**Table 2. OLS Regressions: Factors affecting health status**

(White heteroskedasticity-corrected t-statistics in parentheses)

|_Dependent variables-4_|_Under-5_|_mortality(na_|_turallog)_|_Infant_|_mortality(natu_|_rallog)_|
|---|---|---|---|---|---|---|
|**_Independent variables_**I|_Eq.(2.1)_|_Eq.(2.2)_|_Eq.(2.3)_|_Eq.(2.4)_|_Eq. (2.5)_|_Eq.(2.6)_|
|<br>GDPpercapita inPPPadjusted1990$(In)|<br>-0.44|-0.37|-0.33|-0.47|-0.39|-0.35|
||(-4.94)|(-4.35)|(-3.55)|(-5.82)|(-5.30)|(-4.16)|
|Publichealthspending(Inofshareof|-0.19|0.08|0.15|-0.19|0.09|0.18<br>|
|GDP)|(-2.45)|(0.68)|(1.47)|(-3.02)|(0.99)|(2.25)|
|Indexofcorruption (leastcorrupt=6)||-0.04|||-0.03||
|||(-1.03)|||(-0.91)||
|Qualityofbureaucracy(highest=6)|||0.03|||_0.05_|
||||(0.79)|||(1.56)|
|Indexofcorruptionxpublichealth||-0.07|||-0.08||
|<br>spending(InofshareofGDP)||(-2.36)|||(-2.92)||
|Qualityofbureaucracyxpublic health|||-0.10|||-0.12|
|<br>spending(InofshareofGDP)|||(-3.77)|||(-4.61)|
|<br>Female education|-0.01<br>|-0.01<br>|-0.01<br>|-0.008<br>|-0.008<br>|-0.009<br>|
||(-3.57)|(-3.78)|(-4.22)|(-2.94)|(-3.20)|(-3.95)|
|Incomeinequality|0.009|0.007|0.006|0.01|0.007|0.007|
||(2.14)|(1.61)|(1.51)|(2.41)|(1.70)|(1.84)|
|PredominantlyMuslim|-0.0004|-0.002|-0.001|-0.0003|-0.001|-0.001|
||(-0.26)|(-1.24)|(-0.76)|(-0.27)|(-1.19)|(-0.91)|
|Ethno-linguisticfractionalization|0.40|0.52|0.57|0.09|0.22|0.25|
||(3.00)|(4.15)|(4.29)|(0.73)|(1.99)|(2.18)|
|Accesstosafewater|-0.002|-0.002|-0.003|-0.003|-0.003|-0.004<br>|
||_(-0.55)_|(-0.74)|(-0.93)|(-1.03)|(-1.16)|(-1.64)|
|Degreeofurbanization|0.004|0.002|0.004|0.004|0.002|0.004|
||_(1.45)_|(0.93)|(1.43)|(1.47)|(0.96)|(1.56)|
|Percentageofpopulationagedunder5|0.04<br>|0.04<br>|0.04<br>||||
||(2.43)|(2.74)|(2.73)||||
|Percentageofpopulationagedunder1||||0.16|0.17|0.16|
|||||(2.45)|(2.88)|(2.70)|
|Distance fromtheEquator|-0.15|0.24|0.23|-0.10|0.28|0.32|
||(-0.51)|(0.78)|(0.73)|(-0.38)|(1.05)|(1.16)|
|Dummyfor year1997|<br>0.05|<br>-0.002|<br>0.01|0.02|-0.02|-0.01|
||(0.88)|(-0.03)|(0.03)|(0.41)|(-0.49)|(-0.17)|
|Constant|<br>7.25|<br>6.90|<br>6.40|<br>7.15|<br>6.74|6.26|
||(9.01)|(8.97)|(7.80)|(10.37)<br>|(10.22)<br>|(8.50)<br>|
|R-squared|.92|.94|.93|.91|.93|.93|
|<br>Numberofobservations|148|148|148|169|169|169|

Earlier in this section we have discussed creating a measure of "efficacious" public spending on health. To do this, we interact public health spending with the index of corruption and include this as an additional regressor. To capture the direct effect of corruption on health status, we also include the index of corruption independently. This result is reported in equation (2.2).6 The coefficient on the public health spending is positive and statistically insignificant, but

> 6 One alternative to the standard OLS technique is to allow for serial correlation between two error terms corresponding to the same country. Since we have only two years of time series, there will be at most two error terms per country. Allowing for this type of correlation, and re-running the regressions using the Generalized Least Squares method, our results are very similar to those obtained using OLS. We chose to report the latter because they are considerably more robust to different specifications and sets of independent variables.

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