Future studies should make sure to extensively test the robustness of their results and if possible improve on the methodology in a more fundamental way. This is because even the current ‘best-practice’ precautions against endogeneity – such as using locationindustry averages as instruments of the firm-level indicators, regressing on multiple IC indicators at a time and controlling for the current country, region and industry effects – leave regressions vulnerable to inconsistency and bias, as Carlin et al. (2006) and others point out. For instance, as highlighted above, location-industry averages are used as instruments to alleviate endogeneity stemming from reverse causality. Yet, such endogeneity effects can persist at a more aggregate level as well, because of policy endogeneity and endogenous placement decisions of firms. For instance, using industrylocation averages one may find a strong relationship between the performance of firms and the average quality of telecom services of a specific industry and region. However, as Carlin et al. (2006) point out, regions ‘that are prosperous for a variety of other reasons for which it is not realistically possible to control econometrically also happen to have higher levels of telecom services.’ To counter this effect, a recent paper by HallwardDriemeier, Wallsten and Xu (2006) has included additional city information and sector dummies to at least help ‘control for those more macro issues that affect both the IC variable and the firm’.<sup>102</sup> Nevertheless, the inability to sufficiently control for all factors implies that the endogeneity problem is likely to persist to some extent. In light of this, it is clear that the need arises for more creative instrumental strategies. Again some examples can be found in the literature, for example in Duflo and Pande (2007), who use geographical data to instrument for the endogenous placement of infrastructure, or Datta (2008) and Gibson and Rozelle (2003) who take advantage of the seemingly exogenous placement of road works in specific contexts to assess their impact.

Finally, it should be noted that the underlying assumption of most of the firm-level literature is that changes in the business climate which improve firm performance will translate into broad social benefits. Regression results based on data provided by firmmanagers are thus often straightforwardly translated into policy advice, e.g. to increase competition and lower regulation. In order to reduce the risk of any negative impacts, it may be worthwhile to consider possible competing interests when deducing policy recommendations. For instance, regulations may impact firm productivity negatively but provide benefits to non-managerial social groups.

# _3. Improving Questionnaire Design_

At a fundamental level, it may also be worthwhile to re-think some of the Enterprise Survey questions which determine the raw data on which all analyses are based. For instance, in the era of mobile phones – which are particularly important in many developing countries – the focus on mainline telephone services is anachronistic and misleading. With regard to infrastructure indicators, Straub (2008) makes a number of suggestions for more detailed questions such as firms’ access to alternative transport modes (railways, airports, roads etc.) or the ownership of vehicles.<sup>103</sup>

> 102 Hallward-Driemeier, M., S. Wallsten and L.C. Xu, 2006: 640.

> 103 Straub, S. 2008:  39.

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