The results do not provide evidence of the existence of differences between districts in remote and non-remote areas, but does provide evidence of differences between urban and rural districts. Models 1 and 2 differ only on that model one includes a dummy variable to control for differences between urban and rural districts. This variable is statistically significant in model 1 and has the expected sign (positive). Yet when the variables labor and number of schools are included, the sign of the urban dummy turns negative and non-statistically significant. This is likely because the underlying reasons for an expected difference between rural and urban districts are precisely driven by a expectedly larger number of schools and lower incidence of student labor in urban districts. When the number of schools and labor are controlled for independently, in addition to the urban dummy, the urban dummy becomes negative perhaps capturing some type of spurious effect. Hence model 2 replaces the urban dummy variable for the variables labor and number of schools per square kilometer variables and results in a higher adjusted r-square. The variable remote is not statistically significant in any of the specifications.

Estimation of the Determinants of Education Net Enrollment

|_Dependent Variable: Net Enrollment Rates_|||||
|---|---|---|---|---|
|Variable|OLS<br>(1)|OLS<br>(2)|Fixed effects<br>(3)|Logit-fixed<br>effects<br>(4)|
|Log education spending (per population in school age)|0.02**|0.02**|0.02**|0.14**|
||(2.67)|(2.61)|(3.11)|[.028]<br>|
|||||(4.03)|
||6.9 e-3|0.01|3.2 e-3|<br>0.018|
|Log average district education spending (per population<br>|(0.8)|(1.32)|(0.43)|[0.003]<br>|
|in school age) from 2001 to 2003||||(0.43)|
|Log gross regional product|1.1 e-2**|7.3 e-3*|2.4 e-3|0.02|
||(2.86)|(1.76)|(.64)|[.003]<br>|
|||||(1.01)|
|Share of education personnel spending in total education|-0.015|-0.01|-3.2 e-4|-2.2 e-2|
|spending|(-.92)|(-0.8)|(-0.20)|[-7.3 e-4]<br>|
|||||(-.03)|
|Poverty head count|-0.08|-0.06*|-0.11*|-0.62*|
||(-2.03)|(-1.46)|(-2.32)|[-.11]|
|||||(-2.39)|
|Remote area|-9.91 e-5|-2.3 e-5|-2.0 e-5|1.8 e-4|
||(-.71)|(-.16)|(-.14)|[-3.34 e-5]|
|||||(-0.26)|
|Road Access|1.5 e-4**|1.3 e-4**|4.01 e-5|2.0 e-4|
||(2.75)|(2.56)|(0.8)|[3.72 e-5]<br>|
|||||(.74)|
|Disaster|-3.02 e-4**|-3.0 e-4**|-1.1 e-4|5.9 e-4|
||(-3.65)|(-3.68)|(-1.55)|[1.1 e-4]|
|||||(-1.62)|
|Dummy for urban/rural districts|0.02*<br>(2.21)||||
|Labor||-0.30**|-0.31**|-1.6**|
|||(-5.59)|(-6.24)|[-.30]|
|||||(-4.79)|
|No. of primary and secondary schools per square||4.5 e-3|1.9 e-3|1.7 e-2|
|kilometer||(1.72)|(.76)|[3.1 e-2]|
|||||(1.22)|
|Constant|0.04|0.15|0.30|-0.73|
||(.27)|(1.0)|(1.97)|(-0.92)|
|Adjusted R-square|.29|.36|.56|---|
|No. Obs.|303|299|299|299|

_Source_ : World Bank staff estimates.

_Note:_ t-statistics reported in parentheses **,*,+ denote significance at the 1 percent, 5 percent and 10 percent level, respectively. Column 4 reports the elasticity at mean values of the explanatory variables in brackets.

Gross regional product, road access, and natural disaster variables are significant in the first and second models but turn not significant when the unobserved characteristics at the province level are controlled for. This may reflect the fact that these characteristics are commonly shared by all districts in determined provinces and thus captured by the province dummies.

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