step’ estimation is vulnerable to simultaneity bias as pointed out by Escribano and Guasch. Moreover, they do not control for country effects. Thus, while their indicators may capture some genuine cross-country differences in the three business climate categories, they are also vulnerable to bias if other cross-country effects (such as trade policy, political instability etc.) influence productivity and are also correlated with their indicators.

Dollar et al. (2005) use a sample of Enterprise Surveys from Bangladesh, China, India and Pakistan to regress total factor productivity on a number of controls and five objective business climate variables, including the two infrastructure indicators ‘log of the cost of power losses as a % of sales’ and log ‘time required to obtain a phone line’. The authors find that even after controlling for firm characteristics, geography variables and country-level effects, power losses have a significantly negative effect on productivity. This seems to confirm the importance of electricity in poor countries and more generally the significance of infrastructure for explaining variation in productivity. However, the telecommunications variable has a perversely positive and significant coefficient, but this counter-intuitive result ‘is not robust across all specifications’.

Results based on firm performance variables other than productivity by and large confirm the significant role variations in infrastructure play in explaining differences in firm success. Reinikka and Svensson (2002) use the 1998 Ugandan Industrial Enterprise Survey for a sophisticated short study of the effects of poor infrastructure and deficient public services on the level of private investment. The authors first construct a model to garner hypotheses for the empirical analysis that follows. The model characterizes two decisions – whether a firm buys private, complementary infrastructure capital, and how much it invests in non-complementary, productive capital in the next period. The first empirical estimation thus runs a probit regression of ‘ownership of a generator’ on the number of days of power interruptions from the public grid, the firm’s employment size, the percentage of foreign ownership, a dummy indicating whether the firm exports part of its output, firm profit and age. The model hypothesis is confirmed as public power outages show a significantly positive relationship with the probability of owning a generator.<sup>53</sup> Moreover, a firm is significantly more likely to own a generator if it is a larger firm, an exporter or has a higher percentage of foreign ownership. With respect to the investment decision, the empirical analysis also confirms the model hypotheses. For firms without a generator, investment is found to be negatively related to the number of days of power interruptions. However, ‘an increase in the number of days lost has no statistically significant effect on investment for firms with their own generators’<sup>54</sup> . This comes at a cost, however, for if the public power supply is good (i.e. conditional on few lost days), firms that have installed expensive private electricity infrastructure invest less than firms without a generator. On the whole, Reinikka and Svensson (2002) conclude that their analysis of Ugandan firm level data shows that poor public capital ‘significantly reduces productive investment by firms’.<sup>55</sup> They deduce that a poorly functioning public

> 53 In the empirical analysis the actual public power outcomes are taken as a proxy for ex-ante beliefs of firms that invest in complementary private infrastructure capital in the first period. 54 Reinikka, R. and J. Svensson. 2002: 65.

> 55 _Ibid._ : 67.

16