This paper provides a systematic, empirical assessment of the impact of infrastructure quality on the total factor productivity (TFP) of African manufacturing firms. This measure is understood to include quality in the provision of customs clearance, energy, water, sanitation, transportation, telecommunications, and information and communications technology (ICT).We apply microeconometric techniques to investment climate surveys (ICSs) of 26 African countries carried out in different years during the period 2002-6, making country-specific evaluations of the impact of investment climate (IC) quality on aggregate TFP, average TFP, and allocative efficiency. For each country we evaluated this impact based on 10 different productivity measures. Results are robust once we control for observable fixed effects (red tape, corruption and crime, finance, innovation and labor skills, etc.) obtained from the ICSs. We ranked African countries according to several indices: per * This study is part of the Africa Infrastructure Country Diagnostic (AICD), a project designed to expand the world's knowledge of physical infrastructure in Africa. Financing for AICD is provided by a multi-donor trust fund to which the main contributors are the Department for International Development (United Kingdom), the Public Private Infrastructure Advisory Facility, Agence Française de Développement, and the European Commission. Inquiries concerning the availability of datasets should be directed to vfoster@worldbank.org. The authors are indebted to Vivien Foster and her group of researchers at the World Bank for providing data sets and further insight into the African region.

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

The Investment Climate surveys (ICSs) are valuable instruments which 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 with the quality of the information provided; measurement errors, outlier observations and missing data are frequently found in this datasets. In this paper we discuss the applicability of recent procedures to deal with missing observations in IC surveys. In particular we present a simple replacement mechanism-for application in models with a large number of explanatory variables-, which we call the ICA method, which in turn is a proxy of two methods: multiple imputation and EM algorithm. We evaluate the performance of this ICA method in the context of TFP estimation in extended production functions using ICSs from four countries: India, South Africa, Tanzania and Turkey. We find that the ICA method is very robust and performs reasonably well even under different assumptions on the nature of the mechanism generating missing data.

Investment Climate surveys (ICs) are a recent instrument used by the World Bank to identify key obstacles to country competitiveness and to guide policy reforms and government interventions in developing countries. In this paper, panel data from four ICs of four South East Asian (SEA) countries namely, Indonesia, Malaysia, The Philippines, and Thailand, are pooled to estimate total factor productivity (TFP) and allocative efficiency aspects of firms in each country, using variants of the Olley and Pakes (1996) productivity decomposition. Several economic performance results are disaggregated to obtain country-specific evaluation of the IC impacts. To establish priorities for policy reforms, the corresponding key IC results are organized in five categories: infrastructures, red tape, corruption and crime, finance and corporate governance, quality, innovation and labor skills, and other control variables.

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