26 sector, country group and country fixed effects are taken into account. In all cases but two they are also significant. The exceptions in Table 3b are majority membership in trade unions and whether the firm has audited accounts. In these two cases the inclusion of country fixed effects dilutes the firm experience effects.<sup>8</sup> Days lost due to power outages is a significant determinant of the ranking of electricity as a major or severe constraint. Another regression, not reported here, shows that sales lost is also significant. Similarly, the rate of unionization drives perceptions of labor regulations, bribes paid affect perceptions about corruption, days to clear exports are tied to complaints about customs etc. None of these results are surprising—for example, firms with access to overdrafts are more likely to view high interest rates as a constraint. The results on access to finance are also interesting. After netting out for fixed effects (which include the size factor), having accounts audited substantially decreases the probability that a firm considers access to finance as a serious constraint. This suggests the importance of including measures to increase access to accounting and auditing services in programs to widen financial access.

It is worth reflecting on why the experience variables are significant at all when the fixed effects are included in regressions.<sup>9</sup> If firms are responding to country-level differences in, say, security or power supply, differences should simply appear as fixed effects at the 8 Some might argue that the “experience” variables are also subjective in that they record firm responses rather than a measurement of the actual event. While it is indeed true that surveys ask the firm how many outages it suffers rather than observing the outages directly, it is also unreasonable to argue that firms systematically exaggerate or under-report these events. The only experience variable for which this might be plausible is bribe payments but even in this case, the wording of the question (“in your industry, what is the typical payment”) has, in our opinion, resulted in a higher level of accuracy in the reporting of payments. These results reinforce the correlations with external indicators of the investment climate (that is, from data outside of our surveys) to validate firm perceptions.

9 Recent research by Mengistae and others uses city level means of IC indicators rather than individual observations thereof in the context of performance (i.e. productivity) regressions as a way of mitigating bias due to reverse causality (Mengistae, 2007). This is because they are concerned with measuring the impact of IC on productivity rather than the other way round. The problem of reverse causality is not as important here since we are relating subjective ratings with objective indicators and firm characteristics. In the relationship between firm characteristics and IC ratings, we are using the former as controls rather than variables of primary interest. That would seem to leave measurement error or omitted variables as sources of bias in the estimates and for this reason we include country fixed effects. For most African countries it is not useful, given the small size of the industrial sector, to use city averages. We are grateful to Taye Mengistae for discussion of this issue.