It is important to note that the cost estimates for universal coverage that were presented above were based on the assumption that new infrastructure investments would be needed to reach all households that are currently unserved. (Table 3.11). The analysis of the coverage deficit suggests that this is not in fact the case, and that a significant part of the coverage gap could be bridged by removing barriers that prevent households connecting to existing networks. Overall, it is estimated that this factor could reduce the cost of meeting universal access by as much as 30%, from US $1.4 billion to US$ 1.0 billion.

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Demand side problem<br>only<br>Both supply and<br>demand side problem<br>Supply side problem<br>only<br>ElectricityPiped waterSewerageFixed line telephoneCellular telephone<br>Percentage of households<br><!-- End of picture text -->

**Figure 3.7: Decomposition of coverage deficit**

_Notes_ :<sup>♦</sup> Piped water in dwelling or yard.

_Source:_ World Bank calculations using the ENCOVI 2000, Instituto Nacional de Estadística – Guatemala In order to understand the correlations between specific household characteristics and the decision of whether to connect to a utility service that is already available in the community, a probit regression is used to control for other economic, cultural and geographic variables that may be related with the decision to connect to a service (Table 3.13).

**Table 3.13: Take-up of modern utilities and household characteristics**

<u>(Marginal effects from probit regression are reported)</u>

|Variable|Electricity|Water|Sanitation|Sewerage|Fixed Phone|Cell Phone|
|---|---|---|---|---|---|---|
|Household head characteristics<br>Male|-.021<sup>**</sup><br><sup>*</sup>|-.027<sup>**</sup><br><sup>***</sup>|-.003<br><sup>***</sup>|-.081<sup>***</sup>|-.047<br><sup>***</sup>|.047<br><sup>**</sup>|
|Age|5x10<sup>-4</sup><br>|.001<br>|7x10<sup>-4</sup><br>|.001<br>|.011<br>|-.002 <br>|
|Years of school|.011<sup>***</sup>|.013<sup>***</sup>|.008<sup>***</sup><br>|.015<sup>***</sup><br>|.035<sup>***</sup><br>|.021<sup>***</sup>|
|Indigenous|-.016|-.004|.018<sup>**</sup>|-.067<sup>**</sup>|-.079<sup>*</sup><br>|-.046|
|Speaks Spanish|.026|.010|.016|.010|-.214<sup>*</sup>|.027|
|Household characteristics|||||||
|Business in dwelling|.035<sup>***</sup>|.004<br>|.009<br>|.009<br>|.085<sup>**</sup><br>|.026<br>|
|Income|.003<br>|.007<sup>***</sup><br>|.007<sup>***</sup><br>|.013<sup>***</sup><br>|.025<sup>***</sup>|.007<sup>***</sup><br>|
|Urban area|.056<sup>***</sup>|.052<sup>***</sup>|.063<sup>***</sup>|.280<sup>***</sup>|-.015|-.071<sup>***</sup>|
|Regional dummies|||||||
|Metropolitan|.043<sup>*</sup><br>|.024|.007<br>|.022|-.064<br>|.042|
|North|-.041<sup>*</sup><br><sup>*</sup>|-.038|.031<sup>***</sup><br><sup>**</sup>|-.059|-.160<sup>***</sup>|-.039|
|Northeast|.031|-.064|-.054<br>|-.106|-.034|-.010|
|Southeast|.021|-.024|-.059<sup>***</sup>|-.011<br>|-.033|-.032|
|Central|.019|-.027|.008|.081<sup>***</sup><br><sup>**</sup>|-.080|-.020|
|Southwest|.023<br>|.013|.002<br>|.083<br>|-.035|-.016|
|Petén|-.070<sup>**</sup>|.020|-.080<sup>***</sup>|-.595<sup>***</sup>|-.065|.009|
|F(15,1043)|15.05|10.60|13.79|12.73|34.09|13.08|
|Observations|6,058|6,034|7,144|3,796|2,761|2,592|
|Population size|1,802,063|1,764,457|2,137,789|1,098,917|781,336|826,134|

Results of probit regressions where the dependant variable is 1 if the household uses the service and 0 if it is does not even though available.

27