industrial policy, its spatial aspects and cluster development in ...

Province× year fixed effects included Yes Yes
Pseudo R 2 0.130 0.131
Number of observations 349295 349295
Notes: All regressions are estimated by probit maximum likelihood. Numbers in parenthesis are
asymptotic t-values obtained from robust standard errors adjusted for clustering at the district level.
The regressions include intercept terms, but they are not reported. **, and *** denote statistical
significance at the 5 and 1% levels, respectively. For definition of control variables, see Table 5.5.
stronger pro poor effects in low and medium inequality regions. The magnitude of the
effect suggests that a one SD increase in post-primary school system index leads to a
0.26% reduction in rural poverty in low-inequality districts as compared with 0.08%
poverty reduction in medium-inequality districts. The impact of present levels of Gini
inequality on poverty is relatively weak in our sample where low inequality districts do
not immediately benefit from these investments while the impact on medium-inequality
districts roughly remains unchanged.
5.3.4 Road infrastructure and poverty: Does inequality matter?
How investment in regional road infrastructure affects rural poverty under different
inequality regimes? We investigate the effects of changes in regional road infrastructure
on poverty by classifying districts into three categories of inequality. Low inequality
districts are those where Gini index is 0.5 standard deviations below the mean; high
inequality refer to districts where Gini index is 0.5 standard deviations above the mean
and medium inequality districts refer to all other districts. We incorporate road density
in respective districts into the model by interaction of these three dummy variables with
the road density to capture the slope differentials of road density. Thus the effect of a
change in the district level index of road density on the probability of poverty under
alternative inequality regimes is captured by these interaction terms.
Regression results shown in column (1) of Table 5.10 reveal that holding all else as
constant increase in road density significantly decreases the probability of poverty in all
the low, medium and high inequality districts. For example, a one standard deviation
increase in road density (i.e., 0.125) demonstrates highest levels of poverty reduction in
the low-inequality districts (5.8%), followed by the medium-inequality districts (3.9%)
and then the high-inequality districts (3.5%). Column (2) presents the results of increase
in road density with starting levels of inequality and shows that a one standard deviation
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