Analyses
We used an intention-to-treat (ITT) analysis to assess the impact of treatment assignment on four educational outcomes among children over 6 years old at midline. namely: 1) overall school enrollment, 2) domestic responsibilities as the primary reason for non-enrollment (among the unenrolled), 3) average days of school per week, and 4) average hours of school per day among older siblings. We relied on the following survey items to assess time spent in school: “In the current/most recent school year, in a typical week, how many days does (the child) attend school?” and “In the current/most recent school year, in a typical day, how many hours does (the child) attend school?”. We used a more general survey item (“Is the child currently attending school this year as a student?”) to measure overall school enrollment. We also examined non-enrollment in school due to domestic obligations by recoding the survey item “Why is (the child) not in school this year?”. This item originally contained 15 possible responses (“Too old”, “Ill/disabled”, “Lost interest”, etc.). We were primarily interested in capturing children who were “Needed for housework” or required to “Care for younger siblings”. Because domestic duties and childcare responsibilities likely overlap, both in practice and likely in the minds of survey respondents, we combined these two categories to form a single indicator variable reflecting whether a child was unenrolled in school during the survey year due to domestic (household or childcare) responsibilities.
All models accounted for the baseline value of the outcome variable of interest and geographic clustering/block group; models also incorporated cluster-robust standard errors (at the hamlet level). We used ordinary least squares (OLS) models for continuous outcomes. For dichotomous outcomes, we produced estimates with both logistic and log-binomial models; logistic models are a more common approach, but binomial models (despite their potential convergence issues) allow us to easily estimate risk ratios instead of odds ratios and are appropriate when the outcome is common. We extended our basic models in two ways: first, we stratified by sex to examine the potentially heterogeneous effect of the childcare intervention on girls versus boys. Second, we conducted a per-protocol analysis to assess the effect of the intervention on households that fully complied with their treatment allocation. We defined a “compliant” household as one where all eligible children (6 years old or younger at midline) abided by the household-level treatment assignment. This is an imperfect definition, as a household could be coded as non-compliant even if most (but not all) of its eligible children acted in accordance with their treatment assignment. We expect that more sophisticated markers of compliance will be available at a later date, but this preliminary strategy allows us to better estimate the impact of the intervention among children who were actually exposed. We also stratified these models by sex. All analyses were conducted in Stata 14 (StataCorp. 2015. Stata Statistical Software: Release 14 . College Station, TX: Stata
Results
Descriptive characteristics of the subsample are summarized in Table 1 . The sample remained well-balanced following restriction: treatment and control groups were quite similar across all attributes at baseline. Use of balwadi (Seva Mendir) was reported for 16.4% of all children in treated households at midline, but restricting to eligible children (under 6 years old) yields a more accurate treatment uptake estimate of 28.5%. This was lower than anticipated, likely due to the short window of time (and thus limited opportunity for uptake) from baseline to midline; we expect that household-level uptake of the intervention will increase substantially by the final wave of the study. Use of balwadi among children residing in control households was low (3.2%), as anticipated.
Educational outcomes, including the ability to both read and write and the average time spent in school per day/week, improved slightly in both treatment and control groups from baseline to midline; this is to be expected, as many of these factors would be expected to increase with age. The striking shifts in where children spent most of their day from baseline to midline are likely attributable to seasonal shifts in school attendance (responses to this question would vary based on when in the calendar year they are surveyed) (Verify?).