The table reports only the marginal effect estimates of distance to primary and secondary schools on enrollment. The models include all the covariates included in the models underlying Table 1 that were collected in both years. Unlike the models in Table 1 the variable that controls for household wealth is constructed from data pooled across the two years.

In Niger, data can only be matched at the regional level so the model for that country includes fixed-effects for the six regions. In India the data can be matched at the district level so two models are estimated: one with fixed-effects for the 26 states, and one with fixed-effects for the 440 districts. In Bangladesh the data can be matched all the way down to the cluster level so models that include region, district, or cluster fixed-effects can be estimated.

The results from Bangladesh suggest that controlling for the fixed cluster attributes may well affect the magnitude and significance of the results—but not always in a way that biases against finding large impacts of distance on enrollment. The cross-sectional results suggest that distance to primary school is significantly associated with enrollment (at least at the 10 percent level). But the cluster fixed-effects estimates imply that the impact of distance is smaller in absolute value (–.011 versus –.023 in 1993-94 and –0.029 in 1996-97) and no longer significantly different from zero. In this case the cross-sectional estimates _over_ -estimate the impact of distance on enrollment. Introducing fixed-effects at intermediate levels (state, district) results in progressively smaller point estimates.

**Table 2: Marginal effects of school distance variables from probit model of school participation, rural children aged** **<u>6 to 14</u>**

||Distancetoneares <br>|Margin<br>tprimary school<br>|al effects<br>Distancetonearest<br>|secondary school<br>|
|---|---|---|---|---|
||Marginaleffect|Standard error|Marginaleffect|Standard error|
|**Bangladesh**|||||
|1993-94|-.0225|.0103*|-.0136|.0041**|
|1996-97|-.0291|.0147|-.0042|.0066|
|Pooled|-.0264|.0096**|-.0084|.0039*|
|Pooled region fixed-effects (5 groups)|-.0232|.0090*|-.0081|.0040*|
|Pooled district fixed-effects (61 groups)|-.0179|.0058**|-.0103|.0022**|
|Pooled cluster fixed-effects (258 groups)|-.0110|.0106|-.0123|.0040**|
|**India**|||||
|India 1992-93|-.0008|.0015|-.0007|.0006|
|India 1998-99|.00002|.0007|-.0009|.0006|
|Pooled|-.0004|.0006|-.0008|.0004|
|Pooled state fixed-effects (26 groups)|-.0004|0.0008|-.0010|.0004**|
|Pooled district fixed-effects (440 groups)|-.0004|.0008|-.0012|.0003**|
|**Niger**|||||
|1992|-.0077|.0033*|.00002|.0002|
|1998|-.0237|.0041**|-.0010|.0003**|
|Pooled|-.0123|.0034**|-.0004|.0003|
|Pooledregion fixed-effects (6 groups)|-.0124|.0034**|-.0004|.0003|

_Note_ : Selected results from a model that includes child’s age, adults’ education, characteristics of the head of household, and a set of cluster characteristics. Cluster characteristics are selected to be consistent across the two rounds of data and therefore have minor differences with those in the models included in Table 1. *, ** indicate significance of the underlying probit coefficients at the 5, and 1 percent, respectively. _Source:_ Author’s calculation from DHS data.

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