capital on long-run economic development indicators. Literature has signaled that endogeneity in this context might come from three sources: (a) measurement errors stemming from the use of public capital figures as proxies for infrastructure; (b) omitted variables, which may arise when there is a third variable, unobserved, that affects the infrastructure and growth measure; and (c) the fact that under the simultaneous determination of infrastructure and productivity or output, the bias and inconsistency of standard estimators would follow where infrastructure provision itself positively responds to productivity gains.<sup>7</sup> Possible reasons for such feedback would arise with increased reliance on the private sector for the provision of infrastructure services, or with successful lobbying by industry interest groups that experience either positive productivity gains or constraints on performance due to infrastructure provision.

Various panel data and country studies have tried to address these issues. Röller and Waverman (2001) explicitly model and estimate the impact of telecommunications under simultaneity. In a crosscountry panel estimation, Calderón and Serven (2003, 2005) employ generalized method of moments (GMM) panel estimation methods to control for the possibility of endogeneity, reporting significant improvements in results. Dessus and Herrera (1999) allow for simultaneity in a panel data set for 28 countries. Country-specific time series also confirm the presence of simultaneity between output and infrastructure measures—see Frutos et al. (1998) for Spain, and Fedderke et al. (2005) for South Africa. Also for South Africa, Fedderke and Bogetic (2006)—controlling for the potential endogeneity of infrastructure in estimation—robustly eliminate nearly all evidence of possible overinvestment in infrastructure. Indeed, controlling for the possible endogeneity of infrastructure measures renders the impact of infrastructure capital positive. Romp and Haan (2005) indicate that when simultaneity is taken into consideration, the elasticity estimates found in earlier studies considerably decrease.

Another possibility behind the ambiguous results obtained from empirical studies of public capital impacts on output might simply be that aggregate measures of infrastructure hide the productivity impact of infrastructure at a more disaggregated level. A second batch of studies, focusing mainly on micro data, reveals the existence of the possible indirect impact of infrastructure on economic growth and economic performance beyond the effect of the simple accumulation of capital. Thus, for instance, Shioji (2001) finds that the positive impact of infrastructure arises in panel data on U.S. and Japanese industries once public capital is properly disaggregated. Agénor and MorenoDodson (2006) point out that improvement in the stock of infrastructure can reduce the adjustment costs of private capital by (a) lowering the logistical cost of the investment in private capital, and (b) allowing the replacement of unproductive private investments such as electricity generators or boreholes and wells with more productive investments in machinery and equipment. This assumption has been tested in the context of investment climate assessments (ICAs) with firm-level information. Reinikka and Svensson (1999) show that improvements in the infrastructure stock in Uganda make

> 7 Notice that we avoid using the terms _causality_ or _reverse causality_ , since there is no control group to compare against and the temporal dimension is not large enough to consider Granger-causality concepts _._ Therefore, we use the terms _simultaneity_ and _identification_ , which are more appropriate for ICSs.

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