INFRASTRUCTURE AND GROWTH IN AFRICA

# 1   Methodology

In order to evaluate the impact of infrastructure development on economic growth, we have gathered a large macroeconomic panel data set with information organized in five-year periods for 136 countries over 1960 _–_ 2005.<sup>1</sup> Our panel data set is unbalanced since we do not have complete information for all countries over the sample period. To avoid biases introduced by countries with a very small number of observations, we restrict our sample to countries for which there are at least three consecutive time-series observations.

In the present section we tackle two main problems found in literature on the subject: (a) Evaluating the impact of several types of infrastructure assets instead of the overall infrastructure sector, as well as including some measure of service quality (b) Dealing with the likely endogeneity / reverse causality of infrastructure stocks as well as service quality

## Measuring the quantity and quality of infrastructure

The empirical literature on infrastructure and economic performance has been plagued by an abundance of single-infrastructure-sector assessments. Although most of these papers take a broad theoretical view of infrastructure, they use indicators of a single-infrastructure sector for their empirical analysis. For instance, Easterly (2001) and Loayza, Fajnzylber, and Calderón (2005) use the measure of telephone penetration (main telephone lines per 1,000 people) as a proxy for infrastructure.

The use of single-sector analysis in the empirical assessment of the link between infrastructure and economic performance is mostly explained by the high degree of correlation among measures of infrastructure asset types (such as telecommunications, electricity-generating capacity, road and railway networks, and water and sanitation, among others). The high degree of comovement between the stock measures (and quality measures) of different infrastructure sectors may prevent us from identifying their estimated impact on economic growth.<sup>2</sup> To tackle this problem, we follow the strategy pursued in Calderón and Servén (2004a), and build an aggregate index that summarizes the stock of the different types of infrastructure assets and the indicators of service quality in different infrastructure sectors. These indicators are aggregated using the principal components analysis (PCA), and our aggregate (or synthetic index) is of the first principal components for the underlying variables.<sup>3</sup> In order to construct our aggregate indices of infrastructure quantity and quality, we combine information on physical indicators in three infrastructure sectors: telecommunications, electricity, and 1 Since our focus is on long-run trends in growth rather than its behavior over the business cycle, we work with data averaged over five-year periods to smooth out short-term fluctuations.

2 For example, Calderón and Servén (2004a) found that when infrastructure indicators of telecommunications, power, and roads were included together in a growth-regression equation, the estimated coefficient of power was negative and not statistically significant, whereas either main lines or road networks were statistically insignificant in some regressions.

3 Alesina and Perotti (1996) have used the principal components analysis (PCA) to create a measure of political instability, while Sanchez-Robles (1998) use this method to build an aggregate index of infrastructure stocks.

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