A related issue is that of ‘camels and hippos’ raised by Hausmann and Velasco (2005) and discussed in Gelb et al. (2007) and others. All results are necessarily based on the answers of existing firms that were interviewed. However, if one only interviews those present (‘camels in the desert’), one may miss the crucial constraint (‘water’) of those who have not entered (‘no hippos in the desert’). In other words, a self-selected sample may imply a lack of variation in the explanatory variables that prevents us from noticing a critical constraint. However, in their study of perceptions data, Gelb, Ramachandran, Shah and Turner (2007) argue that such self-selection is hardly ever complete (e.g. hippos can be expected to live in a water hole at the edge of the desert), and that firms that choose to enter in spite of serious constraints (which may force them into costly evasive actions), will perceive them as particularly serious and thus introduce econometrically significant variation.<sup>92</sup> Still, as it stands it is important to recognize that the econometric model above only informs us about the effect of constraints on the sample of existing firms. It is sometimes argued that the more interesting issue is rather the underlying industrial structure (e.g. the camel/hippo ratio in the desert) which should give away the most important constraint (i.e. the absence of hippos indicates that the main constraint is the lack of water). This, however, could only be addressed with completely different models such as “entry” models.

Another general methodological problem is that of multicolinearity. If regressors are correlated with each other, estimates will be inefficient and, as Bastos and Nasir (2004) point out, it may be impossible to ‘know the importance of any one particular indicator since it may be serving as a proxy for other, more relevant variables’<sup>93</sup> . This is a particular problem with the business climate data, as many indicators are closely related. For instance, the prevalence of e-mail usage may largely move with the quality of electricity supply. This counsels caution when interpreting very specific indicators, and emphasizes the importance of choosing a good regression specification. To some extent, variables such as ‘prevalence of e-mail’ should be seen as proxies for broader infrastructure factors. Bastos and Nasir’s (2004) solution is to explicitly aggregate a number of specific indicators into broader measures (‘infrastructure, ‘competition’ etc.), in order to get clearer results at the loss of some (presumably misleading) detail.

Endogeneity – the correlation between the explanatory variables and the error term – is even more serious than multicolinearity, because it causes not only inefficiency and interpretative difficulties, but bias and inconsistency of the estimates. The presence of endogeneity undermines the validity of estimated relationships between business climate indicators and firm performance.

It is unrealistic to assume that firm level business climate indicators are exogenous for a number of reasons. First, a major endogeneity problem arises if relevant explanatory variables are mistakenly omitted from the regression equation and also correlated with relevant included regressors. If this is the case, the estimated parameters of the included regressors will pick up some of the impact on the dependent variable of the omitted variables with which they are correlated. This will distort the estimates of the parameters

> 92 Gelb, A., V. Ramachandran, M. Kedia-Shah and G. Turner. 2007: 27-29. 93 Bastos, F. and J. Nasir. 2004: 10.

27