Decentralization and Corruption in the Philippines

objective than other measures we use, we examine the effects of both household and
public officials perceptions of corruption on the knowledge variable. The effect of
household corruption perceptions is significant in weighted least squares (regression 8.1)
but only marginally significant if the random effects estimator is used (regression 8.2).
The effect of public officials perceptions of corruption are clearer and highly significant
(at 1%) in both the WLS and the random effects estimation. Unsurprisingly if we use the
combined index on corruption, the effect remains highly significant at 1%. The
coefficient of –0.23 means that a one standard deviation increase in corruption reduces
knowledge of required immunizations by 5.5 or 1/4 th of a standard deviation.
Income was also significant in the regression were income, with more correct
responses in municipalities with richer households. Delays in salary payments at the
municipal –but not the facility- level also seemed to adversely affect knowledge, perhaps
due to the drain of the most qualified personnel.
IV.2 Corruption, waiting time and household satisfaction ratings
Next we asked the users of government services –households-, about both their
satisfaction with government services and a somewhat more precise question on waiting
time. Reassuringly the answers to these questions are negatively correlated with each
other and we also use an index composed of both questions. We created these indices on
satisfaction and waiting time after filtering out the propensity to complain using
equations 4.1 and 4.2. Perceptions of national corruption, which are meant to capture the
“gripe” bias are only significant for the satisfaction question. This suggests that it might
be better to ask precise rather than subjective questions to minimize respondent bias. Of
course precise questions may capture less relevant information, so it may be best to ask
both precise and general questions, which is what we do. The best measure may well be
based on both measures and we construct a third measure based on both satisfaction and
waiting time. If the measure were better we would expect the equation predicting it to
have a better fit, indeed the fit of the equation for the composite index is better than for
either component (0.32 rather than 0.22 or 0.29).
We present the results on the determinants of performance measures based on
household responses in Table 9. These equations are estimated at the barangay level.
This is the best way to estimate the equation because households were asked to rate the
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