Minimum Wages in Sub-Saharan Africa: A Primer

Minimum Wages in Sub-SaharanAfrica: A Primer

Haroon Bhorat, Ravi Kanbur, and Benjamin Stanwix

The fraction of workers currently covered by minimum wages in Sub-Saharan Africa

(SSA) is small, but as formality and urbanization increase, wage regulation will become

increasingly relevant. In this analysis, we find that higher minimum wage values are as-

sociated with higher levels of GDP per capita, in both SSA and non-SSA countries. Using

two measures to assess the level at which minimum wages are set, we find that minimum

wages in SSA countries are on average lower—relative to average wages—than most

other comparable regions of the world. Thus, SSA as a whole reflects no particular bias

toward a comparatively more pro–minimum wage policy. Within SSA, however, we ob-

serve that low-income countries set relatively higher minimum wages than middle- or

upper-income countries. We find significant variation in the detail of minimum wage re-

gimes and schedules in the region, as well as large variations in compliance. Notably, sev-

eral countries in SSA have relatively complex minimum wage schedules, and on average

we find high levels of noncompliance among covered workers. We also summarize the

limited research on the employment effects of minimum wages in SSA, which are consis-

tent with global results. By and large, introducing and raising the minimum wage ap-

pears to have small negative employment impacts or no statistically significant negative

impacts. There are country studies, however, where substantial negative effects on em-

ployment are reported—often for specific cohorts. The release of country-level earnings

and employment data at regular intervals lies at the heart of a more substantive,

country-focused minimum wage research agenda for Africa.

Legislated minimum wages apply to many millions of workers around the world.

Over the latter half of the 20th century, almost all countries in Sub-Saharan

Africa (SSA) introduced some form of minimum wage legislation. In many cases,

The World Bank Research ObserverVC The Author 2017. Published by Oxford University Press on behalf of the International Bank for Reconstruction andDevelopment / THE WORLD BANK. All rights reserved. For Permissions, please e-mail: [email protected]:10.1093/wbro/lkw007 Advance Access publication January 3, 2017 32:21–74

these laws apply to workers in specific industries or occupations, and in instances

where wage levels are not set by the state, collectively bargained agreements may

fix wages for specific sectors or occupations. Broadly, however, the introduction of

national laws governing wages is part of an observed regulatory revival in low-

and middle-income countries, where a range of labor regulations aimed at protect-

ing low-paid workers have gradually been introduced (Piore and Schrank 2008).

As will be documented in the paper, there has been widespread adoption of mini-

mum wage legislation as a policy tool in SSA. And yet there has been little work

on the nature, scope, and impact of such laws in the region.

A key distinction must be made between the usually small, formal, wage-

earning sector and usually large, informal, non-wage-earning sector in most

African countries.1 This has implications for minimum wage policy and research.

It is well known that in the overwhelming majority of SSA economies’ subsistence

agriculture and, more recently, urban informal employment dominates the labor

market. In many such countries, wage-earning employees only make up a small

portion of the labor force. As figure 1, below, indicates for a selection of SSA coun-

tries, it is only South Africa where formal salaried employees constitute the

Figure 1. Wage-Earning Employees as a Percentage of Total Employment

020

%40

%60

%80

%

Perc

enta

ge o

f w

ag

e-e

arn

ing

em

plo

ye

es

Mali

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nza

nia

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an

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ve

rag

e

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mbia

Ken

ya

Gro

up

Ave

rag

e

Na

mib

ia

Sou

th A

fric

a

Sources: South Africa: Labour Market Dynamics Study (2013); Kenya: Kenya Integrated Household Budget

Survey (2005–6); Uganda: Uganda National Panel Survey (2012); Mali: Rani et al. (2013); Zambia: Living

Conditions Monitoring Survey (2010); Tanzania: Integrated Labour Force Survey (2005–6); Namibia: Labour

Force Survey (2012); Bhorat, Naidoo, and Pillay (2015).

22 The World Bank Research Observer, vol. 32, no. 1 (February 2017)

majority of the labor force. In the six other countries represented, salaried em-

ployees account for less than half of the labor force, while the sample average is

close to 20 percent.

Minimum wages only apply to wage-earning employees and, in some cases,

only to formal sector wage earners. Wage regulations thus cover only a minority

of the total workforce in most SSA economies. This may go some way to explaining

the dearth of existing research. However, (a) as the urban and formal sector grows,

the experience of wage regulation in the currently covered areas will become sig-

nificant, (b) there can be spill over or “lighthouse” effects on uncovered sectors,2

and (c) as we shall see, minimum wage policy is particularly progressive in the

covered sectors in Sub-Saharan Africa. For these reasons, we believe that an em-

pirical overview of minimum wages in the region is important for the current pol-

icy and analytical discourse.

It is a stylized fact that a large gap exists between de jure and de facto labor reg-

ulation in most low- and middle-income (LMI) countries globally. Simply put, the

levels of noncompliance with minimum wage laws are high. This has been shown

in several studies (Bhorat et al. 2012; Rani et al. 2013; and Almeida and Ronconi

2012) and is supported by the evidence presented in this paper. While it could be

argued that high levels of noncompliance make wage regulation irrelevant, the

reasons for high levels of noncompliance are not yet adequately understood. A study

of the enforcement of minimum wages is of interest in its own right but can also

contribute to a broader research agenda on questions related to the rule of law.

There is a paucity of available household survey data for many SSA countries,

and particularly an absence of reliable information on earnings. This makes rigor-

ous study on the impact of minimum wages difficult, particularly because any at-

tempts to measure minimum wage impacts require pre- and postintervention data,

and this is almost always absent for countries in SSA. The problem is brought into

sharp relief when analyzing one of the most extensive and centralized bodies of in-

formation on global minimum wages—the International Labour Organisation’s

(ILO) TRAVAIL database and the ILO global wage database. While these databases

contain detailed information on minimum wage frameworks, the method of setting

wages in each of the ILO’s member states, and data on enforcement practices for

almost every country, there is very limited information on wages, coverage, or lev-

els of compliance for countries in Africa. This is because for many countries in the

region reliable data on wages either do not exist or are not accessible.

It is thus the purpose of this piece to provide a basic overview of minimum wage

regimes in SSA, where data are available, as well as some more detailed work for

several countries using household survey data. We begin with a brief literature re-

view of more recent minimum wage work and build on Neumark and Wascher’s

(2007) meta-review to provide an updated overview of the empirical wage-

employment trade-off estimates, focused on LMI countries. We then document the

Bhorat et al. 23

level of minimum wages in the region, present simple Kaitz ratios (the Kaitz ratio is

the ratio of the minimum wage to the average wage) and use this to make compari-

sons with wage levels and ratios elsewhere in the world. We proceed to examine com-

pliance levels in seven SSA countries using household survey data and again

compare the outcomes to estimates for a selection of non-SSA LMI countries. We end

by reflecting on possible early lessons for wage-setting regimes in the region and on a

research agenda to inform policy makers on key trade-offs in minimum wage setting.

The Developing Country Literature: A Brief Overview

In most SSA countries, minimum wage laws are used as a policy tool to achieve a

number of objectives, and while there are large cross-country differences, wage

legislation reveals considerable overlap. The stated objectives usually focus on pro-

tecting vulnerable workers from extreme levels of low pay, addressing poverty by

redistributing income from employers to low-wage employees, and encouraging la-

bor productivity. It is well known that there are also risks associated with institut-

ing wage floors, and in most cases country-level legislation recognizes the possible

trade-offs. The costs can include increased unemployment in certain settings, ad-

justed hours of work that disadvantage workers, and the movement of workers

from formal to informal employment—where livelihoods are often more precari-

ous. Rising wages as a result of minimum wage policy, while having positive ef-

fects through driving demand, may also have some impact on the cost of living in

the medium term if firms’ cross-price elasticity response to higher labor costs is to

raise output prices.3 Beyond the various costs and benefits, which are the focus of

a well-established body of literature for developed countries, the attendant issues of

enforcement and compliance are central to any discussion of minimum wages in

SSA. Indeed, it is arguable that most of the impact of a minimum wage policy is

contingent on enforcement and compliance in LMI country settings. We proceed

to briefly analyze the minimum wage literature here.

The empirical work on minimum wages constitutes a large field that is now well

established, with several recent books and papers dedicated to reviewing the main

findings of this literature.4 Studies in the United States were the first to suggest

that the textbook wage-employment trade-offs do not always hold in practice—

that an increase in the minimum wage will not always perfectly predict a decrease

in employment (the seminal paper in the new minimum wage literature being

Card and Krueger 1994). In addition to the potential employment effects, mini-

mum wage laws have been shown to induce adjustments in hours of work and

nonwage benefits, as well as having possible “nonstandard effects” such as influenc-

ing educational decisions and reservation wages.5 The limited but fast-growing body

of work focused on LMI countries has emphasized that the impact of introducing, or


increasing, a minimum wage can have mixed impacts that are often crucially con-

tingent on a variety of factors, including the level at which the minimum wage is

set, broader economic conditions, the nature of the minimum wage intervention, po-

litical economy factors, the enforcement regime, and so on. Existing studies provide

evidence of negative employment effects in some cases but also evidence of no em-

ployment declines in others, with a range of adjustments observed for hours of

work, wages, and nonwage benefits.

In Neumark and Wascher’s (2007) meta-review, they include 15 studies focused

on eight developing countries over the period 1992 to 2006.6 The authors caution

that studying minimum wage effects in developing countries is complicated, partly

due to issues we have already mentioned above that relate to data availability and

quality, but also because the results are often not easily generalizable across countries

or sectors. The majority of findings reviewed by the authors reveal either no effects or

small negative employment effects of minimum wages in LMI country settings. In

Brazil, for example, disemployment effects and reduced work hours are seen to be mi-

nor or nonexistent overall, but more pronounced for individuals with low skills and

lower wages (Fajnzylber 2001; Lemos 2004, 2006, 2007; and Neumark et al.

2006). In Chile, increases in the minimum wage had negative employment effects for

youth and unskilled workers but led to an increase in the employment of women

(Montenegro and Pages 2004). In Colombia, research suggests that disemployment

effects were present, and these were higher for low-skilled workers (Bell 1997; and

Maloney and Nu~nez Mendez 2004). In Costa Rica, increases in minimum wage also

decreased employment and reduced hours worked by employees in covered sectors,

especially those in the lower half of the skill distribution (Gindling and Terrell 2005,

2007). Similar results are found in Indonesia and Trinidad and Tobago (Alatas and

Cameron 2003; Rama 2001; Suryahadi et al. 2003; Hyslop and Stillman 2004;

Comola and De Mello 2011; and Strobl and Walsh 2003).

In the years following the publication of Neumark and Wascher’s (2007) review

paper, a growing body of research has focused on minimum wage effects in LMI

countries (see table A4 in the Appendix for a detailed literature summary). In East

Asia, a number of papers provide new evidence on minimum wage impacts: In

Thailand, Del Carpio, Messina, and Sanz-de-Galdeano (2014) found small disem-

ployment effects on female, elderly, and less-educated workers and large positive

effects on the wages of prime-age male workers; in Vietnam, Del Carpio and Liang

(2013) and Nguyen (2010) estimates showed that low-wage formal sector employ-

ment is negatively affected. In the Phillipines, Lanzona (2012) and Del Carpio,

Margolis, and Okamura (2013) observed disemployment effects only in sectors

with high levels of compliance; and in Indonesia, two papers (Comola and De

Mello 2011; and Harrison and Scorse 2010) found disemployment effects. In

China, Wang and Gunderson (2011) and Fang and Lin (2013) provided some of

the first estimates of minimum wage effects—which are generally negative. In

Bhorat et al. 25

Latin America, several new papers built on existing work to provide new sector-

specific results on employment, but also a nuanced focus on informality, social se-

curity, and poverty (Ham 2013; Gindling 2014; and Kharmis 2013).

The literature on SSA, however, remains rather limited, with published work ex-

isting only for four countries, namely Ghana (Jones 1997), Kenya (Andal�on and

Pages 2008), Malawi (Livingstone 1995), and South Africa (Hertz 2005;

Dinkelman and Ranchhod 2013; Bhorat et al. 2013, 2014a; Bhorat et al. 2014b;

and Garbers 2015). The most comprehensive literature in the SSA region exists for

South Africa and shows that while the introduction of minimum wages had a neg-

ative impact on employment in agriculture, in all other covered sectors no employ-

ment decreases were evident (Dinkelman and Ranchhod 2013; Bhorat et al. 2013;

and Nattrass and Seekings 2014).

In figure 2(a) and (b) below, we construct a graph of employment elasticities es-

timated in the minimum wage literature described above. This includes the 98

Figure 2(a). Minimum Wage-Employment Elasticities for Low- and Middle-Income Countries

-1.5

-1-.

50

.51

Ela

sticity

Indo

ne

sia

Ho

nd

ura

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razil

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nd

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ou

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Ric

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do

ne

sia

Note: The upper dotted line is the median elasticity (-0.08); the lower dotted line is the mean elasticity (-

0.11).

Source: Neumark and Wascher (2007) and authors’ calculations.


papers reviewed in Neumark and Wascher’s (2007) work and 17 more recent

studies focused on LMI countries not included in Neumark and Wascher (2007).

The results include aggregate impacts for all workers but also the employment im-

pacts for specific demographic groups, geographic locations, and sectors. Put differ-

ently, where a study produced elasticity estimates for more than one cohort of

workers, we include each estimate separately. Unlike in the Neumark and

Wascher (2007) review, we have only included those estimates that were statisti-

cally significant, and it is worth noting that 55 percent of reported elasticity esti-

mates reviewed were not statistically significant. A detailed description of each of

these studies is provided in the Appendix.

In figure 2(a), the median elasticity is -0.08, represented by the upper dotted

line on the figure, while the mean is -0.11, shown as the lower dotted line. In fig-

ure 2(b), the averages are slightly higher and the upper dotted line represents a

Figure 2(b). Minimum Wage-Employment Elasticities for High-Income Countries

-4-2

02

Ela

sticity

Un

ited S

tate

s

Ca

na

da

Un

ited S

tate

s

Sw

ed

en

Fra

nce

Gre

ece

Un

ited S

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s

Un

ited S

tate

s

Au

stra

lia

Un

ited S

tate

s

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s

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tate

s

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s

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tate

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tate

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tate

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ites

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da

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nce

Note: The upper dotted line is the median elasticity (-0.23); the lower dotted line is the mean elasticity (-

0.37).


Bhorat et al. 27

median elasticity of -0.23, while the mean for upper-income countries is -0.37,

shown as the lower dotted line. Overall, estimated employment elasticities range

from 2.17 (Katz and Kreuger 1992) to -4.6 (Abowd, Kramarz, and Margolis 1999)

in both samples. The mean and median elasticities suggest that on average the im-

pacts of minimum wage hikes in the countries under review have been marginally

negative. From the sample of 59 developed and 32 developing country estimates,

81 percent of the elasticities were negative while 19 percent were positive. It is im-

portant to note that the absolute value of these coefficients on average is small.

This would suggest that in general the minimum wage has either benign or

slightly negative employment effects. However, the question of impact requires

careful focused empirical investigation on the basis of each minimum wage inter-

vention. In addition, it is crucial to note that in the developing country context dis-

employment effects may be further mitigated due to signficant levels of

noncompliance. We discuss this latter issue in greater detail below.

There are of course a number of studies in both developed and developing coun-

tries that do find substantial negative employment effects relative to the average

elasticities presented above. These outlier results should not be dismissed. As

Neumark and Wascher (2006) note, a study on Canadian teenagers by Baker

et al. (1999) reports an average employment elasticity of -0.25, while work by

Abowd et al. (1999) on France finds that the minimum wage had high negative

impacts on minimum wage earning men aged 25–29 years (who are not protected

by employment promotion contracts), where the employment elasticity was -4.6

relative to a comparable group of men who earn just above the minimum wage. In

Indonesia, Suryahadi et al. (2003) find an overall negative employment elasticity

of -.06, and this is more pronounced for women, where the elasticity is estimated

at -.16. An interesting paper by Feliciano (1998) shows that a decline in real mini-

mum wages in Mexico led to an increase in employment, particularly among

women, where the elasticity ranges from -0.58 to -1.25. In Honduras, Gindling

and Terrell (2007) find a wage employment elasticity of -.46 in the private sector,

while in South Africa Bhorat et al. (2014) find a decrease in agricultural employ-

ment of over 5 percent.

In addition, the figures above underscore the fact that there is a range of poten-

tial impacts of minimum wages on employment. The heterogeneity of outcomes, in

LMI countries in particular, suggests that a variety of context-specific factors inter-

act with the minimum wage. These could include: the level of the minimum wage

relative to average wages, the size of the minimum wage increase, the sector under

consideration (for example, whether it is a tradeable or nontradeable sector), the

timing of wage changes, the change postlaw in the level of worker productivity,

the enforcement regime, and the extent of compliance. While past increases in

minimum wages have generally not had a large negative affect on employment, it

is not the case that such positive outcomes will persist regardless of the level to


which a minimum wage is raised. There is a level beyond which a minimum wage

will begin to negatively affect employment, and this level may differ across geo-

graphic regions, sectors, and firms.

Figure 3, below, visually represents this idea in a basic theoretical construct by

presenting two different impact scenarios. Each of the two lines can be thought of

as labor demand functions representing the employment response to increases in

the minimum wage. The level of employment is on the y-axis, and the level of a

minimum wage increase on the x-axis. It is clear that in the case of the upper line

the minimum wage can be raised to WH (the high wage threshold) without any

impact on employment. Beyond WH, however, any increase in the hypothetical

minimum wage will begin to result in job losses. In the second case, the lower line,

the broad relationship between the minimum wage and employment stays the

same, but, crucially, employment is more sensitive to changes in the minimum

wage where wages can only be raised to WL(the low wage threshold) before they

begin to impact employment negatively.

We could interpret the upper line, for example, as representing a nontradeable

sector that can absorb wage increases more robustly, while the lower line would

represent a sector more sensitive to changes in input costs and perhaps with more

capital/labor replacement possibilities. These elements, together with the list of

abovementioned factors that influence wage-employment trade-offs, all play a role

in determining where the wage threshold lies for a given set of firms, workers, or

sectors.

Figure 3. The Relationship between Minimum Wage Adjustments and Employment

Bhorat et al. 29

One distinct possibility is that minimum wages have larger negative effects

when they are higher relative to average wages. In order to briefly examine this re-

lationship further, we plot the statistically significant point estimates of employ-

ment effects (shown in figures 2[a] and [b]) against the Kaitz ratio for each

country in the study. The figure is shown in the Appendix (figure A1). While this

is a fairly crude approach, the relationship in this case is not statistically signifi-

cant. This could be for several data-specific reasons, such as, for example, that the

employment effects are often calculated for specific subgroups or sectors within a

country for which it is difficult to calculate Kaitz ratios. However, more impor-

tantly, what this statistically insignificant relationship suggests is that the relative

level at which the minimum wage is set is not necessarily the key, or indeed the

only, factor that may impact the employment effects emanating from the mini-

mum wage—a point we emphasize in figure 3 above.

The functions above also suggest a key insight into estimated wage-employment

elasticities: namely that they can be nonlinear. Put differently, if a 50 percent in-

crease in the minimum wage results in a 10 percent drop in employment (where

the wage-employment elasticity is thus -0.2), it is not the case that a 100 percent

increase will result in a 20 percent drop in employment—the elasticity will not re-

main at -0.2. The literature suggests that where increases in a minimum wage are

large and immediate this can result in employment losses, especially for unskilled

workers. More modest increases usually have very few observably adverse effects

and may have large positive impacts on wages and a range of other labor market

outcomes. The positive wage impacts in a legislated increase appear to hold even

in situations of weak enforcement (Dinkelman and Ranchhod 2013; Bhorat et al.

2015).

Minimum Wages in Sub-Saharan Africa

Minimum wage systems in SSA, as elsewhere in the world, can be helpfully classi-

fied into three broad categories: national, sectoral (occupational), and some combi-

nation of the two (hybrid). Needless to say, a national minimum wage system is a

single wage rate that applies to all workers, a sectoral system is one in which there

are separated determinations for workers in particular sectors and/or occupations,

and a combination or hybrid system can exist when the body that decides on mini-

mum wage levels also decides to whom the wage applies and when this should

change. Table A1 (see the Appendix) groups selected African countries into these

three categories. The table serves to show the variety of systems that exist in the

region. In a report by the ILO (2013), the minimum wage frameworks on the con-

tinent are categorized in a similar manner, revealing that a majority of countries

in Africa have adopted some form of sectoral or occupational minimum wage


structure with multiple wage rates rather than a single national minimum wage—

approximately 30 percent versus 61 percent, respectively. Indeed, the percentage

of African countries with multiple minimum wage systems is higher than any of

the other world regions.7 A comprehensive account of wage-setting systems

around the world can be found in Eyraud and Saget (2005).

Beyond the diverse regulatory frameworks, the level at which the wage is set

also varies substantially by the income group that a country falls into. We show

this by grouping SSA countries into low-income (LI), lower-middle-income (LMI),

and upper-middle-income (UMI) categories. We compare 37 SSA countries in these

three groups to a comparable set of 67 non-SSA countries similarly grouped.

Figure 4, below, presents average minimum wage levels by income group for SSA

and non-SSA countries.

The figure reveals substantial differences both across the three country income

groups within SSA and compared to non-SSA countries. As expected, minimum

wage levels are positively correlated with GNI per capita levels. In particular, for

lower-income countries (LICs) in Africa with a GNI per capita of US$1,045 and be-

low, mean minimum wages stood at an average of US$119 (PPP 2013). This in-

creases by 19 percent to $142 for LMI economies and then further by 218 percent

Figure 4. Monthly Minimum Wage Levels by Country Income Group, SSA and Non-SSA

Countries (US$ PPP)

010

020

030

040

050

0M

inim

um

wag

e le

vel (U

S$

PP

P)

LI SSA LI non-SSA LMI SSA LMI non-SSA UMI SSA UMI non-SSA

Notes: LI stands for low income (in black), LMI for lower-middle income (in dark gray), and UMI for upper-

middle income (in light gray).

Sources: ILO global wage database, World Bank WDI.

Bhorat et al. 31

to $366 for UMI African economies. This relationship is explored in further detail

below. In addition, our results suggest that minimum wage levels in SSA are on

average lower than elsewhere in the world for countries in the same income group

(it must be noted that in the LI group our sample of non-SSA countries is limited to

four economies, while in the UMI group our sample of SSA countries is limited to

five countries). In the LMI category, the difference in minimum wage levels is large

and significant at the 5 percent level.

In order to advance this notion of the relationship between country income lev-

els and minimum wage levels, we produce two more figures (5[a] and [b], below).

The figures plot levels of GDP per capita against the level of minimum wages for

the same group of SSA and non-SSA countries used above. We find a relationship

between GDP and the level of the minimum wage that is not dissimilar to what

has been found for the relation between the poverty line and levels of consumption

by Ravallion et al. (2009). The latter noted that based on cross-country evidence,

the value of the national poverty line rises as average consumption levels rise

Figure 5(a). Monthly Minimum Wages and GDP Per Capita (US$ PPP), Africa

AngolaBeninBotswana

Burkina Faso

Burundi

Cameroon

Chad

Comoros

CongoCôte d'Ivoire

Democratic Republic of the Congo

Equatorial Guinea

Ethiopia

Gabon

GambiaGhana

Guinea-Bissau

Kenya

Lesotho

Liberia

Madagascar

Malawi

Mali

Mauritania

Mauritius

Mozambique

Niger

Nigeria

Senegal

South Africa

SudanTanzania, United Republic of

Togo

Uganda

Zambia

010

020

030

040

0M

inim

um

wag

e (

US

$ P

PP

)

5 6 7 8 9 10Log of GDP per capita (US$ PPP)

Fitted values Minimum wage (US$ PPP)

Note: Sample based on 37 African economies, where the latest available data for each country was utilized.



across economies. Levels of economic development are thus positively related to

the country-based poverty line. This relationship appears to hold for minimum

wage levels. As we illustrate then, for a sample of African economies and non-

African developing countries, minimum wage levels are adjusted upwards along

with the increases in GDP per capita.

Specifically, in figure 5(a), the coefficient for the underlying relationship be-

tween the log of GDP per capita and the minimum wage level in SSA is 59.42

(where the level of the minimum wage is on the y-axis). This relationship is similar

but larger for the 67 non-SSA countries presented in figure 5b, where the coeffi-

cient is 125.14. This suggests that across countries minimum wage levels in non-

SSA countries are not only higher relative to levels of GDP compared to minimum

wage levels in SSA countries, but also more responsive to increases in GDP relative

to SSA countries.

Figure 5(b). Minimum Wages and GDP Per Capita (US$ PPP), Non-SSA Developing Countries

Afghanistan

Bangladesh

Haiti

Nepal

Tajikistan

Armenia

Bhutan

Bolivia

El Salvador

Georgia

Guatemala

Guyana

Honduras

IndonesiaIndia

Kyrgyzstan

Lao PDR Moldova

Mongolia

Morocco

Nicaragua

Pakistan

Papua New Guinea

Paraguay

Philippines

Samoa

Solomon Islands

Sri Lanka

Ukraine

Uzbekistan

Vietnam

Albania

Azerbaijan

Belarus

Belize

Bosnia and Herzegovina

Brazil

Bulgaria

China

Colombia

Costa Rica

Cuba

Dominica

Dominican Republic

Ecuador

Fiji

Hungary

Iran, Islamic Rep.

Jamaica

Jordan

Kazakhstan

Lebanon

Libya

Macedonia, FYR

Malaysia

Mexico

MontenegroPanama

Peru

Romania

Serbia

Thailand

Tunisia

Turkey

Turkmenistan

Venezuela, RB

020

040

060

080

010

00

Min

imu

m w

ag

e (

US

$ P

PP

)

6 7 8 9 10Log of GDP per capita (US$ PPP)

Fitted values Minimum wage (US$ PPP)

Note: Sample based on 67 non-SSA developing economies, where the latest available data for each country

was utilized.


Bhorat et al. 33

In attempting to disaggregate minimum wage trends within SSA in greater de-

tail, table 1 below presents country-level data for 21 countries. We focus on the

average level of the minimum wage, the mean wage, and the Kaitz ratio (mini-

mum-to-mean wage). Our estimates of country-based minimum wages represent

the average of all schedules for those economies with more than one minimum.

The Kaitz ratio, in turn, provides an indication of how high the minimum wage is

set relative to average wages. Ideally we would present median wages and use the

median wage to calculate the Kaitz ratio, but, unfortunately, data on median

wages for countries in SSA is rare and would limit our sample even further.

Table 1 reports the most recently available minimum wage rates, grouped ac-

cording to country incomes. We convert all the data into current US$ (PPP) for

the sake of comparison. The first column of minimum wage rates again makes it

clear that despite substantial variation across individual economies, there is a clear

trend showing that minimum wage levels covary positively with country income

group. The group mean and median for UMI countries is thus more than double

those of LI countries. For example, then, UMI economies such as Algeria, Gabon,

and South Africa have legislated average minimum wage levels in excess of $400

per month. LI countries, on the other hand, have promulgated minimum wage lev-

els as low as $26 a month (Burundi).8 The country GDP and minimum wage cor-

relation, though, must be emphasized as an average effect, and from the table it is

clear that there are outliers in each income group.

The mean wage figures in column 2 reveal important cross-country differences,

even within the country groups. Hence, as expected we observe a variance in the

mean wages by country—which is broadly consistent with the GNI per capita of

the country under scrutiny. There remains within this data, however, an interest-

ing approach to measure the tendency of an economy, in a comparative sense, to-

ward setting a higher minimum wage relative to other economies. One can think

of this notion in the following manner: if the ratio of the minimum wage in coun-

try i to the highest minimum wage country, country max, is higher than the ratio

of the mean wage in country i to the highest mean wage country, country max—

then this would reflect a relatively pro–minimum wage policy environment rela-

tive to other countries. Simply put, we are measuring:

Wp ¼Wm

i

Wmmax

Wli

Wlmax

24

35

where if Wp > 1, it reflects a relatively pro–minimum wage policy environment

compared to other economies, whereas if Wp < 1, we suggest a minimum wage

policy environment which is relatively benign. In the figure below, we provide

estimates of Wp for a sample of 66 developing countries. An example from the


data: Chad’s minimum wage mean is 22 percent of the region’s maximum mini-

mum wage. Yet, its mean wage nationally is only 15 percent of the region’s maxi-

mum mean wage. This suggests a relatively pro–minimum wage policy in Chad

when compared with its mean wage differential relative to other developing coun-

tries. Put in numerical terms using the formula presented above, Chad’s Wp value

is 1.45, where a value over 1 suggests a pro–minimum wage policy.

The data suggest that the majority of countries in our sample of 66 have a Wp

ratio below 1, and countries in SSA do not appear to be large outliers in this re-

gard. Indeed, the average figures for each region (presented on the right) suggest

that the EAP, LAC, and ECA regions all have higher Wp ratios than SSA. The over-

all sample average is 0.80.

Figure 7, below, focuses only on the SSA region and presents the mean and me-

dian Wp figures by country income group. There are two notable features here.

Figure 6. Degree of Relative Minimum Wage Policy Bias by Country

0.5

11.5

22.5

mea

n o

f W

p

Cu

ba

Bu

rund

iU

zbe

kis

tan

Kyrg

yzst

an

Sw

azila

nd

Bo

tsw

ana

Ve

ne

zuela

, R

BG

eorg

iaU

gan

da

Se

ne

ga

lM

au

ritius

So

uth

Afr

ica

Ga

bo

nD

om

inic

an

Rep

ub

lic

Ka

zakh

sta

nB

ang

lad

esh

Bo

snia

an

d H

erz

eg

ovin

aM

aced

on

ia, F

YR

M

on

tene

gro

Mexic

oT

anzan

ia

Vie

tnam

Jam

aic

aM

ala

ysia

Azerb

aija

nM

old

ova

Ch

ina

Se

rbia

Pa

na

ma

Mon

go

liaK

enya

Fiji

Nic

ara

gua

India

Mala

wi

Co

sta

Ric

aE

l S

alv

ado

rA

rmen

iaZ

am

bia

Bu

lga

ria

Gh

an

aIn

do

ne

sia

Ukra

ine

Eth

iopia

Be

laru

sB

razil

Alb

an

iaP

eru

R

om

an

iaA

lgeri

aT

ajik

ista

nP

hili

pp

ine

sT

haila

nd

Le

soth

oH

ond

ura

sC

ong

o, R

ep

.E

cua

do

rC

had

Pa

kista

n

Gu

ate

ma

laB

urk

ina

Faso

Mad

ag

asca

rA

fgh

an

ista

nB

oliv

iaC

ong

o, D

em

. R

ep

Arg

entina

SA

Me

an

SS

A M

ea

nE

AP

Mea

nLA

C M

ea

nE

CA

Mea

n

Notes: See Mean on far right for legend: South Asia (SA), Sub-Saharan Africa (SSA), East Asia and Pacific

(EAP), Latin America and the Caribbean (LAC), Europe and Central Asia (ECA). Sample based on 66 countries,

where the latest available data for each country was utilized.

Sources: ILO Global Wage database, World Bank WDI.

Bhorat et al. 35

Firstly, the figure shows that the ratio of minimum wages in Africa to the maxi-

mum minimum wage on the continent is greater than the similar ratio in relation

to mean wages. Secondly, the LIC mean and median values are higher than both

the LMI and UMI sample of countries. This would suggest that despite absolute

minimum wage levels increasing with GNI per capita, low-income African econo-

mies are relatively more pro–minimum wage in their policy setting when com-

pared with the mean relative wages on the continent.

Reverting back to table 1, we provide in column three, the common in-country

measure of the extent to which the minimum wage has “bite”—namely the Kaitz

ratio. The ratio is simply that of the minimum wage to the mean wage.

The Kaitz ratio values range from 0.03 in Burundi to a value of 1.27 in the

Democratic Republic of the Congo. The overall SSA mean is instructive and of use in

and of itself: for the region, minimum wages are 37 percent of mean wages and 28

percent of the median wage. This ratio varies by income classification; so while the

Kaitz is 0.28 at the mean for UMI African economies, it is 0.31 for LMI economies

and 0.46 for LICs. We observe substantially higher ratios for LI countries, where the

group median is 0.16 points above the median for LMI countries in the region and

0.26 points above the UMI median. Simply put, low-income African countries are

setting higher minimum wages relative to their domestic mean wages, compared

with both UMI and LMI economies in the region. When compared with other

Figure 7. Average Wp Values for SSA by Country Income Group0

.51

1.5

Wp

UMI median UMI mean LMI median LMI mean LIC median LIC mean



Table 1. Monthly Average Minimum Wage Estimates: Sub-Saharan Africa

Country Minimum wage (US$ PPP) Mean wage (US$ PPP) Kaitz ratio

Low-income economies

Burkina Faso 138 210 0.66

Burundi 26 256 0.10

Chad 239 371 0.64

Congo, Dem. Rep 68 53 1.27

Ethiopia 77 175 0.44

Madagascar 128 183 0.7

Malawi 49 368 0.13

Tanzania 149 624 0.24

Uganda 65 464 0.10

Group mean 104 300 0.46

Group median 77 256 0.44

Lower-middle-income economies

Congo, Rep. 145 526 0.28

Ghana 128 469 0.27

Kenya 331 979 0.34

Lesotho 242 377 0.64

Senegal 148 983 0.15

Swaziland 94 815 0.12

Zambia 98 252 0.39

Group mean 169 629 0.31

Group median 145 526 0.28

Upper-middle-income economies

Algeria 531 1,003 0.53

Botswana 148 1,287 0.12

Gabon 418 2,356 0.18

Mauritius 218 1,424 0.15

South Africa 517 1,251 0.41

Group mean 366 1,464 0.28

Group median 418 1,287 0.18

Total SSA mean 188 687 0.37

Total SSA median 145 469 0.28

Other regional averages

LAC mean 369 937 0.46

(1.24)*

LAC median 289 859 0.37

(1.32)*

EAP mean 317 884 0.38

(1.03)*

EAP median 284 739 0.32

(1.14)*

SA mean 233 386 0.63

(1.70)*

continued

Bhorat et al. 37

regions, however, the data show that mean and median Kaitz ratios are lower in

SSA. Indeed, the mean and median Kaitz ratios of other regions relative to that of

SSA show that it is only the ECA mean that has a lower Kaitz than that of the SSA

region. This provides some support for the evidence presented in figure 6.

Ultimately, then, the cross-country evidence on minimum wages in Sub-Saharan

Africa indicates firstly that if we simply group countries according to broad income

levels, minimum wages in Sub-Saharan Africa appear in general to be set lower

than those in the rest of the developing world. Secondly, the evidence illustrates a

positive and linear relationship between GDP per capita and the level at which the

minimum wage is set. Consistent with the positive country poverty line—GDP rela-

tionship, and in keeping with other developing countries—African economies reveal

an upward adjustment in the value of the minimum wage as the economies in the

region grow and develop. Thirdly, when assessing the extent to which African gov-

ernments may be more pro–minimum wage, or not, in setting wage levels compared

to other regions, our cross-country evidence indicates that there is no tendency to-

wards progressive minimum wage policy in our sample. Indeed, the regional Kaitz

ratios presented in table 1 are lower for SSA than any other region except ECA.

Finally, it is clear again that when examining the Kaitz index, while SSA economies

overall are setting minimum wages at just over a third of the mean wage, substan-

tially higher ratios are observed for low-income countries in the region.

Variations in African Minimum Wage Schedules:A Country-Level Overview

For the purposes of a cross-country comparison, the above estimates have aggregated

across the different minimum wage schedules that exist within many economies.

Table 1. Continued

Country Minimum wage (US$ PPP) Mean wage (US$ PPP) Kaitz ratio

SA median 255 368 0.59

(2.11)*

ECA mean 325 1,136 0.3

(0.81)*

ECA median 344 1,183 0.28

(1.00)*

Notes: SSA aggregate estimates based on 21 countries, EAP sample based on eight countries, LAC sample based

on 16 countries, SA sample based on four countries, ECA sample based on 17 countries. The latest available data

for each country was utilized. Wages are monthly, and where there is more than one minimum wage schedule,

we have estimated the average minimum wage across the within-country schedules. All estimates are in current

US$ PPP. Asterisk (*) indicates ratio of measure to SSA mean or median.



There is, however, much granularity and nuance that is overlooked when presenting

average minimum wage estimates by country in this fashion. We return here, then,

to a more detailed consideration of minimum wage schedules, focusing on seven SSA

economies for which we have the appropriate microdata.

We have alluded to fact that many minimum wage systems in the region in-

clude a range of sectoral and occupational schedules, and it is these complex

schedules that make a single figure appear blunt as it masks substantial heteroge-

neity at a country level. As an example, the case of Kenya’s minimum wage regime

is instructive in terms of the complexity of minimum wage systems: Kenya has had

an active minimum wage policy since it achieved independence in 1964.

Minimum wages are set by Ministerial order following recommendations by a tri-

partite council and public consultation (ILO 2014). The wage schedule that is pro-

duced is intricate. Wages are set at different rates for agricultural and

nonagricultural occupations, and within these categories there are geographical

and occupational distinctions, each with a unique set of wages. Table A2 presents

daily minimum wage rates for the agricultural sector, which is broken down into

10 categories according to the specific type of employee.9 Within the agricultural

sector, daily wages can range from 203 Kenyan Shillings for a general worker to

370 Kenyan Shillings for a lorry or car driver. Table A3 details monthly minimum

wages for the nonagricultural sector, which is firstly disaggregated by geographic

region into three categories: cities, municipalities and town councils, and other

areas. Within each of these three geographical areas, wages are further delineated

across 15 different employee types. Monthly minimum wages for nonagricultural

workers range from 5,217 to 22,070 Kenyan shillings. In total, Kenya has 55 sep-

arate minimum wage rates.

It is clear how the complexity of Kenya’s wage determination system makes ob-

taining accurate aggregate estimates difficult. But it must be emphasized that

Kenya is not an exception in this regard. South Africa’s legislated minimum wage

schedule, for example, is even more complex for certain sectors, and in total the

country currently has 124 different minimum wage rates. This excludes minimum

wages agreed upon within Bargaining Councils, which would push the number of

specific wage rates in the thousands.

In table 2, below, we provide a brief overview of the number of minimum wage

schedules for 12 countries in SSA. The data show that seven of the 12 countries in

the sample have 10 or more wage schedules, with South Africa being an outlier in

this sample. A differentiated, or complex, wage schedule can be useful in that it

takes account of variations across worker skill levels, geographic regions, and sec-

tors. Yet increasing levels of complexity also makes wage setting, as well as en-

forcement and compliance, more difficult. The ILO (2014) suggests that in general

the complexity of a wage schedule should be commensurate with the county’s re-

source availability, where simpler wage schedules are more suitable if the

Bhorat et al. 39

resources dedicated to minimum wage systems are few. A complex schedule such

as Kenya’s certainly creates a more challenging set of rates to establish each year

and enforce. Labor market regulations in several SSA countries would probably

benefit from a re-assessment of current minimum wage systems in this regard,

with a view toward greater simplification.

Given the complexity of schedules, it is useful to look beyond cross-country ag-

gregates, which, while useful, cannot provide a detailed picture or give any indica-

tion toward levels of compliance with minimum wage laws. In order to do this, we

make use of household survey data for seven SSA countries (South Africa,

Uganda, Kenya, Zambia, Tanzania, Mali, and Namibia).

As tables 2, A2, and A3 indicate, however, there remain significant complexities

within several of these country minimum wage systems—many of which go be-

yond the detail provided by a basic labor force survey. To deal with this, for the

countries with more than one minimum wage we select and focus on two key sec-

tors. The first, which we call the “lower floor” is a low-paid, unskilled sector cov-

ered by a general minimum wage (usually agriculture or “general workers”),

while the second sector we select is higher-paid and medium-skilled (such as retail

trade or working as a clerk), and we call this the “upper floor.”10 This allows us to

present the granularity of minimum wage schedules while at the same time

enabling us to estimate more accurate Kaitz ratios (for specific sectors), as well as

exploring levels of compliance (see the section Minimum Wage Compliance in Sub-

Saharan Africa). To begin, we present kernel density estimates that provide a basic

picture of where specific minimum wages are set relative to the wage distributions

Table 2. Number of Minimum Wage Schedules by African Country

Country Number of wage schedules

Uganda 1

Mali 1

Ghana 1

Malawi 1

Nigeria 2

Botswana 10

Zambia 10

Tanzania 29

Namibia 32

Kenya 55

Ethiopia (public sector) 57

South Africa 124

Average 27

Source: ILO TRAVAIL Database.


Figure 8. (a) Distribution of Wages, Zambia (2010); (b) Distribution of Wages, Tanzania

(2011); (c) Distribution of Wages, South Africa (2013); (d) Distribution of Wages, Namibia

(2012); (e) Distribution of Wages, Uganda (2012); (f) Distribution of Wages, Kenya (2005/6).

(a) (b)

(c) (d)

(e) (f)

Source: Zambia: Living Conditions Monitoring Survey (2010); Tanzania: Integrated Labour Force Survey

(2005/06); South Africa: Labour Market Dynamics Study (2013); Namibia: Labour Force Survey (2012);

Uganda: Uganda National Panel Survey (2012); Kenya: Kenya Integrated Household Budget Survey (2005-

06).

Bhorat et al. 41

in each country.11 We include all employed wage earners in the sample, regardless

of occupation, sector, or whether they are covered by a minimum wage or not.

In all cases, the distributional graphs indicate the lack of a traditional “spike” at

either of minimum wage schedules. This is interesting in two respects: Firstly, un-

like most of the developed country literature, this six-country sample of African

economies shows no evidence of a spike in the wage distribution relevant to the

level of the minimum. Secondly, though, this lack of a spike is also consistent with

fairly fat tails to the left of both minimum wage schedules. Put differently, there is

a significant share of earners not adhering to the minimum wage. This incidence

and deficit of noncompliance is taken up in the next section. Ultimately, the notion

of a subset of African economies displaying relatively high minimum wage levels

that in turn coexist with a fair degree of independence from the country’s self-

same wage distribution is a key result.

Table 3, below, provides more detailed information on minimum wages for the

six SSA countries in the figures above, as well as for Mali. In two of the seven

countries in the table, there are single national minimum wages (Mali and

Uganda), while in the other five there are detailed sectoral and occupational wage

schedules. As noted above, for each of these five countries we select a lower and

upper wage floor and identify the employees covered by these sectoral wage levels.

For the two countries with a single national minimum wage, we simply include all

workers classified as wage-earning employees in our analysis. The table provides

an overview of wage levels, minimum wage levels, coverage, and we also draw at-

tention to the difference between the level of the lower and upper floors, where ap-

plicable. What emerges firstly and most obviously is the within-country variation

in minimum wage levels.

Among the seven countries profiled in table 3 and across the lower and upper

floors in the same country, large variations are apparent both in the level of legis-

lated minimum wages and in mean and median earnings. Column five shows that

minimum wage levels differ substantially across the lower and upper floor categories

within the same country, with wages for workers in lower floor, or unskilled catego-

ries, set at 54 percent of wage levels for upper floor categories on average. While the

heterogeneity in minimum wage levels makes for a complex wage schedule, it also

appears to be reflective of the substantial intersectoral wage inequality, which is evi-

dent in the differences in average wages for the same country. It is also worth noting

the large difference between the mean and median earnings across any one category

of workers, who are assigned here to a lower or upper floor. These estimates are sug-

gestive of high levels of intrasector wage inequality—where mean wages are signifi-

cantly larger than median wages. On average across the group of countries, mean

wages (US$457) are 55 percent higher than median wages (US$251).

Importantly, though, the data reveals an important characteristic of minimum

wage setting in Africa—namely that in general these sectoral wages, given their


specific targeting—will not cover a large share of workers. Apart from Uganda and

Mali, where a national minimum wage exists, coverage of most sectoral minimum

wages is fairly low. In the pursuit of higher wages for vulnerable workers, it is pos-

sible that many African economies do not end up covering a large share of the

wage employed.

To go beyond focusing on the variation in wages and wage levels, we examine

the Kaitz ratios using both mean and median wages for workers in the lower and

upper floor categories. These ratios give an indication of how high minimum wages

are set relative to average wages within in each category and also provide suggestive

evidence regarding compliance, which we take up in more detail shortly.

Figure 9, below, presents the Kaitz ratios for mean wages across categories and

for each country. The ratios vary widely across countries, as well as across the

lower and upper floor categories. In Namibia, for example, for workers in the lower

floor category (“general workers”), the minimum wage is set at 1.5 times the

mean wage, while in Zambia this figure is 0.5.

Overall, however, the average ratio is 0.93 among lower floor workers and 0.63

among upper floor workers. As a point of comparison, the average level of

Table 3. Monthly Minimum Wages (US$ PPP) In Seven African Economies: Upper and LowerFloors

Countries Sector % of totalemployees

Minimum wage Lower/upperfloor

Meanwage

Medianwage(US$ PPP)

South Africa (2013) Lower floor 4 441 0.65 558 329

Upper floor 13 680 1,475 526

Kenya (2005) Lower floor 7 116 0.44 146 96

Upper floor 2 264 470 353

Zambia (2010) Lower floor – 69 0.38 124 78


Tanzania (2007) Lower floor 61 162 0.59 100 52


Namibia (2012) Lower floor – 388 0.63 257 174

Upper floor 4 615 1,057 677

Uganda (2012) National 100 65 N/A 227 107

Mali (2013)*** National 100 132 N/A – –

Mean 0.3 283 0.54 457 251

Notes: Figures only include those classified in household surveys as being wage-earning employees. The

Ugandan minimum wage has not been updated since 1984, thus we take the 1984 rate and adjust for inflation to

obtain a comparable 2012 figure. The data from Mali is taken from Rani et al. (2013), and we are unable to calcu-

late mean or median wages. Wages are in current monthly US$ PPP.

Sources: South Africa: Labour Market Dynamics Study (2013); Kenya: Kenya Integrated Household Budget

Survey (2005–6); Uganda: Uganda National Panel Survey (2012); Mali: Rani et al. (2013); Zambia: Living

Conditions Monitoring Survey (2010); Tanzania: Integrated Labour Force Survey (2005–6); Namibia: Labour

Force Survey (2012).

Bhorat et al. 43

minimum-to-mean wages for nine LMI countries outside of SSA12, presented in

Rani et al. (2013), was 54 percent.

Figure 10 presents the Kaitz ratio using median instead of mean wages, and the

different results are stark, as table 3 suggested. The ratios are significantly higher,

Figure 9. Ratio of Minimum to Mean Wages, Seven African Economies0

.51

1.5

Uganda Zambia Mali South Africa Kenya Namibia Tanzania Average

Lower floor Upper floor

Source: Authors’ calculations.

Figure 10. Ratio of Minimum to Median Wages, Seven African Economies

01

23

Uganda Mali Zambia Kenya South Africa Namibia Tanzania Average

Lower floor Upper floor

Source: Authors’ calculations.


with the average figures above 1, and the black dotted line makes this clear.

Again, comparing these estimates against the sample of non-SSA countries from

Rani et al. (2013), where the average ratio is 0.76, shows that indeed the figures

are high.

Overall, the Kaitz estimates for the SSA region highlight two main aspects of

minimum wages: firstly, the country-specific nature of minimum wage frame-

works, the level at which wages are set and the relation of this level to average

wages for that covered group; and secondly, given that we are only focusing on

covered workers in the two figures above, the data show that minimum wages are

set high relative to average wages and are suggestive of significant noncompliance.

This leads us to explore levels of noncompliance in more detail.

Minimum Wage Compliance in Sub-Saharan Africa

While the Kaitz ratios presented above are indicative of widespread noncompliance

with minimum wage laws, a more robust method is required to investigate further.

We apply an Index of Violation13 from Bhorat, Kanbur, and Mayet (2012) to cal-

culate the level and depth of noncompliance, which provides a more

Figure 11. Average Compliance Rates (V0), African and Developing Country Comparison

0.2

.4.6

.8

Mali

Za

mbia

Ug

an

da

Ken

ya

Sou

th A

fric

a

Na

mib

ia

Ta

nza

nia

SS

A A

ve

rag

e

Vie

t N

am

Mexic

o

Bra

zil

Peru

Co

sta

Ric

a

India

Phili

pp

ines

Tu

rkey

Indo

ne

sia

No

n-S

SA

Avera

ge

Source: Authors’ calculations and Rani et al. (2013).

Bhorat et al. 45

comprehensive picture. To do this, we combine the lower and upper floor catego-

ries presented above where applicable, and we compare the resulting estimates

with those of Rani et al. (2013). The Index of Violation allows us to calculate the

level of noncompliance, or V0, which is simply the percentage of workers who earn

below the minimum wage that applies to them. It also allows us to go beyond this

to calculate the depth of this noncompliance, or V1, which measures how far below

the minimum wage these workers earn on average.

The data in figure 11 clearly show that for most countries in the SSA region, for

the sectors we include, noncompliance is widespread. On average, 58 percent of

workers earn below the minimum wage legislated for them. This is compared to an

average of 30 percent for the non-SSA countries in the figure. This relationship

holds for the mean and median, despite outlier economies such as Tanzania. The

figure also highlights the cross-country differences in levels of noncompliance. In

Zambia, approximately 36 percent of workers earn subminimum wages, while this

rises to 80 percent in Tanzania. These numbers reinforce the picture provided by

the Kaitz ratios above where minimum wage rates were shown to be set high rela-

tive to average wages.

Figure 12 moves beyond the level of compliance to explore how far below the

minimum wage workers are on average. This gives a more nuanced picture of

Figure 12. Average Depth of Noncompliance (V1), African and Developing Country

Comparison

0.2

.4.6

V1

Za

mbia

Ug

an

da

Ken

ya

Sou

th A

fric

a

Na

mib

ia

Mali

Ta

nza

nia

SS

A A

ve

rag

e

Tu

rkey

Vie

t N

am

India

Co

sta

Ric

a

Mexic

o

Phili

pp

ines

Indo

ne

sia

Bra

zil

Peru

No

n-S

SA

Avera

ge

Source: Authors’ calculations and Rani et al. (2013).


noncompliance and how severe noncompliance levels are when examining the av-

erage distance below the minimum wage that workers are earning.

The results suggest an interesting switch: while noncompliance levels were

higher in the African sample of economies, relative levels of noncompliance are in

fact higher in the non-African sample. Hence, our estimates show that the SSA re-

gional average V1 estimate stands at 0.30 while the corresponding figure for the

non-SSA countries is 0.35. This suggests, at least on the basis of this subsample of

developing countries, that while Africa yields to higher levels of noncompliance

than other developing countries, those who are below the minimum wage face a

greater disadvantage in non-African economies. Put simply, absolute levels of non-

compliance are higher in Africa, while relative levels of noncompliance are higher

in non-African developing countries.

Conclusion

Most countries in Sub-Saharan Africa (SSA) have adopted minimum wage regula-

tion. Although the sectors and fraction of workers covered are small given the low

rates of formality and urbanization in SSA, as the number of covered workers

grows, wage regulation will become increasingly significant for the economy as a

whole. In addition, there can be spillover effects in uncovered sectors, which can

be exacerbated, as we show in the paper, when wage regulation is particularly pro-

gressive. Current experience with minimum wages in SSA is thus relevant for ana-

lysts and policy makers as economies may consider redesigning their minimum

wage architecture in particular, and active labor market policy in general.

In examining the variety of minimum wage frameworks in the region, it is clear

that the typology of minimum wage schedules, as well as the levels at which these

minima are set, varies considerably across countries in Africa. Our evidence shows

that higher minimum wage values are associated with higher GDP per capita. In

addition, utilizing two different pieces of evidence, we illustrate that SSA does not

seem to reflect a particular bias towards a more pro–minimum wage policy relative

to other regions of the developing world. Importantly, however, we find that mini-

mum wages in low-income countries in SSA are set at values relative to the coun-

try’s mean wage that are higher than those in lower- and upper-middle income

countries in Africa.

There is limited research on the employment effect of minimum wages in SSA,

but the findings for the four countries (Ghana, Kenya, Malawi, and South Africa)

are consistent with the broad summary of global research. By and large, introducing

and raising the minimum wage has a small negative impact or no measurable nega-

tive impact. However, there is significant variation around this average finding, and

some country studies do present evidence of employment losses in excess of 10

Bhorat et al. 47

percent in certain cases. In short, employment elasticities are neither constant nor

linear. Where increases in a minimum wage are large and immediate, this can result

in employment losses, but more modest increases usually have few observably ad-

verse effects on employment and may have positive impacts on wages.

The great variability in findings on employment can be explained partly by the

great variation in the detail of the minimum wage regimes and schedules country

by country, but also by the variations in compliance. Our data on minimum wage

compliance in Africa illustrates that the continent has higher levels of absolute

noncompliance when compared with other developing countries, but lower levels

of relative noncompliance. However, there is considerable variation across coun-

tries. We find that higher Kaitz indices are associated with higher levels of non-

compliance, but more detailed explanation of noncompliance is an important item

on the research agenda.

This paper has provided an empirical overview of minimum wages in Sub-

Saharan Africa. It is evident that research in this area is still at a very early stage,

with this paper in essence attempting to craft the broad contours of the nature and

extent of minimum wage setting on the continent. While work on minimum wages

is fairly mature in many OECD countries, our understanding of minimum wage

policy and its impact in SSA is not. This is in large part due to a severe lack of nec-

essary data. The release of country-level earnings and employment data at regular

intervals lies at the heart of a future country-focused minimum wage research

agenda for Africa.

Notes

Haroon Bhorat is the Director of the Development Policy Research Unit, School of Economics,University of Cape Town, South Africa; Ravi Kanbur is the T. H. Lee Professor of World Affairs,International Professor of Applied Economics and Management, and Professor of Economics, CornellUniversity; and Benjamin Stanwix is a Senior Researcher in the Development Policy Research Unit,University of Cape Town, South Africa. The authors of this paper acknowledge Benjamin Jourdan forproviding a number of graphs and tables used for the study.

1. In Sub-Saharan Africa, data indicates that approximately 19 percent of the labor force is inwage employment, while 74 percent is in agricultural or nonfarm self-employment (Bhorat, Naidoo,and Pillay 2015). In West Africa, for example, the informal sector accounts for approximately 50 per-cent of national output, over 80 percent of employment and 90 percent of new jobs (Benjamin,Golub, and Mbaye 2015).

2. While there have been several studies analyzing the lighthouse effect of minimum wage lawsin many countries around the world, we know of no such work done for African countries. Relatedresearch on the impact of minimum wage laws in sectors with weak enforcement has been done inSouth Africa (see Dinkelman and Ranchhod 2013 and Bhorat, Kanbur, and Stanwix 2015).

3. Indeed, many countries have wage-setting legislation that takes cost of living measures intoaccount when updating minimum wages.

4. Belman and Wolfson (2014); Neumark, Salas, and Wascher (2014); Schmitt (2013);Neumark and Wascher (2007, 2008).


5. See, for example, Agell and Lommerud (1997) or for a discussion of the possible impacts ofminimum wages from a behavioral perspective, see Falk, Fehr, and Zehnder (2006).

6. The country studies focus on Brazil, Chile, Colombia, Costa Rica, Indonesia, Mexico, PuertoRico, and Trinidad and Tobago. Neumark and Wascher (2007) use the term “developing country” todescribe what we have referred to as low- and middle-income countries.

7. The table below shows the percentage split across world regions between setting a nationalminimum wage versus some form or regional, sectoral, or occupational minimum wage. The percent-ages do not add up to 100 percent given that not all countries in a region have minimum wages.Source: ILO (2013).

8. It should be noted that in the case of Burundi the minimum wage has not been updated since1988; the figure presented here is adjusted for inflation to 2012, priced and converted in US$ PPP.

9. Unskilled employee; stockman, herdsman, and watchman; skilled and semiskilled employees;house servant or cook; farm foreman; farm clerk; section foreman; farm artisan; tractor driver; com-bined harvester-driver; lorry driver or car driver.

10. The specific sectors for each country are as follows:South AfricaLower floor – AgricultureUpper floor – Wholesale and retailKenyaLower floor – AgricultureUpper floor – Employee type [G], see table A2ZambiaLower floor – General workerUpper floor – ClerksTanzaniaLower floor – AgricultureUpper floor – HospitalityNamibia (wages set by collective bargaining)Lower floor – General workersUpper floor – Clerks

11. We only do this for six countries as the data for Mali are taken from Rani et al. (2013).12. Brazil, Costa Rica, India, Indonesia, Mexico, Philippines, Peru, Turkey, Viet Nam.13. If we consider a distribution of wages F(w), where the density function is f(w) and the mini-

mum wage is Wm; then an index of violation can be calculated as follows:

V að Þ ¼ðWm

0

Wm �Wi

Wm

� �a

f wð Þ;

where Wi are individual wages and a is the “violation aversion” parameter such that when: a¼0,V(a) measures the percentage of workers below the minimum; when a¼1, V(a) measures the aver-age gap between Wm; and when a¼2, V(a) is the squared violation gap. This index allows us to cal-culate a family of measures that capture both absolute (V0) and relative (V1, V2) levels ofnoncompliance.

Bhorat et al. 49

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Appendix

Figure A1. Wage-Employment Elasticities and Kaitz Ratios

-4-2

02

Ela

sticity

.2 .3 .4 .5 .6 .7Kaitz Ratio

Elasticity Fitted values


Table A1. Minimum Wage System Classification for Selected African Countries

National Sectoral/occupational Hybrid

Algeria Botswana Burkina Faso

Cameroon Kenya Angola

Cote d’Ivoire Malawi

Egypt Mozambique

Ghana Rwanda

Mali South Africa

Uganda Tanzania

Source: ILO TRAVAIL database.

Bhorat et al. 53

Table A2. Agricultural Minimum Wages, Kenya, 2013

Type of employee Monthly wage (shillings)

Unskilled employee 4,854.35Stockman, herdsman, and watchman 5,606.05House servant or cook 5,541.55Farm foreman 8,757.20Farm clerk 8,757.20Section foreman 5,669.20Farm artisan 5,802.05Tractor driver 6,152.70Combined harvester-driver 6,778.10Lorry driver or car driver 7,113.25

Source: Ministry of Labour Social, Security and Services (2013).

Table A3. Nonagricultural Monthly Minimum Wages, Kenyan Shillings, Kenya, 2013

Type ofemployee

Nonagricultural industry in cities(Nairobi, Mombasa, and Kisumu)

Nonagricultural industry atall municipalities and town councils

Nonagricultural industryat all other areas

a 9,780.95 9,024.10 5,217.95b 10,563.60 9,372.20 6,028.95c 10,911.70 10,116.15 6,223.65d 11,085.70 10,316.00 8,361.35e 12,654.90 11,838.70 9,679.05f 13,201.55 12,184.25 10,071.05g 15,064.65 13,772.75 11,743.30h 16,602.85 15,259.35 13,606.40i 18,329.25 17,101.80 15,434.70j 20,283.90 18,940.40 17,644.60k 22,070.95 20,769.95 19,474.20l 13,201.55 12,184.25 10,071.05m 16,602.85 15,259.35 13,580.60n 17,932.15 17,101.55 15,434.70o 22,070.95 20,769.95 19,474.20

Notes:aGeneral labor, including cleaner, sweeper, gardener, children’s ayah, house servant, day watchman, messenger.bMiner, stone cutter, turn boy, waiter, cook, logger line cutter.cNight watchman.dMachine attendant, sawmill sawyer, machine assistant, mass production machinist, shoe cutter, bakery worker, bakery assis-

tant, tailor’s assistant.eMachinist (made-to-measure), shoe upper preparer, chaplis maker, vehicle service worker (petrol and service stations), bakery

plant hand, laundry operator, junior clerk, wheeled tractor driver (light).fPrinting machine operator, bakery machine operator, plywood machine operator, sawmill dresser, shop assistant, machine

tool operator, dough maker, table hand baker or confectioner, copy-typist, driver (cars and light vans).gPattern designer (draughts-man), garment and dress cutter, single hand oven man, charge-hand baker, general clerk, tele-

phone operator, receptionist, storekeeper.hTailor, driver (medium-sized vehicle).iDyer, crawler tractor driver, salesman.jSaw doctor, caretaker (buildings).kCashier, driver (heavy commercial vehicle), salesman-driver.lUngraded artisan.mArtisan grade III.nArtisan grade II.oArtisan grade I.

Source: Ministry of Labour Social, Security and Services.


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00

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Da

ta:

Av

era

ge

Ea

rnin

gs

Info

rma

tio

nS

yst

em

Neg

ati

ve

emp

loy

men

ta

nd

wo

rkin

gh

ou

rsef

fect

so

f-

14

.4p

erce

nt

an

d5

.1

per

cen

t,re

spec

tiv

ely

,in

the

yea

rfo

llo

win

gth

e

19

98

min

imu

mw

ag

e

incr

ease

.N

ega

tiv

e

emp

loy

men

ta

nd

wo

rkin

gh

ou

rsef

fect

so

f-

5.1

per

cen

ta

nd

5.4

per

cen

t,re

spec

tiv

ely

,in

the

yea

rfo

llo

win

gth

e

19

99

min

imu

mw

ag

e

incr

ease

.

Th

ela

rger

the

pro

po

rtio

no

flo

w-p

aid

wo

rker

sin

afi

rm,

the

hig

her

the

incr

ease

inth

efi

rm’s

av

era

ge

wa

ge.

Jon

es(1

99

7)

Gh

an

aD

ate

:1

97

0–

19

91

Da

ta:

Yea

rbo

ok

of

La

bo

ur

Pro

vid

esfa

irly

stro

ng

evid

ence

tha

tth

e

min

imu

mw

ag

ein

Imp

lied

ela

stic

itie

s

sug

ges

tth

at

ala

rge

pro

po

rtio

no

fth

e

con

tin

ued


Ta

ble

A4

.C

onti

nu

ed

Stu

dy

Co

un

try

Pa

ram

eter

so

fst

ud

yE

mp

loy

men

t/h

ou

rsw

ork

edF

orm

al/

info

rma

lse

cto

rsh

ifts

Wa

ges

Sta

tist

ics

(IL

O),

Inte

rna

tio

na

l

Fin

an

cia

lS

tati

stic

s(I

MF

),

Afr

ica

nE

mp

loy

men

tR

epo

rt

(19

90

),P

enn

Wo

rld

Ta

ble

s,

Wo

rld

Ba

nk

So

cia

lIn

dic

ato

rso

f

Dev

elo

pm

ent

(19

95

),W

orl

d

Ba

nk

’sR

egio

na

l

Pro

gra

mo

nE

nte

rpri

se

Dev

elo

pm

ent

(19

92

)

Gh

an

ah

ad

an

ega

tiv

e

imp

act

on

emp

loy

men

t

inth

efo

rma

lp

riv

ate

sect

or

(-0

.12

ela

stic

ity

)

an

dfo

rma

lp

ub

lic

sect

or

(-0

.17

ela

stic

ity

).

Info

rma

lem

plo

ym

ent

incr

ease

da

sa

resu

lto

f

incr

ease

dm

inim

um

wa

ge.

Em

plo

ym

ent

of

wo

men

.

pu

bli

cse

cto

r

wo

rker

sd

isp

lace

db

y

the

min

imu

mw

ag

e

shif

ted

into

info

rma

l

sect

or

emp

loy

men

t.

Gin

dli

ng

an

d

Ter

rell

(20

07

)

Ho

nd

ura

sD

ate

:1

99

0–

20

04

Da

ta:

Ho

nd

ura

sM

inim

um

Wa

ge

Dec

rees

an

dth

eP

erm

an

ent

Ho

use

ho

ldS

urv

eys

for

Mu

ltip

le

Pu

rpo

ses

Dis

emp

loy

men

tef

fect

sin

the

pri

va

tese

cto

r(-

0.4

6).

Wa

ge

ela

stic

ity

of

0.2

9o

ver

all

.

Ho

wev

er,

the

wel

fare

—th

eto

tal

earn

ing

s—o

flo

w-p

aid

wo

rker

sin

the

larg

efi

rm–

cov

ered

sect

or

fall

sw

ith

hig

her

min

imu

mw

ag

es.

Ala

tas

an

d

Ca

mer

on

(20

03

)

Ind

on

esia

Da

te:

19

90

–1

99

6

Da

ta:

An

nu

al

Su

rvey

of

Ma

nu

fact

uri

ng

Fir

ms

(Su

rvei

Ta

hu

na

nP

eru

sah

aa

nIn

du

stri

,

SI)

So

me

evid

ence

of

a

neg

ati

ve

emp

loy

men

t

imp

act

for

sma

lld

om

esti

c

firm

sb

ut

no

emp

loy

men

t

imp

act

for

larg

efi

rms—

fore

ign

or

do

mes

tic.

Co

mo

laa

nd

De

Mel

lo(2

01

1)

Ind

on

esia

Da

te:

19

96

–2

00

4

Da

ta:

Ind

on

esia

nS

tati

stic

s

Bu

rea

u’s

Na

tio

na

lL

ab

or

Fo

rce

Su

rvey

(Sa

ker

na

s)w

ork

ing

ag

e

(15

to6

5y

ears

),N

ati

on

al

Eco

no

mic

Su

rvey

(Su

sen

as)

,

Ind

ust

ria

lS

urv

ey(S

urv

ei

Ind

ust

ri)

Min

imu

mw

ag

eh

ikes

des

tro

yfo

rma

lse

cto

rjo

bs

(-0

.05

6),

bu

tth

ese

job

s

loss

esa

rem

ore

tha

n

com

pen

sate

dfo

rb

yth

e

exp

an

sio

no

fth

ein

form

al

sect

or

(0.0

74

),

sug

ges

tin

gth

at

min

imu

mw

ag

e

Th

en

ega

tiv

ea

nd

sig

nifi

can

t

coef

fici

ent

on

un

emp

loy

men

t

seem

sto

sug

ges

t

tha

tth

ed

ecre

ase

in

form

al-

sect

or

emp

loy

men

td

ue

to

ari

sein

the

rela

tiv

e

con

tin

ued

Bhorat et al. 63

Ta

ble

A4

.C

onti

nu

ed

Stu

dy

Co

un

try

Pa

ram

eter

so

fst

ud

yE

mp

loy

men

t/h

ou

rsw

ork

edF

orm

al/

info

rma

lse

cto

rsh

ifts

Wa

ges

leg

isla

tio

nis

hu

rtin

g,

inst

ead

of

pro

tect

ing

vu

lner

ab

lew

ork

ers.

va

lue

of

the

min

imu

mw

ag

e

shif

tsw

ork

ers

fro

m

“qu

euin

g”

un

emp

loy

men

tto

the

ina

ctiv

e

po

pu

lati

on

of

the

info

rma

lse

cto

r.

Del

Ca

rpio

,

Ng

uy

en,

an

d

Wa

ng

(20

12

)

Ind

on

esia

Da

te:

19

93

–2

00

6

Da

ta:

An

nu

al

Ma

nu

fact

uri

ng

Su

rvey

(Su

rvei

Ind

ust

rio

rS

I)

an

dth

eN

ati

on

al

So

cio

-

Eco

no

mic

Su

rvey

(Su

sen

as)

Th

eem

plo

ym

ent

effe

cts

of

min

imu

mw

ag

esa

re

sig

nifi

can

ta

nd

neg

ati

ve

am

on

ga

llfi

rms

(-0

.02

33

to-0

.05

42

),b

ut

mo

re

pre

do

min

an

tin

sma

ll

firm

sa

nd

less

edu

cate

d

wo

rker

sa

nd

less

am

on

g

larg

efi

rms

an

dw

ork

ers

wit

hh

igh

sch

oo

l

edu

cati

on

.

Dis

emp

loy

men

tef

fect

s

are

stro

ng

erfo

r

no

np

rod

uct

ion

wo

rker

s

(�0

.05

4)

an

dw

om

en

tha

nfo

rp

rod

uct

ion

wo

rker

s(�

0.0

23

).

Min

imu

mw

ag

esa

rem

ore

bin

din

gin

sma

llfi

rms

tha

nin

larg

efi

rms.

Ha

rris

on

an

d

Sco

rse

(20

10

)

Ind

on

esia

Da

te:

19

90

–1

99

6

Da

ta:

An

nu

al

Ma

nu

fact

uri

ng

Su

rvey

,B

ad

an

Pu

sat

Sta

tist

ik

Res

ult

ssu

gg

est

tha

tth

e

min

imu

mw

ag

ein

crea

ses

led

toem

plo

ym

ent

loss

es

for

pro

du

ctio

nw

ork

ers

acr

oss

all

sect

ors

in

ma

nu

fact

uri

ng

.

A1

per

cen

tin

crea

sein

the

rea

lv

alu

eo

f

the

min

imu

mw

ag

ew

as

ass

oci

ate

d

wit

ha

0.6

75

per

cen

tin

crea

sein

the

rea

lu

nsk

ille

dw

ag

e.

Ind

on

esia

Da

te:

19

93

–2

00

0

con

tin

ued


Ta

ble

A4

.C

onti

nu

ed

Stu

dy

Co

un

try

Pa

ram

eter

so

fst

ud

yE

mp

loy

men

t/h

ou

rsw

ork

edF

orm

al/

info

rma

lse

cto

rsh

ifts

Wa

ges

Ma

gru

der

(20

13

)

Da

ta:

Ind

on

esia

nF

am

ily

Lif

e

Su

rvey

(wa

ves

1,

2,

an

d3

),

Sta

tist

ics

Ind

ust

ry(S

I)

Du

rin

gth

e1

99

0s’

ma

ssiv

efo

reig

n

inv

estm

ent

an

dra

pid

eco

no

mic

gro

wth

,w

ag

es

wer

ein

crea

sed

an

da

“big

pu

sh”

inth

e

eco

no

my

incr

ease

d

do

mes

tic

dem

an

d.

Ob

serv

ing

on

eo

ut

of

the

33

dis

tric

tsin

Ind

on

esia

,

form

al

emp

loy

men

t

incr

ease

da

nd

info

rma

l

emp

loy

men

td

ecre

ase

d

on

lyin

loca

lin

du

stri

es;

tra

dea

ble

ma

nu

fact

uri

ng

firm

ssa

wn

og

row

thin

emp

loy

men

t,a

nd

un

tra

dea

ble

bu

t

no

nin

du

stri

ali

zab

le

serv

ices

saw

an

incr

ease

inin

form

al

emp

loy

men

t.

Sh

ift

fro

min

form

al

to

form

al

emp

loy

men

t

inlo

cal

ind

ust

ries

;

tra

dea

ble

ind

ust

ry

saw

no

mo

vem

ents

;

un

tra

dea

ble

,

no

nin

du

stri

ali

zab

le

serv

ices

saw

ari

sein

info

rma

l

emp

loy

men

t.

Th

eb

ott

om

qu

art

ile

of

the

wa

ge

dis

trib

uti

on

exp

erie

nce

sm

ass

ive

wa

ge

ga

ins

wh

enm

inim

um

wa

ges

gro

w,

so

tha

ta

do

ub

lin

go

fm

inim

um

wa

ges

is

ass

oci

ate

dw

ith

a1

50

per

cen

tto

16

0

per

cen

tin

crea

sein

the

25

thp

erce

nti

le

of

the

wa

ge

dis

trib

uti

on

.

Ra

ma

(20

01

)In

do

nes

iaD

ate

:1

99

3

Da

ta:

La

bo

rF

orc

eS

urv

ey

(Sa

ker

na

s)

Urb

an

un

emp

loy

men

t

dec

rea

sed

by

0p

erce

nt

to

5p

erce

nt.

Th

e

emp

loy

men

tef

fect

s,

ho

wev

er,

va

ried

sub

sta

nti

all

yb

yfi

rmsi

ze:

sma

llfi

rms

ap

pa

ren

tly

Av

era

ge

wa

ges

incr

ease

db

y5

per

cen

t

to1

5p

erce

nt.

con

tin

ued

Bhorat et al. 65

Ta

ble

A4

.C

onti

nu

ed

Stu

dy

Co

un

try

Pa

ram

eter

so

fst

ud

yE

mp

loy

men

t/h

ou

rsw

ork

edF

orm

al/

info

rma

lse

cto

rsh

ifts

Wa

ges

exp

erie

nce

dsu

bst

an

tia

l

dec

rea

ses

in

emp

loy

men

t,w

her

eas

som

ela

rge

firm

sa

ctu

all

y

saw

thei

rem

plo

ym

ent

incr

ease

.W

ork

ers

in

thes

ela

rge

firm

s,th

e

au

tho

rco

ncl

ud

es,

are

the

evid

ent

win

ner

sfr

om

the

min

imu

mw

ag

eh

ike.

Su

rya

ha

di

eta

l.

(20

03

)

Ind

on

esia

Da

te:

19

88

–2

00

0

Da

ta:

Na

tio

na

lL

ab

ou

rS

urv

ey

(Sa

ker

na

s)

Th

eim

po

siti

on

of

min

imu

mw

ag

esh

as

a

neg

ati

ve

an

dst

ati

stic

all

y

sig

nifi

can

tim

pa

cto

n

emp

loy

men

tin

the

urb

an

form

al

sect

or.

Th

e

dis

emp

loy

men

tim

pa

ctis

gre

ate

stfo

rfe

ma

le,

yo

un

ga

nd

less

edu

cate

d

wo

rker

s,w

hil

eth

e

emp

loy

men

tp

rosp

ects

of

wh

ite-

coll

ar

wo

rker

sa

re

enh

an

ced

by

incr

ease

sin

min

imu

mw

ages

(-0

.11

2).

So

me

wo

rker

sw

ho

lose

job

sin

the

form

al

sect

or

an

d

ha

ve

tore

loca

teto

the

info

rma

lse

cto

r

face

low

erea

rnin

gs

an

dp

oo

rer

wo

rkin

g

con

dit

ion

s.

Th

em

inim

um

wa

ge

bec

am

em

ore

bin

din

ga

nd

imp

act

ful

on

the

wa

ge

dis

trib

uti

on

inIn

do

nes

iafr

om

19

88

to

20

00

.

An

da

l� on

an

d

Pa

ges

(20

08

)

Ken

ya

Da

te:

19

98

–1

99

9

Da

ta:

Cen

tra

lB

ure

au

of

Sta

tist

ics’

Inte

gra

ted

La

bo

ur

Fo

rce

Su

rvey

Est

ima

tes

ind

ica

teth

at

a

10

per

cen

tag

ep

oin

t

incr

ease

inth

em

inim

um

tom

edia

nw

ag

era

tio

cou

ldb

ea

sso

cia

ted

wit

h

ad

ecli

ne

inth

esh

are

of

form

al

emp

loy

men

to

f

Min

imu

mw

ag

esw

ere

po

siti

vel

y

ass

oci

ate

dw

ith

wa

ges

of

low

-ed

uca

ted

wo

rker

sa

nd

wo

men

in

no

na

gri

cult

ura

la

ctiv

itie

s,w

hil

en

o

such

rela

tio

nsh

ipis

fou

nd

for

wo

rker

s

ina

gri

cult

ure

.

con

tin

ued


Ta

ble

A4

.C

onti

nu

ed

Stu

dy

Co

un

try

Pa

ram

eter

so

fst

ud

yE

mp

loy

men

t/h

ou

rsw

ork

edF

orm

al/

info

rma

lse

cto

rsh

ifts

Wa

ges

bet

wee

n1

.2p

erce

nt

an

d

5.6

per

cen

ta

nd

an

incr

ease

of

bet

wee

n2

.7

per

cen

ta

nd

5.9

per

cen

tag

ep

oin

tsin

the

sha

reo

fse

lf-e

mp

loy

men

t.

Bel

l(1

99

7)

Mex

ico

Da

te:

19

84

–1

99

0

Da

ta:

Mex

ico

’sA

nn

ua

lIn

du

stri

al

Su

rvey

,M

exic

an

Ex

ues

ta

Na

tio

na

led

eE

mp

leo

Min

imu

mw

ag

esh

ad

vir

tua

lly

no

effe

cto

n

emp

loy

men

tin

the

form

al

sect

or.

Min

imu

mw

ag

esh

ad

vir

tua

lly

no

effe

ct

on

wa

ges

inth

efo

rma

lse

cto

r.

Sig

nifi

can

tn

um

ber

so

fw

ork

ers

are

pa

ida

to

rb

elo

wm

inim

um

wa

ges

.

Bo

sch

an

d

Ma

na

cord

a

(20

10

)

Mex

ico

Da

te:

19

89

–2

00

0

Da

ta:

Mic

rod

ata

,E

ncu

esta

Na

cio

na

ld

eE

mp

leo

Urb

an

o

Ad

ecli

ne

inth

ere

al

va

lue

of

the

min

imu

mw

ag

eex

pla

ins

av

ery

sig

nifi

can

tin

crea

sein

ineq

ua

lity

ob

serv

edin

Mex

ico

bet

wee

nth

ela

te

19

80

sa

nd

late

19

90

s.

Fel

icia

no

(19

98

)M

exic

oD

ate

:1

97

0,

19

80

,1

99

0

Da

ta:

Ind

ust

ria

lC

ensu

s,M

on

thly

Ind

ust

ria

lS

urv

ey

La

rge

red

uct

ion

sin

the

min

imu

mw

ag

ew

ere

fou

nd

toin

crea

se

emp

loy

men

to

ffe

ma

les

ag

ed1

5to

64

yea

rs

(�0

.58

to�

1.2

5).

Dem

an

ded

shif

tsa

wa

y

fro

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lder

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led

ma

les

tow

ard

less

-sk

ille

dm

ale

wo

rker

s.

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stil

lo-

Fre

ema

na

nd

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ema

n

(19

92

)

Pu

erto

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oD

ate

:1

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6–

19

87

Da

ta:

U.S

.D

epa

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ent

of

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r

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imu

mW

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eIn

du

stry

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die

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ula

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r

Pu

erto

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o,

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pu

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rvey

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S)

for

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o

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eim

po

siti

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of

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US

-

lev

elm

inim

um

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ge

to

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erto

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od

isto

rted

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Pu

erto

Ric

an

earn

ing

s

dis

trib

uti

on

,su

bst

an

tia

lly

red

uce

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plo

ym

ent

on

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isla

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-

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ree

da

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w

rem

ark

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at

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ima

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imp

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g

tha

tth

em

inim

um

wa

ge

law

isa

ma

jor

det

erm

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nt

of

act

ua

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ag

esp

aid

.

con

tin

ued

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Ta

ble

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ed

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dy

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un

try

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ram

eter

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orm

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rsh

ifts

Wa

ges

0.9

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llo

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r

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oss

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ust

ries

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aff

ecte

dth

e

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ract

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tics

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the

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tes.

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kss

on

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ko

va

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)

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va

k

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ub

lic

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te:

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98

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00

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Da

ta:

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era

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ve

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)

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20

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h

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ta:

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uth

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r

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ere

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sa

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e,si

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ifica

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.9a

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te:

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00

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20

07

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ta:

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ult

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din

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oti

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in-

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av

er-

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act

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e

nu

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wit

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rea

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2p

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in

20

07

.

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bst

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tia

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sein

farm

wo

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ap

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con

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ued


Ta

ble

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ed

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info

rma

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Wa

ges

Ov

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ag

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rs

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ng

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som

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ap

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at

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urs

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rk

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ease

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e

low

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the

pre

law

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iod

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riv

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rgel

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y

the

fall

inp

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-tim

e

emp

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men

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tz(2

00

5)

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uth

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ica

Da

te:

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tem

ber

20

01

to

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tem

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20

04

Da

ta:

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ab

ou

r

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rce

Su

rvey

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Ho

urs

of

wo

rkp

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dec

rea

sed

(-0

.47

for

wo

men

an

d-0

.28

for

men

),a

nd

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loy

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t

fell

(bet

wee

n-0

.19

an

d-

0.3

3)

for

do

mes

tic

serv

ice

wo

rker

s.

Av

era

ge

wa

ges

of

tho

seem

plo

yed

incr

ease

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ya

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rox

ima

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20

per

cen

t.

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ora

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et

(20

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)

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uth

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ica

Da

te:

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00

–0

7

Da

ta:

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uth

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nL

ab

ou

r

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rce

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rvey

s

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at

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intr

od

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min

imu

m

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ge

law

sh

ad

a

sig

nifi

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tim

pa

cto

n

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men

tin

ag

iven

per

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for

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reta

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mes

tic

wo

rk,

tax

i,

secu

rity

,a

nd

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stry

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ors

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ork

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inth

e

reta

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4.5

per

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t),

secu

rity

(-4

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nt)

,

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iden

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fa

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po

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tic

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ty

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ed.

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fore

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sect

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did

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tex

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ien

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n

incr

ease

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ag

es.

con

tin

ued

Bhorat et al. 69

Ta

ble

A4

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onti

nu

ed

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dy

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un

try

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ram

eter

so

fst

ud

yE

mp

loy

men

t/h

ou

rsw

ork

edF

orm

al/

info

rma

lse

cto

rsh

ifts

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ges

an

dd

om

esti

cw

ork

(-7

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per

cen

t)se

cto

rs

exp

erie

nce

da

red

uct

ion

of

ho

urs

,b

ut

incr

ease

sin

wa

ges

ou

twei

gh

edth

ese

effe

cts.

Mu

rra

ya

nd

va

n

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lbee

k

(20

07

)

So

uth

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ica

Da

te:

Jan

ua

ryto

Ma

rch

20

05

Da

ta:

10

3se

mis

tru

ctu

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inte

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ws

of

larg

e-a

nd

med

ium

-sca

lefa

rmin

g

emp

loy

ers

No

larg

ed

isem

plo

ym

ent

effe

cts

occ

urr

edfo

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rm

wo

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ut

ther

ew

as

som

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dic

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on

tha

t

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ers

sub

stit

ute

da

t

the

low

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ills

ma

rgin

as

wel

la

sa

dju

sted

at

the

inte

nsi

ve

ma

rgin

of

lab

or

tore

du

cew

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ag

e

cost

s.O

na

ver

ag

e,

wo

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sh

ou

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red

uce

db

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een

27

an

d

35

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urs

per

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k,

as

op

po

sed

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est

an

da

rd

45

-ho

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wo

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.

Co

nra

die

(20

04

)S

ou

thA

fric

aD

ate

:2

00

4

Da

ta:

Su

rvey

of

19

0w

ine

an

d

tab

leg

rap

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s

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emp

loy

men

tef

fect

sa

re

fou

nd

wit

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gra

pe

farm

wo

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s(-

0.5

9)

an

d

win

efa

rmw

ork

ers

(�0

.33

).

Na

ttra

ssa

nd

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kin

gs

(20

14

)

So

uth

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ica

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te:

20

10

–1

1

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ta:

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tio

na

lB

arg

ain

ing

Co

un

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nu

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ng

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un

ched

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con

tin

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Ta

ble

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nu

ed

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dy

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un

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info

rma

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ifts

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ges

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com

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riv

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20

10

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hre

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fact

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at

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ate

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at

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0,0

00

job

sin

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cast

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uth

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ica

.

Ga

rber

s(2

01

5,

un

pu

bli

shed

)

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uth

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ica

Da

te:

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97

–2

00

7

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ta:

Oct

ob

erH

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old

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an

dL

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orc

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ey

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gs

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ica

teth

at

form

al

un

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led

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loy

men

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ase

d

by

ap

pro

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ly1

6

per

cen

ta

sa

resu

lto

fth

e

20

03

ag

ricu

ltu

ral

min

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mw

ag

e

reg

ula

tio

n,

of

wh

ich

7.5

per

cen

tis

dir

ectl

y

att

rib

uta

ble

toh

igh

er

un

skil

led

lab

or

cost

s

resu

ltin

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om

the

wa

ge

flo

or.

Th

ere

isa

lso

evid

ence

of

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la

nd

cap

ita

lin

ten

sifi

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on

resu

ltin

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om

the

min

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mw

ag

e.

Del

Ca

rpio

,

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sin

a,

an

d

Sa

nz-

de-

Th

ail

an

dD

ate

:1

99

8–

20

10

Da

ta:

La

bo

rF

orc

eS

urv

eya

nd

Ho

use

ho

ldS

oci

o-E

con

om

ic

Su

rvey

Min

imu

mw

ag

ein

crea

ses

ha

ve

sma

ll

dis

emp

loy

men

tef

fect

so

n

fem

ale

,el

der

ly,

an

dle

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ite

of

sub

sta

nti

al

no

nco

mp

lia

nce

,

the

min

imu

mw

ag

ein

Th

ail

an

dis

bin

din

g,

an

dit

ha

sa

bea

rin

go

na

ctu

al

wa

ge

(0.3

6).

con

tin

ued

Bhorat et al. 71

Ta

ble

A4

.C

onti

nu

ed

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dy

Co

un

try

Pa

ram

eter

so

fst

ud

yE

mp

loy

men

t/h

ou

rsw

ork

edF

orm

al/

info

rma

lse

cto

rsh

ifts

Wa

ges

Ga

ldea

no

(20

14

)

edu

cate

dw

ork

ers

an

d

larg

ep

osi

tiv

eef

fect

so

n

the

wa

ges

of

pri

me-

ag

e

ma

lew

ork

ers

(�0

.05

5).

As

such

,in

crea

ses

inth

e

min

imu

mw

ag

ea

re

ass

oci

ate

dw

ith

incr

ease

s

inh

ou

seh

old

con

sum

pti

on

per

cap

ita

ing

ener

al,

bu

tth

e

con

sum

pti

on

incr

ease

is

gre

ate

sta

mo

ng

tho

se

ho

use

ho

lds

aro

un

dth

e

med

ian

of

the

dis

trib

uti

on

.In

fact

,ri

ses

inth

em

inim

um

wa

ge

incr

ease

din

equ

ali

tyin

con

sum

pti

on

per

cap

ita

wit

hin

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bo

tto

mh

alf

of

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dis

trib

uti

on

.

Del

Ca

rpio

,

Ma

rgo

lis,

an

d

Ok

am

ura

(20

13

)

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e

Ph

ilip

pin

es

N/A

Rea

lm

inim

um

wa

ge

incr

ease

sh

ad

neg

lig

ible

effe

cts

on

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era

ll

emp

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men

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win

gto

the

lim

ited

cov

era

ge

of

min

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mw

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eru

les

an

dh

igh

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mp

lia

nce

.

con

tin

ued


Ta

ble

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ed

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info

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nzo

na

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01

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he

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ilip

pin

es

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Sec

tors

wit

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ge

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mp

lia

nce

exp

erie

nce

dn

ega

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e

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men

tef

fect

s.

Str

ob

la

nd

Wa

lsh

(20

03

)

Tri

nid

ad

an

d

Ta

ba

go

Da

te:

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96

–1

99

8

Da

ta:

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nti

nu

ou

sS

am

ple

Su

rvey

of

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pu

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on

Bo

thla

rge

an

dsm

all

emp

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ers

inso

me

case

s

resp

on

ded

toth

e

min

imu

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ag

eb

yla

yin

g

off

wo

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s.

No

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mp

lia

nce

by

emp

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ers

wa

sp

rov

ento

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asu

bst

an

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sue

in

imp

lem

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ng

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ease

d

min

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es.

Ma

les

wo

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gin

larg

efi

rms

ten

ded

to

ha

ve

thei

rw

ag

ein

crea

sed

toa

tle

ast

the

min

imu

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vel

;so

me

fem

ale

sin

bo

thla

rge

an

dsm

all

firm

sex

per

ien

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aw

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ein

crea

sed

ue

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ge.

Del

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rpio

an

d

Lia

ng

(20

13

)

Vie

tna

mN

/AL

ow

-in

com

eo

ro

ther

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e

vu

lner

ab

lew

ork

ers

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din

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om

en,

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uth

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cen

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bo

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ma

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ts,

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led

,n

on

ma

na

ger

ial

no

np

rod

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ion

wo

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such

as

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ner

so

r

gu

ard

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der

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ers,

an

dth

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y

sma

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rms)

are

pa

rtic

ula

rly

lik

ely

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to

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of

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Bhorat et al. 73

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Documents

Minimum Wages in Sub-Saharan Africa: A Primer