has a significant impact on productivity, explaining some 9 percent of it in total – the second highest percentage after ‘red tape, corruption and crime’<sup>47</sup> .

However, a number of points should be noted when interpreting this result. Firstly, the huge impact of internet access on productivity suggests that this dummy functions as a proxy for better equipped, higher-technology firms rather than just representing internet access _per se_ .<sup>48</sup> This advises caution when deriving policy interpretations. For instance, if the large productivity improvements captured by the internet dummy are actually related to much broader technological differences between firms, then prioritizing putting internet-connections into every firm could be a misguided step. Secondly, one should be careful not to generalize too much from these results and recall that they are based on only three (lower) middle income countries. The relationships may be considerably different in very poor countries where subjective measures indicate that electricity is a more severe problem.<sup>49</sup> Hallward-Driemeier, Wallsten and Xu’s (2006) study underlines that specific aspects of infrastructure are greater constraints in some countries than in others. They use data from an Enterprise Survey of China to regress different firm performance indicators (TFP, investment rate, sales growth, employment growth) on a number of controls<sup>50</sup> and on objective business climate indicators, including two infrastructure ones: loss of sales due to transportation or power problems, and share of employees that use computers<sup>51</sup> . The authors find ‘no evidence that physical infrastructure affects firm performance’ but ‘the impact of technological infrastructure [on productivity] appears to matter significantly’. Hallward-Driemeier et al. conclude that this ‘roughly’ conforms to their knowledge of China, which has relatively few bottlenecks in roads and power after the recent build-up of physical infrastructure, but still faces a ‘binding constraint’ in terms of ‘technological infrastructure’.

In a sample of five Eastern European countries (Kyrgyz Republic, Moldova, Poland, Tajikistan, Uzbekistan), Bastos and Nasir (2004) obtain a similar result as Escribano and Guasch. Regressing TFP on three controls (firm age, export status and ownership) and three aggregate business climate indicators (‘competition’, ‘infrastructure’ and ‘rent predation’<sup>52</sup> ), they find that all three IC measures have the expected sign and are significant at the 1% level. ‘Infrastructure’ accounts for the second largest share of the variation in firm-level productivity, behind ‘competition’ but before ‘rent predation’. What undermines the results of Bastos and Nasir, however, is the fact that their ‘two-

> 47 _Ibid._ : 73.

> 48 Likewise, the other indicators may also capture some additional variation from unobserved variables. In essence, this means that there may still be some omitted variable bias that distorts the estimated parameters, or alternatively, there may be no (or almost no) bias, but the included variable may only be an instrument/proxy for the actual cause of the productivity effect.

> 49 For example, the marginal impact of power outage duration on productivity could be much higher after certain thresholds, which may not be reached in middle-income countries.

> 50 Ownership (share of ownership that is domestic private; share of ownership that is foreign), logs of firm age, city population and city GDP per capita

> 51 in each case city-industry averages are employed to lessen reverse causality

> 52 The authors use principal component analysis to obtain their aggregate indicators. See Annex 2 for the specific indicators on which the aggregate measures are based.

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