TY - BOOK ID - 137686447 TI - Empirical Econometric Evaluation of Alternative Methods of Dealing With Missing Values in Investment Climate Surveys AU - Escribano, Alvaro AU - Guasch, J. Luis AU - Pena, Jorge PY - 2010 PB - Washington, D.C., The World Bank, DB - UniCat KW - Business environment KW - E-Business KW - Economic Theory & Research KW - Enterprise surveys KW - Equipment KW - Imputation method KW - Imputation process KW - Information and Communication Technologies KW - Information and Records Management KW - Information Security & Privacy KW - Innovation KW - Intangible assets KW - Macroeconomics and Economic Growth KW - Manufacturing KW - Marketing KW - Missing data KW - Missing value KW - Missing values KW - Multiple imputation KW - Multiple imputations KW - Private sector KW - Private Sector Development KW - Productivity KW - Result KW - Results KW - Science and Technology Development KW - Statistical & Mathematical Sciences KW - Telecommunications KW - Web UR - https://www.unicat.be/uniCat?func=search&query=sysid:137686447 AB - 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. ER -