MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH Several important limitations of this approach call for the use of panel data models. The simple OLS cross-section is of some interest in that it replicates the results of earlier literature (Canning 1998) and serves to isolate the effect of specific environmental variables that can be identified as relevant from the exploratory data analysis. Nevertheless, it is subject to important methodological limitations. First, the environmental variables only imperfectly control for the myriad differences that arise in different country situations, which can be fully reflected only by means of country-specific factors. Second, the long lag times in the development of infrastructure stocks mean that historic trends play an important role in explaining a country’s current infrastructure endowment. Third, the potential reverse causality from per capita income to infrastructure stocks raises the possibility of endogeneity. All of these issues can be addressed using panel data models that analyze a repeated cross-section for the same countries across a number of years.

Our second step is therefore to estimate an OLS panel model that controls for country differences (fixed effects) that affect the ease or difficulty of providing infrastructure services. This is represented by equation (3), where _yit_ is defined as the infrastructure level for country _i_ at time _t_ , _Xit_ is a matrix of socioeconomic and structural explanatory variables, and _ηi_ is a timeinvariant country-specific fixed effect.

Because the OLS fixed-effects model does not correct for the potential endogeneity of the explanatory variables, our third step is to control for potential endogeneity of relevant regressors using instrumental variables. It seems likely that per capita gross domestic product (GDP) will be endogenous in the specification given in (3). In fact, there is already a large literature based on the concept that causation lies in the opposite direction—that is, that expansion of infrastructure services increases income and income growth. This direction of causality has been examined extensively in Easterly and Serven (2003) and Calderon and Serven (2003, 2004). We draw on this literature to choose appropriate instruments for per capita GDP. Specifically, we employ some of the standard growth regressors from Calderon and Serven (2004), including trade openness (trade as share of GDP), inflation, political risk index, government involvement in economy (government consumption as share of GDP), domestic credit available to the private sector (as share of GDP), and the terms of trade index. Adding these regressors gives us the following equation: _Xit_ contains the same set of regressors included in the cross-sectional OLS study. Per capita GDP is instrumented using the standard growth variables described above. Lastly, it is important to note that any time-invariant variables from the OLS regressions are dropped once country fixed effects are included, which accounts for the smaller sets of coefficients reported for panel data models later in the paper.<sup>1</sup> 1 The regressors used in (4) were also considered in a dynamic panel specification, using the technique developed by Blundell and Bond (1998). These results are reported in Annex A.1 for comparison purposes, 4