test for the validity of the instrument set and the first stage regressions are given in Table
A1. The instruments are both individually and jointly significant in the first stage
regressions of the two endogenous variables. There is also strong evidence of failure to
reject the exogeneity of the instrument set.
Though the result of how education can break the cycle of low income persistence
for the poor is interesting by itself, deriving relevant policy recommendations require
further analysis as to the level of education that can achieve the required results. Table
A2 in the appendices presents regression results of model (5) with four different
specifications for the education variable: continuous (as in the original model) and three
binary variables indicating completion of primary school, completion of secondary
school, and at least some college/professional training. The results show that attainment
of at least a primary school education made no significant contribution to household
income and also failed to significantly reduce income persistence or enhance
convergence. This result is contrary to households whose head had attained either a
secondary or some college education. In these cases, the respective education variable
was highly significant and also caused great reductions in the positive coefficient for the
LID. This is an indication of the role of post-primary education in feeding income growth
and also in breaking (low) income persistence for the poor.
Based on the above findings, Table 3 presents the regression results of the models
with secondary education disaggregated by poverty status. These results show that for
households that are below the poverty line, those whose heads have less than secondary
educations are locked up in a cycle of low income persistence and cumulative
disadvantage. This is in contrast to their counterparts with at least a secondary education
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