limit himself to using models with a reduced number of independent variables with the risk of introducing a more serious omitted variables problem? Or is it preferable to impute missing data in order to be able to use structural models with a wide set of explanatory variables? If we assume the latter as a reasonable solution, the question that arises then is: should we input missing cells in both LHS (independent) and RHS (dependent) variables, or, on the contrary, ,should we satisfy ourselves by replacing missing data in only those explanatory variables of the model?

During recent years statisticians have proposed many alternative methods to handle incomplete datasets that offer substantial improvements over _casewise deletion_ . These approaches may be grouped into two families of methods: maximum likelihood and multiple imputation, see Allison (2001), Meng (2000) and Little and Rubin (1987) for a review. However, these methods depend on easily violated assumptions that, to make things worse, are difficult or even impossible to test. In this paper we discuss the applicability of these methods to four IC surveys with very different patterns of missing data among them: India, Turkey, South Africa and Tanzania. In particular, we propose a simple imputation mechanism (which we call the _ICA method_ ) that in part departs from the EM-algorithm, and that has been widely applied in various empirical works (Escribano et al, 2008a, b; Escribano, Guasch and Pena 2009 and Escribano et al. 2009). We compare the performance of this method with several alternative approaches to deal with incomplete data and we discuss the different assumptions we need to hold for the different imputation mechanisms to work well. We evaluate the validity of the different methods in the context of the extended production function of Escribano and Guasch (2005 and 2008).<sup>7</sup> The extended production function framework used here fits very well with the objective of the paper as the RHS of the equation is compounded by a broad set of explanatory variables.<sup>8</sup> On the other hand, although we concentrate on PF variables, the results of the analysis can be easily extended to any variable with missing information included in the ICSs.

We demonstrate that, besides the imputation method used, a detailed knowledge of the missingness mechanism in the context of ICSs is a requisite. The missing data problem is at the core of statistical and econometric analysis done with ICSs and therefore a proper treatment of the missing data mechanism is inevitable. We also show that the so-called _ICA method_ proposed performs reasonably well, even under very different patterns of missing data. The differences of the _ICA method_ with respect to other more sophisticated imputation mechanisms, such as EM algorithms, multiple imputation, bootstrap methods or Heckman models, are not remarkably significant, so we propose it as a benchmark, a homogeneous, simple and easy to implement method for models with large numbers of covariates in ICSs, and more importantly, for very complex and unbalanced patterns of missing data.

The structure of the paper is as follows. In section 2 we review the patterns of missing data observed in the four IC surveys considered. We compare the original sampling frame with the complete case and we see that in most cases the representativity of the original sample is

> 0% in most cases. However, if we construct models using only those investment climate variables with a response rate higher than 80%, the complete case increases from 20% to 30% of the original sampling frame.

> 7 Although it is straightforward to apply this method to any kind of model, especially those involving a large number of RHS variables or structural system of equations.

> 8 The underlying philosophy of the Escribano and Guasch (2005 and 2008) extended production function is to incorporate in a Cobb-Douglas (or Translog) function a large set of investment climate variables to correct for observable fixed effects.

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