Investment climate Surveys are valuable instruments that improve our understanding of the economic, social, political, and institutional factors determining economic growth, particularly in emerging and transition economies. However, at the same time, they have to overcome some difficult issues related to the quality of the information provided
measurement errors, outlier observations, and missing data that are frequently found in these datasets. This paper discusses the applicability of recent procedures to deal with missing observations in investment climate surveys. In particular, it presents a simple replacement mechanism -- for application in models with a large number of explanatory variables -- which in turn is a proxy of two methods: multiple imputations and an export-import algorithm. The performance of this method in the context of total factor productivity estimation in extended production functions is evaluated using investment climate surveys from four countries: India, South Africa, Tanzania, and Turkey. It is shown that the method is very robust and performs reasonably well even under different assumptions on the nature of the mechanism generating missing data.