Plan of the Volume

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

likely overestimate or underestimate the degree of misallocation because they would reflect both the true share and a sampling error. The WBES-based measure of misallocation would tend to be overestimated if sectors with higher misallocation are overrepresented relative to their shares in the census. Evidence for African countries shows that most industries would have smaller misallocation in the WBES than their dispersion in the census data. Hence, the WBES might underestimate the true misallocation of each sector and therefore underestimate manufacturing productivity dispersion (Cirera, Fattal-Jaef, and Maemir 2018).

Limited Interpretation of Microeconomic Evidence

One of the most widely used measures of firm-level productivity in the literature is total factor productivity revenue (TFPR)— typically defined as the ratio of firms’ sales (or revenues) to input costs (appropriately weighted by their production elasticities). It has been argued that TFPR is a measure of profitability (or firm performance) rather than productivity. Hence, differences in TFPR across firms may capture not only differences in physical efficiency but also differences in prices, which reflect product differentiation and markups in addition to costs (De Loecker and Goldberg 2014). The emergence of (output and less often input) price data and new techniques applied to databases with firm-level prices has enabled researchers to compute more accurate measures of physical efficiency. Evidence on the use of these techniques for emerging markets is presented in Cusolito and Maloney (2018) and references therein.

Future work in Africa needs to distinguish productivity shocks (or technical efficiency) from demand shocks in the measures of TFPR among Sub-Saharan African production establishments. This requires the timely availability and recurrent production of high-quality data on output and input prices at the establishment level—a task that does not preclude improving the country coverage as well as the methodology and periodicity of firm-level censuses. Such new and increased data impose other challenges: (a) wider availability of output price data rather than input price data at the establishment level; (b) reported output prices that are, in most cases, unit values; and (c) the need to undertake surveys at the product level if most manufacturing establishments in a specific sector are multiproduct.

Having greater data availability on output and input prices does not prevent the need to impose more structure to identify the role played by demand shocks in the measured TFPR. Recent research using firm-level census with price data shows that there is still a larger dispersion of TFPR across manufacturing firms in Ethiopia, and this is mirrored by large differences in physical productivity. Prices tend to vary significantly less than productivity levels and do not constitute a major driving factor of TFPR differences (Söderbom 2018).

Plan of the Volume

This volume documents the productivity trends in Sub-Saharan Africa in three different dimensions, assessing productivity at the aggregate level, the sectoral level, and the establishment level. It characterizes the evolution of productivity in the region relative to other countries and regions as well as country groups in Africa classified by their degree of natural-resource abundance and condition of fragility.

The core of this volume rests upon the assessment of the implications for aggregate productivity of production decisions across agricultural farms and manufacturing firms in Sub-Saharan Africa. The next three chapters will present evidence on aggregate productivity from the perspective of production units, using recent household surveys for farmers and firm-level surveys for select African countries as well as frontier estimation techniques. The empirical work presented in this volume can provide further guidance for productivity analysis and the design of a policy agenda for the region.

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Chapter 2, “Needed: Boosting the Contribution of Total Factor Productivity,”

documents the growth performance of Sub-Saharan Africa over the past half century both across countries and across sectors of economic activity. Despite an uptick in labor productivity since 1996, the region has failed to catch up to either high-income countries (notably, the United States) or to groups of middle- to high-income countries such as the EAP5 (Indonesia, Korea, Malaysia, Singapore, and Thailand). The sizable gap in output per worker between SubSaharan African countries and those two benchmark groups is primarily attributed to a lower relative stock of physical and human capital (from the 1960s to the 1980s). During 2000–17, inefficiencies in the region’s factor production use have played an increasing role in explaining this gap.

At the sectoral level, the analysis in this volume unpacks the various industry and services sectors into a five-sector classification: agriculture, manufacturing, nonmanufacturing, market services, and nonmarket services. Sectoral labor productivity in Sub-Saharan Africa exhibits long swings in the medium term over the past quarter century, and it is lower than in the United States, especially in agriculture. Broadly speaking, the structural transformation of Sub-Saharan Africa tends to lag that of other world regions. Agricultural employment shares have declined more slowly and remain higher than in other regions.

Chapter 3, “Resource Misallocation in Sub-Saharan Africa: Firm-Level Evidence,”

documents the extent of resource misallocation across agricultural and manufacturing production units in Sub-Saharan Africa. The agriculture sector analysis uses household-level panel data from the World Bank’s LSMS-ISA initiative for selected countries in the region as well as geographically gridded data on actual and potential crops, crop choices, and land endowments from the FAO’s Global Agronomic Ecological Zones (GAEZ) database. The manufacturing sector analysis uses firm-level manufacturing census data that adequately accounts for small and medium-size firms as well as large formal sector firms. On the other hand, the unavailability of firm-level data for the services sector prevents us from extending the services sector analysis to African countries.

The evidence shows that agriculture and manufacturing in Sub-Saharan Africa are plagued by severe misallocation of resources. The region’s low agricultural productivity is not attributed to the quality of its soil or the amount of rainfall. It is overwhelmingly explained by inefficiencies in the allocation of resources. In manufacturing, the misallocation is captured by TFPR dispersion—which is larger than that of other low- and middle-income countries (China and India) and the efficiency benchmark (United States).

Both agricultural and manufacturing production units tend to face higher distortions in Sub-Saharan Africa than in other regions. In turn, these distortions decelerate the growth of the production units, disincentivize their adoption of productivity-enhancing technologies, and reduce the ability of their peers to learn new techniques.

Chapter 4, “Policies and Institutions that Distort Resource Allocation in Sub-Saharan

Africa,” explains how policies and institutions have distorted the allocation of inputs (capital, land, and labor) across heterogeneous production units. These policies and institutions can be classified into potential sources of misallocation: (a) market imperfections (restricted access to finance, lack of land titling or rental markets, and information frictions affecting market connectivity); (b) statutory provisions (size-dependent taxes and regulations); and (c) discretionary provisions (targeted subsidies and preferential trade policies).

Allocative inefficiencies affect output and productivity levels through three channels: technology, selection (occupational choices), and misallocation. These three channels can be interdependent. For instance, policies or institutions that lead to resource misallocation can potentially generate additional effects through both the selection and technology channels.