**Table 5.16: Cross tabulation of stunting rate against access to modern utilities**

<u>(Proportion of children)</u>

|||Urba<br>|n<br>||Rural<br>||
|---|---|---|---|---|---|---|
||Serv<br>|ice<br>|Pearson<br>|Serv<br>|ice<br>|Pearson<br>|
||Yes|No|Test|Yes|No|Test|
|**Infants: 0-12 months old**|||||||
|Piped water in dwelling or yard|.16|.10|**|.24|.22||
|Community covered by piped water|na|0na||.28|.20||
|Dwelling connected to sewerage|.17|.11||.18|.24||
|Toilet connected to drainage|.17|.03|**<br>**|.05|.25|**|
|Latrine|.17|.03||.24|.25||
|Excusado lavable|.12|.03||.13|.25||
|Only treats water|.15|.21|*|.22|.25||
|Only buys bottled water|.08|.21||0|.25||
|Treats and buys bottled water|.19|.21||.33|.25||
|**Children: 13-59 months old**|||||||
|Piped water in dwelling or yard|.32|.59|***|.60|.60|*|
|Community covered by piped water|.57|.62||.66|.57|<br>|
|Dwelling connected to sewerage|.30|.51|***<br>|.36|.62|***|
|Toilet connected to drainage|.28|.61|***<br>**|.23|.65||
|Latrine|.46|.61|<br>|.62|.65||
|Excusado lavable|.43|.61|*|.30|.65|***|
|Only treats water|.44|.37||.62|.59||
|Only buys bottled water|.19|.37|***|.30|.59|**<br>*|
|Treats and buys bottled water|.27|.37||.40|.59||

_Notes_ : Null hypothesis (homogeneity of users and non-users) is rejected at: ***99%,** 95%,*90%. na: not enough observations were available in this subgroup.

_Source:_ World Bank calculations using the ENCOVI 2000, Instituto Nacional de Estadística – Guatemala However, such cross-tabulations are at best inconclusive since they do not control for the impact of many other health-related variables that are likely to be correlated with access to utility services, such as household income, geographical location, educational attainment, and a variety of other socioeconomic and demographic factors. While in principle it would seem relatively straightforward to control for these in a multivariate regression framework, there are a number of more serious methodological problems that lend caution to modeling the impact of modern utility services on health outcomes.

First, in the case of parentally-reported incidence of disease (such as whether or not children had diarrhea or respiratory illnesses) there may be serious reporting bias, with health-conscious parents being more likely to detect and report the presence of these problems among their children. In this sense, the stunting variable, based on anthropometric measurements, provides a more objective indicator of health status.

Second, there is the serious issue of the potential endogeneity of access to modern utilities in the health production function. That is to say that not only do modern utilities contribute to health, but households with unobserved preferences for health are also more likely to connect to modern utilities. Failure to take this into account could be expected to lead to biased coefficient estimates. The analysis of microenterprise profitability already illustrated how it can be possible to overcome endogeneity problems by using two stage instrumental variables techniques, with the community availability variable acting as an instrument. However, in the case of health production functions, the endogeneity problem affects not only the modern utility coverage variables but also many of the other key explanatory variables, such as family demographics and hygiene behavior. The shear number of potentially endogenous variables complicates the search for instruments and can make the estimation process computationally intractable.

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