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Oxford Development Studies
ISSN: 1360-0818 (Print) 1469-9966 (Online) Journal homepage: http://www.tandfonline.com/loi/cods20
The effect of minimum wages on employment in
emerging economies: a survey and meta-analysis
Stijn Broecke, Alessia Forti & Marieke Vandeweyer
To cite this article: Stijn Broecke, Alessia Forti & Marieke Vandeweyer (2017) The effect of
minimum wages on employment in emerging economies: a survey and meta-analysis, Oxford
Development Studies, 45:3, 366-391, DOI: 10.1080/13600818.2017.1279134
To link to this article: https://doi.org/10.1080/13600818.2017.1279134
Published online: 23 Jan 2017.
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Oxford Development Studies, 2017
VOL. 45, NO. 3, 366–391
The effect of minimum wages on employment in emerging
economies: a survey and meta-analysis
Stijn Broeckea,b, Alessia Fortia
and Marieke Vandeweyera,c
OECD, Paris, France; bIZA, Bonn, Germany; c
Center for Economic Studies, KU Leuven, Leuven, Belgium
Using both qualitative and quantitative (meta-analysis) methods, this
paper reviews the growing evidence on the impact of minimum wages
on employment in 14 major emerging economies (Argentina, Brazil, Chile,
China, Colombia, India, Indonesia, Mexico, Poland, the Philippines, the
Russian Federation, South Africa, Thailand and Turkey). Overall, minimum
wages are found to have only a minimal impact on employment, and there
is evidence of reporting bias towards statistically significant negative results.
More vulnerable groups (e.g. youth and the low-skilled) are marginally more
negatively affected, and there is some indication that higher minimum
wages lead to more informal employment.
Few topics in economics have received as much attention as the minimum wage. In particular, and
despite decades of research, arguments still rage about whether minimum wages have a negative
impact on employment or not.1
On balance, however, most economists would probably agree that
moderate minimum wages are unlikely to undermine employment – although vulnerable groups (like
youth and the low-skilled) may be more likely to suffer adverse employment effects (OECD, 2015).
While the effect of minimum wages on employment has been heavily researched in the developed
world, much less is known about their impact in emerging economies – and yet there are important reasons to believe that their impact might be very different in such settings. For example, emerging economies
are often characterised by high rates of informality and low levels of compliance with the minimum wage,
and so the latter might be expected to have very little or even no effect on employment. On the other hand,
minimum wages in emerging economies are often set at very high levels compared to average wages and
a greater proportion of the workforce is unskilled and earning at or near the minimum wage – meaning
that the negative effect on employment could actually be larger than in more advanced countries.
The true impact of minimum wages on employment outcomes in emerging economies is therefore, to a large extent, an empirical issue. However, while the literature in this field has been growing
rapidly and exponentially (see Figure 1), there has been virtually no attempt to date to review all this
This paper aims to fill this void by reviewing the minimum wage literature for the 14 largest
emerging market economies in the world (as measured by total GDP): the six BRIICS countries
(Brazil, China, the Russian Federation, India, Indonesia and South Africa) as well as Argentina, Chile,
© 2017 Oxford Department of International Development
J21; J31; O10; O57
CONTACT Stijn Broecke email@example.com
OXFORD DEVELOPMENT STUDIES 367
Colombia, Mexico, Poland, the Philippines, Thailand and Turkey.2
While Malaysia completes this list
of the largest emerging economies, no minimum-wage studies could be identified for Malaysia and
so it could not be included in the analysis. The list of countries covered in this paper also provides a
good geographical spread, covering four continents: Africa, Asia, Europe and South America.
Based on both a qualitative (survey) and quantitative (meta-analysis) review of the evidence, we find
that the effect of minimum wages on employment in large emerging economies is very similar to that
observed in more advanced countries: overall, minimum wages seem to have no effect of economic
significance on employment – although studies appear to find that more vulnerable groups (youth
and the low-skilled) are marginally more adversely affected, and that higher minimum wages lead
to more informal employment. There is also clear evidence of reporting bias in favour of statistically
significant negative results. While we investigate whether the impact of minimum wages varies by
other individual and study characteristics, the results are too sensitive to model specification to allow
us to draw any robust conclusions.
Following this introduction, the paper proceeds as follows: We first provide a country-by-country
qualitative survey of the minimum wage literature in emerging economies. We then proceed with a
simple meta-regression analysis (MRA) both of the elasticities obtained in this literature, as well as
of the partial correlations. These simple models are subsequently extended to test and control for
reporting bias and, in the following section, we add a range of additional control variables (including
study fixed effects) to run a multiple MRA. The final section discusses the results and offers some
2. Qualitative literature review
Using standard search engines and economic literature databases, we identified 95 studies covering
13 major emerging economies: Argentina (2), Brazil (19), Chile (13), China (11), Colombia (7), India
(1), Indonesia (10), Mexico (4), Poland (8), the Philippines (2), the Russian Federation (2), South
Africa (9), Thailand (3) and Turkey (5).3
While the main purpose of this paper is to carry out a MRA
of the existing evidence, we believe that a qualitative (survey) review of the literature is still valuable
and should be seen as a useful complement to more quantitative methods. In particular, qualitative
reviews sometimes allow for a more nuanced discussion of certain issues. In addition, because not all
studies we identified contain results which can be used in the meta-analysis, the qualitative survey of
the literature covers a greater number of studies.
1980-1984 1985-1989 1990-1994 1995-1999 2000-2004 2005-2009 2010-2014
Number of individual country studies by year of publication
Figure 1. Minimum wage studies in emerging economies, 1980–2014.
368 S. BROECKE ET AL.
In what follows, we therefore offer a country-by-country review of the impact of the minimum
wage on employment, hours worked and formality, based on the main conclusions reached by each
of the papers surveyed. To guide the discussion (but also for the reader wishing to skip the detailed
country-by-country discussion), Table 1 provides a brief summary of the main findings of this qualitative review, as well as some basic minimum wage and labour market characteristics of the countries
covered to assist the interpretation of the findings. More specifically, Table 1 shows: the Kaitz index (i.e.
the ratio of the minimum to the average wage), the employment rate, and the incidence of informal
employment for each of the countries included in the study.
A few interesting, albeit very tentative, observations emerge from these summary statistics. Firstly,
the employment impact of minimum wages appears to be mixed or zero in most countries studied.
Table 1. Summary of qualitative literature review.
The calculation of minimum and average wages is based on the ILO Global Wage Report data. For the Philippines, the average wage
is calculated as the average of 2012 and 2014, while the minimum wage is calculated as the employment-weighted average of
the regional minimum wages (taken from the Philippine Statistics Authority). For Argentina, the average wage is obtained from
INDEC (2013). Data refer to 2008 for China and 2012 for India. bSource: OECD for Brazil, Chile, Colombia, Mexico, Poland, Russia and Turkey; ILO for Argentina, China, India, Indonesia, Philippines,
South Africa and Thailand 2014 for India. c
Source: ILO (2011), Charmes (2011) (for Chile), DOLE (n.d.) for the Philippines, Central Statistical Office for Poland. The data refer to
persons in informal employment, except in Russia and the Philippines, where it refers to persons employed in the informal sector/
Panel A: country and institutional characteristics
Employment rate, 15+
(2013)b Informal employmentc
South Africa Africa 0.30 43% 33% (2010)
China Asia 0.33 69% 33% (2010)
India Asia 0.40 50% 84% (2009)
Indonesia Asia 0.69 63% 73% (2009)
Philippines Asia 0.87 59% 44% (2008)
Thailand Asia 0.65 71% 42% (2010)
Poland Europe 0.40 50% 5% (2010)
Russia Europe/Asia 0.18 65% 12% (2010)
Turkey Europe/Asia 0.38 46% 31% (2009)
Argentina South America 0.59 56% 50% (2009)
Brazil South America 0.45 59% 42% (2009)
Chile South America 0.45 56% 36% (1995–1999)
Colombia South America 0.60 63% 60% (2010)
Mexico South America 0.28 57% 54% (2009)
Panel B: minimum wage effects
Effect of an increase in the MW on Vulnerable groups more
Employment Hours Formality negatively affected?
South Africa Mostly zero/mixed Mixed Mixed/positive Little research (mixed)
China Mixed Little research (mixed/
Indonesia Mixed Mixed/positive Mixed Yes
Philippines Negative Little research (negative) Yes
Thailand Mixed Little research (positive) Yes
Poland Negative Mixed Yes
Russia Zero (or small) Mixed/negative Yes
Turkey Mixed Mixed Little research (negative) Yes
Argentina Mixed Little research (negative) Yes
Brazil Mostly negative (small) Zero/mixed or negative Mostly negative or zero/
Chile Mostly negative Little research (negative) Little research (negative) Yes
Colombia Mostly negative Little research (negative) Mixed Yes
Mexico Mixed Mixed
OXFORD DEVELOPMENT STUDIES 369
Secondly, when the evidence points towards a negative effect, this tends to be more common in countries with high minimum wages (as a proportion of the average wage), such as the Philippines, Thailand,
Colombia, Brazil and Chile – although there are some exceptions, e.g. Indonesia and Argentina. This
leads us to the third observation, which is that in countries which combine a high minimum wage
with a high incidence of informality, the negative employment effect appears to be muted (which is
the case in both Indonesia and Argentina). Fourthly, when minimum wages are very low (as in the
Russian Federation and South Africa), few negative effects on employment can be detected. Fifthly,
the evidence in nearly all of the countries suggests that vulnerable groups might be more adversely
affected by minimum wages. Finally, the impact of minimum wages on hours worked is much less
well-researched, and the evidence appears inconclusive. Similarly, the relationship between minimum
wages and formality is empirically unclear: while many studies find negative effects, some positive
effects of minimum wages on formality4
have been found in Brazil, Colombia and South Africa.
In contrast to many other Latin American countries, the minimum wage literature in Argentina is very
limited – which is surprising given that Argentina has a relatively high minimum wage. The results of
Groisman (2012, 2015) indicate that a rise in the minimum wage increases the probability of becoming
unemployed or inactive for workers with salaries below the minimum wage, but that this impact is
only significant for unregistered workers. The findings also suggest that formal sector workers have a
higher chance of switching to the informal sector after a minimum wage increase. Both the effects on
employment and on informality are found to be stronger for women, although overall the economic
size of the effect on informality is negligible.
In contrast to Argentina, a relatively large number of studies have investigated the impact of the minimum wage on employment in Brazil. Earlier research tended to find small- to medium-sized negative
effects of the minimum wage on employment (Carneiro, 2001; Fajnzylber, 2001; Foguel, 1998; Foguel,
Ramos, & Carneiro, 2001; Neumark, Cunningham, & Siga, 2006; Soares, 2005). One exception was
a study by Carneiro and Corseuil (2001) who look at the period between 1982 and 1999 and use a
difference-in-differences model. They find much larger negative employment effects, ranging between
−3% and −13% for a 10% increase in the minimum wage. A later series of studies carried out by Lemos
(2004a, 2004b, 2005a, 2005b, 2007, 2009a, 2009b) found no, or negligible, effects – a result confirmed
by Broecke and Vandeweyer (2015) for more recent years.
Two studies have also investigated the effect of Brazil’s state wage floors, but both found that compliance with these state minimum wages is extremely low and that they have had no effect on labour
market outcomes (Corseuil, Foguel, & Hecksher, 2013; Moura & Neri, 2008). While some studies
looked at more vulnerable groups, most of them do not find that they are more negatively affected
(Lemos, 2007, 2009a, 2009b), although one obtains mixed results (Fajnzylber, 2001) and another finds
that vulnerable groups (youth and the low-skilled) are slightly more negatively affected (Broecke &
The relationship between minimum wages and hours worked has barely been investigated in the
context of Brazil, and findings are contradictory: Lemos (2004a) and Broecke and Vandeweyer (2015)
find small negative effects, while Lemos (2007, 2009a) finds no effect at all, and Neumark et al. (2006)
obtain mixed results at the household level.
Similarly, the evidence on the impact of minimum wages on formality is inconclusive in Brazil.
On the one hand, Carneiro (2001, 2004), Carneiro and Corseuil (2001), Foguel et al. (2001), Jales
(2015) and Broecke and Vandeweyer (2015) find that increases in the value of the minimum wage
tend to decrease formal employment and increase informal employment. On the other hand, Lemos
370 S. BROECKE ET AL.
(2009a) finds no proof of employment effects in either formal or informal sectors, while Lemos (2004c,
2009b) also finds some evidence that increases in the minimum wage reduce, rather than increase,
employment of informal workers. Similarly, Fajnzylber (2001) finds larger negative employment effects
in the informal sector (meaning that informality has a tendency to fall as minimum wages increase)
and Foguel (1998) finds a positive effect of minimum wages on formality. Finally, Soares (2005) and
Corseuil et al. (2013) find no effect on formality.
Research on the employment effect of the minimum wage in Chile mainly points to a negative impact
(Beyer & Dussaillant, 2009; Chacra Orfali, 1990; Cowan, Micco, Mizala, Pagés, & Romaguera, 2004;
Infante, Marinakis, & Velasco, 2003; Martinez, Morales, & Valdes, 2001; Miranda, 2013; Paredes &
Riveros, 1989), although one older study (Solimano, 1988) finds a positive impact on employment,
and two others find no or mixed results (Castañeda, 1983; Montenegro & Pagés, 2004).
The evidence from Chile also suggests that the negative impact of minimum wages on employment
is more significant for more vulnerable groups. Paredes and Riveros (1989, 1993), Chacra Orfali (1990)
and Cowan et al. (2004) all conclude that the minimum wage is more likely to have a negative impact
on young people and people with lower educational attainment. Beyer and Dussaillant (2009) find
that the minimum wage has a negative effect on youth employment, with young people mainly transitioning from work to unemployment or inactivity (rather than into study). Montenegro and Pagés
(2004) find that unskilled workers experience negative employment effects, while for skilled workers
the impact is positive. The minimum wage is also found to have a negative effect on the employment
of low-wage workers (Castañeda, 1983; Paredes & Riveros, 1989).
While most Chilean studies do not make a distinction between formal and informal employment
when assessing the impact of minimum wages, Wedenoja (2013) argues that the minimum wage
increases informality. Similarly, only one study investigates the effect of minimum wage increases on
hours worked, and finds that the effect is negative (Grau & Landerretche, 2011).
The impact of minimum wages on labour market outcomes in China has received significant attention from researchers in recent years. Studies analysing aggregate employment generally point to a
small negative impact (Fang & Lin, 2013; Wang & Gunderson, 2012; Xiao & Xiang, 2009) – although
Mayneris, Poncet, and Zhang (2014) argue that increases in the minimum wage have no impact on
aggregate employment because of their positive impact on productivity.
Effects have also been found to differ significantly between the different Chinese regions which vary
in their degree of development. Whereas Wang and Gunderson (2011, 2012) find that the minimum
wage has no effect on employment in the Eastern (most developed) region and a negative impact in
the slower growing Central and Western regions, the exact opposite is found by Ni, Wang, and Yao
(2011) and Fang and Lin (2013). The distinction between urban and rural migrant workers is made
in many studies, with rural workers mostly experiencing stronger negative effects (Ding, 2010; Fang
& Lin, 2013; Luo, Zhou, & Wu, 2011). Negative employment effects are also found to be greater for
low-skilled and female workers (Fang & Lin, 2013; Jia, 2014). Using firm-level data, both Ding (2010)
and Huang, Loungani, and Wang (2014) find stronger adverse effects on low-wage firms. Finally, there
is some evidence that the impact might vary by sector, with negative effects for the manufacturing
sector and positive effects for the construction sector (Luo et al., 2011; Shi, 2011).
When focusing on hours worked rather than on employment, a positive effect of the minimum
wage is found by Xiao and Xiang (2009) – a finding confirmed by Jia (2014) although only for lowskilled men. No research in China has focused on the impact on informality.
OXFORD DEVELOPMENT STUDIES 371
The majority of evidence on minimum wages in Colombia shows a negative impact on employment
and a positive impact on unemployment (Aguirre Botero, 2011; Arango & Pachón, 2004; Bell, 1997;
Hernandez Diaz & Pinzon Garcia, 2006; Sánchez, Duque, & Ruiz, 2009), which is perhaps not too
surprising given the high level of the minimum wage in Colombia. Only one paper looked at the effect
on hours worked, and found a negative effect which is stronger for younger and high-skilled workers
(Arango & Pachón, 2004).
According to Aguirre Botero (2011), youth are most likely to be affected by minimum wages and,
in particular, those with low levels of education or experience, as well as those living in less educated
families. These stronger effects on youth employment are confirmed by Hernandez and Lasso (2003),
Arango and Pachón (2004) and Sánchez et al. (2009), but not by Hernandez Diaz and Pinzon Garcia
(2006). Stronger effects are also found for female and low-skilled workers (Arango & Pachón, 2004;
Bell, 1997; Hernandez Diaz & Pinzon Garcia, 2006; Sánchez et al., 2009).
While Mora Rodriguez (2007) finds that the probability of participation in the informal sector
decreases as the minimum wage rises, Sánchez et al. (2009) show that informality increases.
There has been virtually no research on the effect of minimum wage legislation on employment in
India, which is likely to be a consequence of the complexity of the country’s minimum wage system and
its limited coverage and enforcement (Belser & Rani, 2012). The only study this review encountered
is Soundararajan (2014), who analyses the impact of the minimum wage on employment in the construction industry. Exploiting the difference in minimum wages and the number of labour inspectors
across regions, the author finds a negative effect on employment for low levels of enforcement, and a
positive effect for high levels of enforcement.
The evidence on the effect of minimum wages on employment in Indonesia is mixed, with Chun and
Khor (2010) and Alatas and Cameron (2008) finding no impact, Comola and de Mello (2011) finding a
positive impact, Rama (2001) finding a negative impact, and Islam and Nazara (2000) finding evidence
for both negative and positive effects. Suryahadi, Widyanti, Perwira, and Sumarto (2003), focusing on
the formal sector, find a negative impact on employment, which is stronger for females, low-skilled
youth and part-time workers, while there is evidence of positive effects on white collar employment.
According to Del Carpio, Nguyen, and Wang (2012), only female non-production5
workers and lowskilled production workers in small firms face negative effects of minimum wages. Similarly, Sugiyarto
and Endriga (2008) find negative effects on unskilled workers, but not on skilled ones.
A positive impact on hours worked is found, although not for female workers in rural areas (Chun
& Khor, 2010; Pratomo, 2014). Only a few papers focus on the possible effect of minimum wages on
formality and the evidence is inconclusive. On the one hand, Comola and de Mello (2011) find that
the positive effect on aggregate employment is driven by a positive impact on informal employment
which fully offsets the negative impact on formal employment. This confirms the finding of Chun and
Khor (2010) that the negative impact on low-wage formal sector workers does not translate into aggregate employment losses. On the other hand, Magruder (2013) shows that minimum wages increase
full-time paid work and decrease self-employment, which is evidence for increased formalisation.
For Mexico, evidence on the employment effects of minimum wages is scarce and inconclusive – both
overall as well as for more vulnerable groups. No research in Mexico has looked at the impact of the
372 S. BROECKE ET AL.
minimum wage on either hours worked or formality. Bell (1997) finds that there are no significant
effects on aggregate manufacturing employment, even when a distinction between skilled and unskilled
workers is made, while Samaniego de Villareal and Samaniego Breach (1988) point to a negative relationship between minimum wages and manual employment, and Garza Cantú and Bazaldúa (2002)
also find a negative impact of minimum wage rises on aggregate employment. When looking at different
wage groups, however, the latter authors show that people with low wages experience positive effects
from a minimum wage increase, while the impact on higher wage earners is negative. When defining
vulnerable groups in terms of age and sex, Feliciano (1998) finds that there is no impact on aggregate
male employment, while it is positive for older men. The impact on aggregate female employment is
negative and increases with age.
Most research in the Polish context finds a negative effect of minimum wage rises on employment
(Baranowska-Rataj & Magda, 2015; Bukowski, 2010; Kamińska & Lewandowski, 2015; Majchrowska
& Zółkiewski, 2012; Melnyk, 1996; Ruzik, 2007).
While there are some contradictory findings, the evidence also seems to suggest that the negative
employment effects are greater for youth and in less developed regions. Majchrowska, Broniatowska,
and Żółkiewski (2015) and Ciżkowicz, Kowalczuk, and Rzońca (2014) are two papers which do not
find a negative effect on youth employment, although the former paper does find a more adverse
effect in less-developed regions where there is a higher minimum-to-average wage ratio. By contrast,
Majchrowska and Zółkiewski (2012) find that the minimum wage effect is particularly strong for young
workers over the period 2005–2010, which saw large rises in the minimum wage, as well as in poorer
regions. Kamińska and Lewandowski (2015) also find that job separations following minimum wage
hikes occur primarily among young workers and Baranowska-Rataj and Magda (2015) confirm the
negative impact on youth employment.
Only a handful of papers have investigated the impact of the Polish minimum wage on hours
worked, and the evidence is mixed. Kamińska and Lewandowski (2015) find a negative impact on
hours worked among workers for whom the minimum wage became more binding, but BaranowskaRataj and Magda (2015) find no effect. None of the Polish papers we reviewed looked at the effect
on informality, but then the rate of informality is quite low in Poland and therefore less of an issue.
We could find only two papers which have estimated the impact of minimum wages on employment
in the Philippines. Canales (2014) finds that workers affected by minimum wage increases experience
a reduction in hours worked as well as in the probability of employment. Lanzona (2014) also finds a
negative effect of the minimum wage on employment, particularly among youth, low-skilled workers
2.11. Russian Federation
Research on the effect of minimum wages in the Russian Federation is very limited, which is likely to
be because of the low level of the minimum wage in comparison to average wages. It is therefore not
surprising that the only two papers which look at the employment impact of minimum wages in the
Russian Federation find no, or very little, effect. Kobzar (2009) finds no significant effect on either
aggregate employment or unemployment, and the lack of statistically significant effect on unemployment is confirmed by Muravyev and Oshchepkov (2013). When focusing on different sub-groups,
the latter authors find that the minimum wage has no impact on female unemployment, but does
have a negative impact on youth unemployment. Both papers also look at the effects on the share of
workers in informal employment. While Kobzar (2009) finds no effect, Muravyev and Oshchepkov
OXFORD DEVELOPMENT STUDIES 373
(2013) find a significant positive effect (i.e. minimum wage hikes are associated with an increase in
2.12. South Africa
Although minimum wages were only introduced in South Africa in 1999, there are already several
studies analysing their effects, albeit with conflicting conclusions. While most studies find no effect
on the employment of farm workers (Conradie, 2003, 2005; Murray & van Waelbeek, 2007), Bhorat,
Kanbur, and Stanwix (2014) find that minimum wages reduce the probability of being a farm worker
– a finding confirmed by Bhorat, Cassim, Kanbur, Stanwix, and Yu (2016). For domestic workers, the
evidence is also mixed: individual-level data point to no significant effect on the probability of being a
domestic worker (Bhorat, Kanbur, & Mayet, 2013; Dinkelman & Ranchhod, 2012), but regional-level
analysis shows a negative impact on domestic employment (Hertz, 2005). Bhorat et al. (2013) look at
all sectors with a minimum wage and find no effect on employment in any of them.
The effect on hours worked also differs between sectors. Murray and van Waelbeek (2007) argue
that adjustments in the agricultural sector mainly happen at this intensive margin. This is confirmed by
Bhorat et al. (2014) who find that average hours worked drop after a minimum wage increase. However,
their results also show that average hours increase in the most vulnerable regions, which they attribute to a decline in part-time jobs. The results for domestic workers are again mixed, with Dinkelman
and Ranchhod (2012) and Bhorat et al. (2013) finding no significant effects on hours worked, and
Hertz (2005) finding a negative impact. Bhorat et al. (2013) only find a significant negative effect on
hours worked in the retail and security sectors. For youth, Bhorat et al. (2016) find mixed evidence,
depending on the sector they are employed in: hours worked decline with minimum wage increases
in the private security, agriculture and domestic worker sectors, while they increase in the taxi sector.
In South Africa, minimum wages are found to lead to increased formalisation for both agricultural
and domestic workers (Bhorat et al., 2014; Dinkelman & Ranchhod, 2012). For youth, increased formalisation is only found in the retail sector (Bhorat et al., 2016). However, the impact on formality is
found to differ between people of different sex and ethnicity, with a negative effect (which is stronger
the lower the level of education) for coloured and black women, a positive effect for coloured men,
and no effect for white and black men, or for white women (Millea, Rezek, & Pitts, 2012).
Del Carpio, Messina, and Sanz-de-Galdeano (2014) look at minimum wage increases in Thailand
over the period 1998–2010 and find small disemployment effects for women, older workers and the
low-skilled, but a small positive effect on average hours worked. Ariga (2016) finds that the minimum
wage hikes of 2012 and 2013 had a positive effect on employment – although these findings were
questioned by Lathapipat and Poggi (2016).
Analysis of the impact of minimum wages on the Turkish labour market is scarce and shows mixed
results. Ozturk (2006) and Papps (2012) find a negative impact on employment, while Korkmaz and
Coban (2006) find no impact.
Papps (2012) identifies young and rural workers as those who are most negatively affected by the
minimum wage. This differs from the results of Pelek (2011), who finds no effect on low-skilled youth
employment and a positive impact on the employment rate of young workers with tertiary education.
Whereas Günsoy and Tekeli (2013) find no significant effect of minimum wages on female employment, Ozturk (2006) shows that the low female participation rate can to a large extent be attributed
to minimum wages because the latter reduces the availability of flexible, part-time jobs.
374 S. BROECKE ET AL.
The results on the impact of the minimum wage on hours worked are also contradictory: Ozturk
(2006) finds a positive effect on hours worked, while Papps (2012) finds a negative effect.
Given that young and low-skilled workers have relatively high probabilities of being employed
in the informal sector in Turkey, Pelek (2011) looks at the effect of minimum wages on the share of
formal work for different skill groups of young workers. She finds a positive effect of minimum wages
on informal work for the low-skilled and, to a lesser extent, for medium-skilled young workers.
3. Meta-regression analysis
While a qualitative (survey) review may be useful in getting an overall sense of the results obtained
in the literature, it is difficult to draw robust statistical conclusions from it. In particular, a more
systematic and quantitative approach is necessary in order to obtain an estimate of the ‘average’ size
and significance of the minimum wage effect on employment, and much will depend on subjective
interpretation (both of the authors and the reviewer). Such interpretation is further complicated by
the fact that a qualitative review of the literature does not allow us to test for the existence of bias in
the reporting of the results.
This section therefore attempts to quantify the impact of minimum wages on employment through
a more objective MRA, as described by Stanley and Jarrell (1989) and Stanley and Doucouliagos
(2012). As discussed below, a meta-analysis will enable us to run some formal tests for the presence
of reporting bias in the literature. It will also allow for a more systematic investigation of whether
the effect of minimum wages on employment varies by sub-group studied and/or depends on the
Meta-analyses are becoming increasingly popular to summarise the large number of studies on
minimum wages and employment – although none has specifically looked at emerging economies.
Meta-analyses focusing on developed countries include Card and Krueger (1995), Doucouliagos and
Stanley (2009), Boockmann (2010), de Linde Leonard, Stanley, and Doucouliagos (2014) and Belman
and Wolfson (2014a, 2014b). Nataraj, Perez-Arce, Kumar, and Srinivasan (2014) look at low-income
countries and only cover two emerging economies (India and Indonesia). Chletsos and Giotis (2015)
include a few studies from emerging economies, including Indonesia (6), Brazil (4), Mexico (2), China
(1), Colombia (1) and Poland (1) – but do not distinguish these countries from the other, (largely)
developed countries they include in their study.
3.1. Basic meta-analysis
We run two different types of meta-analysis in this paper. The data collected for the first meta-analysis
consist of all coefficients on the minimum wage variable in regressions where the dependent variable is
employment and where the estimates obtained are in the form of elasticities. As shown in Table 2, this
Table 2. Summary statistics: elasticities.
An elasticity is said to be significant when its p-value is smaller than 0.05. bBecause Bell (1997) studies both Colombia and Mexico, the total number of studies (28) does not equal the sum of the number of
studies per country (29).
N Studies Mean Share negative
Brazil 315 10 −0.005 0.68 0.22 0.06
Chile 21 3 −0.026 0.57 0.24 0.19
China 220 7 0.004 0.60 0.19 0.08
Colombia 25 1 −0.378 0.92 0.64 0.04
Indonesia 84 3 0.029 0.70 0.35 0.19
Mexico 48 3 −0.460 0.73 0.48 0.06
Poland 33 2 −0.145 0.91 0.48 0.03
All 746 28b −0.047 0.68 0.27 0.08
OXFORD DEVELOPMENT STUDIES 375
results in a total of 746 estimates from 28 studies and seven countries (Brazil, Chile, China, Colombia,
Indonesia, Mexico and Poland).6
The approach taken was to include all elasticities presented in each
paper, and not just the authors’ preferred results. This was to minimise the risk of subjectivity creeping
into the analysis and is in line with best practice in the recent meta-analysis literature (Disdier & Head,
2008; Stanley & Doucouliagos, 2012).
Table 2 further shows that the average (unweighted) elasticity from these studies is −.047. In other
words: a 10% increase in the minimum wage decreases employment by .47%, which is a relatively
However, the estimates vary considerably between countries: a small positive effect is
found in China and Indonesia, and relatively strong negative effects are observed in Mexico, Colombia
and also Poland.8
While most estimates are negative, only a third of all coefficients are significantly
different from zero and, as shown in Figure 2, the majority (significant or not) are very close to zero.
This elasticities meta-analysis is our preferred approach because it provides an estimate of the economic effect of minimum wages on employment. However, the big disadvantage of this method is that
a large number of observations which are not reported as elasticities need to be thrown away, which
also results in a diminished country coverage. One way of recovering some of the other estimates and
boosting the sample size for the analysis is to use the ‘partial correlations’ instead of the coefficients
(Stanley & Doucouliagos, 2012). The advantage of using partial correlations is that they are unit-less,
and therefore comparable across studies. However, they are not usually reported in studies and need
to be calculated using the following formula:
where t denotes the t-statistic of the regression coefficient and df the related degrees of freedom.
Similarly, the standard error of the partial correlation needs to be calculated as:
r = t
2 + df
1 − r
-4.2 -3 -2.4 -1.8 -1.6 -1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4
Percent of coefficients
Elasticity of employment to a change in the minimum wage
Figure 2. Distribution of elasticities used for the meta-analysis.
Notes: The data underlying this figure consist of 746 estimates from 28 studies and for 7 countries (Brazil, Chile, China, Colombia, Indonesia, Mexico and
Poland) which could be expressed as elasticities and are used in the first meta-analysis reported in Section 3.1 of this paper. The values on the x-axis
represent the upper bound of a bin with width .2.
376 S. BROECKE ET AL.
One challenge with this approach is that the degrees of freedom are not always reported clearly in
studies and therefore need to be inferred from the information provided by the authors. This is particularly challenging when the models include fixed effects. But, according to Stanley and Doucouliagos
(2012), the calculation of the partial correlation is relatively robust to uncertainty about the degrees
of freedom. A final problem with using partial correlations is that, of course, they are not a measure
of an economic effect, but merely a measure of the strength and direction of the association between
Table 3 shows that this approach boosts the sample size by nearly 50% to 1,083 observations from
56 studies, and adds five countries to the analysis: Argentina, Russia, South Africa, Thailand and
As with the elasticities meta-analysis, all partial correlations were included and not just those
based on the authors’ preferred results. Table 3 shows that the average partial correlation across all
12 countries is −.052. While negative (i.e. the minimum wage has an adverse effect on employment),
this is on the small side of partial correlations usually found in the minimum wage literature, according to the analysis carried out by Doucouliagos (2011). The average partial correlation is negative in
all countries, except Russia and Turkey. It is also small in all countries, except in China (where it is
moderate at −.1) and in Mexico and Thailand (where it is large at −.175 and −.182, respectively). As
in the elasticities analysis, most (two-thirds) of the partial correlations obtained from the emerging
economies literature are negative but, apart from in Colombia, Mexico, Poland and Thailand, only a
small share of these is statistically significant. Moreover, over half of the partial correlations can be
classified as small according to Doucouliagos (2011) criteria (i.e. smaller or equal to ± .045) (see Figure
3). All in all, these results therefore confirm the analysis based on the elasticities sub-sample: while
negative, the impact of minimum wages on employment appears to be small overall.
3.2. Testing for reporting bias
One potential problem with the analysis so far is that the results may partially reflect reporting bias10
rather than a true effect of the minimum wage on employment. Indeed, in their analysis of the effect
of minimum wage rises in the United States, Doucouliagos and Stanley (2009) found that such bias
was an important contributor to the average reported minimum wage effect in the literature. In this
section, we therefore carry out some formal tests for the presence of reporting bias in the research on
minimum wages in emerging economies.
Table 3. Summary statistics: partial correlation coefficients.
A partial correlation is said to be significant when its p-value is smaller than 0.05. bBecause Bell (1997) studies both Colombia and Mexico, the total number of studies (56) does not equal the sum of the number of
studies per country (57).
N Studies Mean Share negative
Argentina 18 1 −0.011 0.72 0.28 0.00
Brazil 384 14 −0.025 0.67 0.20 0.04
Chile 19 5 −0.051 0.68 0.32 0.05
China 230 9 −0.100 0.57 0.25 0.10
Colombia 23 1 −0.048 0.91 0.57 0.04
Indonesia 155 8 −0.010 0.61 0.19 0.13
Mexico 49 3 −0.175 0.71 0.41 0.06
Poland 91 5 −0.091 0.97 0.82 0.01
Russia 2 1 0.040 0.00 0.00 0.00
South Africa 74 6 −0.009 0.57 0.23 0.08
Thailand 11 1 −0.182 0.73 0.45 0.00
Turkey 27 3 0.001 0.67 0.26 0.04
All 1083 56b −0.052 0.67 0.29 0.06
OXFORD DEVELOPMENT STUDIES 377
To do this, we need to adopt a more formal model to estimate the average impact of minimum
wages on employment. More specifically, we relate the effect size e obtained in study i to the overall
effect size 훽0 (intercept) using the following equation:
This, in essence, is what gives us the results reported in Tables 2 and 3. However, if reporting bias is
present, then the effect size is likely to be related to its standard error. The reasoning behind this is
that authors using small samples need larger estimates to achieve the desired significance level and
may therefore resort to data-mining and/or specification search in order to achieve the desired result.
A common method to inspect the presence of such bias visually is to draw a ‘funnel plot’, which
shows the relationship between the effect size (ei
) on the x-axis and the precision of the estimate (as
measured by the inverse of the coefficient’s standard error, sei
) on the y-axis. One would expect this
relationship to be funnel-shaped since more precise estimates (which are plotted higher up the y-axis)
should be closer to the ‘true’ population effect, while less precise estimates (which are plotted lower
down the y-axis) will be scattered more widely around the true effect. The graph’s symmetry is essential
for assessing reporting bias: if the plot leans more heavily towards one side than the other, than this
is indicative of reporting bias.
Panel A of Figure 4 shows a funnel plot based on the minimum wage elasticities summarised in Table
2, and suggests that reporting bias may not be an issue in the studies selected for our meta-analysis.
However, because of the very high precision of some of the estimates, the scale of the y-axis makes it
difficult to visually inspect what goes on near the base of the funnel plot. Panel B therefore reproduces
the figure presented in Panel A, but caps the precision at 100 (equivalent to keeping 65.4% of the observations included in Panel A). With the exception of a few outliers on the left, Panel B again seems to
suggest that reporting bias may not be a major issue.11 Similarly, it is difficult to infer the existence of
any reporting bias from Figure 5, which reproduces the funnel plot for the partial correlations sample.
However, eyeballing reporting bias from a funnel plot is not fool-proof, and so we extend the
meta-analyses run in the previous section to control for the possible presence of such bias. We do this
by adding the standard error of the coefficient, sei
, to the regression:
ei = 훽0 + 휀i (1)
Percent of partial correlations
Figure 3. Distribution of partial correlations used for the meta-analysis.
Notes: The data underlying this figure consist of 1,083 estimates from 56 studies and for 12 countries (Argentina, Brazil, Chile, China, Colombia, Indonesia,
Mexico, Poland, Russia, South Africa, Thailand and Turkey) which could be expressed as partial correlations and are used in the second meta-analysis
reported in Section 3.1 of this paper. The values on the x-axis represent the upper bound of a bin with width .045.
378 S. BROECKE ET AL.
In this regression, β0
denotes the ‘true’ elasticity and β1
indicates the presence or not of reporting bias.
is statistically significant, then reporting bias is present but if it is small, then it is not something
to be much concerned about. If β1
is statistically insignificant and the reporting bias large, then it may
still indicate the presence of reporting bias – but that it is highly variable, which is why it is estimated
ei = 훽0 + 훽1sei + 휀i (2)
Panel A. All coefficients Panel B. Coefficients with precision capped at 100
Effect size (e)
-4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 4.5 -4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 4.5 Precision (1/se)
Effect size (e)
Figure 4. Funnel plots of estimated minimum wage elasticities.
Notes: The data underlying the figure in Panel A consist of 746 estimates from 28 studies and for 7 countries (Brazil, Chile, China, Colombia, Indonesia,
Mexico and Poland) which could be expressed as elasticities and are reported in Table 2. Panel B shows the subset of these estimates with precision
1/se capped at 100 and includes 65.4% of all the estimates included in Panel A.
-1 0 1
Figure 5. Funnel plot of estimated minimum wage partial correlations.
Notes: The data underlying the figure consist of 1,083 estimates from 56 studies and for 12 countries (Argentina, Brazil, Chile, China, Colombia, Indonesia,
Mexico, Poland, Russia, South Africa, Thailand and Turkey) which could be expressed as partial correlations and are reported in Table 3.
OXFORD DEVELOPMENT STUDIES 379
so imprecisely. Note that β1
does not actually represent the magnitude of the reporting bias, but should
be estimated as 훽1se.
Because the studies included in the meta-analysis use different sample sizes and methods, the error
term in Equations (1) and (2) is likely to be heteroscedastic. To control for this, each observation in
Equation (2) can be weighted by the inverse of its standard error, so we obtain the following weighted
least squares model:
is the conventional t-ratio of the reported effect, and 훽o and 훽1
are the coefficients to be estimated. βo
remains the estimate of the average effect, while β1
(now the constant) indicates the presence
(or not) of reporting bias and, as before, the size of this reporting bias (expressed in elasticities rather
than in t-ratios) is computed as 훽1se. In addition, the within-study dependence can be accounted for
by using cluster-robust standard errors.
The results of these various tests for reporting bias are presented in Table 4, both for the elasticities
sample (Panel A) and the partial correlations sample (Panel B). Column (i) contains the results of
Equation (1) and, as discussed above, these are identical to the average effects estimated in Section 3.1.
Column (ii) controls for reporting bias by estimating Equation (2) – i.e. by including the standard error
of the coefficients to the regression. The effect size becomes much smaller as a result of this, and even
turns insignificant in the elasticities regression. There is also clear evidence of (negative) reporting bias,
and the estimated magnitude of this bias is −.021 (i.e. nearly half the effect size found in Column (i)).
Finally, Column (iii) estimates the weighted least squares regression of model (3) with cluster-robust
standard errors. In both the elasticities and the partial correlations models, the estimated effects are
now of no economic significance, while there is still strong evidence of negative reporting bias. In fact,
the estimated size of the reporting bias is at least as large as the effect size found in Column (i). Overall,
two conclusions can be drawn from this analysis: (i) the literature on minimum wages in emerging
economies tends to be biased towards reporting negative employment effects; and (ii) once this bias
is controlled for, no minimum wage effect of economic significance can be detected.
(3) ti = 훽o
+ 훽1 + 휈i
Table 4. Testing for reporting bias.
Notes: Standard errors are reported in parentheses. 훽1
indicates the presence of reporting bias, but the size of the coefficient does not
give an indication of the magnitude of the reporting bias. The latter is reported at the bottom of each panel, and is calculated as 훽1se. ***Significant at the 1% level; **Significant at the 5% level; *
Significant at the 10% level.
(i) (ii) (iii)
+ εi ei
+ β1 sei
+ εi t
) + β1
(with clustered se)
Panel A: coefficients
(effect size) −0.047*** β0
(effect size) −0.013 β0
(effect size) −0.0003***
(0.012) (0.013) (0.0001)
(reporting bias) −0.486*** β1
(reporting bias) −0.782**
Observations 746 Observations 746 Observations 746
Size of reporting bias −0.021*** Size of reporting bias −0.054**
Panel B: partial correlations
(effect size) −0.052*** β0
(effect size) −0.024*** β0
(effect size) −0.001
(0.006) (0.007) (0.003)
(reporting bias) −0.357*** β1
(reporting bias) −0.973***
Observations 1083 Observations 1083 Observations 1083
Size of reporting bias −0.028*** Size of reporting bias −0.075***
380 S. BROECKE ET AL.
3.3. Multiple MRA
The first advantage of MRA is that it allows us to test and control for reporting bias. The second advantage is that it allows us to control simultaneously for a whole host of other variables which might have
an independent effect on the outcome of interest, and which could bias the estimate of the minimum
wage effect if omitted from the regression. For example, minimum wage studies (and estimates within
studies) vary amongst one another both in terms of the populations they cover (e.g. youth, low-skilled,
female, etc.) and how the models are specified (definition of the minimum wage variable used, level of
disaggregation of data, etc.) – and these sample and specification characteristics could, in turn, have
an independent impact on the magnitude of the minimum wage elasticity (or partial correlation). We
therefore extend Equation (3) to include a vector of j such moderator variables, Zij (which are also
divided by the coefficient’s standard error because the model is estimated by weighted least squares).
In addition, Stanley and Doucouliagos (2012) argue that the model should be augmented with a set
of K-variables which identify factors that influence the propensity that an estimate gets published or
reported. Stanley and Doucouliagos (2012) rationalise this as a way of approximating the Inverse Mills
Ratio for each estimate used in the MRA, where publication bias is understood as a type of sample
selection bias. Equation (4) therefore becomes:
We assume that the K- and Z-vectors overlap completely, because we believe that the additional control
variables might potentially affect both the expected effect of the minimum wage on employment (the
Z-vector) and the propensity of a study being selected for publication (the K-vector). In practice, we
control for sample characteristics by including dummy variables for the following categories: lowskilled12; youth13; low-wage14; male and female (with the omitted category consisting of estimates
which do not distinguish between the genders); formal and informal (again, with the omitted category
consisting of non-discriminating estimates); and country (with Brazil as the omitted category). As
model controls, we include dummies for: the average year to which the data refer15; whether the study
has been published in a peer-reviewed journal or not; the type of minimum wage variable used (Kaitz
index, nominal MW, other – with the real MW as the omitted category); and the level of observation of
the data (individual, firm, country or other – with regional-level data as the omitted category). Because
adding K-variables on top of the Z-variables results in a high degree of multicollinearity, we only
retain those K-variables which are statistically significant (i.e. we use a general-to-specific modelling
technique). Note that when adding the K-variables, the reporting bias needs to be estimated as the
combination of the intercept and all the statistically significant K-variables – i.e. �
????1 + ∑
One additional source of potential omitted variable bias in the model just discussed stems from
the fact that estimates from the same study may be influenced by some common unreported or unobserved factors. We therefore also run regressions to which we add study fixed effects, γs
the standard error) – as in Equation (6).16,17 The obvious drawback of including these fixed effects is
that controls for methodological characteristics (as well as the country fixed effects) can no longer be
added separately to the meta-regression. This is because estimates within any one study are generally
obtained using the same model specification (and for the same country), and so will largely be absorbed
by the study fixed effects. Note that the country effects can still be retrieved from the means of the corresponding study fixed effects, and so these will be reported alongside the other results. An additional
complication with introducing study fixed effects is that the effect size becomes more complicated to
interpret. More specifically, the effect size should now be estimated as the average of the fixed effects
훽o + ∑
, where Ns
is the number of dummies (not counting the excluded one):
(4) ti = ????1 + ????o
(5) ti = ????1 + ????o
1Kij + ????i
OXFORD DEVELOPMENT STUDIES 381
Table 5. Multiple meta-regression analysis.
Panel A: coefficients Panel B: partial correlations
(i) (ii) (iii) (iv) (v) (vi) (vii) (viii)
✓ ✓ ✓ ✓
Z-variables ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
K-variablesa ✓ ✓ ✓ ✓
Minimum wageb Precision −0.064* −0.055 0.004*** 0.003*** 0.009 0.051*** 0.041*** 0.040***
(0.035) (0.033) (0.022) (0.013)
Skill Low-skilled −0.001 −0.002* −0.002** −0.002* 0.002 0.001 0.002 0.002
(0.001) (0.001) (0.001) (0.001) (0.002) (0.002) (0.002) (0.002)
Age Young −0.001* −0.001*** −0.001*** −0.002*** 0.000 −0.001 −0.001 −0.001
(0.000) (0.000) (0.000) (0.000) (0.002) (0.001) (0.002) (0.002)
Wage Low-wage −0.006 −0.006 −0.009 −0.009 0.027 0.014 0.014 0.014
(0.006) (0.006) (0.006) (0.006) (0.018) (0.013) (0.013) (0.013)
Gender Female −0.001 0.002 −0.003 −0.003 −0.002 −0.006 −0.009* −0.008
(0.005) (0.005) (0.006) (0.006) (0.005) (0.004) (0.005) (0.005)
Male −0.014 −0.051*** −0.011 −0.037*** 0.001 −0.001 −0.003 −0.007
(0.013) (0.015) (0.015) (0.011) (0.003) (0.003) (0.004) (0.005)
Formality Formal 0.004* 0.003 0.003 0.003 0.004 0.000 −0.009 −0.009
(0.002) (0.002) (0.003) (0.003) (0.005) (0.006) (0.012) (0.012)
Informal 0.003 0.002 0.004** 0.004** 0.060 0.136* 0.046 0.045
(0.002) (0.002) (0.002) (0.002) (0.048) (0.074) (0.039) (0.039)
Country China −0.038*** −0.057*** 0.038 −0.005
(0.008) (0.005) (0.043) (0.043)
Indonesia 0.043 0.045 0.028 0.001
(0.047) (0.046) (0.026) (0.025)
Mexico 0.065 0.157** −0.001 −0.034
(0.059) (0.059) (0.038) (0.031)
Colombia 0.022 0.080 −0.017 −0.068**
(0.070) (0.069) (0.039) (0.032)
Poland −0.123* −0.149** −0.036* −0.036**
(0.062) (0.058) (0.019) (0.016)
Chile 0.071* 0.105*** −0.008 −0.024***
(0.035) (0.035) (0.006) (0.008)
Turkey −0.021 −0.033**
Argentina −0.016 −0.029**
Thailand −0.057 −5.273***
Russia 0.112*** 0.087***
Time 1990–1999 0.060* 0.052 −0.008 −0.029***
(0.034) (0.033) (0.005) (0.009)
≥2000 0.065* 0.053 0.000 −0.030***
(0.034) (0.033) (0.006) (0.007)
Published Yes 0.002* 0.003** 0.006* −0.004
(0.001) (0.001) (0.004) (0.005)
MW variable Kaitz 0.010*** 0.008** 0.002*** 0.002***
(0.003) (0.003) (0.001) (0.000)
Nominal 0.042 0.056 −0.031 −0.015
(0.034) (0.033) (0.021) (0.018)
Other −0.002* 0.001 0.000 0.001*
(0.001) (0.002) (0.001) (0.001)
Firm −0.005 −0.002 −0.010 0.021
(0.036) (0.035) (0.029) (0.030)
Individual −0.001 0.001 0.002 0.001**
(0.001) (0.001) (0.001) (0.001)
Country −0.132* −0.451** −0.020 0.064
(0.076) (0.202) (0.069) (0.073)
382 S. BROECKE ET AL.
Finally, in the partial correlations regressions, it is also important to control for the assumptions made
by authors in calculating their standard errors. This is because, while the partial correlation coefficient
is a function of the degrees of freedom and t-statistic when the latter is calculated on the assumption
of an independent and identically distributed (IID) error term, this is no longer the case once this
assumption is dropped. In practice, we therefore include a dummy variable in the regression coded
as zero for standard errors calculated under the assumption of IID error terms and as one otherwise.
The results of these additional regressions are presented in Table 5, both for the elasticities (Panel
A) and the partial correlations (Panel B). Starting with the elasticities analysis, Column (i) (i.e. without
fixed effects or K-variables) indicates a negative effect similar in size to that reported in Table 4 (i.e. the
‘raw’ effect with no controls for reporting bias) and which is only marginally statistically significant.
Once K-variables are included in the model (Column (ii)), this effect remains similar in size (−.055),
but turns statistically insignificant. It is important to stress that this effect is no longer a general effect
across countries and other characteristics, but rather the effect for the base group (i.e. with all dummy
variables set at their base category). In other words, it is the meta‐estimate of the elasticity for skilled,
older, higher-wage workers of indeterminate gender in Brazil (without differentiation as to whether
they work in the formal or informal sector).18
Finally, adding study fixed effects (Columns (iii) and (iv) of Table 5) turns the effect size marginally
positive, but economically insignificant: a 10% increase in the minimum wage is associated with a
.03–.04% increase in employment. This time, the effect size can be interpreted as an average effect across
countries (although the other dummy variables are still set at their base category19). Turning to the
partial correlations, the effect size obtained in Panel B is always small and positive, and is statistically
significant in three of the four specifications. Overall, the multiple MRA findings therefore confirm
the analysis from the previous section: there is little evidence that the minimum wage in emerging
economies has had a detectible on employment.
Turning to the reporting bias, the elasticities analysis (Panel A of Table 5) provides evidence of a
negative and statistically significant reporting bias, which is almost identical in size to that estimated in
the last column of Table 4. Similarly, the estimated reporting bias in the case of the partial correlations
(6) ti = ????1 + ????o
Note: Standard errors, in parentheses, are clustered at the study-level.
The K-variables are not reported in the table. A general-to-specific approach is used for the K-variables, and only significant K-variables are kept in the regression.
bIn the fixed effects models, the reported precision coefficients are estimated as the weighted mean of the coefficients on the study
fixed effects �
– and therefore no error can be calculated. Significance of the coefficient is estimated using an F-test of
joint significance of the fixed effects.
Error (corrected) is a dummy variable indicating when the standard errors are corrected (i.e. White, serial correlation, heteroscedasticity, etc.).
dIn the models without K-variables effects, reporting bias is estimated as in Table 4. In the models with K-variables, reporting bias is
estimated as �
????1 + ∑
se and the significance is tested using an F-test of the associated coefficients. ***Significant at the 1% level; **Significant at the 5% level; *
Significant at the 10% level.
Panel A: coefficients Panel B: partial correlations
(i) (ii) (iii) (iv) (v) (vi) (vii) (viii)
Error (corrected)c −0.009 −0.020**
N 746 746 746 746 1083 1083 1083 1083
Reporting biasd −0.054* −0.056*** −0.055*** −0.052*** −0.081*** −0.013*** −0.107 −0.106*
Table 5. (Continued).
OXFORD DEVELOPMENT STUDIES 383
analysis (Panel B of Table 5) is remarkably consistent with the estimates reported in Table 4 – although
one of them (in the regression with fixed effects and without K-variables) is statistically insignificant.
Again, therefore, the findings of the multiple MRA confirm the conclusions of the basic meta-analysis
reported in Section 3.2.
In theory, the multiple MRA should also allow us to assess whether the impact of the minimum wage
varies by sample and/or specification characteristics. Here, unfortunately, we find that the results tend
to be sensitive to the choice of model. There is some indication from the elasticities analysis in Table 5
that youth and the low-skilled tend to be more negatively affected by increases in the minimum wage
– but the effect is relatively small, and the finding is not confirmed by the partial correlation analysis.
There is also some indication that increases in the minimum wage tend to lead to small increases in
informal employment – although most of the estimates are statistically insignificant, and one estimate
in the partial correlations analysis is unrealistically high. The results also suggest that published studies
and those using the Kaitz index as a minimum wage variable are slightly more likely to be associated
with a larger positive effect of the minimum wage on employment.20 No definite conclusions can be
drawn about any of the other characteristics (although it is worth remembering that one would not
necessarily expect the same results across the elasticities and partial correlations analyses because the
latter covers a much larger set of studies).
A final remark concerns the effects by country. The models without study fixed effects suggest that
only in studies for Poland is the effect of minimum wages found to be consistently more negative than
in studies for Brazil. However, the analysis in the models without study fixed effects is complicated by
the fact that all estimated country effects need to be estimated relative to the base country (Brazil).
While the country fixed effects drop out of the models with study fixed effects, it is possible to retrieve
the former from the relevant study fixed effects. These are reported in Table 6 and are slightly easier
to interpret since they do not need to be analysed with reference to any base category. The results
indicate that minimum wage studies for Mexico, Colombia and Poland most often report negative
effects of the minimum wage on employment, while positive effects are most often found in studies
for China, Indonesia and South Africa.
The impact of minimum wages on employment is primarily an empirical matter and, in advanced
economies, countless studies have addressed this issue – with some consensus emerging that moderate
increases in a minimum wage (which is set at a reasonable level to start with) are unlikely to lead to
significant employment losses. For policymakers in emerging economies, however, evidence from
developed countries may not be of much use because the characteristics of their labour markets are
Table 6. Country fixed effects estimated from the corresponding study fixed effects.
Notes: Country effects are obtained from the corresponding study fixed effects estimated in the models from Table 5. The column
headings correspond to those in Table 5. Significance is estimated from an F-test of the relevant study fixed effects. ***Significant at the 1% level; **Significant at the 5% level; *
Significant at the 10% level.
Panel A: coefficients Panel B: partial correlations
(iii) (iv) (vii) (viii)
Argentina −0.010 −0.011
Brazil −0.002** −0.002** 0.000*** 0.001***
Chile −0.023 −0.015 0.019*** 0.024***
China 0.036*** 0.033*** 0.105 0.099
Colombia −0.046*** −0.045*** 0.000 0.000
Indonesia 0.013*** 0.012*** 0.055*** 0.056***
Mexico −0.004 −0.018*** −0.047 −0.064**
Poland −0.103*** −0.099 −0.050*** −0.047***
Russia 0.145 0.150
South Africa 0.227*** 0.223***
Thailand −0.036 −0.038
Turkey 0.065 0.068
384 S. BROECKE ET AL.
very different. Recognising this issue, more and more researchers have begun to investigate the impact
of minimum wages in emerging economies and the number of studies has been growing exponentially.
While this boom in research is a welcome development, there has been very little attempt so far at
taking stock of the emerging findings – which is what this paper has sought to address.
Based on evidence for 14 large emerging economies, both the qualitative (survey) review (covering
95 studies) and the meta-analyses of elasticities and partial correlations (covering 28 and 56 studies,
respectively) showed that minimum wages have had little detectable impact on employment. While
more vulnerable groups appear to be more adversely affected by minimum wage rises, the effects tend
to be small on average. These findings are very much in line with the growing consensus around the
impact of minimum wages on employment in more advanced economies.
From a research perspective, the main novelty of this paper has been the application of meta-analysis
techniques to the minimum-wage literature in emerging economies. While meta-analyses have been
commonly used to summarise large numbers of minimum wage studies in the context of developed
countries, to date no such exercise had focused exclusively on emerging economies. The paper also
constitutes an important contribution to the policy debate on minimum wages. More specifically, the
results seem to indicate that, like in more developed countries, regular and modest increases in the minimum wage are unlikely to have a significant negative impact on employment in emerging economies.
Some caution is nevertheless required. While the finding of no employment effect is remarkable
given the large differences in labour market characteristics between emerging and more advanced
economies, the reasons why the minimum wage has no employment effect may in fact be very different. In developed countries, the possible explanations which have generally been advanced for the
lack of effect include: employers with monopsonistic power, the efficiency wage hypothesis, or the
fact that firms use adjustment mechanisms other than employment to adapt to a higher minimum
wage. In emerging economies, however, the reason for not finding any employment effect could be
very different. In particular, the role of non-compliance should be explored in future research as a
possible explanation given that, in many emerging economies, a substantial share of workers still earn
less than the minimum wage (Bhorat & Stanwix, 2013).
Compliance may be low because: the minimum wage is either set too high or too low (Lee & Sobeck,
2012; Rani, Belser, Oelz, & Ranjbar, 2013; Saget, 2008); the system is too complex (Cunningham, 2007;
Rani et al., 2013); there are no legislated fines/punishments for non-compliance; or the minimum
wage is simply not enforced, possibly due to a lack of resources (Kristensen & Cunningham, 2006).
Another important reason why the minimum wage might be difficult to enforce in emerging economies is the existence of a large informal sector. In such contexts, increases in the minimum wage
may have no effect on employment overall because a fall in formal sector employment may simply
be compensated for by a rise in informal sector employment. Indeed, we presented some tentative
evidence that higher minimum wages may lead to more informal employment (although the effect
was of little economic significance).
A second reason to be cautious about the policy implications of this paper is that the findings
presented apply to large emerging economies only – which could be another explanation for why no
employment effect could be detected. Indeed, minimum wages might be expected to have a more
significant impact on employment in small, open economies – and so the conclusions reached here
may not be applicable in those contexts.
The analysis has been valuable from a stock-taking point of view – however, it has also raised a
large number of new questions. Firstly, minimum wage analyses (including those in advanced economies) often take the minimum wage as a given and look at the impact of increases in that minimum
wage on employment. Very few studies ask the question of what would happen in a world without
minimum wages (albeit for obvious, methodological reasons). Secondly, while this study reviewed
95 studies, differences in sub-samples and, in particular, methods used across papers often made it
difficult to compare the results across studies. Indeed, the elasticities meta-analysis could only draw
on a sub-sample of 28 studies which reported findings in the form of elasticities. More consistency and
rigour in reporting findings from minimum wage studies are therefore required. Thirdly, there should
OXFORD DEVELOPMENT STUDIES 385
be a more systematic attempt to investigate the impact of minimum wages on labour market outcomes
other than employment, including hours worked and formality, as well as on different sub-groups.
1. Recent exchanges in the United States bear testimony to this fact. On the one hand, Dube, Lester, and Reich
(2010) and Allegretto, Dube, and Reich (2011, 2013) claim that minimum wages have no impact on employment,
while Neumark, Ian Salas, and Wascher (2014a, 2014b) strongly disagree.
2. The definition of what constitutes an emerging economy varies depending upon the source consulted, and so
any grouping of such countries will necessarily contain some element of arbitrariness. In this paper, we have
followed the IMF classification of countries.
3. Note that the sum of individual country studies adds up to 96 and not 95 – this is because one study included
in our analysis covers two countries.
4. It is important to note that no universal definition of the informal sector is used across the studies. Some authors
define the informal sector as all self-employed workers (Chun & Khor, 2010; Magruder, 2013), while others
explicitly exclude this category (Lemos, 2009a; Wedenoja, 2013). Other definitions include: (i) not having
a signed labour card (Carneiro & Corseuil, 2001; Lemos, 2009a; Wedenoja, 2013); (ii) not having a written
contract (Bhorat, 2014; Dinkelman & Ranchhod, 2012); or (iii) not being registered in the social security
system (Pelek, 2011).
5. According to Del Carpio et al. (2012), production workers are workers who work directly in the production
process, such as forklift operators, whereas non-production workers perform supporting tasks, such as
6. See Appendix 1 for the list of studies included in the elasticities meta-analysis.
7. To put these estimates into context, it is useful to refer back to the famous summary of the minimum wage
literature in the United States carried out by Brown, Gilroy, and Kohen (1982), which concluded that the
disemployment effects of the minimum wage were small and almost exclusively limited to teenagers (aged
16–19 years). For this group, Brown et al. (1982) estimated that a 10% increase in the minimum wage reduced
employment by 0–1.5%.
8. For a number of countries, these findings point in different directions than the qualitative literature review
suggested. However, this may not be entirely surprising since the focus is now on the subset of studies which
report elasticities. It may also to some extent reflect subjectivity in the interpretation of findings by the authors
of the studies we analysed in the survey review.
9. See Appendix 1 for the list of studies included in the partial correlations meta-analysis.
10. We prefer to use the term reporting bias instead of publication bias because many of our estimates are obtained
from working papers which are not published in peer-reviewed journals.
11. We have tested whether our results are sensitive to the inclusion of these outliers. The results (not reported here
but available upon request) indicate that the overall reporting bias in Table 5 reduces somewhat, but that this
does not change the overall conclusions reached in this paper.
12. While the definition of low-skilled differs somewhat between papers, this category includes: less educated,
unskilled or people with incomplete high school education.
13. The definition of young people differs between papers, but tends to cover people aged 15–29 years.
14. The definition of low-wage workers differs between papers, e.g. low-wage, below the minimum wage, between
one and two times the minimum wage.
15. Where studies span more than one year, the average year of the study is calculated and included as a control
variable in the meta-analysis. In practice, we distinguish between papers which use data: older than 1990 (base
category); from between 1990 and 1999; and since 2000.
16. Note that these are added only as Z-variables.
17. In the case of Bell (1997), which covers two different countries, we include two separate fixed effects (one for
18. To obtain an estimate more comparable to that presented in Table 4, one would first need to calculate the
following mean for each group d of dummy variables: Precision+
= Precision + (1 + Nd )
The grand mean of these individual group means would then provide an estimate comparable to the precision
(effect size) term presented in Table 4. For the model presented in Column (i) of Table 5 this gives an estimate
of −.062 – i.e. an estimate similar in size to that presented in Table 4.
19. However, a grand mean effect using the equation in footnote 18 gives very similar estimates (.002 and −.001 for
columns (iii) and (iv), respectively, of Table 5).
20. That being said, Card, Katz, and Krueger (1994) argue that the Kaitz index imports some endogeneity into the
measurement of the minimum wage and thus onto the right-hand side of the equation being estimated. This
could help explain this finding.
386 S. BROECKE ET AL.
The authors wish to thank two anonymous referees as well as the associate editor for very useful comments and suggestions which significantly improved the paper. Feedback on earlier drafts was also received from Andrea Garnero,
Veerle Miranda and Catherine Saget, as well as from participants at the 12th Belgian Day for Labour Economists held
on 4 June 2015 at the University of Leuven, Belgium. All remaining errors are the exclusive responsibility of the authors.
The paper and the opinions expressed therein should not be reported as representing the official views of the OECD
or of its member countries.
No potential conflict of interest was reported by the authors.
Notes on contributors
Stijn Broecke is a labour market economist at the OECD as well as a Research Fellow at IZA. He has previously worked
for the African Development Bank, the UK Civil Service as well as the Ministry of Health in Mozambique. He has
worked on a wide range of topics, including: health, pensions, child poverty, higher education, youth employment,
minimum wages and skills.
Alessia Forti is a Labour Market Economist at the OECD. Her work focuses on providing policy advice to governments
on skills, labour, social and health policies. Prior to joining the OECD, Alessia worked at the Italian Delegation to the
OECD and at the Grameen Bank in Bangladesh.
Marieke Vandeweyer currently works as a labour market economist at the OECD, focusing on topics related to skills
and employability. She is completing her PhD at the University of Leuven, Belgium. Her main areas of research include
the future of work, skills and inequality.
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OXFORD DEVELOPMENT STUDIES 391
Appendix 1. Papers included in meta-analyses
Elasticities Partial correlations
Alatas and Cameron (2008) x
Baranowska-Rataj and Magda (2015) x
Bell (1997) x x
Bhorat et al. (2013) x
Bhorat et al. (2014) x
Bhorat et al. (2016) x
Broecke and Vandeweyer (2015) x x
Carneiro (2004) x
Castañeda (1983) x
Chun and Khor (2010) x
Comola and de Mello (2011) x
Del Carpio et al. (2014) x
Del Carpio et al. (2012) x x
Dinkelman and Ranchhod (2012) x
Fang and Lin (2013) x x
Feliciano (1998) x x
Foguel (1998) x x
Foguel et al. (2001) x
Garza Cantú and Bazaldúa (2002) x x
Grau and Landerretche (2011) x
Groisman (2015) x
Hertz (2005) x
Huang et al. (2014) x x
Islam and Nazara (2000) x x
Kamińska and Lewandowski (2015) x
Kobzar (2009) x
Lemos (2004a) x x
Lemos (2004b) x x
Lemos (2004c) x x
Lemos (2005a) x x
Lemos (2005b) x x
Lemos (2007) x x
Lemos (2009a) x x
Lemos (2009b) x x
Luo et al. (2011) x
Magruder (2013) x
Majchrowska and Zółkiewski (2012) x x
Martinez et al. (2001) x x
Mayneris et al. (2014) x x
Melnyk (1996) x x
Millea et al. (2012) x
Miranda (2013) x x
Montenegro and Pagés (2004) x x
Neumark et al. (2006) x
Ni et al. (2011) x x
Ozturk (2006) x
Papps (2012) x
Pelek (2011) x
Rama (2001) x
Ruzik (2007) x
Shi (2011) x x
Soares (2005) x
Suryahadi et al. (2003) x x
Wang and Gunderson (2011) x x
Wang and Gunderson (2012) x
Xiao and Xiang (2009) x x
THE TURNOVER-REDUCING EFFECTS OF THE
MINIMUM WAGE MAY HARM THE ECONOMY
This article surveys the literature on the effects of the minimum wage and argues that the observed reduction in turnover rates is not
necessarily desirable. If a curvilinear relationship exists between firm productivity and turnover, the effects of the minimum wage on
reducing turnover may create a distance between the actual turnover rate and the optimal rate. Consequently, even if we accept the
proposition that minimum wages have little impact on employment, they may reduce productivity or job growth in sectors not
directly affected by the minimum wage.
JEL codes: J24, J63, J88.
Keywords: labour turnover; minimum wages; productivity.
In recent years, the literature on the impacts of the minimum wage has evolved considerably.
Although the debate is far from settled between those who argue that minimum wages have no
significant employment effects (e.g. Card and Krueger 1994, 1995) and those who argue that the
effects are strongly negative for several subgroups of the population (e.g. Neumark and Wascher
2008), minimum wages could impose economic costs even if they have no discernible effects on jobs
and hours. In summarising the literature on the minimum wage, Schmitt (2013) points out that the
absence of discernible employment effects could be explained by lower turnover. This suggests a
linear relation between turnover and productivity.
This article assumes – but only for the sake of argument – that the minimum wage has no
employment effects. But it does not follow that the minimum wage has no significant deleterious
effects on the economy. Research in the field of management demonstrates an inverted U-shaped
relationship between productivity and turnover (Abelson and Baysinger 1984). It follows that the
economic burden of the minimum wage would be observed elsewhere in the economy or on a
variable other than employment. Thus, the minimum wage could indirectly exert an adverse impact
2. The minimum wage debate
The literature on the minimum wage has greatly expanded since the late 1970s United States
Minimum Wage Study Commission (MWSC), which undertook a review of the effects of
minimum wages on many economic variables. Overall, the Commission found negative
employment effects, especially for younger people, for women and for ethnic minorities (MWSC
1981). Since then, the consensus has eroded, and opinion has been divided between two camps,
*Post-Doctoral Fellow, Texas Tech University. Email: firstname.lastname@example.org
© 2016 Institute of Economic Affairs
which we could label the ‘sceptics’ (those who believe the effects of the minimum wage have
been overstated) and the ‘traditionalists’ (those who believe that the impact of the minimum
wage is quite significant).
The sceptics emerged in the early 1990s as the result of a new strand of literature, initiated by
Card and Krueger (1994),1 which proposed small to insignificant effects of the minimum wage. One
of the most cited studies is that of Dube et al. (2010), who compared restaurant employment
outcomes across 318 pairs of bordering counties with differences in minimum wages between 1990
and 2006. Their results indicated that earnings increased substantially over time with no statistically
significant effects on employment.
These empirical results are hotly disputed by the ‘traditionalists’, chiefly Neumark and Wascher
(2008) and Clemens and Wither (2014), who argue that the MWSC was broadly correct. Some
traditionalists maintain that the size of the effect of the minimum wage will depend on the magnitude
of the increase in the minimum wage and the technical possibility of substituting capital for labour
(see Seltzer 1997 for an illustration of capital substitution). Others argue that the effects are not
observed immediately but are instead delayed and seen only in the pace of employment growth in the
years following the increase in the minimum wage (Meer and West 2013). Yet others stress the
importance of the tradable goods industry. In countries like Canada, where the minimum wage
affects a greater proportion of exporting industries, the effects are much greater since they
undermine competitiveness. ‘This factor might account for the weaker disemployment results’ found
in countries such as the United States and the United Kingdom (Siebert 2015, p. 58). If the minimum
wage affects workers in sectors with non-tradable services (like restaurants), then the effects are seen
through price increases (Addison et al. 2012).
I agree with the traditionalist viewpoint. Small disemployment effects may be statistical artefacts
of poor research designs that do not properly account for certain key variables; or perhaps the
perverse effects of legislating higher wages flow through a multitude of channels that are not fully
evident. However, I will suspend disbelief and accept – strictly for heuristic purposes – that the
empirical evidence of limited disemployment effects is conclusive.
Once this empirical evidence is accepted, what is the channel that explains the result? The
arguments of those who deny negative employment effects of the minimum wage are generally well
known among economists. The arguments of those who argue for small (or even absent) effects are
less well known. One of the arguments advanced relates to the role of frictions.2 In essence, workers
must spend time and resources searching for jobs. In this situation, employers with different wage
rates must deploy significant resources to attract, screen and hire workers. In the absence of a
minimum wage, costs assume the form of unfilled vacancies, rapid turnover and related screening and
training costs (Schmitt 2013, p. 21). The argument is that the minimum wage corrects coordination
failure.3 By raising wages and making them uniform, it makes it easier for employers to attract and
retain workers while turnover is also reduced. In essence, the argument is that the minimum wage
reduces turnover costs in magnitudes larger than the costs of higher wages. For the sake of argument,
let us assume that the empirical literature stressing non-significant or positive effects of minimum
wages on employment is correct. Does it follow that the minimum wage has no deleterious effects?
This article maintains that, if the channel proposed is correct, the effects would be observed not on
employment but on productivity.
This entire argument hinges on the contention that the relation between turnover and
productivity is linear. ‘If the higher wage reduces labour turnover this will also tend to increase
the quantity of labour input because less time is spent on hiring and induction of new workers’
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(Metcalf 2007, p. 29).4 Thanks to the reduction in turnover, matching problems are reduced and
employers need to spend less on training their workers. In this way, the minimum wage reduces
expenditure, thereby increasing demand for labour. However, in the presence of a curvilinear
relationship between productivity and turnover, this argument falters. The main claim of this article is
that if there is such a curvilinear relationship, the minimum wage will create a coordination failure
leading to lower productivity levels.
3. Evidence of a curvilinear relationship between turnover and productivity
If turnover has benefits that must be weighed against its costs, this implies that there could be an
optimal level of turnover. Hence, the relation is curvilinear. It is easy to see why firms like to have a
certain level of turnover. Firing workers allows firms to pursue opportunities for cost reduction and
consolidation. It allows firms to displace poor performers and replace them with new workers of
better quality who may also bring new knowledge into the firm (Kesner and Dalton 1981). The
economics literature does not discuss this in great detail, but the management literature provides
Using a data set from temporary workers in the Netherlands in the late 1990s, Glebbeek and Bax
(2004) found a curvilinear relationship: high turnover was detrimental to productivity, and so was
extremely low turnover, but the inverted U-shape was not observed with certainty. Stronger results
were found by Siebert and Zubanov (2009) using a data set from turnover of sales assistants and
labour productivity in 325 retail clothing stores in Britain between 1995 and 1999. These authors
found that for full-time workers the relationship was linear. But for part-timers there was a clear
inverted U-shaped relationship. Siebert and Zubanov’s was the first study of the issue which was able
to disaggregate the data based on human resources management practices, thus giving it greater
explanatory power. In a sector which tended to be very low-skilled, the low productivity separation
costs and low training costs generated this U-shaped relationship, which had an inflexion point at a
turnover rate of 15 per cent. Many sectors disproportionately affected by the minimum wage find
themselves in a similar situation
Another paper by Batt and Colvin (2011, p. 31) confirmed the linear relationship with ‘core
workers’ who were hired full time. In a recent meta-analysis, Hancock et al. (2013, p. 593) found a
‘weak curvilinear relationship’.
Hence, there is a literature that stresses the non-linearity between turnover and productivity. The
presence of such a relationship could explain the small to zero effects of minimum wages on
employment. However, in such a situation employment is not the proper variable to analyse.
4. Turnover and productivity in the overall economy
If the relation between turnover and productivity is curvilinear overall while being heterogeneous
across firms, the introduction of a minimum wage could push certain industries closer to their
optimum point but increase the distance from the optimum point for the entire economy and lead to
To see how a minimum wage could reduce productivity without necessarily diminishing
employment, let us assume the following scenario in line with the literature that asserts that minimum
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wages have small effects: one sector is composed primarily of unskilled and inexperienced workers
and would be the first affected by the change in the minimum wage, while all other sectors have wage
rates above the minimum level. If the sector affected by the minimum wage experiences the frictions
specified by Schmitt (2013), then the minimum wage could motivate workers to stay longer in this
sector. Training, screening and firing costs would thus be lowered for employers, possibly in
magnitudes sufficient to offset the increase in the minimum wage. Thus, employers would be winners,
and demand for unskilled workers would not be affected. As time passes, an unskilled worker
acquires some human capital through experience and soft skills. Once he has accumulated this
human capital, he becomes more attractive for all firms. However, some firms might be better
matches for this employee than other firms. Productivity is optimised only if this worker is matched
with the employers who most value him. Normally, employers would attract this worker by offering
him the highest wage necessary to secure the match. The high wage rate, commensurate with the
skills and experience acquired, would induce a higher turnover in the sector with unskilled workers.
However, the minimum wage raises the threshold of wage offers that other sectors must exceed to
induce the employees of the first sector to change employer. The higher wage rates caused by the hike
of the minimum wage induce workers to cling to their jobs. In addition, the benefits of incurring
search costs for other jobs are diminished considerably, which probably also leads workers to reduce
their efforts to gather information. By holding on to such jobs, workers remain unmatched to the
employers for whom they could work most productively. The result is a suboptimal level of
productivity. Thus, the minimum wage creates a coordination failure leading to labour misallocation
which generates the said suboptimal productivity.
The evidence in support of such a channel is quite strong: (a) we know that unskilled workers likely to
be earning the minimum wage easily move to more remunerative employment; (b) economists accept
that reducing hiring and firing costs (which increases turnover) leads to faster economic growth,
which implicitly endorses the curvilinear relationship between productivity and turnover; (c) the
literature on matching supports the contention that proper matches between employees and
employers increase productivity, so that lower turnover might mean thinner labour markets with
suboptimal matches; and (d) it provides a bridge between the traditionalist explanation and the
revisionist empirical literature.
First, the turnover of low-skilled workers is well documented. As workers acquire experience in
low-skilled jobs, they can more easily move to more remunerative employment in the future since
experience represents a form of human capital. It also explains findings that the majority of minimum
wage workers move on to more remunerative employment in a short space of time (Smith and
Vavrichek 1992; Long 1999; Carrington and Fallick 2001; Theodos and Bednarzik 2006). We can thus
easily see how the minimum wage could lead to suboptimal matching between employee and
employers. The lower turnover in the unskilled sector incites these workers to remain there even
though they would be more productive elsewhere. In short, other sectors of the economy are
deprived of these potentially more productive workers, who remain employed in sectors where their
experience generates fewer units of output per worker-hour.
Second, economists implicitly assume that there is such a curvilinear relationship between
productivity and turnover when they study ease-of-firing laws and the effects of job destruction.
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Generally, the literature on ease-of-firing laws maintains that higher firing costs increase the natural
rate of unemployment (Bertola 1990; Burda 1992; Garibaldi 1998; Pissarides 2000; Kugler and
Saint-Paul 2004). However, the effect of strong anti-firing laws is a lower level of output. Indeed,
stringent firing regulations considerably reduce productivity and output on top of increasing
unemployment (Bassanini et al. 2009). If economists accept that the lower turnover caused by firing
regulations reduces productivity and output growth, they must accept that there is a curvilinear
relationship between turnover and productivity. If they accept that contention, it must be extended
and applied to the case of an increase in the minimum wage that would yield exactly the same effects.
Third, poor matching between employees and employers indicates that the minimum wage could
create a coordination failure. Evidently, the matching literature in labour economics provides strong
support for this claim (Pissarides 2000; Mortensen and Pissarides 2011). Productivity depends on the
proper matching between job and employee, which in turn depends on a probability distribution
(Pissarides 2000, p. 145). If the minimum wage reduces turnover in one sector as workers are less
tempted to move to other markets, it thins the labour market in that sector. Thinner markets are less
prone to generate proper employee–employer matchings, which would have a significant impact on
productivity (Niederle and Roth 2003; Gan and Li 2004). Consequently, the minimum wage could be
creating a coordination failure by preventing proper matching in sectors down the line and
consequently causing slower growth in employment in other sectors, which could also be evident
through lower productivity growth.
Fourth, this explanation provides a strong ‘traditionalist’ explanation for the empirically small
effects discovered by those who argue that increases in the minimum wage are not problematic.
Consider the debate between Meer and West (2013) and Dube (2013). Meer and West argued that
the minimum wage reduced aggregate employment growth, but Dube pointed out that job growth
had fallen particularly in the manufacturing sector, where few minimum wage workers were present.
Although Meer and West produced disaggregated results that supported their claims and accepted
that there were no statistically significant effects in the manufacturing sector, Dube’s point would be
consistent with the claim that the minimum wage hurt job growth because lower turnover induces
workers to remain in jobs where their productivity is not exploited to the full. Other sectors would
thus see lower productivity growth, and employment growth would slow.
Moreover, the counter-argument made here squares perfectly with the results advanced by Dube
et al. (2010). Their paper was concerned with the effects on the restaurant industry only – which is a
good candidate to act as our theoretical sector composed primarily of unskilled and inexperienced
workers. Addison et al. (2012) and Hirsch et al. (2015) arrived at similar results using the same sector.
The absence of negative employment effects is well explained by lower turnover. However, this does
not mean that there are no negative effects overall. Indeed, the reduced turnover implies that the
restaurant industry does not shed workers or reduce employment growth because it benefits from the
reduced turnover. The cost is merely borne by other industries.
6. A research agenda
Hence, while there is a body of empirical literature (of which I remain sceptical) that maintains that
the minimum wage does not reduce employment significantly, it does not follow that such a measure
has no ill effects. The heuristic exercise undertaken here, wherein the relation between turnover and
productivity is assumed to be curvilinear rather than linear, shows how the effects of the minimum
322 V. GELOSO
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wage could be felt through channels other than employment. Supporters of increases in the minimum
wage must now meet a higher threshold if they want to demonstrate that this measure has positive or
To meet this higher threshold, theoretical and empirical efforts should be devoted to building on
the claims advanced here. The most promising avenue is to consider models of coordination failure.
Indeed, the entire reasoning of this article is that the minimum wage, by reducing turnover in firms
where unskilled workers predominate, moves the overall economy away from the optimal rate of
turnover, so creating a disequilibrium. This fits perfectly with models of coordination failure.
The best starting point would be Ball and Romer’s (1991) important paper supporting the
New Keynesian renaissance, in which they argue that price rigidity causes losses in overall
welfare. Price rigidity generates multiple equilibria that imply coordination failures which lead to
nominal fluctuations affecting real variables. It does not take a leap of faith to extend their
reasoning to wage and turnover: the strategy of firms is affected by the institution of a rigid price
that distorts information and so renders it impossible to coordinate activities in order to reach
equilibrium. In fact, the focus of the New Keynesian literature on coordination failure offers rich
theoretical modelling possibilities.
This insight could best be operationalised through a longitudinal study of firms before and after a
change in the minimum wage in a given area. The idea would be to consider total factor productivity
of firms before the introduction of a substantial increase in the minimum wage. The profile of the
workers in each firm would be analysed so as to identify the industries with the largest shares of
unskilled workers. They would be analysed over a long enough period to establish their rates of
turnover before a hike of the minimum wage. Then the profiles of the workers of all firms would be
analysed to establish their rates of turnover and the evolution of output and productivity. Logically, if
I am correct, we should observe the following effects: (a) a reduction in turnover in all industries, but
especially in those industries with initially high shares of unskilled workers; (b) an increase in the
profitability of firms with initially high shares of unskilled workers; and (c) a reduction in productivity
(or decline in productivity growth if we analyse trend changes) in firms that would have attempted to
poach unskilled workers as they gained experience. Obviously, if observation (b) failed to
materialise, this would imply that the minimum wage does not improve the profitability of firms
sufficiently to avoid a fall in the demand for labour, completely invalidating this line of argument.
However, if effects (a) and (c) are observed, this would mean that the minimum wage does indeed
not reduce employment but instead reduces productivity and overall output.
Such an empirical study would require an inordinate amount of time to design so as to avoid
methodological problems. This is beyond the scope of this article, which seeks to argue that the
alleged absence of employment effects after increases in the minimum wage is no cause for
celebration as perverse effects may be felt elsewhere.
This article’s aim is simple: to show that minimum wages may have adverse effects on the
economy as a whole even if they do not directly reduce employment in the affected sectors. It
argues that if the relationship between productivity and turnover is non-linear, there is an
optimal level of turnover. If the minimum wage pushes turnover away from its optimal rate,
there may be a negative spillover for productivity in other sectors of the economy. Further work
ECONOMIC AFFAIRS VOLUME 36, NUMBER 3 323
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is needed. However, in light of the argument developed here, findings of small or non-existent
employment effects from minimum wages are not sufficient to give this policy agenda a clean bill
1. Although Neumark and Wascher (2008) were the foremost critics of Card and Krueger (1994), the criticisms made by
Bellante and Picone (1999) are the most enlightening and provide a strong rebuttal of the claim that the minimum wage
has limited effects.
2. There is another argument relating to the role of monopsony, but the discussion of the impact of a monopsony would require
another article. Therefore I have opted to tackle only this second argument. The reason for this choice is that, as shown
below, the effects of an increase in the minimum wage could create a coordination failure for the entire economy. Thus, even
if the monopsony argument was valid, the perverse effects identified here would exist too.
3. This is a strong assumption in their model. Although this article is not the proper place to discuss it, it implies that the correct
minimum wage to solve this coordination failure can be identified by government officials. Knowledge of public choice
theory would make anyone sceptical of that assumption.
4. I am quoting Metcalf’s working paper here because he specifies this point in more detail there than in his later published
article in the Journal of Industrial Relations (2008).
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