# Minimum Wage Shocks, Firms and Employment: Evidence from Africa

Rémi Bazillier, María Moraga-Fernández

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UMR 8174

Minimum Wage Shocks, Firms and Employment:
Evidence from Africa

Rémi BAZILLIER, María MORAGA-FERNÁNDEZ

2025.06
# Minimum Wage Shocks, Firms and Employment: Evidence from Africa*

Rémi Bazillier†, María Moraga-Fernández ‡§

March 13, 2025

## Abstract

Despite the widespread use of minimum wage policies to combat poverty in Africa, research on their effects remains limited. This paper addresses this gap by examining the economic impact of minimum wage shocks on firms across the African continent using firm-level data from the World Bank *Enterprise Surveys*. We document that all but four African countries have a minimum wage system. Between 2002 and 2019, we identify 13 shocks—defined as a 30% real increase—in 11 countries. Employing a difference-in-differences approach, we estimate the causal effects of these shocks on labor costs, employment, sales, and productivity. To address potential endogeneity, we instrument minimum wage changes using social protest intensity. We find that minimum wage shocks significantly increase labor costs per worker, suggesting at least a partial enforcement of regulations. However, we detect no robust impact on employment. Instead, firms partly offset higher labor costs through increased sales or productivity. We identify three complementary mechanisms: (1) a cleansing effect, where less productive firms exit; (2) capital investment as an adjustment channel; and (3) a local demand boost, with stronger sales effects in large markets.

**Keywords:** Minimum Wages, Africa, Employment, Firms, Labor regulations

<footnote>*Acknowledgement: This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 956909. We are grateful to Olivier Bargain, Haroon Bhorat, Anthony Black, Clément Bosquet, David Castells Quintana, Lawrence Edwards, Jérôme Gautié, Lukasz Grzybowski, Etienne le Rossignol, Laurine Martinoty, Michael Smith, Josselin Thuilliez and seminar participants at: University of Cape Town, Universitat Autònoma de Barcelona, Bordeaux School of Economics, Ruhr Universität Bochum, Cairo University and Paris 1 - CES, for valuable comments and suggestions. We thank Antoine Castet and Lina Taleb for excellent research assistance; Philipp Schuster, Robert Reinhardt and Pierre Chadefaux for their assistance with GIS data. Finally, acknowledgement is extended to Wilhelm Löwenstein for his continuous insights. Any errors or omissions are solely the responsibility of the authors.</footnote>

<footnote>†Université Paris 1 Panthéon-Sorbonne, CNRS, IRD, Centre d'Économie de la Sorbonne, Paris, France; Email: remi.bazillier@univ-paris1.fr</footnote>

<footnote>‡Université Paris 1 Panthéon-Sorbonne, CNRS, IRD, Centre d'Économie de la Sorbonne, Paris, France.</footnote>

<footnote>§Ruhr University Bochum, Institute of Development Research and Development Policy</footnote>
# 1 Introduction

Minimum wage policies remain among the most debated labor market interventions. Since Stigler (1946), economists have yet to reach a consensus on their employment effects. The well-known debate between Card & Krueger (1994, 2000) and Neumark & Wascher (2000) illustrates this divide, which persists in recent studies across the US (Neumark et al., 2007; Dube et al., 2010; Clemens & Wither, 2019; Cengiz et al., 2019; Neumark & Shirley, 2022), the UK (Machin & Manning, 1994; Stewart, 2004; Manning, 2021), and Germany (Dustmann et al., 2022). Beyond developed economies, research has focused exclusively on middle-income countries. The most recent review (Neumark & Corella, 2021) identified 61 studies on developing countries—all in middle-income economies and only three in Africa, all from South Africa.<fnref n="1" /> Yet, minimum wage policies are also widespread in low-income economies, particularly in Africa (Bhorat et al., 2017; International Labour Organization, 2024). This paper fills this gap by systematically analyzing minimum wage shocks in Africa using firm-level data from a large sample of predominantly low- and lower-middle-income countries.<fnref n="2" />

The limited research on minimum wage policies in Africa<fnref n="3" /> — and more broadly in low-income economies—may stem from economists’ skepticism about their enforcement in contexts where

<footnote n="1">¹Other reviews on minimum wage effects in developing countries include Neumark et al. (2007), Bhorat et al. (2017), and Broecke et al. (2017). Neumark et al. (2007) review 15 studies across eight developing countries, all classified as upper-middle or high income, with none from Africa. Bhorat et al. (2017) extend this review to low- and middle-income countries, adding 17 studies, including only two African cases (South Africa and Ghana). They find that 55% of employment elasticities are insignificant, with a median elasticity of -0.08 and a mean of -0.11. Broecke et al. (2017) highlight minimal employment effects and “a reporting bias towards statistically significant negative results” (p. 366). Neumark & Shirley (2022) estimate an average employment elasticity of -0.061 (-0.102 with their preferred specification) but find stronger negative effects in formal sectors, particularly where the minimum wage is more binding. Again, no study focuses on low-income countries.</footnote>

<footnote n="2">²Our sample includes 20 low-income countries, 19 lower-middle-income countries, and only four upper-middle-income countries.</footnote>

<footnote n="3">³In Africa, most evidence is limited to South Africa. Dinkelman & Ranchhod (2012) found no employment effects for domestic workers despite wage increases. Bhorat et al. (2014) reported job losses in agriculture, while Bhorat et al. (2013) found no impact in retail, domestic work, taxis, security, or forestry. The 2019 national minimum wage showed no early employment effects (Bhorat et al., 2021). Outside South Africa, studies are rare, with only two on Kenya (Andalon & Pagés, 2008) and Ghana (Jones, 1998).</footnote>
wage earners are a minority and informality is widespread. Yet, analyzing these policies remains essential for several reasons. First, minimum wages primarily affect the formal and urban sectors, which are key drivers of structural transformation (McMillan et al., 2014). A dynamic modern sector is a prerequisite for structural change, making it crucial to assess the impact of policies targeting this segment. Moreover, wage employment and urban labor shares are rising in most developing countries, particularly in Africa, broadening the reach of such policies (Bhorat et al., 2017). Second, minimum wage policies can generate spillover effects in informal labor markets. Informal wages may rise due to the lighthouse effect (Boeri et al., 2011; Hallward-Driemeier et al., 2017; Gindling & Terrell, 2005; Comola & De Mello, 2011; Katzkowicz et al., 2021), but they may also decline if a contraction in formal employment increases the supply of informal labor. The seminal two-sector model of Harris & Todaro (1970) links urban minimum wages to rural-urban migration and urban unemployment. In the 1980s and 1990s, the World Bank reinforced concerns that such policies “depress labor incomes where most of the poor are found” (World Bank, 1990, p. 63), particularly in informal sectors. However, this adverse effect on the informal economy depends on whether formal employment is significantly affected—an empirical question that remains open. Lastly, in Africa, where collective bargaining is weak, trade unions have limited channels of influence and often advocate for government-mandated minimum wage increases. This explains why such policies are increasingly implemented across the continent (Bhorat et al., 2017; International Labour Organization, 2024). Understanding their economic effects is therefore essential.

This study provides the first robust causal evidence at the continental level by leveraging a large panel of micro-level firm data. We analyze the effects of minimum wage shocks on labor costs, employment, sales, and productivity in Africa. Specifically, we exploit the richness of the World Bank *Enterprise Surveys* (The World Bank, 2021), which cover a broad set of 43 African countries across 96 survey waves. These surveys aim to construct representative samples
of the private, non-agricultural sector in each country. Taking advantage of the repeated survey
structure, we estimate the causal effects of minimum wage shocks on firm outcomes using a
difference-in-differences approach with a large panel of firms.

Our analysis focuses on the impact of minimum wage shocks rather than the actual levels.
This approach allows us to capture discretionary policy changes that are more plausibly exoge-
nous, rather than gradual adjustments that may be endogenous to economic conditions. To
further mitigate endogeneity concerns, we complement our difference-in-differences strategy with
two additional approaches. First, we employ a “neighboring country” strategy, restricting the
control group to countries within the same sub-region. Second, we implement an instrumental
variable approach, using the intensity of social protests as an instrument. The logic is that most
minimum wage increases that we assess were decided after large social protests which were not
directly explained by firms’ performance, but rather related to broader concerns about living
standards and inflation, which we control for.

Our first contribution is to document minimum wage trends in Africa using ILO data and
other sources, identifying 13 shocks—defined as a 30% real increase—across 11 countries between
2002 and 2019. We also examine the various minimum wage systems in Africa, highlighting a
key fact: all but four countries have some form of minimum wage.<fnref n="4" /> Additionally, we document
each minimum wage shock and analyze the role of social protests in driving these policy changes.

Secondly, our study contributes to the literature by demonstrating that minimum wage shocks
have substantial effects on (formal) firms, even in low-income economies. Specifically, we find a
robust increase in labor costs per worker, suggesting at least partial enforcement of minimum
wage regulations. However, we do not find robust effects on employment. Instead, firms appear
to offset higher labor costs, at least in part, through increased sales or productivity. The effects

<footnote n="4">⁴The exceptions are Djibouti, Somalia, South Sudan, and Zimbabwe. Djibouti previously had a minimum wage, but it was abolished in 1997 and formally removed with the 2006 labor code. Efforts to reintroduce it emerged in 2019, but enforcement has yet to follow. In Zimbabwe, there is no statutory minimum wage; instead, wages are set through other mechanisms.</footnote>
are much stronger for panel firms, which are positively selected, pointing to a potential cleansing
effect of minimum wages.

How can employment levels remain unchanged despite a sharp increase in labor costs? We provide evidence supporting three complementary explanations for this apparent paradox. First, firms are more likely to exit our database following a minimum wage shock, consistent with a cleansing effect (Mayneris et al., 2018). Second, some firms adjust by increasing capital investments. Third, we explore the positive effects on sales. In other contexts, Magruder (2013) shows that minimum wage increases can lead to a high-equilibrium outcome in a big push setting, boosting formal employment through higher local sales - in Indonesia. Similarly, in China, Dautović et al. (2024) find that minimum wage hikes raise consumption levels among low-income households. In our case, we find stronger effects on sales in larger local markets, suggesting a positive local demand effect. To investigate this, we geolocate a significant share of firms in our sample and construct a proxy for local market size using average nighttime light intensity at the urban cluster level. Our results indicate that the effects are most pronounced for firms operating in wealthier urban clusters.

The paper is structured as follows. Section 2 documents minimum wage policies in Africa.
Section 3 presents the data and empirical strategy. Section 4 reports baseline and IV results,
and Section 5 discusses transmission mechanisms. Section 6 concludes.

2 Minimum Wages in Africa

We construct a comprehensive minimum wage database for Africa by systematically integrating
diverse quantitative and qualitative data sources. Our analysis covers **minimum wage systems**
in 51 African countries, building on the classification proposed by Bhorat et al. (2017), which
distinguishes three main categories: *national, sectoral/occupational*, and *hybrid* (a combination
of the first two). We refine and update this classification to reflect recent developments. Figure 1
provides an overview of minimum wage systems across the continent.<fnref n="5" /> An overwhelming majority of these countries have implemented at least one minimum wage determination, with only four countries lacking some sort of minimum wage system. Additionally, 14 countries have adopted a *national* minimum wage policy.

We compile an annual database of **minimum wage levels** covering 45 African countries from 1998 to 2020. Our primary data source is the ILOSTAT Statistics on Wages database (International Labour Organization, 2021), which provides extensive global coverage of minimum wage information.<fnref n="6" /> We use the statutory nominal gross monthly minimum wage, reported annually, which represents the minimum earnings applicable to employees as of December 31 each year. In countries without a nationally mandated minimum wage, we report the prevailing minimum wage in the capital or major city. Alternatively, where multiple regional minimum wages exist, we use an average (e.g., Kenya). For countries with sectoral or occupational minimum wages, we report the rate applied to manufacturing or unskilled workers, following ILOSTAT's methodology.<fnref n="7" />

When data for a specific country and year was missing, we systematically cross-checked with national legislation to fill these gaps. In cases where national legislation was not publicly available or accessible,<fnref n="8" /> we relied on alternative sources such as the annual Human Rights Reports by the US Bureau of Democracy, Human Rights, and Labor, and, in some cases, national media. The latter proved particularly useful in verifying whether a minimum wage adjustment had indeed occurred. Appendix A.3 provides qualitative information on minimum wage-setting mechanisms and minimum wage shocks for each country, along with a detailed list of sources used to update minimum wage data.

<footnote n="5">⁵See Table A.1 in Appendix A for the full list of countries and their respective minimum wage systems.</footnote>

<footnote n="6">⁶See https://ilostat.ilo.org/topics/wages/.</footnote>

<footnote n="7">⁷See ILOSTAT's Statistics on Wages database for details.</footnote>

<footnote n="8">⁸We consulted the ILO's Working Conditions Laws Database and the Global WageIndicator Minimum Wage database, both of which typically provide access to national legislation.</footnote>
To ensure comparability, we converted the statutory nominal gross monthly wage into con-
stant 2015 USD to get statistics on *real* minimum wages. For the sake of the descriptive exercise,
we also converted the nominal minimum wage into current international dollars (PPP).

<figure><img src="image_2.png" /><figcaption>Figure 1: Minimum Wage Systems in Africa</figcaption></figure>

Source: Bhorat et al. (2017) completed by the authors using various sources (International Labour Organization, 2011; WageIndicator, 2021)

We document a strong positive correlation between minimum wage levels and income per
capita (Figure A.1). In current international dollars (PPP), the minimum wage is approximately
150$ in low-income countries, 250$ in lower-middle-income countries and 550$ in upper-middle-
income countries (Figure A.3). Interestingly, these levels appear to be lower than in non-African
countries belonging to the same income group (Bhorat et al., 2017). We also observe regional
heterogeneity with higher minimum wage levels in Northen Africa and lower in Eastern Africa
(Figure A.2).

To further explore the minimum wage relative magnitude, we calculated an “adjusted” Kaitz index, defined as the ratio between the minimum wage and median or average labor costs per worker.<fnref n="9" /> This ratio is used as an indicator of adequacy. An “adequate” minimum wage is considered to be close to 60% of the median wage or 50% of the average wage (Dingeldey et al., 2021, p. 11).<fnref n="10" /> Figure A.4 displays the statistics for our sample. In many countries, the minimum wage is below the threshold defining an “adequate” minimum wage: 29 countries out of 43¹¹ have a minimum-to-average wage ratio below 0.5, while 23 have a minimum-to-median wage ratio below 0.6. On the contrary, minimum wages in 9 and 15 countries can be considered as “too high”, considering the mean and the median, respectively.

Also making use of World Bank *Enterprise Surveys* we next check whether firms were faced with **minimum wage shocks**, defined as an increase in the real value of the minimum wage - defined at the national level - larger than 30%. In order to do so, we use the real values of minimum wages in local currency. We, therefore, focus on cases where minimum wage increases exceeded significantly price increases. Smaller changes that are much more likely endogenous to local economic conditions and price fluctuations are neglected. The goal is to isolate discretionary policy decisions that will most likely require significant adjustments by firms.

In doing so, we identify 13 shocks between 2007 and 2019, 10 of which we can use for the

<footnote n="9">⁹The Kaitz index refers to the ratio between the minimum wage and the median wage, but median wages are usually challenging to obtain for most African countries. Even using statistics on average wages (available from ILOSTAT) renders a limited sample of 25 countries maximum. To address this limitation, we suggest employing labor costs per worker as a suitable proxy for wages. These data are extracted from the World Bank *Enterprise Surveys*, enabling us to construct an “adjusted Kaitz ratio” for 43 African countries, out of which 6 have no minimum wage for the purpose of this study (the 4 mentioned previously, plus Ethiopia, where the minimum wage applies only to the public sector, and Namibia where the minimum wage only applies to the agricultural sector, the domestic workers and construction sector. Only 90 observations are found in our data for construction in such country).</footnote>

<footnote n="10">¹⁰In countries where a larger share of the population has very low wages or works in the informal sector, minimum-to-average wage ratio is considered a better proxy of minimum wage adequacy (Dingeldey et al., 2021, p.12).</footnote>

<footnote n="11">¹¹Of which only 38 have a minimum wage for the purpose of this assessment.</footnote>
empirical analysis.<fnref n="12" /> Table 1 shows those shocks as well as the corresponding nominal and
real increase in the minimum wage. Although we retain a 30% threshold to define a shock, it
should be noticed that shocks are on average much larger (from 40% in Zambia up to 1,903% in
Sierra Leone). The average real increase is 222% and 82.95%, with and without Sierra Leone,
respectively. Appendix A.3 provides extensive qualitative evidence describing those shocks as well
as the minimum wage setting and legislation in the corresponding countries. A common pattern
that emerges is that the decision to increase the minimum wage generally follows important social
protests. In all instances but Gabon, we observe significant demonstrations and strikes either
the year of the minimum wage shock or one or two years before. As pointed out in Bhorat et al.
(2017), collective bargaining is generally weaker in Africa and the proportion of large firms is
also low. Consequently, trade unions turn to the state rather than to companies to help improve
wage conditions for workers. Urging the government to raise the minimum wage replaces wage
negotiations. This is an important characteristic we propose to exploit for our identification
strategy.

## 3 Data

The main goal of this study is to assess the effects of minimum wage shocks on firms’ out-
comes. For that purpose, we exploit the richness of firm-level data provided by the **World
Bank Enterprise Surveys** data (The World Bank, 2021). These data have been used ex-
tensively in the literature in a cross-country framework to assess the effects of various topics
including, among others, corruption, regulations and institutions (Svensson, 2003; Fisman &
Svensson, 2007; Kouamé & Tapsoba, 2019; Chemin, 2020; Amin & Soh, 2021), wages and labor
management (Fafchamps & Söderbom, 2006), trade and productivity (Van Biesebroeck, 2005),

<footnote n="12">¹²For some shocks we lack firm-level data before, i.e. there is no *Enterprise Survey* for that country in the previous years. Thus this study focuses on shocks happening between two available waves.</footnote>
<table><caption>Table 1: Minimum Wage Shocks</caption><thead><tr><th>Country</th><th>Data Year</th><th>Shock Year</th><th>MW (monthly, 2015 USD)</th><th>Nominal MW Increase (%)</th><th>Real MW Increase (%)</th><th>Kaitz Ratio</th><th>MW System</th></tr></thead><tbody><tr><td>Congo, Dem. Rep.</td><td>2009</td><td>2008</td><td>59.40</td><td>234.33</td><td>177.53</td><td>0.25</td><td>Sector./ Occupat.</td></tr><tr><td>Cote d'Ivoire</td><td>2015</td><td>2013</td><td>101.49</td><td>63.90</td><td>58.04</td><td>0.26</td><td>National</td></tr><tr><td>Egypt, Arab Rep.</td><td>2012</td><td>2011</td><td>120.93</td><td>75.00</td><td>56.72</td><td>0.49</td><td>National</td></tr><tr><td>Egypt, Arab Rep.</td><td>2015</td><td>2014</td><td>156.02</td><td>71.43</td><td>54.10</td><td>0.56</td><td>National</td></tr><tr><td>Gabon*</td><td>2007</td><td>2006</td><td>189.08</td><td>81.82</td><td>65.49</td><td>0.33</td><td>National</td></tr><tr><td>Liberia</td><td>2016</td><td>2015</td><td>139.83</td><td>171.73</td><td>168.91</td><td>1.73</td><td>Sector./ Occupat.</td></tr><tr><td>Malawi</td><td>2013</td><td>2012</td><td>23.26</td><td>77.82</td><td>51.14</td><td>0.17</td><td>Sector./ Occupat.</td></tr><tr><td>Niger*</td><td>2008</td><td>2007</td><td>70.53</td><td>41.74</td><td>32.42</td><td>0.18</td><td>National</td></tr><tr><td>Nigeria*</td><td>2013</td><td>2011</td><td>117.65</td><td>227.27</td><td>198.12</td><td>1.37</td><td>National</td></tr><tr><td>Sierra Leone</td><td>2016</td><td>2015</td><td>78.66</td><td>2280.95</td><td>1903.08</td><td>1.56</td><td>National</td></tr><tr><td>Tanzania</td><td>2012</td><td>2010</td><td>53.25</td><td>66.67</td><td>52.31</td><td>0.30</td><td>Sector./ Occupat.</td></tr><tr><td>Zambia</td><td>2012</td><td>2011</td><td>142.28</td><td>56.72</td><td>41.04</td><td>0.88</td><td>Sector./ Occupat.</td></tr><tr><td>Zambia</td><td>2019</td><td>2018</td><td>75.91</td><td>50.00</td><td>39.65</td><td>0.27</td><td>Sector./ Occupat.</td></tr><tr><td>Average</td><td></td><td></td><td>102.18</td><td>269.18</td><td>222.96</td><td>0.64</td><td></td></tr><tr><td>Average w/o Sierra Leone</td><td></td><td></td><td>104.14</td><td>101.54</td><td>82.95</td><td>0.57</td><td></td></tr></tbody></table>

Source: The World Bank (2021); International Labour Organization (2021, 2011); WageIndicator (2021) and authors' calculations.

Note: * denotes a shock which is not included in the empirical analysis due to the lack of an *Enterprise Survey* wave after or before the shock or missing information for some variables.

technology and innovation (Hjort & Poulsen, 2019), access to credit and finance (Asiedu *et al.*, 2013), aid and growth (Chauvet & Ehrhart, 2018) or gender (Alesina *et al.*, 2013).

The World Bank conducted these firm-level surveys in various countries and multiple years in order to get "a representative sample of an economy's private sector", specifically non-agricultural firms. We merged 96 waves from 43 countries which rendered a sample of 33,675 firms surveyed between 2001 and 2019.<fnref n="13" /> Out of this sample, 8,317 observations correspond to panel firms,

<footnote n="13">¹³We originally compiled information for 115 waves in 49 countries in Africa. The lack of data for the variables that we are interested in (e.g. Djibouti lacks macroeconomic indicators before 2013) or a large number of outlying (e.g. Egypt 2007) or deceptive values (e.g. Nigeria's monetary variables in 2007 and 2009 waves) were the reasons why we had to exclude some waves and in some cases some countries.</footnote>
<table><caption>Table 2: Descriptive statistics</caption><thead><tr><th></th><th>Mean</th><th>SD</th><th>Min</th><th>Max</th><th>N</th></tr></thead><tbody><tr><td>Sales, const. 2015 USD (log)</td><td>12.405</td><td>2.189</td><td>5.851</td><td>18.855</td><td>33,675</td></tr><tr><td>Sales, const. 2015 USD (k)</td><td>2,281.374</td><td>8,061.358</td><td>0.347</td><td>154,391.969</td><td>33,675</td></tr><tr><td>Permanent Employment (log)</td><td>3.027</td><td>1.214</td><td>0.000</td><td>6.422</td><td>33,675</td></tr><tr><td>Permanent Employment</td><td>48.515</td><td>84.703</td><td>1.000</td><td>615.000</td><td>33,675</td></tr><tr><td>Labour Cost p/w, const. 2015 USD (log)</td><td>7.399</td><td>1.276</td><td>3.725</td><td>10.830</td><td>33,675</td></tr><tr><td>Labour Cost p/w, const. 2015 USD (k)</td><td>3.434</td><td>5.306</td><td>0.041</td><td>50.531</td><td>33,675</td></tr><tr><td>Sales p/w, const. 2015 USD (log)</td><td>9.378</td><td>1.559</td><td>4.824</td><td>13.695</td><td>33,675</td></tr><tr><td>Sales p/w, const. 2015 USD (k)</td><td>37.226</td><td>77.919</td><td>0.124</td><td>886.310</td><td>33,675</td></tr><tr><td>Minimum Wage Shock 30% (t-1 or t-2)</td><td>0.174</td><td>0.379</td><td>0.000</td><td>1.000</td><td>33,675</td></tr><tr><td>Foreign Ownership</td><td>0.137</td><td>0.344</td><td>0.000</td><td>1.000</td><td>33,675</td></tr><tr><td>State Ownership</td><td>0.017</td><td>0.129</td><td>0.000</td><td>1.000</td><td>33,675</td></tr><tr><td>Exporting Firm</td><td>0.126</td><td>0.332</td><td>0.000</td><td>1.000</td><td>33,675</td></tr><tr><td>Size (small, medium and large)</td><td>1.624</td><td>0.728</td><td>1.000</td><td>3.000</td><td>33,675</td></tr><tr><td>Competition Informal Firms</td><td>0.579</td><td>0.494</td><td>0.000</td><td>1.000</td><td>26,859</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors.

meaning those which are surveyed at least in two waves. With regard to the main outcome variables, we look into the effects of minimum wage shocks on labor costs per worker, permanent employment, sales and productivity, measured as sales per worker. As for the controls, we include firms' characteristics like size, ownership, exporter status and competition from the informal sector. Table 2 provides descriptive statistics for our firm-level variables. With the current definition of minimum wage shocks (higher than 30% in real terms), 17.4% of our sample is considered as treated (i.e. affected by a minimum wage shock).

Both the full sample and the panel sample will be used in the empirical exercise. The panel sample will allow the use of firm fixed effects to focus on within firms' variations and to control for any time-invariant characteristics of firms. Nevertheless, we should be aware that this sample of firms (which are surveyed at least twice) is different from the overall sample. Table 3 shows the respective statistics for panel and non-panel firms which differ in almost every dimension. Panel firms have higher levels of sales, number of employees and labor costs per worker (and thus, wages). It should be highlighted also that the share of firms affected by a minimum wage
<table><caption>Table 3: Descriptive Statistics: Non-panel vs. Panel firms</caption><thead><tr><th rowspan="2"></th><th colspan="2">Non-panel firms</th><th colspan="2">Panel firms</th><th colspan="2">T-test</th></tr><tr><th>Mean</th><th># Obs.</th><th>Mean</th><th># Obs.</th><th>Difference</th><th>p-value</th></tr></thead><tbody><tr><td>Sales, const. 2015 USD (log)</td><td>12.324</td><td>25,358</td><td>12.655</td><td>8,317</td><td>-0.331</td><td>0.000***</td></tr><tr><td>Sales, const. 2015 USD (k)</td><td>2,153.060</td><td>25,358</td><td>2,672.593</td><td>8,317</td><td>-519.533</td><td>0.000***</td></tr><tr><td>Permanent Employment (log)</td><td>2.994</td><td>25,358</td><td>3.129</td><td>8,317</td><td>-0.135</td><td>0.000***</td></tr><tr><td>Permanent Employment</td><td>46.347</td><td>25,358</td><td>55.126</td><td>8,317</td><td>-8.779</td><td>0.000***</td></tr><tr><td>Labour Cost p/w, const. 2015 USD (log)</td><td>7.376</td><td>25,358</td><td>7.468</td><td>8,317</td><td>-0.092</td><td>0.000***</td></tr><tr><td>Labour Cost p/w, const. 2015 USD (k)</td><td>3.394</td><td>25,358</td><td>3.556</td><td>8,317</td><td>-0.162</td><td>0.016*</td></tr><tr><td>Sales p/w, const. 2015 USD (log)</td><td>9.330</td><td>25,358</td><td>9.525</td><td>8,317</td><td>-0.196</td><td>0.000***</td></tr><tr><td>Sales p/w, const. 2015 USD (k)</td><td>36.105</td><td>25,358</td><td>40.642</td><td>8,317</td><td>-4.536</td><td>0.000***</td></tr><tr><td>MW Shock +30%</td><td>0.140</td><td>25,358</td><td>0.270</td><td>8,317</td><td>-0.129</td><td>0.000***</td></tr><tr><td>Foreign Ownership</td><td>0.138</td><td>25,358</td><td>0.134</td><td>8,317</td><td>0.005</td><td>0.266</td></tr><tr><td>State Ownership</td><td>0.018</td><td>25,358</td><td>0.013</td><td>8,317</td><td>0.005</td><td>0.002**</td></tr><tr><td>Exporting Firm</td><td>0.126</td><td>25,358</td><td>0.127</td><td>8,317</td><td>-0.002</td><td>0.716</td></tr><tr><td>Size (small, medium and large)</td><td>1.603</td><td>25,358</td><td>1.689</td><td>8,317</td><td>-0.086</td><td>0.000***</td></tr><tr><td>Competition Informal Firms</td><td>0.572</td><td>19,824</td><td>0.596</td><td>7,035</td><td>-0.024</td><td>0.000***</td></tr><tr><td>Observations</td><td colspan="2">33675</td><td colspan="2"></td><td colspan="2"></td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: † p<0.1 * p<0.05 ** p<0.01 *** p<0.001

shock is also higher for panel firms than non-panel firms (27% vs. 14%).<fnref n="14" />

Further controls at the country level come from the **World Bank Development Indicators** (The World Bank, 2022a) and include GDP per capita (in $t-2$), GDP growth (in $t-2$), Population (in $t$), Inflation (in $t$). Rule of Law averaged between $t-2$ to $t$ is also controlled for (The World Bank, 2022b). We also add a violence indicator built with updated information from ACLED (Raleigh *et al.*, 2010).<fnref n="15" /> All monetary variables (both at the firm and the country level) are converted into (constant) 2015 USD.

Our final sample includes firm-level information from 43 and 27 countries for the full and panel sample, respectively (see figure B.1).

<footnote n="14">¹⁴This large difference is mainly due to the fact that countries affected by a shock happen to be also countries where panel data are available, unlike other countries where panel data are non-existing.</footnote>

<footnote n="15">¹⁵The Armed Conflict Location & Event Data Project (hereafter, ACLED data) is an event-based dataset that provides detailed information on "political violence, demonstrations, and select related non-violent developments around the world". The violence indicator is the result of re-scaling the total number of violent events so that the minimum takes a value of 0 and the maximum takes a value of 10. This is attained by subtracting the minimum value of the series from each value, then dividing it by the difference between the maximum and the minimum, and multiplying it by 10.</footnote>
# 4 Empirical Strategy

## 4.1 Baseline estimates

In order to assess the effects of minimum wage shocks on firms outcome, we propose to estimate the following equation:

$$Y_{i(c,s),t} = \beta_1 MW \ Shock_{c,t-1,t-2} + X_{i(c,s),t}\beta_2 + W_{c,t}\beta_3 + W_{c,t-2}\beta_4 + \lambda_c + \mu_{s,t} + \epsilon_{i,c,s,t} \quad (1)$$

where $i$ is a firm (in country $c$ from sector $s$) in time $t$. The dependent variable $Y_{i(c,s),t}$ is, alternatively, labor costs per worker, the number of permanent employees, total sales, or sales per worker. $MW \ Shock_{c,t-1,t-2}$ represents our minimum wage shock variable, defined as a real minimum wage increase exceeding 30%. If a shock occurs in $t-1$ or $t-2$, the variable takes a value of 1 in $t$ and all subsequent years. If an additional shock occurs in a later period, the variable increments to 2.16 $X_{i(c,s),t}$ are firm-level controls including foreign and state ownership17, whether the firm is an exporting one and the size (when estimating labor costs per worker and sales per worker). On the other hand, $W_{c,t}$ are country controls in $t$ (including the population in logarithm, inflation in %, a measure of rule of law18 and the violence indicator) and $W_{c,t-2}$ in $t-2$ (including GDP per capita and growth).19

Equation 1 includes country fixed effects ($\lambda_c$) and sector-year fixed effects ($\mu_{s,t}$). The identification relies on a quasi-difference-in-difference framework. We compare the within-country changes in the (country) average outcomes between countries affected by a minimum wage shock

<footnote>16This specification ensures that the coefficient on $MW \ Shock_{c,t-1,t-2}$ captures transitions from 0 to 1 and from 1 to 2 (i.e., the effect of experiencing a new shock) but does not capture transitions from 1 to 0 (the effect of no longer experiencing a shock).</footnote>

<footnote>17If the share foreign participation in the firm's capital is at least 10%, the *foreign* dummy takes the value 1. Similarly, if the share of the state's participation in the firm is at least 10%, then the *state* dummy takes the value 1. Firms fully owned by the state are by definition excluded from the sample.</footnote>

<footnote>18This variable is obtained from the World Governance Indicators (The World Bank, 2022b) and is calculated as the average of rule of law between $t-2$ and $t$.</footnote>

<footnote>19We are using lagged values for these two variables since they are very likely affected directly by the shock.</footnote>
and countries not affected by a shock. We control for the sector business cycle (by including sector-year fixed effects), country time-invariant characteristics (through the country fixed effects), firm time-varying (observed) characteristics and country time-varying (observed) characteristics.

We then estimate the following equation for panel firms:

$$Y_{i(c,s),t} = \beta_1 MW \text{ Shock}_{c,t-1,t-2} + X_{i(c,s),t}\beta_2 + W_{c,t}\beta_3 + W_{c,t-2}\beta_4 + \lambda_i + \mu_{s,t} + \epsilon_{i,c,s,t} \quad (2)$$

Equation 2 incorporates firm fixed effects ($\lambda_i$), allowing us to compare within-firm variations in outcome variables between “treated firms” (e.g., those affected by a minimum wage shock) and non-treated firms. By accounting for firm fixed effects, we control for unobserved, time-invariant firm-level characteristics, ensuring that identification relies on within-firm changes over time. As before, we also control for time-varying (observed) firm characteristics, the sector business cycle and country time-varying (observed) characteristics (as well as time-invariant country characteristics). The specification can be interpreted as a generalized difference-in-difference model. While identification is stronger with equation 2, the sample is much smaller since it is restricted to panel firms. The main shortcoming is that, as explained in the previous section, these firms differ from those in the full sample.

All standard errors are clustered at the country level. Our interest lies in assessing the impact of a policy variable (namely, the minimum wage) that maintains a consistent value across all instances within a cluster (the country). This scenario represents a foundational paradigm as elucidated by Moulton (1986, 1990). Likewise, Bertrand et al. (2004) demonstrated the importance of using cluster-robust standard errors in differences-in-differences settings.<fnref n="20" />

<footnote n="20">²⁰Using similar data, several studies cluster the standard errors at the country-year level arguing that the clustering must be done at the level of aggregation of the variable of interest (see Chauvet & Ehrhart (2018) for an instance). If we were to follow this approach, our standard errors would be lower and the significance of</footnote>
**Identifying Assumption and Threat to Identification.** Our identification relies on the assumption that minimum wage shocks are exogenous, in particular to local conditions. This is the rationale for using *shocks* rather than the evolution of the minimum wage. Moreover, we focus on cases where the shock is a *discretionary measure*, and not a simple adjustment to the economic cycle or short-run shocks. As we document it in Appendix A.3, every shock is a political decision implying large adjustments, in practice higher than 40% in real terms. Thus, we focus only on cases where the shock exceeds (by large) inflation, which we control for together with other economic variables (i.e. GDP per capita and GDP growth).

A potential threat to identification would arise if minimum wage shocks were correlated with other unobserved shocks. We mitigate this concern in two ways. First, we control for economic and institutional variables (namely, rule of law) as well as for the level of violence in the country. Second, we systematically include sector-year fixed effects, which capture common shocks affecting all countries simultaneously, but also shocks affecting the same industries. Most of the potential unobserved shocks would be captured by this set of fixed effects. Also, as shown in table 1, shocks take place in very heterogeneous countries (for instance, Zambia versus Egypt or Liberia) in different years.

Although we believe it is very unlikely that the identified minimum wage shocks occur exactly in the same countries and in the same years as other unobserved shocks, we propose two additional identification strategies: the *neighboring-country* strategy, and an instrumental approach based on the intensity of social protests.

<footnote>our results higher. We think, nonetheless, that it is important to take into account the serial correlation within countries. Although it might lead to an over-rejection of our results, we will stick to the more conservative approach of clustering at the country level.</footnote>
## 4.2 The neighboring-country strategy

This identification strategy relies upon a more restricted definition of the control group for each shock. Intuitively, in section 4.1, the control group for a treated firm (i.e. a firm affected by a shock) is a corresponding firm (all things being equal) from a country not affected by a shock the same year. Here, we propose to narrow down the control group to countries from the same sub-region. This strategy is inspired and adapted from the *neighboring-country* strategy in Dube *et al.* (2010)'s paper on the effects of minimum wages in the US at the county level. The rationale is related directly to the quality of the control group as *"contiguous border counties represent good control groups for estimating minimum wage effects if there are substantial differences in treatment intensity within cross-state county-pairs, and a county is more similar to its cross-state counterpart than to a randomly chosen county"* (Dube *et al.*, 2010, p. 949).

In practice, this translates into including county-pairs-year fixed effects, with identification stemming from the comparison with the neighboring counties. In our case, we cannot reach that level as we do not have yearly data for all countries. As a consequence, systematically there is not a neighboring country for which an *Enterprise Survey* wave has been conducted exactly the same year and can be used as a control group. Thus, our second best option is to use a broader unit of comparison and resort instead to African sub-regions.<fnref n="21" /> For such a purpose, we include **sub-region-year fixed effects** in the models. In doing so, we compare only firms within the same sub-region in the same year. Firms affected by a shock (say, for instance, in Malawi in 2013) are compared to firms in another Eastern-African country not affected by a shock and for which we have data exactly the same year (in this case Burundi and Sudan).

<footnote n="21">²¹We follow the classification proposed in the *UN M49 standard*, according to which African countries can be grouped into five main regions: Northern Africa (Algeria, Egypt, Libya, Morocco, Sudan, Tunisia and Western Sahara), Eastern Africa (Burundi, Comoros, Djibouti, Eritrea, Ethiopia, Kenya, Madagascar, Malawi, Mauritius, Mozambique, Rwanda, Seychelles, Somalia, South Sudan, Uganda, United Republic of Tanzania, Zambia, Zimbabwe), Middle Africa (Angola, Cameroon, Central African Republic, Chad, Congo, Democratic Republic of the Congo, Equatorial Guinea, Gabon, Sao Tome and Principe), Southern African (Botswana, Eswatini, Lesotho, Namibia, South Africa) and Western Africa (Benin, Burkina Faso, Cabo Verde, Côte d'Ivoire, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Saint Helena, Senegal, Sierra Leone, Togo).</footnote>
The estimated equations follow:

$$Y_{i(c,s,r),t} = \beta_1 MW \text{ Shock}_{c,t-1,t-2} + X_{i(c,s,r),t}\beta_2 + W_{c,t}\beta_3 + W_{c,t-2}\beta_4 + \lambda_c + \mu_{s,t} + \omega_{r,t} + \epsilon_{i,c,s,r,t} \quad (3)$$

$$Y_{i(c,s,r),t} = \beta_1 MW \text{ Shock}_{c,t-1,t-2} + X_{i(c,s,r),t}\beta_2 + W_{c,t}\beta_3 + W_{c,t-2}\beta_4 + \lambda_i + \mu_{s,t} + \omega_{r,t} + \epsilon_{i,c,s,r,t} \quad (4)$$

with r refering to African sub-region and ω$_{r,t}$ sub-region-year fixed effects. Equation 3 includes country fixed effects while equation 4 includes firm fixed effects (in addition to sector-year fixed effects in both cases).

## 4.3 Instrumental Variable Approach: The Intensity of Social Protests

To strengthen identification, we propose an instrumental variable approach based on the intensity of social protests. As documented in the qualitative analysis of minimum wage shocks (see Appendix A.3), every shock included in our empirical study was preceded by social protests, demonstrations, and/or riots one to two years before the minimum wage increase.

In most developing countries and especially in Africa, small firms dominate the economic landscape (Hsieh & Olken, 2014), and collective bargaining remains weak (International Labour Organization, 2019). Consequently, the minimum wage serves as one of the few labor market policies through which trade unions can exert significant influence in many African countries (Bhorat et al., 2017). Following the typology proposed by Dingeldey et al. (2021) on the intersections between statutory minimum wages and collectively negotiated wages, the minimum wage in these contexts aligns with the so-called “isolated minimum wage” model. As such, “the statutory minimum wage is crucial to influencing wage developments for a large proportion of workers” (Dingeldey et al., 2021, p. 7).

If we have a look at the graphs showing the evolution of minimum wages (provided for each country in Appendix A.3), the adjustments are much less frequent in African countries than
they can be in developed countries. Generally, the nominal minimum wage remains unchanged for a long period before a large adjustment, which takes place on a very irregular basis. Our qualitative description of each shock provides evidence that these adjustments follow social protests, demonstrations and/or riots. These can be concentrated in one sector (e.g. the mining sector in Zambia or the public sector, teachers and doctors in DRC) or involve the whole society (e.g. the anti-government protests in Malawi in 2011 or the 2006 protests in Niger). Except in some cases, demonstrators are not specifically requesting an increase in the minimum wage. In most instances, the protests were triggered by the soaring cost of living and rising food or energy prices. Nonetheless, it appears that an increase in the minimum wage is a frequent answer to such claims. Thus, we argue that the level of social protests (the year of the shock) is a good predictor of a minimum wage shock, ensuring the strength of this potential instrument.

In order to measure the intensity of these protests, we propose to use ACLED data (Raleigh et al., 2010) which covers all political violence and protest events for most countries with detailed information about the dates, actors, locations and fatalities. Among the different categories provided in the dataset, we consider both protests and riots and leave out other categories like battles, violence against civilians, explosions/remote violence and strategic developments.<fnref n="22" /> Similarly to the violence indicator, the total level of protests and riots is transformed into a 0-10 indicator.<fnref n="23" /> To illustrate the potential link with minimum wage shocks, table 4 provides the average number of protests (and riots) when there is a minimum wage shock and when there is not. Firms in countries where there was a minimum wage shock are also those in countries where the number of protests and riots was larger. The differences are striking: using the full sample, the average number of protests is 101.5 when there is no minimum wage shock versus

<footnote n="22">²²We believe that it is unlikely that these types of events are followed by an improvement of working conditions since they are usually linked to major issues other than labor market matters.</footnote>

<footnote n="23">²³The total number of protests and riots happening in the year of the shock is transformed into an indicator ranging from 0 to 10 by subtracting from each value (which is country-year specific) the minimum in the sample, dividing by the difference between the maximum and the minimum and multiplying it by 10.</footnote>
<table><caption>Table 4: Number of protests and riots (t-1, t-2)</caption><thead><tr><th rowspan="2"></th><th colspan="2">No MW Shock</th><th colspan="2">MW Shock</th><th colspan="2">T-test</th></tr><tr><th>Mean</th><th># Obs.</th><th>Mean</th><th># Obs.</th><th>Difference</th><th>p-value</th></tr></thead><tbody><tr><td>Full sample</td><td>101.569</td><td>27,830</td><td>716.579</td><td>5,847</td><td>-615.010</td><td>0.000***</td></tr><tr><td>Panel sample</td><td>77.139</td><td>6,009</td><td>657.428</td><td>2,308</td><td>-580.289</td><td>0.000***</td></tr><tr><td>Country sample</td><td>51.453</td><td>86</td><td>239.500</td><td>10</td><td>-188.047</td><td>0.005**</td></tr></tbody></table>

Source: Raleigh et al. (2010), calculations by the authors. Note: † p<0.1 * p<0.05 ** p<0.01 *** p<0.001

716.5 when there is a minimum wage shock. The difference is also huge when using the panel sample. Without considering the sample size (i.e. considering only one observation per country and year) the differences are also remarkable. All mean tests are significant at least at the 1% level.<fnref n="24" />

So as to be considered valid, the instrument must affect the likelihood of a minimum wage shock without influencing directly firms' outcomes (exclusion restriction). This instrument must be orthogonal to any characteristics which may drive simultaneously the minimum wage shocks and firms' outcomes. An initial threat to identification stems from the potential correlation between social protests and violence. One might think violence may affect negatively firms' performances (including sales and, indirectly, employment). Note that this source of bias would lead to a downward bias in our estimates as the expected correlation between violence and firms' outcomes is negative. To circumvent this concern, we use violence as an additional control. In other words, our strategy is to assess the potential impact of social protests for a given level of violence.<fnref n="25" />

A second challenge might be the correlation between social protests and inflation (or other

<footnote n="24">²⁴Even though we use the total number of protests and riots happening in *the year of the shock* for the main analyses, robustness checks using the average number of protests and riots happening in *the year of the shock and the one before* are provided in the Appendix B. The results in the second stage of the IV strategy are quite similar for the panel sample both in magnitude and significance, while they are smaller and no longer significant in the case of labor costs for the full sample.</footnote>

<footnote n="25">²⁵Note also that we use only protests and riots, excluding more violent events reported in the ACLED database.</footnote>
economic variables). As we have described, price increases are a common trigger of social unrest. Nevertheless, we deal with such potential correlation in different ways. To begin with, all monetary variables are deflated. Thus, results show, for instance, the effect on real sales or real labor costs per worker. Secondly, we control for inflation across all the specifications. What is more, the definition of a minimum wage shock is based on a massive increase in the real value of the minimum wage, meaning once inflation is accounted for. Therefore, the focus is only on cases where the adjustment goes far beyond the price increase. More generally, we also control for economic determinants of firms’ performances (GDP per capita and GDP growth at the country level, and the sector-year fixed effects to control for the business cycle within a specific sector).

A last concern is that our instrument may effectively predict the occurrence of a minimum wage shock but not the variable $MW\ Shock_{c,t-1,t-2}$ as defined in our baseline specification. As outlined in subsection 4.1, $MW\ Shock_{c,t-1,t-2}$ equals 1 if a shock occurs in $t-1$ or $t-2$, affecting that wave, and remains at 1 in subsequent waves. In other words, we argue that the level of protests and riots serves as a strong predictor of changes in $MW\ Shock_{c,t-1,t-2}$. Therefore, our protest variable cannot be directly used as an instrument for $MW\ Shock_{c,t-1,t-2}$.

To address this issue, we follow a two-step procedure inspired by a number of studies from the urban economics field (Combes et al., 2008; Combes & Gobillon, 2015; Bosquet & Combes, 2017). This methodology was designed to isolate agglomeration effects from the role played by individual characteristics. While our goal is obviously different, the two-stage method allows us to identify the separate role of minimum wage shocks on the estimated country-year fixed effects. We will combine this approach with the instrumental variable strategy in the second stage.

In the first step, we regress firms’ outcomes on firm-level characteristics, both time-varying and time-invariant in a model of the type:

$$Y_{i(c,s),t} = X_{i(c,s),t}\beta_2 + \lambda_{c,t} + \mu_s + \epsilon_{i,c,s,t} \quad (5)$$
where $\lambda_{c,t}$ are country-year fixed effects, and $\mu_s$ sector fixed effects. By construction, we can-
not incorporate time-varying country-level controls at this stage, which includes $MW\ Shock_{c,t-1,t-2}$.
The purpose of this first step is to assess the effects of both firm-level characteristics and country-
year fixed effects. The latter capture not only the observed characteristics, including the mini-
mum wage but also any unobserved country-level effects.

In the second step, we regress the *difference* in the country-year fixed effects as estimated
in equation 5 on the *difference* in our minimum wage shock indicator and other country-level
characteristics. This stage allows us to identify the separate effects of minimum wage shocks on
the estimated country-year fixed effect, net of firm-level effects. Notice that by *difference* we
refer to the change from one wave to the following, and we no longer refer to *t* but to *w*, which
stands for wave<fnref n="26" />:

$$
\widehat{\lambda_{c,w}} - \widehat{\lambda_{c,w-1}} = \beta_1 [MW\ Shock_{c,w} - MW\ Shock_{c,w-1}] + W_{c,w}\beta_3 + \epsilon_{c,w} \quad (6)
$$

We argue that this strategy outperforms a first-differences model, as it allows us to include
all firms in the full sample, provided that at least two waves are available for a given country. In
contrast, a first-differences approach would restrict the analysis to panel firms, which constitute
a highly selected subsample (see Table 3).

In equation 6, our estimates are weighted by the total number of observations per country-
wave. This approach enables us to assess the average effect of a minimum wage shock on a
representative African firm while accounting for differences in sample sizes across countries,
which are proportional to country size. We will also estimate equation 6 using equal weights for
each wave to test the robustness of our results. Finally, we will use an alternative specification
that includes wave fixed effects.

<footnote n="26">²⁶Notice that this model uses information from all observations but can only be estimated for waves after the first one.</footnote>
It is at this point that we introduce our two-stage least-square strategy (i.e. the instrumental variable approach). As equation 6 includes the *difference* in the $MW\ Shock_{c,t}$ variable between waves, we instrument this *difference* with the *level* of protests and riots, which was the initial goal. More specifically, our first-stage regression is represented by equation 7:

$$[MW\ Shock_{c,w} - MW\ Shock_{c,w-1}] = \alpha_1 Social\ Protests_{c,w} + W_{c,w}\alpha_2 + \epsilon_{c,w} \quad (7)$$

with $Social\ Protests_{c,w}$ as the index ranging from 0 to 10 and accounting for the number of protests and riots in the year of the shock. Again, we will provide additional estimates including wave fixed effects in equation 7.

The second stage and final equation would then follow:

$$\widehat{\lambda_{c,w}} - \widehat{\lambda_{c,w-1}} = \beta_1[MW\ Shock_{c,w} - MW\ Shock_{c,w-1}] + W_{c,w}\beta_3 + \epsilon_{c,w} \quad (8)$$

with $\widehat{\lambda_{c,w}} - \widehat{\lambda_{c,w-1}}$, the coefficients estimated in equation 5 and $[MW\ Shock_{c,w} - MW\ Shock_{c,w-1}]$ the values predicted in the first stage (equation 7).

# 5 Results

## 5.1 Baseline results

Table 5 presents the results from the **country fixed effects** model for the full sample (equation 1). We find that minimum wage shocks lead to a significant increase in total labor costs per worker. On average, firms in countries affected by a shock experience labor costs that are 36% higher than those in unaffected countries, *ceteris paribus*. While we do not directly observe wages—and total labor costs per worker also include social contributions—this effect is likely driven by wage increases in affected firms. This finding suggests at least a minimum level of
<table><caption>Table 5: Full sample: OLS results, country fixed effects</caption><thead><tr><th></th><th>(1)<br>Labour costs p/w</th><th>(2)<br>Permanent employment</th><th>(3)<br>Sales</th><th>(4)<br>Sales p/w</th></tr></thead><tbody><tr><td>Minimum Wage Shock 30% (t-1 or t-2)</td><td>0.360*<br>(0.171)</td><td>0.046<br>(0.104)</td><td>0.258<br>(0.326)</td><td>0.204<br>(0.249)</td></tr><tr><td># Obs.</td><td>33,675</td><td>33,675</td><td>33,675</td><td>33,675</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.319</td><td>0.261</td><td>0.315</td><td>0.264</td></tr><tr><td>% Treated Obs.</td><td>17.363</td><td>17.363</td><td>17.363</td><td>17.363</td></tr><tr><td># Countries</td><td>43</td><td>43</td><td>43</td><td>43</td></tr><tr><td>Sample</td><td>Full</td><td>Full</td><td>Full</td><td>Full</td></tr><tr><td>Firms' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Sector x Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Region x Year FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001

compliance with the new wage regulations. Even for workers already earning above the minimum wage, a large statutory increase may act as a signal, prompting firms to raise wages more broadly.

Conversely, we find no significant impact on employment, with the coefficient remaining slightly positive but close to zero. This null effect could stem from either a lack of enforcement by firms or alternative adjustment mechanisms. However, the former explanation appears inconsistent with the observed increase in labor costs per worker reported in column (1). According to standard labor demand theory, a rise in the minimum wage should lead to employment reductions. Yet, firms may be offsetting higher labor costs through increased sales or productivity gains. Indeed, columns (3) and (4) indicate significant positive effects on both outcomes: firms in affected countries report, on average, 26% higher total sales and 20% higher sales per worker compared to those in unaffected countries, all else being equal. In the following sections, we further investigate these potential adjustment mechanisms.

Similarly, Table 6 presents the results using **firm fixed effects** instead (equation 2). Since
panel firms constitute only a small fraction of the full sample, the number of observations is significantly lower in these estimates, while the proportion of treated firms is notably higher (27.75% compared to 17.38% in the previous specification). Interestingly, when we re-estimate the model with country fixed effects using this restricted sample, the results remain broadly consistent with those reported in Table 5 (see Table B.1 in Appendix B).<fnref n="27" />

Once firm fixed effects are included (Table 6), the coefficients increase both in magnitude and statistical significance. For labor costs per worker, we observe a 65% increase in firms affected by a shock relative to unaffected firms, holding other factors constant. This effect is highly significant at the 0.1% level. While this estimate may appear large, it is consistent with the scale of the minimum wage increases analyzed, which average +83% (or +269% when including Sierra Leone).

The effect on employment, although slightly larger than in previous specifications, remains statistically indistinguishable from zero. However, the impact on both total sales and sales per worker is substantial (+61% and +56%, respectively), with magnitudes comparable to the increase in labor costs per worker and significance at the 0.1% level. This supports the notion that firms may have offset rising labor costs through higher sales and/or productivity gains.

It is important to highlight that panel firms, by construction, are surviving firms—those that exit the market (potentially due to the minimum wage shock) are no longer observed in subsequent waves. The stronger effect for panel firms compared to non-panel firms aligns with the idea of a "cleansing effect," whereby less productive firms may be forced out following a shock. This mechanism has been documented in other contexts, such as China, where Mayneris et al. (2018) found no employment impact but a positive effect on productivity for surviving

<footnote n="27">²⁷The coefficient for labor costs per worker is slightly higher (0.422), reflecting the selection of panel firms. However, the coefficients for employment and sales per worker are not statistically significant, despite being similar in magnitude to those in the full sample. The effect on total sales is only marginally significant. Table B.2 further explores this by restricting the sample to panel countries (i.e., those with available panel firms). The lack of significance—particularly, the marginal significance of the effect on labor costs per worker—suggests that the observed effects are not solely driven by the composition of panel countries.</footnote>
<table><caption>Table 6: Panel sample: OLS results, firm fixed effects</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>Labour costs p/w</th><th>Permanent employment</th><th>Sales</th><th>Sales p/w</th></tr></thead><tbody><tr><td>Minimum Wage Shock 30% (t-1 or t-2)</td><td>0.647***<br>(0.159)</td><td>0.057<br>(0.082)</td><td>0.614***<br>(0.163)</td><td>0.560***<br>(0.151)</td></tr><tr><td># Obs.</td><td>8,317</td><td>8,317</td><td>8,317</td><td>8,317</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.383</td><td>0.732</td><td>0.648</td><td>0.414</td></tr><tr><td>% Treated Obs.</td><td>27.750</td><td>27.750</td><td>27.750</td><td>27.750</td></tr><tr><td># Countries</td><td>27</td><td>27</td><td>27</td><td>27</td></tr><tr><td>Sample</td><td>Panel</td><td>Panel</td><td>Panel</td><td>Panel</td></tr><tr><td>Firms' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Sector x Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region x Year FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001

firms exposed to a minimum wage increase. We will explore this mechanism in section 6.1.

We present the results of the *neighboring-country strategy* in Annex B.5. The impact on labor costs per worker is confirmed: we estimate a +27% increase for the full sample (with country fixed effects) and an even larger +87% increase for panel firms (with firm fixed effects) (see Tables B.12 and B.13). For sales and sales per worker, we observe positive but somewhat noisy effects, which are significant only for panel firms. The employment effects remain statistically insignificant for the panel sample, with the coefficient close to zero. However, for the full sample, employment shows a positive and significant effect at the 5% level.<fnref n="28" />

<footnote n="28">²⁸Additionally, Tables B.14 and B.15 present estimates for the panel sample and the full set of panel countries, respectively, incorporating country and sub-region-year fixed effects. The results for the panel sample closely align with the within-firm estimates, though statistical significance is somewhat lower. In contrast, the full sample of panel countries yields larger and more significant effects, reinforcing the positive impact on labor costs and sales, with no evidence of a negative employment effect.</footnote>
## 5.2 Instrumental Variable Results

We now turn to the results obtained using the instrumental variable (IV) approach and the two-step methodology outlined in Section 4.3. Table 7 presents the first-stage estimates for both the panel and full samples, confirming the strength of our instrument. The coefficient on the IV is positive and highly significant at the 0.1% level in both cases. Moreover, the Kleibergen-Paap Wald F-statistic and Cragg-Donald Wald F-statistic both exceed the commonly accepted thresholds,<fnref n="29" /> providing strong evidence of a robust correlation between social protests and riots and the likelihood of a minimum wage shock. Notably, inflation is included as a control variable in the first-stage regression (along with other country-level controls), and as expected, we also find a positive correlation between inflation and the occurrence of a minimum wage shock.

Subsequently, we move on to the second stage (equation 8), whose results are displayed in table 8. The coefficient should be interpreted as the effect of minimum wage shocks on the outcome of interest, once the individual characteristics of firms have been taken into account (in the first step).<fnref n="30" />

Previous OLS results are confirmed with a slightly higher magnitude. Minimum wage shocks (instrumented by social protests) have a strong positive effect on labor costs per worker. These results also confirm the non-negative effect on employment. Indeed, the coefficient for the full sample shows a positive effect which is significant at the 1% level. This is consistent across samples as it is significant at the 10% level for the panel sample, although the magnitude is much smaller. This apparent paradox seems to be explained by the increase in sales and sales per worker. The results are generally stronger for panel firms (see table 9).

As robustness, we provide additional estimates in Appendix B. Tables B.3, B.4, and B.5

<footnote n="29">²⁹The critical values for the Kleibergen-Paap statistic range between 5.53 and 16.38, while a value of 10 is typically used as a rule of thumb for the Cragg-Donald statistic.</footnote>

<footnote n="30">³⁰As we are estimating the difference in the country-year fixed effects, an increase in this difference - explained by a minimum wage shock - would lead to an increase in the outcome of interest.</footnote>
<table><caption>Table 7: Instrumental variable strategy: first stage</caption><thead><tr><th></th><th>(1)<br>Full sample</th><th>(2)<br>Panel sample</th></tr></thead><tbody><tr><td>Protests and riots indicator (0-10)</td><td>0.104***<br>(0.024)</td><td>0.115***<br>(0.015)</td></tr><tr><td>GDP per Capita (log) (t-2)</td><td>-0.076<br>(0.058)</td><td>-0.109<br>(0.090)</td></tr><tr><td>GDP Growth (%) (t-2)</td><td>0.032†<br>(0.019)</td><td>0.021<br>(0.023)</td></tr><tr><td>Population (log) (t)</td><td>-0.110†<br>(0.057)</td><td>-0.132†<br>(0.080)</td></tr><tr><td>Inflation (%) (t)</td><td>0.012<br>(0.009)</td><td>0.034*<br>(0.015)</td></tr><tr><td>Rule of Law (average t and t-2)</td><td>-0.040<br>(0.081)</td><td>-0.165<br>(0.122)</td></tr><tr><td>Violence indicator (0-10)</td><td>0.030†<br>(0.018)</td><td>0.018<br>(0.018)</td></tr><tr><td>Constant</td><td>2.180*<br>(0.981)</td><td>2.760*<br>(1.275)</td></tr><tr><td>Kleibergen-Paap Wald F-Statistic</td><td>19.440</td><td>61.940</td></tr><tr><td>Cragg Donald Wald F-Statistic</td><td>15,450.515</td><td>4,854.060</td></tr><tr><td># Obs.</td><td>22,226</td><td>5,943</td></tr><tr><td># Countries</td><td>36</td><td>27</td></tr><tr><td>Country FE</td><td>No</td><td>No</td></tr><tr><td>Sector FE</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001. Sector FE and firms' controls included in the first step of the strategy as indicated in equation 5.

display the results obtained when using the same weight for each wave. The results are similar although the significance is lower, especially for the full sample. Then tables B.6, B.7, and B.8 use as an instrument the protests and riots indicator for the year of the shock *but also the previous one*. Finally, tables B.9, B.10, and B.11 include wave fixed effects. The results are also fairly similar, even though, again significance is lower, particularly in the case of the full sample.
<table><caption>Table 8: Full sample: IV strategy, second stage</caption><thead><tr><th></th><th>(1)<br>Labour costs p/w</th><th>(2)<br>Permanent employment</th><th>(3)<br>Sales</th><th>(4)<br>Sales p/w</th></tr></thead><tbody><tr><td>+30% MW shock (diff.)</td><td>0.733*<br>(0.289)</td><td>0.383**<br>(0.118)</td><td>1.016***<br>(0.226)</td><td>0.577†<br>(0.326)</td></tr><tr><td># Obs.</td><td>22,226</td><td>22,226</td><td>22,226</td><td>22,226</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.199</td><td>0.079</td><td>0.277</td><td>0.307</td></tr><tr><td>% Treated Obs.</td><td>26.307</td><td>26.307</td><td>26.307</td><td>26.307</td></tr><tr><td># Countries</td><td>36</td><td>36</td><td>36</td><td>36</td></tr><tr><td>Kleibergen-Paap Wald F-Statistic</td><td>19.440</td><td>19.440</td><td>19.440</td><td>19.440</td></tr><tr><td>Cragg Donald Wald F-Statistic</td><td>15,450.515</td><td>15,450.515</td><td>15,450.515</td><td>15,450.515</td></tr><tr><td>Sample</td><td>Full</td><td>Full</td><td>Full</td><td>Full</td></tr><tr><td>Firms' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Sector FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001. Sector FE and firms' controls included in the first step of the strategy as indicated in equation 5.

<table><caption>Table 9: Panel sample: IV strategy, second stage</caption><thead><tr><th></th><th>(1)<br>Labour costs p/w</th><th>(2)<br>Permanent employment</th><th>(3)<br>Sales</th><th>(4)<br>Sales p/w</th></tr></thead><tbody><tr><td>+30% MW shock (diff.)</td><td>0.863***<br>(0.100)</td><td>0.091†<br>(0.049)</td><td>1.005***<br>(0.126)</td><td>0.895***<br>(0.117)</td></tr><tr><td># Obs.</td><td>5,943</td><td>5,943</td><td>5,943</td><td>5,943</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.213</td><td>0.208</td><td>0.092</td><td>0.081</td></tr><tr><td>% Treated Obs.</td><td>38.836</td><td>38.836</td><td>38.836</td><td>38.836</td></tr><tr><td># Countries</td><td>27</td><td>27</td><td>27</td><td>27</td></tr><tr><td>Kleibergen-Paap Wald F-Statistic</td><td>61.940</td><td>61.940</td><td>61.940</td><td>61.940</td></tr><tr><td>Cragg Donald Wald F-Statistic</td><td>4,854.060</td><td>4,854.060</td><td>4,854.060</td><td>4,854.060</td></tr><tr><td>Sample</td><td>Panel</td><td>Panel</td><td>Panel</td><td>Panel</td></tr><tr><td>Firms' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Sector FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001. Sector FE and firms' controls included in the first step of the strategy as indicated in equation 5.
# 6 Mechanisms

Overall, we find no significant effect on employment, a result that aligns with several studies conducted in different countries. For example, Bhorat et al. (2017) report that 55% of employment elasticities are not statistically significant in their review of literature on developing countries. This null effect on employment cannot be attributed only to weak enforcement, as we observe a positive and robust effect on labor costs per worker across various specifications. This finding indicates that there is at least some degree of compliance with minimum wage laws.

Our results also suggest that minimum wage shocks may not only affect workers earning the minimum wage but could also lead to wage increases for those earning slightly above the minimum. Figure 2 illustrates the effect of minimum wage shocks by decile of firms, categorized by their average labor costs per worker. The data shows that almost all deciles are impacted by a minimum wage shock, with the strongest effects observed in firms within the second to sixth deciles. However, we do not find a significant effect on firms in the highest deciles (at least for the full sample). Additionally, the first decile shows no significant impact, which may indicate challenges in enforcement among very low-paying firms. It is important to interpret these results with caution. We do not have data on individual wages, but rather on the average labor costs per worker, which may mask the wage conditions of different groups within firms. Furthermore, we cannot account for the composition of the workforce, making it difficult to determine how many workers are paid below or near the minimum wage in each firm. As a result, average labor costs per worker may hide important wage heterogeneity within firms.

We next examine the effect of a minimum wage shock based on firms' proximity to the minimum wage level. Specifically, we categorize firms into three groups: (i) firms with average labor costs per worker below the minimum wage, (ii) firms with average labor costs per worker between one and two times the minimum wage, and (iii) firms with average labor costs per worker
<figure><img src="image_3.png" /><figcaption>Figure 2: Effects of Minimum Wage Shock by Decile of labor Costs per Worker</figcaption></figure>

Source: The World Bank (2021), calculations by the authors. Note: the point estimates represented correspond to the betas for the minimum wage shock indicator interacted with each decile.
<table><caption>Table 10: Share of firms by Average Labour Costs per Worker</caption><thead><tr><td></td><td>Firms</td><td>Percent</td></tr></thead><tbody><tr><td>Lower than MW</td><td>9,691</td><td>28.776</td></tr><tr><td>Between MW and two MW</td><td>7,838</td><td>23.274</td></tr><tr><td>Over two MW</td><td>16,148</td><td>47.950</td></tr><tr><td>Total</td><td>33,677</td><td>100</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: The number of firms with labour costs per worker larger than twice the minimum wage include firms in countries where no minimum wage regulations apply (3484 firms).

exceeding twice the minimum wage. The first group accounts for 28.78% of the sample, indicating a significant level of non-compliance among a sizable share of firms. The second group, comprising firms with labor costs between one and two times the minimum wage, represents 23.28% of the sample. Meanwhile, firms with labor costs above twice the minimum wage constitute the largest category, at 47.95% of the sample (see Table 10). This classification allows us to better understand how wage adjustments following a minimum wage shock vary depending on firms’ initial wage structures.

The results in Table 11 indicate that the impact of minimum wage shocks extends beyond firms near the minimum wage threshold. Large increases in the minimum wage appear to trigger broader wage adjustments, affecting firms that already pay above the minimum wage. Specifically, firms with average labor costs exceeding the minimum wage also experience an increase in labor costs per worker. However, as expected, the impact is somewhat stronger for firms paying below the minimum wage. For panel firms (using firm fixed effects, see Table 12), the impact of minimum wage shocks appears to be strongest among firms that had average labor costs above the minimum wage but below twice the minimum wage prior to the shock.<fnref n="31" /> Despite this, the

<footnote n="31">³¹It is important to note that the categorization for panel firms is done ex-ante, based on their labor costs in the previous wave. This approach allows us to leverage information from before the shock occurred. In contrast, for the full sample, categorization is done ex-post, using labor costs after the shock. This distinction arises because pre-shock labor costs are not observed for the full sample. Consequently, the ex-post classification may introduce a downward bias in the estimated effects, as the observed labor costs may already reflect the increase due to the shock (i.e., labor costs prior to the shock may have been even lower).</footnote>
<table><caption>Table 11: Full sample: OLS results, country fixed effects, by level of labour costs per worker</caption><thead><tr><th></th><th>(1)<br>All firms</th><th>(2)<br>Lower than MW</th><th>(3)<br>Between MW and Two MW</th><th>(4)<br>Over Two MW</th></tr></thead><tbody><tr><td>Labour costs p/w</td><td>0.364*<br>(0.171)</td><td>0.501***<br>(0.114)</td><td>0.382**<br>(0.112)</td><td>0.480***<br>(0.110)</td></tr><tr><td>Permanent employment</td><td>0.046<br>(0.104)</td><td>-0.234†<br>(0.122)</td><td>-0.096<br>(0.080)</td><td>0.048<br>(0.092)</td></tr><tr><td>Sales</td><td>0.258<br>(0.326)</td><td>-0.366<br>(0.246)</td><td>0.081<br>(0.287)</td><td>0.484*<br>(0.230)</td></tr><tr><td>Sales p/w</td><td>0.212<br>(0.256)</td><td>-0.132<br>(0.160)</td><td>0.177<br>(0.242)</td><td>0.436*<br>(0.198)</td></tr><tr><td># Obs.</td><td>33,675</td><td>9,677</td><td>7,826</td><td>16,141</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.250</td><td>0.221</td><td>0.249</td><td>0.338</td></tr><tr><td>% Treated Obs.</td><td>17.363</td><td>23.158</td><td>21.607</td><td>11.846</td></tr><tr><td># Countries</td><td>43</td><td>35</td><td>37</td><td>43</td></tr><tr><td>Sample</td><td>Full</td><td>Lower than<br>MW</td><td>Between MW<br>and Two MW</td><td>Over Two MW</td></tr><tr><td>Firms' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Sector x Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Region x Year FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001

results indicate that the effect of the minimum wage shock remains positive and substantial across all firm categories.

How the level of employment can remain unchanged after a strong increase in labor costs has already been investigated in the literature, providing different potential explanations for this apparent paradox. For instance, recent evidence suggests that minimum wages can affect the survival rate of firms (Mayneris et al., 2018) while other studies propose a push in local demand as the main reason (Magruder, 2013). These and other ideas are tested in the coming subsections.
<table><caption>Table 12: Panel sample: OLS results, firm fixed effects, by level of labour costs per worker</caption><thead><tr><th></th><th>(1)<br>All firms</th><th>(2)<br>Lower than MW</th><th>(3)<br>Between MW and Two MW</th><th>(4)<br>Over Two MW</th></tr></thead><tbody><tr><td>Labour costs p/w</td><td>0.640***<br>(0.160)</td><td>0.910*<br>(0.346)</td><td>1.004***<br>(0.226)</td><td>0.843***<br>(0.158)</td></tr><tr><td>Permanent employment</td><td>0.057<br>(0.082)</td><td>-0.105<br>(0.141)</td><td>0.132<br>(0.187)</td><td>-0.103<br>(0.078)</td></tr><tr><td>Sales</td><td>0.614***<br>(0.163)</td><td>-0.063<br>(0.409)</td><td>1.407***<br>(0.258)</td><td>0.534**<br>(0.158)</td></tr><tr><td>Sales p/w</td><td>0.557**<br>(0.151)</td><td>0.042<br>(0.325)</td><td>1.276***<br>(0.207)</td><td>0.637***<br>(0.167)</td></tr><tr><td># Obs.</td><td>8,317</td><td>1,828</td><td>1,734</td><td>4,636</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.414</td><td>0.354</td><td>0.363</td><td>0.441</td></tr><tr><td>% Treated Obs.</td><td>27.750</td><td>42.505</td><td>32.641</td><td>20.298</td></tr><tr><td># Countries</td><td>27</td><td>24</td><td>23</td><td>27</td></tr><tr><td>Sample</td><td>Full</td><td>Lower than MW</td><td>Between MW and Two MW</td><td>Over Two MW</td></tr><tr><td>Firms' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Sector x Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region x Year FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001

## 6.1 Minimum Wages and Firm's Survival Rates: A cleansing effect

Our findings indicate that the rise in labor costs following a minimum wage shock is, at least for some firms, offset by an increase in total sales or sales per worker. However, this adjustment may not be sufficient for all firms, potentially leading to higher exit rates among those unable to absorb the added labor costs.

The data do not allow for the direct observation of firm survival rates, as panel firms—those observed at least twice—constitute only a small sub-sample of the full sample. However, by definition, panel firms are surviving firms, which is not necessarily the case for non-panel firms.
This could explain why these companies, on average, are more productive, pay higher wages, and generate higher sales. Given that our results are primarily driven by panel firms, this may point towards a *cleansing effect* associated with minimum wage shocks.

Although we cannot directly observe the survival rate of firms, we can track whether firms exit the panel. Most firms are interviewed only once, while panel firms are interviewed multiple times (from two to four times). In this section, we examine whether a minimum wage shock increases the probability of exiting the survey. Of course, firms may exit the survey without exiting the market. By construction, the attrition rate is quite high, due to the small proportion of panel firms. We aim to test whether a minimum wage shock affects this attrition rate, which would be consistent with the notion of a cleansing effect. The results are presented in Table 13.<fnref n="32" /> Attrition is high for the full sample (almost 70%), but we find that a minimum wage shock increases the probability of exiting the survey by 5 percentage points. When focusing on panel firms, attrition is much lower (by construction), but the effect of a minimum wage shock is substantial. All else equal, the probability of exiting the survey increases by 40 percentage points for firms facing a minimum wage shock. While we acknowledge that this result cannot be directly interpreted as the impact of the shock on firm survival rates, we argue that it is consistent with the existence of a *cleansing effect*.

## 6.2 Minimum Wages and Local Demand

As suggested in Magruder (2013), minimum wages may act as a ‘big push,’ stimulating local product demand and promoting formalization in non tradable, industrializable industries. Accordingly, the observed increase in sales may reflect a rise in local demand. To test whether the results from Section 5 align with this idea— given that we cannot directly measure the effect on

<footnote n="32">³²The number of observations decreases because we exclude the last wave for each country, as we cannot observe whether firms exit the survey in the subsequent wave.</footnote>
<table><caption>Table 13: Probability to exit the survey</caption><thead><tr><th></th><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td>MW Shock in the next wave</td><td>0.051**<br>(0.015)</td><td>0.404***<br>(0.012)</td></tr><tr><td># Obs.</td><td>14,486</td><td>5,044</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.129</td><td>0.217</td></tr><tr><td>% Treated Obs.</td><td>18.687</td><td>16.971</td></tr><tr><td># Countries</td><td>27</td><td>27</td></tr><tr><td>Mean Dep. Var</td><td>0.696</td><td>0.128</td></tr><tr><td>Sample</td><td>Full</td><td>Panel</td></tr><tr><td>Country FE</td><td>Yes</td><td>Yes</td></tr><tr><td>Sector x Year FE</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm FE</td><td>No</td><td>No</td></tr><tr><td>Region x Year FE</td><td>No</td><td>No</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001

local demand— we propose two alternative approaches. First, we will investigate whether the effect varies by market potential, using the location of firms and nighttime light data. Second, we will examine whether there are heterogeneous effects by sector, with the expectation of stronger effects in sectors closer to consumer demand

## 6.2.1 Heterogeneity by Market Potential

We test the hypothesis that the effects of minimum wage shocks vary with the size of the local markets in which firms operate. To investigate this, we leverage the location data of WBES firms and use average nighttime light (NTL) intensity at the urban cluster level as a proxy for local market potential. Annex B.2 details the methodology used to geolocate WBES firms, define urban cluster boundaries, and compile relevant data sources. Figure B.2 presents a map of the WBES firms we successfully geolocated.

Using the kernel density distribution of NTL intensity shown in Figure B.3, we classify firms into three groups based on their local market size: (i) firms located in cities with NTL intensity
below the 2nd decile are categorized as operating in small markets, (ii) those between the 2nd and 8th deciles are classified as medium-market firms, and (iii) those in cities with NTL intensity above the 8th decile are considered to operate in large markets. We then examine the differential effects of minimum wage shocks across these market segments.<fnref n="33" />

<table><caption>Table 14: Panel sample: Effects of Minimum Wage Shock by Market Potential</caption><thead><tr><th></th><th>(1)<br>Labour costs p/w</th><th>(2)<br>Permanent employment</th><th>(3)<br>Sales</th><th>(4)<br>Sales p/w</th></tr></thead><tbody><tr><td>MW Shock 30% in Small Market</td><td>0.279<br>(0.542)</td><td>-0.110<br>(0.181)</td><td>0.832<br>(0.663)</td><td>0.945<br>(0.565)</td></tr><tr><td>MW Shock 30% in Medium Market</td><td>0.622***<br>(0.162)</td><td>0.091<br>(0.082)</td><td>0.590***<br>(0.159)</td><td>0.501**<br>(0.149)</td></tr><tr><td>MW Shock 30% in Large Market</td><td>0.562*<br>(0.204)</td><td>0.227†<br>(0.118)</td><td>0.782*<br>(0.298)</td><td>0.556*<br>(0.220)</td></tr><tr><td># Obs.</td><td>6,945</td><td>6,945</td><td>6,945</td><td>6,945</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.400</td><td>0.726</td><td>0.650</td><td>0.425</td></tr><tr><td>% Treated Obs.</td><td>26.177</td><td>26.177</td><td>26.177</td><td>26.177</td></tr><tr><td># Countries</td><td>27</td><td>27</td><td>27</td><td>27</td></tr><tr><td>Sample</td><td>Panel</td><td>Panel</td><td>Panel</td><td>Panel</td></tr><tr><td>Firms' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Sector x Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region x Year FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr></tbody></table>

Source: The World Bank (2021); Li et al. (2020), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001

While we do not observe an effect for any of the outcomes in none of the three market sizes for the full sample (see Table B.16 in Appendix B), the results for the panel sample are insightful (see Table 14). Firms operating in medium and large markets are those increasing labor costs the most but also those experiencing an increase in sales and sales per worker. This is more so the

<footnote n="33">³³Specifically, we estimate the following equations for both the full and panel samples: $Y_{i(c,s),t} = \sum_{m=1}^{3} \beta_m \text{MW Shock}_{c,t-1,t-2} * \text{Market}_m + \sum_{m=1}^{3} \alpha_m \text{Market}_m + X_{i(c,s),t} \beta_4 + W_{c,t} \beta_5 + W_{c,t-2} \beta_6 + \lambda_c + \mu_{s,t} + \epsilon_{i,c,s,t}$, $Y_{i(c,s),t} = \sum_{m=1}^{3} MW \text{Shock}_{c,t-1,t-2} * \text{Market}_m + \sum_{m=1}^{3} \alpha_m \text{Market}_m + X_{i(c,s),t} \beta_2 + W_{c,t} \beta_3 + W_{c,t-2} \beta_4 + \lambda_i + \mu_{s,t} + \epsilon_{i,c,s,t}$ where MW Shock$_{c,t-1,t-2}$*Market$_m$ is the interaction between the shock variable and the market size dummies.</footnote>
case for those in large markets, as the magnitude of the coefficients is larger (see columns 3 and 4).
These results suggest that firms operating in larger markets are more capable of compensating
for an increase in labor costs through sales than those operating in smaller markets. Thus,
our results may point to the previously mentioned increase in local product demand following
a minimum wage shock. In addition, we find further evidence of the non-negative effect on
employment across markets as the coefficient is only marginally significant in large markets but
positive.

6.2.2 Heterogeneity by Sector

We turn next to a heterogeneity analysis by sector as an indirect way to test the local demand
mechanism. When looking at the effects by sector, we do not find considerable differences between
service and manufacturing firms (see tables B.17 and B.18 for the full and the panel samples,
respectively), although the effect for labor costs is larger in the former while those for sales and
sales per worker are larger in the latter.

This similarity, however, hides a strong heterogeneity between sectors. Figure 3 shows the
estimated coefficients for each sector for our four main outcomes, for both the full and the panel
sample<fnref n="34" />. We should be cautious as the number of observations is rather small in some sectors.
However, in general, we can see that the strongest effects within manufacturing take place in the
garment and food industries. This is particularly the case for panel firms. In the service sector,
the strongest effects are observed in retail sales and transport. We argue that these sectors are
more likely to directly benefit from a local demand boom linked to wage increases following a
minimum wage shock.

<footnote n="34"><sup>34</sup>The coefficients plotted for each sector correspond to the $\beta_s$ in the following specifications for the full and the panel samples, respectively:</footnote>

<footnote>$$Y_{i,c,s,t} = \beta_s MW \ Shock_{c,t-1,t-2} * Sector_s + \beta_2 X_{i,c,s,t} + \beta_3 W_{c,t} + \beta_4 W_{c,t-2} + \lambda_c + \mu_{s,t} + \epsilon_{i,c,s,t}$$</footnote>

<footnote>$$Y_{i,c,s,t} = \beta_s MW \ Shock_{c,t-1,t-2} * Sector_s + \beta_2 X_{i,c,s,t} + \beta_3 W_{c,t} + \beta_4 W_{c,t-2} + \lambda_i + \mu_{s,t} + \epsilon_{i,c,s,t}$$</footnote>

<footnote>where $MW \ Shock_{c,t-1,t-2} * Sector_s$ is the interaction between the shock variable and the sector dummies.</footnote>
<table><caption>Table 15: Full sample: Effects of Minimum Wage Shock by Sector</caption><thead><tr><th></th><th>(1)<br>Labour costs p/w</th><th>(2)<br>Permanent employment</th><th>(3)<br>Sales</th><th>(4)<br>Sales p/w</th></tr></thead><tbody><tr><td>MW Shock 30% in Service Sector</td><td>0.406*<br>(0.176)</td><td>0.038<br>(0.105)</td><td>0.253<br>(0.329)</td><td>0.208<br>(0.249)</td></tr><tr><td>MW Shock 30% in Manufacturing Sector</td><td>0.307†<br>(0.164)</td><td>0.056<br>(0.106)</td><td>0.264<br>(0.329)</td><td>0.200<br>(0.250)</td></tr><tr><td># Obs.</td><td>33,675</td><td>33,675</td><td>33,675</td><td>33,675</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.319</td><td>0.261</td><td>0.315</td><td>0.264</td></tr><tr><td>% Treated Obs.</td><td>17.363</td><td>17.363</td><td>17.363</td><td>17.363</td></tr><tr><td># Countries</td><td>43</td><td>43</td><td>43</td><td>43</td></tr><tr><td>Sample</td><td>Full</td><td>Full</td><td>Full</td><td>Full</td></tr><tr><td>Firms' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Sector x Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Region x Year FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001

## 6.3 Minimum Wages and Capital Intensity: The Role of Fixed Assets

Neoclassical production theory suggests that as labor costs rise, firms have an incentive to adopt more capital-intensive production methods (Arrow *et al.*, 1961; Cobb & Douglas, 1928; Hicks, 1963). In response to a minimum wage increase, firms may adjust their capital intensity accordingly. While we lack high-quality data to directly measure changes in capital stock, we leverage information on firms' acquisition of fixed assets to assess their behavior following a minimum wage shock.<fnref n="35" />

On average, one-third of firms report purchasing a fixed asset in the fiscal year covered by the data. Table 17 shows that this proportion increases following a minimum wage shock, with the effect being statistically significant in the panel sample (column 2). This supports the

<footnote n="35">³⁵Specifically, we use responses to the question: “*Did this establishment purchase any fixed assets in the last fiscal year?*”</footnote>
<table><caption>Table 16: Panel sample: Effects of Minimum Wage Shock by Sector</caption><thead><tr><th></th><th>(1)<br>Labour costs p/w</th><th>(2)<br>Permanent employment</th><th>(3)<br>Sales</th><th>(4)<br>Sales p/w</th></tr></thead><tbody><tr><td>MW Shock 30% in Service Sector</td><td>0.715***<br>(0.184)</td><td>-0.058<br>(0.078)</td><td>0.483**<br>(0.158)</td><td>0.541***<br>(0.127)</td></tr><tr><td>MW Shock 30% in Manufacturing Sector</td><td>0.611***<br>(0.159)</td><td>0.117<br>(0.091)</td><td>0.683***<br>(0.179)</td><td>0.569**<br>(0.180)</td></tr><tr><td># Obs.</td><td>8,317</td><td>8,317</td><td>8,317</td><td>8,317</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.383</td><td>0.732</td><td>0.648</td><td>0.414</td></tr><tr><td>% Treated Obs.</td><td>27.750</td><td>27.750</td><td>27.750</td><td>27.750</td></tr><tr><td># Countries</td><td>27</td><td>27</td><td>27</td><td>27</td></tr><tr><td>Sample</td><td>Panel</td><td>Panel</td><td>Panel</td><td>Panel</td></tr><tr><td>Firms' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Sector x Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region x Year FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001

idea that firms respond differently to substantial minimum wage hikes, with capital investment emerging as a potential adjustment mechanism to boost sales or productivity and offset rising labor costs. Our findings align with policy simulations by Bauducco & Janiak (2018) for the U.S., which predict an increase in capital stock following a minimum wage increase, as well as with empirical evidence from Vietnamese (Nguyen, 2017, 2019) and Chinese (Liu & Wu, 2023) firms. However, they contrast with studies that found no effect (Haepp & Lin, 2017) or a decline in capital investment in response to minimum wage hikes in China and the U.S., respectively (T. Gustafson & D. Kotter, 2023).

The effect on the probability of purchasing a fixed asset varies slightly across sectors, with a stronger response observed in manufacturing compared to services (column 4). From a neoclassical perspective, this aligns with the idea that manufacturing industries exhibit a higher marginal rate of technical substitution. In other words, service sector firms face greater constraints in
<figure><img src="image_4.png" /><figcaption>Figure 3: Effects of Minimum Wages Shocks by Sector</figcaption></figure>

Source: The World Bank (2021), calculations by the authors. Note: the point estimates represented correspond to the betas for the minimum wage shock indicator interacted with each sector.

substituting capital for labor following a rise in labor costs, whereas manufacturing firms, which are more reliant on machinery, can adjust more easily. However, given that we do not find a significant negative impact on employment, these results may suggest complementarity rather than substitutability between labor and capital.

These findings also suggest that firms may seek to enhance productivity through capital investment as labor costs rise. In other words, to offset higher labor expenses, firms might invest in capital to improve efficiency. Another possible explanation relates to increased consumer demand following wage increases. Firms may need to expand their production capacity through capital
<table><caption>Table 17: Probability to buy a fixed asset</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Minimum Wage Shock 30% (t-1 or t-2)</td><td>0.026<br>(0.038)</td><td>0.219***<br>(0.032)</td><td></td><td></td></tr><tr><td>MW Shock 30% in Service Sector</td><td></td><td></td><td>0.019<br>(0.040)</td><td>0.180***<br>(0.036)</td></tr><tr><td>MW Shock 30% in Manufacturing Sector</td><td></td><td></td><td>0.033<br>(0.038)</td><td>0.241***<br>(0.032)</td></tr><tr><td># Obs.</td><td>29,921</td><td>7,235</td><td>29,921</td><td>7,235</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.146</td><td>0.198</td><td>0.146</td><td>0.198</td></tr><tr><td>% Treated Obs.</td><td>19.458</td><td>29.883</td><td>19.458</td><td>29.883</td></tr><tr><td># Countries</td><td>43</td><td>26</td><td>43</td><td>26</td></tr><tr><td>Average dep. var.</td><td>0.341</td><td>0.354</td><td>0.341</td><td>0.354</td></tr><tr><td>Sample</td><td>Full</td><td>Panel</td><td>Full</td><td>Panel</td></tr><tr><td>Country FE</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Sector x Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm FE</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Region x Year FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001

investment to meet this higher demand, an effect likely to be more pronounced in manufacturing industries. This interpretation aligns with the results presented in Section 6.2. Finally, the fact that these effects are significant only for panel firms further suggests that survival is contingent on firms' ability to adapt their production processes.

## 6.4 Other Mechanisms

Thus far, our analysis indicates that minimum wage shocks did not lead to a decline in permanent employment. However, firms may still adjust their overall labor costs by reducing more precarious and flexible forms of employment, such as informal or temporary jobs. By cutting these types of labor, firms can maintain the number of permanent employees while still adapting to higher wage costs. This section examines these two potential adjustment mechanisms.

Moreover, the absence of an overall employment effect may mask heterogeneity across firms.
We have already shown that this is not the case when differentiating by sector, as the effect
remains statistically indistinguishable from zero in both services and manufacturing. In this
section, we explore whether our results vary by firm size, given that some theories suggest labor
regulations are more strictly enforced in larger firms.

6.4.1 Minimum Wage Shocks and Competition from the Informal Sector

To investigate whether some firms adjust by shifting to the informal economy following a large
minimum wage increase, we examine the impact of minimum wage shocks on competition from
the informal sector. Since we cannot directly observe transitions from the formal to the informal
economy or track the number of informal employees, we use firms’ perceptions of competition
from the informal sector as a proxy. Specifically, the World Bank Enterprise Surveys ask firms
whether they face competition from unregistered or informal firms. An increase in this variable
may suggest two possible scenarios.

First, we hypothesize that firms facing this type of competition may themselves possess informal characteristics. Although the surveyed firms are formally registered, we adopt a broader understanding of formality that extends beyond the classic definition of registration.<fnref n="36" />

Second, if the first scenario does not hold, an increase in perceived informal competition could
signal an expansion of the informal economy following a minimum wage shock, particularly within
the panel sample. We therefore propose using this dummy variable as the dependent variable to
assess how minimum wage shocks influence firms’ perceptions of informal competition.

Table 18 presents the results for both the full (columns 1 and 3) and panel (columns 2 and 4)
samples. Overall, we do not observe a significant effect on the likelihood of facing competition
from informal firms in the full sample, although the coefficient is positive. However, panel

<footnote n="36">³⁶While the ILO defines the informal sector as all activities "not covered or insufficiently covered by formal arrangements," other definitions of informality have emerged. For example, Löwenstein (mimeo) characterizes the informal sector as "stricken by absolute poverty," with informal firms operating below the poverty line.</footnote>
<table><caption>Table 18: Competition from informal or unregistered firms</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Minimum Wage Shock 30% (t-1 or t-2)</td><td>0.071<br>(0.070)</td><td>0.229**<br>(0.066)</td><td></td><td></td></tr><tr><td>MW Shock 30% in Service Sector</td><td></td><td></td><td>0.035<br>(0.070)</td><td>0.232**<br>(0.069)</td></tr><tr><td>MW Shock 30% in Manufacturing Sector</td><td></td><td></td><td>0.108<br>(0.070)</td><td>0.226**<br>(0.067)</td></tr><tr><td># Obs.</td><td>26,858</td><td>6,039</td><td>26,858</td><td>6,039</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.136</td><td>0.190</td><td>0.138</td><td>0.190</td></tr><tr><td>% Treated Obs.</td><td>20.981</td><td>32.042</td><td>20.981</td><td>32.042</td></tr><tr><td># Countries</td><td>42</td><td>26</td><td>42</td><td>26</td></tr><tr><td>Sample</td><td>Full</td><td>Panel</td><td>Full</td><td>Panel</td></tr><tr><td>Country FE</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Sector x Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm FE</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Region x Year FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001

firms report an increase in competition from the informal sector. This finding is particularly interesting because these firms, which have survived the shock, are presumed to remain in the formal economy—at least officially. Therefore, we interpret this result as suggesting an increase in informality, which could explain the heightened perception of competition from informal firms. Notably, there is no significant sectoral difference, as both manufacturing and service firms report a similar increase in perceived informal competition.

### 6.4.2 Is there an adjustment through temporary employment?

As previously discussed, firms may adjust to higher labor costs by transitioning to the informal economy. Alternatively, they may reduce other forms of precarious employment to maintain the number of permanent employees. One such form is temporary employment. Columns 2 and 3 in Tables 19 and 20 present the effects of minimum wage shocks on this variable. Our results do not support this potential adjustment mechanism, as we find no significant effect on temporary
<table><caption>Table 19: Full sample: OLS results, country fixed effects</caption><thead><tr><th></th><th>(1)<br>Perm. Emp.</th><th>(2)<br>Temp. Emp. (&gt;0)</th><th>(3)<br>Temp. Emp. (all)</th><th>(4)<br>Total emp.</th></tr></thead><tbody><tr><td>Minimum Wage Shock 30% (t-1 or t-2)</td><td>0.046<br>(0.104)</td><td>-0.065<br>(0.137)</td><td>0.494<br>(0.326)</td><td>0.052<br>(0.093)</td></tr><tr><td># Obs.</td><td>33,675</td><td>11,773</td><td>31,342</td><td>33,675</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.261</td><td>0.198</td><td>0.113</td><td>0.262</td></tr><tr><td>% Treated Obs.</td><td>17.363</td><td>14.542</td><td>18.423</td><td>17.363</td></tr><tr><td># Countries</td><td>43</td><td>43</td><td>43</td><td>43</td></tr><tr><td>Sample</td><td>Full</td><td>Full</td><td>Full</td><td>Full</td></tr><tr><td>Firms' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Sector x Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Region x Year FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001

employment across all samples. This holds for both the full sample (Table 19) and the panel sample (Table 20), as well as for firms reporting any level of temporary employment (column 2) and all firms, including those reporting zero temporary employment (column 3)<fnref n="37" />. The effect is only marginally significant—though positive—for the panel sample in the latter case. The effect on total employment is also not significantly different from zero (column 4).

### 6.4.3 Does the size of firms matter?

The seminal paper by Tybout (2000) argues that there exists a "missing middle" in the firm size distribution of developing countries, with labor regulations identified as one of the key obstacles. This concept is rooted in the model of firm size distribution with heterogeneous

<footnote n="37">³⁷Note that the sample size decreases due to missing values for this variable. Temporary employment is treated as zero when the data is missing for the purpose of calculating total employment but is kept as missing in the analysis specifically focusing on temporary employment. We argue that imputing zero in this case would artificially censor the data. However, since permanent employment is reported by all firms, assuming total employment equals permanent employment for these firms should not substantially distort the results. Despite some loss in accuracy, we retain the full sample for the sake of comparability.</footnote>
<table><caption>Table 20: Panel sample: OLS results, firm fixed effects</caption><thead><tr><th></th><th>(1)<br>Perm. Emp.</th><th>(2)<br>Temp. Emp. (&gt;0)</th><th>(3)<br>Temp. Emp. (all)</th><th>(4)<br>Total emp.</th></tr></thead><tbody><tr><td>Minimum Wage Shock 30% (t-1 or t-2)</td><td>0.057<br>(0.082)</td><td>-0.058<br>(0.154)</td><td>0.874†<br>(0.471)</td><td>0.117*<br>(0.055)</td></tr><tr><td># Obs.</td><td>8,317</td><td>1,435</td><td>7,765</td><td>8,317</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.732</td><td>0.497</td><td>0.243</td><td>0.728</td></tr><tr><td>% Treated Obs.</td><td>27.750</td><td>19.721</td><td>28.822</td><td>27.750</td></tr><tr><td># Countries</td><td>27</td><td>27</td><td>27</td><td>27</td></tr><tr><td>Sample</td><td>Panel</td><td>Panel</td><td>Panel</td><td>Panel</td></tr><tr><td>Firms' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Sector x Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region x Year FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001

workers and entrepreneurs proposed by Rauch (1991). Interestingly, this model uses the example of a "minimum formal sector wage," which is only enforced for firms "greater than a certain size" (Rauch, 1991, p. 33). In this framework, larger firms, which face higher labor costs per worker, are able to exploit their productivity advantages, allowing more talented entrepreneurs to operate larger firms. The additional profits derived from being larger more than offset the increased labor costs. As a result, less productive entrepreneurs remain in smaller firms. The existence of this "missing middle" has been challenged by Hsieh & Olken (2014), but in response, Tybout (2014) notes that "policies and market conditions in some developing countries have discouraged production at mid-sized firms, as opposed to small or large firms, relative to an undistorted plant size distribution" (Tybout, 2014, p. 235). The underlying argument is that small firms are not subject to regulations, whereas mid-sized firms are more likely to be affected.

Tables 21 and 22 present the results by firm size for the full and panel samples, respectively. Contrary to the literature's suggestion, small firms are indeed affected by minimum wage shocks.
<table><caption>Table 21: Full sample: Effects by firms’ size</caption><thead><tr><th></th><th>(1)<br>All firms</th><th>(2)<br>Small Firms (&lt;20)</th><th>(3)<br>Medium Firms (20-99)</th><th>(4)<br>Large firms (+100)</th></tr></thead><tbody><tr><td>Labour costs p/w</td><td>0.364*<br>(0.171)</td><td>0.466*<br>(0.177)</td><td>0.162<br>(0.155)</td><td>0.530**<br>(0.155)</td></tr><tr><td>Permanent employment</td><td>0.046<br>(0.104)</td><td>0.002<br>(0.050)</td><td>-0.009<br>(0.057)</td><td>-0.125<br>(0.079)</td></tr><tr><td>Sales</td><td>0.258<br>(0.326)</td><td>0.232<br>(0.296)</td><td>0.027<br>(0.265)</td><td>0.148<br>(0.213)</td></tr><tr><td>Sales p/w</td><td>0.212<br>(0.256)</td><td>0.230<br>(0.275)</td><td>0.036<br>(0.234)</td><td>0.273<br>(0.204)</td></tr><tr><td># Obs.</td><td>33,675</td><td>17,636</td><td>11,044</td><td>4,959</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.250</td><td>0.254</td><td>0.231</td><td>0.230</td></tr><tr><td>% Treated Obs.</td><td>17.363</td><td>16.421</td><td>17.965</td><td>19.339</td></tr><tr><td># Countries</td><td>43</td><td>43</td><td>43</td><td>42</td></tr><tr><td>Sample</td><td>Full</td><td>Small</td><td>Medium</td><td>Large</td></tr><tr><td>Firms' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Sector x Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Region x Year FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001

When estimating the effects on labor costs per worker, the coefficient is positive across all firm sizes, with stronger effects observed for both small and large firms. For mid-sized firms, the effect is not significant. We do not find any significant impact on employment for any firm size group. Regarding sales and productivity, the coefficients for all firms pooled together are not significant. However, the coefficients for small firms are larger (though still imprecise) compared to those for larger firms.

Our estimates are generally stronger within firms (i.e., for the panel sample, Table 22). The labor cost effects are much more pronounced for small firms, marginally significant for medium-sized firms, and not significant for large firms, with the magnitude for small firms being twice
<table><caption>Table 22: Panel sample: Effects by firms' size</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr><tr><th></th><th>All firms</th><th>Small Firms (&lt;20)</th><th>Medium Firms (20-99)</th><th>Large firms (+100)</th></tr></thead><tbody><tr><td>Labour costs p/w</td><td>0.640***<br>(0.160)</td><td>0.896***<br>(0.177)</td><td>0.470†<br>(0.263)</td><td>0.447<br>(0.284)</td></tr><tr><td>Permanent employment</td><td>0.057<br>(0.082)</td><td>0.055<br>(0.055)</td><td>-0.039<br>(0.051)</td><td>0.451*<br>(0.205)</td></tr><tr><td>Sales</td><td>0.614***<br>(0.163)</td><td>0.735**<br>(0.227)</td><td>0.239<br>(0.326)</td><td>0.088<br>(0.270)</td></tr><tr><td>Sales p/w</td><td>0.557**<br>(0.151)</td><td>0.680**<br>(0.217)</td><td>0.278<br>(0.324)</td><td>-0.363<br>(0.379)</td></tr><tr><td># Obs.</td><td>8,317</td><td>3,249</td><td>1,692</td><td>1,002</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.414</td><td>0.420</td><td>0.425</td><td>0.373</td></tr><tr><td>% Treated Obs.</td><td>27.750</td><td>30.009</td><td>25.650</td><td>25.250</td></tr><tr><td># Countries</td><td>27</td><td>27</td><td>27</td><td>24</td></tr><tr><td>Sample</td><td>Panel</td><td>Small</td><td>Medium</td><td>Large</td></tr><tr><td>Firms' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Sector x Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region x Year FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001

as large as the effects for medium and large firms. Positive effects on sales and productivity are also only observed for small firms. As noted earlier, panel firms are selected by definition and represent those that have survived. These results suggest that at least a portion of dynamic small firms are able to compensate for the minimum wage shocks they face by increasing their sales and sales per worker.
# 7 Conclusions

Although empirical evidence remains limited, developing countries are not exempt from the ongoing debate surrounding the effects of minimum wages. In many cases, collective bargaining is weaker (International Labour Organization, 2019), leading trade unions to rely more on the state than on individual firms to improve workers' wage conditions. In such contexts, unions often press the government to raise the minimum wage, effectively substituting direct wage negotiations between employers and employees. In line with this, we document that the vast majority of African countries have implemented at least one minimum wage determination, with only four lacking a minimum wage system altogether. We leverage this widespread adoption to assess the impact of minimum wage shocks (+30% in real terms) on various firm outcomes, using World Bank *Enterprise Survey* data from 42 African countries across 96 waves.

Our results demonstrate that the effect of minimum wage shocks on labor costs per worker is consistent across different specifications and remains robust to various identification strategies. This suggests a degree of enforcement even in contexts typically characterized by low compliance. Moreover, substantial minimum wage increases appear to affect not only firms paying below the minimum wage on average, but also those paying above it—and even those paying more than twice the minimum wage. Although individual wages are not observed, this may indicate spillover effects extending beyond low-wage workers.

Contrary to the predictions of economic theory, we find no consistent (negative) effect on employment. Instead, firms appear to offset the increase in labor costs through higher sales and improved productivity. This adjustment is more pronounced for panel firms (i.e., firms observed at least twice in our data), suggesting a potential cleansing effect: only those firms that survive and can offset higher costs through larger sales are observed repeatedly. This aligns with existing literature (Mayneris et al., 2018) and is further supported by our findings on the probability of a firm exiting the data, which serves as a proxy for survival rates. Several potential mechanisms
behind this result are discussed in this paper.

To begin with, manufacturing sectors such as garments and food industries, as well as service sectors like retail sales, are among those experiencing the largest effects. These sectors, being closer to consumer demand, are more likely to benefit from a local demand shock following wage increases. Similarly, our analysis by market potential indicates that firms operating in larger markets are better able to compensate for labor cost increases through higher sales, which further supports the local demand boost channel. Additionally, our results reveal that firms adjust by investing in capital stock, as evidenced by the increased likelihood of purchasing fixed assets in response to the wage shocks.

Furthermore, in contrast to some literature suggesting that labor regulations are only enforced for firms "greater than a certain size" (Rauch, 1991, p. 33), we find that the effects on labor costs per worker and sales are primarily driven by small firms, particularly in the panel sample.

The final potential adjustment mechanism is an increase in informality following a minimum wage shock. While the data we use do not allow us to directly assess the effect on informal employment or firms, we reject the hypothesis that minimum wage shocks significantly reduce employment in formal firms, which would typically be the primary driver of an increase in informal labor supply. However, one possibility is that the reallocation from formal to informal employment occurs as a result of formal firms exiting the market. The finding that panel firms report increased competition from unregistered firms following a minimum wage shock aligns with this hypothesis. Future research will be necessary to quantify the size of this effect.
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# Appendix A Minimum Wages in Africa

## A.1 Minimum Wage Systems in Africa

<table><caption>Table A.1: Minimum Wage Systems in Africa</caption><thead><tr><th>National</th><th>Sectoral / Occupational</th><th>Hybrid</th><th>No Minimum Wage</th></tr></thead><tbody><tr><td>Algeria</td><td>Botswana</td><td>Angola</td><td>Djibouti</td></tr><tr><td>Benin</td><td>Chad</td><td>Burkina Faso</td><td>Somalia</td></tr><tr><td>Cameroon</td><td>Congo, DR</td><td>Burundi</td><td>South Sudan</td></tr><tr><td>Cape Verde</td><td>Equatorial Guinea</td><td>Lesotho</td><td>Zimbabwe</td></tr><tr><td>Central African Republic</td><td>Eritrea</td><td>Morocco</td><td></td></tr><tr><td>Comoros</td><td>Eswatini</td><td>Senegal</td><td></td></tr><tr><td>Congo, Rep.</td><td>Ethiopia</td><td>Sudan</td><td></td></tr><tr><td>Cote d'Ivoire</td><td>Guinea</td><td>Tunisia</td><td></td></tr><tr><td>Egypt</td><td>Kenya</td><td></td><td></td></tr><tr><td>Gabon</td><td>Liberia</td><td></td><td></td></tr><tr><td>Gambia</td><td>Libya</td><td></td><td></td></tr><tr><td>Ghana</td><td>Madagascar</td><td></td><td></td></tr><tr><td>Guinea-Bissau</td><td>Malawi</td><td></td><td></td></tr><tr><td>Mali</td><td>Mozambique</td><td></td><td></td></tr><tr><td>Mauritania</td><td>Namibia</td><td></td><td></td></tr><tr><td>Niger</td><td>Rwanda</td><td></td><td></td></tr><tr><td>Nigeria</td><td>Tanzania</td><td></td><td></td></tr><tr><td>Sierra Leone</td><td>Togo</td><td></td><td></td></tr><tr><td>South Africa</td><td>Zambia</td><td></td><td></td></tr><tr><td>Uganda</td><td></td><td></td><td></td></tr></tbody></table>

Source: Bhorat et al. (2017) completed by the authors using various sources (International Labour Organization, 2011; WageIndicator, 2021)
A.2 Statistics on Minimum Wages

<figure><img src="image_5.png" /><figcaption>Figure A.1: Minimum Wage and GDP per capita</figcaption></figure>

Source: Minimum Wages: International Labour Organization (2021, 2011); WageIndicator (2021). GDP per capita: The World Bank (2022a)
<figure><img src="image_6.png" /><figcaption>Figure A.2: Minimum Wage by African Regions</figcaption></figure>

Source: International Labour Organization (2021, 2011); WageIndicator (2021).
<figure><img src="image_7.png" /><figcaption>Figure A.3: Minimum Wage by Income levels</figcaption></figure>

Source: International Labour Organization (2021, 2011); WageIndicator (2021).
<figure><img src="image_8.png" /><figcaption>Figure A.4: Adjusted Kaitz Ratio in Africa</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>(b) Median labor costs</figcaption></figure>

Source: International Labour Organization (2021, 2011); WageIndicator (2021); The World Bank (2021), calculations by the authors. The “adjusted” Kaitz ratio is defined as the ratio between minimum wage and the average or median labor costs per worker (monthly). The map in subfigure (a) is calculated using the average and the one in subfigure (b), the median. Average (or median) labor costs per worker is extracted from the last wave available from World Bank *Enterprise Surveys*.
## A.3 Description of Minimum Wage Shocks

### A.3.1 Ivory Coast

#### Minimum wage shock<fnref n="38" />

The minimum wage in Ivory Coast remained unchanged for 19 years until the Decree 2013-791, 20/11/2013³⁹ increased it from FCFA 36,607 to FCFA 60,000. In nominal terms, this is a 63.9% increase, while in constant terms (that is, deflated using the GDP deflator) it entailed a 58% rise in the minimum wage, affecting the 2016 wave (fiscal year 2015).

Tripartite negotiations between the government, trade unions and employers began in 2007. An agreement was reached in 2009 but the rise was only enacted in 2013. Before the start of negotiations, several strikes and social protests over soaring living costs had taken place in 2007. In 2008, during the international food price crisis, people marched in various cities such as Abidjan, Yopougon, Port-Boué, Attécoubé or Grand Bassam to raise their voices against the escalating cost of living. Demonstrations lasted a few days with one protester killed in April. In April 2010, transport workers set on strike for a week to complain about high food and fuel prices and demand tax cuts. An agreement on the minimum wage was reached only one year after these protests.

#### Minimum wage fixing

The minimum wage in Ivory Coast is decided by the government after tripartite (or bipartite) body discussions. A decree establishing the interprofessional minimum wage (*salaire minimum interprofessionnel garanti*) must be passed after the recommendation from the Consultative labor

Thomson Reuters, *Ivory Coast transporters end strike: union*, 17 April 2010

Al Jazeera, *Protests force Ivory Coast tax cuts*, 1 April 2008

Le Figaro, *Côte d'Ivoire: le salaire minimum gagne 60%*, 20 November 2013

Guardian, *Côte d'Ivoire raises minimum wage for workers after 19 years*, 21 November 2013

<footnote n="38">³⁸Sources:</footnote>

<footnote n="39">³⁹Décret n° 2013-791 du 20 novembre 2013 portant revalorisation du salaire minimum interprofessionnel garanti, en abrégé SMIG.</footnote>
<figure><img src="image_10.png" /><figcaption>Figure A.5: Evolution of Minimum Wage in Ivory Coast</figcaption></figure>

Source: International Labour Organization (2021); The World Bank (2021)

Commission<fnref n="40" />.

## A.3.2 Democratic Republic of Congo

### Minimum wage shock

The minimum wage shock we look into is the one affecting the 2010 wave. The minimum wage in the Democratic Republic of Congo (salaire minimum interprofessionnel garanti) was CDF 8,710 per month in 2007 and changed to CDF 29,120 in 2008. In constant terms, the minimum wage was CDF 6,390.414 in 2007 and CDF 17,735.37 in 2008. This implies a 177.5% increase (234% increase in nominal terms). The minimum wage had remained the same ever since introduced in 2003 by Decree 080/2002, 3/07/2002⁴¹, fixing the minimum wage as established in

<footnote n="40">⁴⁰Article 31.6 in the 1995 labor Code reads: «Des décrets pris après avis de la Commission Consultative du Travail fixent les salaires minima interprofessionnels garantis».</footnote>

<footnote n="41">⁴¹Décret n° 080/2002 du 3 juillet 2002 portant fixation du salaire minimum interprofessionnel garanti, des allocations familiales minima et de la contre-valeur du logement</footnote>
Act 015/2002, 16/10/2001⁴², defining the labor Code. The Ordonnance 08/040, 30/04/2008⁴³,
fixing the minimum wage, modified the former legislation.

Through 2007 and 2008 there were some strikes that affected mainly the public sector as
teachers, university professors and doctors claimed higher wages<fnref n="44" />. However, the private sector
also faced the same troubles: because of high inflation rates, and despite the increase in minimum
wages, families could only afford a meal per day<fnref n="45" />.

Even if paid in Congolese francs, minimum wage negotiations were made in dollar terms. The fact that the currency depreciated made workers lose at least 50% of their purchase power given that the SMIG was not revised for years after the 2008 increase and the daily rate equalled USD 1 as of 2017⁴⁶.

Minimum wage fixing

According to the Labor Code, Article 87 the President of the Republic must issue a decree upon the recommendation of the Minister responsible for Labor and Social Welfare and following consultation with the National Labor Council. The decree has to establish the guaranteed minimum inter-professional wages, as well as the rates of minimum family allowances and, in the absence of collective agreements, the minimum wages by occupational category. <fnref n="47" />

<footnote n="42">⁴²Loi n° 015/2002 du 16 octobre 2002 portant Code du Travail</footnote>

<footnote n="43">⁴³Ordonnance n° 08/040 du 30 avril 2008 portant fixation du salaire minimum interprofessionnel garanti, des allocations familiales minima et de la contre-valeur du logement</footnote>

<footnote n="44">⁴⁴https://www.ei-ie.org/index.php/en/item/17201:drc-strikes-suspended-after-successful-negotiations, https://reliefweb.int/report/democratic-republic-congo/situation-humanitaire-en-rdc-semaine-du-17-au-22-aoc3%BBt-2008, https://www.france24.com/fr/20081007-le-mouvement-greve-prend-lampleur-a-kinshasa-reportage-rd</footnote>

<footnote n="45">⁴⁵https://blog.courrierinternational.com/afrikarabia/2008/05/17/rdc-vie-chere-et-tensions-sociales/</footnote>

<footnote n="46">⁴⁶https://www.rfi.fr/fr/afrique/20170323-rdc-salaires-smig-hausse-kuku , https:// www.ifsi-isvi.be/general/rdcongo-enjeu-syndicalisme/ , https://www.cgt.fr/actualites/ international/droit-situation-demploi-precarisee-mobilisation/appel-la-greve-generale-le</footnote>

<footnote n="47">⁴⁷« Un décret du Président de la République, pris sur proposition du Ministre ayant le Travail et la Prévoyance Sociale dans ses attributions, après avis du Conseil National du Travail, fixe les salaires minima interprofessionnels garantis ainsi que les taux des allocations familiales minima, et à défaut de conventions collectives ou dans leur silence, les salaires minima par catégorie professionnelle. »</footnote>
<figure><img src="image_11.png" /><figcaption>Figure A.6: Evolution of Minimum Wage in DRC</figcaption></figure>

Source: International Labour Organization (2021); The World Bank (2021)

### A.3.3 Arab Republic of Egypt

#### Minimum wage shock

Two are the shocks affecting Egyptian firms that we use in our empirical analysis. The first shock affected the 2013 wave: the minimum wage increased from LE 400 in 2010 to LE 700 in 2011, which renders a 75% change. In constant terms, the minimum wage increased from LE 926.71 to LE 1452.35, which means a 56.72% change. The second shock is the one affecting the 2016 wave: the minimum wage remained at LE 700 in 2013, after the change in 2011, and increased again to LE 1,200 in 2014. This implies a 71.43% change in nominal terms and a 54.1% change in real terms (from LE1,118.12 to LE1,722.98).

In 2010, 1.9 million citizens were earning less than LE 700 so the government decided to set the minimum wage at such value for fiscal year 2011-2012 but targeted further increases to reach the LE 1,200 floor in 5 years, which was the case in 2014. A 10% tax on capital gains
was introduced and a 5 percentage point increase in income tax was approved to help finance
the restructuring of the wage system<fnref n="48" />. The minimum wage had already been increased from
LE35 to LE400 in 2010 after thousands of public workers and factory employees had held strikes
over their low salaries because of inflation and commodity price increases. The seemingly high
increase was regarded as trivial given the price hikes<fnref n="49" />.

Minimum wage fixing

Minimum wages in Egypt are decided by the National Council for Wages, which is formed by decree of the prime minister and made up of eight experienced ministers and heads of different national agencies, four employer representatives and four trade union representatives. Other specialists are welcome to attend upon invitation but have no vote. The Council must set the minimum wage at the national level depending on the cost of living and «providing the methods and measures guaranteeing the realisation of balance between wages and prices.» The minimum wage is to cover all workers with the exception of public workers, domestic service workers and dependent family members of the employer. It is also within the council's tasks to set minimum periodical yearly increases of «at least 7% of the basic salary on the ground of which the social insurance contributions are reckoned»<fnref n="50" />.

A.3.4 Gabon

Minimum wage shock

There was a minimum wage increase in Gabon that took place in 2006 and affected the 2009
wave (fiscal year 2007). However, because we lack an additional wave before, we cannot assess
the effect of such a shock<fnref n="51" />.

<footnote n="48">⁴⁸https://dailynewsegypt.com/2011/06/01/egypt-sets-minimum-wage-at-le-700-sees-26-pct-growth/</footnote>

<footnote n="49">⁴⁹https://www.ilo.org/dyn/travail/docs/438/_www.businessweek.com_ap_financialnews_ D9J5E7VG0.pdf</footnote>

<footnote n="50">⁵⁰Decree 983 of 2003 concerning the establishment of a National Council for Wages comprehensively lists all tasks expected to be carried out by the National Council and describes the minimum wage fixing procedure.</footnote>

<footnote n="51">⁵¹In econometric terms this means that the change from *MW Shock*<sub>c,t-1,t-2</sub> = 0 to *MW Shock*<sub>c,t-1,t-2</sub> = 1</footnote>
<figure><img src="image_12.png" /><figcaption>Figure A.7: Evolution of Minimum Wage in Egypt</figcaption></figure>

Source: International Labour Organization (2021); The World Bank (2021)

The minimum wage increased from FCFA 44,000 in 2005 to FCFA 80,000 in 2006 after the
*Decree 855/PR/MTE, 9/11/2006, fixing the minimum wage* was passed with effect from October
$1^{52}$. It had remained unchanged since it was modified in 1985$^{53}$. In nominal terms this is an
81.82% change. In real terms, the minimum wage increased from FCFA 33,782.66 to FCFA
55,906.13 between those years, a 65.49% rise. The increase took place after the re-election of
President Omar Bongo in November 2005.

It was not only the minimum wage that increased but also the “*pont d’indice*” which increased
from 400 to 425. This is used to calculate the salary for public agents. For instance, a normal
3<sup>rd</sup> grade civil servant earned 40x400+44,000 in 2005 and 40x425+80,000 in 2006. Thus, for
*cannot be identified as the indicator remains constant across all surveyed firms in Gabon.*

$^{52}$Décret n° 855/PR/MTE du 9 novembre 2006, fixant le salaire minimum interprofessionnel garanti en République gabonaise

53 Décret n° 1036/PR/MTE du 19 juin 1985, portant réajustement du salaire minimum interprofessionnel garanti
the public sector the wages increased even more<fnref n="54" />. Allegedly, the procedure requested by law to
examine economic and working conditions and to recommend a minimum wage rate to the pres-
ident had not been followed since 1994 given the austerity policy recommended by international
financial institutions.<fnref n="55" />

Minimum wage fixing
---

The government decides on a national minimum wage for all workers after tripartite (or bipartite) discussions. The National Wages Committee, composed of four government representatives, and one representative from each of the four most important employers' organisations and trade unions, has to provide the government with a recommendation on the minimum wage. Although the government may also set specific rates for certain occupations or sectors not covered by a collective agreement, in practice, the government sets the minimum wage at the national level.

The frequency of minimum wage adjustment is not set forth in the legislation. However, the National Wage Committee must meet at least once every three years<fnref n="56" />.

A.3.5 Liberia

Minimum wage shock

The minimum wage shock considered is the one affecting the 2017 wave (fiscal year 2016).

The minimum wage was USD 52 per month in 2014 and increased to USD 141.3 in 2015⁵⁷.

The increase occurred after health worker protests, following the Ebola crisis<fnref n="58" />. The floor had

remained unchanged for decades<fnref n="59" /> until the *Decent Work Act* was passed in 2015. The increase

<footnote n="54">⁵⁴http://www.bdpgabon.org/archives/content/view/4209/1/</footnote>

<footnote n="55">⁵⁵https://2009-2017.state.gov/j/drl/rls/hrrpt/2006/78735.htm</footnote>

<footnote n="56">⁵⁶The minimum wage fixing procedure is described in *Loi no 3/94 du 21 novembre 1994 portant Code du travail and Decret n° 642/PR/MTEFP du 18 juin 1999, fixant la composition de la Commission Nationale d'Etude des Salaires*.</footnote>

<footnote n="57">⁵⁷The minimum wage is set in USD</footnote>

<footnote n="58">⁵⁸Source: VOA Voice of America English News, *Liberian Health workers to Strike Over Ebola Pay*, 8 October 2014</footnote>

<footnote>Al Jazeera, *Liberia nurses threaten strike over Ebola pay*, 12 October 2014</footnote>

<footnote n="59">⁵⁹*Labor Law, as enacted by the National Legislature, Reproduced by the Ministry of Labor, Liberia* (the date is</footnote>
<figure><img src="image_13.png" /><figcaption>Figure A.8: Evolution of Minimum Wage in Gabon</figcaption></figure>

Source: International Labour Organization (2021); The World Bank (2021)

meant a 171.73% change in nominal terms. In real terms, the minimum wage changed from USD 23.27 to USD 62.58, implying a 168.91% increase⁶⁰.

There is some evidence that by February 2016 the law had not been enforced yet and the new minimum wage was expected to come into effect in March 2016. In addition, it seems like workers were not satisfied with the increase since it did not adjust to their current realities.⁶¹ Indeed, the initial proposal envisioned a higher minimum wage that was not approved given the disagreement between the Senate and the Houses of Parliament.⁶²

## Minimum wage fixing

unclear, but the document says the section regarding the minimum wage was added in 1977).

⁶⁰US Human Rights Report 2016 describes a different minimum wage before the increase. However, the initial level reported is even lower than what is reported in the law (USD 0.17 per hour), and the change is only recorded in the 2016 report. Nevertheless, we will stick to what the law says (i.e. USD 0.25 per hour or USD 52 per month).

⁶¹https://thenewdawnliberia.com/new-labor-law-march-1-enforcement-requires-commitment-sincerity/, https://thenewdawnliberia.com/senate-finally-passes-decent-work-bill/

⁶²https://allafrica.com/stories/201505201121.html
The Decent Work Act established the Minimum Wage Board, consisting of the Minister of Labor as the Chairperson, and four more people appointed by the President based on recommendations by the National Tripartite Council. The members of the Board shall «from time to time» review and recommend minimum wages promoting decent work in the country. Such recommendations shall consider living standards in the community, the amount necessary to keep up with the health, efficiency and well-being of workers and their families, the state of economic development and growth (including productivity) and the impact that increasing minimum wages might have on the competitiveness and viability of businesses. Recommendations on minimum wages shall apply to all employees covered by the Decent Work Act and may differ by economic sector and occupation or be defined in terms of piece rates when it is the case. The Minimum Wage Board shall also take into account the most appropriate means and time frame for introducing minimum wage changes.

On an annual basis, the Board shall submit a report to the Minister of Labor for its subsequent publication in the Annual Report of the Ministry of Labor. All recommendations must be thoroughly justified and have to be submitted within 60 days after concluding that a new minimum wage should be set. The Minister of Labor shall then publish the recommendations made by the Minimum Wage Board in the Official Gazette within 60 days. After being published the minimum wage order takes effect.<fnref n="63" />

### A.3.6 Malawi

#### Minimum wage shock

Firms in the 2014 wave in Malawi (fiscal year 2013) were affected by a minimum wage shock as the minimum wage was increased to MWK 8,242 per month in 2012 from MWK 4,635 the previous year. This corresponds to a 77.82% increase in nominal terms. In real terms, the

<footnote n="63">⁶³Decent Work Act, 2015</footnote>
<figure><img src="image_14.png" /><figcaption>Figure A.9: Evolution of Minimum Wage in Liberia</figcaption></figure>

Source: International Labour Organization (2021); The World Bank (2021)

minimum wage was MWK 19,127.22 in 2011 and MWK 28,908.15 in 2012, which is equal to a 51.14% increase. The rise followed anti-government protests that took place in the main cities on 19 July 2011. Malawians marched against recurrent fuel and foreign exchange reserve shortages due to fuel crises and strict control of foreign exchange exports. Foreign exchange controls were decided after the UK (Malawi's major aid donor) suspended part of its aid programme, followed by the World Bank, the AfDB, the EU, Germany and Norway. The first protests then turned into two days of riots, with violent disruptions leading to 20 deaths<fnref n="64" /> and causing further civil unrest's. After the passing of President Mutharika in April 2012, the new administration led by Joyce Banda raised the minimum wage in July 2012 following an agreement between the consortium of trade unions, employer organizations, the Economic Association of Malawi and

<footnote n="64">⁶⁴Source: The Guardian, *Malawi protesters killed during anti-regime riots*, 21 July 2011
Industrial Global Union, *19 dead after violent disruption of civil protests in Malawi*, 26 July 2011
Human Rights Watch, *Malawi: Use Restraint in Upcoming Protests*, 17 August 2011</footnote>
the Ministry of Labor. Further increases followed<fnref n="65" />.

In spite of the fact that the 2012 increase was welcomed among workers and even though these increases might look huge<fnref n="66" />, people were soon complaining again. Due to the devaluation of the currency and high inflation, workers were facing serious difficulties in affording a living with their current wages. This was particularly the case of public workers who were involved in massive and lengthy strikes in 2013 that even led to the closing of schools and the international airport. People blamed the reforms based on the austerity policy demanded by the IMF.<fnref n="67" />

*Minimum wage fixing*

The Minister of Labor, if she/he believes it is convenient to set a minimum wage for any group of employees, shall consult with workers' organizations and employers relevant to the affected wage earners in order to fix the appropriate level of minimum wage. After such consultations, the Minister can publish in the Gazette the wage order prescribing the minimum wage to be paid to the affected group of wage earners.

When prescribing minimum wages, the Minister shall consider, if possible, workers' and families' needs, the general level of wages, the cost of living, social security, the living standards of other social groups and economic factors such as productivity or the employment effect that wages might have.

The consultation of the Minister with workers' and employers' representatives in order to reconsider the levels of minimum wages shall take place at least once every three years<fnref n="68" />.

<footnote n="65">⁶⁵The minimum wage was drastically increased in 2014 by 73.68% to MWK 14,315 (+43.68% to MWK 32,627.33 in real terms).</footnote>

<footnote n="66">⁶⁶https://www.nyasatimes.com/malawi-increase-minimum-wage-rate-over-100-percent/#:
~:text=%E2%80%9CWith%20effect%20from%20July%201,ECAMA)%20and%20Ministry%20of%20Labour,
https://www.nyasatimes.com/malawi-to-give-least-paid-civil-servants-over-40-pay-rise/</footnote>

<footnote n="67">⁶⁷https://reliefweb.int/report/malawi/malawi%E2%80%99s-president-faces-crisis-confidence,
https://www.reuters.com/article/malawi-strike-idINL6NOBLDP220130221</footnote>

<footnote n="68">⁶⁸Employment Act, 1999 (No. 6 of 2000). *Government Gazette, Supplement, 2000-05-19.*</footnote>
<figure><img src="image_15.png" /><figcaption>Figure A.10: Evolution of Minimum Wage in Malawi</figcaption></figure>

Source: International Labour Organization (2021); The World Bank (2021)

### A.3.7 Niger

#### Minimum wage shock

In 2007, there was a minimum wage shock affecting the 2009 wave in Niger (fiscal year 2008). However, given that this is the first wave available, we cannot assess the effect as there is no previous information to compare to<fnref n="69" />.

The minimum wage was CFA 20,000 in 2006 and increased to CFA 28,347 in 2007, this is a 41.74% change. In real terms, the minimum wage stood at CFA 28,240.35 in 2006 and changed to CFA 37,395.09 in 2007, meaning a 32.43% increase. The minimum wage had remained unchanged for four years until it was increased in 2006⁷⁰, changed again in 2012⁷¹ and has

<footnote n="69">⁶⁹Similarly to Gabon, we cannot identify the change from $MW\ Shock_{c,t-1,t-2} = 0$ to $MW\ Shock_{c,t-1,t-2} = 1$. In this case, the indicator would remain 1 since the first available wave.</footnote>

<footnote n="70">⁷⁰Décret n° 2006-058/PRN/MFP/T du 8 mars 2006 fixant le nouveau taux horaire du salaire minimum inter-professionnel garanti.</footnote>

<footnote n="71">⁷¹Décret n° 2012-359/PRN/MFP/T du 17 août 2012 fixant le nouveau taux horaire du salaire minimum inter-professionnel garanti (SMIG).</footnote>
remained unmodified ever since.

In July 2006, hundreds of Nigeriens marched through Niamey's streets to protest against the soaring costs of public services and commodity prices, including fuel. Such peaceful protests then became weekly until mid-July when they paralysed the country for a month. The national decision to raise the minimum wage followed later that year<fnref n="72" />.

### Minimum wage fixing

The government publishes a decree with the new minimum wages after consultation with the social partners, specifically, the Consultative Commission of Work. The Consultative Commission of Work sets the interprofessional minimum wage as well as the minimum wage for each professional category and the overtime limits, night work limits, holidays and seniority and attendance bonuses<fnref n="73" />. In practice, each occupation and sector has a differentiated «taux horaire».

### A.3.8 Nigeria

#### Minimum wage shock

The Nigerian government raised the minimum wage by 227.3% in nominal terms in 2011 (from N 5,500 to N 18,000 per month), following a series of protests and right before the elections. In real terms, this meant a still large increase of 198.1% after the *National Minimum Wage (Amendment) Act, 2011* was passed. Because of the lack of reliable data, we cannot assess this increase as a shock in our empirical exercise<fnref n="74" />.

In March 2011, doctors went on strike claiming a pay rise in Lagos state hospitals. In April, the minimum wage rise was enacted but not immediately implemented. The increase soon

<footnote n="72">⁷²Source: Guardian, *Niger rights groups protest cost of living*, 06 July 2006
The New Humanitarian, *Protests against soaring cost of living*, 10 July 2006</footnote>

<footnote n="73">⁷³The minimum wage fixing procedure is described in the Labor Code (Ordonnance n. 96-039 du 29 Juin 1996 portant Code du Travail).</footnote>

<footnote n="74">⁷⁴There are two additional waves of Nigerian World Bank *Enterprise Survey* data, one for 2007 and another one for 2009. The misleading magnitude and distribution of monetary variables and the lack of information on the measurement system (and currency) prevented us from using them. We argue that it is dangerous to assess the effect of a minimum wage shock if the labor costs distribution seems deceptive beforehand.</footnote>
<figure><img src="image_16.png" /><figcaption>Figure A.11: Evolution of Minimum Wage in Niger</figcaption></figure>

Source: International Labour Organization (2021); The World Bank (2021)

evolved into a complex issue, involving different social actors, federal governments, the central government, and workers. In August and September 2011, social unrest started again as people protested against the non-implementation of the National Minimum Wage (Amendment) Act. The Nigeria Labour Congress and Trade Union Congress warned of potential nationwide strikes if the minimum wage was not implemented for workers in all grade levels and across all states. The increase in the minimum wage meant, however, a huge increase in public spending, exceeding in some states the stipulated thresholds for recurrent and capital expenditure<sup>75</sup>.

## Minimum wage fixing

The minimum wage is set at the national level by the government. The *National Minimum Wage Act, 1981* does not establish with which organs the government should discuss the min-

<footnote n="75"><sup>75</sup>Source: Al Jazeera, *Nigerian doctors strike work*, 2 May 2011
Daily Trust, *Nigeria: Anambra Workers Protest Over Minimum Wage, Embark On Strike*, 09 August 2011, https://www.vanguardngr.com/2011/08/minimum-wage-nlc-warns-govs-against-violation/, https://www.vanguardngr.com/2011/10/minimum-wage-workers-new-burden/</footnote>
<figure><img src="image_17.png" /><figcaption>Figure A.12: Evolution of Minimum Wage in Nigeria</figcaption></figure>

Source: International Labour Organization (2021); The World Bank (2021)

imum wage although a tripartite committee was formed to facilitate discussions for the 2000 increase. The law does not set forth the criteria to be followed or the frequency of adjustment either. The minimum wage can also be determined through decentralized collective bargaining. In such a case, unless explicitly excluded from the scope of the National Minimum Wages Act, the rates set in the collective agreement must be as high as the national minimum wage.

The national rate must apply to all workers in Nigeria (including civil servants) with the exception of part-time workers, workers paid on a commission or piece-rate basis, seasonal employees, workers in merchant shipping or civil aviation, and employees in establishments employing fewer than 50 workers. The law also foresees exceptions for disabled workers in case the employer has been granted an exemption from the Minister of Labor<sup>76</sup>.

<footnote n="76">⁷⁶See article I in section 4 in the *National Minimum Wage Act, 1981*.</footnote>
## A.4 Sierra Leone

### Minimum wage shock

In 2015, Sierra Leone raised the minimum wage to LE 500,000 per month from LE 21,000, the level at which it stood since 1997. This is a 2,281% increase in nominal terms. In real terms, this is a 1,903% hike (from LE 9,889 to LE 198,079). This shock affected the firms in the 2017 wave (fiscal year 2016)<fnref n="77" />.

The minimum wage increase followed violently repressed riots that erupted in the mining sector over wages and inequalities between national workers and expatriates during 2012 and 2013. In April 2012, workers in the *African Minerals*' mine in Sierra Leone<fnref n="78" /> rioted for two days over pay. The protests were severely and violently repressed by the authorities, resulting in one dead and many injured. In December of that year, miners from the Koidu diamond mine<fnref n="79" /> set on strike claiming a bonus payment. The riots left two deaths as the police opened fire on protesting workers.<fnref n="80" />

The following year was, nevertheless, marked with protests linked to the Ebola crisis, including protests over pay of workers at high risk <fnref n="81" />.

### Minimum wage fixing

According to the *Minimum Wage Act, 1997* the minimum wage should be decided by the Minister of Labor after consultation with the Joint National Board as established under the *Regulation of Wages and Industrial Relations Act 1971. No. 18*. The Minister is in charge of publishing the order through a statutory instrument amending the minimum wage in the

<footnote n="77">⁷⁷The Statutory Instrument No.6 of 2014 amended the Minimum Wage Act, 1997 (No. 1 of 1997) with effect from January 1 2015.</footnote>

<footnote n="78">⁷⁸*African Minerals* is a mining company operating in Sierra Leone and listed in the London Stock Exchange</footnote>

<footnote n="79">⁷⁹Koidu Limited is an Israeli-owned company devoted to diamond mining in Sierra Leone.</footnote>

<footnote n="80">⁸⁰Thomson Reuters, *African Minerals workers riot in Sierra Leone*, 20 April 2012
Thomson Reuters, *Violent strike halts work at Sierra Leone Koidu diamond mine*, 21 December 2012, https://www.refworld.org/docid/51b8515918.html</footnote>

<footnote n="81">⁸¹https://www.reuters.com/article/us-health-ebola-leone-idUKKCNOI32G720141014; https://www.bbc.com/news/world-africa-30191938</footnote>
<figure><img src="image_18.png" /><figcaption>Figure A.13: Evolution of Minimum Wage in Sierra Leone</figcaption></figure>

Source: International Labour Organization (2021); The World Bank (2021)

*Minimum Wage Act, 1997* section 2. The law does not set forth how frequently the minimum rate is revised or the criteria to be followed.

### A.4.1 United Republic of Tanzania

The minimum wage in Tanzania was set at TZS 48,000 per month in 2009 and changed to TZS80,000 after the *Regulation of Wages and Terms of Employment Order* came into force in May 2010⁸². This is a 66.67% nominal increase. In real terms, the minimum wage in 2009 stood at TZS 81,474.66 and increased to TZS 124,090.9 in 2010, which corresponds to a 52.31% change. This shock affected firms in the 2013 wave (fiscal year 2012).

Although it was not a nationwide social protest, teachers marched in February 2009 claiming their salary arrears in the Northwest of the country. This localized protest was severely repressed as government officials ordered physical punishments for 32 teachers. In addition, workers of the

<footnote n="82">⁸² *Government Notice No. 172 published on 30/04/2010*</footnote>
Tanzania Railways started a strike throughout the country in September 2009 against delays in wage payments<fnref n="83" />.

In May 2010, the sole Labour federation in Tanzania, to which a third of the formal work-force belonged, organised a nationwide strike to claim better wages and pensions and reduced income taxes. However, it was called off and both the government and the federation reached an agreement to increase minimum wages and exempt low-paid government employees from income taxation<fnref n="84" />.

Later on, the minimum wage increased again from TZS80,000 to TZS100,000 in 2013 (where it actually stands now), although this is not captured in our data<fnref n="85" />.

## Minimum wage fixing

The Minister of Labor may designate a wage board for a particular sector and area, tasked with conducting an inquiry into compensation and employment conditions within that sector and area. The wage board should consist of a chairperson, someone nominated by the Labor, Economic and Social Council representing the employees, and someone nominated by the Labor, Economic and Social Council representing the employers. The board may also question anyone possessing information pertinent to the ongoing investigation or even conduct public hearings or facilitate negotiations between trade unions, employers and employers' associations in the sector.

Subsequently, the wage board is required to submit its findings and recommendations on minimum wage on any terms and conditions of employment particular to that sector or area to the Minister. In case the negotiations of the board with trade unions, employers and employers' associations in the sector lead to a collective agreement on the minimum wage or the like employment conditions, the board may recommend extending the agreement to the full sector and area as long as the parties involved were representative enough.

<footnote n="83">⁸³Source: Thomson Reuters, *Tanzania teachers caned for seeking salaries*, 13 February 2009
The Independent, *Tanzania Railways workers on strike*, 15 September 2009</footnote>

<footnote n="84">⁸⁴https://2009-2017.state.gov/documents/organization/160147.pdf</footnote>

<footnote n="85">⁸⁵*Labour Institutions Wage Order 2013, Government Notice No.196* published on 28/06/2013</footnote>
The Minister may then issue a wage order determining the minimum wage or other conditions of employment after considering the recommendation of the board. In case the Minister does not accept such a recommendation, it might be referred back to the wage board for reconsideration. If the Minister does not issue a wage order within 60 days, then she/he should refer to the National Assembly within 14 days and explain the reasons for not issuing an order <fnref n="86" />.

<figure><img src="image_19.png" /><figcaption>Figure A.14: Evolution of Minimum Wage in Tanzania</figcaption></figure>

Source: International Labour Organization (2021); The World Bank (2021)

## A.4.2 Zambia

Two are the minimum wage shocks that affect our data for Zambia. The first is the one affecting the 2013 wave (fiscal year 2012): the minimum wage changed from ZMW 268 in 2010 to ZMW 420 after the *Minimum Wages and Conditions of Employment Order, 2011* was passed<fnref n="87" />, which

<footnote n="86">⁸⁶The minimum wage fixing procedure is described in Part V, Wage Boards, in the *Labour Institutions Act* (No. 7 of 2004).</footnote>

<footnote n="87">⁸⁷ILOSTAT registered an increase to ZMW420 but actually the order says ZMW 419. https://www.ilo.org/dyn/travail/docs/2275/MWCE%20-%20Shop%20Workers%20Order%202011.pdf</footnote>
renders a 56.72% change. In constant terms, the minimum wage increased from ZMW 268 to ZMW 378, which means a 41.04% change. The minimum wage was increased again to ZMW 700 with the *Minimum Wages and Conditions of Employment Order, 2012* (this is a 66.67% increase in nominal terms and 55.77% in real terms). Thus, there were two consecutive shocks affecting firms in the 2013 wave.

Soon after the new president was appointed in the 2011 elections, the minimum wage was increased. It looks like this responded to the claims of workers in the mining sector who were paid even below the minimum wage back then<fnref n="88" />. Riots were especially harsh the following year when miners killed their Chinese manager while striking in protest against the delays in implementing the new minimum wage<fnref n="89" />.

The second shock affecting our Zambian data (wave 2019, fiscal year 2019) took place when the minimum wage was increased again from ZMW 700 (where it stood since the 2012 rise) to ZMW 1,050 in 2018 <fnref n="90" />. This implies a 50% change in current terms. In real terms, the minimum wage changed from ZMW381.68 to ZMW533.02, this is a 39.65% change<fnref n="91" />.

It seems like strikes were widespread in 2017 and 2018 and it not only concerned miners but also public workers in the post service, council workers who had not been paid for months and doctors who were demanding better work conditions, including higher wages<fnref n="92" />. The Labor Commissioner even demanded unions to stabilise the situation to avoid unnecessary strikes as minimum wage increases were indeed under consideration<fnref n="93" />.

<footnote n="88">⁸⁸https://www.reuters.com/article/ozatp-zambia-strikes-20111010-idAFJ0E7990KG20111010, https://mg.co.za/article/2011-10-10-zambian-workers-set-to-strike/</footnote>

<footnote n="89">⁸⁹https://www.bbc.com/news/world-africa-19135435, https://www.thenewhumanitarian.org/report/96073/zambia-dreaming-minimum-wage</footnote>

<footnote n="90">⁹⁰*Minimum Wages and Conditions of Employment Order, 2018*</footnote>

<footnote n="91">⁹¹According to ILOSTAT, the minimum wage was increased to ZMW1,698.6 but this includes allowances. We will consider ZMW 1,050 which is the minimum wage that corresponds to the category ILOSTAT is considering (Handy boy, Office boy (Category I), which is the lowest) but without the allowances. We do this for the sake of consistency as the data for the previous years excluded such allowances.</footnote>

<footnote n="92">⁹²https://www.wsws.org/en/articles/2018/12/28/work-d28.html, https://www.zambiawatchdog.com/strike-by-doctors-continue/, https://www.zambiawatchdog.com/zampost-workers-go-on-strike-2/</footnote>

<footnote n="93">⁹³http://www.daily-mail.co.zm/prevent-strikes-unions-told/</footnote>
## Minimum wage fixing

The Minister of Labor may, by statutory order, prescribe the rates to be paid to workers in case she/he thinks that there is no current adequate provision for the regulation of minimum wages or minimum conditions of employment for any category of workers. The *Minimum Wages and Conditions of Employment Act* does not set forth the procedure to be followed by the Minister. Nevertheless, in case there is a trade union representing the workers affected by the statutory order, this should be consulted before issuing it.

Any worker affected by a statutory order fixing their minimum wage or conditions of employment may apply for a review of such order to the Minister of Labor<fnref n="94" />.

<figure><img src="image_20.png" /><figcaption>Figure A.15: Evolution of Minimum Wage in Zambia</figcaption></figure>

Source: International Labour Organization (2021); The World Bank (2021)

<footnote n="94">⁹⁴The minimum wage fixing procedure is described in the *Minimum Wages and Conditions of Employment (Shop Workers) Order (Order 119 of 1997)*.</footnote>
# Appendix B Additional Statistics, Results and Robustness Checks

## B.1 Sample

<figure><img src="image_21.png" /><figcaption>Figure B.1: Full / Panel samples and Minimum Wage Shocks</figcaption></figure>

Source: The World Bank (2021), calculations by the authors.
## B.2 Location of WBES firms

The World Bank *Enterprises Surveys* provide information on the location of firms within countries. We primarily use the variable "a3x", which includes the name of the city/town/village ('physical location of the establishment as determined by the screener questionnaire'), when available. Alternatively, we use the variable "a2", which provides the sampling region (a regional classification of the establishment as defined in the sample frame). Based on this information, we carefully review the location details, manually correcting discrepancies or variations in spelling. When the location information was not precise enough (e.g., 'South-West'), we drop it from the dataset. Similarly, we exclude cases where there is uncertainty about the exact city name or potential confusion between two cities. After correcting the list of cities, we use the OpenCage Geocoding API<fnref n="95" /> in Stata to obtain the precise GPS coordinates. In doing so, we geolocate 24,038 observations for the full sample (71% of the sample) and 6,945 observations for the panel sample (83% of the sample).

We then match the location with urban clusters using the GHSL Data Package (European Commission, 2023).<fnref n="96" /> The city boundaries are retrieved using the GHS Settlement Model Grid (Schiavina et al., 2023; European Commission and Statistical Office of the European Union, 2021) using classes 30 ("Urban Centre grid cells")<fnref n="97" /> and classes 23 ("Dense Urban Cluster grid cell")<fnref n="98" />.

The last step is to use nighttime light (NTL) data by Li *et al.* (2020). We match the city boundaries identified in the previous step with luminosity values within the same clusters. We use average NTL intensity in 2000. The dataset by Li *et al.* (2020) is a harmonised time series

<footnote n="95">⁹⁵https://opencagedata.com/tutorials/geocode-in-stata.</footnote>

<footnote n="96">⁹⁶We used the raster files with 1km resolution in the Mollweide coordinates system</footnote>

<footnote n="97">⁹⁷"An urban centre consists of contiguous grid cells (4-connectivity cluster) with a density of at least 1,500 inhabitants per km² of permanent land, and has at least 50,000 inhabitants in the cluster with smoothed boundaries (3-by-3 conditional majority filtering) and <15 km² holes filled".</footnote>

<footnote n="98">⁹⁸"A Dense Urban Cluster consists of contiguous cells (4-connectivity cluster) with a density of at least 1,500 inhabitants per km² of permanent land and has at least 5,000 and less than 50,000 inhabitants in the cluster".</footnote>
<figure><img src="image_22.png" /><figcaption>Figure B.2: Location of WBES firms</figcaption></figure>

Source: The World Bank (2021), location computed by the authors using OpenCage Geocoding AI.
from 1992 to 2021 which provides "lights from cities, towns, and other continuous lighting areas at night", with luminosity values ranging from 0 to 63. Figure B.3 shows the Kernel Density. The data seem to follow a multimodal distribution, with two distinct peaks after the dashed vertical lines depicted in the plot. These values correspond to the NTL intensity for the 2nd and the 8th deciles (25.83 and 57.2, respectively). Accordingly, we classify firms into three groups: (i) those located in cities with NTL intensity below the first cutoff are said to operate in small markets, (ii) those in between cutoffs are said to belong to medium markets, and (iii) those in cities with NTL intensity above the second cutoff are considered to be operating in large markets.<fnref n="99" />

<figure><img src="image_23.png" /><figcaption>Figure B.3: Nighttime Light Intensity Distribution: Kernel Density Plot</figcaption></figure>

Source: Li et al. (2020), calculations by the authors. Note: kernel = epanechnikov, bandwidth = 1.9927.

<footnote n="99">⁹⁹This classification roughly corresponds to those in the bottom 15% (small markets), those in the top 26% (large markets) and those in between (59% in medium sized markets). Provided the large number of repeated values (recall that all firms operating in the same city have the same data), all deciles do not contain the same number of observations.</footnote>
## B.3 Baseline results

Tables B.1 and B.2 show the country fixed effects specification results using the panel firms and panel countries sample, respectively. While the former includes only firms that are at least twice in our data, the latter considers the full sample for those countries that have a panel structure (i.e. for which at least some observations are observed twice or more). The rationale is to assess whether the different results in the panel and full sample are driven by the differences in the firm versus country fixed effects, by the panel (surviving) firms or by the country sample for which the panel structure is available. These results should be interpreted with caution as the sample is intentionally limited for the purpose of this exercise.

<table><caption>Table B.1: Panel sample: OLS results, country fixed effects</caption><thead><tr><th></th><th>(1)<br>Labour costs p/w</th><th>(2)<br>Permanent employment</th><th>(3)<br>Sales</th><th>(4)<br>Sales p/w</th></tr></thead><tbody><tr><td>Minimum Wage Shock 30% (t-1 or t-2)</td><td>0.422*<br>(0.185)</td><td>0.053<br>(0.075)</td><td>0.297†<br>(0.169)</td><td>0.229<br>(0.178)</td></tr><tr><td># Obs.</td><td>8,317</td><td>8,317</td><td>8,317</td><td>8,317</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.304</td><td>0.254</td><td>0.308</td><td>0.242</td></tr><tr><td>% Treated Obs.</td><td>27.750</td><td>27.750</td><td>27.750</td><td>27.750</td></tr><tr><td># Countries</td><td>27</td><td>27</td><td>27</td><td>27</td></tr><tr><td>Sample</td><td>Panel</td><td>Panel</td><td>Panel</td><td>Panel</td></tr><tr><td>Firms' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Sector x Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Region x Year FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001

## B.4 Instrumental variable Results

On the other hand, tables B.3, B.4, and B.5 provide further instrumental variable results for the two-step procedure borrowed from the urban economics literature. The difference between these
<table><caption>Table B.2: Panel countries full sample: OLS results, country fixed effects</caption><thead><tr><th></th><th>(1)<br>Labour costs p/w</th><th>(2)<br>Permanent employment</th><th>(3)<br>Sales</th><th>(4)<br>Sales p/w</th></tr></thead><tbody><tr><td>Minimum Wage Shock 30% (t-1 or t-2)</td><td>0.340†<br>(0.192)</td><td>-0.055<br>(0.082)</td><td>0.079<br>(0.190)</td><td>0.140<br>(0.170)</td></tr><tr><td># Obs.</td><td>26,230</td><td>26,230</td><td>26,230</td><td>26,230</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.308</td><td>0.267</td><td>0.300</td><td>0.238</td></tr><tr><td>% Treated Obs.</td><td>22.291</td><td>22.291</td><td>22.291</td><td>22.291</td></tr><tr><td># Countries</td><td>27</td><td>27</td><td>27</td><td>27</td></tr><tr><td>Sample</td><td>Full-<br>Panel</td><td>Full-<br>Panel</td><td>Full-<br>Panel</td><td>Full-<br>Panel</td></tr><tr><td>Firms' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Sector x Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Firm FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Region x Year FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001

and the ones in the 5 section is the number of observations since only one observation per wave (country-year) is considered.

Tables B.6, B.7, and B.8 report the results for the extended IV strategy using average protests and riots during the year of the shock and the previous one (i.e. instead of using only those during the year of the shock). The results for the full sample are no longer significant in the second stage, while for the panel sample, they remain strong and significant.

Similarly, tables B.9, B.10, and B.11 report the results for the extended IV strategy but including this time wave fixed effects in the IV steps (recall that no fixed effects were added at this point in the main section). The results for the panel sample are still significant for labor costs and sales (and to a lesser extent for sales per worker), while for the full sample, they are only significant (but positive) for employment.
<table><caption>Table B.3: Instrumental variable strategy: first stage</caption><thead><tr><th></th><th>(1)<br>Full sample</th><th>(2)<br>Panel sample</th></tr></thead><tbody><tr><td>Protests and riots indicator (0-10)</td><td>0.069*<br>(0.039)</td><td>0.085***<br>(0.031)</td></tr><tr><td>GDP per Capita (log) (t-2)</td><td>-0.062<br>(0.049)</td><td>-0.066<br>(0.087)</td></tr><tr><td>GDP Growth (%) (t-2)</td><td>0.012<br>(0.021)</td><td>0.001<br>(0.023)</td></tr><tr><td>Population (log) (t)</td><td>-0.040<br>(0.050)</td><td>-0.048<br>(0.065)</td></tr><tr><td>Inflation (%) (t)</td><td>0.013<br>(0.011)</td><td>0.032***<br>(0.011)</td></tr><tr><td>Rule of Law (average t and t-2)</td><td>-0.046<br>(0.090)</td><td>-0.204<br>(0.149)</td></tr><tr><td>Violence indicator (0-10)</td><td>0.022<br>(0.017)</td><td>-0.009<br>(0.021)</td></tr><tr><td>Constant</td><td>1.042<br>(0.977)</td><td>1.170<br>(1.196)</td></tr><tr><td>Kleibergen-Paap Wald F-Statistic</td><td>3.230</td><td>7.768</td></tr><tr><td>Cragg Donald Wald F-Statistic</td><td>3.810</td><td>4.302</td></tr><tr><td># Obs.</td><td>53</td><td>40</td></tr><tr><td># Countries</td><td>36</td><td>27</td></tr><tr><td>Country FE</td><td>No</td><td>No</td></tr><tr><td>Sector FE</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001. Sector FE and firms' controls included in the first step of the strategy as indicated in equation 5.
<table><caption>Table B.4: Full sample: IV strategy, second stage</caption><thead><tr><th></th><th>(1)<br>Labour costs p/w</th><th>(2)<br>Permanent employment</th><th>(3)<br>Sales</th><th>(4)<br>Sales p/w</th></tr></thead><tbody><tr><td>+30% MW shock (diff.)</td><td>0.222<br>(1.079)</td><td>0.635<br>(0.483)</td><td>0.759<br>(0.697)</td><td>0.043<br>(1.088)</td></tr><tr><td># Obs.</td><td>53</td><td>53</td><td>53</td><td>53</td></tr><tr><td>Adj. R<sup>2</sup></td><td>-0.030</td><td>-0.497</td><td>-0.086</td><td>-0.069</td></tr><tr><td>% Treated Obs.</td><td>18.868</td><td>18.868</td><td>18.868</td><td>18.868</td></tr><tr><td># Countries</td><td>36</td><td>36</td><td>36</td><td>36</td></tr><tr><td>Kleibergen-Paap Wald F-Statistic</td><td>3.230</td><td>3.230</td><td>3.230</td><td>3.230</td></tr><tr><td>Cragg Donald Wald F-Statistic</td><td>3.810</td><td>3.810</td><td>3.810</td><td>3.810</td></tr><tr><td>Sample</td><td>Panel</td><td>Panel</td><td>Panel</td><td>Panel</td></tr><tr><td>Firms' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Sector FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001. Sector FE and firms' controls included in the first step of the strategy as indicated in equation 5.

<table><caption>Table B.5: Panel sample: IV strategy, second stage</caption><thead><tr><th></th><th>(1)<br>Labour costs p/w</th><th>(2)<br>Permanent employment</th><th>(3)<br>Sales</th><th>(4)<br>Sales p/w</th></tr></thead><tbody><tr><td>+30% MW shock (diff.)</td><td>0.946***<br>(0.262)</td><td>0.260<br>(0.246)</td><td>0.930**<br>(0.324)</td><td>0.627†<br>(0.369)</td></tr><tr><td># Obs.</td><td>40</td><td>40</td><td>40</td><td>40</td></tr><tr><td>Adj. R<sup>2</sup></td><td>-0.261</td><td>-0.181</td><td>-0.311</td><td>-0.197</td></tr><tr><td>% Treated Obs.</td><td>25.000</td><td>25.000</td><td>25.000</td><td>25.000</td></tr><tr><td># Countries</td><td>27</td><td>27</td><td>27</td><td>27</td></tr><tr><td>Kleibergen-Paap Wald F-Statistic</td><td>7.768</td><td>7.768</td><td>7.768</td><td>7.768</td></tr><tr><td>Cragg Donald Wald F-Statistic</td><td>4.302</td><td>4.302</td><td>4.302</td><td>4.302</td></tr><tr><td>Sample</td><td>Panel</td><td>Panel</td><td>Panel</td><td>Panel</td></tr><tr><td>Firms' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Sector FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001. Sector FE and firms' controls included in the first step of the strategy as indicated in equation 5
<table><caption>Table B.6: Instrumental variable strategy: first stage</caption><thead><tr><th></th><th>(1)<br>Full sample</th><th>(2)<br>Panel sample</th></tr></thead><tbody><tr><td>Protests and riots indicator (0-10) (year of the shock and previous one)</td><td>0.108**<br>(0.036)</td><td>0.126***<br>(0.015)</td></tr><tr><td>GDP per Capita (log) (t-2)</td><td>-0.012<br>(0.062)</td><td>-0.050<br>(0.105)</td></tr><tr><td>GDP Growth (%) (t-2)</td><td>0.041†<br>(0.022)</td><td>0.023<br>(0.024)</td></tr><tr><td>Population (log) (t)</td><td>-0.025<br>(0.071)</td><td>0.002<br>(0.090)</td></tr><tr><td>Inflation (%) (t)</td><td>0.010<br>(0.010)</td><td>0.031*<br>(0.015)</td></tr><tr><td>Rule of Law (average t and t-2)</td><td>-0.107<br>(0.103)</td><td>-0.212<br>(0.150)</td></tr><tr><td>Violence indicator (0-10)</td><td>-0.019<br>(0.031)</td><td>-0.059*<br>(0.028)</td></tr><tr><td>Constant</td><td>0.301<br>(1.248)</td><td>0.162<br>(1.283)</td></tr><tr><td>Kleibergen-Paap Wald F-Statistic</td><td>9.097</td><td>70.538</td></tr><tr><td>Cragg Donald Wald F-Statistic</td><td>9,725.699</td><td>4,555.862</td></tr><tr><td># Obs.</td><td>22,226</td><td>5,943</td></tr><tr><td># Countries</td><td>36</td><td>27</td></tr><tr><td>Country FE</td><td>No</td><td>No</td></tr><tr><td>Sector FE</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001. Sector FE and firms' controls included in the first step of the strategy as indicated in equation 5.
<table><caption>Table B.7: Full sample: IV strategy, second stage</caption><thead><tr><th></th><th>(1)<br>Labour p/w</th><th>(2)<br>Permanent employment</th><th>(3)<br>Sales</th><th>(4)<br>Sales p/w</th></tr></thead><tbody><tr><td>+30% MW shock (diff.)</td><td>0.274<br>(0.569)</td><td>0.532**<br>(0.202)</td><td>1.006***<br>(0.303)</td><td>0.397<br>(0.503)</td></tr><tr><td># Obs.</td><td>22,226</td><td>22,226</td><td>22,226</td><td>22,226</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.187</td><td>-0.107</td><td>0.279</td><td>0.301</td></tr><tr><td>% Treated Obs.</td><td>26.307</td><td>26.307</td><td>26.307</td><td>26.307</td></tr><tr><td># Countries</td><td>36</td><td>36</td><td>36</td><td>36</td></tr><tr><td>Kleibergen-Paap Wald F-Statistic</td><td>9.097</td><td>9.097</td><td>9.097</td><td>9.097</td></tr><tr><td>Cragg Donald Wald F-Statistic</td><td>9,725.699</td><td>9,725.699</td><td>9,725.699</td><td>9,725.699</td></tr><tr><td>Sample</td><td>Panel</td><td>Panel</td><td>Panel</td><td>Panel</td></tr><tr><td>Firms' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Sector FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001. Sector FE and firms' controls included in the first step of the strategy as indicated in equation 5.

<table><caption>Table B.8: Panel sample: IV strategy, second stage</caption><thead><tr><th></th><th>(1)<br>Labour p/w</th><th>(2)<br>Permanent employment</th><th>(3)<br>Sales</th><th>(4)<br>Sales p/w</th></tr></thead><tbody><tr><td>+30% MW shock (diff.)</td><td>0.755***<br>(0.105)</td><td>0.123†<br>(0.066)</td><td>1.049***<br>(0.115)</td><td>0.904***<br>(0.115)</td></tr><tr><td># Obs.</td><td>5,943</td><td>5,943</td><td>5,943</td><td>5,943</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.273</td><td>0.199</td><td>0.067</td><td>0.074</td></tr><tr><td>% Treated Obs.</td><td>38.836</td><td>38.836</td><td>38.836</td><td>38.836</td></tr><tr><td># Countries</td><td>27</td><td>27</td><td>27</td><td>27</td></tr><tr><td>Kleibergen-Paap Wald F-Statistic</td><td>70.538</td><td>70.538</td><td>70.538</td><td>70.538</td></tr><tr><td>Cragg Donald Wald F-Statistic</td><td>4,555.862</td><td>4,555.862</td><td>4,555.862</td><td>4,555.862</td></tr><tr><td>Sample</td><td>Panel</td><td>Panel</td><td>Panel</td><td>Panel</td></tr><tr><td>Firms' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Sector FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001. Sector FE and firms' controls included in the first step of the strategy as indicated in equation 5.
<table><caption>Table B.9: Instrumental variable strategy: first stage</caption><thead><tr><th></th><th>(1)<br>Full sample</th><th>(2)<br>Panel sample</th></tr></thead><tbody><tr><td>Protests and riots indicator (0-10)</td><td>0.081***<br>(0.023)</td><td>0.118***<br>(0.026)</td></tr><tr><td>GDP per Capita (log) (t-2)</td><td>-0.128<br>(0.078)</td><td>-0.260*<br>(0.114)</td></tr><tr><td>GDP Growth (%) (t-2)</td><td>0.037†<br>(0.020)</td><td>0.035<br>(0.030)</td></tr><tr><td>Population (log) (t)</td><td>0.044<br>(0.100)</td><td>-0.029<br>(0.169)</td></tr><tr><td>Inflation (%) (t)</td><td>0.022**<br>(0.008)</td><td>0.026*<br>(0.013)</td></tr><tr><td>Rule of Law (average t and t-2)</td><td>0.118<br>(0.122)</td><td>0.127<br>(0.136)</td></tr><tr><td>Violence indicator (0-10)</td><td>-0.002<br>(0.019)</td><td>-0.003<br>(0.024)</td></tr><tr><td>Kleibergen-Paap Wald F-Statistic</td><td>11.986</td><td>20.857</td></tr><tr><td>Cragg Donald Wald F-Statistic</td><td>7,288.963</td><td>2,838.167</td></tr><tr><td># Obs.</td><td>22,226</td><td>5,943</td></tr><tr><td># Countries</td><td>36</td><td>27</td></tr><tr><td>Country FE</td><td>No</td><td>No</td></tr><tr><td>Wave FE</td><td>Yes</td><td>Yes</td></tr><tr><td>Sector FE</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001. Sector FE and firms' controls included in the first step of the strategy as indicated in equation 5.
<table><caption>Table B.10: Full sample: IV strategy, second stage</caption><thead><tr><th></th><th>(1)<br>Labour costs p/w</th><th>(2)<br>Permanent employment</th><th>(3)<br>Sales</th><th>(4)<br>Sales p/w</th></tr></thead><tbody><tr><td>+30% MW shock (diff.)</td><td>0.428<br>(0.464)</td><td>0.646*<br>(0.239)</td><td>0.666<br>(0.401)</td><td>-0.050<br>(0.487)</td></tr><tr><td># Obs.</td><td>22,226</td><td>22,226</td><td>22,226</td><td>22,226</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.240</td><td>-0.280</td><td>0.259</td><td>0.236</td></tr><tr><td>% Treated Obs.</td><td>26.307</td><td>26.307</td><td>26.307</td><td>26.307</td></tr><tr><td># Countries</td><td>36</td><td>36</td><td>36</td><td>36</td></tr><tr><td>Kleibergen-Paap Wald F-Statistic</td><td>11.986</td><td>11.986</td><td>11.986</td><td>11.986</td></tr><tr><td>Cragg Donald Wald F-Statistic</td><td>7,288.963</td><td>7,288.963</td><td>7,288.963</td><td>7,288.963</td></tr><tr><td>Sample</td><td>Panel</td><td>Panel</td><td>Panel</td><td>Panel</td></tr><tr><td>Firms' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Wave FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Sector FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001. Sector FE and firms' controls included in the first step of the strategy as indicated in equation 5.

<table><caption>Table B.11: Panel sample: IV strategy, second stage</caption><thead><tr><th></th><th>(1)<br>Labour costs p/w</th><th>(2)<br>Permanent employment</th><th>(3)<br>Sales</th><th>(4)<br>Sales p/w</th></tr></thead><tbody><tr><td>+30% MW shock (diff.)</td><td>0.735**<br>(0.215)</td><td>0.171<br>(0.105)</td><td>0.614*<br>(0.266)</td><td>0.428†<br>(0.211)</td></tr><tr><td># Obs.</td><td>5,943</td><td>5,943</td><td>5,943</td><td>5,943</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0.473</td><td>0.241</td><td>0.474</td><td>0.575</td></tr><tr><td>% Treated Obs.</td><td>38.836</td><td>38.836</td><td>38.836</td><td>38.836</td></tr><tr><td># Countries</td><td>27</td><td>27</td><td>27</td><td>27</td></tr><tr><td>Kleibergen-Paap Wald F-Statistic</td><td>20.857</td><td>20.857</td><td>20.857</td><td>20.857</td></tr><tr><td>Cragg Donald Wald F-Statistic</td><td>2,838.167</td><td>2,838.167</td><td>2,838.167</td><td>2,838.167</td></tr><tr><td>Sample</td><td>Panel</td><td>Panel</td><td>Panel</td><td>Panel</td></tr><tr><td>Firms' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Countries' Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country FE</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Wave FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Sector FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Source: The World Bank (2021), calculations by the authors. Note: Robust standard errors in parentheses, clustered at the country level. † p<0.1 * p<0.05 ** p<0.01 *** p<0.001. Sector FE and firms' controls included in the first step of the strategy as indicated in equation 5.