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WP/20/30

Competition, Competitiveness and Growth in Sub-Saharan Africa

by Reda Cherif, Sandesh Dhungana, Xiangming Fang, Jesus Gonzalez-Garcia, Miguel Mendes, Yuanchen Yang, Mustafa Yenice, and Jung Eun Yoon

IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

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© 2020 International Monetary Fund WP/20/30

IMF Working Paper

African Department

Competition, Competitiveness and Growth in Sub-Saharan Africa

Prepared by Reda Cherif, Sandesh Dhungana, Xiangming Fang, Jesus Gonzalez-Garcia, Miguel Mendes, Yuanchen Yang, Mustafa Yenice, and Jung Eun Yoon*

Authorized for distribution by Papa N’Diaye February 2020

IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Abstract

Does greater product market competition improve external competitiveness and growth? This paper examines this question by using country-and firm-level data for a sample of 39 sub-Saharan African countries over 2000–17, as well as other emerging market economies and developing countries, and finds that an improvement in domestic competition is associated with a signficant increase in real GDP per capita growth rate, achieved mainly through an improvement in export competitiveness and productivity growth. Price levels, including of essential items, are also generally lowered with an increase in competition. Moreover, at the firm-level, evidence shows that greater competition—proxied through a decline in corporate market power—is associated with an increase in firm’s investment and the labor’s share in output. These effects are more pronounced in the manufacturing sector and among domestic firms compared to foreign firms. JEL Classification Numbers: D2, D4, E2, F6, J3, K2, L1, L4

Keywords: Markups; Market Power; Competition; Sub-Saharan Africa

Authors’ E-Mail Addresses: Reda Cherif: [email protected]; Sandesh Dhungana: [email protected]; Xianming Fang: [email protected]; Jesus Gonzalez-Garcia: [email protected]; Miguel Mendes: [email protected] ; Yuanchen Yang: [email protected]; Mustafa Yenice: [email protected]; Jung Eun Yoon: [email protected]

*The research underlying this paper was initiated in the context of the preparation of IMF’s October 2019 Regional Economic Outlook for Sub-Saharan Africa. We would like to thank Mahvash Saeed Qureshi and David Robinson for their guidance, as well as IMF colleagues for helpful comments and suggestions, and Charlotte Vazquez for editorial assistance. The usual disclaimer applies.

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Contents

I. INTRODUCTION................................................................................................................. 4

II. FRAMEWORK FOR THE EMPIRICAL ANALYSIS....................................................... 6

III. DATA ................................................................................................................................. 7 A. Country-level Competition Data...................................................................................... 7 B. Firm-level Competition Data ........................................................................................... 7 C. Other data ....................................................................................................................... 10

IV. STYLIZED FACTS.......................................................................................................... 10 A. Country-level Competition Indicators ........................................................................... 10 B. Firm-Level Competition Indicators................................................................................ 11 C. Competition across Sectors ............................................................................................ 13

V. COMPETITION AND MACROECONOMIC PERFORMANCE ................................... 14 A. Growth ........................................................................................................................... 14 B. Channels of Transmission .............................................................................................. 15 C. Welfare ........................................................................................................................... 15

VI. FIRM DYNAMICS AND COMPETITION .................................................................... 17 A. Investment...................................................................................................................... 17 B. Exports ........................................................................................................................... 18 C. Labor Shares................................................................................................................... 18 D. Productivity.................................................................................................................... 19

VII. HOW TO BOOST COMPETITION IN DOMESTIC MARKETS? .............................. 19 A. Product Market Liberalization ....................................................................................... 19 B. Competition Policies ...................................................................................................... 20 C. Complementary Policies ................................................................................................ 22

VIII. CONCLUSION.............................................................................................................. 23

References............................................................................................................................... 25

Tables...................................................................................................................................... 28

Figures..................................................................................................................................... 42

I. INTRODUCTION

Competition among firms is generally deemed an essential driving force of market economies. It ensures an efficient allocation of resources as factors are allocated to their best use, and generates firm dynamics that boost innovation, productivity growth, and external competitiveness—translating into macroeconomic gains.2 Moreover, by limiting unfair pricing, discriminatory practices, and rent extraction, competition is seen to have significant welfare, employment generation, and distributional implications as well.

The expected benefits of competition are, however, more likely to accrue in the absence of market distortions. Where market distortions exist— whether in advanced economies or low-income countries—it is often argued that competition, especially from foreign entrants, can hurt the domestic industry and create dominant firms that end up stifling competition and harming consumer welfare. Such concerns generally lead to trade and other regulatory barriers that restrict the entry of private firms in domestic markets. Nevertheless, many of these fears can be mitigated by implementing an appropriate policy framework that encompasses the opening of the market along with a strong competition law and enforcement agency. By and large, existing evidence shows that competition and a well-crafted competition policy framework can help to improve welfare and other macroeconomic outcomes (Dutz and Hayri 1999; UNCTAD 2004; Aghion and Griffith 2005; OECD 2014).

Despite the advantages of competition, markets are often characterized by anticompetitive practices and structures, especially in developing countries. Sub-Saharan Africa is no exception— monopolies, especially state-owned, are widely prevalent, and single operators hold large market shares in key sectors in many countries. The lack of competition has significant potential costs, hurting the poor through higher prices of essential items and undermining external competitiviness and economic growth.3 Although the issue of declining competition and rising corporate market power has received much attention in recent years in the context of advanced and emerging market economies (Autor and others 2017; De Loecker and Eeckhout 2018; De Loecker and others 2018; IMF 2019a), a systematic analysis for emerging market economies and developing countries, and in particular for sub-Saharan Africa, remains lacking.

Against this background, this paper aims to broaden the understanding of the state of product market competition in sub-Saharan Africa by bringing together country and firm-level data from several sources to explore the following key questions. How has product market competition in sub-Saharan Africa evolved over the years and how does it compare to other regions? What are the macroeconomic implications of competition for external competitiveness, economic growth, and consumer welfare? How does competition affect firm behavior and performance to generate observed macroeconomic outcomes? What role

2 The dynamic efficiency gains conferred by competition are based on the Schumpeterian “creative destruction” hypothesis, which postulates that competition drives innovation and constant change, leading the least productive firms to exit the market and the most productive firms to survive (Schumpeter 1942). To reap the dynamic benefits of competition, however, firms must be able to enter, upgrade, and exit easily. 3 World Bank (2016), for example, estimates that retail prices of essential food items are at least 24 percent higher in African cities than in other major cities around the world, while cement prices are, on average, about 183 percent higher than world prices.

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does macroeconomic policy, including competition policy, play in promoting competition in the region?

The analysis, based on a sample of 39 sub-Saharan African countries covering the period 2000–17, shows that competition in the region remains generally low relative to the rest of the world. Specifically, country-level indicators show that, on average, sub-Saharan Africa lags advanced and emerging market economies in both domestic and foreign competition, though it is on par with other developing economies. Specifically, more than 70 percent of the countries in the region fall in the bottom half of countries globally in terms of domestic and foreign competition indicators. The low level of domestic competition is related to the market dominance of a few large firms, the absence or weak enforcement of competition policies, structural and regulatory barriers to entry, and the distortive effects of tax regimes. Meanwhile, foreign competition is mainly impeded by high trade barriers, which may also indirectly affect domestic competition by restricting access to intermediate inputs.

Firm-level indicators of competition—such as markups and profitability—provide deeper insights into sectoral market structures and suggest that markups and profitability are generally higher in sub-Saharan African countries compared to other emerging market economies and developing economies.4 Both profitability and markups in the region vary considerably across sectors and country groups but tend to be higher in the services sectors (such as hotels and restaurants, information and communications, and transportation), and among oil exporters relative to other country groups. In general, there is a strong association between the number of competitors faced by a firm and its markup and profitability, suggesting that reducing barriers to business entry could play an important role in boosting competition and improving market dynamics.

The empirical analysis shows that sub-Saharan Africa has much to gain from promoting competition. Moving from the median value of the competition intensity index for sub- Saharan African countries to the top quartile of the global distribution is associated with an average increase in the real GDP per capita growth rate of about 1 percentage point, achieved mainly through an improvement in export competitiveness and productivity growth. Also, an international comparison of price levels suggests that prices, including of essential items, are on average about 20 percent higher in sub-Saharan African countries than in other emerging market and developing economies. Higher competition can help to significantly lower prices of consumer and intermediate goods, thereby improving welfare and competitiveness.

The analysis of firm-level analysis shows that firm behavior responds to market structure, generating the observed macroeconomic patterns. Specifically, a decline in firm markups is significantly associated with an increase in investment and exports, productivity growth, and labor’s share of output. The effects of market power are more pronounced in the

4 While several variables, most notably market shares, have been used in previous studies as an indicator of competition, this paper uses profitability and markup measures given limited firm-level data availability for sub-Saharan African countries, which makes it difficult to compute market shares. In principle, differences in profitability and markup—which broadly speaking capture the divergence between the product price and the cost of production—could be reflecting differences in the return to capital and in productivity, thus the empirical analysis presented below attempts to control for these factors. The paper also analyzes the persistence of profits and markups, as in competitive markets, the process of firm entry and exit should imply a mean-reverting behavior of these variables.

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manufacturing sector relative to services, and stronger for domestic firms relative to majority foreign-owned firms.

The rest of the paper is organized as follows. Section II presents the general framework for the empirical analysis. Section III introduces the data used in the empirical analysis. Section IV presents some stylized facts pertaining to product market competition in sub-Saharan Africa. Section V analyzes the macroeconomic implications of greater competition, while Section VI delves into the firm-level effects. Section VII explores ways to help improve competition in the sub-Saharan African region. Section VIII concludes.

II. FRAMEWORK FOR THE EMPIRICAL ANALYSIS

The idea that competition is an important driving force of market economies that affects economic growth can be traced back to Adam Smith’s Wealth of Nations, penned more than two centuries ago.5 Since then, a voluminous body of literature has examined the effect of competition on economic growth and welfare. Theoretically, the relationship is ambiguous: rivalry among firms can encourage innovation and boost productivity growth, but it can also stifle innovation and growth by limiting the expected returns for firms from innovating (Aghion and Griffith 2005). Also, it may be the case that open and competitive systems can also enable firms in dominant positions to entrench themselves and work toward closing the system and impeding growth (Rajan and Zingales 2004).

Cross-country empirical studies, however, generally indicate a strong positive relationship between competition and growth resulting from a more efficient allocation of resources and increased investment, innovation, productivity, and export competitiveness (OECD 2014; Goodwin and Pierola Castro 2015). Competition is also observed to have important welfare and distributional implications by lowering prices for consumers and downstream producers, generating income and employment opportunities, and reducing discriminatory practices (Begazo and Nyman 2016). The empirical analysis using country level data presented below focus on the effects of competition in domestic markets on the growth of per capita GDP and the channels for those effects. Also, cross country data is used to evaluate the relationship of domestic competition and the price level of comparable consumption baskets in different countries in order to assess the effects on consumer welfare of different levels of economic competition in domestic markets.

Obviously, the effects of competition in domestic markets at the aggregate level are the result of effects that materialize at the firm level. Therefore, any assessment at the aggregate level should be corroborated with a firm-level analysis. The empirical analysis using micro data presented below is similar to other studies ––as De Loecker and others 2018 and IMF 2019–– but focuses on the relationship of corporate market power and investment, exports, productivity and labor shares in emerging and developing countries, and in particular in sub-Saharan Africa. These groups of countries have not been studied in previous literature.

5 See, for example, Smith (1776), Book II, Chapter II, p. 329, para. 106.

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III. DATA

To assess the competitiveness and growth implications of product market competition in sub-Saharan Africa, country and firm-level data is compiled from various sources, as described below.

A. Country-level Competition Data

Global Competitiveness Index (GCI). The main macro-level indicators of competition are obtained from the World Economic Forum’s Global Competitiveness Index (GCI) and include overall competition, domestic competition, and foreign competition. These indicators—based on macroeconomic data, as well as responses to surveys provided by business leaders—are available annually over 2006–17 for 44 sub-Saharan African countries (Table 1). The level of competition is measured on a range of 1 (low) to 7 (best). Domestic and foreign competition are further disaggregated into several factors such as the intensity of local competition, effectiveness of anti-monopoly policy, ease of doing business, etc. (Table 2).6 For the regression analysis, the indicator capturing the intensity of local competition is used as the main variable to capture the market structure and extent of competition faced by the firms.7

Bertelsmann Stiftung Transformation Index (BTI). The Bertelsmann Stiftung Transformation Index is another source of country-level competition information included in this paper for robustness purposes. Two subcomponents of BTI: market-based competition and anti-monopoly policy, which range between 0 and 10 (best), are considered here. The scores of BTI are mainly based on opinion surveys among country experts and are available every two years over 2006-18 for 36 sub-Saharan African countries (listed in Table 3).

B. Firm-level Competition Data

Firm profitability and markups are common indicators to assess the level of competition faced by firms. For profitability, the Lerner Index—defined as the price-marginal cost difference relative to price (P−MC)/P—is often used as a proxy, while markups— theoretically defined as the price to marginal cost ratio (P/MC)—are generally proxied in recent literature following De Loecker and Warzynski (DLW, 2012).8 Specifically, based on the firm’s cost minimization problem (first-order condition), DLW show that a firm’s markup (μ) can be expressed as:

𝑃𝑃 𝛽𝛽𝑘𝑘 𝜇𝜇 = = (1) 𝑀𝑀𝑀𝑀 𝛼𝛼𝑘𝑘

where βk is the output elasticity of any input k and αk is the expenditure share of the same input in sales. Following DLW, αk is approximated by the expenditure share of variable

6 The GCI has been revised since 2018. Data for the new GCI 4.0 version is available for 2018 only and is not included in the analysis as it is not comparable to earlier data. 7 The sample used in the regression analysis covers 25 sub-Saharan African countries and ends in 2014 due to the lack of data availability for the control variables included in the estimations. 8 In theory, under perfect competition, a firm’s price and marginal cost are equalized. Thus, the differential between price and marginal cost could be interpreted as the ability of a firm to influence the market.

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inputs in sales and βk is approximated by the output elasticity of variable costs. The former is firm specific while the latter is usually assumed to be the same across firms in the same industry (DLW; Diez and others2019). Thus, for a firm j in industry i, taking the log of firm markup yields the following equation:

𝑙𝑙𝑙𝑙�𝜇𝜇𝑖𝑖𝑖𝑖� = 𝑙𝑙𝑙𝑙(𝛽𝛽𝑖𝑖) − 𝑙𝑙𝑙𝑙(𝛼𝛼𝑖𝑖) (2)

To construct empirical measures of firm profitability and markup, two data sources are used: the World Bank’s Enterprise Survey and the Orbis database.

World Bank Enterprise Survey (WBES). The WBES collects information from a representative sample of firms by conducting face-to-face interviews, and focuses on formal firms in the manufacturing and services sectors with 5 or more employees. The survey provides information on a broad set of aspects and several variables of the firms including size, ownership, sector, geographic region, financial information, and information about the business environment in which firms operate. The WBES data is mostly cross-sectional and interviews may not be repeated with the same firms over the years.

The WBES data used in the paper comprises 39 sub-Saharan African countries over 2006–18, listed in Table 4. For comparative purposes, data for other 89 emerging market economies and developing countries are also used. For the empirical analysis all variables from the WBES are trimmed at the top and bottom percentiles to remove extreme values, and no sector with less than 100 observations is included in the regression analysis to ensure a sufficiently representative sample. After cleaning up the data, about 41,000 and 10,000 firm-level observations—mostly pertaining to the manufacturing sector—are obtained for sub-Saharan Africa and other emerging market economies and developing countries, respectively (Table 5).

To construct measures of firm profitability and markup using the WBES data, some approximations are made considering data availability. Firm profitability is constructed as the difference between annual sales (proxy for revenue) and the cost of variable inputs to annual sales, as follows:9

𝑆𝑆𝑆𝑆𝑙𝑙𝑒𝑒𝑠𝑠 − 𝐿𝐿𝑆𝑆𝐿𝐿𝐿𝐿𝐿𝐿 𝐶𝐶𝐿𝐿𝑠𝑠𝐶𝐶𝑠𝑠 − 𝑀𝑀𝑆𝑆𝐶𝐶𝑒𝑒𝐿𝐿𝑀𝑀𝑆𝑆𝑙𝑙𝑠𝑠 𝑆𝑆𝑙𝑙𝑎𝑎 𝑀𝑀𝑙𝑙𝐶𝐶𝑒𝑒𝐿𝐿𝑖𝑖𝑒𝑒𝑎𝑎𝑀𝑀𝑆𝑆𝐶𝐶𝑒𝑒 𝑀𝑀𝑙𝑙𝑖𝑖𝑖𝑖𝐶𝐶𝑠𝑠 𝑆𝑆𝑆𝑆𝑙𝑙𝑒𝑒𝑠𝑠

To proxy firm markup given in equation (2), 𝑙𝑙𝑙𝑙(1/𝛼𝛼𝑖𝑖) is used, which is constructed as the log ratio of annual sales to variable costs (that is, the sum of labor cost and the cost of raw materials and intermediate goods).10

9 The Lerner index is often proxied in the literature using the ratio of Earnings Before Interest and Taxes (EBIT) to operating revenue (IMF, 2019). The WBES, however, does not provide data on the EBIT. 10 To compute markups as defined in equation (2), longitudinal information is required to estimate output elasticities, 𝛽𝛽𝑖𝑖 , which is lacking in WBES data. However, under the assumption that output elasticities to variable inputs are similar across countries in the sample, including industry-specific effects in the estimation precludes the need to include these elasticities

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Orbis Bureau van Dijk (Moody’s Analytics). The Orbis dataset provides harmonized cross-country financial information for both privately held and publicly listed firms. The information is usually gathered from local companies that collect information from corporate registers. The raw data obtained from Orbis requires intensive cleaning. The cleaning procedures include dropping duplicate values, reporting mistakes, and data that was not assigned to a specific industry. Cleaning data also involves quality checks to identify outliers and aberrant observations. Filters are applied to the annual growth rates of some variables such as sales, operating revenue, and the number of employees to eliminate extreme observations. Finally, variables are scaled to allow cross-country comparability. Data for 28 sub-Saharan African countries over 2000-17 (with gaps for some countries) are obtained from the Orbis database (Table 6). About 73 percent of the firms for sub-Saharan Africa pertain to three countries: South Africa, Nigeria and Mauritius. To ensure that the regression results are representative for a broad group of countries, the sub-Saharan African data is complemented with data for 53 other emerging economies and developing countries. About 30 percent of the observations in the emerging markets and developing countries sample and 40 percent of the observations in the sub-Saharan Africa sample belong to the manufacturing sector, while the rest are dominated by the services sector (Table 7).

The construction of the variables using the Orbis database is mainly based on Díez and others (2019). To compute markups, two approaches are used. In the first approach, markups are obtained as the ratio of operating Revenue (OR) to the cost of goods sold (COGS), which includes direct labor and materials costs. This measure, shown below, is similar to the measure computed using WBES data and allows direct comparisons of the indicators between the two databases:

𝑂𝑂𝑖𝑖𝑒𝑒𝐿𝐿𝑆𝑆𝐶𝐶𝑀𝑀𝑙𝑙𝑂𝑂 𝑅𝑅𝑒𝑒𝑅𝑅𝑒𝑒𝑙𝑙𝑖𝑖𝑒𝑒 − 𝐶𝐶𝐿𝐿𝑠𝑠𝐶𝐶𝑠𝑠 𝐿𝐿𝑜𝑜 𝐺𝐺𝐿𝐿𝐿𝐿𝑎𝑎𝑠𝑠 𝑆𝑆𝐿𝐿𝑙𝑙𝑎𝑎𝑠𝑠 𝑂𝑂𝑖𝑖𝑒𝑒𝐿𝐿𝑆𝑆𝐶𝐶𝑀𝑀𝑙𝑙𝑂𝑂 𝑅𝑅𝑒𝑒𝑅𝑅𝑒𝑒𝑙𝑙𝑖𝑖𝑒𝑒

The second approach involves estimating output elasticities for different sectors.11 Using the available panel data for firms in several sectors, output elasticities (the β’s in equation 1) are estimated without assuming that sectoral technologies are the same across countries. To estimate the β’s, the control function approach of Olley and Pakes (1996) is applied, following the methodology of DLW and Diez and others (2019). This methodology assumes that for each firm i, the production function is represented by a Cobb-Douglas model as follows:

𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛽𝛽𝑅𝑅𝑖𝑖𝑖𝑖 + 𝛼𝛼𝑘𝑘𝑖𝑖𝑖𝑖 + 𝑤𝑤𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 (3)

where yit is output, vit is variable input, kit is the stock of capital and wit is productivity. For each industry in the NACE Rev. 2 classification (see Eurostat 2008), a two-step approach to estimate β is followed. In the first step, output is estimated based on variable inputs and fixed capital (and their squares and interaction terms) and the residuals obtained from this

in the regressions. Moreover, even if the output elasticity for specific sectors is assumed to vary across countries, the country-fixed effects included in the regressions would help to absorb those variations. 11 This approach is applied to construct markups for the regression analysis based on Orbis data.

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estimation are interpreted as productivity. In the second step, productivity is assumed to follow a Markov process and is obtained using a GMM estimation of equation (3) assuming moment conditions consistent with intuitive assumptions about the timing of investment decisions (see Diez and others 2019 for more details).

C. Other Data

Product market liberalization. To assess the pace of product market reforms in sub- Saharan countries, data is obtained from Alesina and others (forthcoming). The sample provides annual information over 1973-2014 for 14 sub-Saharan African countries (Burkina Faso, Cameroon, Côte D’Ivoire, Ethiopia, Ghana, Kenya, Madagascar, Mozambique, Nigeria, Senegal, South Africa, Tanzania, Uganda and Zimbabwe), which account for about 80 percent of the region’s output. This database covers information on reforms pertaining to different sectors such as trade, the current and capital accounts, telecommunications and electricity, agriculture, and financial liberalization.

Competition policy framework. To analyze the state of anti-trust frameworks across sub-Saharan African countries, data is gathered from two sources: the World Bank–African Competition Forum (WB-ACF), and an IMF desk survey of country authorities. The WB-ACF data is based on surveys for 36 countries jointly administered by the World Bank and the African Competition Forum over 2010–15 (World Bank 2016). The survey provides cross-sectional information on several aspects of competition including the existence and enforcement of competition laws, price control regulations, and the degree of independence, annual budgets and staff size of the competition authorities. The IMF data is based on a survey of competition authorities designed specifically for this paper. The survey was conducted in May–June 2019 and elicited response from 37 jurisdictions, including 29 separate jurisdictions and one regional body.

Other variables. Additional macroeconomic variables necessary for the empirical analyisis are collected from various sources such as the IMF’s World Economic Outlook, the World Development Indicators, etc. (see Table 8).12

IV. STYLIZED FACTS

A. Country-level Competition Indicators

Based on the World Economic Forum’s product market competition indicator, overall competition in the region is, on average, significantly lower than in advanced and emerging market economies but similar to that observed in the rest of developing countries (Figure 1). More than 40 percent of the countries in sub-Saharan Africa are in the bottom quartile of the global distribution of the competition index, while more than 70 percent are below the world median (Figure 2).13 These patterns are also observed in other available country-level

12 See Table 9 for a list of non-sub Saharan African countries included in the country and firm-level empirical analysis.

13 The methodology to compute the World Economic Forum’s product market competition index was revised in 2018. The revised index, while not strictly comparable to earlier years, portrays a similar picture for sub-Saharan Africa relative to other countries in terms of domestic and foreign competition (Figure 4).

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competition indicators such as the Bertelsmann Stiftung Transformation Index, which shows a notable difference between sub-Saharan Africa and other countries in terms of market competition (Figure 3).14

The low level of competition in most sub-Saharan African countries can be attributed to low levels of both domestic and foreign competition. The weak domestic competition environment mainly stems from the market dominance of a few firms, lack of effective competition policies, structural and regulatory barriers to entry, and the distortive effects of prevalent fiscal regimes (Figure 5). Low foreign competition is to a large extent driven by trade barriers, which have declined significantly over the last two decades but remain relatively high (Figure 6). Trade barriers—both tariff and non-tariff related—tend to limit direct competition from foreign firms but could also indirectly affect domestic competition by restricting the availability of inputs (or by making them more expensive).

A look across the different country groups in the region shows considerable heterogeneity in the state of competition across markets. Non-resource intensive countries generally have the most competition-prone market structures, while oil exporters have the least, probably reflecting the structure of these economies, with limited diversification, significant import protection, and the prevalence of a few large firms in the extractive industry (Figure 7, panel 1). Domestic competition, however, appears to have increased over the last decade in all country groups, with non-resource- intensive countries recording the largest improvement, mainly due to an improvement in the ease of doing business. Across subregions, competition is significantly lower in central Africa, while it is the highest across southern African countries (Figure 7, panel 2).

B. Firm-Level Competition Indicators

Firm-level competition indicators—such as profitability and markups—corroborate the country-level indicators and show that the extent of competition faced by firms in the region is indeed limited.15 These indicators are not readily available for sub-Saharan Africa but they are constructed for the purpose of this paper using detailed information obtained from two data sources: the World Bank Enterprise Survey, which provides mostly cross-sectional information on over 10,000 firms in 39 sub-Saharan African countries during 2006–18; and the Orbis database, which provides time-series information on about 500 firms in 18 sub-Saharan African countries during 2000–17, resulting in nearly 9,000 firm-level observations.16

Based on these datasets, average firm profitability in sub-Saharan African countries is significantly higher (10–20 percent) compared to other emerging market economies and

14 The World Economic Forum’s competition indicator is based on both subjective (opinion surveys of business executives) and objective (tariff rates, number of regulatory procedures, etc.) components. The Bertelsmann Stiftung Transformation Index is based on opinion surveys of country experts. 15 While in absolute terms the markup and profitability measures may not necessarily reflect the extent of market competition under fairly general assumptions such as similar technologies, a comparison across firms and countries could inform on differences in market power. 16 The number of firms covered in both databases varies considerably across countries, but more than 90 percent of the firms in the WBES and 50 percent of the firms in the Orbis database belong to the manufacturing sector.

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developing countries (Figure 8).17 Firm markups are also about 11 percent higher in sub-Saharan African countries relative to other countries at a similar level of development, thereby implying a lower degree of competition in the region.18

The derived firm profitability and markup measures are positively associated with each other by construction, but also with other measures of market concentration, such as the number of competitors faced by firms.19 Countries characterized by a higher share of firms reporting fewer competitors tend to record higher average firm profitability and markups—suggesting that removing barriers to entry and encouraging more firms to enter the market could bolster competition and reduce corporate market power (Figure 9). Notably, for a given share of firms reporting few competitors, profitability and markups across sub-Saharan African countries tend to be higher than in other emerging market economies and developing countries, indicating a relatively higher degree of corporate market power in the region.

A look across country groups within sub-Saharan Africa shows average firm markups and profitability are higher among oil-exporting countries by about 16 and 8 percent, respectively, relative to other countries (Table 10). Similarly, central African countries tend to have significantly higher markups and profitability (by about 8 percent and 18 percent, respectively) compared to other regions within sub-Saharan Africa.20 The higher markups among oil exporters and in central African countries are consistent with Figure 3 above, which shows a relatively low level of product market competition at the macro level on these countries.

In terms of the dynamics of markups, the lack of consistent firm-level time-series data for most sub-Saharan African countries makes it difficult to draw definitive conclusions; however, the available information suggests an increase in markups in some countries, including the region’s two largest economies: Nigeria and South Africa (Figure 10). These trends are consistent with other studies (Aghion, Braun, and Fedderke 2008; Fedderke, Obikili, and Viegi 2018; De Loecker and Eeckhout 2018), which also document rising firm markups in these countries and globally. More generally, the analysis shows that markups are highly persistent in sub-Saharan Africa, with the half-life of markups being almost twice as

17 Firm profitability is often captured by an empirical measure of the Lerner index—the ratio of operating earnings to sales (IMF 2019a). Given the limited availability of data on operating earnings in the WBES for sub-Saharan African firms, profitability is defined as the difference between firm sales and the cost of inputs to firm sales, and using Orbis data, it is measured as operating revenue to the cost of goods and services. 18 Theoretically, markup is defined as the price to marginal cost ratio. However, given the unavailability of data on marginal costs, the following proxies are used here: 1) the log of the ratio of sales to the cost of inputs when considering the WBES data; and 2) the log ratio of revenue turnover to costs when considering the Orbis database. With these definitions, markup values (profits) higher than 1 (0) can be considered as indicators of market power, as they suggest a divergence between prices and costs. 19 While market share (that is, firm sales to total industry sales in a given period) is a commonly used measure of market concentration, given the lack of data on the entire size of the market, in particular in the informal segment, it is not the preferred measure for the analysis here. Nevertheless, market shares calculated as a check with the databases mentioned are strongly positively correlated with both firm markups and profitability. 20 In addition to average markup, markup dispersion within sectors is also significantly higher among the oil exporters, as well as in central African countries. As noted by Lerner (1934), markup dispersion could lead to a misallocation of resources resulting in efficiency losses.

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long in countries in the region than in other emerging market economies and developing countries.21

Evaluating the behavior of markups across the different types of firms in the region indicates that majority state-owned and foreign-owned firms tend to have higher markups than other firms, especially in the manufacturing sector. By contrast, small firms tend to have lower markups than medium and large firms (Figure 11). These patterns are consistent with those for other emerging market and developing economies—but it is worth noting that the share of mostly state-owned firms in the sample for sub-Saharan Africa is almost double that for other emerging market economies and developing countries.

C. Competition across Sectors

The macro and firm-level competition indicators presented above suggest generally low levels of competition in sub-Saharan Africa, but are all sectors equally anticompetitive across countries? To answer this question, the computed firm profitability and markup measures are aggregated across sectors to gauge the degree of sectoral competition in the region. The results show considerable variation across sectors in sub-Saharan Africa, with both profitability and markups being the highest in the nontradable sectors, such as hotels and restaurants, wholesale and retail trade, and construction, based on the WBES database, and in other services, information and communications, financial intermediation, and transportation, based on the Orbis database, which has larger coverage of firms in the services sector (Tables 11–12). On average, markups tend to be lower in the manufacturing sector, especially among textile and leather producers.

Comparing the profitability and markup measures for countries in sub-Saharan Africa with those for other emerging market and developing economies indicates that competition is weaker in the region across nearly all sectors, with the average difference in markups equivalent to about 7 percent (Figure 12). In general, however, there is a strong positive correlation (about 0.9) between sectoral markups in sub-Saharan African countries and other countries, suggesting that the pattern of sectoral competition tends to be similar across countries.22

Sectoral markups are also generally positively correlated across country groups within sub- Saharan Africa, except for central African countries, which tend to have higher markups in most manufacturing industries along with the services sector (Table 13). On average, commodity exporters— both oil and other—also tend to have higher markups in the manufacturing sector than the non-resource-intensive countries.

21 The half-life of firm markups—obtained by estimating an autoregression (AR(1)) model of markups, while controlling for different firm, industry, and country-level characteristics and year effects—is about 1 year for the sub-Saharan African sample compared to 0.5 years for other emerging market economies and developing countries. 22 While higher returns to capital resulting in higher profitability may be expected in low-income countries relative to advanced economies given their low level of capital endowment, the relatively higher markups in most sub-Saharan African countries compared to other developing economies with similar capital endowment structure suggest that the high level of profitability/markups cannot be fully attributed to higher returns to capital. More generally, the equality between returns to capital and the marginal product of capital relies on the assumption of perfect competition in capital markets (Caselli and Feyrer 2007), which generally does not hold in low-income countries.

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V. COMPETITION AND MACROECONOMIC PERFORMANCE

Does the low level of competition prevalent across sub-Saharan Africa affect macroeconomic performance? This section presents different models using country level data to evaluate the effects of competition in domestic markets on the growth of per capita GDP and the most important channels for those effects, as investment, non-commodity exports and productivity growth. Given the importance of the indfluence of competition on productivity growth, a closer look is taken on the influence of competition in domestic markets on innovation and technological advancement.

A. Growth

What are the macroeconomic implications of domestic market competition? Competition can stimulate economic growth by ensuring an efficient allocation of resources, encouraging investment, boosting innovation and productivity, and promoting exports. Also, competition can also have important welfare and distributional implications through its effects on prices and output. To analyze the effect of competition on economic growth and its determinants (such as investment, exports, productivity), regressions of the following form are estimated:

𝑀𝑀𝑆𝑆𝑀𝑀𝐿𝐿𝐿𝐿𝑖𝑖,𝑖𝑖 = 𝛽𝛽𝐶𝐶𝐿𝐿𝑖𝑖𝑖𝑖,𝑖𝑖 + 𝛾𝛾𝛾𝛾𝑖𝑖,𝑖𝑖 + 𝛼𝛼𝑖𝑖 + 𝛼𝛼𝑖𝑖 + +𝜀𝜀𝑖𝑖𝑖𝑖 (4)

where the dependent variable 𝑀𝑀𝑆𝑆𝑀𝑀𝐿𝐿𝐿𝐿𝑖𝑖,𝑖𝑖 represents alternative macroeconomic indicators for country i at time t (such as real GDP per capita growth rate, the share of private investment in GDP, non-oil exports in percent of GDP, and indicators of innovation and technological readiness). 𝐶𝐶𝐿𝐿𝑖𝑖𝑖𝑖,𝑖𝑖 is the indicator of the intensity of local competition obtained from the World Economic Forum with values ranging between 1 (low) and 7 (high). 𝛾𝛾𝑖𝑖,𝑖𝑖 is a vector of control variables selected according to the specification used that includes lagged (log) real GDP per capita, share of investment in output, trade openness, change in terms of trade, years of schooling, trading partner growth, institutional quality, and public debt in percent of GDP. αi and αt are country and year-fixed effects, respectively and 𝜀𝜀𝑖𝑖𝑖𝑖 is the random error term. The model is estimated using ordinary least squares (OLS) and standard errors are clustered at the country level.

The positive relationship between competition and growth is borne out by data used in this paper. Equation (4) is estimated for the world sample, which includes advanced, emerging market, and developing economies (depending on data availability), as well as for samples comprising emerging market economies and developing countries (EMEDEV) and sub- Saharan African (SSA) countries only. The results show that competition is positively associated with higher economic growth in both the world and EMEDEV samples—with the estimated coefficient being statistically significant at the 1 percent level for the world sample and at the 5 percent level for the EMEDEV sample (Table 14). Based on these results, an increase in the competition intensity index from the median level for sub-Saharan African countries to the top quartile of the global distribution implies an average increase in the real GDP per capita growth rate of about 1 percentage point (Figure 13). The impact is

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economically relevant as the average real GDP per capita growth rate in PPP terms in sub- Saharan Africa after 2010 has been 1 percent.23 Restricting the sample to sub-Saharan African countries (cols. 3 and 6), the coefficient of competition remains positive but loses statistical significance (p-value: 0.2).24 The results are robust to addressing potential endogeneity concerns by using the instrumental variable-two stage least squares (IV-2SLS) methodology with two types of instruments: (i) the first two lagged values of the competition index; and (ii) a regional competition index that is the average of the competition indices for the nearby countries in the region (Table 15).

B. Channels of Transmission

What are some of the channels through which competition lifts economic growth? Analyzing the effect of competition on private investment, non-oil exports and labor productivity, the results show a positive but statistically weak association of the local competition intensity index and investment (percent of GDP) but a strongly positive association with exports (percent of GDP) and labor productivity growth. Specifically, an improvement in the competition index from the median value for sub-Saharan Africa to the top quartile of the global distribution is associated with an increase in exports by 1.7 percent of GDP and labor productivity growth by about 1 percentage point (Table 16). The improvement in export competitiveness could be a result of a boost in productivity as competition encourages high- productivity firms to enter the export market (the ‘self-selection effect’) and/or makes the existing exporting firms more productive to compete with other firms (the ‘learning-by- doing’ effect; Wagner 2012). Innovation and technological advancement could partly explain the increase in productivity as competition could induce firms to invest in research and development, new management practices, and better technology to improve competitiveness (Holmes and Schmitz 2010). The results reported in Table 17 support this assertion and show that competition is strongly positively association with greater innovation and technological readiness across countries. 25

C. Welfare

To analyze the effect of competition on welfare, comparable data on international prices for different items is obtained from the World Bank’s International Comparison Program (ICP)— only cross-country data for the year 2011 is used—and the following regression is estimated:

𝑃𝑃𝑖𝑖 = 𝜌𝜌𝐶𝐶𝐿𝐿𝑖𝑖𝑖𝑖 + 𝜃𝜃𝛾𝛾𝑖𝑖 + 𝛼𝛼1𝐸𝐸𝑀𝑀𝑖𝑖 + 𝛼𝛼2𝐷𝐷𝐶𝐶𝑖𝑖 + 𝛼𝛼3𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖 + 𝜇𝜇𝑖𝑖 (5)

23 Several emerging market and developing economies in the sample, such as Colombia, Mauritius, and Morocco, have achieved a sustained increase in the competition intensity index over the last decade equivalent to an increase from the median level for sub-Saharan African countries to the top quartile of the world distribution. 24 These results of sub-Saharan Africa should be interpreted with caution given the limited sample size for the region, which covers a period with few observed changes in competition indicators. 25 Competition could also impact economic growth through some other channels. For example, studies indicate that inadequate access to finance has been a chronic impediment to growth in sub-Saharan Africa (IMF 2018), while available evidence suggests that competition in the banking sector is strongly associated with financial inclusion and access (Mengistu and Perez-Saiz 2018).

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where Pi represents an internationally comparable price of a product or service (such as food, health, education, etc.) or the aggregate individual consumption basket for country i; Comi is the local competition intensity indicator, with values ranging between 1 (low) and 7 (high); X is a vector of control variables including (log) real GDP per capita, logistical efficiency, trade openness, and foreign direct investment (FDI) in percent of GDP; EME, DEV and SSA are dummy variables taking the value of one if country i is an emerging market economy, a developing country, or a sub-Saharan African country, respectively, and zero otherwise (advanced countries are taken as the reference category); and μ is a random error term. Equation (5) is estimated using OLS with robust standard errors. For comparative purposes, the equation is first estimated without including the competition index but controlling for other macro-structural characteristics.

The results indicate that the prices of most products and services—including food, utilities, furniture, health services, communication, education, as well as machinery and equipment— are on average significantly higher in sub-Saharan Africa than in other emerging market economies and developing countries (Table 18). It is worth noting that prices in sub-Saharan Africa tend to be higher for food, clothing, and health services, items that tend to carry a larger weight in the consumption basket of low-income households (Figure 14). Prices for intermediate inputs used in production—such as utilities and machinery and equipment—are also significantly higher in the region relative to other emerging market and developing economies (Table 19).

Looking at price levels across the different regions in sub-Saharan Africa it is apparent that, on average, east Africa has the lowest prices for goods, followed by west Africa, though prices of most services (such as health, communications, restaurants and hotels, as well as utilities) are not statistically different among the regions (Table 20). Accounting explicitly for foreign competition by including measures of trade openness and foreign direct investment in the regressions shows that greater foreign competition also helps to lower prices. The higher price levels in the services sector are consistent with the higher firm markups across regions in the services sector reported above. Differentiating between sub-Saharan Africa countries based on their exchange rate regime, such that CFA franc zone countries are considered relative to the non-CFA franc zone countries, the results show no statistically significant difference in the price levels in most cases, except for some nontradable sectors such as health services, communication, and recreation (Table 21).

Increasing competition may help to lower prices as indicated by the strongly negative relationship between the local competition intensity index and prices of most goods and services, thereby improving welfare and the external competitiveness of economies. Specifically, moving from the median level of the competition index for sub-Saharan Africa to the top quartile of the global distribution is, on average, associated with about an 8 and 14 percent reduction in the prices of food items and health services, respectively, and a 10 percent decline in the price of the overall individual consumption basket (Figure 15).

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Finally, some important points are worth noting. While domestic competition indicators explain a large part of the average price differential between sub-Saharan Africa and other emerging market and developing economies, they do not fully account for it. This suggests that other macro-structural factors may also play a role in pushing up the price levels across sub-Saharan African countries.26 Also, greater trade openness and FDI are strongly negatively associated with price levels across countries and including these variables in the estimations lowers the price difference between sub-Saharan Africa and other countries but the differential is not eliminated in most cases, indicating again that other macro-structural factors may also be at play in pushing prices higher in the region.

VI. FIRM DYNAMICS AND COMPETITION

To estimate the effect of competition on the behavior of firms along several dimensions as investment, export orientation, labor share and productivity, a baseline regression with the following explanatory variables is estimated. Indicator of competition intensity: the markup, as described above, the variable of interest; firm specific controls as size (number of employees in logs); direct exports27 (share of direct exports in sales); dummy variables indicating whether foreign and private ownership of firms is greater than 50 percent; age (number of years since establishment); and a dummy variable for the number of competitors (equal to one if the number of competitors facing the firm is less than five and zero otherwise); macroeconomic controls: the log of real GDP per capita in PPP terms (from Penn World Table 9.0), and industry, country and year fixed effects.

In these regressions, investment, exports and labor shares are normalized by value added (sales minus cost of materials and intermediate inputs) and log transformed (except exports, given some zero observations), and two indicators of productivity are used: labor productivity and total factor productivity. Benchmark regressions are restricted to industries with at least 100 observations to avoid small sample bias and estimated for a sample of emerging and developing economies including sub-Saharan Africa; for comparisons, a sample comprising sub-Saharan African countries is also estimated (Table 22).

A. Investment

Investment is measured with the purchase of equipment reported by firms to sales. A standard definition used in the literature the investment rate, which is investment as a ratio of the stock of fixed capital in the previous period, but WBES does not offer firm-level time series and the variable available to proxy the stock of capital (cost to re-purchase its machinery) has limited coverage, especially in SSA. The estimation results indicate that higher firm markups are associated with lower investment with an elasticity of about 0.5–0.7, and it is highly statistically significant across both samples. This result is robust to alternative specifications (such as scaling by sales instead of value added and including additional

26 While countries in sub-Saharan Africa tend to have large informal markets, including available indicators of the size of the informal market in the estimations (such as the share of firms competing against unregistered/informal firms in the country; or the share of informal employment in total employment) does not alter the results significantly. 27 Except for regressions (3) and (4) in Table 22 where exports are the dependent variable and where we exclude this variable.

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control variables). Among other factors, private ownership appears to matter with majority private-owned firms investing more relative to public firms while larger and older firms tend to invest less, and there is no strong evidence that foreign ownership or exporting status matter for investment.

B. Exports

We measure exports as the share of exports to firm’s value added, to represent both export orientation and the ability to compete in international markets. The value of exports is backed out from WBES using the share of direct exports in sales. As a sizable share of firms (about 30 percent) in the sample do not export, a Tobit regression is also estimated to verify robustness of the results. The results for the benchmark specification show that higher markups are associated with significantly lower exports in both samples. Also, the results suggest that firms facing fewer competitors and older firms export less, while larger firms and foreign firms tend to export more.

C. Labor Shares

The dependent variable measuring labor share is labor cost to value added. The effect of markups on labor share is negative and significant across samples, with an elasticity of about one, suggesting that competition can have significant distributional consequences. More specifically, the results suggest that increased competition leading to lower market power and markups is associated with higher shares of labor in total value added. Also, the results show that older, exporting, and foreign owned firms tend to have lower labor shares.

In summary, firm level evidence supports the country-level results and show that lower market power and markups are statistically significantly associated with higher firm investment and exports in emerging market economies and developing countries including sub-Saharan Africa. Using WBES data—and controlling for firm characteristics, as well as country and year-fixed effects—the results show that a 1 percent decline in markups is associated with an increase in investment and exports of about 0.7 percent and 0.2 percent of the firm’s value added, respectively (Figure 16). Notably, the labor share is also significantly associated with firm markups, with a 1 percent decline in markup implying a one percentage point in the share of output that is remunerated to labor.

Restricting the sample to sub-Saharan African countries portrays a similar picture and indicates a strong negative association between firm markup and investment, exports, and labor shares. Including an additional indicator of competition in the regressions such as the number of competitors faced by the firm, shows also that, on average, firms facing fewer competitors have lower exports, labor shares, and investment though the association is statistically significant for exports only. The largely cross-sectional nature of the WBES data does not allow testing for the association between firm markup and productivity growth.

It is important to note that the Orbis data also captures the time dimension of firm behavior, allowing for a more refined measurement of markups and assessing their impact on

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productivity growth.28 Controlling for fixed and time-varying firm-, industry-, and country- level characteristics, the results show that a 1 percent decline in markups is associated with a 1–1.4 percent increase in firm’s investment to value added ratio and about a 1 percent increase in the share of labor in a firm’s output in emerging market and developing economies including in sub-Saharan Africa (Figure 16)

D. Productivity

Estimation results using Orbis data, presented in Table 23, show that lower markups are significantly associated with higher labor and total factor productivity growth, with a 1 percent decline in markups implying a 0.8 percentage point increase in the rate of productivity growth.29

In summary, the findings from firm-level data echo the results of earlier studies, which show—mostly in the context of advanced economies—that firms with higher markups and greater market power tend to have lower investment, productivity growth, and labor shares (Nickell 1996; Autor and others 2017; Gutiérrez and Philippon 2017; IMF 2019a), While at the same time, the findings do not support the view that stronger competition discourages innovation. Also, the results suggest that the association between markups and investment, labor share, and productivity growth is nearly twice as strong in the manufacturing sector as in the services sector—implying that weak competition in the manufacturing sector may have a greater impact on economic growth compared to the services sector. Differentiating firms based on their ownership structure does not show any statistically significant difference in their response to market power (publicly or privately owned firms), however, for a given increase in markups, domestically owned firms have significantly lower investment and labor shares compared to their foreign counterparts.

VII. HOW TO BOOST COMPETITION IN DOMESTIC MARKETS?

Given the benefits of competition, how can it be strengthened, in particular in sub-Saharan Africa? Several factors are important, most notably enforcement of a strong competition policy framework that encompasses, among other things, product market liberalization, the adoption of an adequate competition law, an independent enforcement body, and competition advocacy. Other policies—notably, trade, fiscal, and structural—that facilitate business activity and reduce barriers to entry also play a critical role in stimulating competition.

A. Product Market Liberalization

The liberalization of product markets typically includes a transfer of production from state- owned enterprises (SOEs) to private firms, elimination of price controls, and developing regulatory bodies to facilitate private sector activity. Prior to the 1980s, most sub-Saharan African economies were state led with SOEs largely dominating domestic markets. Product

28 Following De Loecker and Warzynski (2012), the markups based on Orbis data used for the regression analysis are constructed as the log ratio of the output elasticity of inputs to the expenditure share of inputs in sales. 29 While labor share in output is positively associated with competition, this does not necessarily imply an increase in unit labor costs due to an improvement in productivity growth, as well as a general decline in price levels.

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market reforms were initiated as part of a broader set of structural reforms that included trade policy liberalization in the early 1980s followed by current account and financial liberalization in the 1990s (Figure 17). Product market liberalization (notably in three key sectors: telecommunications, electricity, and agriculture) followed soon after in the late 1990s and encompassed a shift from public to private ownership, development of independent regulatory bodies, and the elimination (or reduction) of price controls.30

Existing evidence suggests that such reforms have generally helped to boost productivity and growth in developing economies, including in sub-Saharan Africa (Ostry, Prati, and Spilimbergo 2009; Robinson, Gaertner, and Papageorgiou 2011). The reform momentum, however, appears to have slowed down over the last decade, with SOEs still dominating markets in many sub-Saharan African countries, especially in the utilities and transportation sectors (McKinsey Global Institute 2016; Sibiya and others 2018).31 According to the OECD–World Bank Product Market Regulations database, some sub-Saharan African countries (Kenya, Senegal, South Africa) are among the most restrictive in terms of allowing entry into the network and services sectors, while price controls are also widely prevalent— for instance, about two-thirds of the sub-Saharan African countries surveyed by the World Bank (2016) reported the existence of regulations that allow for price controls. In this context, pursuing further product market reforms, especially in the network and services sector, reducing regulatory and structural barriers to firm entry and exit, and improving the overall investment climate could catalyze private sector development and boost competition and growth.32 Although the small size of domestic markets and the large fixed costs associated with some sectors (especially utilities, telecommunications, and transportation) imply that natural monopolies may arise, unbundling the components such that those more amenable to competition are separated and opened for competition could help to improve economic outcomes.33

B. Competition Policies

An adequate competition policy framework is essential to derive the expected developmental benefits from product market reforms and protect consumer welfare. Enforcement of a robust competition policy framework comprises the development of antitrust laws, setting up independent and well-functioning institutions, and judicial support. The results of the survey conducted for this paper show that there has been significant progress in the adoption of antitrust laws since the 2000s in sub-Saharan Africa, with the number of countries with a competition law more than doubling from 12 in 2000 to 31 by 2019 (Figure 18).34 In general,

30 The structural reforms index is obtained from Alesina and others (forthcoming) and is available for 14 economies in sub-Saharan Africa: Burkina Faso, Cameroon, Côte d’Ivoire, Ethiopia, Ghana, Kenya, Madagascar, Mozambique, Nigeria, Senegal, South Africa, Tanzania, Uganda, and Zimbabwe. 31 The slowdown in the product market reform momentum is evident from the limited improvement in the overall competition indicator, as noted in Figure 1. 32 Further reforms in the network sector could, for example, include liberalizing the telecommunications and wholesale electricity markets and fully unbundling electricity generation, transmission, and distribution. Some electricity unbundling reforms have already been introduced in Ethiopia, Ghana, Kenya, and Nigeria. 33 For example, in the electricity sector, transmission and distribution tend to be the noncompetitive components, but generation and retailing are more amenable to competition (OECD 2001). Studies show that when such reforms induce competition, industry performance is significantly improved (Zhang, Parker, and Kirkpatrick 2008). 34 These statistics are based on an IMF desk survey of competition authorities in member countries in the region.

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these laws are based on those of advanced economies—typically covering merger control, collusive practices, and the abuse of dominance issues—and have been operationalized by setting up competition agencies. Also, most countries have a competition law or unified legal framework to deal with competition issues, and the wide gap between the setting up of legal frameworks and the dates of effective implementation: on average, a lag of about 6 years is reported. Also, only in 32 percent of the cases, implementation of anti-trust frameworks occurred in tandem with the legal setup, and in at least three cases, it took more than a decade for the legal framework to be implemented.

Despite this progress in the adoption of competition laws and the establishment of competition agencies, notable improvements in domestic market competition have not been witnessed in most countries as noted previously. One reason for this disconnect is that well- functioning antitrust frameworks require not only a sound legal setup, but also independent regulatory bodies, adequate financial resources, and suitably qualified staff to pursue anticompetition investigations. Viewed against these benchmarks, antitrust frameworks in the region present a mixed picture. According to World Bank (2016), about one-third of the countries in the region with a competition law have competition agencies that fall under the purview of another government body, potentially undermining their independence. The financial resources allocated to competition agencies are often limited, with few reporting any self-financing from penalties.35 The availability of technical staff also varies—while the Competition Commission of South Africa (CCSA) has more than 130 technical staff, about one-third of the surveyed countries reported employing fewer than 10 staff members. On average, agencies in the region report investigating two cases a year, with the clear exception of Kenya and South Africa, which investigate about 500 cases a year.36

The variation in the competition policy frameworks in the region is reflected in the perception-based indicators of the effectiveness of antitrust enforcement. For example, based on the World Economic Forum’s effectiveness of anti-monopoly policy index, Kenya and South Africa are among the best performers in the region, while oil exporters lag behind. It is also striking that in the region’s oil exporters, the perceived effectiveness of antitrust frameworks has declined during the last decade, highlighting the need for persistent efforts to maintain a robust antitrust framework (Figure 19).37 More generally, adopting a competition law is not a panacea, and proper enforcement of the law needs to be ensured to foster private investment and enterprise development.

In enforcing competition laws, the regional dimension is becoming increasingly important. The small size of domestic markets in most sub-Saharan African countries implies that large

35 Both World Bank (2016) and the IMF country desk survey conducted for this paper also show that there is a significant variation in the annual budgets of competition agencies in sub-Saharan Africa, ranging from less than 0.001 percent of GDP to 0.06 percent of GDP. In 2017–18, the CCSA had the largest budget, of $22 million in nominal terms (0.01 percent of GDP), followed by Kenya ($6 million). Relative to its economic size, Seychelles Fair Trading Commission had the largest budget. 36 The CCSA is the most active antitrust authority in the region. In 2017–18 alone, it prohibited 12 mergers, levied about 0.01 percent of GDP in penalties, and finalized 193 enforcement cases (CCSA Annual Report 2017–18). The increase in markups and market concentration in South Africa, however, suggests that more needs to be done to stimulate competition and check anticompetitive practices. 37 In some cases, the reversals correspond to the onset of conflict, which weakened the general institutional and fiscal capacity in the affected countries.

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firms may operate in multiple jurisdictions to reap economies of scale, or a few large firms across countries may form cartels to limit foreign competition in their jurisdictions and exploit consumers. A case in point is that of the cement industry, where nine regional firms produce more than 50 percent of the cement, and anticompetitive practices have regional dimensions (World Bank 2016).38 Limiting such regional anticompetitive behavior requires cross-country cooperation. Some agencies have initiated bilateral cooperation, including informal information sharing and signing memoranda of understanding, such as between Kenya and South Africa and among Malawi, Tanzania, and Zambia (World Bank 2016). In addition, supranational competition authorities for blocs like the Common Market for Eastern and Southern Africa (COMESA) and the West African Economic and Monetary Union (WAEMU) have started operating regional merger control regimes and facilitating investigative actions at a regional level. Nevertheless, further regional cooperation remains necessary to tackle the growing challenges from pan-regional monopolies and cartels, especially in view of greater expected trade and investment flows in the context of the African Continental Free Trade Agreement (AfCFTA).

C. Complementary Policies

Competition policies are essential but may not be enough to increase competition without complementary macroeconomic policies, notably trade, foreign investment, and fiscal policies. In the context of sub-Saharan Africa, several studies show that trade barriers—both tariff and nontariff—hurt overall competition and competitiveness (World Bank 2012; Cadot and others 2015). The African Continential Free Trade Agreement (AfCFTA), which aims to boost regional trade and economic integration, is thus likely to help improve economic competition across the region. The agreement envisions the elimination of tariffs on most goods, the liberalization of trade of key services, and the reduction of nontariff obstacles to international trade. These reforms that are expected to stimulate trade and growth in the region (IMF 2019b). In pursuing regional integration, however, the mutually reinforcing relationship among trade, investment, and competition policies should be considered: trade and investment liberalization stimulate competition, but an effective competition policy framework is needed to ensure that gains from foreign competition are realized and markets are not taken over by a few large firms engaging in unfair trading practices.

Competition is also influenced by government interventions and fiscal policies. For example, preferential tax treatment to selected firms or the selective implementation of policies can hamper competition by creating an uneven playing field. Public procurement policies that benefit certain firms—whether state or privately owned—can also hurt competition and entrench the dominant position of large firms.39 Also, inefficient customs administrations

38 The CCSA investigated and fined the four largest cement producers in 2008 for colluding to segment markets across countries (See CCSA Annual Report 2009–10. 39 Collusive practices can infiltrate public procurement systems even if the process does not deliberately favor certain undertakings. In 2012, for example, the Zambian Competition and Consumer Protection Commission investigated irregularities in bids for a government subsidy program, alleging that two firms divided their bids to avoid competing against each other (World Bank 2016). Based on the investigations, the commission levied sanctions and the government broadened the tender process. This case illustrates the need for competition authorities to work closely with public procurement agencies to make procurement processes competition-friendly and to remain vigilant of platforms allowing competitor contact.

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can adversely impact trade and foreign competition. Fiscal policies and public procurement systems thus need to be carefully designed, and customs administration systems need to be strengthened and modernized so as not to undermine competition. In cases where certain firms or sectors need to be subsidized for the provision of a public good, the costs and benefits of the incentives at play should be clearly analyzed, including their effects on economic competiton.

VIII. CONCLUSION

Product market competition in sub-Saharan Africa is low relative to the rest of the world. Country-level data suggest that more than 70 percent of countries in the region are below the global median in terms competition indicators. Firm markups—directly calculated using enterprise data—corroborate the macro-level observations and suggest that, on average, markups in sub-Saharan African countries are higher than in other emerging market economies and developing countries, especially in the services sectors. In addition, a comparison of the price levels of internationally comparable products and services indicates that prices in the region are relatively higher than in other regions at a similar level of development, which can at least partly be attributed to low product market competition.

The empirical analysis suggests that an increase in competition can help to improve economic growth and welfare through increased productivity, export competitiveness and lower consumer prices. These findings are supported by firm-level evidence, which shows that market structure significantly affects firms’ behavior and performance, which ultimately shapes macroeconomic outcomes. Specifically, a decline in markups is significantly associated with an increase in firm investment, exports, productivity growth, and labor’s share in output. These effects are more pronounced in the manufacturing sector relative to services and tend to be stronger for domestic firms relative to foreign-owned firms.

These results remark the need to strengthen product market competition in sub-Saharan Africa. Although product market reforms were undertaken in several countries in the region in the late 1990s and early 2000s and helped to boost competition and conferred growth gains, the reform momentum has stalled in recent years. Thus, despite the almost three-fold increase in the number of countries that have enacted competition laws since 2000, progress on the ground remains limited.

As several factors affect competition, a holistic approach that encompasses the following key elements is needed to stimulate competition in the region: a) product market reforms that reduce structural and regulatory barriers to private sector participation in the goods and services markets and improve the ease of doing business; b) an effective competition policy framework, which includes an adequate competition law along with an independent, adequately funded, and staffed enforcement agency; c) complementary trade and foreign direct investment policies that bolster foreign competition and improve access to intermediate inputs; and d) carefully designed fiscal policies and procurement systems that do not distort competition by benefiting a few market players.

These policies are individually important and also mutually reinforcing. For example, trade and investment liberalization help to stimulate competition, but an effective competition

24

policy framework is essential to ensure that gains from foreign competition are realized by the whole economy and a few large firms do not dominate markets and implement unfair trading practices. In the same vein, development policies aimed at the advancement of certain sectors deemed as essential to boosting productivity and growth should not give way to a decline in competition and and increase in corporate market power that would impose costs on the rest of the economy and offset the potential effects of the original policies. Furthermore, in the current context of increasing regional trade and integration, cooperation among national competition authorities needs to be strengthened to tackle any anticompetitive practices of large pan-regional firms. More generally, countries need to maintain a stable and sound macroeconomic and institutional environment to attract private investment and ensure that policies to stimulate competition have traction.

25

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TABLES

Table 1. Sub-Saharan Africa: Country Coverage of the WEF Competition Index Country Years Country Years Country Years Angola 2010–14 Guinea 2013–14 Senegal 2007–14 Botswana 2006–14 Kenya 2006–14 Sierra Leone 2012–14 Burkina Faso 2006–14 Liberia 2012–13 South Africa 2006–14 Cameroon 2006–14 Madagascar 2006–14 Tanzania 2006–14 Côte d'Ivoire 2008–14 Malawi 2008–14 Uganda 2006–14 Ethiopia 2006–14 Mali 2006–14 Zambia 2007–14 Gabon 2012–14 Mozambique 2006–14 Zimbabwe 2006–14 Gambia, The 2006–14 Namibia 2006–14 Ghana 2008–14 Nigeria 2006–14

Source: World Economic Forum.

Table 2. Components of the World Economic Forum's Competition Index

Competition Domestic Competition Foreign Competition Intensity of local competition Prevalence of trade barriers Effectiveness of anti-monopoly policy Trade tariffs Extent of market dominance Prevalence of foreign ownership Effect of taxation on incentives to invest Business impact of rules on FDI Total tax rate Burden of customs procedures Number of procedures required to start a business Imports as a percentage of GDP Time required to start a business Agricultural policy costs Source: World Economic Forum, 2018, Methodology and Computation of the Global Competitiveness Index 2017–18.

Table 3. Sub-Saharan Africa: Country Coverage of the BTI Country Years Country Years Country Years Angola 2006–18 Ethiopia 2006–18 Niger 2006–18 Benin 2006–18 Ghana 2006–18 Nigeria 2006–18 Botswana 2006–18 Guinea 2006–18 Rwanda 2006–18 Burkina Faso 2006–18 Kenya 2006–18 Senegal 2006–18 Burundi 2006–18 Lesotho 2006–18 Sierra Leone 2006–18 Côte d'Ivoire 2006–18 Liberia 2006–18 South Africa 2006–18 Cameroon 2006–18 Madagascar 2006–18 South Sudan 2006–18 Central African Republic 2006–18 Malawi 2006–18 Tanzania 2006–18 Chad 2006–18 Mali 2006–18 Togo 2006–18 Congo, Dem. Rep. 2006–18 Mauritius 2006–18 Uganda 2006–18 Congo, Rep. 2006–18 Mozambique 2006–18 Zambia 2006–18 Eritrea 2006–18 Namibia 2006–18 Zimbabwe 2006–18 Source: Bertelsmann Stiftung Foundation. Note: Data available every two years.

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Table 4. Sub-Saharan Africa: Country Coverage of the WBES Country Years Obs. Country Years Obs. Country Years Obs. Angola 2006, 2010 238 Eswatini 2006, 2016 111 Namibia 2006, 2014 114 Benin 2009, 2016 82 Gambia, The 2006, 2018 92 Niger 2009, 2017 46 Botswana 2006, 2010 155 Ghana 2007, 2013 472 Nigeria 2007, 2014 1355 Burkina Faso 2009 36 Guinea 2006, 2016 140 Rwanda 2006, 2011 90 Burundi 2006, 2014 147 Guinea-Bissau 2006 47 Senegal 2007, 2014 401 Cabo Verde 2009 43 Kenya 2007, 2013, 2018 892 Sierra Leone 2017 56 Cameroon 2009, 2016 149 Lesotho 2016 61 South Africa 2007 677 Central African Republic 2011 15 Liberia 2017 61 South Sudan 2014 59 Chad 2009, 2018 96 Madagascar 2009, 2013 320 Tanzania 2006, 2013 385 Congo, Dem. Rep. 2006, 2010, 2013 344 Malawi 2009, 2014 136 Togo 2009, 2016 45 Côte d'Ivoire 2009, 2016 157 Mali 2007, 2010, 2016 385 Uganda 2006, 2013 386 Eritrea 2009 28 Mauritius 2009 100 Zambia 2007, 2013 540 Ethiopia 2011, 2015 387 Mozambique 2007, 2018 522 Zimbabwe 2011, 2016 609 Source: World Bank Enterprise Survey.

Table 5. Industry Coverage of the WBES Emerging Market

Industry Classification Economics and Sub-Saharan Developing Countries Africa

Manufacture of: Manufacture of basic metals 1253 168 Chemicals and chemical products 3633 550 Coke, refined petroleum products and nuclear fuel 79 20 Electrical machinery and apparatus n.e.c. 1386 94 Fabricated metal products, except machinery and equipment 3143 906 Food products and beverages 7777 2693 Furniture; manufacturing n.e.c. 1481 1175 Machinery and equipment n.e.c. 2380 196 Medical, precision and optical instruments, watches and clocks 211 7 Motor vehicles, trailers and semi-trailers 865 51 Other non-metallic mineral products 2965 450 Other transport equipment 140 20 Paper and paper products 612 111 Radio, television and communication equipment and apparatus 239 17 Rubber and plastics products 2780 333 Textiles 3340 331 Tobacco products 156 16 Wearing apparel; dressing and dyeing of fur 4506 1478 Wood, wood products, except furniture 1038 445 Luggage, handbags, footwear, etc; tanning/ dressing leather 1111 182

Others Publishing, printing and reproduction of recorded media 1077 547 Recycling 99 9 Construction 161 42 Hotels and restaurants 140 32 Retail trade, except of motor vehicles and motorcycles; 372 43 Sale, maintenance and repair of motor vehicles 83 16 Wholesale trade and commission trade, except of motor 174 47

Total 41201 9979 Source: World Bank Enterprise Survey.

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Table 6. Sub-Saharan Africa: Country Coverage of the Orbis Country Years Obs. Country Years Obs. Benin 2011–16 4 Mauritius 2001–17 984 Botswana 2001–17 154 Mozambique 2003–16 31 Burkina Faso 2009–17 10 Namibia 2000–17 62 Cameroon 2014–16 3 Nigeria 2001–17 1334 Cape Verde 2006–16 35 Rwanda 2010–17 12 Côte d'Ivoire 2003–17 263 Senegal 2009–17 23 Ethiopia 2011–15 4 Seychelles 2014–17 8 Gabon 2001–17 12 South Africa 2000–17 3432 Gambia 2014–15 2 Swaziland 2003–17 18 Ghana 2000–17 246 Togo 2011–17 11 Kenya 2000–17 430 Uganda 2003–17 45 Liberia 2004–11 8 Republic of Congo 2002–17 94 Malawi 2001–17 63 Zambia 2002–17 147 Mali 2012–16 5 Zimbabwe 2000–17 389 Source: Orbis Bureau van Dijk. Note: Data shows gaps for some countries.

Table 7. Industry Coverage of the Orbis

Emerging Market Industry Classification Economies and Sub-Saharan

Developing Countries Africa Agriculture, hunting, forestry 38994 273 Construction 184291 164 Electricity, gas and water supply 33345 143 Financial intermediation 122691 315 Hotels and restaurants 41219 249 Manufacturing 573862 3385 Mining and quarrying 26556 317 Other services 24694 158 Real estate, renting, and business activities 285924 602 Transport and storage 89038 260 Information and communication 35333 637 Wholesale/retail trade; repair of 668317 1326

Source: Orbis database.

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Table 8. Variable Definitions and Data Sources

Variable Description Sources Antitrust frameworks data Data on anti-trust frameworks World Bank (2016) Competition frameworks in SSA Data on competition frameworks IMF, AFR survey Enterprise Survey data Diverse firm level data World Bank General gov. gross debt to GDP In percent IMF, WEO database Global Competitiveness Index Scores World Economic Forum Inflation rate In percent IMF, WEO database Institutional quality Score ICRG Market liberalization data Scores Alesina, et al (2019) Orbis data Diverse firm level data ORBIS Bureau van Dijk Population Millions World Bank, WDI Private investment In percent of GDP IMF, WEO database Real GDP In billions of national currency IMF, WEO database Real GDP growth in trading partners In percent IMF, WEO database Real GDP, PPP In billions of international dollars IMF, WEO database Real price of investment goods Index PWT 9.0 Share of investment in GDP In percent World Bank, WDI Share of population in working age In percent World Bank, WDI Terms of trade Index IMF, WEO database Trade openess In percent World Bank, WDI Transformation Index Scores Bertelsmann Stiftung Foundation Years of schooling Years World Bank, WDI

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Table 9. List of Non-Sub-Saharan African Countries Country Database Country Database Country Database Afghanistan BTI, WBES Guatemala WEF, BTI, WBES, Orbis Paraguay WEF, BTI, WBES, Orbis Albania WEF, BTI, WBES, Orbis Guyana WBES Peru WEF, BTI, WBES, Orbis Algeria WEF, BTI, Orbis Haiti WEF, BTI Philippines WEF, BTI, WBES Antigua and Barbuda WBES Honduras WEF, BTI, WBES, Orbis Poland WEF, BTI, WBES Argentina WEF, BTI, WBES Hong Kong SAR WEF Portugal WEF Armenia WEF, BTI, WBES, Orbis Hungary WEF, BTI, WBES Qatar WEF, BTI Australia WEF Iceland WEF Romania WEF, BTI, WBES, Orbis Austria WEF India WEF, BTI, WBES Russia WEF, BTI, WBES Azerbaijan WEF, BTI, WBES, Orbis Indonesia WEF, BTI, WBES, Orbis Samoa WBES Bahamas, The WBES Iran WEF, BTI, Orbis Saudi Arabia WEF, BTI Bahrain WEF, BTI Iraq BTI, WBES, Orbis Serbia WEF, BTI, WBES, Orbis Bangladesh WEF, BTI, WBES, Orbis Ireland WEF Singapore WEF, BTI Barbados WBES Israel WEF Slovak Republic WEF, BTI Belarus BTI, WBES Italy WEF Slovenia WEF, BTI Belgium WEF Jamaica WEF, BTI, WBES, Orbis Solomon Islands WBES Belize WBES Japan WEF Somalia BTI Bhutan BTI, WBES, Orbis Jordan WEF, BTI, WBES, Orbis Spain WEF Bolivia BTI, WBES, Orbis Kazakhstan WEF, BTI, WBES, Orbis Sri Lanka WEF, BTI, WBES, Orbis Bosnia and Herzegovina BTI, WBES, Orbis Korea WEF, BTI St. Kitts and Nevis WBES Brazil WEF, BTI, WBES, Orbis Kosovo BTI, WBES, Orbis St. Lucia WBES Brunei Darussalam WEF Kuwait WEF, BTI St. Vincent and the Grenadines WBES Bulgaria WEF, BTI, WBES, Orbis Kyrgyz Republic BTI, WBES, Orbis Sudan BTI, WBES, Orbis Cambodia BTI, WBES, Orbis Lao P.D.R. BTI, WBES, Orbis Suriname WEF, WBES Canada WEF Latvia WEF, BTI Sweden WEF Chile WEF, BTI, WBES Lebanon WEF, BTI, WBES, Orbis Switzerland WEF China WEF, BTI, WBES Libya BTI Syria WEF, BTI Colombia WEF, BTI, WBES, Orbis Lithuania BTI Taiwan Province of China BTI, Orbis Costa Rica WEF, BTI, WBES, Orbis Luxembourg WEF Tajikistan BTI, WBES Croatia WEF, BTI, WBES Malaysia WEF, BTI, WBES Thailand WEF, BTI, WBES, Orbis Cyprus WEF Malta WEF Timor-Leste, Dem. Rep. of WBES Czech Republic BTI Mauritania BTI, WBES Tonga WBES Denmark WEF Mexico WEF, BTI, WBES, Orbis Trinidad and Tobago WEF, WBES Djibouti WBES Micronesia, Fed. States of WBES Tunisia WEF, BTI, WBES, Orbis Dominica WBES Moldova WEF, BTI, WBES, Orbis Turkey WEF, BTI, WBES, Orbis Dominican Republic WEF, BTI, WBES, Orbis Mongolia BTI, WBES, Orbis Turkmenistan BTI Ecuador WEF, BTI, WBES, Orbis Montenegro, Rep. of BTI, WBES Ukraine WEF, BTI, WBES Egypt WEF, BTI, WBES, Orbis Morocco WEF, BTI, WBES, Orbis United Arab Emirates WEF, BTI El Salvador WEF, BTI, WBES, Orbis Myanmar WEF, BTI, WBES, Orbis United Kingdom WEF Estonia WEF, BTI Nepal BTI, WBES, Orbis United States WEF Fiji WBES Netherlands WEF Uruguay WEF, BTI, WBES Finland WEF New Zealand WEF Uzbekistan BTI, WBES, Orbis France WEF Nicaragua WEF, BTI, WBES, Orbis Vanuatu WBES FYR Macedonia BTI, WBES, Orbis Norway WEF Venezuela WEF, BTI, WBES, Orbis Georgia BTI, WBES, Orbis Oman WEF, BTI Vietnam WEF, BTI, WBES, Orbis Germany WEF Pakistan WEF, BTI, WBES Yemen WEF, BTI, WBES, Orbis Greece WEF Panama WEF, BTI, WBES Grenada WBES Papua New Guinea BTI, WBES, Orbis

Source: Author's compilation.

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Table 10. Sub-Saharan Africa: Firm Markup and Profitability

Oil ex porters Other resource-intensiv e

By Resource Intensity Markup Profitability

0.82 0.51 0.69 0.45

Non-resource-intensiv e 0.64 0.42

Central Africa

By Region Markup Profitability

0.82 0.51 East Africa 0.66 0.44 Southern Africa 0.62 0.43 West Africa 0.65 0.42

EMEDEV excl. SSA 0.57 0.39 Source: IMF staff estimates based on the World Bank Enterprise Survey. Note: Profitability is defined as the difference between revenue and the cost of inputs relative to revenue. Markup is defined as the log ratio of sales to the cost of inputs. EMEDEV = Emerging market economics and developing countries; SSA = sub-Saharan Africa.

Table 11. Sub-Saharan Africa: Average Sectoral Profitability and Markup Based on WBES Profitability Markup

Hotels and restaurants 0.62 1.17 Wholesale trade, except of motor vehicles 0.56 1.03 Retail trade, excl. motor vehicles/cycles 0.54 1.00 Construction 0.52 0.98 Manuf. of food products and beverages 0.48 0.77 Manuf. of motor vehicles/trailers 0.48 0.76 Manuf. of electrical machinery/apparatus n.e.c. 0.48 0.76 Manuf. of basic metals 0.47 0.75 Manuf. of other non-metallic mineral products 0.47 0.74 Manuf. of chemicals/chemical products 0.47 0.73 Manuf. of rubber and plastics products 0.46 0.72 Publishing, printing 0.46 0.71 Manuf. of wood/wood products 0.46 0.71 Manuf. of furniture; manufacturing n.e.c. 0.44 0.67 Manuf. of fabricated metal products 0.44 0.66 Manuf. of wearing apparel; dressing/dyeing 0.44 0.66 Manuf. of machinery and equipment n.e.c. 0.43 0.64 Manuf of leather products 0.42 0.63 Manuf. of textiles 0.41 0.59 Manuf. of paper and paper products 0.39 0.57 Average 0.47 0.76

Source: IMF staff estimates based on the World Bank Enterprise Survey. Notes: Profitability is defined as the ratio of the difference between sales and cost of labor, raw materials and intermediate inputs to sales. Markup is defined as log ratio of sales to cost of labor, raw materials and intermediate inputs.

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Table 12. Sub-Saharan Africa: Average Sectoral Profitability and Markup Based on Orbis Profitability Markup

Hotels and restaurants 0.63 1.17 Other services 0.57 1.12 Information and communication 0.58 1.03 Financial intermediation 0.55 0.91 Transport and storage 0.44 0.67 Real estate, renting and business activities 0.41 0.63 Mining and quarrying 0.35 0.51 Agriculture, hunting and forestry 0.36 0.49 Manufacturing 0.36 0.48 Electricity, gas and water supply 0.33 0.43 Construction 0.27 0.34 Wholesale/retail trade; repair of motor vehicles and g 0.27 0.34

Source: IMF staff estimates based on the Orbis database.

Table 13. Sub-Saharan Africa: Average Sectoral Markup by Country Group

Industry

Manf. of food products and beverages Manf. of tobacco products Manf. of textiles Manf. of wearing apparel; dressing/dyeing of fur Leather tanning/dressing; leather products Manf. of wood/wood products Manf. of paper and paper products Publishing, printing and reproduction of recorded media Manf. of coke, refined petroleum products, nuclear fuel Manf. of chemicals and chemical products Manf. of rubber and plastics products Manf. of other non-metallic mineral products Manf. of basic metals Manf. of fabricated metal products Manf. of machinery and equipment n.e.c. Manf. of electrical machinery and apparatus n.e.c. Manf. of radio, television, communication equipment Manf. of medical, precision, optical instruments, watches Manf. of motor vehicles, trailers and semi-trailers Manf. of other transport equipment Manf. of furniture; manufacturing n.e.c. Recycling Construction Sale, maintenanc, repair of motor vehicles Wholesale trade, excl. motor Retail trade, excl. motor vehicles Hotels and restaurants

Oil Exporters

Other Resource- Intensive

Non- Resource- Central Intensive

East Southern West

Markup legend

< 0.5

0.5-1.0

>1.0

No data

Source: IMF staff estimates based on World Bank Enterprise Survey. Notes: Markup is defined as log ratio of sales to cost of labor, raw materials, and intermediate inputs.

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Table 14. Competition and Real GDP Per Capita Growth Real GDP Per Capita Growth

World EMDEs SSA World EMDEs SSA (1) (2) (3) (4) (5) (6)

Local competition 1.500*** 1.510** 1.035 1.353*** 1.268** 0.233 (0.529) (0.628) (0.809) (0.502) (0.593) (0.589)

Real GDP per capita (lag) -14.122*** -14.516*** -15.803** -17.050*** -17.053*** -18.910*** (1.947) (2.363) (6.102) (2.084) (2.425) (5.839)

Investment 14.997*** 14.432*** 4.270 12.025*** 11.549*** 8.364** (3.495) (4.069) (3.853) (3.138) (3.345) (3.332)

Years of schooling -0.073 -0.440 -0.311 -0.337 -0.764 -1.514 (0.756) (1.050) (2.252) (0.745) (1.031) (1.906)

Trade openess 0.918 0.845 0.975 2.130* 2.390* 2.145 (1.054) (1.273) (2.457) (1.189) (1.387) (1.685)

Terms of trade change 0.012 0.022* 0.012 0.009 0.018 0.026* (0.010) (0.011) (0.014) (0.011) (0.012) (0.013)

Trading partner growth 0.472*** 0.337* -0.038 0.512*** 0.402** -0.157 (0.158) (0.175) (0.209) (0.152) (0.154) (0.263)

Institutional quality 1.712*** 0.915 3.530* 1.202** 1.018 2.989* (0.646) (0.828) (1.714) (0.600) (0.739) (1.529)

Public debt -0.053*** -0.084*** -0.152*** (0.018) (0.022) (0.029)

Price of capital formation 0.634 0.598 7.718** (2.437) (3.309) (3.101)

Country/Year fixed effects Yes Yes Yes Yes Yes Yes Observations 971 678 179 963 670 179 R-squared 0.610 0.560 0.515 0.627 0.586 0.619 No. of Countries 121 88 25 120 87 25

Source: IMF staff estimates. Note: Dependent variable is real GDP growth per capita (in PPP terms). Competition is the WEF’s Index fo Intensity of Local Competition, ranging from 1 to 7 (best). Investment, trade opennes, and public debt in percent of GDP. Terms of trade change in percent. All specifications include constant, country and fixed year effects. Clustered standard errors at country level in parenthesis. ***,** and * denote statistical significance at the 1, 5, and 10 percent levels, respectively. EMDEs = Emerging markets economics and developing countries; SSA = sub-Saharen Africa.

Table 15. Competition and Real GDP Per Capita Growth: IV-2SLS Lagged Values of Local Competition Intensity Index Regional Average of Local Competition Intensity Index

World EMDEs SSA World EMDEs SSA World EMDEs SSA World EMDEs SSA (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Local competition 1.978* 2.646** 3.337** 2.058** 2.583** 1.161 11.624** 11.532 6.814 10.648** 10.187 6.025 (1.063) (1.311) (1.652) (1.038) (1.291) (1.751) (5.303) (7.933) (4.254) (5.242) (7.496) (4.112)

Real GDP per capita (lag) -20.468*** -20.313*** -25.294*** -24.998*** -25.270*** -27.093*** -20.197*** -18.655*** -19.227** -21.609*** -19.871*** -20.499*** (2.887) (3.996) (6.840) (3.179) (4.043) (6.789) (4.269) (4.697) (7.771) (4.037) (4.189) (7.589)

Investment 16.307*** 15.891*** 10.250* 13.729*** 13.402*** 12.566*** 12.535** 13.750** 2.676 10.512** 11.636* 3.485 (4.618) (5.074) (5.426) (4.078) (4.454) (4.379) (5.339) (6.399) (5.082) (5.201) (6.091) (4.523)

Years of schooling -0.234 -0.457 -0.247 -0.618 -0.985 -0.688 -0.387 -0.280 -0.809 -0.561 -0.540 -0.760 (1.072) (1.541) (2.702) (1.051) (1.506) (2.103) (0.718) (0.869) (2.530) (0.696) (0.880) (2.228)

Trade openess 0.323 -0.651 -0.809 1.674 1.350 1.677 -3.231 -2.824 0.516 -2.089 -1.362 2.022 (1.419) (1.604) (3.297) (1.332) (1.533) (2.380) (2.799) (3.435) (3.165) (2.837) (3.554) (2.383)

Terms of trade change 0.011 0.019 -0.006 0.002 0.008 -0.007 -0.007 -0.002 -0.001 -0.009 -0.003 0.000 (0.013) (0.015) (0.018) (0.012) (0.014) (0.015) (0.017) (0.024) (0.020) (0.016) (0.022) (0.017)

Trading partner growth 0.365** 0.373** 0.065 0.421*** 0.499*** 0.014 0.475** 0.225 -0.099 0.527*** 0.309 -0.144 (0.158) (0.189) (0.241) (0.155) (0.162) (0.278) (0.194) (0.248) (0.248) (0.186) (0.230) (0.255)

Institutional quality 1.360* -0.352 2.127 0.766 -0.415 0.742 0.281 -1.205 2.837 0.125 -0.896 2.250 (0.741) (1.043) (2.484) (0.702) (0.958) (1.900) (1.257) (2.069) (2.326) (1.168) (1.958) (2.158)

Public debt -0.068*** -0.109*** -0.204*** -0.033 -0.054 -0.105** (0.025) (0.037) (0.063) (0.023) (0.040) (0.042)

Country/Year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 739 512 131 733 506 131 971 678 179 963 670 179 No. of Countries 118 85 23 117 84 23 121 88 25 120 87 25

Source: IMF staff estimates Note: Dependent variable is real GDP growth per capita (in PPP terms). Competition WEF’s Index of Intensity of Local Competition, ranging from 1 to 7 (best). Investment, trade openness, and public debt in percent of GDP. Terms of trade change in percent. Columns 1- 6 are estimated using the first two lags of local competition as instruments; Columns 7-12 use regional average local competition score as instrument. All specifications include a constant, and country and fixed year effects. Statistics in parentheses denote clustered standard errors at country level. ***,** and * indicate statistical significance at the 1, 5, and 10 percent levels, respectively.

36

Table 16. Competition, Investment, Exports and Productivity Investment Non-Oil Exports Labor Productivity Growth

World EMDEs SSA World EMDEs SSA World EMDEs SSA

Local competition (1) 0.213

(2) 0.160

(3) 3.304*

(4) 1.598*

(5) 1.186

(6) 2.704*

(7) 0.916**

(8) 1.090*

(9) 1.200

(0.772) (0.983) (1.859) (0.832) (0.884) (1.544) (0.452) (0.556) (0.934) Real GDP per capita (lag) 4.726 2.552 -9.634 -8.410* -7.959 0.376 -1.156 5.413 22.420**

(3.386) (4.329) (12.182) (4.968) (6.164) (4.461) (5.378) (7.569) (9.265) Terms of trade change 0.018 0.019 0.038 -0.013 -0.014 0.009

(0.016) (0.017) (0.031) (0.013) (0.014) (0.022) Trade openess -1.423 -0.659 4.200 0.818 0.468 4.725

(2.177) (2.628) (5.162) (1.273) (1.534) (2.933) Growth (lag) 0.247*** 0.193*** 0.147

(0.050) (0.049) (0.087) Real interest rate -0.066 -0.059 0.100

(0.062) (0.070) (0.066) Institutional quality -0.220 -1.502 4.010* 0.474*** 0.576*** 0.339 0.010 0.261 4.735*

(0.779) (0.917) (2.288) (0.177) (0.201) (0.225) (0.559) (0.862) (2.311) Trading partner growth -7.097** -5.080* -12.863***

(3.282) (2.817) (3.498) REER (log) -0.674 -0.840 0.099

(1.261) (1.366) (2.369) Investment 11.835*** 13.039*** 8.073*

(3.852) (4.594) (4.429) Years of schooling 0.108 -0.106 1.929

(0.508) (0.740) (1.989)

Country/Year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 1,069 748 190 1,354 935 238 989 683 180 R-squared 0.738 0.738 0.762 0.965 0.935 0.935 0.510 0.494 0.516 No. of Countries 104 76 23 125 90 25 122 88 25

Source: IMF staff estimates. Note: Dependent variable is share of investment to GDP in cols. 1-3, non-oil exports to GDP in cols. 4-6, and labor produtivity growth in cols. 7-9. Competition is the World Economic Forum's Intensity of Local Competition index that ranges from 1 to 7 (best). All specifications include a constant, and country and fixed year effects. Statistics in parentheses denote clustered standard errors at country level. ***,** and * indicate statistical significance at the 1, 5, and 10 percent levels, respectively. EMDEs = Emerging markets economics and developing countries; SSA = sub-Saharen Africa.

Table 17. Competition and Innovation Innovation Technological Readiness

World EMDEs SSA World EMDEs SSA (1) (2) (3) (4) (5) (6)

Local competition 0.168*** 0.181*** 0.108 0.166*** 0.200*** 0.134** (0.040) (0.048) (0.086) (0.051) (0.054) (0.054)

Real GDP per capita (lag) -0.076 0.018 0.483* 0.046 0.220 0.252 (0.153) (0.200) (0.242) (0.212) (0.242) (0.300)

Trade openess -0.060 -0.065 0.090 0.100 -0.004 0.003 (0.072) (0.090) (0.210) (0.113) (0.125) (0.137)

Investment 0.004* 0.004 0.001 -0.006 -0.006 -0.006 (0.003) (0.003) (0.005) (0.004) (0.004) (0.004)

Institutional quality 0.018 0.059 0.055 -0.101* -0.102 0.121 (0.047) (0.060) (0.135) (0.060) (0.071) (0.148)

Country/Year fixed effects Yes Yes Yes Yes Yes Yes Observations 1,264 868 233 1,232 858 233 R-squared 0.968 0.862 0.806 0.968 0.915 0.876 No. of Countries 123 87 25 120 86 25

Source: IMF staff estimates Note: Dependent variable is the World Economic Forum's innovation index in cols. 1-3, and technological readiness index in cols. 4-6. Competition is the World Economic Forum's Intensity of Local Competition index that ranges from 1 to 7 (best). All specifications include a constant, and country and fixed year effects. Statistics in parentheses denote clustered standard errors at country level. ***,** and * indicate statistical significance at the 1, 5, and 10 percent levels, respectively. EMDEs = Emerging markets economics and developing countries; SSA = sub-Saharen Africa.

37

Table 18. Competition and Price Levels Food & Alcohol & Clothes & Utilities (Water & Furniture Health Transport Communication Beverages Tobacco Footwear Electricity)

Real GDP per capita (log) 0.145*** 0.153*** 0.214*** 0.281*** 0.186*** 0.299*** 0.049 0.163*** Emerging market -0.307*** -0.248** -0.202 -0.607*** -0.214*** -0.370*** -0.485*** -0.216 Developing excl. SSA -0.330*** -0.431*** -0.333*** -0.657*** -0.316*** -0.318** -0.649*** -0.470*** Sub-saharan Africa -0.056 -0.179 -0.450*** -0.110 -0.168* -0.024 -0.599*** -0.094 Logistical index -0.033 0.095 -0.065 0.195 -0.048 0.344*** -0.018 -0.059 Constant 3.525*** 2.908*** 2.917*** 1.289** 2.988*** 0.420 4.436*** 3.339***

Observations 141 141 141 141 141 141 141 141 R-squared 0.585 0.588 0.514 0.663 0.616 0.683 0.588 0.337

Recreation Education Hotels Miscellaneous Construction Household Individual Machinery & Goods & Services Consumption Consumption Equipment

Real GDP per capita (log) 0.172*** 0.352*** 0.230*** 0.190*** 0.073 0.151*** 0.186*** -0.002 Emerging market -0.374*** -0.693*** -0.341*** -0.362*** -0.633*** -0.395*** -0.413*** -0.053 Emerging -0.441*** -0.637*** -0.331*** -0.450*** -0.648*** -0.485*** -0.469*** -0.096** Sub-saharan Africa -0.224* -0.031 0.006 -0.431*** -0.692*** -0.296*** -0.238** 0.017 Logistical index -0.013 0.431*** 0.002 0.071 0.100 0.021 0.048 -0.034 Constant 2.988*** -0.142 2.376*** 2.493*** 3.974*** 3.200*** 2.736*** 4.799***

Observations 141 141 141 141 141 141 141 141 R-squared 0.592 0.733 0.562 0.769 0.516 0.696 0.737 0.147

Source: IMF staff estimates. Notes: Dependent variable is log of internationally comparable prices of respective items. Emerging market, developing country, and sub-Saharan Afria are (mutually exclusive) dummy variables with advanced country as the base category. All specifications include a constant. Robust standard errors are computed. ***,**,* indicate statistical significance at the 1,5, and 10 percent levels, respectively.

Table 19. Internationally Comparable Price Levels Across Regions Food & Beverages Alcohol & Clothes & Furniture Utilities Miscellaneous Machinery &

Tobacco Footwear Goods & Services Equipment

Local competition -0.082** -0.086** 0.008 0.009 -0.294*** -0.306*** -0.129*** -0.137*** 0.042 0.034 -0.074 -0.082* -0.058** -0.062** Real GDP per capita (log) 0.165*** 0.163*** 0.176*** 0.169*** 0.240*** 0.237*** 0.199*** 0.197*** 0.295*** 0.291*** 0.218*** 0.217*** 0.008 0.007 Emerging market -0.320*** -0.306*** -0.209*** -0.203** -0.279** -0.242** -0.230*** -0.204*** -0.588*** -0.559*** -0.359*** -0.338*** -0.072* -0.061 Developing country -0.344*** -0.330*** -0.399*** -0.403*** -0.392*** -0.351*** -0.346*** -0.318*** -0.665*** -0.635*** -0.459*** -0.435*** -0.114** -0.103** Sub-Saharan Africa -0.107 -0.086 -0.090 -0.088 -0.581*** -0.525*** -0.203** -0.164** -0.024 0.019 -0.418*** -0.385*** -0.003 0.013 Logistics index 0.046 0.077 0.159* 0.174* 0.109 0.186* 0.064 0.119** 0.275*** 0.337*** 0.183*** 0.227*** 0.003 0.026 Trade openness -0.001*** -0.001*** -0.000 -0.001 -0.001** -0.002*** -0.001** -0.001*** -0.001*** -0.002*** -0.001*** -0.001*** -0.000** -0.001** FDI to GDP -0.002** -0.001 -0.005*** -0.003*** -0.004 -0.003** -0.001** Constant 3.570*** 3.511*** 2.453*** 2.457*** 3.768*** 3.605*** 3.228*** 3.115*** 0.778 0.652 2.331*** 2.233***

Observations 122 121 122 121 122 121 122 121 122 121 122 121 122 121 R-squared 0.658 0.669 0.656 0.667 0.599 0.615 0.690 0.711 0.753 0.762 0.823 0.830 0.202 0.224

Health services Transport Communication Recreation Restaurants & Individual Household Hotels Consumption Consumption

Local competition -0.145* -0.156** -0.107* -0.111* -0.155** -0.170** -0.138*** -0.148*** -0.144*** -0.152*** -0.106*** -0.115*** -0.120*** -0.127*** Real GDP per capita (log) 0.326*** 0.324*** 0.069* 0.066 0.211*** 0.213*** 0.224*** 0.222*** 0.283*** 0.284*** 0.208*** 0.207*** 0.171*** 0.169*** Emerging market -0.390*** -0.355*** -0.496*** -0.482*** -0.257* -0.219 -0.408*** -0.376*** -0.387*** -0.365*** -0.427*** -0.402*** -0.416*** -0.393*** Developing country -0.319*** -0.281** -0.687*** -0.675*** -0.503*** -0.452*** -0.469*** -0.433*** -0.357*** -0.329*** -0.491*** -0.463*** -0.509*** -0.485*** Sub-Saharan Africa -0.057 -0.004 -0.612*** -0.592*** -0.215 -0.152 -0.252*** -0.204** -0.044 -0.009 -0.260*** -0.222*** -0.336*** -0.302*** Logistics index 0.486*** 0.560*** 0.062 0.093 0.026 0.104 0.091 0.156** 0.083 0.129 0.163*** 0.216*** 0.132** 0.180*** Trade openness -0.001** -0.002*** -0.001 -0.001* -0.001* -0.002*** -0.002*** -0.002*** -0.002*** -0.002*** -0.001*** -0.001*** -0.001*** -0.001*** FDI to GDP -0.005** -0.002 -0.005** -0.004*** -0.003** -0.003*** -0.003*** Constant 0.553 0.399 4.581*** 4.529*** 3.521*** 3.329*** 3.010*** 2.868*** 2.486*** 2.377*** 2.791*** 2.680*** Observations 122 121 122 121 122 121 122 121 122 121 122 121 122 121 R-squared 0.711 0.720 0.625 0.634 0.402 0.416 0.692 0.710 0.641 0.647 0.799 0.812 0.756 0.770

Source: IMF staff estimates. Notes: Dependent variable is internationally comparable price of the respective items. Emerging market, developing country, sub-Saharan Africa are (mutually exclusive) dummy variables with advanced country as the base category. All specifications include a constant. Robust standard errors are computed. ***,**,* indicate statistical significance at the 1, 5 and 10 percent levels, respectively.

38

Table 20. Internationally Comparable Price Levels Across Regions in Sub-Saharan Africa Food & Beverages Alcohol & Clothes & Furniture Utilities Miscellaneous Machinery &

Tobacco Footwear Goods & Services Equipment Local competition -0.076* -0.079* 0.011 0.014 -0.284*** -0.294*** -0.109** -0.115** 0.064 0.057 -0.055 -0.061 -0.063** -0.066** Real GDP per capita (log) 0.155*** 0.153*** 0.168*** 0.161*** 0.227*** 0.223*** 0.177*** 0.174*** 0.270*** 0.266*** 0.196*** 0.195*** 0.010 0.008 Emerging market -0.325*** -0.309*** -0.212*** -0.207** -0.285** -0.247** -0.239*** -0.212*** -0.599*** -0.568*** -0.369*** -0.346*** -0.072 -0.061 Developing country -0.348*** -0.332*** -0.401*** -0.406*** -0.395*** -0.354*** -0.350*** -0.321*** -0.670*** -0.639*** -0.464*** -0.438*** -0.116** -0.104** East Africa -0.236*** -0.217** -0.047 -0.050 -0.669*** -0.620*** -0.311*** -0.277** -0.151 -0.113 -0.542*** -0.512*** -0.024 -0.010 West Africa -0.048 -0.026 -0.199* -0.200* -0.600*** -0.541*** -0.280*** -0.239** -0.103 -0.057 -0.478*** -0.442*** 0.036 0.053 Central Africa -0.023 0.009 -0.086 -0.078 -0.481*** -0.404** -0.016 0.039 0.176 0.238 -0.234 -0.188 -0.036 -0.014 Southern Africa -0.068 -0.046 -0.011 -0.004 -0.505*** -0.451*** -0.077 -0.038 0.119 0.163 -0.294*** -0.262*** -0.006 0.010 Logistics index 0.049 0.082 0.165* 0.178* 0.114 0.192* 0.070 0.126** 0.282*** 0.347*** 0.189*** 0.235*** 0.004 0.028 Trade openness -0.001*** -0.001*** -0.000 -0.000 -0.001** -0.002*** -0.001*** -0.001*** -0.001*** -0.002*** -0.001*** -0.001*** -0.000** -0.001** FDI to GDP -0.002** -0.001 -0.005*** -0.004*** -0.004 -0.003** -0.001** Observations 122 121 122 121 122 121 122 121 122 121 122 121 122 121 R-squared 0.676 0.688 0.661 0.673 0.602 0.619 0.708 0.732 0.758 0.767 0.833 0.840 0.212 0.234

Health services Transport Communication Recreation Restaurants & Individual Household Hotels Consumption Consumption

Local competition -0.113 -0.123 -0.105* -0.107* -0.119 -0.134* -0.124** -0.134*** -0.124** -0.132** -0.098** -0.105*** -0.112*** -0.118*** Real GDP per capita (log) 0.300*** 0.297*** 0.056 0.052 0.217*** 0.219*** 0.213*** 0.211*** 0.258*** 0.258*** 0.193*** 0.191*** 0.154*** 0.152*** Emerging market -0.401*** -0.364*** -0.502*** -0.489*** -0.252* -0.211 -0.412*** -0.380*** -0.399*** -0.374*** -0.434*** -0.408*** -0.423*** -0.400*** Developing country -0.322*** -0.282** -0.692*** -0.682*** -0.488*** -0.433*** -0.470*** -0.433*** -0.364*** -0.333*** -0.496*** -0.467*** -0.515*** -0.490*** East Africa -0.198 -0.151 -0.600*** -0.586*** -0.261 -0.199 -0.368*** -0.324*** -0.284* -0.249 -0.367*** -0.334*** -0.454*** -0.424*** West Africa -0.146 -0.090 -0.730*** -0.713*** -0.173 -0.101 -0.229** -0.177 -0.006 0.035 -0.268*** -0.228*** -0.346*** -0.310*** Central Africa 0.236 0.311 -0.619*** -0.593*** 0.145 0.233* -0.107 -0.039 0.175 0.227 -0.171 -0.118 -0.255** -0.207* Southern Africa 0.064 0.117 -0.473*** -0.455*** -0.416*** -0.356*** -0.221*** -0.174** 0.081 0.116 -0.168* -0.131 -0.222*** -0.188** Logistics index 0.489*** 0.566*** 0.073 0.102 -0.000 0.083 0.091 0.159** 0.091 0.141 0.170*** 0.223*** 0.141** 0.190*** Trade openness -0.001** -0.002*** -0.001 -0.001* -0.001** -0.002*** -0.002*** -0.002*** -0.002*** -0.002*** -0.001*** -0.001*** -0.001*** -0.001*** FDI to GDP -0.005** -0.002 -0.005** -0.004*** -0.003** -0.003*** -0.003*** Observations 122 121 122 121 122 121 122 121 122 121 122 121 122 121 R-squared 0.719 0.729 0.632 0.641 0.420 0.436 0.701 0.721 0.670 0.678 0.805 0.818 0.765 0.779

Source: IMF staff estimates. Notes: Dependent variable is internationally comparable price of the respective items. Emerging market, developing country, and sub- Saharan African regions are (mutually exclusive) dummy variables with advanced country as the base category. All specifications include a constant. Robust standard errors are computed. ***,**,* indicate statistical significance at the 1, 5 and 10 percent levels, respectively.

Table 21. Internationally Comparable Price Levels: CFA vs Non-CFA Franc Zone Food & Alcohol & Clothes & Furniture Utilities Miscellaneous Machinery &

Beverages Tobacco Footwear Goods & Services Equipment Local competition -0.080** -0.084** 0.007 0.009 -0.290*** -0.302*** -0.126*** -0.134*** 0.046 0.037 -0.072 -0.080 -0.058** -0.062** Real GDP per capita (log) 0.169*** 0.167*** 0.175*** 0.169*** 0.248*** 0.246*** 0.206*** 0.204*** 0.302*** 0.299*** 0.223*** 0.222*** 0.008 0.007 Emerging market -0.319*** -0.305*** -0.209*** -0.203** -0.277** -0.240** -0.228*** -0.202*** -0.587*** -0.557*** -0.359*** -0.337*** -0.072* -0.061 Developing country -0.343*** -0.329*** -0.399*** -0.403*** -0.391*** -0.350*** -0.345*** -0.316*** -0.664*** -0.633*** -0.459*** -0.434*** -0.114** -0.103** Sub-Saharan Africa -0.126* -0.106 -0.089 -0.085 -0.621*** -0.565*** -0.237** -0.198** -0.059 -0.015 -0.441*** -0.408*** -0.001 0.015 CFA franc zone 0.077 0.077 -0.007 -0.009 -0.007 -0.009 0.134* 0.135* 0.136 0.138 0.154 0.156 -0.006 -0.005 Logistics index 0.039 0.070 0.160* 0.175* 0.160* 0.175* 0.052 0.107* 0.262** 0.326*** 0.070 0.116 0.004 0.027 Trade openness -0.001*** -0.001*** -0.000 -0.000 -0.000 -0.000 -0.001*** -0.001*** -0.001*** -0.002*** -0.002*** -0.002*** -0.000** -0.001** FDI to GDP -0.002** -0.001 -0.001 -0.004*** -0.004 -0.003** -0.001** Observations 122 121 122 121 122 121 122 121 122 121 122 121 122 121 R-squared 0.661 0.672 0.656 0.667 0.656 0.667 0.696 0.718 0.755 0.763 0.646 0.652 0.202 0.225

Health services Transport Communication Recreation Restaurants & Individual Household Hotels Consumption Consumption

Local competition -0.140* -0.151* -0.107* -0.110* -0.146** -0.161** -0.134** -0.144*** -0.140** -0.149*** -0.104*** -0.112*** -0.118*** -0.125*** Real GDP per capita (log) 0.337*** 0.335*** 0.070* 0.067 0.229*** 0.232*** 0.233*** 0.232*** 0.292*** 0.292*** 0.213*** 0.211*** 0.175*** 0.173*** Emerging market -0.388*** -0.353*** -0.496*** -0.482*** -0.254* -0.214 -0.406*** -0.374*** -0.386*** -0.363*** -0.426*** -0.401*** -0.415*** -0.392*** Developing country -0.318*** -0.279** -0.687*** -0.675*** -0.501*** -0.449*** -0.468*** -0.431*** -0.356*** -0.327*** -0.490*** -0.462*** -0.509*** -0.484*** Sub-Saharan Africa -0.110 -0.057 -0.614*** -0.594*** -0.302** -0.239* -0.296*** -0.248*** -0.083 -0.047 -0.283*** -0.245*** -0.356*** -0.322*** CA franc zone 0.208* 0.211* 0.007 0.008 0.346*** 0.350*** 0.175** 0.177** 0.154 0.156 0.088 0.090 0.078 0.079 Logistics index 0.468*** 0.542*** 0.062 0.093 -0.005 0.075 0.076 0.141* 0.070 0.116 0.155*** 0.208*** 0.125** 0.174*** Trade openness -0.001** -0.002*** -0.001 -0.001* -0.001* -0.002*** -0.002*** -0.002*** -0.002*** -0.002*** -0.001*** -0.001*** -0.001*** -0.001*** FDI to GDP -0.005** -0.002 -0.005** -0.004*** -0.003** -0.003*** -0.003*** Observations 122 121 122 121 122 121 122 121 122 121 122 121 122 121 R-squared 0.714 0.724 0.625 0.634 0.422 0.437 0.699 0.718 0.646 0.652 0.801 0.813 0.758 0.772

Source: IMF staff estimates. Notes: Dependent variable is internationally comparable price of the respective items. Emerging market, developing country, sub-Saharan Africa are (mutually exclusive) dummy variables with advanced country as the base category. CFA franc zone is a dummy variable, which equals to one if the country is part of the CFA franc zone and zero otherwise. All specifications include a constant. Robust standard errors are computed. ***,**,* indicate statistical significance at the 1, 5 and 10 percent levels, respectively.

39

Table 22. Competition and Firm Behavior. World Bank Enterprise Survey Data Investment Exports Labor Share

EMDEs SSA EMDEs SSA EMDEs SSA (1) (2) (3) (4) (5) (6)

Markup -0.724*** -0.539*** -0.168*** -0.053*** -1.236*** -1.311*** (0.031) (0.081) (0.008) (0.010) (0.010) (0.028)

Log real GDP per capita (PPP) 0.335 0.107 -0.114* -0.043 0.125** 0.223** (0.221) (0.385) (0.059) (0.043) (0.049) (0.088)

No. of competitors -0.015 0.026 -0.124*** -0.027*** -0.011 0.003 (0.027) (0.062) (0.006) (0.008) (0.008) (0.018)

Size (in logs) -0.193*** -0.116*** 0.142*** 0.078*** -0.000 -0.000 (0.010) (0.031) (0.003) (0.005) (0.000) (0.000)

Direct exports (in pct. of sales) -0.041 -0.527*** -0.156*** -0.078 (0.052) (0.199) (0.016) (0.064)

Foreign ownership -0.060 0.062 0.264*** 0.113*** -0.127*** -0.173*** (0.046) (0.096) (0.019) (0.020) (0.016) (0.033)

Private ownership 0.196** 0.477** -0.068*** -0.051** -0.012 0.000 (0.083) (0.228) (0.026) (0.021) (0.023) (0.054)

Age -0.004*** -0.006** -0.002*** -0.001 -0.001*** -0.001 (0.001) (0.002) (0.000) (0.000) (0.000) (0.001)

Constant -4.134** -3.645 0.840* 0.243 -1.868*** -2.167*** (1.964) (3.312) (0.495) (0.372) (0.437) (0.762)

Observations 17,933 3,598 41,956 8,110 41,956 8,110 R-squared 0.117 0.068 0.158 0.164 0.408 0.407 Industry fixed effects Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes Country fixed effects Yes Yes Yes Yes Yes Yes

Source: IMF staff estimates. Notes: Dependent variable in cols. (1)-(2) is log of equipment purchase to value added; in cols. (3)-(4) is log of exports to value added; in cols. (5)-(6) is log of labor cost to value added. Markup is log of sales to cost of inputs. No. of competitors is a binary variable equal to one if the no. of competitors reported by the firm is less than 5 and zero otherwise. Foreign and private ownership are binary variables equal to one if foreign and private ownership of the firm is greater than 50 percent, respectively, and zero otherwise. All specifications include log real GDP per capita, a constant, and industry, year, and country fixed effects. Statistics in parentheses are robust standard errors. ***,** and * indicate statistical significance at the 1, 5, and 10 percent levels, respectively. EMEDEV=Emerging market economics and developing countries; SSA=Sub-Saharan Africa.

40

Table 23. Emerging and Developing Countries: Competition and Firm Behavior. Orbis Data Investment Labor Share Labor Productivity Growth Total Factor Productivity Growth

All firms Excl. Excl. All firms Excl. Excl. All firms Excl. Excl. All firms Excl. Excl. financial financial financial financial financial financial financial financial sector sector sector sector sector sector sector sector

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Markup -1.275*** -1.313*** -1.421*** -0.580*** -0.587*** -0.687*** -0.829*** -0.841*** -0.828*** -0.832*** -0.827*** -0.728***

(0.094) (0.095) (0.129) (0.037) (0.037) (0.083) (0.080) (0.081) (0.155) (0.015) (0.015) (0.020) Firm size 0.136*** 0.141*** 0.156*** 0.151*** 0.161*** 0.066* 0.447*** 0.473*** 0.335*** 0.007** 0.007** 0.004

(0.037) (0.039) (0.048) (0.022) (0.022) (0.037) (0.039) (0.043) (0.051) (0.003) (0.003) (0.004) Age -0.049** -0.050** -0.047** 0.125*** 0.122*** 0.110*** 0.043*** 0.042*** 0.040*** -0.021** -0.021** -0.025***

(0.020) (0.020) (0.020) (0.009) (0.009) (0.011) (0.015) (0.015) (0.015) (0.009) (0.009) (0.008) Log Real GDP per capita 0.834*** 0.854*** 0.837*** -0.187 -0.103 0.225 -0.092 -0.060 -0.209 0.156*** 0.155*** 0.175***

(0.267) (0.272) (0.280) (0.146) (0.149) (0.161) (0.211) (0.218) (0.246) (0.039) (0.039) (0.038) Domestic ownership 0.066 0.070 0.122 -0.095 -0.087 -0.047 -0.130 -0.121 -0.201 -0.015 -0.007 0.006

(0.785) (0.783) (0.778) (0.536) (0.536) (0.542) (0.205) (0.205) (0.213) (0.041) (0.043) (0.039) Real GDP growth 0.014*** -0.006** -0.002 -0.001*

(0.005) (0.003) (0.004) (0.001) Trade openness -0.004 -0.008*** -0.001 -0.000

(0.003) (0.002) (0.002) (0.000) Institutional quality -0.073 -0.045 0.148** -0.026**

(0.077) (0.051) (0.075) (0.011) Labor productivity (lag) -0.051** -0.047** -0.076**

(0.023) (0.024) (0.037) Total factor productivity (lag) -0.063*** -0.063*** -0.014

(0.011) (0.012) (0.016)

Observations 19,422 19,018 9,913 33,563 32,951 13,206 12,736 12,477 5,433 105,760 101,353 73,095 R-squared 0.048 0.048 0.073 0.107 0.108 0.110 0.122 0.124 0.115 0.358 0.356 0.356 No. of firms 7,769 7,637 3,051 11,296 11,124 3,529 5,281 5,200 1,932 68,368 64,491 52,542 Firm/country/year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Industry x Year effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Other macro control variables No No Yes No No Yes No No Yes No No Yes

Source: IMF staff estimates. Notes: Dependent variable is (log) investment to value added in cols. (1)-(3), (log) share of labor in value added in cols. (4)-(6), labor productivity growth in cols. (7)-(9), and total factor productivity growth in cols. (10)-(12). Lagged values of markup and firm-level characteristics are used in cols. (7)-(12) to mitigate potential reverse causality concerns. Clustered standard errors at the firm level are reported in parentheses. ***,** and * indicate statistical significance at the 1,5, and 10 percent levels, respectively.

Table 24. Sub-Saharan Africa: Competition and Firm Behavior. Orbis Data All firms Excl. Excl. All firms Excl. Excl. All firms Excl. Excl. All firms Excl. Excl.

financial financial financial financial financial financial financial financial sector sector sector sector sector sector sector sector

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Markup -0.844*** -1.077*** -1.037*** -0.699*** -0.848*** -0.865*** -0.304 -0.317 -0.333 -0.659*** -0.562*** -0.562***

(0.19) (0.27) (0.27) (0.14) (0.20) (0.21) (0.21) (0.20) (0.20) (0.05) (0.07) (0.08) Age -0.0235 -0.0282 -0.0255 0.106*** 0.0799*** 0.0769*** 0.0557*** 0.0442*** 0.0403** -0.0383*** -0.0345*** -0.0430***

(0.02) (0.03) (0.03) (0.02) (0.02) (0.02) (0.01) (0.01) (0.01) (0.00) (0.01) (0.01) Log real GDP per capita 0.554 0.762 0.778 0.307 1.034* 0.579 -0.517 -0.246 0.0258 0.0436 0.00669 0.22

(0.48) (0.60) (0.80) (0.30) (0.40) (0.42) (0.61) (0.55) (0.54) (0.07) (0.12) (0.22) Firm size 0.275*** 0.265*** 0.0909 0.0999 0.261* 0.263* 0.0165 0.0186

(0.07) (0.07) (0.06) (0.06) (0.11) (0.10) (0.01) (0.01) Real GDP growth 0.0126 -0.0175* -0.0111 -0.00996*

(0.01) (0.01) (0.01) (0.00) Trade openness -0.00563 -0.00399 -0.00542 0.00112

(0.01) (0.01) (0.01) (0.00) Institutional quality 0.0245 0.113 -0.0938 -0.0817

(0.19) (0.12) (0.16) (0.05) Labor productivity (lag) -0.186** -0.172* -0.165*

(0.07) (0.07) (0.07) Total factor productivity (lag) -0.0116 -0.0509 -0.0604

(0.04) (0.08) (0.08)

Observations 3,192 1,923 1,879 4,105 2,376 2,320 1,123 1,123 1,104 4,496 2,209 2,159 R-squared 0.037 0.072 0.074 0.076 0.069 0.077 0.147 0.158 0.157 0.288 0.27 0.282 Firm/country/year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Industry x Year effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Other macro control variables No No Yes No No Yes No No Yes No No Yes

Source: IMF staff estimates. Notes: Dependent variable is (log) investment to value added in cols. (1)-(3), (log) share of labor in value added in cols. (4)-(6), labor productivity growth in cols. (7)-(9), and total factor productivity growth in cols. (10)-(12). Lagged values of markup and firm-level characteristics are used in cols. (7)-(12) to mitigate potential reverse causality concerns. Clustered standard errors at the firm level are reported in parentheses. ***,** and * indicate statistical significance at the 1,5, and 10 percent levels, respectively.

41

Table 25. Sub-Saharan Africa: Competition and Firm Behavior. Orbis Data Investment Labor Share Labor Total Factor

productivity Productivity (1) (2) (3) (4) (5) (6) (7) (8)

Markup -2.353*** -2.420*** -1.017*** -1.196*** -1.381*** -1.556*** -0.648*** -0.614*** (0.19) (0.21) (0.13) (0.18) (0.19) (0.23) (0.03) (0.03)

Firm size 0.170* 0.174* 0.0755 0.0338 0.371*** 0.349*** 0.00938* 0.00764 (0.07) (0.07) (0.05) (0.06) (0.07) (0.07) (0.00) (0.00)

Age -0.868 -1.148 -0.417 -0.726 -0.0276 -0.39 0.038 -0.0737 (0.57) (1.55) (0.44) (0.72) (0.11) (1.02) (0.03) (0.17)

Log Real GDP per capita 58.93 94.06 -25.21 12.43 1.36 20.68 -3.298 3.855 (40.12) (116.00) (32.17) (9.28) (7.23) (66.40) (1.68) (10.59)

Domestic ownership -0.203 -0.199 0.584 0.569 0.536*** 0.530*** 0.0927** 0.0893** (0.75) (0.74) (0.81) (0.82) (0.06) (0.06) (0.03) (0.03)

Real GDP growth -0.468 0.738 -0.006 0.0207 (0.89) (0.75) (0.17) (0.01)

Trade openness -0.688 0.0516 -0.261 -0.0425 (0.95) (0.10) (0.76) (0.05)

Institutional quality 19.74 29.52 -1.34 0.0203 (16.29) (30.92) (3.70) (0.45)

Labor productivity (lag) -0.044 -0.0253 (0.04) (0.05) -0.0594** (0.05)

Total factor productivity (lag) -0.0229 -0.0249

Observations 6253 5389 8460 6821 3582 2970 24719 23036 R-squared 0.111 0.118 0.17 0.174 0.122 0.122 0.336 0.359 Firm/country/year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Industry x Year effects Yes Yes Yes Yes Yes Yes Yes Yes

Source: IMF staff estimates. Notes: Dependent variable is (log) investment to value added in cols. (1)-(2), (log) share of labor in value added in cols. (3)-(4), labor productivity growth in cols. (5)-(6), and total factor productivity growth in cols. (7)-(8). Sample is restricted to manufacturing firms in the Orbis database. Lagged values of markup and firm-level characteristics are used in cols. (5)-(8) to mitigate potential reverse causality concerns. Clustered standard errors at the firm level are reported in parentheses. ***,** and * indicate statistical significance at the 1,5, and 10 percent levels, respectively.

Source: Bertelsmann Stiftung Transformation Index (BTI). Note: Higher values indicate greater competition. The difference between the average in sub-Saharan Africa and advanced and emerging market economies is statistically significant in both periods. DEV = Developing countries; SSA = sub-Saharan Africa.

Ind ex

Ind

ex

Sh ar

e o f c

ou ntr

ies

Ad va

nc ed

Ad va

nc ed

Em er

gin g

Em er

gin g

DE V

ex cl.

S SA

De ve

lop ing

ex

cl. S

SA SS

A

Ad va

nc ed

SS A

Ad va

nc ed

Em er

gin g

DE V

ex cl.

S SA SS

A Em

er gin

g Ad

va nc

ed De

ve lop

ing

ex cl.

S SA

Em er

gin g

SS A

DE V

ex cl.

S SA SS

A

Overall competition Domestic Foreign competition competition

First quartile Second quartile Third quartile Fourth quartile

Source: World Economic Forum, Global Competitiveness Index. Note: Figures in the bars indicate the number of countries in a specific quartile.

Figure 3. Selected Groups of Countries: Competition Indicators (BTI), 2008–18 10 9 8 7 6 5 4 3

Overall competition Domestic Foreign competition competition

Source: World Economic Forum, Global Competitiveness Index. Note: Index ranges from 1 to 7, with higher values indicating greater competition; DEV = Developing countries; SSA = sub-Saharan Africa.

Figure 2. Sub-Saharan Africa: Comparison with Quartiles of Global Distribution, 2007–17

100%

80%

60%

40%

20%

0%

2 2 2 7 7 7

10 9 9

14 14 13

5

4

3

Av erage 2008–12

Av erage 2014–18

Av erage 2013–17 Av erage 2007–12

42

FIGURES

Figure 1. Selected Groups of Countries: Product Market Competition, 2007–17

6

3

43

Figure 4. Selected Groups of Countries: Revised WEF Competition Indicators, 2018

75 65

Note: In panel 1 index ranges from 1 to 7, with higher values indicating better performance. In panel 2 higher values indicate low ease of doing business. DEV = Developing countries; SSA = sub-Saharan Africa.

Figure 6. Selected Groups of Countries: Foreign Competition Indicators, 2013–17

11 9 7 5 3 1

Source: World Economic Forum, Global Competitiveness Index. Note: Higher values indicate low business impact of rules on FDI, high prevalence of foreign ownership, low non-tariff trade barriers, and higher trade tariffs. DEV = Developing countries; SSA = sub-Saharan Africa.

Intensity of local Extent of market Effectiveness of Effect of competition dominance anti-monopoly taxation on

policy incentives to invest

55 45 35

0

10

Procedures to start a business Days to start a business

Business impact of rules

on FDI

Trade tariffs Prevalence of Prevalance of trade barriers foreign

ownership

Ind ex

4

Source: World Economic Forum, Global Competitiveness Index.

Ad va

nc ed

Em erg

ing DE

V e xc

l. S SA SS

A Ad

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ed Em

erg ing

DE V e

xc l. S

SA SS A

Ad va

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Em erg

ing DE

V e xc

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A Ad

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ed Em

erg ing

DE V e

xc l. S

SA SS A

Ind ex

Ind

ex

Ad va

nc ed

Em erg

ing DE

V e xc

l. S SA SS

A Ad

va nc

ed Em

erg ing

DE V e

xc l. S

SA SS A

Ad va

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Em erg

ing DE

V e xc

l. S SA SS

A Ad

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erg ing

DE V e

xc l. S

SA SS A

Ad va

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ing De

v. ex

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A SS

A Ad

va nc

ed Em

erg ing

De v.

ex cl.

SS A

SS A

20

Note: Higher values indicate greater competition. DEV = Developing countries; SSA = sub-Saharan Africa.

Figure 5. Selected Groups of Countries: Domestic Competition Indicators, 2013–17

1. Product Market Environment 2. Regulatory Barriers 6 30

Nu mb

er 5

Ad va

nc ed

Ad va

nc ed

Em erg

ing

DE V e

xc l. S

SA

Em erg

ing De

v. ex

cl. SS

A SS

A Ad

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ed Em

erg ing

De v.

ex cl.

SS A

SS A

SS A

Ad va

nc ed

Em erg

ing

DE V e

xc l. S

SA SS A

Domestic Distortive effect market of taxes and

competition subsidies on competition

Source: World Economic Forum. Global Competitiveness Index 4.0.

Extent of market

dominance

Competition in services

44

Figure 7. Sub-Saharan Africa: Product Market Competition, 2007–17

1. By Resource Intensity 2. By Region 5.0 5.0

Ind ex

Av erage 2013–17 Av erage 2007–12 Av erage 2013–17 Av erage 2007–12 4.5 4.5

4.0

3.5

Ind ex

4.0

3.5

3.0 3.0

Oi l e

xp or

ter s

Ot he

r r es

ou rce

- int

en siv

e No

n- re

so ur

ce -

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xp or

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e No

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ce -

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siv e

Ce ntr

al Af

ric a

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rn A

fri ca

So uth

er n A

fric a

W es

t A fric

a

Ce ntr

al Af

ric a

Ea ste

rn A

fri ca

So uth

er n A

fric a

W es

t A fric

a

Ce ntr

al Af

ric a

Ea ste

rn A

fri ca

So uth

er n A

fric a

W es

t A fric

a

ve

Overall competition Domestic Foreign competition Overall competition Domestic Foreign competition competition competition

Source: World Economic Forum, Global Competitiveness Index. Note: Index ranges from 1 to 7, with higher values indicating greater competition.

Figure 8. Selected Groups of Countries: Firm-Level Competition Indicators 1.0

Emerging market and developing economies (excl. SSA)

*** *

*** * Sub-Saharan Africa

Profitability Profitability Markup Markup (WBES) (Orbis) (WBES) (Orbis)

Pr ofi

tab ilit

y / M

ar ku

p 0.8

0.6

0.4

0.2

0.0

Sources: IMF staff estimates based on the World Bank Enterprise Survey (WBES) and Orbis databases. Notes: Profitability is defined as the difference between revenue and the cost of inputs relative to revenue. Markup is defined as the log ratio of sales to the cost of inputs using the WBES database, and as the log ratio of revenue turnover to costs using the Orbis database. *** and * indicate statistically significant difference in the mean values between the two groups at the 1 and 10 percent levels, respectively. SSA = sub-Saharan Africa.

Figure 9. Selected Groups of Countries: Competition Indicators and Number of Competitors

1. Firm Profitability 2. Firm Markup

0.8 1.4 0.7 1.2 0.6 1.0

y = 0.14***x + 0.344

M ar

ku p

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

y = 0.31***x + 0.475

0.2 0.4 0.6 0.8

0.5 0.4 0.3 Pr

ofi tab

ilit y

0.8

0.6

0.4 0.2 0.2 0.1

0 0.0

Share of firms reporting few er than 5 competitors Share of firms reporting few er than 5 competitors Sub-Saharan Africa Sub-Saharan Africa Emerging market economics and developing countries (excl. SSA) Emerging market economics and developing countries (excl. SSA)

Source: IMF staff estimates based on World Bank Enterprise Survey data.

45

Figure 10. Sub-Saharan Africa: Firm Markups, 2002–17 180 170 Nigeria 160 South Africa

90 100 110 120 130 140 150

Ind ex

20 02

= 10

0 Sub-Saharan Africa

2002 04 06 08 10 12 14 16 Source: IMF staff estimates based on the Orbis database. Note: The series show averages over all firms for each year.

Figure 11. Selected Groups of Countries: Markup by Firm type

1. Based on WBES Data 1.0

0.8

0.6

0.4

0.2

0.0 Majority Majority Small public foreign firms owned owned

EMEDEV (excl. SSA)

*** *** ***

*** ** *

Yes No 0.6 0.5 0.4 0.3 0.2 0.1

0

***

*** ***

*** Yes No

Majority Majority Small public foreign firms owned owned

SSA

2. Based on Orbis Data

Majority Majority Small Majority Majority Small public foreign firms public foreign firms owned owned owned owned

EMEDEV (excl. SSA) SSA Source: IMF staff estimates. Notes: Markup is defined as log ratio of sales to cost in panel 1. and as the log ratio of turnover to costs in panel 2. Small firms are defined as those with number of employees less than 20. Majority public and foreign owned firms are defined as those with public and foreign ownership of more than 50 percent, respectively. ***,** and * indicate statistical significance at the 1, 5, and 10 percent levels, respectively. EMEDEV (excl. SSA) = Emerging markets economics and developing countries excluding sub-Saharan Africa.

46

Figure 12. Selected Groups of Countries: Firm Markups by Sector 2. Based on Orbis Data 1. Based on WBES Data

0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

Hotels and restaurants Hotels and restaurants Wholesale trade, excl. motor vehicles

Retail trade, excl. motor vehicles/cycles Other services Construction

Information and communication Manuf. of food products and beverages Manuf. of motor vehicles/trailers Financial intermediation

Manuf. of electrical machinery/apparatus Manuf. of basic metals Transport and storage

Manuf. of other non-metallic mineral… Real estate, renting, Manuf. of chemicals/chemical products and business activities Manuf. of rubber and plastics products Mining and quarrying

Publishing, printing Manuf. of wood/wood products Agriculture, hunting, and forestry

Manuf. of furniture Manufacturing Manuf. of fabricated metal products

Manuf. of wearing apparel; dressing/dyeing Electricity, gas, and water supply Manuf. of machinery and equipment

Manuf. of leather products Construction Manuf. of textiles Wholesale/retail trade; Repair of

Manuf. of paper and paper products motor vehicles and goods

Sub-Saharan Africa Emerging market economies and developing countries (excl. SSA) Source: IMF staff estimates. Notes: Markup is defined as the log of the ratio of sales to cost in panel 1 and the log of the ratio of revenue turnover to costs in panel 2. Manuf. = Manufacturing; WBES = World Bank Enterprise Survey; SSA = sub-Saharan Africa.

Figure 13. Sub-Saharan Africa: Competition and Macroeconomic Performance

Growth Private Non-oil exports Labor investment productivity

growth

Source: IMF staff estimates. Note: Statistics are based on the regressions results reported in Annex Table 2.13 (col. 1), Annex Table 2.15 (cols. 1, 4, and 7), and represent the change in the respective macroeconomic variables for an increase in the World Economic Forum's local competition intensity index from the median for sub-Saharan African countries to the top decile of the global distribution. ***, ** and * indicate statistical significance at the 1 and 5 percent levels, respectively.

***

**

**

0.0

0.4

0.8

1.2

1.6

2.0

Pe rc

en tag

e p oin

ts

47

Figure 14. Sub-Saharan Africa: Price Differentials with Other Country Groups 60

40 *** *** *** ** *** *** ***

0

Advanced economies –20 Developing economies excl. SSA *** Emerging market economies

–40

20 ***

Pe rc

en t

Food and Clothes and Health Individual beverages footwear consumption

basket Sources: IMF staff calculations based on World Bank, International Comparison Program data.. Note: The bars show the average difference in price levels between sub-Saharan Africa and other country groups. ***, ** denote statistically significant differences at 1 and 5 percent levels, respectively. SSA = sub-Saharan Africa.

Figure 15. Sub-Saharan Africa: Impact of Increased Local Competition on Prices

–35

–30

–25

–20

–15

–10

–5

0

Pe rc

en t

Clothes and Health Food and Individual footwear services beverage consumption

Pe rc

en t

Source: IMF staff calculations, based on data from the World Bank International Comparison Program. Note: The bars show the effect of an increase in the indicator of local competition intensity from the median in sub-Saharan Africa to the top decile of the world distribution.

Figure 16. Estimated Impact of Markups on Firm Performance

1. WBES Data 2. Orbis Data 1.6 1.6

*** Sub-Saharan Africa *** *** 1.2 1.2 Emerging market economies and *** developing countries *** ***

*** 0.8 ***

Pe rc

en t

0.8 ***

0.4 0.4 ***

*** 0.0 0.0

*** ***

Investment to value Exports to value Labor share to value Investment to Labor share to Labor Total factor added added added value added value added productivity productivity

growth growth

Source: IMF staff estimates. Notes: Bars show the estimated impact of a 1 percent decline in firm markups, defined as the log of the ratio of sales to cost in panel 1 and the log of output elasticity to input relative to the expenditure share of the input in sales in panel 2. Emerging market economies and developing countries include sub-Saharan Africa. *** indicates statistical significance at the 1 percent level. WBES = World Bank Enterprise Survey.

48

Figure 17. Sub-Saharan Africa: Structural Reforms, 1973–2014

0.9 Trade

Financial market 0.6

Current account

0.3 Capital account

Product market (telecom, electricity) 0.0

Ind ex

1973 77 81 85 89 93 97 01 05 09 13 Source: Alesina and others, forthcoming. Notes: Average across 14 countries for which data is available. Higher values indicate greater liberalization.

Figure 18. Sub-Saharan Africa: Competition Frameworks

Does the jurisdiction have a competition law or unified legal

framework ?

Is the implementation of competition policy in the jurisdiction centralized ?

Does the jurisdiction have a formal competition policy?

Does the jurisdiction have an independent competition/anti-trust

agency?

Share of positiv e answers

Source: IMF staff estimates. Note: The scores denote the percentage of respondent authorities who answered yes to the question. Total respondents equal to 37.

Figure 19. Sub-Saharan Africa: Anti-Monopoly Enforcement 4.0

3.5

0 25 50 75 100

2006 2017

Ind ex

3.0

2.5

2.0

Oil exporters Other resource-intensive countries Non-resource-intensive countries

Source: World Economic Forum. Note: Index ranges from 1 to 7, with higher values indicating more comprehensive anti-monopoly policy enforcement.

  • I. INTRODUCTION
  • II. FRAMEWORK FOR THE EMPIRICAL ANALYSIS
  • III. DATA
  • IV. STYLIZED FACTS
  • V. COMPETITION AND MACROECONOMIC PERFORMANCE
  • VI. FIRM DYNAMICS AND COMPETITION
  • VII. HOW TO BOOST COMPETITION IN DOMESTIC MARKETS?
  • VII. HOW TO BOOST COMPETITION IN DOMESTIC MARKETS?
  • VIII. CONCLUSION
  • References
  • Tables
  • Figures
  • Blank Page