Data and Measurement Issues

10 b oostin G Pro D u C tivity in s ub- sA h A r A n Afri CA

role of allocative inefficiencies in explaining productivity differences between establishments across Sub-Saharan African countries relative to those of other benchmark countries or regions.

The establishment-level analysis identifies and discusses the different policies and institutions that affect productivity and drive the misallocation of resources across farms and firms in Sub-Saharan Africa. Specifically, it discusses a comprehensive but not exhaustive set of policies and institutions that are categorized by these potential sources of misallocation (Restuccia and Rogerson 2017): • Market imperfections. The analysis discusses credit market imperfections (that is, lack of access to finance due to the lack of collateral); lack of land titling, affecting the allocation of land; and information frictions, affecting producers that are not connected to markets or farmers who have inadequate information on weather forecasts. • Statutory provisions. Also discussed are size-dependent policies—more specifically, tax provisions and regulations that depend on features of the different production units (such as size and age) as well as trade policies that protect specific categories of goods. • Discretionary provisions. In addition, the report captures government provisions that favor or penalize certain types of production units—for instance, subsidies to farmers, low-interest lending to specific firms, and preferential market access for specific groups of producers, among others.

Finally, this report—launched and financed by the World Bank’s Office of the Chief Economist of the Africa Region (AFRCE)—is part of the Bank’s programmatic agenda on the drivers of productivity worldwide, emphasizing the factors that explain the productivity gap of emerging markets (and, notably, Sub-Saharan African countries) relative to the high-income world. Box 1.1 succinctly describes the goals of some of these research projects.

One of the main challenges of empirical work in low- and middle-income countries, notably in Sub-Saharan Africa, is the issue of data quality. The poor quality of the data on macroeconomic, financial, and structural indicators for less-developed countries and for economies with large informal sectors— particularly in Sub-Saharan Africa—has been well documented (Jerven 2010, 2013a, 2013b, 2013c).

Empirical work on productivity in SubSaharan Africa is plagued by problems concerning data availability, comparability, and quality. At the national level, these problems are often tied to issues of capacity: The production of high-quality data for national income and product accounts (NIPA), consumption surveys, and firm-level censuses is technically complex. It involves the largescale mobilization of sizable financial and human resources as well as the setup of robust quality control mechanisms. Additionally, the failure of statistical offices to adhere to methodological and operational standards leads to data comparability and quality issues (Beegle et al. 2016).

At the aggregate level, problems with NIPA quality in Sub-Saharan African countries have been extensively reported (Jerven 2010). Inaccuracies in the output and productivity data reported by national statistical systems have led to (potentially) misleading country productivity rankings. Output and productivity estimates in international currency showed significant variation across countries because of the varying reliability of the data sources or differences in the methods chosen to express the data in international currency.9

Output and productivity estimates across African countries can also be volatile, not only because of the low quality of statistical services but also partly because of the large weight of sectors (such as agriculture) that are prone to volatile domestic shocks and vulnerable to fluctuating international commodity prices. This report highlights some of the data production problems facing the region’s