health workers at public primary schools and primary health centers in several countries including Peru. They found that 44% of health workers in Peru were absent at least once during two unannounced visits. Moreover, the absence rate was higher in rural areas.

To investigate whether public spending is indeed less effective in rural areas due to differences in quality, I assess the impact of public expenditure on alternative child health outcomes. The results are found in **Table 7.** I ran two IV panel specifications where the probability of suffering a life threatening illness and the weight for height z-score of children are the dependent variables. In rural areas increases in public spending increase the probability of suffering a life threatening illness and reduce weight for age. In contrast, in urban areas public expenditure has not statistically significant impact on these outcomes. The results support the claim that rural areas have less effective public services<sup>50</sup> .

**Table 7: IV Estimation - Impact of changes in public expenditure on changes in child** **<u>health outcomes by area</u>**

|_Dependent variable_|Probability of s<br>threatenin|uffering a life<br>g illness|Z-sc<br>(weight f|ore<br>or age)|
|---|---|---|---|---|
||Rural|Urban|Rural|Urban|
|Total Public Spending|0.060|0.074|-0.128|0.038|
||(0.022)**|(0.047)|(0.026)***|(0.052)|
|Other Controls|YES|YES|YES|YES|
|N|701|1228|693|1216|

Source: Young Lives Study Round 1 and 2, ENAHO 2002, 2006, and Ministry of Economic and Finance Budgetary Expenditure for 2000–2006. Note: Robust standard errors * Significant at 10%; ** significant at 5%; *** significant at 1%. Spending measured in 100 million soles. The instrument for all the IV estimations is unanticipated mining revenues. For all estimations the IV is strong. In all specifications I used as controls child’s age, household characteristics (number of children under 5 years of age, availability of drinking water, household size, and wealth score), community characteristics (price of rice, population size, a dummy for living in the highlands, and a poverty index) and the level of regional debt payments. In addition, for the 1st estimation I include controls for water and air pollution as well as the predicate height for age z-score of the child (calculated using rice prices). In the second estimation I include a control for predicted health status. I clustered the standard errors at the region-year level and I used individual, time, and regional fixed.

50 In all specifications I used as controls child’s age, household characteristics (number of children under 5 years of age, availability of drinking water, household size, and wealth score), community characteristics (price of rice, population size, a dummy for living in the highlands, and a poverty index) and the level of regional debt payments. In addition, for the 1<sup>st</sup> estimation I include controls for water and air pollution as well as the predicate height for age z-score of the child (calculated using rice prices). In the second estimation I include a control for predicted health status. I clustered the standard errors at the region-year level and I used individual, time, and regional fixed.

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