metadata: authors: 0: author: Dauda,Seidu
1: author: Nyman,Sara
2: author: Cassim,Aalia
count: South Africa
docna: 0: docna: Product Market Competition, Productivity,
            and Jobs : The Case of South Africa
docty: Policy Research Working Paper
abstracts: cdata!: The degree of concentration and market
            power in South African markets has been the topic of much
            policy discussion. However, there has been little evidence
            on what drives market power and the impact of the degree of
            competition in South African markets on economic outcomes.
            This paper improves on previous markup estimates for South
            Africa using a methodology developed by De Loecker and
            Warzynski (2012) applied to tax administrative data for
            2010–14. The paper then explores the firm-level determinants
            of the estimated markups and assesses the link between
            competition and firm-level outcomes, including productivity,
            employment, and wages. The analysis finds that average
            markups across the economy appear to have risen between 2010
            and 2014. Larger firms, higher-intensity exporters, and
            firms with greater sales shares charge higher markups than
            comparator firms in South Africa, even after controlling for
            efficiency. Moreover, lower product market competition has a
            significant, negative effect on productivity growth,
            employment growth, and wage growth in South African
            manufacturing industries. Higher sales-weighted and
            value-added-weighted average industry-level markups are
            associated with lower industry-level entry rates. The
            findings highlight the importance of implementing sound
            pro-competition government interventions and the significant
            economic benefits associated with such policies.
geo_reg_key: 517195,517191
repnme: repnme: Product Market Competition, Productivity,
            and Jobs : The Case of South Africa
display_title: Product Market Competition,
            Productivity, and Jobs : The Case of South Africa
listing_relative_url: /research/2019/12/31646891/product-market-competition-productivity-jobs-case-south-africa
publishtoextweb_dt: 2019-12-17T00:00:00Z
origu: EFI-FCI-TIC-Investment Climate (ETIIC)
no_of_pages: 40
author_keys: 0: author_key: 1374042
1: author_key: 1342471
model_extractions: Policy Research Working Paper 9084 Product Market Competition, Productivity, and Jobs The Case of South Africa _Seidu Dauda Sara Nyman Aalia Cassim_ Equitable Growth, Finance and Institutions Practice Group December 2019
Policy Research Working Paper 9084

# **Abstract**

The degree of concentration and market power in South African markets has been the topic of much policy discussion. However, there has been little evidence on what drives market power and the impact of the degree of competition in South African markets on economic outcomes. This paper improves on previous markup estimates for South Africa using a methodology developed by De Loecker and Warzynski (2012) applied to tax administrative data for 2010–14. The paper then explores the firm-level determinants of the estimated markups and assesses the link between competition and firm-level outcomes, including productivity, employment, and wages. The analysis finds that average markups across the economy appear to have risen between 2010 and 2014. Larger firms, higher-intensity exporters, and firms with greater sales shares charge higher markups than comparator firms in South Africa, even after controlling for efficiency. Moreover, lower product market competition has a significant, negative effect on productivity growth, employment growth, and wage growth in South African manufacturing industries. Higher sales-weighted and value-added-weighted average industry-level markups are associated with lower industry-level entry rates. The findings highlight the importance of implementing sound pro-competition government interventions and the significant economic benefits associated with such policies.

This paper is a product of the Equitable Growth, Finance and Institutions Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sdauda@worldbank.org or snyman@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Product Market Competition, Productivity, and Jobs: The Case of South Africa<sup>1</sup> Seidu Dauda Sara Nyman Aalia Cassim _JEL classification_ : D24, J21, J31, L11, L13, O47.

_Keywords_ : Markup, Product Market Competition, Productivity Growth, Job Creation, Wage Growth, South Africa.

> 1 The authors would like to thank the National Treasury of South Africa and the South African Revenue Service for providing access to the data for this paper, Julian Koschorke for invaluable assistance, and Marek Hanusch, Tania Begazo, Mariana Iootty de Paiva Dias, Christine Qiang, Yue Li, and Liberty Mncube as well as others at the Competition Commission of South Africa for providing insightful comments.
# 1. Background

Product market competition is often cited as a key driver of productivity and economic growth – and thus is an important piece in the puzzle of boosting South Africa’s economic performance. At present, there is significant discussion on the level of competition faced by firms across South African industries. This in part stems from concerns that product markets have remained relatively concentrated since the end of apartheid, with newer and smaller firms unable to successfully compete against larger incumbents that benefit from scale and other advantages. This has implications for the degree of rivalry in markets. Historically, there have been four main conglomerate groupings which have controlled the majority of economic activity (Roberts, 2004) - Anglo American Corporation, Sanlam, Liberty Life and Rembrandt/Remgro. Together, they held 80.2 percent of JSE market capitalization in 1985 and 59.8 percent in 2002. In addition, Roberts pointed out that the 1996 manufacturing survey suggested the largest four firms account for more than half of output in 47 percent of the 57 main product groupings. Fedderke et al. (2018) find that concentration ratios for the top 5 percent of firms across industries have increased across most industrial categories from before 2001 to 2010 and 2012.<sup>2</sup> In part, currently observed market structures result from South Africa’s legacy of exclusion (World Bank Group, 2018). However, they can also be reinforced by a lack of pro-competition principles in current government interventions in some markets, making it difficult for South Africa to overcome its historical legacy. South Africa’s overarching economic development plans have in fact advocated for competition policies in the past. The New Growth Path adopted in 2011suggests that competition is key to generating employment and investment opportunities, as well as lower prices for businesses and consumers. In a similar vein, the National Development Plan (National Planning Commission, 2012) suggests that bolstering competition in high value-added sectors and diversifying industry are key to bringing more individuals into the labor market and improving the skill base of the economy. While South Africa has made important strides in implementing its

> 2 When discussing product markets in South Africa, there are two distinct types of “concentration” which have typically been part of policy dialogue: i) concentration of economic ownership across the economy; and ii) concentration within specific markets. While the two are linked, it is crucial to distinguish between them since they have different implications. The former encompasses the concentration of economic ownership in a few parent firms across markets, while the latter refers to concentration within a specific market (or set of substitutable products). It is the latter type of concentration that is spoken of in the context of competition outcomes.

2
competition law over the last two decades, there is still room to enhance a broader set of public policies – such as regulation of network industries, trade policy and industrial policy - in a way that can foster competition and thereby address national social and economic objectives.

An assessment of the current level of competition in a market is important to inform policy recommendations. Market structure is only one potential reflection of competition and a more thorough assessment requires going beyond structure to understand firm interaction and conduct. In particular, this calls for an understanding of firms’ exercise of market power. Typically, market power refers to the degree to which a firm is able to maintain its prices above marginal cost for a sustained period of time.<sup>3</sup> This conduct shapes incentives for improvements in productivity and innovation and drives economic outcomes. Obtaining a measure of a firm’s markup (price charged relative to marginal cost) can be a better way of understanding market power than the existing proxies of competition. Concentration is problematic if it reflects a high degree of undue market power. However, it can also be an efficient market structure when markets are characterized by scale economies and network effects. Moreover, a key factor for a firm’s market power is the threat of entry as well as the existence of actual competitors in the market. Thus, concentration is most problematic when it is the result of protections that shield incumbents from competition and when large firms are not adequately incentivized to compete through effective ex post competition enforcement. Ensuring effective ex post enforcement is particularly important in the context of markets where demand is relatively small compared to the efficient scale of production, while procompetition ex ante government interventions are crucial for setting the stage for effective rivalry across markets.

While the body of empirical evidence on the extent of competitive pressure within the context of South African manufacturing industries is growing, it is still relatively scanty. In addition, the lack of firm-level data (up until 2015) has been prohibitive, particularly in terms of the statistical methods that can be used, to examine competition. The objective of this paper is two-fold: (1) We estimate indicators of product market competition using a new and more robust methodology than previous studies. By doing so, the paper contributes and enhances the ongoing debate on the extent of competitive pressure within South African manufacturing industries. (2) We explore the

> 3 Although it is worth noting in less traditional markets that are multi-sided this definition is less useful.

3
determinants of firm-level markups in formal manufacturing industries and then, using these markups as a proxy of product market competition, we examine the link between competition and firm-level outcomes such as productivity growth, employment and wages. This paper provides a novel contribution to the analysis of competitive conditions in South African markets in three ways: by generating additional evidence using a more comprehensive firm-level data set than most previous studies and by providing improved estimations of markups as well as new results on the link between competition and key economic outcomes. For example, we are not aware of any literature in the South African context that has linked behavioral proxies of competition, such as markup, to employment and wages.

# 1.1. Review of the literature

# _Measuring competition in South Africa_

In respect of markups, some evidence seems to suggest that manufacturing markups in South Africa have been significantly higher than they are in comparable industries world-wide (in particular in the United States) and they are also non-declining (Fedderke et al., 2007) manufacturing markups in the period 1990 to 1997; Aghion et al (2008) up to 2004). Fedderke et al. (2018) were the first to calculate firm-level markups for the South African manufacturing sectors using administrative tax data (SARS-NT panel). They find sector-specific relationships between markups, levels of market concentration and barriers to entry which might be one channel through which firms are able to maintain dominant positions in certain sectors. Additional empirical evidence by Klein (2011) and Fedderke (2013) also supports the conclusion that markups are relatively high. However, recent empirical work by Zalk (2014) and Du Plessis et al. (2015) points to conflicting findings suggesting that firms in South African manufacturing industries do not enjoy significantly higher markups than firms in comparator countries. While Zalk (2014) used similar data sets and price-cost margin (PCM) measures as Aghion et al (2008) but treated the data sets differently, Du Plessis et al. (2015) used different data sets. In addition, Edwards and Van de Winkel (2005) find average markups are below those in the United States and are close to the median of other countries. They ascribe the divergence from previous papers’ findings to the use of a different methodology as well as a different time horizon. More recently, De Loecker and Eeckhout (2018) have found a modest increase of markups in South Africa over the period from 1980 to 2016 using data primarily on publicly listed companies.

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## _Impact of competition on economic outcomes_

While several studies have attempted to measure trends in industrial competition in South Africa, there is a dearth of literature that examines the impact of South Africa’s market structure on economic outcomes. Fedderke and Naumann (2011) find that concentration, as measured by the Gini and Rosenbluth coefficients, lowers investment rates. Fedderke and Szalontai (2009) find that higher concentration is detrimental to output growth, lowers labor productivity and raises labor unit costs. In addition, Aghion et al. (2008) find a strong positive effect of product market competition, as measured by price-cost margins, on productivity growth. This paper builds on and complements these previous studies to further understand the impact of competition on productivity growth as well as on labor market outcomes.

# 2. Measuring competition and treatment of data

2.1. Estimated markups and other commonly used proxies of competition While previous literature for South Africa has estimated structural and behavioral measures of competition such as Hirschman Herfindahl Index (HHI) and PCM, these measures have several important limitations (refer to the Annex for details on some of these issues). Noting the caveats associated with these measures, the indicator of competition used in this paper’s analysis is estimated firm-specific markups derived following the methodology developed by De Loecker and Warzynski (DLW, 2012) and pioneered by Hall (1986). This methodology has several advantages over indicators that have typically been used in South Africa to date. In this regard, our analysis is novel within the context of South Africa.

The DLW method is a structural estimation of firm-level markup, while accounting for productivity shocks. It relies on the idea that, for a cost-minimizing firm, the output elasticity of a variable production input that is free of adjustment costs equals its expenditure share in total revenue when price equals marginal cost of production. Therefore, any divergence between the output elasticity and the share of that variable input in total revenue represents a firm’s markup if competition is imperfect. The choice of the variable input that is free of adjustment costs is important. We use intermediate inputs as the variable input free of adjustment costs. The 5
methodology does not require data on firm-level prices or require accurate marginal cost estimates, and it leads to more reliable estimates of markup than PCM, as it accounts for potential endogeneity issues by accounting for productivity shocks. Since a firm’s unobserved productivity is potentially correlated with its input choices, not accounting for productivity shocks would otherwise lead to biased markup estimates.<sup>4</sup> See section E of the Annex for a brief discussion on the empirical framework for obtaining the firm-level markups and the output elasticities. For a more detailed exposition, refer to De Loecker and Warzynski (2012).

Since the application of the DLW method involves estimating a production function, we recover the Solow residual or total factor productivity (herein referred to as TFP or productivity) under the method. Given that we do not observe firm-level prices, we are unable to estimate a quantity-based productivity (TFPQ). Instead, we recover a measured revenue-based productivity (TFPR) after deflating all nominal variables by the appropriate deflators reported in the data set. While we do not have physical quantities or detailed price indices to convert revenues into physical output, De Loecker and Warzynski (2012) show that using revenue data to recover markup does not affect the temporal changes in the level of the markup or the estimated association between markups and firm-level variables, which we examine in this paper. It might, however, have an effect on the level of the estimated markup. We estimate the production function using the Ackerberg, Caves, and Frazer (ACF, 2015) two-step estimator, which corrects for potential endogeneity and collinearity issues.<sup>5</sup> A key caveat of this is that recent work has noted problems in applying the ACF estimator to the gross output production function (Gandhi, Navarro, and Rivers, 2018). We estimate the production function at the two-digit ISIC industry level, which allows for technology to vary across industries to some extent, although it should be noted that there is likely to be significant firm heterogeneity even within narrowly-defined industries.<sup>6</sup> However, our use of the more flexible

> 4 The bias may be due to simultaneity between output and variable inputs. Simultaneity arises because productivity is known to profit-maximizing firms (but unobservable to the econometrician) when they choose their input level. Firms will expand their output (by using more inputs) due to positive productivity shocks. Thus, the positive correlation between the unobserved productivity shock and input levels biases the estimates of the output elasticities. 5 We use the Stata routine “prodest” – developed by Rovigatti and Mollisi (2016) – to estimate the production function and recover the output elasticity of intermediate inputs and labor, and the total factor productivity measure, as well as the first-stage residual to be used to correct for measurement error in output or sales.

> 6 Estimating the production function at the 4-digit level may be possible but given the data at hand this would greatly restrict the number of observations available.

6
translog gross output production function specification allows for firm-time variation in the estimated output elasticities through differences in input use.

Due to restrictions in the data set, we restrict the markup and productivity analysis to firms in the manufacturing industries. because the proxy variable for materials (cost of sales) is missing for a sizable number of non-manufacturing firms in the sample, particularly those in the services sectors such as financial and insurance activities, real estate activities, and professional activities like legal and accounting services.<sup>7</sup>

# 2.2. Data

We use the SARS-NT firm-panel data set based on tax administrative data. This firm-level panel data are created using the South African Corporate Income Tax (CIT) information sourced from SARS. Although data are available for the years 2008-2017, we start our sample from 2010 given that there is a considerable proportion of firms with missing sales revenue information in 2008 and 2009 relative to the other years - a key variable underpinning this analysis. We have therefore conducted our analysis using data for the years 2010-2017. However, due to observed issues with some variables (including sales and employment) for 2015, we have chosen to present analysis for 2010-2014 as our primary set of results. Nevertheless, it is worth noting that estimated effects using data for 2010-2017 are in line with those using a sample up to 2014, albeit of different magnitude. Finally, since the data are derived from tax administrative data, only formal firms are included in the data set and the results that follow must be read in this context.

In order to improve our estimated competition indicators, in treating the data, we carefully considered a number of issues, considering principles of Industrial Organization. As an example, we made important decisions with respect to missing values of the core variables of interest and unreliable wage data in some years for some firms. In addition, we selected variables for the analysis based on how well they capture the underlying microeconomic concepts required to gain an accurate estimation of competition dynamics. For instance, we selected a wage measure that

> 7 In addition, there are various conceptual and empirical problems relating to the measurement of output, prices, and productivity in the non-manufacturing sectors (see https://www.brookings.edu/research/productivity-in-the-services- <u>sector/).</u>

7
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Underlying this are differences in the dynamics within specific industries. For example, between 2010 and 2014, 67 percent of industries experienced a rise in unweighted mean industry markups (with the average rise being 13 percent over that period) while the remaining 33 percent experienced a decline in markups (average decline of 10 percent).

_Table 1. Change in industry markups across the manufacturing sector between 2010-2014_

|Measure of overall industry<br>markups|The proportion<br>industries with a<br>change in ov|of manufacturing<br>positive/negative<br>erall markups|The average ch<br>markup for in<br>positive/neg|ange in industry<br>dustries with a<br>ative change|
|---|---|---|---|---|
||Positive|Negative|Positive|Negative|
|Unweighted mean|67%|33%|13%|-10%|
|Sales weighted mean|63%|37%|33%|-10%|
|Employment weighted mean|67%|33%|36%|-14%|
|Valued added weighted mean|70%|30%|66%|-11%|
|Average industry median|72%|28%|4%|-3%|

Note: Markups estimated using methodology developed by De Loecker and Warzynski (2012) Industries that have seen the greatest increases in mean markups as observed in the data include: manufacture of chemicals, pharmaceuticals, textiles and machinery. It is worth noting that these industries may be those that are becoming more intensive in capital usage and R&D over time (particularly industries like pharmaceuticals) and that this increase reflects those dynamics. These markups do not account for differences in efficiency and therefore their increases may reflect efficiency gains rather than increases in pricing power. Of policy interest for South Africa is also that, while several food and beverages industries have seen modest increases in markups, others show a similar magnitude of decreases in markups.

The rest of this analysis will shed light on some potential determinants of markups and discuss key findings of the empirical analysis of related economic outcomes. The markups used are estimated following the methodology laid out in De Loecker and Wazynski (2012). We recover these markups while controlling for variables which could affect the optimal choice of input demand for the firm. These include export status and age, which are used as controls.

9
# 3.1. Determinants of firm markups and prices

As a first step in this analysis, we look to relate a firm’s markups and pricing behavior to various firm-specific characteristics in a fixed effects regression framework. We cluster standard errors at the firm level. Key firm-specific characteristics of interest here are the relationship between a firm’s share in total sales of the 3-digit subsector (hereafter referred to as sales share and used as a proxy for market share since the definition of a true relevant market from an antitrust perspective is not available in the data), whether it is a new entrant, its export status, size and age.<sup>9</sup> The specification we take to the data is given by where a firm’s logged markup (μ��) is modeled as a function of its contemporaneous characteristics X��with unknown weights β, α is a constant, δ� are time-specific effects (to account for economic shocks or economy-wide changes in policy), and ε�� is the error. X��contains firm-specific variables of specific interest in the context of South Africa including the firm’s sales share,<sup>10</sup> the export status of the firm (the share of export sales in overall sales of the firm<sup>11</sup> ), whether or not the firm is a new entrant,<sup>12</sup> as well as various control variables including: firm age,<sup>13</sup> number of employees<sup>14</sup> and total capital stock (proxied by the book value of tangible assets) in order to capture differences in size and factor intensity. The firm’s level of current productivity, lagged productivity or its level

> 9 Throughout this paper, we include categorical variables of export status, size and age in order to assess the differential effects between different cohorts. This is consistent with previous literature on South Africa including, for example, Aterido et al, 2019.

> 10 Note that in calculating sales shares, we use only domestic sales given that domestic market concentration is most relevant for competition dynamics within South Africa. Not excluding export revenues can provide a misleading picture on domestic market concentration.

> 11 Classified into three categories: Exporters with a share of exports in sales of: less than 11 percent (low-intensity exporters); greater than or equal to 11 percent and less than 25 percent (medium-intensity exporters); and greater than or equal to 25 to 100 percent (high-intensity exporters), with the base category being non-exporting firms. While dummies based on export share in sales are a common determinant in the empirical literature, there does not appear to be a commonly agreed upon cut-off threshold. As a result, the thresholds used in this analysis are informed by thresholds that could be found elsewhere in the literature, as well as the distribution of the available data.

> 12 A new entrant would be a firm in a specific year that has a reported birth year equal to the current year and reported positive sales.

> 13 The firm’s age (in five categories: 6 to 10 years, 11 to 20 years, 21 to 20 years, 31 to 40 years and above 40 years). The base category to which the coefficients on the age dummies compare is the group of firms aged 1 to 5 years. 14 The firm’s size based on the number of employees (6 to 10 employees, 11 to 20 employees, 21 to 50, 51 to 100, 101 to 250, 251 to 100 and over 1000 employees). The base category to which the coefficients on the size dummies compare is the group of firms with 1 to 5 employees.

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of productivity growth was included in various specifications to control for other technological and cost factors.<sup>15</sup> It should be noted that the introduction of productivity into the model changes the interpretation of the dependent markup variable. In general, high markups could be the result of either high prices or low marginal cost, i.e. high productivity. Introducing productivity into the model allows us to control for differences in marginal cost between firms. Thus, specifications that include productivity control for differences in marginal cost, which lets the other elements of β pick up the variation in average prices related to the market power and demand conditions faced by firms with different characteristics. The coefficient on the productivity variable will pick up the passthrough effect of marginal cost changes on overall markups.

Firm characteristics that are likely to impact future productivity have been included as controls in the first-stage regression to recover markups – here, these characteristics include export status and sales share. However, we are not interpreting the elements of β as causal parameters but instead are testing whether, on average, new entrants, exporters, and firms with higher sales shares have different markups. These results have not been documented for South Africa before as far as we are aware. It is relevant to note that the markup is being calculated on the entirety of a firm’s production including production that is sold in international markets and across a variety of products.

The first specification examines the determinants of markup. In the second specification we control for current levels of revenue-based productivity. Since markups are determined by a combination of price levels and marginal costs (a measure of efficiency), controlling for the latter allows us to interpret the coefficients of these models as determinants of pricing levels. Findings are generally robust across regression specifications. Table 2 shows the empirical determinants for firm-level markups. Results are discussed below.

> 15 Persistence in behavior may make the markup process dynamic, however, like De Loecker and Wazynski (2012) and many others in the literature, we are primarily interested in how firms’ markups relate to contemporaneous characteristics. Nevertheless, as an additional check we have tried a dynamic specification of this model (not shown here) and the results on exports, sales share and firm size remain comparable.

11
_Table 2: Determinants of markup and market power in South African manufacturing industries_

||Model 1|Model 2|Model 3|
|---|---|---|---|
|Low-intensity exporters|-0.010<sup>***</sup>|0.002|-0.009<sup>***</sup>|
||(0.002)|(0.001)|(0.002)|
|Medium-intensity exporters|-0.018<sup>***</sup>|-0.002|-0.014<sup>***</sup>|
||(0.003)|(0.002)|(0.004)|
|High-intensity exporters|-0.016<sup>***</sup>|0.010<sup>***</sup>|-0.009<sup>*</sup>|
||(0.005)|(0.004)|(0.005)|
|Sales share|0.741<sup>***</sup>|2.048<sup>***</sup>|0.724|
||(0.260)|(0.328)|(0.454)|
|Age: 6-10 years|-0.004<sup>**</sup>|0.004<sup>**</sup>|-0.006<sup>**</sup>|
||(0.002)|(0.002)|(0.002)|
|Age: 11-20 years|-0.003|0.005<sup>**</sup>|-0.007<sup>*</sup>|
||(0.003)|(0.002)|(0.004)|
|Age: 21-30 years|0.003|0.001|-0.008|
||(0.004)|(0.004)|(0.005)|
|Age: 31-40 years|0.006|-0.005|-0.001|
||(0.008)|(0.006)|(0.009)|
|Age: 41 years or more|0.015|-0.009|0.005|
||(0.011)|(0.009)|(0.011)|
|Firm size: 6-10 employees|0.016<sup>***</sup>|0.107<sup>***</sup>|0.023<sup>***</sup>|
||(0.002)|(0.003)|(0.003)|
|Firm size: 11-20 employees|0.035<sup>***</sup>|0.202<sup>***</sup>|0.042<sup>***</sup>|
||(0.003)|(0.004)|(0.004)|
|Firm size: 21-50 employees|0.053<sup>***</sup>|0.307<sup>***</sup>|0.062<sup>***</sup>|
||(0.003)|(0.006)|(0.005)|
|Firm size: 51-100 employees|0.059<sup>***</sup>|0.410<sup>***</sup>|0.065<sup>***</sup>|
||(0.005)|(0.008)|(0.007)|
|Firm size: 101-250 employees|0.060<sup>***</sup>|0.518<sup>***</sup>|0.067<sup>***</sup>|
||(0.007)|(0.011)|(0.009)|
|Firm size: 251-1000 employees|0.042<sup>***</sup>|0.619<sup>***</sup>|0.055<sup>***</sup>|
||(0.014)|(0.017)|(0.014)|
|Firm size: over 1000 employees|0.018|0.714<sup>***</sup>|0.035<sup>**</sup>|
||(0.014)|(0.019)|(0.014)|
|Log of capital stock|-0.000|-0.002<sup>**</sup>|-0.000|
||(0.001)|(0.001)|(0.001)|
|Entrants|0.022|-0.013|-0.003|

12
||(0.017)|(0.012)|(0.021)|
|---|---|---|---|
|Log of TFP||0.388<sup>***</sup><br>(0.008)||
|Log of lagged TFP|||-0.010<sup>**</sup><br>(0.005)|
|Firm effects|Yes|Yes|Yes|
|Year effects|134986|134986|93465|
|No. of observations|0.017|0.344|0.016|
|Within R-squared|-0.010<sup>***</sup>|0.002|-0.009<sup>***</sup>|
||(0.002)|(0.001)|(0.002)|

Notes: All models include firm and year fixed effects. Standard errors clustered at the firm level are parentheses. ***, **, and * indicate significance at 1 percent, 5 percent, and 10 percent. The reference categories for the exporter, age, and firm size groups are “Non-exporters”, “Age: 2-5 years”, and “Firm size: 1-5 employees”, respectively.

# Sales share

A simple model of oligopoly competition between firms in prices or quantities would imply a positive relationship between sales share and markup, since the elasticity of demand faced by a firm is decreasing in its market share. Indeed, this relationship is often postulated in South Africa and the results obtained confirm a significant positive relationship between the two variables, i.e. firms with higher sales share being able to charge higher markups. The average change in markup and prices per 1 percentage point change in sales share is around 1 percent and 7 percent respectively.

# Export status

A number of theoretical international trade models have predictions regarding markups and firmlevel export status. Most models suggest that exporting firms would be more productive (either through self-selection of more productive firms into export or through “learning by exporting”) and therefore would have higher markups due to lower costs per unit of production.<sup>16</sup> Another theory could be that firms lower their markups when exporting to more competitive foreign markets. As a result, markups for exporting firms would be lower on average and there would be a clear pattern of markups falling as the share of exports in the firm’s sales increases.<sup>17</sup> In understanding the association between export status and markups, the method we have used here

> 16 See for example Bernard et al. (2003) and Melitz and Ottaviano (2008).

> 17 See for example Hornok and Muraközy (2018).

13
has the advantage that we can control for productivity improvements that are endogenous to past export status (as hypothesized in the first theory outlined above) and thus focus on whether there are other factors that affect the pricing power of exporters, such as the competitive pressure faced in foreign markets (as per the second theory).

Our results suggest that neither of these predicted correlations between a firms’ markups and its export status hold in the case of South Africa. Table 2 shows that markups charged by exporters are on average significantly lower than those charged by non-exporters. However, after controlling for efficiency, this effect disappears and high-intensity exporters (firms with above 25 percent of sales from export) appear to in fact have higher pricing power than other firms. This might indicate that high-intensity exporters tend to participate in less competitive markets, either at home or abroad.

Firm size (in terms of employment) When various firm sizes are compared (using the number of employees as the measure of firm size) relative to the control group (of firms with 1-5 employees), larger firms have higher markups and charge higher prices. When controlling for efficiency, firms’ prices monotonically increase with firm size, with the estimated magnitudes being significantly different from each other.<sup>18</sup> While firms with between 6 and 10 employees have prices that are around 11.3 percent higher than those of the smallest firms, firms with more than 1,000 employees charge prices that are 104.2 percent higher than those of the comparator group.<sup>19</sup> Given that the results remain robust with the inclusion of productivity and lagged productivity in the regression, it appears that the effect is at least partially due to larger firms charging higher prices and not solely driven by differences in efficiency.<sup>20</sup>

> 18 The existence of a positive correlation between sales share and firm size across the sample may mean that the estimated effect of these variables is dampened.

> 19 The estimation of the magnitudes of the dummy variables in semilogarithmic equations follows Kennedy (1981). 20 Note that the dynamic version of the markup regression suggests that firm size is positively associated with the degree to which their markup changes from period to period.

14
# 3.2. Competition and economic outcomes

Having identified some of the determinants of firm markups in the South African context, the analysis now turns to identifying the impact of markups (as a measure of the market power held by firms) on economic outcomes that are relevant for South African policy makers. The results shed light on the relationship between competition and productivity, employment, wages, as well as firm entry.

## Productivity growth

The following sections model these outcomes using the below set up and controlling for firm fixed effects:

## ∆lnTFP�� �α� �α� ln μ���� �X��β�δ� �γ� �ε�� [2]

where TFP�� is total factor productivity and μ���� is the lagged markup. Since the estimates of productivity and markup are both affected by output prices, there may be some correlation between contemporaneous productivity and markups. Although regressing contemporaneous productivity on lagged values of markup should partly mitigate this concern, it is worth keeping this caveat in mind. X�� is a vector of control variables, which include export status (using the same categorizations explained in the section above), variables to capture labor and capital use, firm age, whether the firm is a new entrant, sales share, and lagged TFP growth. Table 3 shows the determinants of productivity growth of South African firms. Both model specifications presented below capture similar information regarding the relationship between labor and capital and can be seen as robustness checks.

_Table_ 3 _: Effects of weaker competition on productivity growth in South African manufacturing industries_

||Model 1|Model 2|
|---|---|---|
|Log of lagged markup|-0.631<sup>***</sup>|-0.630<sup>***</sup>|
||(0.033)|(0.034)|
|Low-intensity exporters|-0.007|-0.013<sup>**</sup>|
||(0.006)|(0.006)|
|Medium-intensity exporters|-0.012|-0.019<sup>**</sup>|
||(0.009)|(0.009)|
|High-intensity exporters|-0.042<sup>***</sup>|-0.047<sup>***</sup>|

15
||(0.015)|(0.016)|
|---|---|---|
|Sales share|-3.127<sup>***</sup>|-3.593<sup>***</sup>|
||(0.764)|(0.879)|
|Age: 6-10 years|0.030<sup>***</sup>|0.024<sup>***</sup>|
||(0.006)|(0.007)|
|Age: 11-20 years|0.030<sup>***</sup>|0.021<sup>**</sup>|
||(0.009)|(0.009)|
|Age: 21-30 years|0.018|0.012|
||(0.012)|(0.012)|
|Age: 31-40 years|-0.008|-0.004|
||(0.024)|(0.025)|
|Age: 41 years or more|0.008|0.015|
||(0.038)|(0.039)|
|Firm size: 6-10 employees|-0.165<sup>***</sup>||
||(0.007)||
|Firm size: 11-20 employees|-0.316<sup>***</sup>||
||(0.010)||
|Firm size: 21-50 employees|-0.463<sup>***</sup>||
||(0.014)||
|Firm size: 51-100 employees|-0.612<sup>***</sup>||
||(0.019)||
|Firm size: 101-250 employees|-0.760<sup>***</sup>||
||(0.027)||
|Firm size: 251-1000 employees|-0.938<sup>***</sup>||
||(0.044)||
|Firm size: over 1000 employees|-1.288<sup>***</sup>||
||(0.044)||
|Log of capital stock|0.021<sup>***</sup>||
||(0.003)||
|Log of capital intensity||0.045<sup>***</sup>|
|||(0.003)|
|Lagged TFP growth|-0.305<sup>***</sup>|-0.293<sup>***</sup>|
||(0.010)|(0.010)|
|Firm effects|Yes|Yes|
|Year effects|Yes|Yes|
|No. of observations|60913|60913|
|Within R-squared|0.310|0.265|

16
Notes: All models include firm and year fixed effects. Standard errors clustered at the firm level are parentheses. ***, **, and * indicate significance at 1 percent, 5 percent, and 10 percent. The reference categories for the exporter, age, and firm size groups are “Non-exporters”, “Age: 1-5 years”, and “Firm size: 1-5 employees”, respectively.

Previous empirical literature – both theoretical and empirical –indicates that competition can drive greater productivity growth. It does this through: i) within-firm effects (where competition acts as a disciplining device, encouraging firms to become more efficient and to innovate), and ii) acrossfirm effects whereby competition ensures that more productive firms increase their market share at the expense of the less productive. Aghion et al (2008) provided the baseline study of the relationship between markups and productivity growth in South Africa through a study of publiclylisted companies. They find that higher markups have a large negative impact on productivity growth for publicly-listed firms in South African manufacturing industries.

Our results confirm that competition is positively related to productivity growth in South Africa. The results indicate that higher markups – and by extension lower competition – negatively impact productivity growth. The results are highly significant across model specifications, as presented in Table 3. Our results suggest that a 10 percent reduction in average markups in manufacturing industries is associated with a 0.1 percent increase in productivity growth. To put this in context, this productivity gain would have taken South Africa’s negative productivity growth for manufacturing industries into positive territory over the period between 2010 and 2014.

The results also suggest that South African exporters have lower productivity growth than nonexporters, particularly in the case of firms with over 25 percent of their sales coming from export. While this may appear contrary to the theoretical prediction of learning-by-exporting, it could be in line with the theory that more productive firms self-select into export – assuming that more productive firms have less room for productivity growth. It could also be the case that exporting firms have been able to compete despite their low productivity growth due to support from the South African government directed at export sectors.<sup>21</sup> Future work could explore these potential explanations further.

> 21 Between the years 2006 and 2012, foregone tax revenue from tax incentives amounted to around 1.3 percent of GDP on average (World Bank, 2016). And figures appear similar from 2013 to 2016 according to the 2018 Budget Review. The majority of these tax expenditures are directed towards the Automotive Production and Development Program (APDP) which is aimed at exporters, and towards the diesel refund scheme available for primary producers

17
There is a negative relationship between firm size, as measured by employment, and productivity growth and the negative effect becomes larger with increasing firm size. Firms with 6 to 10 employees have 15.2 percent lower productivity growth than the comparator category (firms with 5 employees or less), while firms with more than 1,000 employees have 60.9 percent lower productivity growth than the smallest firms. A test on the equality of the coefficients on firm size confirms that the estimated size effects are statistically different from each other, thus confirming the productivity differential between different firm size categories. This is in line with recent results from Aterido et al. (2019), who found that larger firms were less productive, but it is opposed to Kreuser and Newman (2018) who find larger firms to be more productive than smaller firms. Unlike these previous papers, our results have the advantage of overcoming a common criticism of productivity studies whereby revenue-based productivity measures can be confounded by differences in markups across firms. Our findings confirm that larger firms are less productive even after controlling for markups. While further research would be needed to uncover the reasons for this, a possible explanation could be found in industrial policy support and protections for larger but relatively less efficient firms.

# Employment and wages

South Africa’s unemployment rate in 2018 stood at roughly 27 percent.<sup>22</sup> Young people are particularly affected, with national estimates suggesting that youth unemployment stood at 53.4 percent in 2018.<sup>23</sup> As a result, employment creation is a key priority for South African policy makers.

Both theoretical models and empirical work tend to suggest that a lack of competition in product markets stifles employment in the long term and/or on aggregate. The basic intuition is that less

> on land and offshore (e.g. agriculture, mining and fishing). Furthermore, there are smaller incentive schemes targeted explicitly at exporting enterprises, e.g. Sector Specific Assistance Scheme (SSAS) Project Funding for Emerging Exporters or the Export Marketing and Investment Assistance (EMIA) scheme. In addition, there are numerous additional financial incentives available through various agencies that do not appear as tax expenditures (e.g. grants or subsidized loads). 22 National estimates suggest that unemployment stood at 26.92 percent in 2018, while the ILO estimates unemployment for 2018 to be at 26.96 percent. Source: World Development Indicators (WDI) Database (2019). 23 WDI Database (2018).

18
intense competition raises prices above marginal cost, reducing output demanded by consumers and, therefore, reduces labor demanded by producers.<sup>24</sup> An increase in prices also reduces real wages, which could theoretically reduce the supply of labor if the substitution effect outweighs the income effect or boost labor supply if the income effect outweighs the substitution effect. The impact of competition on wages has been relatively less examined than the impact on employment and productivity. Existing studies have focused on the effect on wage inequality rather than on wage levels or growth – and these have been mixed in the findings.<sup>25</sup> We are not aware of any literature in the South African context that has linked competition, as proxied by a behavioral indicator such as markup, to employment and wages.

# _Employment growth_

To test the relationship between competition and employment growth in South Africa we use the following specification: ∆lnl�� �α� �α� ln μ���� �Z��β�α�∆lnw�� �α� ln TFP���� �α� ∆ln l���� �δ� �γ� �ε�� [3] Where l�� is employment in firm i at time t. Z�� is a vector of control variables that includes export status (using the same categorizations explained in the section above), firm age, whether the firm is a new entrant, sales share, and log of lagged capital stock.<sup>26</sup> We also control for real wage growth. Since we do not observe true firm level wage rates, we impute the level of firm wages (w��) using data on the total wage bill divided by firm-level employment.

The findings reported in Table 4 suggest that firms that face lower competition (i.e. those that have higher pricing power) have lower employment growth – and this holds whether we control for productivity growth or not. A 10 percent reduction in average observed markups leads to a 0.12 percent increase in employment growth. While the impact on employment growth may seem small,

> 24 See for example Spector (2004) and Blanchard and Giavazzi (2003) for examples of theoretical work; and Griffith, Harrison, and Macartney (2007); Fiori et al. (2012); and Nicoletti and Scarpetta (2005) for examples of empirical work. World Bank – OECD (2017) also contains a review of the links between competition and employment outcomes.

> 25 See World Bank – OECD (2017) for a review on the links between competition and wages.

> 26 Alternative specifications replacing lagged productivity with contemporaneous productivity growth were also tested as robustness checks (not shown). Results were consistent with those shown here.

19
it represents more than a doubling of mean employment growth in manufacturing industries (of 0.07 percent) between 2010 and 2014.

As discussed above, measured markups have two dimensions: pricing power and efficiency. Either of these could be linked to employment outcomes. However, since we control for efficiency levels through the inclusion of lagged productivity in the regression, our results can be interpreted as reflecting the impact of firms’ pricing power.

_Table_ 4 _: Effects of weaker competition on employment growth in South Africa manufacturing industries_

||Model 1|Model 2|
|---|---|---|
|Log of lagged markup|-1.014<sup>***</sup>|-1.124<sup>***</sup>|
||(0.043)|(0.044)|
|Low-intensity exporters|0.016|-0.006|
||(0.010)|(0.009)|
|Medium-intensity exporters|0.030<sup>*</sup>|0.001|
||(0.016)|(0.014)|
|High-intensity exporters|0.051<sup>**</sup>|0.005|
||(0.025)|(0.021)|
|Sales share|3.312<sup>***</sup>|-0.561|
||(0.991)|(0.492)|
|Age: 6-10 years|-0.017|-0.029<sup>***</sup>|
||(0.011)|(0.009)|
|Age: 11-20 years|-0.013|-0.032<sup>**</sup>|
||(0.015)|(0.013)|
|Age: 21-30 years|-0.000|-0.014|
||(0.020)|(0.017)|
|Age: 31-40 years|-0.000|-0.016|
||(0.029)|(0.029)|
|Age: 41 years or more|0.024|0.037|
||(0.042)|(0.047)|
|Real wage growth|-0.170<sup>***</sup>|-0.137<sup>***</sup>|
||(0.009)|(0.007)|
|Log of lagged TFP|0.874<sup>***</sup>||
||(0.029)||
|Lagged employment growth|-0.147<sup>***</sup>|-0.165<sup>***</sup>|

20
||(0.007)|(0.006)|
|---|---|---|
|Log of lagged capital stock|-0.007<sup>**</sup>|-0.017<sup>***</sup>|
||(0.003)|(0.003)|
|TFP growth||-0.920<sup>***</sup>|
|||(0.026)|
|Firm effects|Yes|Yes|
|Year effects|Yes|Yes|
|No. of observations|60293|60293|
|Within R-squared|0.337|0.473|

Notes: All models include firm and year fixed effects. Standard errors clustered at the firm level are parentheses. ***, **, and * indicate significance at 1 percent, 5 percent, and 10 percent. The reference categories for the exporter and age groups are “Non-exporters” and “Age: 1-5 years”, respectively.

# _Wage growth_

To test the relationship between competition and wage growth we use the following specification: ∆w�� �α� �α� ln μ���� �Z��β�α� ln TFP���� �α�∆TFP���� �α�∆ln w���� �α�∆l�� �δ� � γ� �ε�� [4] where w�� is the average wage at firm i at time t. Z��is the vector of control variables for the growth regression that includes export status (using the same categorizations explained in the section above), firm age, as well as measures of factor usage by the firm.

Several factors can shape the relationship between competition and wages, including the stance of labor market regulations and the bargaining position of workers (which affects the distribution of rents) as well as the level of productivity. For example, firms that face lower competition may be less productive and therefore may pay lower wages. However, since we control for productivity in our model, we would not expect to pick up the effect of this channel. On the other hand, firms that face lower competition may earn higher margins which can then be shared with workers (depending on wage bargaining structures).

Our results suggest that firms that face lower competition (higher pricing power) have lower wage growth (Table 5). A 10 percent reduction in average observed markups leads to a 0.04 percent 21
increase in wage growth. This would have almost negated the decline in mean wage growth observed in South African manufacturing industries 2010 and 2014 (-0.05 percent).

At the same time, sales share is positively associated with wage growth. By contrast, greater firm size (as measured by employment levels) appears to be highly negatively related to wage growth. The larger is the current number of employees, the lower is the wage growth. This might indicate that larger firms are able to rely more on reputation, job security or other non-wage factors to attract workers.

_Table_ 5 _: Effects of weaker competition on wage growth in South Africa manufacturing industries_

||Model 1|Model 2|
|---|---|---|
|Log of lagged markup|-0.407<sup>***</sup>|-0.463<sup>***</sup>|
||(0.048)|(0.047)|
|Lagged TFP growth|0.418<sup>***</sup>|0.412<sup>***</sup>|
||(0.031)|(0.031)|
|Log of lagged TFP|-0.200<sup>***</sup>|-0.132<sup>***</sup>|
||(0.041)|(0.038)|
|Low-intensity exporters|0.023|0.021|
||(0.015)|(0.015)|
|Medium-intensity exporters|0.010|0.008|
||(0.027)|(0.027)|
|High-intensity exporters|0.053|0.053|
||(0.034)|(0.034)|
|Sales share|2.920<sup>***</sup>|2.881<sup>***</sup>|
||(0.854)|(0.849)|
|Age: 6-10 years|-0.016|-0.019|
||(0.019)|(0.019)|
|Age: 11-20 years|-0.009|-0.012|
||(0.025)|(0.025)|
|Age: 21-30 years|0.047|0.044|
||(0.036)|(0.036)|
|Age: 31-40 years|0.030|0.034|
||(0.057)|(0.057)|
|Age: 41 years or more|0.122|0.128|
||(0.090)|(0.089)|

22
|Firm size: 6-10 employees<br>Firm size: 11-20 employees|-0.112<sup>***</sup><br>(0.020)<br>-0.192<sup>***</sup><br>(0.027)||
|---|---|---|
|Firm size: 21-50 employees|-0.254<sup>***</sup><br>(0.035)||
|Firm size: 51-100 employees|-0.318<sup>***</sup>||
||(0.051)||
|Firm size: 101-250 employees|-0.365<sup>***</sup>||
||(0.066)||
|Firm size: 251-1000 employees|-0.471<sup>***</sup><br>(0.103)||
|Firm size: over 1000 employees|-1.103<sup>***</sup>||
||(0.107)||
|Log of capital stock|0.017<sup>***</sup>||
||(0.004)||
|Employment growth|-0.555<sup>***</sup>|-0.592<sup>***</sup>|
||(0.017)|(0.015)|
|Log of capital intensity||0.028<sup>***</sup>|
|||(0.004)|
|Lagged real wage growth|-0.438<sup>***</sup>|-0.436<sup>***</sup>|
||(0.015)|(0.015)|
|Firm effects|Yes|Yes|
|Year effects|Yes|Yes|
|No. of observations|60293|60293|
|Within R-squared|0.343|0.342|

Notes: All models include firm and year fixed effects. Standard errors clustered at the firm level are parentheses. ***,

**, and * indicate significance at 1 percent, 5 percent, and 10 percent. The reference categories for the exporter, age, and firm size groups are “Non-exporters”, “Age: 1-5 years”, and “Firm size: 1-5 employees”, respectively.

# 3.3. Industry-level entry rates

The last set of regressions presented in this section (Table 6) provides insights into the relationship between the level of competition in an industry and industry-level entry, i.e. the number of new firms entering specific industries.<sup>27</sup>

> 27 The entry variable definition is based on a firm’s first year of sales.

23
The results indicate that if industry-level competition is measured using average markups across all firms in an industry, there no evidence of a significant effect of average markups on entry in that industry. However, when weighting the average markup by sales or by value-added in order to better understand how the market power of firms with a more prominent market position affects industry-level entry, we find a significant negative relationship between markup and entry rate. The difference in the magnitude of effect between the unweighted and sales- or value-addedweighted averages suggests that entry might be dampened in the presence of firms with a larger market presence that also hold greater market power – perhaps because these firms have some ability to foreclose entry into their industries or that they benefit from policy- or natural barriers that hinder entry and protect their positions. It is worth noting that the relationship does not hold when weighting by employment, suggesting that it may be a firms’ market position in terms of sales that may be more relevant for entry dynamics than its position in terms of employment.

_Table 6: Relationship between competition and industry-level entry rates_

||Model 1|Model 2|Model 3|Model 4|
|---|---|---|---|---|
|Lagged average markup|-0.004||||
||(0.009)||||
|Lagged sales-weighted markup||-0.054<sup>**</sup><br>(0.023)|||
|Lagged VA-weighted markup|||-0.022<sup>***</sup>||
||||(0.004)||
|Lagged employment-weighted markup||||-0.009|
|||||(0.021)|
|Average age of firms|-0.006|-0.006|-0.007|-0.006|
||(0.012)|(0.011)|(0.011)|(0.012)|
|Log of market-level total employment|0.063|0.080|0.079|0.063|
||(0.104)|(0.102)|(0.102)|(0.102)|
|Average capital intensity|-0.000|-0.000|-0.000|-0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Log of average R&D expenditure|-0.000|-0.000|-0.000|-0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Sector effects|Yes|Yes|Yes|Yes|
|Year effects|Yes|Yes|Yes|Yes|
|No. of observations|240|240|240|240|

24
Within R-squared 0.022 0.030 0.034 0.022 Notes: The dependent variable is the productivity dispersion of the sample (as measured by the standard deviation of log TFP). All models include industry and year fixed effects. Standard errors clustered at the industry level are in parentheses. ***, **, and * indicate significance at 1 percent, 5 percent, and 10 percent.

# 4. Conclusion

The degree of concentration and competition in South African markets and the extent to which firms enjoy relatively high markups have been topics of much policy discussion. However, there has been little evidence to date on what drives markups and market power, and the impact that the degree of competition in South African markets may have on economic outcomes. In this paper we estimate markups for South Africa’s manufacturing sector using a methodology developed by De Loeker and Warzynski (2012). We use these estimated markups first to examine the determinants of markups and market power, and second to identify the impact of product market competition (using markups and controlling for efficiency levels) on economic outcomes.

Overall, average industry markups appear to have risen between 2010 and 2014 (Figure 1). For the mean industry markup, this pattern is more noticeable when firm markups are weighted either by sales or employment, suggesting that the rise is driven disproportionately by larger firms. Median markups within an industry have risen more rapidly than mean industry markups, which could suggest that the overall rise in markups has come disproportionately from firms within the middle of the industry distribution of markups (rather than very high or very low markup firms).

We find that firms with a higher share of sales in their industry and larger firms (in terms of employees) charge higher markups and appear to have higher pricing power as one might expect. Meanwhile, firms with relatively high export intensity (over 25 percent of sales) charge lower markups but have higher pricing power.

In terms of the second objective and in the context of South Africa’s poor economic performance over the past decade, this paper provides evidence of the impact that low levels of competition have had on firm performance. Our key findings suggest that firms that face lower competition (i.e. have greater pricing power) have lower productivity growth. After controlling for markups, we also find that small firms tend to show higher productivity growth than large firms. One hypothesis for this could be that industrial policies may have frequently targeted larger firms, thus 25
leading to protections or market distortions that have dampened incentives for productivity gains among these firms and hindered their exit from the market.

Another important finding, which specifically addresses South Africa’s persistently high unemployment rate, is that firms that face lower levels of competition have lower employment growth. This points to the output expansion effect of greater competition, which would imply the expansion of employment opportunities as competition is boosted. We also find that firms that face greater competition display greater wage growth, even when controlling for productivity - potentially because such firms need to increase their wages to attract more employees.

The industry-level analysis suggests that industries that have higher sales-weighted or valueadded-weighted average markups have lower rates of entry, indicating that the presence of larger firms with market power may dampen entry.

The findings of this paper highlight the importance of implementing sound pro-competition regulations and government interventions for productivity growth and employment. In addition to continuing to enforce the competition law, ensuring that sector policies and industrial policies are designed to promote contestability and a level playing field within manufacturing markets will be key. In particular, the results suggest that the economic benefits from such policies are significant and that outcomes such as promoting employment growth and wage growth can be boosted through pro-competition reforms.

This study lends itself to further research. A key question for additional work would be to understand the policy drivers of the degree of competition, including the effects of industrial policy design on market power and market dynamics or those of past sectoral reforms or competition law enforcement on the degree of competition in markets.<sup>28</sup> For example, for some of the patterns we observe with respect to the productivity of exporters and larger firms, further research would be beneficial to understand their drivers. It would also be interesting to further understand the effects of participation in Global Value Chains and exporting intensity on the degree of competition faced by firms and the extent to which that affects pricing behavior.

> 28 For an overview of competition enforcement in South Africa, see Purfield et al, 2016.

26