different assumptions and patterns of missing data. This is supported by section 4.4, where we saw that IC variables are able to explain a rather large proportion of the variability of the MDM in all the countries.

# **7. Conclusions**

When the missing data mechanism (MDM) is ignorable, the objective of the imputation methods is not to augment the sample size, but to preserve the sample representativity, to gain efficiency in the estimation and to retrieve for the analysis a large number of very expensive interviews. The alternative to these methods is the complete case or _listwise deletion_ , which is not a panacea even when the MDM is ignorable. Operating with the complete case is only acceptable if incomplete cases attributable to missing data comprise a small percentage, say 5% or less, of the number of total cases (Schafer, 1997), and when the complete case preserves the representativeness of the original sampling frame. In addition, in models with a large number of regressors, the problem of missing data may encourage analysts to leave out of the regression some explanatory variables with a high proportion of missing values. As Cameron and Trivedi (2005) point out, this practice may be misleading as it leads to an omitted variables problem, which could be more serious than the missing data problem _per se_ . The first question we raise in this paper is, hence, whether the researcher should do something about the missing values when dealing with investment climate surveys (ICSs).

In the context of ICSs, a large proportion of the sample size is lost in the complete case and the representativeness of the original sample frame is, to some extent, modified. Given these results, the MDM can in no way be considered as missing completely at random (MCAR), and consequently a complete case could lead to inconsistent and inefficient results. In order to overcome this problem, we propose a imputation mechanism that fits well with the characteristics of ICSs—with unbalanced patterns of missing data and a low proportion of available observations in the complete case—likely to be used to construct structural models composed of single, or even systems of, equations with a large number of explanatory variables, all of them containing missing data.

The imputation method proposed, which we call the _ICA method_ , departs from the class of EM type algorithms and relies on the expectation of the imputed variables conditional to the sector, region and size they belong to. The performance of the ICA method depends on several characteristics of the MDM, such as the number of variables replaced or the proportion of missing values in the complete case; but especially, it depends on the nature of the MDM: _missing at random (MAR) or non-ignorable_ . Taking this into account, we analyze the MDM of four countries with very different patterns of missing data (India, Turkey, South Africa and Tanzania) to find out to what extent the MDM can be treated as MAR or not. Although not conclusive on the nature of the MDM, the descriptive analysis shows that this has to do with a variety of IC determinants, such as informality and corruption and also with the capacity of the firms. More dynamic firms engaged in R&D, quality, innovation of new products, technologies and operating in more exigent and competitive 89