MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH The income and demographic variables that proved statistically significant in the cross-sectional OLS results were also statistically significant in the fixed effects panel results. To a greater extent than before, however, variables capturing the economic structure of the economy (export orientation, and shares of agriculture and manufacturing compared with services) become statistically significant in explaining infrastructure stocks, particularly in the case of roads and ICT. It is important to remember, however, that these results do not yet account for dynamics in infrastructure provision, nor have they been corrected for endogeneity.

**Table 3.3 Results of OLS fixed effects panel data model**

|Datain logs<br>Variable|Density of<br>roads|Density of<br>roads (arable)|Density of<br>paved roads|Density of<br>paved roads<br>(arable)|Access to water|Access to<br>sanitation|Access to<br>electricity|Generating<br>capacity|Internet<br>connections|Mobile<br>telephones|Fixed-line<br>telephones|
|---|---|---|---|---|---|---|---|---|---|---|---|
|I|0.051|0.079|0.196|0.227|–0.022|0.126|0.371|0.247|1.768|2.059|0.633|
|ncome|(3.25)**|(4.49)**|(10.60)**|(11.15)**|–0.47|–1.33|(2.52)*|(11.87)**|(7.73)**|(11.77)**|(27.68)**|
|Ubiti|0.244|0.131|0.477|0.365|0.664|0.141|1.096|0.504|2.316|3.158|0.986|
|ranzaon|(3.52)**|–1.69|(5.90)**|(4.10)**|(3.30)**|–0.36|(5.11)**|(5.50)**|(2.88)**|(4.72)**|(10.98)**|
|Population|0.366|0.094|0.587|0.312|0.103|1.01|0.119|0.062|21.884|16.909|1.524|
|density|(6.43)**|–1.48|(8.76)**|(4.24)**|–0.64|(3.12)**|(2.18)*|–0.84|(26.44)**|(25.86)**|(20.14)**|
|Infrastructure aid|–0.005|–0.011|–0.011|–0.017|–0.004|0.019|0.006|0.003|–0.019|0.08|–0.003|
|(per capita)|–1.87|(3.90)**|(3.76)**|(5.08)**|–0.54|–1.25|–0.17|–0.96|–0.8|(3.98)**|–0.85|
|Exports|0.001|0|0.001|0.001|0.001|0|0.04|0.004|0.073|0.034|0.006|
|<br>(% GDP)|–1.13|–0.35|(2.20)*|–1.82|–0.35|–0.01|–0.29|(5.98)**|(11.28)**|(6.85)**|(8.60)**|
|Agriculture|–0.004|–0.003|0.001|0.002|–0.001|0.009|0.188|–0.005|–0.112|–0.154|–0.01|
|(% GDP)|(3.40)**|(2.58)**|–0.95|–1.38|–0.49|–1.43|–1.17|(3.29)**|(7.11)**|(11.30)**|(5.69)**|
|Manufacturing|0.001|0.002|–0.001|0.001|–0.007|0.01|0.398|0.004|–0.151|–0.205|–0.002|
|<br>(% GDP)|–0.85|–1.43|–0.37|–0.27|–1.56|–1.13|(2.85)**|(2.23)*|(7.55)**|(13.39)**|–0.99|
|C|–3.401|–0.185|–6.602|–3.424|4.764|–1.021|–0.22|–10.59|–92.962|–70.033|–5.856|
|onstant|(11.67)**|–0.57|(19.38)**|(9.13)**|(5.72)**|–0.61|–0.15|(27.63)**|(23.72)**|(23.53)**|(14.93)**|
|Observations|1919|1910|1875|1866|167|165|85|2223|1100|1230|2323|
|Plant fixed<br>effects|0.21|0.07|0.39|0.24|0.67|0.52|0.68|0.22|0.68|0.76|0.73|

_Sources_ : As for table 2.1.

The results from the instrumental variables estimation indicate endogeneity bias in the OLS specification. If—as suggested by Calderón and Servén (2004) and others that expansion of infrastructure is associated with higher levels of income and growth, then we would expect the OLS coefficient on per capita GDP to be upwardly biased. Indeed, the results of our instrumental variables estimation show that the magnitude of the coefficient on per capita GDP decreased substantially once we controlled for endogeneity using the standard regressors from the growth literature, as discussed above. The traditional Hausman test of the exogeneity of our instruments indicates that they are appropriate in most infrastructure sectors. The exception is in the estimates for access to water and sanitation, where the limited number of observations (from just two years of data) may have affected the results. But it is clear that addressing the endogeneity of per capita GDP is critical to obtaining consistent coefficient estimates.

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