levels), and is therefore also capturing nonlinear relations between market shares and IC variables coming from (4.3a). To know to what extent these nonlinear terms are affecting this relation, we perform simulation experiments<sup>18</sup> on INF, IC, and C variables, and evaluate the consistency of the results with the ones obtained from the previous mixed O&P decomposition—see (4.4b). The IC simulations are done variable by variable (one at a time) keeping the rest of the variables constant; that is, we propose a scenario in which the level of one of the IC variables improves by 20 percent in all establishments (20 percent less power outages, 20 percent less shipment losses, etc). We compute the corresponding rate of change of aggregate productivity, average productivity, and allocative efficiency caused by such a 20 percent improvement. We repeat the same experiment for the rest of the IC and C variables, and, for comparative purposes, we also evaluate the relative group of IC variables.

# **5. The contribution of infrastructure to the investment climate (IC) of Africa**

In section 4 we described the econometric methodology used in section 6 to assess the impact of infrastructure on productivity. We suggested three key elements of empirical evaluations of infrastructure and other IC and C impacts on productivity: the marginal productivity effects, the percentage contributions of infrastructure to aggregate log productivity (mean and efficient components), and the simulations of infrastructure improvements on aggregate productivity (in levels).

In this section we focus on presenting the results of infrastructure contributions to aggregate productivity from simulation experiments. In addition to the results of the econometric analysis, we consider African firms’ perceptions of the main obstacles to economic performance. In the first subsection the objective is to assess how African firms perceive infrastructure quality. This is followed in the next subsections by the results of the econometric analysis, focusing on the infrastructure’s effect on productivity after controlling for other IC factors. Finally, to complement the robustness of the results we check the consistency of the conclusions obtained from both the IC contributions to average log TFP and from the TFP simulation experiments.

_Do African firms perceive infrastructure as an obstacle to growth?_ In ICSs, firms are asked to rate a number of IC factors as obstacles to economic performance. The survey options offered are _no obstacle_ , _minor obstacle_ , _moderate obstacle_ , _major obstacle_ , or _very severe obstacle_ on a broad range of IC aspects: infrastructure, red tape, corruption and crime, finance, and labor skills.

Figure 5.1 shows the degree to which each group of IC factors is perceived by firms as an obstacle to economic development. These perceptions are sorted in descending order by their perceived contribution to the total, after being normalized to 100. For example, in panel A of figure 5.1, we observe that in Cape Verde 25 percent of firms believe infrastructure to be a major or very severe obstacle to economic growth; 40 percent find red tape, corruption, and crime as a major or very severe

> 18 We are indebted to Ariel Pakes for this suggestion.

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