Table 9.
Regression results on multidimensional poverty incidence and deprivation score using multilevel models
| Outcomes | MPI poor | C vector | MPI poor | C vector |
|---|---|---|---|---|
| Sample | All sample | All sample | Sample age < 18 | Sample age < 18 |
| Models | Logistic | Linear | Logistic | Linear |
| Coefficient | Coefficient | Coefficient | Coefficient | |
| Group (Ref: Control) | ||||
| Group χ2(2) | 2.53 | 1.78 | 2.55 | 1.80 |
| Bridges | −0.458 (0.292) | −0.017 (0.013) | −0.456 (0.289) | −0.017 (0.013) |
| Bridges PLUS (Bridges +) | −0.160 (0.230) | −0.008 (0.011) | −0.160 (0.227) | −0.008 (0.011) |
| Time (Ref: Baseline) | ||||
| Time χ2(4) | 49.47*** | 80.63*** | 22.02*** | 34.75*** |
| Year 1 (Y1) | −0.313 (0.230) | −0.012 (0.010) | −0.292 (0.228) | −0.012 (0.010) |
| Year 2 (Y2) | −0.434** (0.191) | −0.022*** (0.006) | −0.393* (0.205) | −0.021*** (0.006) |
| Year 3 (Y3) | −0.744** (0.305) | −0.028*** (0.010) | −0.678** (0.300) | −0.026** (0.010) |
| Year 4 (Y4) | −1.179*** (0.198) | −0.058*** (0.007) | −0.911*** (0.214) | −0.044*** (0.008) |
| Group X time | ||||
| Group X Time χ2(8) | 27.48*** | 34.38*** | 27.37*** | 33.75*** |
| Bridges X Y1 | −0.688** (0.317) | −0.045*** (0.013) | −0.691** (0.315) | −0.044*** (0.013) |
| Bridges + X Y1 | −0.658** (0.276) | −0.033*** (0.012) | −0.654** (0.275) | −0.033*** (0.012) |
| Bridges X Y2 | −0.616** (0.300) | −0.035*** (0.010) | −0.648** (0.311) | −0.035*** (0.010) |
| Bridges + X Y2 | −1.176*** (0.257) | −0.051*** (0.011) | −1.197*** (0.273) | −0.051*** (0.011) |
| Bridges X Y3 | −0.859** (0.404) | −0.048*** (0.013) | −1.026** (0.403) | −0.051*** (0.014) |
| Bridges + X Y3 | −1.168*** (0.381) | −0.057*** (0.013) | −1.241*** (0.375) | −0.060*** (0.013) |
| Bridges X Y4 | −0.195 (0.317) | −0.009 (0.011) | −0.330 (0.382) | −0.016 (0.013) |
| Bridges + X Y4 | −0.888*** (0.266) | −0.032*** (0.011) | −1.167*** (0.327) | −0.044*** (0.013) |
| Constant | 1.054*** (0.203) | 0.347*** (0.009) | 1.040*** (0.201) | 0.347*** (0.009) |
| Variance of school random intercepts | 0.246 (0.084) | 0.001 (0.000) | 0.247 (0.087) | 0.001 (0.000) |
| Variance of child random slopes (time) | 0.259 (0.047) | 0.001 (0.000) | 0.245 (0.053) | 0.000 (0.000) |
| Variance of child random intercepts | 3.255 (0.568) | 0.007 (0.001) | 2.837 (0.534) | 0.007 (0.001) |
| Covariance of child slopes and intercepts | −0.587 (0.127) | −0.001 (0.000) | −0.471 (0.122) | −0.001 (0.000) |
| Variance of residual | N/A | 0.008 (0.000) | N/A | 0.008 (0.000) |
| Outcomes | MPI poor | C vector | MPI poor | C vector |
| Observations | 6231 | 6231 | 5821 | 5821 |
| N | 1382 | 1382 | 1381 | 1381 |
| Pairwise comparisons | ||||
| Y0 bridges versus control | −0.458 (0.292) | −0.017 (0.013) | −0.456 (0.289) | −0.017 (0.013) |
| Y0 bridges + versus control | −0.160 (0.230) | −0.008 (0.011) | −0.160 (0.227) | −0.008 (0.011) |
| Y0 bridges + versus bridges | 0.298 (0.247) | 0.008 (0.011) | 0.296 (0.244) | 0.009 (0.011) |
| Y1 bridges versus control | −1.147*** (0.285) | −0.061*** (0.015) | −1.147*** (0.285) | −0.061*** (0.015) |
| Y1 bridges + versus control | −0.818** (0.265) | −0.042** (0.014) | −0.815** (0.267) | −0.042** (0.014) |
| Y1 bridges + versus bridges | 0.328 (0.256) | 0.020 (0.012) | 0.333 (0.261) | 0.020 (0.013) |
| Y2 bridges versus control | −1.074*** (0.285) | −0.052*** (0.014) | −1.104*** (0.294) | −0.052*** (0.014) |
| Y2 bridges + versus control | −1.336*** (0.281) | −0.059*** (0.013) | −1.357*** (0.292) | −0.060*** (0.013) |
| Y2 bridges + versus bridges | −0.262 (0.243) | −0.008 (0.013) | −0.254 (0.253) | −0.008 (0.013) |
| Y3 bridges versus control | −1.317** (0.381) | −0.065*** (0.015) | −1.482*** (0.393) | −0.068*** (0.016) |
| Y3 bridges + versus control | −1.328*** (0.379) | −0.066*** (0.015) | −1.401*** (0.381) | −0.068*** (0.015) |
| Y3 bridges + versus bridges | −0.011 (0.337) | −0.001 (0.014) | 0.081 (0.354) | −0.000 (0.014) |
| Y4 bridges versus control | −0.654 (0.324) | −0.025 (0.013) | −0.786 (0.405) | −0.033 + (0.014) |
| Y4 bridges + versus control | −1.048*** (0.289) | −0.041** (0.013) | −1.328*** (0.342) | −0.052*** (0.013) |
| Y4 bridges + versus bridges | −0.395 (0.310) | −0.015 (0.013) | −0.541 (0.426) | −0.020 (0.015) |
School-level cluster robust standard errors in parentheses
p < 0.10;
p < 0.05;
p < 0.01;
p < 0.001
“Multilevel models have the advantage of being statistically efficient in accounting for the clustering nature of data, where multiple individuals are nested within each school, and multiple observations across time are nested within each individual. Specifically, children from the same school are likely to be correlated with each other, and the repeated measures for each individual are likely to be not independent. Multilevel models allow us to estimate the school-level random intercepts, individual-level random intercepts, and the individual random slopes across survey time points. In each model, we included study group status (Bridges and Bridges PLUS, with control group serving as the reference group), time dummy(ies), and their interactions. The interactions allowed us to identify the intervention effects accounting for any baseline differences in outcomes of interest across the three study arms before the intervention as well as accounting for changes in outcomes across time shared by the three arms (Wang et al., 2018).” The continuous outcome, C vector, were estimated using linear mixed models (LMMs) with the resulting regression coefficients estimating the mean change in the outcome per unit change in the predictor via the -mixed- command in Stata 14. The binary outcomes, MPI poverty incidence, were estimated using a logistic GLMM via the Stata -melogit- command with the coefficients being reported. We modeled the covariance structure to be unstructured, which made the least assumptions regarding the covariance structure, and we estimated the cluster-adjusted robust standard errors with school ID treated as the clustering variable