intergenerational transmission of human capital as documented in Rossin-Slater and Wust (2020), nor do they include any social externalities that could arise from an increase in tax revenues following an earnings increase, from a lower reliance on public assistance, from improvements in health (Carneiro and Ginja, 2014; Brotman et al., 2016) and the resulting lower burden on health systems, or from decreases in crime (Heckman et al., 2010).

# **4. Results**

In this section, we present average effect sizes from robust variance meta-regressions. We show results for our full sample of studies and for high-income countries and low- and middleincome countries separately. We also present separate estimates for interventions that expanded preprimary education services and interventions that aimed to improve quality in existing preprimary classrooms.

We could not compute average effects when the sample size was too small to obtain trustworthy p-values, when the degrees of freedom fell below 4, or when all extracted outcomes came from a single study; cells corresponding to these situations are blank in our tables. Appendix 4 presents the figures with the corresponding study-specific standardized effect sizes by outcome category.<sup>21</sup>

## **Demand for preprimary education programs**

In our review, 10 studies - equally distributed between high-income countries and low- and middle-income countries - report impacts on children’s take-up of offered preprimary education

> 21 Figure 1 in the Appendix summarizes the results presented in sections 4.1 and 4.2.

24
services. Table 5, Panel A presents the average effect sizes of the impact of preprimary interventions on preprimary school participation and progression for the full sample of studies. On average, the odds of children being enrolled in or attending preprimary education are 1.4 sd higher for children who were given access to preprimary education programs compared to those who did not have access. This is equivalent to a 30 percentage point increase in school participation.<sup>22</sup> Although most studies only reported participation in the program under evaluation, some also reported participation in _any_ preprimary education services. Results from these studies indicate that preprimary interventions improve overall enrolment and attendance, suggesting that the improvements in program-specific participation do not solely reflect substitution away from existing services (Kline & Walters, 2016; Brinkman et al., 2017; Berkes et al., 2019; Spier et al., 2020).

# **Impacts on children’s skills in the preprimary period**

Panel B of Table 5 presents robust variance meta-regression results for cognitive skills related to language, literacy, and math, as well as for outcomes that either relate to general cognition or represent indices that combine multiple cognitive skills. Panel C of Table 5 presents results for non-cognitive skills: executive functioning, social-emotional skills, and behavior. Taken together, our results suggest that preprimary interventions improve children’s skills in both high-income countries and low- and middle-income countries, with higher impacts estimated among studies evaluating quality improvements in existing preprimary programs compared to studies evaluating expansions in services.<sup>23</sup>

> 22 See Appendix Figure 1 for study-specific estimates on school participation and progression during the preprimary years.

> 23 Studies in the sample inconsistently reported detailed program features of the evaluated interventions or measured the quality of classroom instruction. Thus, it is not possible to identify exactly what underlies program success or failure or the persistence of impacts.  Given the importance of teachers to children’s skill development during the preprimary period, Appendix Table 8 presents average effect sizes of the impact of preprimary interventions on teacher practices in the classroom.  Average effect sizes pooled across country-income groups suggest that preprimary interventions can improve teachers’ practices in the classroom by an average of 0.473 sd (column 1). Similarly, preprimary programs, particularly if they include school meals and nutrition counselling for parents, can improve children’s health during and after the preprimary period. Average effect sizes for health outcomes during the preprimary period are small and statistically insignificant (effect size: 0.032; standard error: 0.029; and N=27).

25
# **4.2.1 Cognitive skills**

When we pool outcomes across all domains of cognitive skills, across country-income contexts, and across intervention goals, preprimary education interventions raise children’s cognitive skills by an average of 0.15 sd during the pre-primary period (Table 5 Panel B Column 1). While the estimated average effect size is higher for studies from high-income countries in Column 2 (0.18 sd) than for studies from low- and middle-income countries in Column 3 (0.12 sd), these estimates are statistically indistinguishable (see Appendix Table 7 for the difference between the two estimates and its associated standard error). On the other hand, studies evaluating interventions that aimed to improve the quality of existing services generated significantly higher impacts on average (0.20 sd in Column 5) than studies of expansions in services (0.10 sd in Column 4) (again see Appendix Table 7 for a formal test of this difference).

We have sufficient sample size to test whether these gains are present separately for all the developmental domains we pooled together as cognitive skills.<sup>24</sup> For language, literacy, and math, the average effect sizes for the full sample of extracted outcomes are positive and statistically significant, suggesting gains of 0.108 sd in language, 0.216 in literacy, 0.224 in math, and 0.084 in general skills (Table 5, Panel B, column (1)). This advantage over children in comparison groups who either did not attend preprimary education or did not experience increased investment in their preprimary classrooms is also evident for the general cognition category. The larger gains in literacy and math may be explained by the fact that children are less likely to engage in activities in the home that build these skills compared to language skills. In studies from high-income countries, preprimary interventions lead to a significant 0.115 sd increase in language scores, a 0.238 sd increase in literacy scores, and a 0.360 sd increase in math scores. In low- and middle-income countries, preprimary education interventions raise math scores by a significant 0.164 sd, on average. The estimated average effect size for language in studies from these countries is positive but fails to reach conventional levels of statistical significance. Our sample size of observations and studies measuring literacy in these countries was too small to estimate an average effect size.

> 24 Appendix Figures 2–6 present study-specific effect sizes for cognitive skills (language, literacy, math, and general).

26
# **4.2.2 Non-cognitive skills**

Table 5 Panel C presents average effect sizes for outcomes measuring non-cognitive skills: executive functioning, social-emotional skills, and behavior. When pooled across income levels (column 1) and across all outcomes, estimated effects suggest that preprimary education interventions lead to significant gains of 0.121 sd in non-cognitive skills. Again, we cannot statistically distinguish impacts estimated from studies in high-income countries from those estimated in studies from low- and middle-income countries (see Appendix Table 7 for the estimated difference between the two effects and its precision). In this case, however, estimated impacts from studies evaluating modifications to existing programs (0.159 sd in Column 5) largely drive the pooled average effect size, whereas the average impact is smaller and insignificant (0.048 sd) among the smaller sub-sample of studies evaluating preprimary expansions that also measured non-cognitive skills.

When we examine specific domains, we see that executive functions improve by an average of 0.095 sd (p<0.10). This average effect is driven largely by interventions evaluated in high-income countries, most of which directly aimed to improve these types of skills, and which increased these skills by an average of 0.169 sd (p<0.01). We lack sufficient sample size to estimate a separate average effect size for executive functions among studies from low- and middleincome countries.

Preprimary education interventions also significantly improved social-emotional skills by an average of 0.115 sd when we pool across both income groups, with high-income countries and low- and middle-income countries demonstrating similar significant effects of 0.094 and 0.130 sd, respectively (columns 2 and 3). The average effect size for outcomes related to child behavior in studies from high-income countries was a statistically insignificant 0.07 sd. Studies conducted in low- and middle-income countries did not collect this outcome.<sup>2526</sup>

> 25 In high-income contexts, the largest effect sizes, both positive and negative, come from a US study reporting a statistically significant improvement in behavior of 1.06 sd to -0.77 for externalizing behavior following a teacher intervention to reduce children’s behavioral problems (Raver et al., 2009). In low- and middle-income contexts, standardized effect sizes range from a significant 0.45 sd on a social composite index following exposure to a preprimary curriculum that promoted social and emotional abilities through play in India (Dillon et al., 2017), to an insignificant -0.08 sd in prosocial scores following a preprimary school expansion in Cambodia (Bouguen et al., 2018).

> 26 Appendix Figures 7–10 graph study-specific effect sizes and visually demonstrate the overall positive but still mixed results for non-cognitive skills.

27
# **Persistence of effects beyond preprimary education**

This section reports robust variance meta-regression results for the average effect size of preprimary interventions _after_ the preprimary period. If preprimary education imparts skills that help children learn and makes subsequent education efforts more effective, then we should expect to see the benefits of preprimary interventions persist when studies track children into post-preprimary education or adulthood.

Unfortunately, fewer studies estimate impacts beyond the preprimary period and no studies in low- and middle-income countries report impacts measured in adulthood. Thus, we often lack statistical power for inference. Table 6 (Panels A–C) reports average effect sizes for outcomes measuring school participation and progression, cognitive skills, and non-cognitive skills when children are between the ages of 6 and 18 years. On average, the time elapsed between the preprimary intervention and outcome measurement for the post-preprimary period is around 5 years, ranging from 1 to 15 years. Overall, the results suggest that preprimary interventions can generate advantages that last beyond the preprimary period, both in cognitive skills and noncognitive skills.

## **4.3.1 School participation and progression**

The meta-regression results in Table 6 Panel A suggest a positive but statistically insignificant average effect size of 0.142 sd for the impact of preprimary interventions on subsequent school participation and progression in the post-preprimary period.<sup>27</sup> The studyspecific effect sizes in Appendix Figures 12-13 suggest differences across income contexts. Preprimary interventions can improve outcomes like high-school graduation rates in highincome countries (Rossin-Slater & Wust, 2020; Gray-Lobe et al., 2021), but the overall effect of these interventions on school participation and progression appears to be low after the preprimary period. On the other hand, in low- and middle-income contexts, effect sizes are positive and significant in most studies, suggesting that preprimary interventions can improve school participation beyond the preprimary years.

> 27 Though we extracted school participation and progression outcomes in adulthood from studies in our sample, we lack statistical power to estimate the average effect size and its precision.

28
## **4.3.2 Cognitive skills**

When we pool effect sizes across high-income countries and low- and middle-income contexts and aggregate outcomes into a single category of cognitive skills, we find a significant persistent advantage of 0.071 sd in the post pre-primary period following interventions that either expanded or improved preprimary education (Table 6, Panel B). Sample sizes preclude a complete breakdown by skill, country-income context, and intervention goal, but average effect sizes are generally similar across these sub-samples.<sup>28</sup>

## **4.3.3 Non-cognitive skills**

The estimated average effect size pooled across income contexts and across outcomes in Panel C of Table 6 suggests significant persistence of advantages in non-cognitive skills of 0.068 sd. In the post-preprimary period, children benefitting from preprimary interventions show a significant advantage of 0.094 sd in social-emotional skills. Again, estimated effect sizes are similar in magnitude and statistically indistinguishable across country-income contexts.<sup>29</sup>

# **Heterogeneity by socioeconomic status**

This section reports average effect sizes estimated from Equation (5) when we limit our sample to coefficients estimated separately for subpopulations defined by their socioeconomic status. Given the infrequency of separate estimates for these subpopulations, we only report estimates for outcomes related to school participation and progression and for all skills pooled together, controlling for both the individual’s level of education at the time of measurement and for country-income context whenever possible.

> 28 Appendix Figures 13–16, which plot study-specific effect sizes for cognitive skills, suggest that the limited longitudinal evidence is mixed. Some studies exhibit economically and statistically meaningful impacts; others report negative but statistically insignificant results. In high-income contexts, for example, effect sizes—when standardized—could be as high as the significant 0.38 sd in first-grade math scores following a change in the preprimary math curriculum and teacher training/coaching (Clements et al., 2013), and as low as the significant -0.14 sd in a state achievement test following an offer of a slot in a subsidized pre-k program (Lipsey et al., 2018). Similarly, in low-income contexts, standardized effect sizes could be as high as a significant 0.23 sd in literacy scores measured in first grade when an additional year of preprimary education is offered to children (Spier et al., 2020), and as low as a statistically insignificant -0.05 sd in literacy scores following a teacher training and coaching program (Wolf et al., 2019b).

> 29 As for outcomes related to cognitive skills, study-specific effect sizes in Appendix Figures 17–19 make apparent both low sample sizes for executive functions, social-emotional skills, and behaviors after the preprimary period and mixed results, as well as a high count of statistically insignificant effects.

29
The average effect sizes across these pooled outcome categories in Table 7 provide suggestive evidence that pre-primary interventions can have greater impacts on more disadvantaged populations. For instance, for outcomes related to school participation and progression, estimated effects are 0.096 sd higher on average for children coming from lower socioeconomic backgrounds, compared to their less disadvantaged peers, although this advantage in gains is not statistically significant. However, average effect sizes are 0.05 sd significantly higher for disadvantaged populations when it comes to skills.

# **Economic analysis**

In Table 8, we present our findings from an economic evaluation of a subset of studies in low- and middle-income countries that reported sufficient information for us to compare costs per child to an estimate of the gain in lifetime earnings the child would receive from an improvement in cognitive skills. Panel A reports calculations for a discount rate of 3 percent, while Panel B presents results from a more conservative rate of 5 percent. Ganimian et al. (2021) report a range of benefit-to-cost ratios following the same method for a discount rate of 3 percent, which we directly report in Panel A.<sup>30</sup> In Panel A, our smallest benefit-to-cost ratio is 3.5 when we use the smallest estimate of the returns to cognitive skills (4.3 percent per standard deviation) to translate the smallest estimate of gains in cognitive skills to gains in earnings for the Spier et al. (2020) study, which assessed the impact of adding an additional year of preprimary education for four-year old children on top of the mandatory year prior to the start of first grade in Bangladesh. Our largest benefit-to-cost ratio is 103.5 when we use the largest estimate of the returns to cognitive skills (13 percent per standard deviation) and the largest estimate of the returns to social-emotional skills (15 percent per standard deviation) to translate the smallest estimated average treatment effects into earnings for the Wolf et al. (2019a) study. This study assessed the impact of an in-service teacher training program in Ghana that sought to help teachers transition to a more holistic, child-centered curriculum.<sup>31</sup> Although both estimates of the benefit-to-cost ratio are quite high, it is

> 30 Appendix Table 4 documents the assumptions underlying these calculations as well as data sources for parameters such as the labor force participation rate and growth in real wages.

> 31 Though poor children did show positive and significant gains in cognitive skills in Brinkman et al. (2017), the average treatment effects for all cognitive skills in the full sample were not statistically distinguishable from zero. However, even in the full sample, there were average gains in social-emotional skills. If we

30
understandable that the ratio would be higher for a teaching training program implemented once with some refresher sessions than for the provision of an additional year of education.

Panel B uses the more conservative discount rate of 5 percent to translate lifetime earnings into a present value (Haacker et al., 2020). We still find benefit-to-cost ratios all above 1. The minimum value for this ratio is 14.2 for the Gallego et al. (2021) study, which assessed the impact of a change in the math curriculum. The maximum value, which is for the Wolf et al. (2019a) study, is still quite large at 49.7.

# **5. Discussion and conclusion**

This study investigates whether current investments in preprimary education are too low through a systematic review of quasi-experimental evidence from around the world. Using 798 outcomes extracted and standardized from 54 studies, we use robust variance meta-regression to assess the returns to investment in preprimary education by establishing whether on average there is demand for preprimary programs and the extent to which preprimary education improves children’s skills during the preprimary period. To more concretely gauge returns, we also combine estimates from the literature on the relationship between improvements in cognitive skills and future earnings with study-specific estimates of improvements in these skills in a subset of studies from low- and middle-income countries that report per-child costs.

We find that interventions that expand access to preprimary education led to significant increases in the take-up of preprimary education services and school participation during the preprimary period. On average, preprimary education programs significantly improve children’s cognitive and social-emotional skills and executive functions in the short run, suggesting that these kinds of services promote learning and skill development better than children’s alternative care options during the preprimary period. A translation of these effects into gains in children’s earnings suggest sizable benefit-to-cost ratios, ranging from 1.7 to 103.5.

Although fewer studies track children beyond the preprimary years, we find evidence of smaller but statistically significant persistent effects of preprimary interventions in both cognitive skills and non-cognitive skills. Not only do these estimates highlight the potential for preprimary education programs to generate lasting learning gains among young children, but they also estimate wage returns only for these gains in social emotional skills with a discount rate of 3 percent, we still find sizable benefit-to-cost ratios of 8.1 (lower bound) to 24.5 (upper bound).

31
underscore the role that meta-analysis can play in overcoming estimation challenges related to statistical power that may face individual studies, particularly small-sample longitudinal studies.

Average effect sizes in studies from high-income countries could not be statistically distinguished from average effect sizes in low- and middle-income countries for both cognitive and non-cognitive skills, both during and after the preprimary period. Estimated impacts were also similar in magnitude across the income spectrum. This points to the potential for preprimary education programs to improve the school readiness of young children even in contexts with limited state capacity.

This set of results, together with the currently low coverage rates in low- and middle-income countries, suggest that current levels of investment in preprimary education may be suboptimal. That is, an increase in spending on preprimary education coverage and quality may improve the overall efficiency of education spending, particularly if investments first target children from lower socioeconomic backgrounds. Given that children from disadvantaged backgrounds tend to have very low access to preprimary education and alternative learning opportunities (McCoy et al., 2018), minimum quality thresholds for services may depend on local conditions. Thus, even preprimary interventions that do not cover every aspect of quality could still improve learning outcomes for very disadvantaged children (Cascio & Schanzenbach, 2014). Our results suggest that existing preprimary programs have on average promoted higher skill development compared to what children would have experienced in the absence of these programs (either at home or in alternative programs).

While our sample of 798 estimated effect sizes is large, we recognize some limitations in our study design that may affect the magnitude and significance of the average effect sizes we estimate for school participation and progression and skills. For example, we restricted our search to studies that had been published in peer-reviewed journals or through formal working paper series. Given levels of publication-bias estimated multiple disciplines (Ioannidis, 2008; Ioannidis et al., 2017), this decision might bias us toward a positive and significant average effect.

On the other hand, we also aimed to extract all relevant outcomes from each included study, even if they might not be considered the best metrics to assess our outcomes of interest. This decision, and the attendant measurement error (attenuation bias) in many extracted outcomes, could bias us toward extracting multiple insignificant effects.

32
Nevertheless, from our large sample of published studies and formal working papers, we find positive and often persistent average effects of preprimary interventions on school participation and progression, as well as on cognitive and non-cognitive skills, during the preprimary period and beyond. Future research that aggregates evidence across studies would ideally try to understand variation in effectiveness based on program and child characteristics. The identification of what makes programs most effective could help make the returns and costeffectiveness of preprimary education programs even higher.

33
# **References**

Bailey, D.H., G.J. Duncan, F. Cunha, B.R. Foorman, and D.S., and Yeager. 2020a. "Persistence and FadeOut of Educational-Intervention Effects: Mechanisms and Potential Solutions." _Psychological Science in the Public Interest_ 21(2): 55-97.

- Bailey, M.J., B.D. Timpe, and S. Sun. 2020b. _Prep School for poor kids: The long-run impacts of Head Start on Human capital and economic self-sufficiency._ (No. w28268): National Bureau of Economic Research.

- Baird, S., F.H. Ferreira, B. Özler, and M. Woolcock. 2014. "Conditional, unconditional and everything in between: a systematic review of the effects of cash transfer programmes on schooling outcomes." _Journal of Development Effectiveness_ 6 (1): 1-43.

- Baker, M., J. Gruber, and K. Milligan. 2015. _Non-cognitive deficits and young adult outcomes: The longrun impacts of a universal child care program._ (No. w21571): National Bureau of Economic Research.

- Baker, M., J. Gruber, and K. Milligan. 2008. "Universal child care, maternal labor supply, and family wellbeing." _Journal of Political Economy_ 116 (4): 709-745.

- Banerjee, A,, Sh. Cole, E. Duflo, and L. Linden. 2007. "Remedying Education: Evidence from Two Randomized Experiments in India." _Quarterly Journal of Economics,_ 122 (3): 1235-1264.

- Belfield, C., A.B. Bowden, A. Klapp, H. Levin, R. Shand, and S. Zander. 2015. "The economic value of social and emotional learning." _Journal of Benefit-Cost Analysis_ 6 (3): 508-544.

- Berkes, J., A. Bouguen, D. Filmer, and T. Fukao. 2019. _Improving Preschool Provision and Encouraging Demand: Heterogeneous Impacts of a Large-Scale Program._ Washington D.C.: The World Bank Working Paper Series.

- Berlinski, S., S. Galiani, and M. Manacorda. 2008. "Giving children a better start: Preschool attendance and school-age profiles." _Journal of Public Economics_ 92 ((5-6)): 1416-1440.

- Bernal, R., and S.M. Ramírez. 2019. "Improving the quality of early childhood care at scale: The effects of “From Zero to Forever”." _World Development_ 118: 91-105.

- Bernal, R., O. Attanasio, X. Pena, and M. Vera-Hernández. 2019. "The effects of the transition from homebased childcare to childcare centers on children’s health and development in Colombia." _Early Childhood Research Quarterly_ 47: 418-431.

- Bouguen, A., D. Filmer, K. Macours, and S. Naudeau. 2018. "Preschool and parental response in a second best world evidence from a school construction experiment." _Journal of Human Resources_ (The World Bank Working Paper Series) 53 (2): 474-512.

- Brinkman, S.A., A. Hasan, H. Jung, A. Kinnell, and M. Pradhan. 2017. "The impact of expanding access to early childhood education services in rural Indonesia." _Journal of Labor Economics_ 35 (S1): S305-S335.

- Brodeur, A., N. Cook, and A. Heyes. 2020. "Methods matter: P-hacking and publication bias in causal analysis in economics." _American Economic Review_ 110 (11): 3634-3660.
- Brotman, L.M., S. Dawson-McClure, D. Kamboukos, K.Y. Huang, E.J. Calzada, K. Goldfeld, and E. Petkova. 2016. "Effects of ParentCorps in prekindergarten on child mental health and academic performance: Follow-up of a randomized clinical trial through 8 years of age." _JAMA Pediatrics_ 170 (12): 1149-1155.

- Campbell, F., G. Conti, J.J. Heckman, S.H. Moon, R. Pinto, E. Pungello, and Y. Pan. 2014. "Early childhood investments substantially boost adult health." _Science_ 343 (6178): 1478-1485.

- Carneiro, P., and R. Ginja. 2014. "Long-term impacts of compensatory preschool on health and behavior: Evidence from Head Start." _American Economic Journal: Economic Policy_ 6 (4): 135-73.

Cascio, E.U. 2015. _The promises and pitfalls of universal early education._ IZA World of Labor.

- Cascio, E.U., and D.W. Schanzenbach. 2014. _Proposal 1: Expanding preschool access for disadvantaged children._ Policies to address poverty in America, p.19.

- Cascio, E.U., and D.W., and Schanzenbach. 2013. "The impacts of expanding access to high-quality preschool education ." _National Bureau of Economic Research_ (No. w19735).

- Chetty, R., J.N. Friedman, N. Hilger, E. Saez, D.W. Schanzenbach, and D. Yagan. 2011. "How does your kindergarten classroom affect your earnings? Evidence from Project STAR." _The Quarterly Journal of Economics_ 126 (4): 1593-1660.

- Clements, D.H., J., Sarama, C.B. Wolfe, and M.E. Spitler. 2013. "Longitudinal evaluation of a scale-up model for teaching mathematics with trajectories and technologies: Persistence of effects in the third year." _American Educational Research Journal_ 50 (4): 812-850.

- Conti, G., J.J. Heckman, and R. Pinto. 2016. "The effects of two influential early childhood interventions on health and healthy behaviour." _The Economic Journal_ 126 (596): F28-F65.

- Cunha, F., and J., Heckman. 2007. "The technology of skill formation." _American Economic Review_ 97 (2): 31-47.

- Currie, J. M. 2001. _Early childhood intervention programs: what do we know?_ Brookings Institution, Brookings Roundtable on Children.

- Dean, J.T., and S. Jayachandran. 2020. "Attending kindergarten improves cognitive development in India, but all kindergartens are not equal."

Dehaene, S. 2010. _Reading in the brain: The new science of how we read._ Penguin Group USA.

- Deming, D. 2009. "Early childhood intervention and life-cycle skill development: Evidence from Head Start." _American Economic Journal: Applied Economics_ 1 (3): 111-34.

- DeWind, N. K., J. Park, M. G. Woldorff, and E. M. Brannon. 2019. "Numerical encoding in early visual cortex." _Cortex_ 114: 76-89.

Diamond, A. 2013. "Executive functions." _Annual Review of Psychology_ 64: 135-168.

Dillon, M.R., H. Kannan, J.T. Dean, E.S. Spelke, and E. Duflo. 2017. "Cognitive science in the field: A preschool intervention durably enhances intuitive but not formal mathematics." _Science_ 357 (6346): 47-55.
- Duflo, E., P. Dupas, and M. Kremer. 2011. "Peer Effects, Teacher Incentives, and the Impact of Tracking: Evidence from a Randomized Evaluation in Kenya ." _American Economic Review_ 10 (5): 1739-74.

- Duncan, G.J., and K. Magnuson. 2013. "Investing in preschool programs." _Journal of Economic Perspectives_ 27 (2): 109-32.

- Evans, D.K., and A., Popova. 2016. "What really works to improve learning in developing countries? An analysis of divergent findings in systematic reviews." _The World Bank Research Observer_ 31 (2): 242-270.

- Evans, D.K., and F. Yuan. 2021. "What We Learn about Girls' Education from Interventions that Do Not Focus on Girls." _The World Bank Economic Review._

- Evans, D.K., P. Jakiela, and H.A. Knauer. 2021. "The impact of early childhood interventions on mothers." _Science_ 372 (6544): 794-796.

- Fernald, L.C., A. Weber, E. Galasso, and L. Ratsifandrihamanana. 2011. "Socioeconomic gradients and child development in a very low income population: evidence from Madagascar." _Developmental Science_ 14 (4): 832-847.

- Fernald, L.C., E. Prado, and Kariger, P. Raikes, A. 2017. _A toolkit for measuring early childhood development in low and middle-income countries._ Washington D.C.: The World Bank.

- Galasso, E., and A. Wagstaff. 2019. "The aggregate income losses from childhood stunting and the returns to a nutrition Intervention aimed at reducing stunting." _Economics & Human Biology_ (Policy Research Note World Bank) 34: 225-238.

- Gallego, F.A., E. Näslund-Hadley, and M. Alfonso. 2021. "Changing Pedagogy to Improve Skills in Preschools: Experimental Evidence from Peru." _The World Bank Economic Review_ 35 (1): 261286.

- Ganimian, A.J., K. Muralidharan, and C.R. Walters. 2021. _Augmenting State Capacity for Child Development: Experimental Evidence from India._ (No. w28780): National Bureau of Economic Research.

- Garces, E., D. Thomas, and J. Currie. 2002. "Longer-term effects of Head Start." _American Economic Review_ 92 (4): 999-1012.

- Gelber, A., and A. Isen. 2013. " Children's schooling and parents' behavior: Evidence from the Head Start Impact Study." _Journal of Public Economics_ 101: 25-38.

- Haacker, M., T.B. Hallett, and R. Atun. 2020. "On discount rates for economic evaluations in global health." _Health Policy and Planning_ 35 (1): 107-114.

- Hamlin, J., T. Ullman, J. Tenenbaum, and N. Baker, C. Goodman. 2013. "The mentalistic basis of core social cognition: Experiments in preverbal infants and a computational model." _Developmental Science_ 16 (2): 209-226.

- Havnes, T., and M. Mogstad. 2011. "No child left behind: Subsidized child care and children's long-run outcomes." _American Economic Journal: Economic Policy_ 3 (2): 97-129.

- Heckman, J.J. 2006. "Skill formation and the economics of investing in disadvantaged children." _Science_ 312 (5782): 1900-1902.
Heckman, J.J. 2007. "The economics, technology, and neuroscience of human capability formation. ." _Proceedings of the National Academy of Sciences_ 104(33): 13250-13255.

- Heckman, J.J., S.H. Moon, R. Pinto, P.A. Savelyev, and A. and Yavitz. 2010. "The rate of return to the HighScope Perry Preschool Program." _Journal of Public Economics_ 94 (1-2): 114-128.

- Hedberg, E.C. 2011. _ROBUMETA: Stata module to perform robust variance estimation in meta-regression with dependent effect size estimates._ Boston: Boston College Working Papers in Economics.

- Hedges, L.V., E. Tipton, and M.C. Johnson. 2010. "Robust variance estimation in meta‐regression with dependent effect size estimates." _Research Synthesis Methods_ 1 (1): 39-65.

- Hendren, N., and B. Sprung-Keyser. 2020. "A unified welfare analysis of government policies." _The Quarterly Journal of Economics_ 135 (3): 1209-1318.

- Hidrobo, M., J. Hoddinott, N Kumar, and M. Olivier. 2018. "Social protection, food security, and asset formation." _World Development_ (101): 88-103.

- Horowitz, J.L., and C.F. Manski. 2000. "Nonparametric analysis of randomized experiments with missing covariate and outcome data." _Journal of the American Statistical Association_ 95 (449): 77-84.

Ioannidis, J.P. 2008. "Why most discovered true associations are inflated." _Epidemiology_ 640-648.

Ioannidis, J.P., T.D. Stanley, and H. Doucouliagos. 2017. "The power of bias in economics research."

- Jara‐Ettinger, J., E Gibson, and Kidd, C. Piantadosi, S. 2016. "Native Amazonian children forego egalitarianism in merit‐based tasks when they learn to count." _Developmental Science_ 19 (6): 11041110.

- Johnson, R.C., and C.K. Jackson. 2019. "Reducing inequality through dynamic complementarity: Evidence from Head Start and public school spending." _American Economic Journal: Economic Policy_ 11 (4): 310-49.

- Kline, P., and C.R. Walters. 2016. "Evaluating public programs with close substitutes: The case of Head Start." _The Quarterly Journal of Economics,_ 131 (4): 1795-1848.

- Knudsen, E.I. 2004. "Sensitive periods in the development of the brain and behavior." _Journal of Cognitive Neuroscience_ 16 (8): 1412-1425.

- Knudsen, E.I., J.J. Heckman, J.L. Cameron, and J.P. Shonkoff. 2006. "Economic, neurobiological, and behavioral perspectives on building America’s future workforce." _Proceedings of the national Academy of Sciences_ 103 (27): 10155-10162.

- Kremer, M., and A. Holla. 2009. "Improving education in the developing world: what have we learned from randomized evaluations?" _Annu. Rev. Econ._ 1 (1): 513-542.

- Krueger, A.B. 1999. "Experimental estimates of education production functions." _The Quarterly Journal of Economics_ 114 (2): 497-532.

- Lee, D.S. 2009. "Training, wages, and sample selection: Estimating sharp bounds on treatment effects." _The Review of Economic Studies_ 76 (3): 1071-1102.

Lipsey, M.W., and D.B., Wilson. 2001. _Practical meta-analysis._ SAGE publications, Inc.
- Lipsey, M.W., D.C. Farran, and K. Durkin. 2018. "Effects of the Tennessee Prekindergarten Program on children’s achievement and behavior through third grade." _Early Childhood Research Quarterly_ 45: 155-176.

- Martinez, S., S. Naudeau, and V. Pereira. 2017. _Preschool and child development under extreme poverty: evidence from a randomized experiment in rural Mozambique._ Washington D.C.: The World Bank.

- McCoy, D.C., C. Salhi, H. Yoshikawa, M. Black, P. Britto, and G. Fink. 2018. "Home-and center-based learning opportunities for preschoolers in low-and middle-income countries." _Children and Youth Services Review_ 88: 44-56.

- McCoy, D.C., H. Yoshikawa, K.M. Ziol-Guest, G.J. Duncan, H.S. Schindler, K. Magnuson, R. Yang, A. Koepp, and J.P., Shonkoff. 2017. "Impacts of early childhood education on medium-and long-term educational outcomes." _Educational Researcher_ 46 (8): 474-487.

- Meager, R. 2019. "Understanding the average impact of microcredit expansions: A bayesian hierarchical analysis of seven randomized experiments." _American Economic Journal: Applied Economics_ 11 (1): 57-91.

- Naudeau, S., S. Martinez, P. Premand, and D. Filmer. 2011. "Cognitive development among young children in low-income countries, is No Small Matter." In _The Impact of Poverty Shocks, and Human Capital Investments in Early Childhood Development_ , by editor Harold Alderman, 9-50. Washington D.C.: The World Bank.

- Nores, M., and W.S. Barnett. 2010. "Benefits of early childhood interventions across the world:(Under) Investing in the very young." _Economics of Education Review_ 29 (2): 271-282.

- Ozler, B., L. C. H. Fernald, P. Kariger, C. McConnell, M. Neuman, and E. Fraga. 2018. "Combining preschool teacher training with parenting education: A clusterrandomized controlled trial." _Journal of Development Economics_ (The World Bank.) 133: 448–467.

- Phillips, D., M. Lipsey, K. Dodge, R Haskins, D. Bassok, M. Burchinal, G. Duncan, M. Dynarski, K. Magnuson, and Ch. Weiland. 2017. _The Current State of Scientific Knowledge on PreKindergarten Effects._ Washington D.C.: The Brookings Institute.

- Raver, C.C., S.M. Jones, C. Li-Grining, F. Zhai, M.W. Metzger, and B. Solomon. 2009. "Targeting children's behavior problems in preschool classrooms: A cluster-randomized controlled trial." _Journal of Consulting and Clinical Psychology_ 77 (2): 302.

- Rossin-Slater, M., and M. Wüst. 2020. "What is the Added Value of Preschool for Poor Children? LongTerm and Intergenerational Impacts and Interactions with an Infant Health Intervention." _American Economic Journal: Applied Economics_ 12 (3): 255-86.

- Schady, N., J. Behrman, M.C. Araujo, R. Azuero, R. Bernal, D. Bravo, F. Lopez-Boo, et al. 2014. _Wealth gradients in early childhood cognitive development in five Latin American countries._ Washington D.C.: The World Bank.

- Schultz, T.P. 2010. "Population and Health Policies." In _Handbook of Development Economics 5_ , edited by Mark Rosenzweig and Dani Rodrik, 4785-4881.
- Shonkoff, J.P., D.A. Phillips, and National Research Council. 2000. _The developing brain._ National Academies Press (US): In From neurons to neighborhoods: The science of early childhood development. .

- Spelke, E., and K. Shutts. forthcoming. "Chapter 1: Learning in the Early Years." In _Quality Early Learning: Nurturing Children's Potential_ , edited by A. Devercelli and M Bendini.

- Spier, E., K. Kamto, A. Molotsky, A. Rahman, N. Hossain, Z. Nahar, and H. and Khondker. 2020. "Bangladesh Early Years Preschool Program Impact Evaluation."

- Tamis-LeMonda, C.S, K. E Adolph, S. A. Lobo, L. B. Karasik, S.. Ishak, and K. A. Dimitropoulou. 2008. "When infants take mothers' advice: 18-month-olds integrate perceptual and social information to guide motor action." _Developmental Psychology_ 44 (3): 734.

- Tanner‐Smith, E.E, and E. Tipton. 2014. "Robust variance estimation with dependent effect sizes: Practical considerations including a software tutorial in Stata and SPSS." _Research Synthesis Methods_ 5 (1): 13-30.

- Tanner-Smith, E.E., E. Tipton, and J.R. Polanin. 2016. "Handling complex meta-analytic data structures using robust variance estimates: A tutorial in R." _Journal of Developmental and Life-Course Criminology_ 2 (1): 85-112.

- Tipton, E. 2015. "Small sample adjustments for robust variance estimation with meta-regression." _Psychological Methods_ 20 (3): 375.

- Tipton, E., J.E. Pustejovsky, and H. Ahmadi. 2019. "Current practices in meta‐regression in psychology, education, and medicine." _Research Synthesis Methods,_ 10 (2): 180-194.

- Tomasello, M., M. Carpenter, J. Call, and T., Moll, H. Behne. 2005. "Understanding and sharing intentions: The origins of cultural cognition." _Behavioral and brain sciences_ 28 (5): 675-691.

- UIS, (UNESCO Institute for Statistics). 2018. _UIS Stat._ Accessed March 4, 2021. http://data.uis.unesco.org/.

- UNICEF. 2019. _A World Ready to Learn: Prioritizing quality early childhood education. Global Report. ._ New York: UNICEF.

- van Huizen, T., and J. Plantenga. 2018. "Do children benefit from universal early childhood education and care? A meta-analysis of evidence from natural experiments." _Economics of Education Review_ 66: 206-222.

- Vivalt, E. 2020. "How much can we generalize from impact evaluations?" _Journal of the European Economic Association_ 18 (6): 3045-3089.

- Wang, Y., R. Williams, L. Dilley, and D. M. Houston. 2020. "A meta-analysis of the predictability of LENATM automated measures for child language development." _Developmental Review_ 57: 100921.

- Wolf, S., J.L. Aber, J.R. Behrman, and E. and Tsinigo. 2019a. "Experimental impacts of the "Quality Preschool for Ghana” interventions on teacher professional well-being, classroom quality, and children’s school readiness." _Journal of Research on Educational Effectiveness_ 12 (1): 10-37.
- Wolf, S., J.L. Aber, J.R. Behrman, and M. Peele. 2019b. "Longitudinal causal impacts of preschool teacher training on Ghanaian children’s school readiness: Evidence for persistence and fade‐out." _Developmental Science_ 22 (5): e12878.

- World Bank. 2013. _What Matters Most for Early Childhood Development: A Framework Paper._ Washington, D.C: World Bank. SABER Working Paper Series.

- World Bank. 2018. _World Development Report 2018 : Learning to Realize Education's Promise._ Washington, DC: World Bank.

- Yoshikawa, H., C. Weiland, J. Brooks-Gunn, M.R. Burchinal, L.M. Espinosa, W.T. Gormley, J. Ludwig, K.A. Magnuson, D. Phillips, and M.J. Zaslow. 2013. _Investing in Our Future: The Evidence Base on Preschool Education. Society for Research in Child._ Society for Research in Child Development.

- Yuan, S., and C. Fisher. 2009. "Really? She blicked the baby? Two-year-olds learn combinatorial facts about verbs by listening." _Psychological Science_ 619-626.

- Zelazo, P.D., U. Müller, D. Frye, S. Marcovitch, G. Argitis, J. Boseovski, J.K. Chiang, et al. 2003. "The development of executive function in early childhood." _Monographs of the society for research in child development_ i-151.
# **Figures and Tables**

**Figure 1: Children in early childhood education by country income and household wealth**

<!-- Start of picture text --> 90%<br>0.784<br>80%<br>70%<br>0.637<br>60% 0.557<br>0.482<br>50%<br>40%<br>30% 0.264 0.271<br>20% 0.155<br>10% 0.076<br>0%<br>Low Income Low-Middle Income High-Middle Income High Income<br>Bottom quintile Top quintile<br><!-- End of picture text --> _Notes_ : Data from the Multiple Indicator Cluster Surveys, reported in McCoy et al. (2018)
**Figure 2: Children receiving stimulation at home and attending early childhood education by country income**

<!-- Start of picture text --> 100%<br>0.092 0.228 0.421 0.607<br>90%<br>0.14<br>High stimulation & preprimary<br>80% 0.29 Low stimulation & preprimary<br>0.539 High stimulation & no preprimary<br>0.471<br>70% 0.658 Low stimulation & no preprimary<br>0.708<br>60%<br>50%<br>0.834<br>40% 0.875<br>30% 0.386<br>20%<br>0.226<br>10%<br>0.116<br>0% 0.074<br>Low Income Low-Middle High-Middle High Income<br>Income Income<br><!-- End of picture text --> _Notes_ : Data from the Multiple Indicator Cluster Surveys, reported in McCoy et al. (2018)
# **Figure 3: Study Selection Process**

<!-- Start of picture text --> Studies identified through Studies identified through<br>database searches other sources (e.g. experts)<br>(N=183) (N=87)<br>Studies excluded from<br>Screening 1: Citation - Abstract<br>Screening Stage (N=111)<br>Total studies screened Reasons for exclusion:<br>- Not center-based program<br>- Not targeting children 3 to 6<br>(N=270) - Not peer-reviewed journal article or<br>WBG/NBER working paper series<br>- Not quasi-experimental or<br>experimental study<br>- Duplicates/working papers of final<br>published papers<br>Full-text studies assessed<br>Studies excluded from<br>for eligibility Screening 2: Full Text<br>Screening Stage (N=105)<br>(N=159)<br>Reasons for exclusion:<br>--Study IE quality checks<br>(identification strategy, attrition,<br>compliance etc.)<br>-No impact estimate, standard errors,<br>confidence interval, p-value, sample<br>size etc.<br>-Not enough information to calculate<br>effect size<br>Total studies included<br>for review or data<br>extraction Notes: [Identification/Search Stage: Phase 1 and<br>Phase 2]: includes searching literature from<br>(N=54) Economics, Education and Psychology, SIEF<br>Portfolio, Google Scholar, ProQuest search (WB<br>Librarian), external reviewers, experts, hand search<br>and bibliographies. [Screening 1: Phase 3]: review of<br>citations and abstracts to eliminate duplicates, not<br>published papers and papers that do not estimate<br>rigorously the impacts of preprimary programs or<br>policies. [Screening 2: Phase 4]: review of full text<br>studies, eliminate papers based on lack of a rigorous<br>quantitative analysis (e.g. identification strategy,<br>attrition, compliance etc.). [Data Extraction: Phase<br>5]: coding of information at the study, intervention<br>and outcomes (effect size) level.<br>dentification<br> I<br> Screening 1<br>creening 2<br> S<br> Included<br><!-- End of picture text -->
<!-- Start of picture text -->
* o<br>wrens wa ; [Noway] ae<br>[Denmark<br>1<br>“ Bangladesh<br>5 2 1<br>The Gambia 1<br>[ Cambodia |<br>[ Colombia ] 1 [ Ghana |<br>k<br>NUMBER OF STUDIES l<br>HIGHINCOME COUNTRIES: 1<br>a:28 1 [Mozambique]<br>:1 22» (reas)guay<br>LOW INCOME COUNTRIES<br>; 11 Lugy]<br>21 _ Regent|]<br>NODATA i<br>IBRD 45716 | ua<br>DECEMBER 2021<br><!-- End of picture text -->
# **Table 1: Search and inclusion criteria for studies**

|**Panel A:**Search||
|---|---|
|_Sources_|APA PcycNet, Academic Search Elite/EBSCO host, Econlit, ERIC, IDEAS,<br>ScienceDirect, Social Science, Research Network (SSRN), Google Scholar, ProQuest<br>Database, MEDLINE, NBER, WBG Working Papers, Strategic Impact Evaluation Fund<br>(SIEF) Portfolio, JSTOR, JOLIS library catalogue – International Monetary Fund,<br>World Bank and International Finance Corporation, PsycINFO, PubMed, and Web of<br>Science|
|_Search terms_|Preprimary OR preprimary OR Pre-school OR Kindergarten OR early childhood<br>development OR early childhood education OR early child development OR early<br>child education OR early learning OR early cognitive development OR early skills<br>development) AND (impact evaluation OR field experiment OR randomized OR<br>program evaluation OR meta-analysis OR systematic review OR synthesis review OR<br>qualitative study)>2005. Age 3 to 6;Countries: Global|
|**Panel B**: Inclusion crit|eria|
|_Program beneficiaries_|Children aged 3-6 years|
|_Interventions_|Programs that provide group-based childcare to children 3 to 6 years old in a center<br>setting with a formal or informal developmental and educational focus. This includes<br>formal preprimary schools, community schools, preschools, kindergarten, pre-<br>kindergarten, or daycare with an educational component. We also include<br>supplementary / co-interventions targeting parents, teachers, or other inputs (i.e.,<br>teacher training, targeting curricula, pedagogical approaches, infrastructure) whose<br>primary outcomes of interest are either child outcomes, or intermediary outcomes that<br>affect the quality of a pre-school program.|
|_Study design_|Studies implementing experimental and quasi-experimental methods with a credible<br>source<br>of<br>exogenous<br>variation.<br>We included studies<br>employing one<br>of<br>the<br>following designs: randomized control trial (RCT), regression discontinuity design<br>(RDD), differences-in-differences (DID), instrumental<br>variables<br>(IV), and matching methods. At least one treatment arm must be able to isolate the<br>effect of a preprimary program.|
|---|---|
|_Outcomes of interest_||
||Child outcomes: school participation and progression, cognitive skills, social-emotional<br>skills, behavior, long-term educational attainment, adult health, and labor outcomes<br>Teacher/parent (adult) outcomes: classroom practices and engagement in stimulation<br>activities|
|_Publication type_|Peer-reviewed<br>journal<br>articles,<br>WB/NBER<br>Working<br>Papers,<br>and<br>other<br>working/discussion papers only if they included a formal institution/citation. We<br>excluded studies that are not part of a working paper series (including PhD<br>Dissertations, job market papers, and conference working papers) and other<br>institutional policy publications.|
|_Publication date_|Any|
|_Geography_|Global|
**Table 2: Study characteristics**

||Mean|Standard<br>deviation|
|---|---|---|
||(1)|(2)|
|Focused on high-income country|0.67|0.48|
|Publication timing|||
|_2007-2012_|0.33|0.48|
|_2013-2018_|0.41|0.50|
|_2019-2021_|0.26|0.44|
|Published in peer-reviewed journal|0.85|0.36|
|Discipline|||
|_Economics_|0.50|0.50|
|_Education_|0.44|0.50|
|_Psychology_|0.06|0.23|
|Randomized control trial|0.70|0.46|
|Evaluation of expansion in coverage|0.46|0.50|

_Notes_ : Observations are studies, N=54.
**Table 3: Characteristics of interventions**

||High-income|Low- & middle-|
|---|---|---|
||countries|income countries|
||(1)|(2)|
|Program category|||
|_Teacher professional development_|0.55|0.69|
||(0.07)|(0.08)|
|_Subsidized or free access_|0.49|0.03|
||(0.07)|(0.03)|
|_Change in curriculum_|0.29|0.16|
||(0.02)|(0.07)|
|_Change in pedagogy_|0.04|0.13|
||(0.03)|(0.06)|
|_Provision of materials_|0.14|0.28|
||(0.05)|(0.08)|
|_Provision of new staff_|0.00|0.09|
||(0.00)|(0.05)|
|_Provision of health and nutrition services_|0.08|0.00|
||(0.04)|0.00|
|_School construction_|0.02|0.34|
||(0.02)|(0.09)|
|_Parental engagement_|0.04|0.50|
||(0.03)|(0.09)|
|_Community outreach_|0.00|0.16|
||(0.00)|(0.07)|
|_Extension of school day_|0.02|0.00|
||(0.02)|(0.00)|
|_Teacher payments_|0.00|0.09|
||(0.00)|(0.05)|
|Type of provision|||
|_Formal preprimary_|0.90|0.56|
||(0.04)|(0.09)|
|_Community-based preprimary_|0.00|0.44|
||(0.00)|(0.09)|
|_Daycare with educational component_|0.10|0.00|
||(0.04)|(0.00)|
|School management|||
|_Public_|0.57|0.63|
||(0.07)|(0.09)|
|_Public-private_|0.43|0.25|
||(0.07)|(0.08)|
|_Community_|0.00|0.13|
||(0.00)|(0.06)|
|Teacher formally qualified|0.80|0.50|
||(0.06)|(0.09)|
|Program targets disadvantaged population|0.71|0.66|
|_Notes_: Observations are evaluated program|s, N=141 interv<br>(0.07)|entions or contrasts<br>(0.09)|

_Notes_ : Observations are evaluated programs, N=141 interventions or contrasts. Standard errors of presented means are in parentheses.
**Table 4: Characteristics of extracted outcomes**

||Mean|Standard<br>deviation|Median|
|---|---|---|---|
||(1)|(2)|(3)|
|Sample size|502,289|3,299,459|2,449|
|Fraction that are estimates for separate SES subpopulations|0.24|0.43||
|Fraction measured during period||||
|_Preprimary (3-5 years)_|0.63|0.48||
|_Post-preprimary (6-18 years)_|0.29|0.45||
|_Adulthood (after 18 years)_|0.08|0.08||
|Fraction measuring domain||||
|<br>_School participation and progression_|0.19|0.39||
|_Language_|0.11|0.32||
|_Literacy_|0.06|0.24||
|_Math_|0.08|0.27||
|_General cognitive_|0.06|0.23||
|_Executive function_|0.09|0.29||
|_Social-emotional learning_|0.14|0.34||
|_Behavior_|0.09|0.28||
|_Health_|0.11|0.32||
|_Notes_: Observations are extracted outcomes from studies in<br>_Teacherpractices_|the sampl<br>0.06|e, N=798. S<br>0.24|ES refers t|

_Notes_ : Observations are extracted outcomes from studies in the sample, N=798. SES refers to socioeconomic status
**Table 5: Average effects of preprimary interventions on participation and skills** **_during_ the preprimary period**

||Pooled sample|High-income|Low- & middle-<br>income|Expansion|Quality|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|**Panel A: Schoolparticipation and**|**progression**|||||
|_School participation and progression_|1.4048**<br>(0.4776)|||1.538**<br>(0.513)||
|N|29|||26||
|**Panel B: Cognitive skills**||||||
|_Pooled skills_|0.1474***<br>(0.0259)|0.1787***<br>(0.0431)|0.1223***<br>(0.0330)|0.0950***<br>(0.0263)|0.197***<br>(0.0420)|
|N|131|76|55|58|73|
|_Language_|0.1084***|0.1150**|0.0992|0.0770***|0.157**|
||(0.0295)|(0.0367)|(0.0526)|(0.0226)|(0.0591)|
|N|53|35|18|23|30|
|_Literacy_|0.2162***<br>(0.0634)|0.2385**<br>(0.0783)|||0.213*<br>(0.0844)|
|N|31|28|||24|
|_Math_|0.2238***<br>(0.0469)|0.3605**<br>(0.1108)|0.1643***<br>(0.0406)||0.265***<br>(0.0602)|
|N|28|10|18||16|
|_General_|0.0844*<br>(0.0367)||0.0803<br>(0.0464)|0.0866<br>(0.0472)||
|N|19||16|16||
|**Panel C: Executive functions and s**|**ocial-emotional**|**skills**||||
|_Pooled skills_|0.1209***|0.1354**|0.1138***|0.0488|0.159***|
||(0.0279)|(0.0494)|(0.0330)|(0.0412)|(0.0317)|
|N|114|75|39|32|82|
|_Executive functions_|0.0951*<br>(0.0387)|0.1693*<br>(0.0778)|||0.176***<br>(0.0365)|
|N|42|31|||33|
|_Social-emotional skillls_|0.1150***<br>(0.0301)|0.0935**<br>(0.0318)|0.1301**<br>(0.0463)||0.117***<br>(0.0329)|
|N|50|22|28||32|
|_Behavior_|0.0750|0.0750||||
||(0.0500)|(0.0500)||||
|N|22|22||||

_Notes_ : Standard errors estimated using small sample adjustments as in Tipton (2015) are in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Number of observations (N) refers to number of study-specific average effects used in the specification. Specifications that pool skills use one large category for extracted outcomes - either cognitive skills (which combines language, literacy, math, and general cognitive skills) or what is typically referred to as non-cognitive skills (executive functions, social-emotional skills, and behaviors). See Appendix 1 for details on definitions of school participation and progression; language, literacy, math, and general skills; executive functions; social-emotional skills; and behavior. Cells are blank when the sample size is too small to be able to obtain trustworthy p-values, when the degrees of freedom fall below 4 or when all observations came from a single study.
**Table 6: Average effects of preprimary interventions on participation and skills** **_after_ the preprimary period**

||Pooled sample|High-income|Low- & middle-<br>income|Expansion|Quality|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|**Panel A: Schoolparticipation andp**|**rogression**|||||
|_School participation and progression_|0.1423<br>(0.0888)|||0.131<br>(0.106)||
|N|22|||20||
|**Panel B: Cognitive skills**||||||
|_Pooled skills_|0.0718***|0.0604|0.0862**|0.0604|0.0865*|
||(0.0236)|(0.0375)|(0.0311)|(0.0336)|(0.0346)|
|N|62|23|39|31|31|
|_Literacy_|0.0626<br>(0.0344)<br>14|||0.0655<br>(0.0515)<br>9||
|_Math_|0.0571*<br>(0.0311)|0.0648<br>(0.0566)|0.0512<br>(0.0361)|0.0535<br>(0.0547)|0.0622<br>(0.0399)|
||29|7|22|9|20|
|_General_|0.0603<br>(0.0476)<br>10|||||
|**Panel C: Executive functions and so**|**cial-emotional s**|**kills**||||
|_Pooled skills_|0.0686***<br>(0.0190)||0.0750***<br>(0.0178)|||
|N|70||34|||
|_Social-emotional skillls_|0.0945**<br>(0.0342)||0.1056**<br>(0.0361)|||
|_otes_: Standard errors estimated using s<br><0.1 Number of observations (N) refers<br>ool skills use one large category for ext<br>eneral cognitive skills) or what is typic<br>ehaviors). See Appendix 1 for details<br>eneral skills; executive functions; socia<br>ble to obtain trustworthy p-values, whe<br>Tipton, 2015).<br>N|mall sample adju<br>to number of stud<br>racted outcomes -<br>ally referred to as<br>on definitions of<br>l-emotional skills;<br>n the degrees of fr<br><br>34|stments as in Tip<br>y-specific avera<br>either cognitive<br>non-cognitive s<br>school participa<br>and behavior. C<br>eedom fall belo|ton (2015) are in p<br>ge effects used in t<br>skills (which comb<br>kills (executive fun<br>tion and progress<br>ells are blank whe<br>w 4 or when all obs<br><br>21|arentheses. *** p<br>he specification.<br>ines language, l<br>ctions, social-em<br>ion; language, li<br>n the sample size<br>ervations came fr|<0.01, ** p<0.05,<br>Specifications tha<br>iteracy, math, an<br>otional skills, an<br>teracy, math, an<br>is too small to b<br>om a single stud|

_Notes_ : Standard errors estimated using small sample adjustments as in Tipton (2015) are in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Number of observations (N) refers to number of study-specific average effects used in the specification. Specifications that pool skills use one large category for extracted outcomes - either cognitive skills (which combines language, literacy, math, and general cognitive skills) or what is typically referred to as non-cognitive skills (executive functions, social-emotional skills, and behaviors). See Appendix 1 for details on definitions of school participation and progression; language, literacy, math, and general skills; executive functions; social-emotional skills; and behavior. Cells are blank when the sample size is too small to be able to obtain trustworthy p-values, when the degrees of freedom fall below 4 or when all observations came from a single study (Tipton, 2015).
# **Table 7: Difference in average effects of preprimary interventions by socioeconomic status**

|Pooled<br>sample<br>(1)|High-income<br>(2)|Low- &<br>middle-<br>income<br>(3)|
|---|---|---|
|**Panel A: Schoolparticipation andprogression**|||
|Low socio-economic status<br>0.0959<br>(0.0685)|0.0938<br>(0.0686)||
|N<br>32|24||
|**Panel B: Skills(Pooled)**|||
|Low socio-economic status<br>0.0535*<br>(0.0246)|||
|N<br>82|||

_Notes_ : Standard errors estimated using small sample adjustments as in Tipton (2015) are in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Number of observations (N) refers to number of study-specific average effects used in the specification. Appendix Table 2 presents the groups classified as "low socio-economic" sub-populations. All specifications include a constant term (the average effect size), which is omitted here. The coefficient on the low socio-economic status variable represents the average difference between study-specific average effect sizes between children coming from low and high socio-economic backgrounds.
|**Table 8: Returns to cognitive and soc**|**ial-emotional s**<br>Ttl t|**kills from**|**low- an**|**d middle**|**-income**|**studies**||Lbd|Upper-bound|
|---|---|---|---|---|---|---|---|---|---|
||oa coss<br>per child<br>(dollars)|Preprim|ary-augm|entation t|o lifetime|earnings (|dollars)|ower-oun<br>benefit-to-cost<br>ratio|<br>benefit-to-cost<br>ratio|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|**Panel A:**Discount rate = 3%||||||||||
|India (Ganimian et al, 2021)||||||||11.1|11.8|
|India (Dillon et al, 2017)|$53|$913|$2,845|$1,429|$302|$2,234|$817|5.7|54.0|
|Malawi (Ozler et al, 2018)|$72|$782|$2,010|$1,109|$259|$1,487|$586|3.6|28.1|
|Ghana (Wolf et al, 2019a)|$16|$563|$1,657|$855|$186|$1,280|$478|11.6|103.5|
|Peru (Gallego et al, 2021)|$37|$3,264|||$1,080|||29.2|88.2|
|Bangladesh(Spier et al,2020)|$145|$1,542|$4,175|$2,244|$510|$3,143|$1,212|3.5|28.8|
|**Panel B**: Discount rate = 5%||||||||||
|India (Dillon et al, 2017)|$53|$411|$1,281|$643|$136|$1,006|$368|2.6|24.3|
|Malawi (Ozler et al, 2018)|$72|$370|$950|$524|$122|$703|$277|1.7|13.3|
|Ghana (Wolf et al, 2019a)|$16|$271|$796|$411|$89|$615|$230|5.6|49.7|
|Peru (Gallego et al, 2021)|$37|$1,587|||$525|||14.2|42.9|
|Bangladesh(Spier et al,2020)|$145|$738|$1,999|$1,074|$244|$1,505|$580|1.7|13.8|
|High cognitive wage returns||Yes|Yes|Yes|No|No|No|||
|Low cognitive wage returns||No|No|No|Yes|Yes|Yes|||
|High social-emotional wage returns||No|Yes|No|No|Yes|No|||
|Low social-emotional wage returns||No|No|Yes|No|No|Yes|||

_Notes_ : For each country, we first calculate the present discounted value of a child's lifetime earnings, assuming that the child will work from age 22 to 65. To do this, we use the average nominal monthly wages for the latest year available, as well as the labor force participation rate and the average growth in real wages for the 2015-2019 period. See Appendix Table 4 for these parameters and our sources of data for all studies except for Ganimian et al, (2021), for which we directly use their estimates of benefit to cost ratios. In Panel A, when discounting future earnings to the present value, we assume a discount rate of 3 percent per year. In Panel B, we assume 5 percent per year. To calculate gains to lifetime earnings from preprimary interventions, we use the lowest significant treatment effect on cognitive skills reported in standard deviations in the paper and the lowest significant treatment effect on social-emotional skills. We use high and low estimates from the literature on the returns to a standard deviation increase in cognitive or social-emotional skills. Kline and Walters (2016) use an annual return of 13 percent per standard deviation of cognitive skills, while Galasso and Wagstaff (2018) use 4.3 percent. For the returns to social-emotional skills, Belfield et al (2015) use a range of 4 percent to 15 percent. We vary our assumptions on these returns in Columns 2 through 7. Because papers (with the exception of Ganimian et al., (2021)) reported costs per child in US dollars, we discounted lifetime earnings to the year costs were incurred and used nominal exchange rates collected by the International Monetary Fund to convert earnings to dollars in the year costs were incurred. In Columns 8 and 9, we report the ratio of benefits to costs for an individual child.
# **Appendix—For Online Publication Only**

## **Appendix 1. Mapping of extracted outcomes**

This section describes the skill categories we used to aggregate outcomes extracted from each study.

_Literacy_ outcomes include early reading skills (e.g., letter/word recognition, decoding, print and phonological awareness), and writing, as well as reading comprehension and performance in achievement tests in studies that focus on longer-term impact. We classify constructs like expressive and receptive vocabulary, and oral comprehension as _language_ .

_Math_ outcomes include early mathematics knowledge and skills such as numerical identification and shape recognition, as well as math test scores beyond preprimary. Under _general cognition_ we mapped more general knowledge and learning skills ranging from general intellectual ability, such as problem solving and communication skills, to summary indexes of achievement scores on literacy, language, and math skills, as captured by instruments such as the Early Development Instrument (Janus and Offord, 2007), International Development and Early Learning Assessment (Pisani, Borisova, and Dowd 2015), and the Woodcock–Johnson (Woodcock, McGrew, & Mather, 2001).

We classified outcomes representing constructs such as attention, inhibition, working memory, and cognitive flexibility as _executive functions. Social-emotional_ outcomes include social cognition, social competence, emotional regulation, prosociability, and relationships with peers, among others. _Behavioral outcomes_ include reported or observed measures of aggression, internalizing and externalizing behavior problems, conduct, and disciplinary actions during preprimary and basic education, as well as indicators such as crime, arrests, convictions, and incarceration during adulthood. There is some overlap across these last three outcome categories, which include a very wide range of measures that are often grouped together in the early childhood development literature.

_Health outcomes_ include anthropometric measures such as weight, height, and their standardized equivalents (WAZ, or z-scores for weight-for-age and HAZ, or z-scores for height-for-age), as
well as indicators for general child health. For adults, health included both physical and mental health. _Motor development_ encompasses indicators of fine and gross motor development.

# **Appendix 2. Process for standardizing outcomes across studies**

## **Continuous variables**

To standardize continuous variables that were not already standardized, we needed to scale the estimated coefficient (or “raw” effect size, ESraw) by the standard deviation (sd) of the outcome variable for the control group at follow-up to get the standardized effect _d_ .

Very few studies, however, report this standard deviation. When the standard deviation was not available, we use an approximation for standard deviation using the sample sizes of treatment (NT) and control (NC) groups: where _SE_ corresponds to the standard error of ESraw.

When studies did not include even a standard error of the estimated coefficient, we used the p- value (or an approximation of it) to estimate the standardized effect size for outcomes using the following formula: where _qnorm_ gives us the z-score of the _p_<sup>th</sup> quantile of the normal distribution.

Some studies reported baseline or follow-up sample sizes only for the entire sample, not separately for the treatment and control groups. In these cases, we assume that the total sample size was equally distributed across experimental groups, unless information in the text or elsewhere indicated otherwise. For example, in the Baker et al., (2008) differences-in-differences study, we assigned 23 percent of the sample to the treatment group since 23 percent of the Canadian population lives in Quebec. When the follow-up sample size for an estimation is not specified, we assume the sample size reported at baseline and adjust for attrition, if reported.
We use the following formula to calculate the standard errors associated with _d_ , where _t_ corresponds to the t-statistic corresponding to the raw estimated coefficient.

Finally, if a study only reported stars to indicate statistical significance at the 1, 5 or 10 percent levels, we calculate the t-stat, assuming the following p-values: 0.005, 0.04, and 0.09, respectively.<sup>1</sup>

# **Binary variables**

To standardize binary variables, we first have to convert raw effect sizes into odds ratios (OR) using follow-up means of success (enrollment rate, for example) and failure (non-enrollment rate).

For randomized studies conducted at the individual level that report raw means at follow-up, we would need the outcome variable means at follow-up— MeanC, the outcome mean for the control group at follow-up and and MeanT = MeanC + ESraw, the mean of the treatment group at followup. Because some studies do not report raw means at follow-up and instead report estimates of impact adjusted by baseline characteristics or fixed effects, we can also add the impact estimate of treatment obtained from the regression model to the follow-up mean of the control group to get the covariate-adjusted success rate in the treatment group.

When treatment is assigned at an aggregated level (for example, schools or communities) rather than the individual level, estimated standard errors must also take into account intracluster correlation in the outcome variable. Otherwise, we might overstate the precision of the estimates. We follow Wilson (2011) and convert the logged OR (ln⁡(OR)) into a standardized effect size ( _d_ ). As the logistic distribution is similar to the normal distribution, we can convert each ln⁡(OR) into a _d_ using the following formula:

> 1 Some studies only reported “Not significant” or p-value = 0 or p-value = 1. For these cases, we assume p-value = 0.55, 0.005, and 0.99, respectively.
Then, the standard error of _d_ can be calculated using the standard error of the coefficient estimate for the treatment indicator from the regression model as follows: where _z_ is either a z- or t-test associated with the treatment effect from the regression model. As we did for continuous variables, we used an approximation to calculate the t-stat when studies only reported levels of significance.

In sum, to calculate standardized effects sizes and their standard errors within each study for binary variables, we needed the outcome mean in the control group at follow-up and the estimated coefficient. These two elements allowed us to calculate the covariate-adjusted followup mean outcome in the treatment group. Then, we calculate ln(OR) and convert it into _d_ using Equation (4). Finally, we calculate the standard error of d using the formula (5).

When the follow-up mean was not reported for the sample or for a subgroup (T or C), we assumed that the follow-up mean was equal to the baseline mean for the control group. We excluded a study from a regression if it only reported an impact estimate without any reference to a control mean at baseline or follow-up. We also excluded studies from a regression when the follow-up mean outcome in the control group plus the covariate-adjusted impact estimate from the regression model was larger than 1, as in these cases, it is not possible to calculate an odds ratio.
# **Appendix 3. Tables**

## **Appendix Table 1: Characteristics of the included studies for review**

||**Country**<br>(1)|**Program Name**<br>(2)|**Study**<br>(2)|**Intervention Components**<br>(3)|**Discipline**<br>(4)|**Publication Type**<br>(5)|**IE Design**<br>(6)|
|---|---|---|---|---|---|---|---|
||**_HIC - High In_**|**_come Countries_**||||||
|**1**|Canada|Quebec Universal Childcare<br>Program|(Baker et al. 2008)|Subsidized or free access|Economics|Journal article|DID|
|**2**|Chile|Chile - Un Buen Comienzo|(Bowne et al. 2016b)|Teacher PD/<br>Pedagogy change/<br>Provision of materials|Education|Journal article|RCT +<br>HLM|
|**3**|Chile|Chile - Un Buen Comienzo|(Yoshikawa et al. 2015)|Teacher PD/<br>Pedagogy change/<br>Provision of materials|Psychology|Journal article|RCT|
|**4**|Denmark|Denmark Preschool<br>Expansion (1933-1960)|(Rossin-Slater and Wust 2020)|Preschool construction|Economics|Journal article|DID|
|**5**|Norway|Norway Universal Childcare<br>Program I|(Drange et al 2016)|Subsidized or free access/<br>Curriculum|Economics|Journal article|DID|
|**6**|Norway|Norway Universal Childcare<br>Program II|(Havnes and Mogstad 2011)|Subsidized or free access|Economics|Journal article|DID|
|**7**|Norway|Norway Universal Childcare<br>Program II|(Havnes and Mogstad 2015)|Subsidized or free access|Economics|Journal article|DID + QR|
|**8**|Spain|Spain Universal Childcare<br>Program|(Felfe et al 2014)|Subsidized or free access/<br>Teacher PD/<br>Curriculum|Economics|Journal article|DID|
|**9**|U.S.|USA High/Scope Perry<br>Preschool Program|(Heckman et al 2010)|Subsidized or free access|Economics|Journal article|RCT|
|**10**|U.S.|USA Boston Pre-K - Building<br>Blocks Curriculum|(Clements et al. 2011)|Teacher PD/<br>Curriculum/<br>Provision of materials|Education|Journal article|RCT +<br>HLM|
|**11**|U.S.|USA Boston Pre-K - Building<br>Blocks Curriculum|(Clements et al. 2013)|Teacher PD/<br>Curriculum/<br>Provision of materials|Education|Journal article|RCT +<br>HLM|
|**12**|U.S.|USA Boston Pre-K -<br>Subsidized Preschool|(Weiland and Yoshikawa<br>2013)|Subsidized or free access/<br>Teacher PD/<br>Curriculum|Education|Journal article|RDD|
|**13**|U.S.|USA Boston Pre-K -<br>Subsidized Preschool|(Gray-Lobe et al 2021)|Subsidized or free access/<br>Teacher PD/|Economics|Journal article|RCT + IV|
Curriculum

|**14**|U.S.|USA Great Start Teacher PD<br>Initiative|(Neuman and Cunningham<br>2009)|Teacher PD|Education|Journal article|RCT +<br>ANOVA|
|---|---|---|---|---|---|---|---|
|**15**|U.S.|USA Head Start - CSRP|(Raver et al. 2008)|Teacher PD/<br>Health and nutrition|Education|Journal article|RCT +<br>HLM|
|**16**|U.S.|USA Head Start - CSRP|(Raver et al. 2009)|Teacher PD/<br>Health and nutrition|Psychology|Journal article|RCT +<br>HLM|
|**17**|U.S.|USA Head Start - CSRP|(Raver et al. 2011)|Teacher PD/<br>Health and nutrition|Education|Journal article|RCT +<br>HLM|
|**18**|U.S.|USA Head Start - CSRP|(Watts et al 2018)|Teacher PD/<br>Health and nutrition|Education|Journal article|RCT|
|**19**|U.S.|USA Head Start - Increased<br>Spending|(Johnson and Jackson 2019)|Subsidized or free access|Economics|Journal article|DID + IV|
|**20**|U.S.|USA Head Start - REDI<br>Program|(Bierman et al. 2008)|Teacher PD/<br>Curriculum/<br>Provision of materials/<br>Parental engagement|Education|Journal article|RCT +<br>HLM|
|**21**|U.S.|USA Head Start - Subsidized<br>Preschool|(Bitler et al 2014)|<br>Subsidized or free access|Economics|Working Paper|RCT + IV|
|**22**|U.S.|USA Head Start - Subsidized<br>Preschool|(Carneiro and Ginja 2014)|Subsidized or free access|Economics|Journal article|RDD|
|**23**|U.S.|USA Head Start - Subsidized<br>Preschool|(Frisvold and Lumeng 2011)|Subsidized or free access/<br>Preschool day extension|Economics|Journal article|DID|
|**24**|U.S.|USA Head Start - Subsidized<br>Preschool|(Kline and Walters 2016)|Subsidized or free access|Economics|Journal article|RCT|
|**25**|U.S.|USA Head Start - Subsidized<br>Preschool|(Bloom and Weiland 2015)|Subsidized or free access|Education|Working Paper|RCT +<br>HLM|
|**26**|U.S.|USA Head Start - Subsidized<br>Preschool|(Bailey et al 2020)|Subsidized or free access|Economics|Journal article|DID|
|**27**|U.S.|USA Head Start - Teacher<br>PD & PATHS Curriculum|(Hamre et al 2012b)|Teacher PD/<br>Curriculum|Education|Journal article|RCT +<br>HLM|
|**28**|U.S.|USA Head Start - Teacher<br>PD Program II|(Pianta et al. 2017)|Teacher PD|Education|Journal article|RCT +<br>HLM|
|**29**|U.S.|USA Head Start - Teacher<br>PD Program I|(Powell et al. 2010)|Teacher PD|Education|Journal article|RCT +<br>HLM|
|**30**|U.S.|USA Oklahoma/Tulsa<br>Universal Pre-K|(Gormley et al. 2011)|Subsidized or free access|Education|Journal article|PSM|
|**31**|U.S.|USA ParentCorp Project|(Brotman et al. 2016)|Teacher PD/<br>Curriculum/<br>Parental engagement|Education|Journal article|RCT + PSM|
|---|---|---|---|---|---|---|---|
|**32**|U.S.|USA Pre-K Programs (State<br>level)|(Wong et al. 2008)|<br>Subsidized or free access|Economics|Journal article|RDD|
|**33**|U.S.|USA Tennessee Pre-K|(Lipsey et al. 2018)|Subsidized or free access|Education|Journal article|RCT +<br>HLM|
|**34**|U.S.|USA Tools of the Mind<br>Curriculum|(Barnett et al. 2008)|Teacher PD/<br>Curriculum/<br>Provision of materials|Education|Journal article|RCT|
|**35**|U.S.|USA Tools of the Mind<br>Curriculum|(Blair and Raver 2014)|Teacher PD/<br>Curriculum|Psychology|Journal article|RCT|
|**36**|U.S.|USA Tools of the Mind<br>Curriculum|(Diamond et al 2007)|Teacher PD/<br>Curriculum|Economics|Journal article|RCT|
||**_LMIC - Low-_**|**_& Middle-Income Countries_**||||||
|**37**|Argentina|Argentina Preschool<br>Construction|(Berlinski et al. 2009)|Preschool construction/<br>Curriculum|Economics|Journal article|DID|
|**38**|Bangladesh|Bangladesh Early Years<br>Preschool Program|(Spier at al 2020)|Subsidized or free access|Education|Technical Report|RCT + IV|
|**39**|Cambodia|Cambodia Preschool<br>Construction II|(Berkes et al 2019)|Preschool construction/<br>Community outreach/<br>Parental engagement|Economics|Working Paper|RCT|
|**40**|Cambodia|Cambodia Preschool<br>Construction I|(Bouguen et al. 2018)|Preschool construction/<br>Teacher PD/<br>Provision of materials|Economics|Journal article|RCT|
|**41**|Colombia|Colombia Preschools - HIM<br>& FE Programs|(Andrew et al. 2019)|Teacher PD/<br>Provision of materials/<br>Provision of staff|Economics|Working Paper|RCT|
|**42**|Gambia|Gambia Preschool<br>Construction|(Blimpo et al 2019)|Preschool construction/<br>Curriculum/<br>Teacher PD|Education|Working Paper|RCT|
|**43**|Ghana|Quality Preschool for Ghana<br>(QP4G)|(Wolf 2019)|Teacher PD/<br>Parental engagement|Education|Journal article|RCT +<br>HLM|
|**44**|Ghana|Quality Preschool for Ghana<br>(QP4G)|(Wolf and Peel 2019)|Teacher PD/<br>Parental engagement|Education|Journal article|RCT +<br>HLM|
|**45**|Ghana|Quality Preschool for Ghana<br>(QP4G)|(Wolf et al. 2018)|Teacher PD/<br>Parental engagement|Education|Journal article|RCT +<br>HLM|
|**46**|Ghana|Quality Preschool for Ghana<br>(QP4G)|(Wolf et al. 2019)|Teacher PD/<br>Parental engagement|Education|Journal article|RCT +<br>HLM|
|**47**|India|India Pratham Game-Based<br>Math Curriculum|(Dillon et al. 2017)|Curriculum/<br>Provision of materials|Economics|Journal article|RCT|
|---|---|---|---|---|---|---|---|
|**48**|India|India’s Integrated Child<br>Development Services<br>(ICDS)|(Ganimian et al 2021)|Provision of staff|Economics|Journal article|RCT|
|**49**|Indonesia|Indonesia Preschool<br>Construction|(Brinkman et al. 2017)|Preschool construction/<br>Teacher PD/<br>Community outreach|Education|Journal article|DID + IV|
|**50**|Malawi|Malawi Community-based<br>Childcare Centers|(Ozler et al. 2018)|Teacher PD/<br>Teacher Payments/<br>Parental engagement|Economics|Journal article|RCT|
|**51**|Mozambique|Mozambique Preschool<br>Construction|(Martinez et al. 2017)|Preschool construction/<br>Teacher PD/<br>Parental engagement|Economics|Working Paper|RCT|
|**52**|Paraguay,<br>Peru|Inquiry and Problem-Based<br>Pedagogy (IPP)|(Bando et al 2019)|Teacher PD/<br>Pedagogy change/<br>Provision of materials|Economics|Working Paper|RCT|
|**53**|Peru|Inquiry and Problem-Based<br>Pedagogy (IPP)|(Gallego et al 2019)|Teacher PD/<br>Pedagogy change/<br>Provision of materials|Economics|Journal article|RCT|
|**54**|Uruguay|Uruguay Preschool<br>Construction|(Berlinski et al. 2008)|Preschool construction|Economics|Journal article|IV|
# **Appendix Table 2: Characteristics of the programs evaluated**

|**Country**|**Program name**|**Intervention components**|**Provision type**|**Target**<br>**group**<br>**(years)**|**Target**<br>**population**|**Type of**<br>**location**|**Implementer type**|**Number**<br>**of studies**<br>**in review**|
|---|---|---|---|---|---|---|---|---|
|(1)|(2)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**_HIC - High In_**|**_come Countries_**||||||||
|**Canada**|Quebec Universal Childcare<br>Program|Subsidized or free access|Daycare (w/education<br>component)|3-4|General|Urban & Rural|Government|1|
|**Chile**|Chile - Un Buen Comienzo|Teacher PD/<br>Pedagogy change/<br>Provision of materials|Formal classroom|4-5|Disadvantaged|Urban|Government|2|
|**Denmark**|Denmark Preschool<br>Expansion (1933-1960)|Preschool construction|Preschool - formal<br>classroom|3-7|Disadvantaged|Urban & Rural|Government +<br>Private|1|
|**Norway**|<br>Norway Universal Childcare<br>Program I|Subsidized or free access/<br>Curriculum|Formal classroom|5-6|General|Urban & Rural|Government|1|
|**Norway**|Norway Universal Childcare<br>Program II|Subsidized or free access|Daycare (w/education<br>component)|3-6|General|Urban & Rural|Government +<br>Private|2|
|**Spain**|Spain Universal Childcare<br>Program|Subsidized or free access/<br>Teacher PD/<br>Curriculum|Daycare (w/education<br>component)|3|General|Urban & Rural|Government +<br>Private|1|
|**U.S.**|USA High/Scope Perry<br>Preschool Program|Subsidized or free access|Preschool - formal<br>classroom|3-5|Disadvantaged|Urban & Rural|Government|1|
|**U.S.**|USA Boston Pre-K - Building<br>Blocks Curriculum|Teacher PD/<br>Curriculum/<br>Provision of materials|Formal classroom|3-5|Disadvantaged|Urban|Government|2|
|**U.S.**|USA Boston Pre-K -<br>Subsidized Preschool|Subsidized or free access/<br>Teacher PD/<br>Curriculum|Formal classroom|4-5|General|Urban|Government|2|
|**U.S.**|USA Great Start Teacher PD<br>Initiative|Teacher PD|Formal classroom|3-5|Disadvantaged|Urban|Government + NGO|1|
|**U.S.**|USA Head Start - CSRP|Teacher PD/<br>Health and nutrition|Formal classroom|3-5|Disadvantaged|Urban & Rural|Government +<br>Private|4|
|**U.S.**|USA Head Start - Increased<br>Spending|Subsidized or free access|Formal classroom|4|Disadvantaged|Urban & Rural|Government|1|
|**U.S.**|USA Head Start - REDI<br>Program|Teacher PD/<br>Curriculum/<br>Provision of materials/<br>Parental engagement|Formal classroom|4|Disadvantaged|Urban & Rural|No info|1|
|**U.S.**|USA Head Start - Subsidized<br>Preschool|<br>Subsidized or free access|Formal classroom|4|Disadvantaged|Urban & Rural|Government +<br>Private|6|
|**U.S.**|USA Head Start - Teacher PD<br>& PATHS Curriculum|Teacher PD/<br>Curriculum|Formal classroom|3-4|Disadvantaged|No info|Government|1|
|**U.S.**|USA Head Start - Teacher PD<br>Program I|Teacher PD|Formal classroom|4|Disadvantaged|Urban & Rural|No info|1|
|**U.S.**|<br>USA Head Start - Teacher PD<br>Program II|Teacher PD|Formal classroom|4|Disadvantaged|Urban|Government +<br>Private|1|
|**U.S.**|USA Oklahoma/Tulsa<br>Universal Pre-K|Subsidized or free access|Formal classroom|4|Disadvantaged|Urban|Government|1|
|**U.S.**|USA ParentCorp Project|Teacher PD/<br>Curriculum/<br>Parental engagement|Daycare (w/education<br>component)|4|Disadvantaged|Urban|Government|1|
|---|---|---|---|---|---|---|---|---|
|**U.S.**|USA Pre-K Programs (State<br>level)|<br>Subsidized or free access|Formal classroom|4|General|Urban & Rural|Government|1|
|**U.S.**|USA Tennessee Pre-K|Subsidized or free access|Formal classroom|4-5|Disadvantaged|Urban & Rural|Government|1|
|**U.S.**|USA Tools of the Mind<br>Curriculum|Teacher PD/<br>Curriculum|Formal classroom|4-5|Disadvantaged|Urban|Government|3|
|**_LMIC - Low-_**|**_& Middle-Income Countries_**||||||||
|**Argentina**|Argentina Preschool<br>Construction|Preschool construction/<br>Curriculum|Formal classroom|3-5|Disadvantaged|Urban|Government|1|
|**Bangladesh**|Bangladesh Early Years<br>Preschool Program|Subsidized or free access|Formal classroom|4|General|Rural|Government|1|
|**Cambodia**|<br>Cambodia Preschool<br>Construction I|Preschool construction/<br>Teacher PD/<br>Provision of materials|Community-based|3-6|Disadvantaged|Rural|Government|1|
|**Cambodia**|Cambodia Preschool<br>Construction II|Preschool construction/<br>Community outreach/<br>Parental engagement|Community-based|3-5|General|Urban & Rural|Government|1|
|**Colombia**|Colombia Preschools - HIM<br>& FE Programs|<br>Teacher PD/<br>Provision of materials/<br>Provision of staff|Community-based|2-5|Disadvantaged|Urban & Rural|Government + NGO|1|
|**Gambia**|Gambia Preschool<br>Construction|Preschool construction/<br>Curriculum/<br>Teacher PD|Community-based|3-6|Disadvantaged|Rural|Government|1|
|**Ghana**|Quality Preschool for Ghana<br>(QP4G)|Teacher PD/<br>Parental engagement|Formal classroom|4-6|Disadvantaged|Urban & Rural|Government|4|
|**India**|<br>India Pratham Game-Based<br>Math Curriculum|<br>Curriculum/<br>Provision of materials|Formal classroom|5|Disadvantaged|Urban|International Non-<br>Governmental (ING)|1|
|**India**|India’s Integrated Child<br>Development Services (ICDS)|Provision of staff|Formal classroom|3-6|Disadvantaged|Urban & Rural|Government|1|
|**Indonesia**|Indonesia Preschool<br>Construction|Preschool construction/<br>Teacher PD/<br>Community outreach|Community-based|3-6|Disadvantaged|Rural|Government + IO|1|
|**Malawi**|Malawi Community-based<br>Childcare Centers|<br>Teacher PD/<br>Teacher Payments/<br>Parental engagement|Community-based|3-5|Disadvantaged|Rural|Government + ING|1|
|**Mozambique**|Mozambique Preschool<br>Construction|<br>Preschool construction/<br>Teacher PD/<br>Parental engagement|Community-based|3-5|Disadvantaged|Rural|International Non-<br>Governmental (ING)|1|
|**Paraguay,**<br>**Peru**|Inquiry and Problem-Based<br>Pedagogy (IPP)|<br>Teacher PD/<br>Pedagogy change/<br>Provision of materials|Formal classroom|5|General|Urban & Rural|Government|2|
|**Uruguay**|Uruguay Preschool<br>Construction|Preschool construction|Formal classroom|3-5|General|Urban & Rural|Government|1|
# **Appendix Table 3: Mapping of subpopulations to high and low socioeconomic status.**

|**Low socioeconomic status**|**High socioeconomic status**|
|---|---|
|Assets below median|Assets above median|
|Black|Non-Black|
|Father not at home|Father at home|
|Free lunch|Not free lunch|
|Low income|High income|
|Mother less educated|Mother more educated|
|Mother without education|Mother with education|
|Mother without high school|Mother with high school|
|No parent with high school|One parent with high school|
|Parents with high school or less|Parents with more than high school|
|Poor|Non-poor|
|Single-parent|Two-parent|
|Stunted child|Unstunted child|
|Non-White|White|
||Mother with college|
|Hispanic||
|High-povertyschools||
# **Appendix Table 4: Parameter values and data sources for calculating benefit-to-cost ratio for preprimary interventions in lowand middle-income countries**

||Labor force<br>participation<br>rate|Average<br>nominal<br>monthly<br>wage<br>(country<br>currency)|Annual<br>real wage<br>growth|Lowest<br>treatment<br>effect on<br>cognitive<br>skills (SD)|Lowest<br>treatment<br>effect on<br>social-<br>emotional<br>skills (SD)|Data sources|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(5)|(6)|(7)|
|India (Dillon et al, 2017)|46.3|13143|0|0.09|0.165|Periodic Labour Force Survey, 2019;<br>India Ministry of Statistics and<br>Programme Implementation;<br>BloombergNewswire|
|Malawi (Ozler et al, 2018)|37.2|125000|3.4|0.185|0.252|Integrated Household Survey, 2017 ;<br>National Statistical Office of Malawi|
|Ghana (Wolf et al, 2019a)|57|618|2.16|0.107|0.18|Living Standards Survey, 2017;<br>Ghana Statistical Service; Bloomberg<br>Newswire|
|Peru (Gallego et al, 2019)|77.4|1570|1.7|0.19|---|Encuesta Nacional de Hogares, 2019;<br>ILO SIALC; BloombergNewswire|
|Indonesia (Brinkman et al, 2017)|70.1|2913897|-0.2|---|0.158|National Labour Force Survey, 2020;<br>Statistics Indonesia of the Republic<br>of Indonesia; BloombergNewswire|
|Bangladesh (Spier et al, 2020)<br>Notes: Missing recent data for averag|59.4<br>e real wage growth|12016<br>for Ghana, we|3<br>averaged valu|0.25<br>es across other|0.37<br>countries. Coun|Labour Force Survey, 2017;<br>Bangladesh Bureau of Statistics<br>try currencies were the following:|

Notes: Missing recent data for average real wage growth for Ghana, we averaged values across other countries. Country currencies were the following: India (INR), Malawi (MWK), Mozambique (MZN), Ghana (GHS), Peru (PEN), Indonesia (IDR), and Bangladesh (BDT)
|**Appendix Table 5: Average effects o**|**fpreprimary i**|**ntervention**|**s onparticipa**|**tion and ski**<br>Expa|**lls****_during_ the**<br>nsion|**preprimary**<br>Qu<br>impro|**period**<br>ality<br>vement|
|---|---|---|---|---|---|---|---|
||||Low- &||Low- &||Low- &|
||Pooled<br>sample|High-<br>income|<br>middle-<br>income|High-<br>income|<br>middle-<br>income|High-<br>income|<br>middle-<br>income|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|**Panel A: Schoolparticipation andp**|**rogression**|||||||
|_School participation and progression_|1.4048**<br>(0.4776)|||||||
|N|29|||||||
|**Panel B: Cognitive skills**||||||||
|_Pooled skills_|0.1474***|0.1787***|0.1223***|0.1117***|0.0762|0.2492**|0.1576***|
||(0.0259)|(0.0431)|(0.0330)|(0.0270)|(0.0504)|(0.0794)|(0.0416)|
|N|131|76|55|25|33|51|22|
|_Language_|0.1084***|0.1150**|0.0992|||0.1427*||
||(0.0295)|(0.0367)|(0.0526)|||(0.0692)||
|N|53|35|18|||23||
|_Literacy_|0.2162***|0.2385**||||0.2402**||
||(0.0634)|(0.0783)||||(0.0937)||
||31|28||||22||
|_Math_|0.2238***|0.3605**|0.1643***|||||
||(0.0469)|(0.1108)|(0.0406)|||||
||28|10|18|||||
|_General_|0.0844*||0.0803|||||
||(0.0367)||(0.0464)|||||
||19||16|||||
|**Panel C: Executive functions**<br>|**and social-emotional**<br>|**skills**<br>|||
|---|---|---|---|---|
|_Pooled skills_|0.1209***|0.1354**|0.1138***|0.1830***|
||(0.0279)|(0.0494)|(0.0330)|(0.0531)|
|N|114|75|39|66|
|_Executive functions_|0.0951*|0.1693*|||
||(0.0387)|(0.0778)|||
|N|42|31|||
|_Social-emotional skills_|0.1150***|0.0935**|0.1301**||
||(0.0301)|(0.0318)|(0.0463)||
|N|50|22|28||
|_Behavior_|0.0750|0.0750|||
||(0.0500)|(0.0500)|||
|N|22|22|||

_Notes:_ Standard errors estimated using small sample adjustments as in Tipton (2015) are in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Number of observations (N) refers to number of study-specific average effects used in the specification. Results under the columns of “Expansion” or “Quality” are estimated average effects from studies evaluating interventions oriented to increase preschool expansion or to improve preschool quality, respectively. Specifications that pool skills use one large category for extracted outcomes - either cognitive skills (which combines language, literacy, math, and general cognitive skills) or what is typically referred to as non-cognitive skills (executive functions, social-emotional skills, and behaviors). See Appendix 1 for details on definitions of school participation and progression; language, literacy, math, and general skills; executive functions; social-emotional skills; and behavior. Cells are blank when the sample size is too small to be able to obtain trustworthy estimations, when the degrees of freedom fall below 4 or when all observations came from a single study.
|**Appendix Table 6: Average effects o**|**fpreprimary**|**interventions**|**onparticipa**|**tion and ski**<br>Expa|**lls****_after_ thep**<br>nsion|**reprimary p**<br>Qu<br>impro|**eriod**<br>ality<br>vement|
|---|---|---|---|---|---|---|---|
||||Low- &||Low- &||Low- &|
||Pooled<br>sample|High-<br>income|<br>middle-<br>income|High-<br>income|<br>middle-<br>income|High-<br>income|<br>middle-<br>income|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|**Panel A: Schoolparticipation andp**|**rogression**|||||||
|_School participation and progression_|0.1423<br>(0.0888)|||||||
|N|22|||||||
|**Panel B: Cognitive skills**||||||||
|_Pooled skills_|0.0718***|0.0604|0.0862**|0.0205|||0.0488*|
||(0.0236)|(0.0375)|(0.0311)|(0.0304)|||(0.0231)|
|N|62|23|39|18|||26|
|_Language_||||||||
|N||||||||
|_Literacy_|0.0626<br>(0.0344)<br>14|||||||
|_Math_|0.0571*|0.0648|0.0512||||0.0231|
||(0.0311)|(0.0566)|(0.0361)||||(0.0317)|
||29|7|22||||17|
|_General_|0.0603<br>(0.0476)<br>10|||||||
|**Panel C: Executive functions**<br>|**and social-emotional skills**<br> <br>|
|---|---|
|_Pooled skills_|0.0686***<br>0.0750***<br>(0.0190)<br>(0.0178)|
|N|70<br>34|
|_Executive functions_||
|N||
|_Social-emotional skills_|0.0945**<br>0.1056**|
||(0.0342)<br>(0.0361)|
|N|34<br>21|
|_Behavior_||
|N||

_Notes:_ Standard errors estimated using small sample adjustments as in Tipton (2015) are in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Number of observations (N) refers to number of study-specific average effects used in the specification. Results under the columns of “Expansion” or “Quality” are estimated average effects from studies evaluating interventions oriented to increase preschool expansion or to improve preschool quality, respectively. Specifications that pool skills use one large category for extracted outcomes - either cognitive skills (which combines language, literacy, math, and general cognitive skills) or what is typically referred to as non-cognitive skills (executive functions, social-emotional skills, and behaviors). See Appendix 1 for details on definitions of school participation and progression; language, literacy, math, and general skills; executive functions; social-emotional skills; and behavior. Cells are blank when the sample size is too small to be able to obtain trustworthy estimations, when the degrees of freedom fall below 4 or when all observations came from a single study.
**Appendix Table 7: Average effects of preprimary interventions on participation and skills during preprimary and post-preprimary**

||School<br>p<br>|participati<br>rogression<br>|on and<br> <br>|C<br>|**Preprimar**<br>ognitive ski<br>|**y**<br>lls<br>|Executiv<br>e<br>|e functions a<br>motional ski<br>|nd social-<br>lls<br>|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|_Preprimary program_|0.3030**|----|----|0.1474***|0.1222***|0.1915***|0.1209***|0.1150***|0.1576***|
||(0.0932)|||(0.0259)|(0.0332)|(0.0397)|(0.0279)|(0.0331)|(0.0305)|
|_High-income_||----|----||0.0541<br>(0.0537)|||0.0138<br>(0.0586)||
|_Expansion_||----|----|||-0.0957**<br>(0.0482)|||-0.1066*<br>(0.0514)|
|N|22|----|----|131|131|131|114|114|114|
||School<br>p|participati<br>rogression|on and<br>|**P**<br>C|**ost-preprim**<br>ognitive ski|**ary**<br>lls|Executiv<br>e|e functions a<br>motional ski|nd social-<br>lls|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|_Preprimary program_|0.0736<br>(0.0539)|----|----|0.0718***<br>(0.0236)|0.0863**<br>(0.0311)|0.0856**<br>(0.0342)|0.0686***<br>(0.0190)|----|----|
|_High-income_||----|----||-0.0260<br>(0.0487)|||----|----|
|_Expansion_||----|----|||-0.0239<br>(0.0481)||----|----|
|N|12|----|----|62|62|62|70|----|----|

Notes: Standard errors estimated using small sample adjustments as in Tipton (2015) are in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Number of observations (N) refers to number of study-specific average effects used in the specification. Specifications that pool skills use one large category for extracted outcomes - either cognitive skills (which combines language, literacy, math, and general cognitive skills) or what is typically referred to as non-cognitive skills (executive functions, social-emotional skills, and behaviors). "High income" indicates whether the study reports results from high-income countries, while "Expansion" indicates whether the preprimary intervention increased preschool coverage as opposed to improving preschool quality. Cells are blank when the sample size is too small to be able to obtain trustworthy estimations, when the degrees of freedom fall below 4 or when all observations came from a single study.
# **Appendix Table 8: Average effects of preprimary on teacher practices and health**

||Pooled sample|High-income|Low- & middle-<br>income|
|---|---|---|---|
||(1)|(2)|(3)|
|Teacher practice in the classroom|0.4729***<br>(0.0994)|||
|N|47|||
|Health during preprimary|0.0324||0.0280|
||(0.0290)||(0.0312)|
|N|27||23|

_Notes_ : Standard errors estimated using small sample adjustments as in Tipton (2015) are in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Number of observations (N) refers to number of study-specific average effects used in the specification. Cells are blank when the sample size is too small to be able to obtain trustworthy estimations, when the degrees of freedom fall below 4 or when all observations came from a single study.
# **Appendix 4. Figures**

|Appendix Figure 1: Average effects of preprimary interventions on participation and skills_during and after_|
|---|
|the preprimary period .............................................................................................................................................. 20|
|Appendix Figure 2: Effect sizes on school participation and progression (preprimary) ................................ 21|
|Appendix Figure 3: Effect sizes on language skills in HICs (preprimary) ...................................................... 22|
|Appendix Figure 4: Effect sizes on language skills in LMICs (preprimary) .................................................... 23|
|Appendix Figure 5: Effect sizes on cognitive literacy skills (preprimary) ........................................................ 24|
|Appendix Figure 6: Effect sizes on cognitive math skills (preprimary) ............................................................ 25|
|Appendix Figure 7: Effect sizes on cognitive general skills (preprimary) ........................................................ 26|
|Appendix Figure 8: Effect sizes on executive function skills (preprimary) ...................................................... 27|
|Appendix Figure 9: Effect sizes on social-emotional skills in HICs (preprimary) ........................................... 28|
|Appendix Figure 10: Effect sizes on social-emotional skills in LMICs (preprimary) ...................................... 29|
|Appendix Figure 11: Effect sizes on behavioral skills (preprimary) ................................................................. 30|
|Appendix Figure 12: Effect sizes on school participation and progression (post-preprimary) ..................... 31|
|Appendix Figure 13: Effect sizes on school participation and progression (adulthood) ................................ 32|
|Appendix Figure 14: Effect sizes on language skills (post-preprimary) ........................................................... 33|
|Appendix Figure 15: Effect sizes on cognitive literacy skills (post-preprimary) ............................................. 34|
|Appendix Figure 16: Effect sizes on cognitive math skills (post-preprimary) ................................................. 35|
|Appendix Figure 17: Effect sizes on cognitive general skills (post-preprimary) ............................................. 36|
|Appendix Figure 18: Effect sizes on executive function skills (post-preprimary) ........................................... 37|
|Appendix Figure 19: Effect sizes on social-emotional skills (post-preprimary) .............................................. 38|
|Appendix Figure 20: Effect sizes on behavioral skills (post-preprimary and adulthood) .............................. 39|
|Appendix Figure 21: Effect sizes on teaching practices in HIC (preprimary) .................................................. 40|
|Appendix Figure 22: Effect sizes on teaching practices in LMICs (preprimary & post-preprimary) ........... 41|
|Appendix Figure 23: Effect sizes on health and motor outcomes (preprimary) .............................................. 42<br>Appendix Figure 24: Effect sizes on health and motor outcomes (post-preprimary and adulthood) .......... 43|

_<u>Note: These Appendix Figures present study-specific average treatment effects of preprimary programs on outcomes</u> expressed in terms of standard deviations. Results are drawn from 54 experimental and quasi experimental studies that report impacts on a wide variety of outcome categories. Effect sizes to the right of zero represent an improvement of the outcome, while effect sizes to the left of zero represent a worsening of the outcome. (*) indicates that the connotation of the outcome is negative, and the representation of the effect size has been adjusted to show either an improvement or worsening according to each specific case._
<!-- Start of picture text --> Preprimary Post-preprimary<br>School participation* ! Fo —-—<br>| |<br>Cognitive skills (pooled) } -e4 | -eH<br>I |<br>SEL and EF skills (pooled) : Heo : HeH<br>| |<br>wo Language | eH |<br>=||<br>3<br>=) Literacy | -—@—_H Te<br>Oxe) Math | +—e— Les<br>= | |<br>rs) General te -+—-@—H<br>I |<br>Executive function Ie |<br>I |<br>Socio-emotional skills | te eo<br>Behavior | |<br>!<br>0 2 4 6 0 2 4 6<br>Effect size (standard deviations)<br>* School participation is expressed in percentage points.<br><!-- End of picture text -->
<!-- Start of picture text --> School participation & progression - (Preprimary)<br>Study countrycodeType of intervention ES (98%.Ch = Outcome_variable:<br>High Income - Pre-primary<br>(Bitler et al 2014) USA (HS) - Subsidized Preschool —— 1.93 (1.53, 2.33)7, Head Start attendance (3 y.o.}<br>(Bitler et al 2014) USA (HS) - Subsidized Preschool ——— 1.99 (1.59, 2.38)T, Head Start attendance (4 yo.)<br>(Bitler et al 2014) USA (HS) - Subsidized Preschool > 0.30 (0.09, 0.59)7, Head Start attendance (5 y.o.}<br>(Bloom and Weiland 201554. (HS) - Subsidized Preschool Se a 0.70 (0.35, 1.05)7, Head Start Attendance (4 yo.)<br>(Cameiro and Ginja 201864. (H5) - Subsidized Preschool —— 1.70: (1.30, 2.10)T, Head Start enrollment (4 y.0.) Male<br>(Kline and Walters 201654. (H3) - Subsidized Preschool —@ 4.23: (4.01, 4.45)T, Head Start attendance (pooled) (4-5 yo.)<br>(lipsey etal. 2018) USA Subsidized Pre-K ~0.05 (-0.13, 0.02], School attendance (5 yo.)<br>Low & Middle Income - Pre-primary<br>(Spier at al 2020) BSD Subsidized Preschool (Additional year) —_— 3.82 (3.441, 4.24)T, Enrollment in Early Years Preschool Program (5 yo.)<br>(Brinkman etal. 2017) IDN Preschool Construction & TT & Gommunity Outreach + 0.43 (0.35, 0.51)T, Enrolled in playgroup (4-5 yo.)<br>(Brinkman etal. 2017) IDN Preschool Construction & TT & Gommunity Outreach + 0.88 (0.57, 0.78), Enrollment duration in playgroup (months) (4-5 yo.)<br>(Berkes et al 2019) KHM Preschool Construction +> 0.23 (0.08, 0.40)71, Enrollment in any preschool (4.5 yo.)<br>(Berkes et al 2019) KHM Preschool Construction & Community Outreach 0.21 (0.02. 0.40)T2, Enroliment in any preschool (4.5 yo.)<br>(Berkes et al 2019) KHM Preschool Construction & Community Outreach & Parental Engagement ~ 0.29 (0.11, 0.47) 73, Enrollment in any preschool (4.5 yo.)<br>(Berkes et al 2019) KHM Preschool Construction 0.14 (-0.01, 0.28371, Enroliment in any preschool (5.6 yo.)<br>(Berkes et al 2019) KHM Preschool Construction & Community Outreach =~ 0.27 (0.07, 0.48) 72, Ever enrolled in any preschool (5.5 yo.)<br>(Berkes et al 2019) KHM Preschool Construction & Community Outreach & Parental Engagement - 0.41 (0.11, 0.51) 73, Ever enrolled in any preschool (5.5 yo.)<br>(Berkes et al 2019) KHM Preschool Construction 0.05 (-0.00, 0.08)11, Months enrolled in preschool (6.6 yo.)<br>(Berkes et al 2019) KHM Preschool Construction & Community Qutreach 0.05 (-0.00, 0.11)72, Months enrolled in preschool (5.6 yo.)<br>(Berkes et al 2019) KHM Preschool Construction & Community Qutreach & Parental Engagement 0.07 (0.04, 0.13)73, Months enrolled in preschool (5.5 y-o.)<br>(Berkes et al 2019) KHM Preschool Construction ~~ 2.23 (1.97, 2.48)71, Ever enrolled in community preschool (5.5 y.o.}<br>(Berkes et al 2019) KHM Preschool Construction & Community Outreach ~~ 2.49 (2.25, 2.73) 72, Ever enrolled in community preschool (5.5 yo.)<br>(Berkes et al 2019) KHM Preschool Construction & Community Qutreach & Parental Engagement —— 2.34 (2.02, 2.65) 72, Ever enrolled in community preschool (5.5 yo.)<br>(Berkes et al 2019) KHM Preschool Construction > 0.38 (0.39, 0.43) 71, Months enrolled in community preschool (5.5 yo.)<br>(Berkes et al 2019) KHM Preschool Construction & Community Outreach > 0.51 (0.45, 0.56) 72, Months enrolled in community preschool (5.5 y.o.)<br>(Berkes et al 2019) KHM Preschool Construction & Community Outreach & Parental Engagement +> 0.40 (0.34, 0.45) 73, Months enrolled in community preschool (5.5 y.o.)<br>(Bouguen et al. 2018) KHM Preschool construction & TT & Provision of material — 0.85 (0.50, 1-21)T, Ever attended formal preschool (4-9 yo.)<br>(Ozler et al. 20134) NW TT & Coaching > 0.26 (0.09. 0.43)71, Attending preschool (CBC) (5.5 y.o.)<br>(Ozler et al. 20134) NW TT & Coaching & Teacher Stipend + 0.28 (0.11, 0.46) T2, Attending preschool (CBC) (5.5 y.o.)<br>(Ozler et al. 2013) NW TT & Coaching & Parental Engagement +> 0.22 (0.07, 0.38)73, Attending preschool (CBC) (5.5 y.o.)<br>NOTE: Weights are from random effects analysis<br>4.45 Qo 4.45<br><!-- End of picture text -->
<!-- Start of picture text --> Language - (Preprimary - HICs)<br>Study ountrycode Type of intervention ES (95% Cl) ‘Outcome_variable<br>High Income - Pre-primary<br>(Baker et al. 2008) CAN Subsidized Childcare (Expansion) 0.02 (-0.05, 0.08)T, PPVT score, scaled (4 y.o.)<br>(Barnett et al. 2008) USA TT & Coaching & Provision of material 0.22 (-0.03, 0.47)T, PPVT-IIl English vocabulary development. (3-4 yo.)<br>(Barnett et al. 2008) USA. TT & Coaching & Provision of material 0.11 (40.05, 0.27)T, Expressive One-Word Picture Vocabulary Test (3-4 yo.)<br>(Barnett et al. 2008) USA TT & Coaching & Provision of material 0.34 (-0.02, O0.70)T, Receptive and expressive language skills (3-4 y.o.)<br>(Bierman et al. 2008) USA (HS) - TT & Coaching & Curriculum —— 0.15 (0.02, 0.28) T, Language skills: vocabulary (4 y.o.}<br>(Bierman et al. 2005) USA (HS) - TT & Coaching & Curriculum -0.07 (-0.31, 0.1777, Language skills: grammatical understanding (4 y.0_}<br>(Bierman et al. 2005) USA (HS) - TT & Coaching & Curriculum 0.04 (-0.27, 0.1907, Language skills: sentence imitation (4 yo.)<br>(Bierman et al. 2008) USA. (HS) - TT & Coaching & Curiculum —_— _—— 0.25 (0.04, 0.46) T, Language‘communication (Parent report) (4 yo.)<br>(Bitler et al 2074) USA (HS) - Subsidized Preschool —~_ 0.18 (0.10, 0.26) T, PPVT Score (4 yo.)<br>(Bitler et al 2074) USA (HS) - Subsidized Preschool 0.07 (-0.01, 0.15)T, PPVT Score (5 yo.)<br>(Blair and Raver 2014) USA TT & Coaching & EF Curriculum 0.08 (-0.07, 0.43)T, Vocabulary (546 yo.)<br>(Bloom and Weiland 2015) USA (HS) - Subsidized Preschool —> 0.15 (0.07, 0.23) T, Receptive vocabulary (PPT) (4 y.o.}<br>(Bloom and Weiland 2015) USA (HS) - Subsidized Preschool 0.01 (-0.03, 0.05)T, Oral comprehension (W-OC) (4 y.0.)<br>(Kline and Walters 2016) USA (HS) - Subsidized Preschool > 0.25 (0.19, 0.31) T, PPVT and WII Tests score (4-5 y.o.)<br>(Kline and Walters 20196) USA (HS) - Subsidized Preschool 0.09 (-0.00, 0.19)T, PPVT and WII Tests score (5-6 yo.)<br>(Pianta et al. 2017) USA (HS) TT 0.08 (-0.05, 0.11) 11, PPVT Receptive Language (4.5-5 yo.)<br>(Pianta et al. 2017) USA. (HS) Coaching & Feedback “0.01 (-0.12. 0.1092, PPVT Receptive Language (4.5-5 yo.)<br>(Pianta et al. 2017) USA (HS) TT & Coaching & Feedback 0.02 (-0.11, 0.15) T3, PPVT Receptive Language (4.5-5 yo.)<br>(Pianta et al. 2017) USA (HS) TT ~0.05 (-0.13, 0.03971, WJ-IIl Expressive Language (4.5-5 y.o.)<br>(Pianta et al. 2017) USA (HS) Coaching & Feedback 0.07 (-0.15, 0.01972, WU-IIl Expressive Language (4.5-5 y.o.)<br>(Pianta et al. 2017) USA (HS) TT & Coaching & Feedback 0.10 (-0.23, 0.03973, WJ-IIl Expressive Language (4.5-5 y.o.)<br>(Pianta et al. 2017) USA (HS) TT 0.17 (-0.10, 0.44771, Multiple word (4.5-5 y.o.)<br>(Pianta et al. 2017) USA (HS) Coaching & Feedback —>— 0.26 (0.05, 0.47) T2, Multiple word (4.5-5 yo.)<br>(Pianta et al. 2017) USA (HS) TT & Coaching & Feedback 0.00 (-0.22, 0.04)72, Multiple word (4.5-5 y.o.}<br>(Pianta et al. 2017) USA (HS) TT 0.20 (-0.04, 0.44)11, Single word* (4.5-5 y.o.)<br>(Pianta et al. 2017) USA (HS) Coaching & Feedback 0.06 (-0.15, 0.27)T2, Single word* (4.5-5 y.o.)<br>(Pianta et al. 2017) USA (HS) TT & Coaching & Feedback 0.00 (-0.13, 0.43)T3, Single word* (4.5-5 yo.)<br>(Powell et al. 2010) USA (HS) -1T 0.03 (-0.15, 0.087, Receptive vocabulary (PP'VT) (4 y.0.)<br>(Raver et al. 2011) USA (HS) - TT & Coaching & Mental Health Counselling —_——_—— 0.34 (0.07, 0.61) T, Receptive vocabulary (PPT) (3-5 yo.)<br>(Weiland and Yoshikawa 201064 Subsidized Pre-K & TT & Curriculum —>— 0.44 (0.27, 0.61) T, Receptive vocabulary (PPT) (4-5 yo.)<br>(Wong et al. 2003) USA(MI) Subsidized Pre-K (Michigan) 0.13 (-0.56, 0.200 (MI), PPVT (5 y.0.)<br>(Wong et al. 2008) USA(MJ) Subsidized Pre-K (New Jersey) —e 0.8 (0.02, 0.70) T (NU), PPVT (5 y.0.)<br>(Wong et al. 2008) USA(OK) Subsidized Pre-K (Oklahoma) -_—) 0.28 (0.01, 0.55) T (OK), PPVT (5 y.0.)<br>(Wong et al. 2008) USA(SC) Subsidized Pre-K (South Carolina) 0.04 (-0.09, 0.17)T (SC), PPVT (5 yo.)<br>(Wong et al. 2008) USA(WW) 9 Subsidized Pre-K (West Virginia) 0.16 (-0.38, 0.68)T (WV), PPVT (5 yo.)<br>NOTE: Weights are from random effects analysis<br>704 o 704<br><!-- End of picture text -->
<!-- Start of picture text --> Language - (Preprimary - LMICs)<br>Study countrycode Type of intervention ES (85% Chy Outcome_variable<br>Low & Middle Income - Pre-primary<br>(Andrew et al 2079) COL Provision of Material’ Statt 4.10 (40.23, 0.08) 71, Language development (3-6 yoo.)<br>(Andrew at al. 2079) COL TT & Prowision of Material’ Staff 0.05 (0.08, 0.17) 12, Language development (2-6 y-0.}<br>(Andrew etal 2019) COL TT & Provision of Material/Siatt — 0.14 (0.04, 0.25) 1. 72, Language development (3-6 y.o.)<br>(Blimpo et al 2079) ‘GMB Preschool Construction & TT & Play Curriculum 0.17 (40.41, 0.07) Ti. Languageand Auditory Skills (3-5 yo.)<br>(Blimpe et al 2078) ‘GMB TT & Play curriculum 0.171 (40.18, 0.40) 72, Languageand Auditory Skills (3-6 yo.)<br>(Brinkman etal. 2017) IDN Preschool Gonstruction & TT & Community Outreach 0.13 (40.01, 0.27) 7, Languageand cognitive development (EDI) (4-5 yo.)<br>(Ganimian et al 2021) IND Provision of staff 04 0.20,002) 9 T, Language score (2-8 y.0.)<br>(Berkes etal 2019) KHM Preschool Construction 0.05 (-0.01,0.10) Ti. Language (4.5 y.o.)<br>(Berkes etal 2019) KHM Preschool Construction & Community Outreach 0.03 (-0.05,0.10) 2. Language (4.5 y.o.)<br>(Berkes etal 2019) KHM Preschool Construction & Community Outreach & Parental Engagement 0.04 (40.02, 0.10) Ta, Language (4.5 yo)<br>(Berkes etal 2019) KHM Preschool Construction 0.05 (40.00,0.17) 71, Language (5.5 y.o.)<br>(Berkes etal 2019) KHM Preschool Construction & Community Outreach 0.04 (-0.06, 0.08) 12, Language (5.5 y.0.)<br>(Berkes etal 2019) KHM Preschool Construction & Community Outreach & Parental Engagement 0.03 (0.04, 0.00) 13. Language (5.5 y.o.)<br>(Bouguen et.al. 2078) KHM Preschool construction & TT & Provision of material 0.04 (40.18, 0.08) T, Receptive vocabulary (PPVT) (44-6 yo.)<br>(Martinez et al. 2017) Moz Preschool Construction & TT & Parental Engagement 0.13 (0.01, 0.25) TT, Receptive vocabulary (PPWT) (3-5 yo.)<br>(Ozler et al. 2018) WW TT & Goaching 40.03 (40.15, 0.08) 71, Language skills (Malawi DATS) (5.5 yo.)<br>(Ozler et al. 2073) NWI TT & Coaching & Teacher Stipend 0.09 (-0.06,0.23) 12, Language skills (Malawi DATS) (5.5 y.0.)<br>(Ozler at al. 2018) MW TT & Coaching & Parental Engagement —>— 0.19 (0.05, 0.32) 73. Language skills (Malawi DATS) (5.5 y.o.)<br>NOTE: Weights are from random effects anahysis<br>-.418 tt] 818<br><!-- End of picture text -->
<!-- Start of picture text --> Cognitive Literacy - (Preprimary)<br>Study countrycode Type of intervention ES (85% Cl) ‘Outcome_variable<br>High Income - Pre-primary<br>(Yoshikawa et al. 2015) CHL TT & Coaching & Provision of material -0.07 (-0.30, 0.1677, Vocabulary (5.5 yo.)<br>(Yoshikawa et al. 2015) CHL TT & Coaching & Provision of material 0.09 (-0.21, 0.20)T, Letterword identification (5.5 y.0.)<br>(roshikawa et al. 2015) CHL TT & Coaching & Provision of material -0.03 (-0.13, 0.0777, Early writing (5.5 yo.)<br>(Barnett et al. 2008) USA. TT & Coaching & Provision of material 0.03 (-0.21, 0.27)T, Early literacy skills (2-4 y.0.}<br>(Bierman et al. 2003) USA. (HS)-T7 & Coaching & Curriculum — 0.28 (0.16, 0.62) T, Literacy skills: blending (4 y.o.)<br>(Bierman et al. 2003) USA. (HS)-TT & Coaching & Curriculum ~~ 0.25 (0.14, 0.56) T, Literacy skills: elision (4 yo.)<br>(Bierman et al. 2003) USA (HS) - TT & Coaching & Curriculum 0.96 (-0.03, 0.35)T, Literacy skills: print awareness (4 yo.)<br>(Bierman et al. 2003) USA (HS) - TT & Coaching & Curriculum 0.17 (-0.01, 0.35)T, Reading activities (Parent report) (4 yo.)<br>(Blair and Raver 2014) USA TT & Coaching & EF Curriculum 0.07 (-0.03, .17)T, Reading (5-6 yo.)<br>(Bloom and Weiland 2015) USA (HS) - Subsidized Preschool + 0.17 (0.08, 0.26) T, Early reading (WLW) (4 y.0.)<br>(Pianta et al. 2017) USA (HS) TT 0.00 (0.00, 0.00) T1, TOPEL Phonological Awareness (4.5-5 y.0.)<br>(Pianta et al. 2017) USA (HS) Coaching & Feedback 0.03 (-0.14, 0.17) T2, TOPEL Phonological Awareness (4.5-5 y.0.)<br>(Pianta et al. 2017) USA (HS) TT & Coaching & Feedback -0.07 (-0.21, 0.06773, TOPEL Phonological Awareness (4.5-5 yo.)<br>(Pianta et al. 2017) USA (HS) 1T 0.05 (-0.05, 0.15)71, TOPEL Pring Knowledge (4.5-5 yo.)<br>(Pianta et al. 2017) USA (HS) Coaching & Feedback 0.01 (-0.11, 0.08)T2, TOPEL Pring Knowledge (4.5-5 yo.)<br>(Pianta et al. 2017) USA (HS) TT & Coaching & Feedback 0.01 (-0.14, 0.12973, TOPEL Pring Knowledge (4.5-5 y.0.)<br>(Powell et al. 2010) USA (HS)-TT 0.70 (-0.06, 0.28)T, Letter-word recognition (4 yo.)<br>(Powell et al. 2010) USA (HS)-TT > 0.29 (0.18, 0.40) T, Leiter knowledge (4 y.0.)<br>(Powell et al. 2010) USA (H8)-1T —o— 0.22 (0.03, 0.41) T, Concepts about print (4 yo.)<br>(Powell et al. 2010) USA (HS)-1T 0.17 (0.02, 0.32) T, Writing (4 y.0.)<br>(Powell et al. 2010) USA (HS)-TT 0.18 (0.07, 0.35) T, Literacy skills: blending (4 y.0.)<br>(Raver et al. 2011) USA (HS) -TT& Coaching & Mental Health Counselling — 0.63 (0.37, 0.89) T, Leiter naming (3-5 yo.)<br>(Weiland and Yoshikawa 20 12)54 Subsidized Pre-K & TT & Curmiculum —~- 0.62 (0.43, 0.81) T, Early Reading (WJ Letter Word ID) (4-5 y.0.)<br>(Wong et al. 2008) USA (MI) Subsidized Pre-K (Michigan) —————— "1.9 0.05, 2.13) T (MI), Print Awareness (5 y.o.)<br>(Wong ef al. 2008) USA(NJ) Subsidized Pre-K (New Jersey} 0.22 (0.01, 0.63) T (NJ), Print Awareness (5 yuo.)<br>(Wong et al. 2002) USA(OK) Subsidized Pre-K (Oklahoma) 0.43 (-0.07, 0.93)T (OK), Print Awareness (5 y.0.)<br>(Wong et al. 2008) USA(SC) Subsidized Pre-K (South Carolina) —————_— 0.78 (0.04, 1.53) T (SC), Print Awareness (5 yo.)<br>(Wong et al. 2008) USA (WW) = ‘Subsidized Pre-K (West Virginia) SH 0.92 (0.04, 1.30) T (WV), Print Awareness (5 y.0.)<br>Low & Middle Income - Pre-primary<br>(Spier at al 2020) BSD Subsidized Preschool (Additional year) > 0.25 (0.11, 0.20) T, Emergent literacy (5 y.o.}<br>(Wolfat al. 20782) GHA TT& Coaching 0.11 (0.00, 0.22) T1, Early Literacy (4-6 y.0.)<br>(Wolf et al. 20188) GHA TT & Coaching & Parental Engagement -0.03 (-0.16, 0.10772, Early Literacy (4-6 yo.)<br>NOTE: Weights are from random effects analysis<br>-2.13 0 2.13<br><!-- End of picture text -->
# Cognitive Math - (Preprimary)

<!-- Start of picture text --> Study countrycode Type of intervention ES (85% Cl} ‘Outcome_variable<br>High Income - Pre-primary<br>(Blair and Raver 2014) USA TT & Coaching & EF Curriculum ~~ 0.13 (0.02, 0.24) T, Math (5-6 yo.)<br>(Bloom and Weiland 2015) USA (HS) - Subsidized Preschool +> 0.12 (0.06, 0.13) T, Early numeracy (WJ-AP) (4 yo.)<br>(Clements et al. 2011) USA TT & Coaching & Math Curiculum —P——- sd (0.36, 1,03) T, Rasch total test score (4-6 yo.)<br>(Raver et al. 2011) USA (HS) - TT & Coaching & Mental Health Counselling —_— 0.54 (0.30, 0.73) T, Early Math Skills (3-5 yo.)<br>(Weiland and Yoshikawa 201354 Subsidized Pre-K & TT & Curriculum —— 0.58 (0.40, 0.73) T, Numeracy (W-J Applied Problems) (4-5 y.0.)<br>(\Veiland and Yoshikawa 201354 Subsidized Pre-K & TT & Curriculum —>— 0.50 (0.29, 0.71) T, Numeracy and Geometry (Short Form) (4-5 yo.)<br>(Wong et al. 2008) USA(MI) Subsidized Pre-K (Michigan) —_———ho —_— 0.53. (0.02, 1.04) T (MI). Math (5 yo.)<br>(Wong et al. 2008) USA(MJ) Subsidized Pre-K (New Jersey) 0.23 (0.04, 0.44) T (NJ), Math (5 yo.)<br>(Wong ef al. 2008) USA(OK) Subsidized Pre-K (Oklahoma) 0.34 (-0.06, 0.74)T (OK), Math (5 yo.)<br>(Vong et al. 2008) USAC) Subsidized Pre-K (West Virginia) 0.06 (0.14, 0.28)T (WW), Math (5 yo.)<br>Low & Middle Income - Pre-primary<br>(Spier at al 2020) BGD Subsidized Preschool (Additional year) — 0.28 (0.11, 0.47) T, Emergent numeracy (5 y.0.)<br>(Wolfet al. 20198) ‘GHA TT & Coaching 0.11 (0.01, 0.20) 11, Early Math Skills (4-6 yo.)<br>(Wolfet al. 20792) ‘GHA. TT & Coaching & Parental Engagement 0.03 (40.13, 0.0872, Early Math Skills (4-6 y.0.)<br>(Dillon et al. 2017) IND Math Curriculum = 0.25 (0.17, 0.33) 71, Math composite (ASER math) (5 yo.)<br>(Dillon et al. 2077) IND Socio-emotional Curriculum 0.00 (0.09, 0.17) T2, Math composite (ASER math) (5 yo.)<br>(Ganimian et al 7021) IND Provision of staff ~~ 0.20 (0.17, 0.41) T, Math seare (3-6 yo.)<br>(Berkes et al 2019) KHM Preschool Construction 0.06 (0.00, 0.12) T1, Early numeracy (4.5 y.0.)<br>(Berkes et al 2019) KHM Preschool Construction & Community Quireach 0.04 (-0.05_ 0.08)T2, Early numeracy (4.5 y.0.)<br>(Berkes et al 2019) KHM Preschool Construction & Community Outreach & Parental Engagement 0.06 (-0.00_ 0.12)T3, Early numeracy (4.5 y.o.}<br>(Berkes et al 2019) KHM Preschool Construction 0.07 (0.04, 0.08)71, Early numeracy (5.5 y.o.}<br>(Berkes et al 2019) KHM Preschool Construction & Community Outreach 0.04 (40.07, 0.0572, Early numeracy (5.5 y.o.)<br>(Berkes et al 2019) KHM Preschool Construction & Community Outreach & Parental Engagement 0.04 (-0.05_ 0.08)T3, Early numeracy (5.5 y.o.}<br>(Bando et al 2019) PER IPP Pedagogy & TT & Provision of Material -_— 0.19 (0.07, 0.31) Ta (PER), Math test score (Peru 2012) (5 y.o.)<br>(Gallego et al 2018) PER IPP Pedagogy & TT & Provision of Material ~~ 0.21 (0.09, 0.33) T, EGMA - Overall test score (5 y.o.)<br>(Gallego et al 2018) PER IPP Pedagogy & TT & Provision of Material > 0.23 (0.11, 0.25) T, EGMA - Shapes items test score (5 y.o.)<br>(Gallego et al 2078) PER IPP Pedagogy & TT & Provision of Material = 0.18 (0.09, 0.29) T, EGMA - Numeracy items test score (5 y.0.)<br>(Bando et al 2079) PRY IPP Pedagogy & TT & Provision of Material al 0.20 (0.10, 0.20) T1 (PR), Math test score (Paraguay 2011) (5 yo.)<br>(Bando et al 2078) PRY IFP Pedagogy & TT & Provision of Material +> 0.17 (0.09, 0.25) T2 (PR), Math test score (Paraguay 2013) (5 yo.)<br>NOTE: Weights are from random effects analysis<br>-1.08 it] 1.08<br><!-- End of picture text -->
<!-- Start of picture text --> Cognitive General - (Preprimary)<br>Study countrycode Type of intervention ES (85% Cl} Outcome_varable<br>High Income - Pre-primary<br>(Bamettet al. 2008) USA TT& Coaching & Provision of material 0.05 (-0.78, 0.29) 7, Problem solving and visual-motor proficiency (3-4 y.o.)<br>(Lipsey et al. 2078) = USA Subsidized Pre-K > 0.24 (0.70,0.38) 7, WHI Achievernent Battery (4 yo.)<br>(Lipsey etal. 2072) USA Subsidized Pre-K 0.02 (0.07, 0.11) 7, WIL Achievernent Battery (5 y.0.)<br>Low & Middle Income - Pre-primary<br>(Spierat al 2020) BGD Subsidized Preschool (Additional year) —— $s. (008,044) T Approachesto learning (5 yo.)<br>(Wolfet al. 2019) GHA TT& Coaching —_— 0.16 (0.04,0.29) 71, School readiness composite (IDELA score) (449 yo.)<br>(Wolfet al. 2019) GHA TT& Coaching & Parental Engagement 0.03 (40.70, 0.15) 72, School readiness composite (IDELA score) (444 yo.)<br>(Brinkman et al. 2017) IDN Preschool Construction & TT& Community Outreach O08 (40.08, 0.23) 7, Communication and general knowledge (EDI) (4-6 yo.)<br>(Berkes et 3! 2019) KHM Preschool Construction — 0.05 (0.01,0.09) 71, Cognitive development Index (4.5 y.0.)<br>(Berkes et a! 2019) KHM Preschool Construction & Community Outreach 0.07 (40.04, 0.08) 72, Cognitive development Index (4.5 yoo.)<br>(Berkes et a! 2019) KHM Preschool Construction & Community Outreach & Parental Engagement 0.05 (0.00,0.09) 73, Cognitive development Index (4.5 yoo.)<br>(Berkes et a! 2018) KHM Preschool Construction 0.03 (-0.02, 0.08) 71, Cognitive development Index (5.5 yo.)<br>(Berkes et a! 2019) KHM Preschool Construction & Community Outreach 0.07 (0.07, 0.04) T2, Cognitive development Index (5.5 yoo.)<br>(Berkes et a! 2019) KHM Preschool Construction & Community Outreach & Parental Engagement O02 (40.04, 0.08) 73, Cognitive development Index (5.5 yoo.)<br>(Bouguen etal. 2078) KHM Preschool construction & TT& Provision of material 0.11 (-0.25, 0.06) T,.AS&:- Communication (4-9 yo.)<br>(Bouguen et al. 2018) KHM Preschool construction & TT& Provision of material 0.13 (0.27, 0.01) T,.A50- Problem Solving (4-9 y.0.)<br>(Bouguen et al. 2078) KHM Preschool construction & TT& Provision of material 0.08 (40.20, 0.02) T, Cognitive development index (4-6 yoo.)<br>(Martinez et al. 2017) Moz Preschool Construction & TT& Parental Engagement —— 0.77 (0.07,0.28) T,.A5O- Communication (3-5 yo.)<br>(Martinez et al. 2017) Moz Preschool Construction & TT& Parental Engagement — 0.77 (0.08,0.24) 7, ASO- Problem Solving (3-5 y.0.)<br>(Martinez et al. 2017) Moz Preschool Construction & TT& Parental Engagersent — — 0.17 (O.07,0.27) = 7, Cognitive index (3-5 y.0.}<br>NOTE: Weights are from random effects analysis<br><!-- End of picture text -->
<!-- Start of picture text --> Executive Function - (Preprimary)<br>Study countrycade Type of inberventian ES (95% Cl} Quicome_variabile<br>High Income - Pre-primary<br>(Baker el al. 2008) CAN Subsidized Childcare (Expansion) 0.05 (40.09, -0.01) T, Hyperactivity-inaitlention® (4 y.0.)<br>(Biermasn et al. 2006) USA (HS) -TT& Goaching & Curriculum ——— 0.25 (0008, 0.85) 7, Task oviemiation (Observer report) (4 y.0.)<br>(Bierman et al. 2006) USA (HS) -TT& Coaching & Curriculum 0.14 (4008, 0.36) 7, Atlention problems (Teacher report)" (4 y.0.)<br>(Bierman(Btair and etRaweral. 2006) 2014) USAUSA (HS)TT8 Gaaching-TT& Coaching & EF Curriculum & Curriculum 0.1 04 (0 .00,.00, 0.20)0-28) T ,. Adention Working Memory problems(5-8 (Parenty.0.) report)" (4 y.0.)<br>(Blair(Btair(Blair andandand Rawer 2014}RawerRaver 2014}2014} USAUsAUSA TTTTTT&&& CoachingCaschingCoaching4 & EF EF Curriculum Curriculum 0.12 (4002,10 . 2008 ( 40.460003 , 0. 0.26)19)86) T,T,T, AccuracyExecutiveRAN (S-6 yop Function(percent correct- Reaction responding)Time (5-6 yo.)(5-4 y.0.)<br>(Btair(Blair aneand RaverRaver 2014)2014} USAUSA TTTT&& CoachingCoaching && EFEF CurriculumCurriculum 0.900.00 (0009,(4000, 7.89)2000) T,T, RavenDot ProbeMatrices:[Attention(Fluid contred)inteligence/reasoning)(5-6 y.0.) (56 y.0.)<br>(Diamond etal 2007) USA TT& EF Curriculum —— ‘O47 (0.74, 0.80) T. Dots Incongrumnttask (%. conmect responses) (5 yo.)<br>(Diamond etal2007) USA TT& EF Currisulurn —_—>— O48 (0.15, 0.81) 7, Flanker task (% comect respanses) (5 yo}<br>(Diamond etal 2007) USA TT& EF Curriculum —— 0.85 (0.32, 0.98) TT, Reverse flanker task (% correct responses} (5 y.0.)<br>(Diarnesd etal 2007) USA TT& EF Curriculum —— 0.59 (0.06, 0.72) T, Dots Mixed task (9% of children passingthe test} (5 yoo.)<br>(Garmley et al. 2011) USA Subsidized Pre-K (Oklshorna) 0,02 (0.20, 0.18) T1, Dizobedient* (4 yo.)<br>(Garmley et al. 20171} USA ‘Subsidized Pre-K (Oklshorns) 0.09 (003, 0.21) 11, Attention problerns* (4 yuo.)<br>(Garmley et al. 2011) USA Subsidized Pre-K (Oklshorna) > 0.19 (0.07, 0.31) 74, Aiientivenessindex (4 y.0.)<br>(Garmley et al. 2011} USA ‘Subsadised Preschool HS (Oklahoma) 90.15 (0.40, 0.10) T2, Desobedient* (4 y.0.)<br>(Garmley et al. 2011} USA ‘Subsidized Preschool HS (Oklahoma) 0.05 (4h21, 0.31) T2, Attention problemns*(4 yo.)<br>(Garmley et al. 2011) USA Subsidized Preschool HS (Oklahoma) 0.08 (40.26, 0.38) T2,Alientivencssindex (4 y.0.)<br>(Pianta et al 2017) USA 4H) TT 0.10 (0.21, 0.01) TA, Inhibitory control (4.5-5 0.)<br>(Pianta ef al 2017) USA (HS) Coaching & Feedback +> 0.20 (0.06, 0.34) 12, Inhibitory control (4.6-5 0.)<br>(Pianta et al 2017) USA (HS) TT & Coaching & Feedback 0.24 (40.11, 0.59) TS, Inhibitory control (4.5-5 0.)<br>(Raver at al. 2071) USA (H8) -TT& Coaching & Mental Health Counselling —p— 0.37 (0.08, 0.71) 7; Executive Functioning (3-5 y.0.)<br>(Raver et al. 2071) USA (HS) -TT& Coaching & Mental Health Counselling 0.20 (410, 0.50) T. Effortiul Control (3-5 yo.)<br>(Rewer et al. 2011) USA (HS) -TT4 Coaching & Mental Health Counselling —>— 10.43 (0.05, 0.81) —T, Attention!Imputsiveness (3-5 7.0.)<br>(Weiland and Yoshikawa 207 3)USA Subsidized Pre-K & TT & Curriculum ~~ 0.21 (0.06, 0.37) T. Inhibitory Control (Pencil Tap) (4-5 yo.)<br>(Weiland and Yoshikawa 2073j)USA Subsidized Pre-K & TT & Curriculum 10.24 (0.02, 0.48) T, Working Memory (Backward Digit Saam) (4-5 yo.)<br>(Weiland and Yoshikawa 2013)USA ‘Subsidized Pre-K & TT & Curriculum ~> 10.24 (0.06, 0.42) T, Working Memory (Forward Digit Span) (4-5 y.0.)<br>(Wailand and Yoshikwwa 2073\SA ‘Subsidized Pre-K & TT & Curriculum —_ 0.28 (0.10, 0.48) 7, Adention Shifting (Dimentional Change Card Sort} (4-6 y..)<br>(Weiland and Yoshikawa 2073j)USA Subsidized Pre-K & TT & Curriculum 0.11 (0.08, 0.20) T, Attention Shating (TOO Attention) (4-5 ye.)<br>Low& Middle Income - Pre-primary<br>(Spier at al 2020) BSD ‘Subsidized Preschool (Additional year) O10 (008, 0.28) T, Executive Functioning (5 yc.)<br>(Wolfet al. 2079) GHA TT Cuaching 0.11 (0.04, 0.28) 14, Executive Functioning (4-6 y-0.)<br>(Wolfet al. 20192) GHA TT& Gaaching & Parental Engagement 0.04 (40.11, 0.19) 12, Executive Functioning (4-6 yo.)<br>(Ganimian et al 2021) IND Provisan of staff + 0.18 (008, 0.28) TT, Executive function score (3-6 yo.)<br>(Berkes ef al 2019) KHM Preschool Construction 0.04 (4001, 0.08) 11, Executive function (4.6 yo.)<br>(Berkes ef al 20795) KHM Preschool Construction & Community Guireach O00 (4005, 0.06) T2, Executive function (4.6 yo.)<br>(Berks: et al 2019) KHM Preschool Construction & Cammunity Quireach & Parental Engagement 0.04 (40001, 0.08) TS, Executive function (4.5 yo.)<br>(Berkes ef al 2019) KHM Preschool Construction 90.07 (-0.08, 0.04) Ti, Executive function (5.5 yo.)<br>(Berkes ef al 20795) KHM Preschool Construction & Community Guireach 90065 (0.10, 0.01) T2, Executive function (5.6 yo.)<br>(Berkes ef al 20195) KHM Preschool Construction & Community Guireach & Parental Engagement 0.07 (000d, 0.07) TS, Executive function (5.5 yo.)<br>(Bouguen et al. 2078) KHM Preschool construction & TT & Pravision of material 90.01 (90.74, 0.92) T. Woodcock - Johnson » memorytest (4-6 yoo.)<br>NOTE: Weight: are from random effects: analysis<br>1.89 oO 1.49<br><!-- End of picture text -->
# Social-emotional - (Preprimary - HICs)

<!-- Start of picture text --> Study eountyeode == Type of intervention ES (5% Cl) Outeome_variable<br>High Income - Pre-primai©y<br>(Barnett et al. 2008) USA TT & Coaching & Provision of mateisl — es -0.50 (-0.02, 0.08) 1, S5R5 The Social Skills Rating System (3-4 y.0.)<br>(Barnett et al. 2008) USA TT & Goaching & Provision of material 0.14 (-0.12, 0.40) T, Cognitive abilities and achievemen (WWUR} (3-4 ya)<br>(Barnett et al. 2008) USA TT & Goaching & Provision of material 0.17 (4.37, 0.15) T. WHER LW Cognitive abilities and achievement (3-4 yo.)<br>(Bierman et al. 2008) USA (HS) - TT & Coaching & Curriculum 0.21 (-0.01, 0.43) T, Social cognition: emotion identification (4 y.0.)<br>(Bienman et al. 2008) USA (HS) - TT & Coaching & Curriculum —p>—_ 0.23 (0.02, 0.44) T, Social cognition: emotion recognition (4 y.0.}<br>(Bierman et al. 2008} USA (HS) - TT & Coaching & Curriculum —>~— 0.35 (0.17, 0.59) T, Social cognition: CST competent responses (4 y.0.)<br>(Bierman et al. 2008} USA (HS) - TT & Coaching & Curriculum —_—_ 0.28 (0.03, 0.53) T, Social cognition: CST inept responses* (4 y.0_)<br>(Bierman et al. 2008} USA (H5)-TT & Coaching & Curriculum 0.24 (-0.05, 0.53) T, Social competence (Teacher report) (4 yo.)<br>(Bierman et al. 2008} USA (HS) - TT & Coaching & Curriculum 0.08 (-0.06, 0.24) T, Social competence (Parent report} (4 yoo.)<br>(Bierman et al. 2008} USA (H5)-TT & Coaching & Curriculum 0.26 (-0.03, 0.55) T, Social competence (Obsenver report) (4 y.0.)<br>(Gormley et al. 2011) USA Subsidized Pre-K (Ciklahoma) 0.00 (-0.04,0.22) Ti Apathia* (4 yo)<br>(Gormley et al. 2011) USA Subsidized Pre-K (Ciklahoma) ——— 0.15 (0.02, 0.28) Ti, Timid* (4 yo.)<br>(Gormley et al. 2011) USA Subsidized Preschool HS (Oklahoma) 0.01 (-0.15, 0.17) T2, Apathic* (4 y.o.)<br>(Gormley et al. 2011) USA Subsidized Preschool HS (Oklahoma) 0.27 (-0.01, 0.55) T2, Timid* (4 y.o.)<br>(Hamre et al 2012) USA (HS) - TT & Coaching & Curriculum —— 0.22 (0.08, 0.35) Ti, Social competence (4-5 yo.)<br>(Hamre et al 2012) USA (HS) - TT & Coaching & Curriculum -0..00 (-0.00, 0.00) Ti, Social problems* (4-5 y.0.}<br>(Lipsey et al. 2013) USA Subsidized Pre-K. 0.10 (-0.04, 0.23) T, Interpersonal Skills (5 yoo.)<br>(Lipsey et al. 2018) USA Subsidized Pre-K 0.05 (-0.00,0.18) T, Peer Relations (5 yo.)<br>(Lipsey et al. 2018) USA Subsidized Pre- 0.04 (-0.10, 0.17) T, Feelings about school (5 yo.)<br>(Weiland and Yoshikawa 2013) USA Subsidized Pre-K £ TT & Curriculum —>— 0.79 (0.02, 0.36) T, Emotion Recognition (4-5 yo.)<br>(Weiland and Yoshikawa 2013) = USA Subsidized Pre-K & TT & Curriculum 0.03 (-0.12,0.18) —T, Positive Emotion (TOQ) (4-5 yo.)<br>(Weiland and Yoshikawa 2013) USA Subsidized Pre-K £ TT & Curriculum 0.07 (-0.00, 0.23) T, Impulsive Control (TOO) (4-5 y.0.)<br>NOTE: Weights are from random effects analysis<br>925 0 925<br><!-- End of picture text -->
<!-- Start of picture text --> Social-emotional - (Preprimary - LMICs)<br>Study countryeede = Type of intervention ES (85% Ch Outcome_variable<br>Low & Middle Income - Pre-primary<br>(Spier at al 2020) BGD Subsidized Preschool (Additional year} + 0.37 (0.21, 0.53) T, Socicemotional skills (5 yoo.)<br>(Andrew et al 2019) = COL Provision of Materiali Stat? 0.01 (0.18, 0.18) TI, ASG: SE (3-H yo)<br>(Andrew ef al. 2019) COL TT & Provision of Material/Staft 0.03 (40.14,0.21) 72, ASO SE (2-H yo.)<br>(Andrew et al. 2019) COL TT & Provision of Material/Staf? O05 (4.11.0.21) 771, T2, ASO: SE (3-H yo.)<br>(Wolfet al.20703) GHA TT & Coaching —>— 0.18 (0.05,0.31) 71, Socksemotional skills (4-6 yo.)<br>(Wolfet al.20793) GHA TT & Coaching & Parental Engagement 0.13 40.10, 0.38) = 72, Socicemotional skills (4-6 yoo.)<br>(Brinkman etal. 2017) IDM Preschool Construction & TT & Community Outreach —_— 0.23 (0.08, 0.38) T, Social competence (EDI) (4-5 yo.)<br>(Brinkman etal. 2017) IDN Preschool Construction & TT & Community Outreach 0.07 (40.13,.0.15) 9 T, Emotional maturity (EDI) (4-5 y.o.)<br>(Dillon et al. 2017) IND Math Curriculum 0.12 (-0.01,.0.28) 71, Social composite (5 y.o.}<br>(Dillon et al. 2017) IND Socio-emotional Curriculum — os (0.27, 0.83) T2, Social composite (5 y.o.)<br>(Berkes et al 2019) KHM Preschool Construction O.07 (4.07,.0.14) 9 71, Socicemotional problems* (4.5 y.o_)<br>(Bermes et al 2019) KHM Preschool Construction & Community Qutreach — 0.10 (0.02, 0.19) T2, Socicemotional problems* (4.5 y.o_)<br>(Berkes et al 2019) KHM Preschool Construction & Community Outreach & Parental Engagement —>— 0.12 (0.03, 0.20) T3, Socisemotional problems* (4.5 y.o.)<br>(Bermes et al 2019) KHM Preschool Gonstruction 0.01 (0.08, 0.08) 71, Socicemotional problems* (5.5 y.o_}<br>(Berkes et al 2019) KHM Preschool Construction & Community Outreach 0.01 0.07, 0.09) 2, Socicemotional problems* (5.5 y.o_)<br>(Bermes et al 2019) KHM Preschool Construction & Community Qutreach & Parental Engagement 0.00 (4.08,0.09) 13, Socicemotional problems* (5.5 y.o_}<br>(Bouguen etal. 2018) KHM Preschool construction & TT & Provision of material 0.068 240.06,0.18) 7, SDQ- Emotion (446 yo.)<br>(Bouguen etal. 2018) KHM Preschool construction & TT & Provision of material 0.04 (-0.20,0.12) 7, SDQ- Conduct (4-4 yo.)<br>(Bouguen etal. 2018) KHM Preschool construction & TT & Provision of material 0.01 20.13,0.18) 9 7, SDQ.- Hyperactivity* (446 yoo.)<br>(Bouguen etal. 2018) KHM Preschool construction & TT & Provision of material 0.00 40.13,0.14) 7, S0Q- Peart (4-8 yo)<br>(Bouguen etal. 2018) KHM Preschool construction & TT & Provision of material 0.08 (40.21, 0.04) T, SDQ.- Prosocial (446 yoo.)<br>(Bouguen etal. 2018) KHM Preschool construction & TT & Provision of material 0.02 (-0.10, 0.07) 7, Noncognitive index (4-6 y.0.)<br>(Ozler et al. 2018) MM TT & Coaching 0.06 (-0.18,0.06) 71, Total difficulties (SD) (5.5 yo.)<br>(Galer et al. 3018) MW TT & Coaching & Teacher Stipend 0.06 (40.03,0.21) 2, Total difficulties (5D0) (5.5 yo.)<br>(Ozler et al. 2018) NWI TT & Coaching & Parental Engagement OOF 4.03, 0.17) 73, Total difficulties (502) (5.5 yo.)<br>(Galer et al. 3018) MW TT & Coaching 0.11 (-0.04, 0.28) = Ti, Prosocial (SQ) (5.5 yo.)<br>(Ozler et al. 2018) NWI TT & Coaching & Teacher Stipend 0.03 4.13,0.18) 2, Prosocial (SD0) (5.5 yo)<br>(Ozler et al. 2018) MM TT & Coaching & Parental Engagement ——> 0.25 (0.10, 0.40) T3, Prosocial (SOO) (5.5 yo.)<br>NOTE: Weights are from random effects analysis<br>-626 0 625<br><!-- End of picture text -->
<!-- Start of picture text --> Behavior - (Preprimary)<br>Study countrycode Type of intervention ES (95% Cl} ‘Qutcome_variable<br>High Income - Pre-primary<br>(Baker et al. 2008) CAN Subsidized Childcare (Expansion) 0.07 (-0.11, 1.03) T, Separation anxiety seore* (4 y.0.)<br>(Baker et al. 2008) CAN Subsidized Childcare (Expansion) 0.07 (-0.11, 0.03) T. Physical aggression and oppesition* (4 y.0.)<br>(Yoshikawa etal 2015) CHL TT & Coaching & Provision of material 0.76 (-0.03, 0.35) T, Selfregulation and bow problem behavior scale (5.5 y.o_)<br>(Bierman et al. 2003) USA (H5)-TT& Coaching & Curriculum 0.21 (0.01, 0.41) T, Social cognition: CST aggressive responses" (4 y.0.}<br>(Bierman et al. 2003) USA (H5)-TT& Coaching & Curriculum 0.28 (-0.00, 0.58) T. Aggression (Teacher report}* (4 y.0.)<br>(Bierman et al. 2008) USA (H5)}-TT& Coaching & Curriculum 0.13 (-0.02, 0.23) T. Aggression (Parent report)* (4 yo.)<br>(Bierman et al. 2008) USA (H5)-TT& Coaching & Curriculum 0.19 (-0.12, 0.50) T, Aggression (Observer report}* (4 y.0.}<br>(Bierman et al. 2008) USA (H5)-TT& Coaching & Curriculum O17 (-0.04, 0.33) T, Leaming engagement (Teacher report) (4 y.0.}<br>(Gormleyet al.2071) USA Subsidized Pre-K (Oklahoma) 70.04 (-0.55,0.43) 71, Aggression* (4 yo.)<br>(Gormleyet al.2071) USA Subsidized Preschool HS (Oklahoma) 0.02 (-40.23,0.19) = 72, Aggression* (4 y.0.)<br>(Lipseyet al.2072) USA Subsidized Pre-K 0.10 (-0.01, 0.21) T, Preparednessfor grade (5 y.o.)<br>(Lipseyet al.2018) USA Subsidized Pre-K 0.04 (-0.08, 0.18) T, Work-related Skills (5 yoo.)<br>(Lipseyet al.2018) USA Subsidized Pre-K 0.08 (-0.06, 0.21) T, Behavior problems" (5 y.0.}<br>(Pianta et al. 2017) USA (H5) TT 0.03 (-0.10, 0.18) T1, Persistence/engagement (4.5-5 yo.)<br>(Pianta et al. 2017) USA (H5)} Coaching & Feedback 0.07 (-0.21, 0.07) 9 72, Persistence/engagement (4.5-5 yo.)<br>(Pianta et al. 2017) USA (H5) TT & Coaching & Feedback 0.14 (0.00, 0.27) T3, Persistence/engagement (4.5-5 yo.)<br>(Raveret al.2000) USA (H5)-TT& Coaching & Mental Health Counselling —_-—) 1.06 (0.54, 1.53) T, Behavior Problems Index (Intemalizing)* (4-5 yo.)<br>(Raveret al.2000) USA (H5)-TT& Coaching & Mental Health Counselling —>—__ (0.98 (0.52, 1.44) T, Behavior Problems Index (Extemalizing)* (4-5 yo.)<br>(Raveret al.2000) USA (H5)-TT& Coaching & Mental Health Counselling —_———_ 0.82 (0.22, 1.42) T. G-TRF Intemalizing 100 item indesx* (4-5 y.0.)<br>(Raveret al.2006) USA (H5)}-TT& Coaching & Mental Health Counselling _ 0.92 (0.47, 1.37) T. C-TRF Extemalizing 100 item index* (4-5 y.0.)<br>(Raveret al.2000) USA (H5)-TT& Coaching & Mental Health Counselling 0.11 (-0.19, 0.41) T, PIPPS Iintemalizing/Disruptive index* (4-5 y.0.)<br>(Raveret al.2008) USA (HS)-TT& Coaching & Mental Health Counseling —j 40.77 (-1.50, -0.04) T, PIPPS Extemalizing/Disruptive index* (4-5 y.o.)<br>NOTE: Weights are from random effects analysis<br>-1.58 Qo 1.58<br><!-- End of picture text -->
# School Participation & Progression - (Post-preprimary)

Study countrycode Type: of intervention ES (95% Cl) Outcome_varable High Income - Basic (Felfe et al 2014) ESP Subsidized Childcare & TT & Curriculum 0.11 (-0.23, 0.007, Falling behind a grade in primary* (12 y.0.) (Fetfe et al 2014) ESP Subsidized Childcare & TT & Curriculum -0.08 (-0.25, 0.087, Falling behind a grade at secondary* (15 y.0.) (Drange et al 2076) NOR Subsidized Kindergarten & Play Curriculum ~0.04 (-0.04, 0.027, School performance (test score) (15-16 y.o.) (Brotman et al. 2018) USA TT & Curmiculum & Parental Engagement 0.21 (-0.08, 0.5171, Academie performance (7 y.0.) (Cameiro and Ginja 201454 (H5} - Subsidized Preschool 0.97 (-0.13, 2.0777, In high school (16-17 yo.) Male (Gray-Lobe et al 2027) USA Subsidized Pre-K & TT & Coaching & Curriculum 0.12 (-0.01, 0.2477, Enrollment in college (18 y.0.) (Gray-Lobe et al 2021) USA Subsidized Pre-K & TT & Coaching & Curriculum 0.15 (0.00, 0.30) T. Graduated high school (13 yo.) (Lipsey et al. 2078) USA Subsidized Pre-K, 70.03 (-0.11, 0.057, School attendance (8 yo.) (Lipsey et al. 2078) USA Subsidized Pre-K ~0.04 (-0.08, 0.077, School attendance (6 y.0.) (Lipsey et al. 2078) USA Subsidized Pre-K ~0.04 (-0.12, 0.047, School attendance (7 y.o.) (Watis et al 2013) USA (H5)}-TT & Coaching & Mental Health Counselling 0.19 (-0.01, 0.40)1, Self-reported GPA (10-11 y.0.) Low & Middle Income - Basic (Spier at al 2020) BED Subsidized Preschool (Additional year) (0.14 (-0.31, 0.58)7, Primary school enrollment (6 y.0.) (Brinkman et al. 2017) IDN Preschool Construction & TT & Community Outreach > (0.68 (0.60, 0.78) T, Enrolled in playgroup (8-0 yo.) (Brinkman et al. 2017) IDN Preschool Construction & TT & Community Outreach > 0.86 (0.74, 0.97) T, Enrollment duration in playgroup (months) (2-9 y.o. (Martinez etal. 2017) Moz Preschool Consiruction & TT & Parental Engagement > 0.21 (0.09, 0.32) T. Enrolled at primary school (5-8 y.o.) (Martinez et al. 2017) Maz Preschool Construction & TT & Parental Engagement ~> 0.19 (0.07, 0.30) T, Ever goneto primary school (5-9 yo.) (Martinez etal. 2017) Moz Preschool Construction & TT & Parental Engagement > 0.12 (0.04, 0.21) T, Appropriate grade for age (5-9 y.o.) (Martinez et al. 2017) Maz Preschool Construction & TT & Parental Engagement 0.41 (-0.17_ 0.38)T, Dropout from school* (5-8 yo.) (Martinez et al. 2017) Maz Preschool Construction & TT & Parental Engagement > 0.14 (0.05, 0.22) T, Schooling index (5-2 y.o0.) (Berlinski et al. 2008) URY Preschool Construction 0.04 (-0.03, 0.02), Years of schooling at age 7 (7 y.0.) (Berlinski et al. 2008) URY Preschool Construction -0.00 (-0.03, 0.027, Years of schooling at age & (8 yo.) (Berlinski et al. 2008) URY Preschool Construction 0.04 (0.02, 0.03)7, Years of schooling at age 9 (8 y.o.) NOTE: Weights are from random effects analysis -2.07 Lt) 2.07
<!-- Start of picture text --> School Participation & Progression - (Adult)<br>Study cauntrycode Type of intervention ES (05% Cl) ‘Outcome variable<br>High Income - Adult<br>(Rossin-Slater and Wust 2020/NK Preschool Construction = 0.15 (0.04, 0.28) T, Years of schooling (30-60 y.0.)<br>(Rossin-Slater and Wust 2020/NK Preschool Construction 0.04 (0.02, 0.08) T, More than compulsory education (30-00 yo.)<br>(Haynes and Mogstad 2011) NOR Subsidized Childcare (Expansion) 0.07 (0.01, 0.02) T, Years of completed education (33-28 yo.)<br>(Haynes and Mogstad 2011) NOR Subsidized Childcare (Expansion) 0.03 (0.01, 0.04) T, Attended college (23-36 yo.)<br>(Haynes and Mogstad 2011) NOR Subsidized Childcare (Expansion) 0.03 (0.01, 0.05) T, High school dropout* (33-36 y.o.)<br>(Bailey et al 2020) USA (HS) - Subsidized Preschool 0.09 (0.02, 0.76) T, Professional or doctoral degree (25-54 y.o.)<br>(Bailey et al 2020) USA (HS) - Subsidized Preschool + 0.13 (0.06, 0.20) T, Years of schooling (25-54 y.0.)<br>(Bailey et al, 2020) USA (HS) - Subsidized Preschool 0.06 (-0.01, 0.13)T, High school completion (25-54 y.o.)<br>(Bailey et al, 2020) USA (HS) - Subsidized Preschool 0.07 (-0.00, 0.14) T, Attended college (25-54 yo.)<br>(Bailey et al, 2020) USA (HS) - Subsidized Preschool + 0.12 (0.05, 0.19) T, Completed college (25-54 y.o.)<br>(Cameiro and Ginja 2014) USA (HS) - Subsidized Preschool 1.25 (-0.15, 2.65) T, High school diploma (20-21 yo.) Male<br>(Cameiro and Ginja 2014) USA (HS) - Subsidized Preschool 0.12 (40.88, 0.6571, Ever in college (20-21 y.o.) Male<br>(Gray-Lobe et al 2021) USA Subsidized Pre-K & TT & Coaching & Curriculum 0.02 (-0.17, 0.21)T, College graduation (22 ya.)<br>(Heckman et al 2070) USA Subsidized Preschool He 1-40 (0.42, 2.37) T, High school graduation (19 yo.) Female<br>(Heckman et al 2070) USA Subsidized Preschool —_—_—) 1.20 (0.49, 1.92) T, Grade point average (19 y.o.) Female<br>(Heckman et al 2070) USA Subsidized Preschool _}_— 0.79 (0.23, 1.35) T, Highest grade completed (19 y.o.) Female<br>(Heckman et al 2070) USA Subsidized Preschool 0.04 (-0.11, 0.20)T, High school graduation (19 yo.) Male<br>(Heckman et al 2070) USA Subsidized Preschool 0.11 (40.47, 0.68)T, Grade point average (19 y.o.) Male<br>(Heckman et al 2070) USA Subsidized Preschool 0.08 (-0.38, 0.55)T, Highest grade completed (19 y.o.) Male<br>NOTE: Weights are from random effects analysis<br>“2.65 o 2.65<br><!-- End of picture text -->
<!-- Start of picture text --> Study countrycode = Type of intervention ES (85% Cl) Outcome_variable<br>High Income - Basic<br>(Bitler et al 2074) USA (HS) - Subsidized Preschool 0.07 (40.07,0.09) = T, PPVT Score (8 yo.)<br>(Bitler et al 2074) USA (HS) - Subsidized Preschool 0.04 (40.04,0.12) 9 7, PPVT Score (7 ye.)<br>(Blair and Raver 2014) USA TT& Coaching & EF Curriculum 0.70 (40.00, 0.20) = T, Vocabulary (6-7 yoo.)<br>(Kline and Walters 2018) USA, (HS) - Subsidized Preschool O04 (40085, 0.14) 0 7, PPVT and WII Tests score (6-7 yoo.)<br>Low & Middle Income - Basic<br>(Brinkman et al. 2017} IDM Preschool Construction & TT & Community Outreach O05 (40085,0.18) 9 7, Language and cognitive development (ED) (8-9 y.0.}<br>(Martinez et al. 2017) Moz Preschool Construction & TT & Parental Engagement —[——— ts 1.43 (0.14, 0.72) T, Language and cognitive development (EDI) (6 y.o.)<br>(Ozter et al. 2018) MW TT& Coaching 0.03 (40.14,0.21) 71, PPVT(T yo)<br>(Ozer at al. 2018) hl TT& Coaching & Teacher Stipend 0.10 (40.08,0.30) T2, PPVT(7? yo.)<br>(Ozter et al. 2018) Na TT& Coaching & Parental Engagement 0.70 (4008, 0.30) 73, PPVT(? yo)<br>MOTE: Weights are from random effects analysis<br>718 0 719<br><!-- End of picture text -->
<!-- Start of picture text --> Cognitive Literacy - (Post-preprimary)<br>Study countrycode = Type of intervention ES (95% Clhp Outcome_variable<br>High Income - Basic<br>(Felfeet al 2014) ESP Subsidized Childcare & TT & Curriculum _ + 0.15 (0.02, 0.28) T, Readingseores (15 y.c.}<br>(Blair and Raver 2014) USA TT & Coaching & EF Curriculum _ 0.14 (0.02, 0.25) T, Reading (5-7 y.0.)<br>(Cameiro and Ginja 2014) USA (HS) - Subsidized Preschool 0.10 (40.22, 0.01) T, PLAT-RIR Word recognition (12-13 yo.) Male<br>(Carneiro and Ginja 2014) USA (HS) - Subsidized Preschool 0.07 (-0.19, 0.04) T, PIAT-RC Reading comprehension (12-13 y.o.) Male<br>(Gray-Lobe et al 2027) USA Subsidized Pre-K & TT & Coaching & Curriculum 0.15 (-0.02, 0.37) T, SAT-Verbal score above state bottom quartile (13 y.o.)<br>(Gray-Lobe et al 2027) USA Subsidized Pre-K & TT & Coaching & Curriculum 0.17 (-0.05, 0.28) T. SAT-Writing score above state bottom quartile(15 y.0.)<br>(Lipsey et al. 2078) USA Subsidized Pre-K 0.05 (-0.14, 0.03) T, StateAchievementTest - Reading (8 y.o.)<br>Low & Middle Income - Basic<br>(Beriinski et al. 2008) ARG Prechool Construction ————_— 73 (0.03, 0.43) T, Spanish test score (8-13 yoo.)<br>(Spier at al 2020) BoD Subsidized Preschool (Additional year) $+ 0.23 (0.07, 0.30) T, Emergent literacy (5 y.o.)<br>(Spier at al 2020) BSD Subsidized Preschool (Additional year) 40.03 (40.17,0.11) T, EGRA- Familiar Words (8 yo.)<br>(Wolf 2078) GHA TT & Coaching 0.17 (-0.00, 0.22) Ti, Literacy (6-3 y.o.)<br>(Wolf 2078) GHA TT & Coaching & Parental Engagement 0.04 (-0.14, 008) 72, Literacy (6-8 y.o.)<br>(Wolfet al.2078b) GHA TT & Coaching 0.08 (-0.05. 0.22) = 71, Earty Literacy (5-7 y.o.)<br>(Wolfet al. 207 8b) GHA TT & Coaching & Parental Engagement 0.05 (40.18, 0.08) 99 12, Barty Literacy (5-7 yo.)<br>NOTE: Weights are from random effects analysis<br>-42T i] 427<br><!-- End of picture text -->
# Cognitive Math - (Post-preprimary)

Study countyeode Type of intervention Es (95% Cl) Qutcome_variable High Income - Basic (Felfe et31 2014) ESP Subsidized Childcare & TT & Curriculum 0.05 (-0.05, 0.15) T, Math scores (15 yo.) (Blair and Rawer 2014) USA TT & Coaching & EF Curriculum 0.07 (-0.08, 0.17) T, Math (6-7 yo.) (Cameiro and Ginja 2014) USA (HS) - Subsidized Preschool 0.02 (40.100, 0.13) T, PIAT- Math test score (12-13 yo.) Male (Clements et al. 2073) USA, TT & Coaching & Math Curriculum (3 years) —— 0.38 (0.22, 0.54) Ti, Math test scorein first grade (REMA) (6 yo.) (Clements et al. 2073) USA. TT & Coaching & Math Curriculum (2 years) —>— 0.21 (0.05, 0.37) T2, Math test score in first grade (REMA) (6 yo.) (Gray-Lobe et al 2021) USA, Subsidized Pre-K & TT & Coaching & Curriculum 0.16 (-0.01, 0.32) T, SAT-Math score above state bottom quartile (18 y.o.) (Lipseyet al.2078) USA Subsidized Pre-K —~—0.14 (-0.25, 0.03) TT, State Achievement Test-Mathematics (8 y.o.) Low & Middle Income - Basic (Berlinski et al. 2008) ARG Prechool Construction —>— 0.24 (0.05, 0.43) T, Math test score (8-13 y.a.) (Spierat al 2020) BGD Subsidized Preschool (Additional year) —>— 0.30 (0.10, 0.50) T, Emergent numeracy (6 yo.) (Spierat al 2020) BGD Subsidized Preschool (Additional year) 0.05 (40.08, 0.19) T. EGMANumerical Identification (6 yc.) (Spierat al 2020) BGD Subsidized Preschool (Additional year) 0.14 (0.05, 0.27) T, EGMANumber Discrimination (6 yo.) (Spierat al 2020) BGD Subsidized Preschool (Additional year) -0.08 (-0.48, 0.30) T, EGMAMissing Numbers (6 y.a.) (Molt 2079) GHA TT & Coaching 0.00 (-0.01, 0.01) Ti, Math (6-8 yo.) (Wolf 2079) GHA TT & Geaching & Parental Engagement 0.10 (-0.20, -0.00) 12, Math (6-8 yo.) (Wolfet all. 2019b} GHA TT & Coaching 0.08 (0.08, 0.14) Ti, Early Math Skills (5-7 yo} (Wolfet al. 2019} GHA TT & Coaching & Parental Engagement 0.05 (-0.16, 0.06) T2, Early Math Skills (5-T yo) (Dillon et al. 2017) IND Math Curriculum ~~ 0.12 (0.03, 0.21) Ti, Math composite (ASER math) (6 yo.) (Dillon et al. 2017) IND Socio-emotional Curriculum 0.01 (40.07, 0.10) T2, Math composite (ASER math} (6 yo.) (Dillon et al. 2017) IND Math Curriculum ~~ 0.14 (0.04, 0.24) Ti, Math composite (ASER math) (6.5 y.c.} (Dillon et al. 2017) IND Socio-emotional Curriculum 0.06 (0.03, 0.15) T2, Math composite (ASER math} (6.5 yc.) (Ozler et al. 2018) Mi TT & Coaching 0.08 (-0.21,0.04) 9 11, Early grade math (7 y.0.) (Ozler et al. 2018) MW TT & Coaching & Teacher Stipend 0.08 (-0.16, 0.10) T2, Early grade math {7 yc.) (Ozler et al. 2018) MI TT & Coaching& Parental Engagement 0.02 (-0.15, 0.11) T3, Early grade math (7 yc) (Bando etal 2019) PER IPP Pedagogy & TT& Provision of Material 0.38 (0.02, 0.74) T3 (PER), Math test score (Penu 2012) (8 yc.) (Gallego etal 2019) PER IPP Pedagogy & TT& Provision of Material 0.04 (0.05, 0.14) T, EGMA4Overall test score (6 yo.) (Gallego et! 2019) PER IPP Pedagogy & TT& Provision of Material ~~ 0.13 (0.03, 0.23) T. EGMAShapes items test score (6 yo.) (Gallego etal 2019) PER IPP Pedagogy & TT& Provision of Material 0.09 (0.11, 0.13) T, EGMANumeracy items test score (5 yo) (Bando etal 2019) PRY IPP Pedagogy & TT& Provision of Material —fes 47 (0.02, 0.92) Ti (PRY), Math test score (Paraguay 2011) (9 yo.) (Bando etsl 2019) PRY IPP Pedagogy & TT& Provision of Material 0.39 (0.02, 0.76) T2 (PRY). Math test score (Paraguay 2013) (8 yo.) NOTE: Weights are from random effects analysis 919 0 818
<!-- Start of picture text --> Study eountryoode Type of intervention ES (95% Ch} Outeome_wvariable<br>High Income - Basic<br>(Gray-Lobe etal 2021) USA Subsidized Pre & TT & Coaching & Curriculum 0.14 (-0.02, 0.30) T, SA4T-Reasoning score about state bottom quartile (18 y-o.}<br>(Lipsey et al. 2078) USA Subsidized Prec 0.07 (-0.18.00.04) 0 T, WI AchievementBattery (8 y.c.)<br>(Lipsey et al. 2078) usa Subsidized Pre-K 40.09 (-0.47, 40.01) T, State Achievement Test- Science (8 yo.)<br>(Lipsey et al. 2018) USA Subsidized PreK 0.01 (-0.12,.0.09) 7, WHI AchievementBattery (6 yo}<br>(Lipsey et al. 2078) USA Subsidized Pret 40.08 (-0.18,0.02) T, WHI AchievementBattery (7 yo.)<br>Low & Middle Income - Basic<br>(Spier at al 2020) BGD Subsidized Preschool (Additional year) 0.05 (-0.17, 0.21) T, Approaches. to leanning (8 yoo.)<br>(Wolfet al.2078b) ‘GHA TT & Coaching 0.13 (-0.01, 0.26) Ti, School readiness composite (IDELA score} (5-7 yoo.)<br>(Wolfet al.2078b) GHA TT & Coaching & Parental Engagement 40.08 (-0.417, 0.08) 2, School readiness composite (IDELA score} (5-7 yo.)<br>(Brinkman etal 2017) IDN Preschool Construction & TT & Community Outreach 0.01 (-0.24, 0.27) T, Gommunication and general knowledge (ED!) (8-8 y.o)<br>(Martinez ef al. 2017) Moz Preschool Construction & TT & Parental Engagement —_— 0 (0.07, 0.57) T, Communication and general knowledge (ED!) (6 yo.)<br>NOTE: Weights are from random effects analysis<br>-.673 Oo S73<br><!-- End of picture text -->
<!-- Start of picture text --> Study countrycode Type of intervention ES (85% Cl) Outcome_variable<br>High Income - Basic<br>(Watts et al 2018) USA (HS}-1T & Coaching & Mental Health Counselling —_— 0.20 (0.01. 0.40) T, Executive Function - Accuracy (10-11 y.o.}<br>(Waits et al 2018) USA. (H5}-1T & Coaching & Mental Health Counselling 0.00 (-0.20, 0.20) TT. Executive Function - Reaction Time (10-11 y-o.}<br>Low & Middle Income - Basic<br>(Berlinski et al. 2008) ARG Prechool Construction 0.06 (0.00, 0.12) T, Share of students paying attention (9-13 y.o.)<br>(Spier at al 2020) BoD Subsidized Preschool (Additional year) 0.02 (-0.14, 0.18) 9 T, Executive Functioning (6 y-o.)<br>(Wolf 2078) GHA TT & Goaching —_—_—?-— 0.12 (0.02,0.22) T4, Cognitive flexibility (6-8 y.0.)<br>(Wolf 2018) GHA TT & Coaching 0.08 (-0.04, 0.18) T4, Inhibitory control (6-8 y.0.)<br>(Wolf 2078) GHA TT & Coaching & Parental Engagement 0.05 (-0.05, 0.15) 2, Cognitive flexibility (6-8 yo.)<br>(Wolf 2078) GHA TT & Coaching & Parental Engagement 0.07 (-0.02, 0.16) 2, Inhibitory control (6-8 yo.)<br>(Wolf et al. 2019b) GHA TT & Coaching 0.11 (-0.02,0.25) 1, Executive Functioning (5-7 y.o.)<br>(Wolf et al. 2019b) GHA, TT & Coaching & Parental Engagement 40.07 (0.20, 0.07) 2, Executive Functioning (5-7 yo.)<br>NOTE: Weights are from random effects analysis<br>-4 o 4<br><!-- End of picture text -->
<!-- Start of picture text --> Social-emotional - (Post-preprimary)<br>Study countrycodTyp e of intervention ES (95% Cl) Outcome_variable<br>High Income - Basic<br>(Lipsey et al. 2078) USA Subsidized Pre-K 0.05 (-0.06, 0.16) T, Interpersonal Skills (8 y.0.)<br>(Lipsey et al. 2013) USA Subsidized Pre 70.05 (+-0.18, O.07)T, Peer Relations (8 y.o.)<br>(Lipsey et al. 2078) USA Subsidized Pre-K ~0.00 (-0.13, 0.12)T, Feelings about school (8 y.0_)<br>(Lipsey et al. 2072) USA Subsidized Pre-K ~0.07 (-0.21, 0.08), Interpersonal Skills (6 y.0.)<br>(Lipsey et al. 2078) USA Subsidized Pre-K 0.02 (-0.18, 0.12)T, Interpersonal Skills (7 y.0.)<br>(Lipsey et al. 2018) USA Subsidized Pre-K 0.02 (-0.13, 0.16) T, Peer Relations (6 y.0.)<br>(Lipsey et al. 2078) USA Subsidized Pre-K 0.03 (-0.10, 0.16) T, Peer Relations (7 y.0.)<br>(Lipsey et al. 2078) USA Subsidized Pre-K ~0.15 (-0.28, -0.017, Feelings about school (6 y.0.)<br>(Lipsey et al. 2078) USA Subsidized Pre-K 0.08 (-0.21, 0.08)T, Feelings about school (7 yo.)<br>(Watts et al 2018) USA (HS)- TT & Coaching & Mental Health Counselling (0.14 (-0.06, 0.35) T, Emotional regulation (EGNG}Angry D-Prime* (10-11 y.o.)<br>(Watts et al 2078) USA (H5)- TT & Coaching & Mental Health Counselling _—_e 0.31 (0.09, 0.54) T, Emotional regulation (EGNG)Angry RT (adjusted)* (10-11 yo.)<br>(Watts et al 2018) USA (HS)- TT & Coaching & Mental Health Counselling 0.06 (-0.18, 0.31) T, Emotional regulation (EGNG}-Sad D-Prime* (10-11 yo.)<br>(Watts et al 2018) USA (HS)- TT & Coaching & Mental Health Counselling —_—)— 0.23 (0.02, 0.44) T, Emotional regulation (EGNG}-Sad RT (adjusted)* (10-11 y.0.)<br>Low & Middle Income - Basic<br>(Spier atal 2020) BGD Subsidized Preschool (Additional year) —+— 0.34 (0.14, 0.54) T, Socioemotional skills (6 y.o.)<br>(Wolf 2018) GHA TT & Coaching 0.08 (-0.04, 0.20) T1, Prosocial (6-8 yo.)<br>(Wolf 2018) GHA TT & Coaching 0.11 (-0.02, 0.24) T1, Social competence (6-2 y.0.)<br>(Wolf 2078) GHA TT & Coaching & Parental Engagement (0.05 (-0.08, 0.13) T2, Prosocial (6-8 yo.)<br>(Wolf 2018) GHA TT & Coaching & Parental Engagement (0.04 (-0.10, 0.13) T2, Social competence (6-8 y.0.}<br>(Wolfet al. 2019b) GHA TT & Coaching 0.13 (0.00, 0.26) T1, Socioemotional skills (5-7 yo.)<br>(Wolfet al. 2019b) GHA TT & Coaching & Parental Engagement 0.01 (-0.12, 0.14) T2, Secioemotional skills (5-7 y.o.)<br>(Brinkman et al. 207 7—M Preschool Construction & TT & Community Outreach 0.02 (-0.12, 0.17) T, Social competence (EDI) (8-9 y.0.)<br>(Brinkman et al. 207 7—N Preschool Construction & TT & Community Outreach — 0.16 (0.02, 0.29) T, Emotional maturity (EDI) (8-8 yo.)<br>(Dillon et al. 2017) IND Math Curriculum 0.03 (40.12, 0.17) T1, Social composite (6 yo.)<br>(Dillon et al. 2017) IND Sacio-emotional Curriculum _———— O.17 (0.01, 0.22) T2, Social composite (6 yo.)<br>(Dillon et al. 2017) IND Math Curriculum 0.01 (-0.14, 0.16) T1, Social composite (6.5 yo.)<br>(Dillon et al. 2017) IND Secio-emotional Curriculum 0.14 (-0.02, 0.30) T2, Social composite (6.5 y.0.)<br>(Martinez et al. 2017MOzZ Preschool Construction & TT & Parental Engagement (0.33 (-0.13, 0.78) T, Social competence (EDI!) (6 y.0.)<br>(Martinez et al. 2017MOzZ Preschool Consiruction & TT & Parental Engagement 0.30 (-0.14, 0.74) T, Emotional maturity (EDI) (6 yo.)<br>(Gzler et al. 2018) MW TT & Coaching 0.10 (-0.01, 0.21) T1, Total difficulties (SDQ) (7 yo.)<br>(Ozler etal. 2013) MV TT & Coaching & Teacher Stipend 0.10 (-0.04, 0.24) T2, Total difficulties (SDQ) (7 y.o.)<br>(Ozler etal. 2012) MW TT & Coaching & Parental Engagement 0.05 -O.17, G.07)73, Total difficulties (SD) (7 y.o.)<br>(Ozler et al. 2013) MV TT & Coaching 0.05 (-0.18, 0.08)T1, Prosocial (SD) (7 y.0.}<br>(Gzler et al. 2018) MW TT & Coaching & Teacher Stipend 0.01 (-0.15, 0.13)T2, Prosocial (SDQ) (7 y.0.}<br>(Ozler etal. 2013) MW TT & Coaching & Parental Engagement 0.00 [-0.14, 0.14) T3, Prosocial (SDQ) (7 yo.)<br>NOTE: Weights. are irom random effects analysis<br>-.788 O 786<br><!-- End of picture text -->
<!-- Start of picture text --> Behavior - (Post-preprimary & Adult)<br>Study countrycode Type of intervention ES (86% Ch) Qutcome_variable<br>High Inconne - Basic<br>(Carmeiro and Ginja 207854. (HS) - Subsidized Preschool 0.14 (-0.39, 0.119, School damage* (12-12 y.o.) Male<br>(Carneiro and Ginja 2018 6A (HS) - Subsidized Preschool 0.13 (0.01, 0.24) T, Behavioral Problems Index* (12-13 y.o.) Male<br>(Carneiro and Ginja 201864 (HS) - Subsidized Preschool 0.68 (-0.07, 1.43)T, Arrested, convicted* (16-17 y.o.) Male<br>(Gray-Lobe et al 2021) USA Subsidized Pre-K & TT & Coaching & Curriculum 0.09 (-0.10, 0.28)T, Ever suspended* (14-15 y.o.)<br>(Gray-Lobe et al 2021) USA Subsidized Pre-K & TT & Coaching & Curriculum 0.06 (-0.01, 0.13)T, Number of suspensions* (14-18 y.o.)<br>(Gray-Lobe et al 2021) USA Subsidized Pre-K & TT & Coaching & Curriculum “0.07 (-0.21, 0.077, Ever truant* (14-18 y.0.)<br>(Gray-Lobe et al 2021) USA Subsidized Pre-K & TT & Coaching & Curriculum 0.04 (-0.03, 0.17)T, Times truant* (14-18 y.0.)<br>(Gray-Lobe et al 2021) USA Subsidized Pre-K & TT & Coaching & Curriculum 0.05 (-0.02, 0.12)T, Days absent* (14-18 yo.)<br>(Gray-Lobe et al 2021) USA Subsidized Pre-K & TT & Coaching & Curriculum 1.08 (-0.07, 2.23)T, Juvenile incarceration* (14-13 y.o.)<br>(Lipsey et al. 2013) USA Subsidized Pre-K 0.04 (-0.09, 0.16)T, Preparedness for grade (8 y.o.)<br>(Lipsey et al. 2013) USA Subsidized Pre-K 0.02 (-0.09, 0.12)T, Work-related Skills ( yo.)<br>(Lipsey et al. 2013) USA Subsidized Pre-K 0.02 (-0.16, 0.107, Behavior problems* (2 y.o.)<br>(Lipsey et al. 2018) USA Subsidized Pre-K 0.04 [-0.12. 0.047, Disciplinary actions: all offenses* (3 y.o.)<br>(Lipsey et al. 2013) USA Subsidized Pre-K 0.08 (-0.20, 0.047, Preparedness for grade (6 y.o.)<br>(Lipsey et al. 2018) USA Subsidized Pre-K 0.07 (-0.19, 0.067, Preparedness for grade (7 y.0.)<br>(Lipsey et al. 2018) USA Subsidized Pre-k 0.11 (-0.24, 0.029, Work-related Skills (6 yo.)<br>(Lipsey et al. 2018) USA Subsidized Pre-K 0.05 (-0.18, 0.077, Work-related Skills (7 yo.)<br>(Lipsey et al. 2013) usa Subsidized Pre-K -0.06 [-0.19, 0.077, Behavior problems* (6 y.o.)<br>(Lipsey et al. 2018) USA Subsidized Pre-K 0.05 (-0.08, 0.18)T, Behavior problems" (7 y.0.)<br>(Watts et al 2018) USA (HS) - TT & Coaching & Mental Health Counselling 0.03 (-0.19, 0.25)T, Behavior problems-internalizing* (10-11 y.o.)<br>(Watts et al 2018) USA (HS) - TT & Coaching & Mental Health Counselling 0.12 (-0.14, O.37)T, Behavior problems-extermalizing* (10-11 yo.)<br>High Income - Adult<br>(Bailey et al 2020) USA (HS) - Subsidized Preschool 0.03 (-0.04, 0.10)T, Incarceration* (25-64 y.0.)<br>(Carneiro and Ginja 207864 (HS) - Subsidized Preschool 0.73 (0.02, 1.44) T, Arrested, convicted* (20-21 y.o.) Male<br>(Heckman et al 2070) USA Subsidized Preschool 0.48 (-0.07, 1.0357, Total aresis* (40 y.0.) Female<br>(Heckman etal 2070) USA Subsidized Preschool 0.30 (-0.28, O.87)T, Any arrests* (40 y.o.) Female<br>(Heckman et al 2070) USA Subsidized Preschool 0.46 (-0.04, 0.82)T, Total arrests* (40 y.0.) Male<br>(Heckman etal 2070) USA Subsidized Preschool 0.62 (-0.29, 1.52)T, Any arrests* (40 y.o.) Male<br>Low & Middle Income - Basic<br>(Berlinski etal. 2008) ARG Prechool Construction 0.07 (0.01, 0.13) T, Share of students putting in a lot of effort (8-13 yo.)<br>(Berlinski etal. 2009) ARG Prechool Construction 0.02 (-0.08, 0.027, Share of students being disciplined* (9-12 yo.)<br>(Berlinski et al. 2008) ARG Prechool Construction 0.07 (0.01, 0.12) T, Share of students participating in class (9-13 yo.)<br>(Wolf 2019) ‘GHA TT & Coaching > 0.15 (0.03, 0.27) 71, Behavioral regulation (6-8 y.o.)<br>(Wolf 2018) (GHA TT & Coaching & Parental Engagement 0.14 (0.02, 0.26) T2, Behavioral regulation (6-3 y.o.)<br>NOTE: Weights are from random effects analysis<br>-2.23 oO 223<br><!-- End of picture text -->
<!-- Start of picture text --> Teacher practices - (Preprimary - HICs)<br><!-- End of picture text -->

|Study|countryce|de<br>Timeline|Type ofintervention|ES(95% Cl)|‘Outcome_variable|
|---|---|---|---|---|---|
|High Income - Pre-primary||||||
|(Neuman andCunningham 20|08) USA|1 year after|TT|0.07 (-0.02, 0.04)|9 71, Teacher practice—ELLCO/JCHELLO|
|(Neuman andCunningham 20|08) USA|1 year after|TT+ Coaching<br>—~p—|0.92 (0.25, 1.39)|2, Teacher practice-ELLOOVCHELLO|
|(Powell et al.2010)|USA|1 year after|(HS) -TT<br>—_—+—_|0.90(0.58, 7.39)|T,Classroomliteracy environment (ELLCO Score}|
|(Powell et al.2010)|USA|1 year after|(HS) -TT<br>—_—>—|0.92(0.51,1.33)<br>|9 7.Language, Literacy andCurriculum|
|(Powell etal 2010)|USA|Tyearafter<br>=|(H3)-1T|0.07 (-0.55, 0.89)|7, Word definition instruction|
|(Powell et al.2010)|USA|Tyearafter<br>|= (H5)-1T|0.82(-0.06, 1.30)|T,Gode-focused instruction|
|(Powell et al.2010)|USA|1 year after|(H5) -TT|0.16(-0.46,0.78)|— T,Languageeliciting prompts: Book reading|
|(Powell et al.2010)|USA|1 year after|(HS) -TT|0.45(-0.18, 1.08)|TT,Languageeliciting prompts: Free Play|
|(foshikawa et al.2075)|CHL|1 year after|TT&Coaching & Provision of material|0.38(0.02,0.73)|— T,Emotional support|
|(foshikawa et al.2075)|CHL|1 year after|TT & Coaching & Frovision of material|0.06 (-0.13, 0.24)|TT, Instructional support|
|(foshikawa et al.2075)|CHL|1 year after|TT & Coaching & Frovision of material|0.43 (0.02, 0.84)|7, Classroom organization|
|(Bowne etal20186)|CHL|2years after<br>|= TT&Coaching & Provision ofmaterial<br>-_—|_<br>4.28(0.70, 1.88)|— T,Literacy instruction (minutes)|
|(Bowne etal20186)|CHL|2years after|TT&Coaching & Provision ofmaterial<br>————|0.76(0.18, 1.34)|— T,Vocabulary support in literacy (minutes)|
|(Bowne et al.20186)|CHL|2years after|TT&Coaching & Provision ofmaterial<br>—_———|0.83 (-1.-41, -0.20)|T,Othertopic instruction (minutes)|
|(Bowne et al.20186)|CHL|2yearsafter|TT&Coaching & Provision ofmaterial<br>—_—~_—|-0..90 (-1.57, -0.41)|T.Vocabulary support inothertopic(minutes)|
|(Bowne etal20186)|CHL|2years after|TT&Coaching & Provision of material<br>_|4.410(0.52, 7.67)|— T, Literacy instruction (proportion)|
|(Bowne et al.20186)|CHL|2yearsafter|TT&Coaching & Provision ofmaterial<br>—}——|0.82(0.24, 7.40)|7.Vocabulary support in literacy(proportion)|
|(Bowne et al.20186)|CHL|2years after|TT&Coaching & Provision ofmaterial<br>—_——|0.82 (-1.40, -0.24)|T,Othertopic instruction (proportion)|
|(Bowne et al.20185)|CHL|2yearsafter<br>|= TT&Coaching & Provision ofmaterial<br>_—_— >_>|-1.28 (-1.28,0.70)|T.Vocabulary support inother topic (proportien)|
|(Neuman andCunningham 20|08) USA|months after|TT|0.23 (40.52, 0.98)|71, Teacher practice-ELLCO/CHELLO|
|(Neuman andCunningham 20|08) USA|months after|TT+ Coaching<br>—j—|O.7T (0.23, 1.31)|2, Teacher practice-ELLCO/CHELLO|
|(Yoshikawa et al.2075)|CHL|@ months after|TT&Coaching & Frovision ofmaterial<br>—)_——|0.21 (0.25, 1.38)|— T.Emotional support|
|(Yoshikawa et al.2015)|CHL|& months ater|TT&Coaching & Provision of material|0.44(40.07, 0.94)|_ T. Instructional support|
|(foshikawa et al.2075)|CHL|G months after|TT&Coaching & Provision of material<br>_——|0.46(0.02,0.90)|T,Classroomorganization|
|(Raver et al. 2008)|USA|7 months after|(HS)-TT&Coaching&Mental Health Counselling<br>——|0.20(0.53, 1.25)|T.Positive climate|
|(Raver et al. 2008)|USA|7 months after|(H5)-1T&Coaching&Mental Health Counselling<br>—~—|0.84(0.31,0.97)<br>|— T.Negetive climate*|
|(Raver et al. 2008)|USA|7 months after|(HS)-TT&Coaching&MentalHealth Counselling<br>_—>—|0.53(015,091)|T. Teacher sensitivity|
|(Raver et al. 2008)|USA|7 months after|(HS)-TT&Coaching&Mental Health Counselling|0.52(40.02, 1.06)|T.BehaviorManagement|
|NOTE:Weightsarefromran|domeffectsana|lysis||||
||||-1.86<br>i]|1.86||
# Teacher practices - (Preprimary & Post-preprimary - LMICs)

Study countrycode Timeline Type of intervention ES (95% Cl) Outcome_variable Low & Middle Income - Pre-primary (Wolf et al. 20794) GHA 1year after TT & Coaching ~~ 0.56 (0.35, 0.77) 141, Fidelity checklist (no. of activities) (Wolf et al. 20794) GHA 1year after TT & Coaching -0.11 (0.63, 0.41) 71, Facilitating deeper learning (Wolf etal. 20719a) GHA lyear after TT & Coaching —_— 0.65 (0.16, 1.13) 71, Emotional support and behavior management (Wolf et al. 20794) GHA year after TT & Coaching —p—_ 0.52 (0.04, 1.00) 11, Supporting student expression (Wolf et al. 20794) GHA year after TT & Coaching & Parental Engagement ~~ 0.61 (0.39, 0.34) T2, Fidelity checklist (no. of activities) (Wolf et al. 20794) GHA year after TT & Coaching & Parental Engagement -0.16 (-0.73, 0.41) T2, Facilitating deeper learning (Wolf etal. 20719a) GHA year after TT & Coaching & Parental Engagement —_e— 0.65 (0.16, 1.15) 12, Emotional support and behavior management (Wolf et al. 20794) GHA year after TT & Coaching & Parental Engagement 0.09 (-0.41, 0.60) T2, Supporting student expression (Ozleretal. 2013) MW! 1.5 years after TT & Coaching 0.56 (0.01, 1.10) 11, Classroom observation index (5.5 y.o.) (Ozleret al. 2013) MW 1.5 years after TT & Coaching & Teacher Stipend —_—~eo— 4.21 (0.64, 1.77) T2, Classroom observation index (5.5 y.o.} (Ozleret al. 2013) MW 1.5 years after TT & Coaching & Parental Engagement —>— 1.00 (0.45, 1.54) 13, Classroom observation index (5.5 y.o.) (Wolf and Peel 2019)GHA 2 years after TT & Coaching 0.21 (-0.01, 0.44) 141, Fidelity checklist (no. of activities) (Wolf and Peel 2019)GHA 2 years after TT & Coaching 0.30 (-0.77, 0.16) 11, Facilitating deeper learning (Wolf and Peel 2019)GHA 2 years afler TT & Coaching 0.36 (-0.04, 0.76) 11, Emotional support and behavior management (Wolf and Peel 2019)GHA 2 years afler TT & Coaching -0.34 (-0.90, 0.21) T1, Supporting student expression (Wolf and Peel 2019)GHA 2 years after TT & Coaching & Parental Engagement 0.25 (0.02, 0.47) T2, Fidelity checklist (no. of activities) (Wolf and Peel 2019)GHA 2 years after TT & Coaching & Parental Engagement -0.42 (-0.92, 0.09) T2, Facilitating deeper learning (Wolf and Peel 2019)GHA 2 years after TT & Coaching & Parental Engagement 0.14 (0.92, 1.19) T2, Emotional support and behavior management (Wolf and Peel 2019)GHA 2 years after TT & Coaching & Parental Engagement —_—— 0.66 (-1.23, -0.13)T2, Supporting student expression Low & Middle Income - Basic (Ozleret al. 2013) MW 3 years after TT & Coaching 0.30 (-0.30, 0.90) T1, Classroom observation index (7 y.o.) (Ozleretal. 2018) MW 3 years after TT & Coaching & Teacher Stipend 0.32 (-0.25, 0.90) T2, Classroom observation index (7 y.o.) (Ozleret al. 2018) MI 3 years after TT & Coaching & Parental Engagement 0.14 (-0.45, 0.72) T3, Classroom observation index (7 y.o.) NOTE: Weights are fram random effects analysis #) -1 0 1 2
<!-- Start of picture text --> Health and motor outcomes - (Preprimary)<br>Study countrycodeType of intervention ES (95% Gl) ‘Outeome_variable<br>High Income - Pre-primary<br>(Frisvold and Lumeng 207064 (HS) - Subsidized Preschool & Extended Preschool Day —_>—_—_—_—— 0.417 (0.02, 0.21) T. Obese* (4 yo.)<br>(Frisvold and Lumeng 207064 (HS) - Subsidized Preschool & Extended Preschool Day 0.11 (-0.02, 0.24) T, Qvenweight*(4 yo.)<br>(Frisvold and Lumeng 207064 (HS) - Subsidized Preschool & Extended Preschool Day 0.04 (-0.34, 0.22)T, Underwveight*(4 yoo.)<br>(Frisvold and Lumeng 20 106A (HS) - Subsidized Preschool & Extended Preschool Day 0.09 (-0.068, 0.24) T, BM z-score* (4 yo.)<br>Low & Middle Income - Pre-primary<br>(Spier at al 2020) BGD Subsidized Preschool (Additional year} ————— 0.28 (0.14, 0.42) TT. Motor development(5 yo.)<br>(Spier at al 2020) BGD Subsidized Preschool (Additional year) 0.08 (-0.08, 0.24) T, Motor development (6 y.0.}<br>(Blimpo et al 2078) ‘GMB Preschool Construction & TT & Play Curriculum 0.04 (-0.31, 0.2371, Fine Motor Skills (3-6 yo.)<br>(Blimpoet al 2018) GMB TT& Pisy curriculum 0.16 (0.11, 0.43) T2, Fine Motor Skills (3-8 y.o.)<br>(Brinkman etal. 2017) IDN Preschool Construction & TT & Community Outreach 0.03 (40.17, 0.72)T, Physical health and wellbeing (EDI) (4-5 y.o.}<br>(Berkes et al 2019) KHM Preschool Construction —_— 0.07 (0.01, 0.13) 11, Fine motor skills (4.5 y.0.)<br>(Berkes. et al 2019) KHM Preschool Construction & Community Outreach 0.00 (-0.08, 0.07)T2, Fine motor skills (4.5 y0.)<br>(Berkes et al 2079) KHM Preschool Construction & Community Outreach & Parental Engagement 0.05 (-0.01, 0.12) T3, Fine motor skills (4.5 y.0.)<br>(Berkes et al 2079) KHM Preschool Construction 0.02 (-0.08, 0.04)71, Fine motor skills (6.5 y.0.)<br>(Berkes et al 2019) KHM Preschool Construction & Community Outreach —>— 0.08 (-0.14, 0.0172, Fine motor skills (5.5 y.0.)<br>(Berkes. et al 2019) KHM Preschool Construction & Community Outreach & Parental Engagement 0.05 (-0.12. 0.07)T3, Fine motor skills (5.5 yo.)<br>(Bouguen etal. 2078) KHM Preschool construction & TT & Provision of material 0.10 (-0.00, 0.20) T.ASO - Gross Motor (46 y.0.)<br>(Bouguen etal. 2078) KHM Preschool construction & TT & Prowigion of material “0.07 (-0.21, O.08)T, ASO - Fine Motor (4-6 yo.)<br>(Bouguen etal. 2018) KHM Preschool construction & TT & Provision of material 0.01 (-0.09, 0.08)T. Heightfor Age (445 y.o.}<br>(Bouguen etal. 2078) KHM Preschool construction & TT & Provision of material 0.04 (-0.10, Q.08)T. Weightfor Age (448 y.0.)<br>(Bouguen etal. 2078) KHM Preschool construction & TT & Provision of material 0.04 (-0.08, 0.71) T. Motor development index (446 yoo.)<br>(Bouguen etal. 2078) KHM Preschool construction & TT & Provision of material 0.11 (-0.08, 0.08)T, Anthropometrics inde: (4-8 yo.)<br>(Martinez et al. 2017) Moz Preschool Construction & TT & Parental Engagement _ 0.15 (0.07, 0.24) T. ASO - Fine Motor (3-5 y.0.)<br>(Martinez et al. 2017) Moz Preschool Construction & TT & Parental Engagement 0.08 (-0.03, 0.18) T, ASQ - Gross Motor (3-5 y.0.)<br>(Martinez et al. 2017) Moz Preschool Construction & TT & Parental Engagement 0.06 (-0.06, 0.70) T. Health index (3-5 yo.)<br>(Ozer at al. 2018) MV TT & Coaching 0.11 (-0.22, 0.01)T1, Fine motor skills (5.5 yo.)<br>(Ozler et al 2018) MW TT & Coaching & Teacher Stipend 0.412 (40.24, 0.01)T2, Fine motor skills (6.5 y.0.)<br>(Ozler et al. 2013) MW TT & Coaching & Parental Engagement 0.01 (-0.11, 0.13) T3, Fine motor skills (5.5 y.0.)<br>NOTE: Weights are from random effects analysis<br>434 0 454<br><!-- End of picture text -->
# Health outcomes - (Post-preprimary & Adult)

|Study|countryce|de<br>Type of interventian||Es(95%Cl)<br>‘Outcome_variable|
|---|---|---|---|---|
|High Income - Basic|||||
|(Brotmanetal.2018)|USA,|TT& Curiculum &ParentalEngagement|—-|0.44(0.15,0.73)<br>9T.Mental health problems* (7yo.)|
|(Cameiro and Ginja 2014)|USA|(HS) - Subsidized Preschool||0.20 (-1.83, 0.84)<br>T, Drug use* (12-13 y.o.) Male|
|(Cameiro and Ginja2014)|Usa|(HS) -Subsidized Preschool|——|0.26 (0.04,0.48)<br>T,Qwenweight* (12-13yo.) Male|
|(Cameiro and Ginja2014)|USA|(HS) -Subsidized Preschool||0.63 (-0.19, 1.43)<br>T,Aleoholuse* (12-13 y.9.)Male|
|(Cameiro and Ginja2014)|USé|(HS) -Subsidized Preschool||0.40 (-0.48, 1.28)<br>T.Ewer smoke* (12-13 yo.) Male|
|(Cameiro and Ginja 2014)|USA|(HS) -Subsidized Preschool||0.38 (-0.04,0.81)<br>T, Frequent visits to doctor’(12-13 y.0.)Male|
|(CarmeinoandGinja2014)|USA,|(HS)-SubsidizedPreschool||><br>2.00(2.00,2.00)<br>T,Useofmedicines*(12-12y.o.)Male|
|<br>(Cameiro and Ginja 2014)|USA|<br>(HS) - Subsidized Preschool|—|<br> <br>0.24 (0.05,0.43)<br>TT, Owenweight* (16-17 yoo.) Male|
|(Cameiro and Ginja 2014)|Usa|(HS) - Subsidized Preschool||0.23 (-0.60, 1.06)<br>T, Birth control (16-17 yo.) Male:|
|(Carmeinoand Ginja 2014)|USA,|(HS) -Subsidized Preschool||0.68 (-0.42, 1.79)<br>T. Health status (16-17 yo.)Male|
|(Cameiro and Ginja 2014)|USA|(HS) - Subsidized Preschool||0.04 (0.08, 0.11)<br>7. Ever drunk* (16-17 yo.) Male|
|(Cameiro and Ginja 2014)|USé|(HS) - Subsidized Preschool||0.18 (-0.60, 0.87)<br>T, Eversex* (16-17 yo.) Male|
|(Carmeino and Ginja2014)|USA,|(HS) -Subsidized Preschool|+>|0.19(0.07,0.31)<br>T.GESD Depression percentile*(16-17yo.) Male|
|High Income -Adult|||||
|(Rossin-Slater<br>and Wust 2020|) DNK|Preschool Construction||0.04 (0.02,0.06)<br>T. Survival beyondage95(30-50 y-o.}|
|(Cameiro and Ginja 2014)|USé|(HS) - Subsidized Preschool||0.30 (-0.82, 1.22)<br>T. Birth control (20-21 yo.) Male|
|(Heckmanet al2010)|USA|Subsidized Preschool||0.34 (-0.84, 1.52)<br>T,No healthproblems (19yo.) Female|
|(Heckmanet 312010)|usA.|Subsidized Preschool||0.20 (-0.10, 1.70) T, Alive(40 y.c.) Female|
|(Heckmanet al2010)|USA|Subsidized Preschool||0.18 (-0.49, 0.84)<br>T,Notobaccouse(27 y.o.)Female|
|(Heckmanet al2010)|USA,|Subsidized Preschool||0.234 (-0.89, 1.57)<br>T. Infrequent alcoholuse (27y.o.)Female|
|(Heckmanet al2010)|USA,|Subsidized Preschool||0.20 [-1.34, 0.93) T. Routine annualhealthexam (27 y-0.) Female|
|(Heckmanet al2010)|USA|Subsidized Preschool||-0.58 (-1.23, 0.07) T.No healthproblems (19 y-o.) Male|
|(Heckmanet al2010)|Usa|Subsidized Preschool||0.80 (-0.10, 1.70) T, Alive(40 y.0.) Male|
|(Heckmanet al2010)|USA|Subsidized Preschool||0.20 (-0.20, 0.70)<br>T.Notobaccouse (27yo.)Male|
|(Heckmanet al2010)|Usa|Subsidized Preschool||0.52 (-0.17, 1.21)<br>T. infrequent alcohol use (27y.o.)Male|
|(Heckmanet al2010)|USA|Subsidized Frescheal||0.08 (-0.05,0.10)<br>T, Routine annualhealthexam (27 y.0.) Male:|
|Low & Middle Income - Basic|||||
|(Brinkmanet al.2017)|IDN|Preschool Construction &TT&CommunityOutreach||0.10 (-0.04, 0.25)<br>T,Physical healthandwell-being (ED!) (8-9 y.0.)|
|(Martinez et al. 2017)|Moz|Preschool Construction £TT& Parental Engagement||0.20 (-0.00, 0.60)<br>T, Physical healthandwell-being (EDI) (8 y.o.)|
|NOTE: Weights arefromran|dom effects a|nalysis|||
|||-2|o|2|
# **Appendix 5. Included studies**

- Andrew, A., O. Attanasio, R. Bernal, L.C. Sosa, S. Krutikova, and M. Rubio-Codina. 2019. _Preschool quality and child development ._ (No. w26191): National Bureau of Economic Research.

- Bailey, M.J., B.D. Timpe, and S. Sun. 2020. _Prep School for poor kids: The long-run impacts of Head Start on Human capital and economic self-sufficiency ._ (No. w28268): National Bureau of Economic Research.

- Baker, M., J. Gruber, and K. Milligan. 2008. "Universal child care, maternal labor supply, and family well-being." _Journal of Political Economy,_ 116 (4): 709-745.

- Bando, R., E. Näslund-Hadley, and P. Gertler. 2019. _Effect of inquiry and problem based pedagogy on learning: Evidence from 10 field experiments in four countries._ (No. w26280): National Bureau of Economic Research.

- Barnett, W.S., K. Jung, D.J. Yarosz, J. Thomas, A. Hornbeck, R. Stechuk, and S. Burns. 2008. "Educational effects of the Tools of the Mind curriculum: A randomized trial." _Early Childhood Research Quarterly_ 23 (3): 299-313.

- Berkes, J., A. Bouguen, D. Filmer, and T. Fukao. 2019. _Improving Preschool Provision and Encouraging Demand: Heterogeneous Impacts of a Large-Scale Program._ Washington D.C.: The World Bank Working Paper Series.

- Berlinski, S., S. Galiani, and M. Manacorda. 2008. "Giving children a better start: Preschool attendance and school-age profiles." _Journal of Public Economics_ 92 (5-6): 1416-1440.

- Berlinski, S., S. Galiani, and P. Gertler. 2009. "The effect of pre-primary education on primary school performance." _Journal of Public Economics_ 93 (1-2): 219-234.

- Bierman, K.L., C.E. Domitrovich, R.L. Nix, S.D. Gest, J.A. Welsh, M.T. Greenberg, C. Blair, K.E. Nelson, and S. Gill. 2008. "Promoting academic and social‐emotional school readiness: The Head Start REDI program." _Child Development_ 79 (6): 1802-181.

- Bitler, M.P., H.W. Hoynes, and T. Domina. 2014. _Experimental evidence on distributional effects of Head Start._ (No. w20434): National Bureau of Economic Research.

- Blair, C., and C.C. Raver. 2014. "Closing the achievement gap through modification of neurocognitive and neuroendocrine function: Results from a cluster randomized controlled trial of an innovative approach to the education of children in kindergarten." _PloS One_ 9 (11): e112393.

- Blimpo, M., P.M. Carneiro, P. Jervis, and T. Pugatch. 2019. _Improving access and quality in early childhood development programs: Experimental evidence from the Gambia._ Washington D.C.: World Bank Policy Research Working Paper, (8737).
Bloom, H.S., and C. Weiland. 2015. _Quantifying variation in Head Start effects on young children's cognitive and socio-emotional skills using data from the National Head Start Impact Study._ Available at SSRN 2594430.

- Bouguen, A., D. Filmer, K. Macours, and S. Naudeau. 2018. "Preschool and parental response in a second best world evidence from a school construction experiment." _Journal of Human Resources_ (The World Bank Working Paper Series) 53 (2): 474-512.

- Bowne, J.B., H. Yoshikawa, and C.E. Snow. 2016. "Experimental impacts of a teacher professional development program in early childhood on explicit vocabulary instruction across the curriculum." _Early Childhood Research Quarterly_ 34: 27-39.

- Brinkman, S.A., A. Hasan, H. Jung, A. Kinnell, and M. Pradhan. 2017. "The impact of expanding access to early childhood education services in rural Indonesia." _Journal of Labor Economics_ 35 (S1): S305-S335.

- Brotman, L.M., S. Dawson-McClure, D. Kamboukos, K.Y. Huang, E.J. Calzada, K. Goldfeld, and E. Petkova. 2016. "Effects of ParentCorps in prekindergarten on child mental health and academic performance: Follow-up of a randomized clinical trial through 8 years of age." _JAMA Pediatrics_ 170 (12): 1149-1155.

- Carneiro, P., and R. Ginja. 2014. "Long-term impacts of compensatory preschool on health and behavior: Evidence from Head Start." _American Economic Journal: Economic Policy_ 6 (4): 13573.

- Clements, D.H., J. Sarama, M.E. Spitler, A.A. Lange, and C.B. Wolfe. 2011. "Mathematics learned by young children in an intervention based on learning trajectories: A large-scale cluster randomized trial." _Journal for Research in Mathematics Education_ 42 (2): 127-166.

- Clements, D.H., J., Sarama, C.B. Wolfe, and M.E. Spitler. 2013. "Longitudinal evaluation of a scaleup model for teaching mathematics with trajectories and technologies: Persistence of effects in the third year." _American Educational Research Journal_ 50 (4): 812-850.

- Diamond, A., W.S. Barnett, J. Thomas, and S. Munro. 2007. "Preschool program improves cognitive control." _Science_ 318 (5855): 1387.

- Dillon, M.R., H. Kannan, J.T. Dean, E.S. Spelke, and E. Duflo. 2017. "Cognitive science in the field: A preschool intervention durably enhances intuitive but not formal mathematics." _Science_ 357 (6346): 47-55.

- Drange, N., T. Havnes, and A.M. Sandsør. 2016. "Kindergarten for all: Long run effects of a universal intervention." _Economics of Education Review_ 53: 164-181.

- Felfe, C., N. Nollenberger, and N. Rodríguez-Planas. 2015. "Can’t buy mommy’s love? Universal childcare and children’s long-term cognitive development." _Journal of Population Economics_ 28 (2): 393-422.

- Frisvold, D.E., and J.C. Lumeng. 2011. "Expanding exposure can increasing the daily duration of head start reduce childhood obesity?" _Journal of Human Resources_ 46 (2): 373-402.
- Gallego, F.A., E. Näslund-Hadley, and M. Alfonso. 2021. "Changing Pedagogy to Improve Skills in Preschools: Experimental Evidence from Peru." _The World Bank Economic Review_ 35 (1): 261-286.

- Ganimian, A.J., K. Muralidharan, and C.R. Walters. 2021. _Augmenting State Capacity for Child Development: Experimental Evidence from India._ (No. w28780): National Bureau of Economic Research.

- Gormley, W.T., D.A. Phillips, K. Newmark, K. Welti, and S. Adelstein. 2011. "Social‐emotional effects of early childhood education programs in Tulsa." _Child Development_ 82 (6): 20952109.

- Gray-Lobe, G., P.A. Pathak, and C.R. Walters. 2021. _The Long-Term Effects of Universal Preschool in Boston._ (No. w28756): National Bureau of Economic Research.

Hamre, B. K., R. C. Pianta, A. J. Mashburn, and J. T. Downer. 2012. "Promoting young children's social competence through the preschool PATHS curriculum and MyTeachingPartner professional development resources." _Early Education & Development_ 23 (6): 809-832.

- Havnes, T., and M. Mogstad. 2015. "Is universal child care leveling the playing field?" _Journal of Public Economics_ 127: 100-114.

- Havnes, T., and M. Mogstad. 2011. "No child left behind: Subsidized child care and children's long-run outcomes." _American Economic Journal: Economic Policy_ 3 (2): 97-129.

Heckman, J.J., S.H. Moon, R. Pinto, P.A. Savelyev, and A. and Yavitz. 2010. "The rate of return to the HighScope Perry Preschool Program." _Journal of Public Economics_ 94 (1-2): 114-128.

- Johnson, R.C., and C.K. Jackson. 2019. "Reducing inequality through dynamic complementarity: Evidence from Head Start and public school spending." _American Economic Journal: Economic Policy_ 11 (4): 310-49.

- Kline, P., and C.R. Walters. 2016. "Evaluating public programs with close substitutes: The case of Head Start." _The Quarterly Journal of Economics,_ 131 (4): 1795-1848.

- Lipsey, M.W., D.C. Farran, and K. Durkin. 2018. "Effects of the Tennessee Prekindergarten Program on children’s achievement and behavior through third grade." _Early Childhood Research Quarterly_ 45: 155-176.

- Martinez, S., S. Naudeau, and V. Pereira. 2017. _Preschool and child development under extreme poverty: evidence from a randomized experiment in rural Mozambique._ Washington D.C.: The World Bank.

Neuman, S.B., and L. Cunningham. 2009. "The impact of professional development and coaching on early language and literacy instructional practices." _American Educational Research Journal_ 46 (2): 532-566.
- Ozler, B., L. C. H. Fernald, P. Kariger, C. McConnell, M. Neuman, and E. Fraga. 2018. "Combining preschool teacher training with parenting education: A clusterrandomized controlled trial." _Journal of Development Economics_ (The World Bank.) 133: 448–467.

- Pianta, R., B. Hamre, J. Downer, M. Burchinal, A. Williford, J. Locasale-Crouch, C. Howes, K. La Paro, and C. Scott-Little. 2017. "Early childhood professional development: Coaching and coursework effects on indicators of children’s school readiness." _Early Education and Development_ 28 (8): 956-975.

- Powell, D.R., K.E. Diamond, M.R. Burchinal, and M.J. Koehler. 2010. "Effects of an early literacy professional development intervention on head start teachers and children." _Journal of Educational Psychology_ 102 (2): 299.

-

- Raver, C.C., S.M. Jones, C. Li Grining, F. Zhai, K. Bub, and E. Pressler. 2011. "CSRP’s impact on low‐income preschoolers’ preacademic skills: self‐regulation as a mediating mechanism." _Child Development_ 82 (1): 362-378.

- Raver, C.C., S.M. Jones, C. Li-Grining, F. Zhai, M.W. Metzger, and B. Solomon. 2009. "Targeting children's behavior problems in preschool classrooms: A cluster-randomized controlled trial." _Journal of Consulting and Clinical Psychology_ 77 (2): 302.

- Raver, C.C., S.M. Jones, C.P. Li-Grining, M. Metzger, K.M. Champion, and L. Sardin. 2008. "Improving preschool classroom processes: Preliminary findings from a randomized trial implemented in Head Start settings." _Early Childhood Research Quarterly_ 23 (1): 10-26.

- Rossin-Slater, M., and M. Wust. 2020. "What is the Added Value of Preschool for Poor Children? Long-Term and Intergenerational Impacts and Interactions with an Infant Health Intervention." _American Economic Journal: Applied Economics_ 12 (3): 255-86.

- Spier, E., K. Kamto, A. Molotsky, A. Rahman, N. Hossain, Z. Nahar, and H. and Khondker. 2020. "Bangladesh Early Years Preschool Program Impact Evaluation."

- Watts, T.W., J. Gandhi, D.A. Ibrahim, M.D. Masucci, and C.C. Raver. 2018. "The Chicago School Readiness Project: Examining the long-term impacts of an early childhood intervention." _PloS One_ 13 (7): e0200144.

- Weiland, C., and H. Yoshikawa. 2013. "Impacts of a prekindergarten program on children's mathematics, language, literacy, executive function, and emotional skills." _Child Development_ 84 (6): 2112-2130.

- Wolf, S. 2019. "Year 3 follow-up of the ‘Quality Preschool for Ghana’interventions on child development." _Developmental Psychology_ 55 (12): 2587.

- Wolf, S., and M.E. Peele. 2019. "Examining sustained impacts of two teacher professional development programs on professional well-being and classroom practices." _Teaching and Teacher Education_ 86: 102873.

- Wolf, S., J.L. Aber, J.R. Behrman, and E. and Tsinigo. 2019a. "Experimental impacts of the "Quality Preschool for Ghana” interventions on teacher professional well-being, classroom quality,
and children’s school readiness." _Journal of Research on Educational Effectiveness_ 12 (1): 1037.

- Wolf, S., J.L. Aber, J.R. Behrman, and M. Peele. 2019b. "Longitudinal causal impacts of preschool teacher training on Ghanaian children’s school readiness: Evidence for persistence and fade‐out." _Developmental Science_ 22 (5): e12878.

- Wong, V.C., T.D. Cook, W.S. Barnett, and K. Jung. 2008. "An effectiveness‐based evaluation of ‐

- five state pre kindergarten programs." _The Journal of the Association for Public Policy Analysis and Management_ 27 (1): 122-154.

- Yoshikawa, H., D. Leyva, C.E. Snow, E. Treviño, M. Barata, C. Weiland, C.J. Gomez, et al. 2015. " Experimental impacts of a teacher professional development program in Chile on preschool classroom quality and and child outcomes." _Developmental Psychology_ 51 (3): 309.