<mark>analysis than that of a municipality expanding its childcare centers such that there are 15 more seats per 1,000 children. Because papers using the same empirical approach tend to have similar treatments, the results are more comparable within each methodology than across methodologies.</mark>

# **<mark>RESULTS BY ANALYSIS</mark>**

<mark>The 22 papers included in our review that attempt to identify impacts of institutional childcare rely on a wide range of methodologies: Randomized Controlled Trials, Regression Discontinuity, Difference-in-Difference, Quasi-Experimental, and Instrumental Variables designs. Each of the methods offers advantages and limitations for interpretation of findings for policy design. Two dimensions are of particular importance: plausibility of causal interpretation and external validity. We organize our discussion below moving through this continuum.</mark> <mark>We start by discussing RCTs, the gold standard for causal interpretations but the findings of which are frequently limited to a specific population and may not warrant extrapolation to a similar intervention implemented at the national scale. Three studies are included in this category.</mark> <mark>We next discuss RD design studies, which largely rely on discontinuity in the age of eligibility for preschools. In this category, again, a number of assumptions need to be tested for causal interpretation of impacts (such as continuous density around the cutoff point and similarity of individuals on either sides of the cutoff point). Similarly, these studies provide estimates around the discontinuity, which may not lend to an extrapolation for populations further away from discontinuity (e.g., for mothers of children of different ages). Three studies in our review fall into this category.</mark> <mark>The largest category in our review relies on DiD design (either double or triple differences). It includes nine papers, which largely take advantage of national rollouts of institutional childcare programs. These papers offer estimates that are more likely to be valid for the national level programs.</mark><sup>4</sup> <mark>However, a number of assumptions need to hold for the resulting estimates to be interpreted as causal. Some of these assumptions are not testable; for example, we cannot test if the error term conceals unobserved heterogeneity correlated with preschool rollout and maternal labor market outcomes. Others, such as the parallel trends assumption, are testable but they are not consistently tested in every paper in the review. We point to these limitations when discussing the results.</mark> <mark>The last two categories rely on quasi-experiments using waitlists (two papers) and IV (five papers) approaches. Both approaches require more stringent assumptions for causal interpretations, compared to RCTs and RD designs; and unlike DiD designs, they are limited to specific populations.</mark><sup>5</sup>

> 4 Some of the programs analyzed targeted low-income populations; in such cases, the estimates are valid for the country level population of eligible households (low-income households, for example).

> 5 <mark>While IV studies may generate national level estimates, studies included in this review were limited to specific populations and small-scale programs.</mark>

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