in areas without access to electricity or to be dependent on an unreliable public grid’<sup>44</sup> , given that they lack the scale economies to operate a generator efficiently. Lee et al. (1999) suggest that since a very large share of new jobs in developing countries are created by small firms, the negative impact of infrastructure deficiencies on employment creation is potentially huge.<sup>45</sup> Regrettably, the potential links between the disproportional infrastructure problems of small firms and job creation are not followed up by the authors and no tentative cost estimates in terms of jobs are provided.

Lee et al.’s analyses demonstrate the concrete insights that can be gained from descriptive firm-level statistics of infrastructure variables. Their specific results are somewhat dated by now, but the concerns they address remain relevant today. The Enterprise Survey database, which contains similar statistics for more than 100 countries, is an extremely valuable new resource in that respect.

While descriptive statistics are useful to establish basic facts, regression analyses have provided a more detailed view of the relationship between infrastructure and firm performance indicators. Escribano and Guasch (2005) use ES data from Guatemala, Honduras and Nicaragua to calculate ten different measures of firm productivity. The productivity measures are then regressed on a number of controls as well as a broad array of objective business climate variables of which four are infrastructure indicators - the log of average duration of power outages, the log of the number of days to clear customs for imports, the log of shipment losses as fraction of sales as well as a dummy for internet access. The regressions are carried out both for a pooled sample and for each of the three countries separately. The authors find that in all regressions the infrastructure variables always have the expected signs, and the vast majority is significant. The authors are confident enough in their results to interpret the elasticities straightforwardly, noting that for the pooled sample: ‘a one percent increase in the average duration (hours per day) of power outages decreases productivity between 0.02 and 0.1 percent, depending on the productivity measure used. It mainly affects old plants...a one percent increase in the fraction of shipment losses will decrease productivity between 1.23 and 2.53 percent. This is most important in old and small firms...firms with access to internet are between 11% and 15% more productive [than] those firms without’<sup>46</sup> Escribano and Guasch go to great length to avoid bias and inconsistency in their analysis. They take care to avoid simultaneity problems, control for country, industry and year effects with dummies and also including at least two firm characteristics as controls (age and share of imported inputs). Regressions are run on all variables at a time to avoid omitted variable bias. Moreover, they use region-industry averages of the IC variables as instruments to alleviate reverse causality. Escribano and Guasch show that infrastructure

> perceiving infrastructure as a greater _relative_ constraint. There may be structural reasons – such as larger firms’ greater demands on the various elements of the business climate  – that on average lead larger firms to report higher absolute constraint rankings in the various categories.

> 44 Aterido, R., M. Hallward-Driemeier and C. Pagés. 2007: 15.

> 45 Lee, K.S., A. Anas and G. Oh. 1999: 2140.

> 46 Escribano, A.; and J. Guasch. 2005: 55.

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