metadata: authors: 0: author: Asmal,Zaakhir
1: author: Bhorat,Haroon Ismail
2: author: Kanbur,Ravi
3: author: Ranzani,Marco
4: author: Paci,Pierella
count: Mauritius
docna: 0: docna: Minimum Wages and Labor Supply in an
            Emerging Market : The Case of Mauritius
docty: Policy Research Working Paper
subtopic: Labor Standards,Labor Markets,Food & Beverage Industry,Business Cycles and Stabilization Policies,Common Carriers Industry,Plastics & Rubber Industry,General Manufacturing,Textiles, Apparel & Leather Industry,Pulp & Paper Industry,Construction Industry,Educational Sciences,Rural Labor Markets
teratopic: Social Protections and Labor,Macroeconomics and Economic Growth,Industry,Education,Rural Development
abstracts: cdata!: This paper investigates the effect of
            multiple minimum wages, known as remuneration orders, on
            employment and working hours in Mauritius. Using data
            between 2004 and 2014, the analysis indicates that a 10
            percent increase in the minimum wages brings about a
            slightly positive effect on employment in the covered
            sector, with an estimated employment elasticity of 0.113,
            which is within the range of elasticities found in previous
            studies of employment effects of minimum wages in low- and
            middle-income countries. The positive employment effect of
            minimum wages is also associated with a 2.3 percent increase
            in average working hours for men but a 1.8 percent decline
            in average working hours for women in the covered sector. In
            the uncovered sector, the significant positive effect along
            the intensive margin, estimated at 4.2 percent, is driven by
            changes in labor supply among men.
geo_reg_key: 517191
repnme: repnme: Minimum Wages and Labor Supply in an
            Emerging Market : The Case of Mauritius
display_title: Minimum Wages and Labor Supply in an
            Emerging Market : The Case of Mauritius
listing_relative_url: /research/2018/12/30706009/minimum-wages-labor-supply-emerging-market-case-mauritius
topicv3: Labor Markets m1326488 855,Manufacturing Sectors m1326521 1281,Education m1327048 470,Minimum Wages m1327199 1671,Jobs m1503931 2670
publishtoextweb_dt: 2018-12-18T00:00:00Z
historic_topic: Social Protections and Labor,Macroeconomics and Economic Growth,Industry,Education,Rural Development
no_of_pages: 35
author_keys: 0: author_key: 925956
1: author_key: 94124
2: author_key: 927986
3: author_key: 144977
model_extractions: WPS8681 Policy Research Working Paper 8681 Minimum Wages and Labor Supply in an Emerging Market The Case of Mauritius _Zaakhir Asmal Haroon Bhorat Ravi Kanbur Marco Ranzani Pierella Paci_ Poverty and Equity Global Practice December 2018
Policy Research Working Paper 8681

# **Abstract**

This paper investigates the effect of multiple minimum wages, known as remuneration orders, on employment and working hours in Mauritius. Using data between 2004 and 2014, the analysis indicates that a 10 percent increase in the minimum wages brings about a slightly positive effect on employment in the covered sector, with an estimated employment elasticity of 0.113, which is within the range of elasticities found in previous studies of employment effects of minimum wages in low- and middle-income countries. The positive employment effect of minimum wages is also associated with a 2.3 percent increase in average working hours for men but a 1.8 percent decline in average working hours for women in the covered sector. In the uncovered sector, the significant positive effect along the intensive margin, estimated at 4.2 percent, is driven by changes in labor supply among men.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at mranzani@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Minimum Wages and Labor Supply in an Emerging Market: The Case of Mauritius**

Zaakhir Asmal<sup>*1</sup> Haroon Bhorat<sup>*</sup> Ravi Kanbur<sup>**</sup> Marco Ranzani<sup>***</sup> Pierella Paci<sup>***</sup>

## JEL: J23; J31; J38.

Keywords: Minimum Wages; Employment; Hours of Work; Mauritius.

> 1 *: Development Policy Research Unit, School of Economics, University of Cape Town. **: Cornell University ***: World Bank. The findings, interpretations and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent.
# **Introduction**

Minimum wage systems exist in about 90 percent of the International Labour Organization’s (ILO) 193 member states (Benassi, 2011). These minimum wage regulations aim to protect low-paid workers and typically take one of two broad forms: A single base minimum wage rate for a country or region, or a more complex system of multiple minimum wage rates by sector and/or occupation. In Mauritius, minimum wages have been legislated in the latter form, with specified wage rates for varying occupations across 30 industry-specific remuneration orders. There has, however, been little work on the nature and impact of these stipulated wage rates in the country. In this paper, we thus consider the effects of these minimum wages on employment and hours worked in Mauritius.

The standard textbook theoretical model of minimum wages predicts that in a competitive equilibrium, there is a negative relationship between minimum wages and employment. Early studies on the effect of the minimum wage on employment, dating as far back as the work of Stigler (1946), predicted that in a competitive labor market, the relationship between minimum wages and employment is a negative one. A growing number of later studies however suggested that the relationship between the minimum wage and employment is not necessarily always negative (Katz and Krueger, 1992; Card, 1992 a, b).

Based on the work of Neumark and Wascher (2007) and 17 more recent studies focusing on low- and middle-income (LMI) countries, DPRU (2016) presents the employment effects of minimum wage increases across various developed and developing countries derived from a review of 115 studies. The results include aggregate impacts for all workers coupled with the employment impacts for specific demographic groups, regions and sectors. Overall, employment elasticities in the studies reviewed a range from 2.17 (Katz and Krueger, 1992) to

-4.6 (Abowd, Kramarz, and Margolis, 1999). The mean and median of all the cumulative elasticities are -0.22 and -0.11, respectively, suggesting that on average the impacts of various minimum wage hikes in the countries under review have been marginally negative.

For LMI countries specifically, DPRU (2016) notes 32 statistically significant minimum wageemployment elasticity estimates from 11 countries. The elasticities range from a negative value of -1.3 to a positive value of 1, with a median elasticity of -0.08 and a mean elasticity of -0.11. Empirical studies on the effect of increased minimum wages on employment in LMI countries thus suggest that increases in wages will have either benign or only slightly negative employment effects.

This is the first study to consider the effect of minimum wages on employment in Mauritius specifically. Using data between 2004 and 2014, we find that a 10 percent increase in the minimum wages brings about a slightly positive effect on employment in the covered sector, with an estimated employment elasticity of 0.113. This is within the range of elasticities found in previous studies of employment effects of minimum wages in LMI countries.

2
We also find that the positive employment effect of minimum wages in Mauritius is associated with a 2.3 percent increase in average working hours for men but a 1.8 percent decline in average working hours for women in the covered sector, for a 10 percent increase in the minimum wage. In the uncovered sector, a significant hours of work effect, driven by an increase in average male work hours, is found for the sector overall with a 10 percent increase in the minimum wage being associated with an increase in average number of hours worked of 4.2 percent.

Overall, the results suggest the presence of monopsony power in the low-wage labor market in Mauritius with increases in minimum wages being associated with a slightly positive effect on employment in the covered sectors. In addition, it seems that increases in the minimum wage have the effect of increasing average work hours for males in both the covered and uncovered sectors. However, for women, there is a decline in average working hours for women in the covered sector, suggesting that industries in which females are more likely to be represented may be responding to increased minimum wages by decreasing the number of hours worked by female employees rather than firing workers, despite the overall positive employment effect.

The rest of the paper is organized as follows. We start with a brief consideration of how minimum wages are set in Mauritius within the complex remuneration order wage system. Thereafter, we introduce the data source used throughout the analysis and consider how legislated minimum wages have changed over the period under review. We then outline our methodology and present our findings. We also briefly note the limitations of our study and take note of minimum wage compliance which may affect our estimation of effects.

# **1 Wage Fixing in the Private Sector**

The framework for establishing minimum wages and conditions of employment in the private sector is set either by the remuneration regulations by the National Remuneration Board (NRB), Collective Agreement or an award of the Employment Relations Tribunal (Figure 1).<sup>2</sup>

> **2** The NRB is a quasi-judicial body formerly set up under Section 45 of the Repealed Industrial Relations Act which is now deemed to have been set up under Section 90 of the Employment Relations Act 2008.

3
# ~~<u>=</u> a~~
The 30 ROs are currently applied both at the sectoral and occupational levels, stipulating different wages to workers covered by each order.<sup>4</sup> This complexity is further compounded by the fact that some ROs are set at an industry or sector level with occupational variation, while others are set at the occupational level with sub-occupational variation. Therefore, an office messenger in the sugar industry could potentially be covered by the sectoral RO or the general RO for office attendants. Under the current minimum wage architecture, the likelihood of multiple coverage for workers therefore increases considerably.

A further layer of complexity is added by the fact that for many occupations or occupational categories wages are stipulated by number of years of work experience.

A vertical division has been established between organized sectors in which wages are regulated by collective bargaining and sectors in which the government considers that workers do not have real bargaining power necessary to fix minimum wages. The fixing of minimum wages by collective bargaining can be provided for by law or result from national practice. Further to a collective bargaining process, a collective agreement is reached.<sup>5</sup> However, the Collective Agreement cannot contain a provision reducing the wage below that provided in the ROs. Workers covered by the ROs can also be covered by collective agreements. When a collective agreement is not reached, the matter is referred to the Commission for Conciliation and Mediation. The sectors covered by a collective agreement in Mauritius are the sugar industry, bus transport, construction, port services, hotels and catering services.

Table 1 below and Table 6 in the Appendix clearly illustrate the sectoral and occupational variation in legislated wages that underlies this complex wage system. Table 1 illustrates the complexity of the system by showing the number of job title categories and the total number of wage rates specified within each RO in the year 2016. Overall, over 2,000 individual wage rates were specified in that year.

**Table 1: Numbers of Wage Rates specified within Remuneration Orders in Mauritius** **<u>(2016)</u>**

|**RO (MW) category**|**Corresponding**<br>**broad industry**<br>**sector/s**|**Number of job**<br>**title categories**|**Years of**<br>**experience**<br>**categories per**<br>**job title**|**Total number of**<br>**wage rates**<br>**specified**|
|---|---|---|---|---|
|Attorney and Notary<br>Employees|Finance and<br>professional|2|15 years (clerk)<br>20years(secretary)|35|

increases which applies to all workers – even those who are not covered by ROs. The increase in wages is higher for those in the lower wage brackets and lower for those at the upper end. The Salary Compensation System focuses on supporting low wages and caters for the vulnerable segment of the society by increasing their purchasing power. Further, the quantum for the salary compensation is fixed after various tripartite consultations.

**4** Wages for public sector workers (namely, those working in parastatals and local authorities) and those in the finance and banking sector are excluded from these ROs. Furthermore, managerial positions in all sectors are excluded.

**5** The Collective Agreement can be drawn where a recognized trade union/a group of recognized trade unions/a joint negotiating panel and an employer reach an agreement on the terms and conditions of employment.

5
|**RO (MW) category**|**Corresponding**<br>**broad industry**<br>**sector/s**|**Number of job**<br>**title categories**|**Years of**<br>**experience**<br>**categories per**<br>**job title**|**Total number of**<br>**wage rates**<br>**specified**|
|---|---|---|---|---|
||services (admin and<br>support)||||
|Baking industry|Manufacturing|24|3 job titles specified<br>by years of<br>experience; up to 8<br>years|39|
|Banks fisherman and frigo-<br>workers|Agriculture,<br>forestryand fishing|2|NA|2|
|Block-making and<br>Construction|Construction<br>(Possibly, mining<br>andquarrying)|26|Most job titles<br>specified by year; up<br>to 8 years|100|
|Catering and Tourism|Transport, food,<br>accommodation<br>and ICT (food and<br>accommodation);<br>CSP (Arts,<br>entertainment and<br>recreation)|52|Most job titles<br>specified by year: up<br>to 7 years; 4 years<br>for most|196|
|Cinema Employees|CSP (Arts,<br>entertainment and<br>recreation)|12|NA; rates for some<br>by number of<br>showsper month|19|
|CleaningEnterprises|Across all sectors|13|<br>8years for all|104|
|Retail Trades|Wholesale and<br>retail|34|8 years for all|272|
|Domestic Workers|<br>Private households|8|NA|8|
|Electrical, Engineering,<br>Mechanical|Manufacturing|11|Most job titles<br>specified by year; up<br>to 7years|63|
|Export enterprises|Manufacturing|11|Most job titles<br>specified by year; up<br>to 9years|47|
|Factory employees|Manufacturing|10|Most job titles<br>specified by year; up<br>to 8years|70|
|Field crop and orchard<br>workers|Agriculture,<br>forestryand fishing|6|NA|6|
|Light metal and wooden<br>furniture|Manufacturing|11|All job titles<br>specified by year; up<br>to 8years|64|
|Livestock workers|Agriculture,<br>forestry and fishing|4|Supervisor specified<br>by year(5 years)|8|
|Newspapers and periodicals|Manufacturing|10|All job titles<br>specified by year; up<br>to 15years|90|
|Nursing Homes|CSP (Health)|16|Most job titles<br>specified by year; up<br>to 10years|74|
|Office Attendants|Finance and<br>professional<br>services (admin and<br>support);<br>CSP(admin)|2|Both job titles<br>specified by year<br>(10 years)|20|
|Pre-primary School Employees|<br> <br>CSP (Education)|7|Most job titles<br>specified by year; up<br>to 10years|26|
|Printing industry|Manufacturing|16|<br>Most job titles<br>specified by year; up<br>to 10years|85|

6
|**RO (MW) category**|**Corresponding**<br>**broad industry**<br>**sector/s**|**Number of job**<br>**title categories**|**Years of**<br>**experience**<br>**categories per**<br>**job title**|**Total number of**<br>**wage rates**<br>**specified**|
|---|---|---|---|---|
|Private Secondary School|CSP (Education)|13|Most job titles<br>specified by year; up<br>to 15years|111|
|Public Transport (Buses)|Transport, food,<br>accommodation<br>and ICT (transport<br>and storage)|35|Most job titles<br>specified by year; up<br>to 10 years|322|
|Road Haulage Industry|Transport, food,<br>accommodation<br>and ICT (transport<br>and storage)|6|All job titles<br>specified by year; 8<br>years|48|
|Salt ManufacturingIndustry|Manufacturing|7|NA|7|
|SecurityGuards|Across all sectors|1|8years|8|
|Sugar Industry (Agricultural)|Agriculture,<br>forestryand fishing|18|NA|18|
|Sugar Industry (Non-<br>agricultural)|Manufacturing|35|Many job titles<br>specified by year; up<br>to 6 years. Some<br>job titles further<br>categorized<br>accordingtograde|58|
|Tailoring Trade|Manufacturing|5|Learner specified by<br>year;5years|9|
|Tea Industry|Agriculture,<br>forestry and fishing<br>Manufacturing|32|NA|32|
|Travel Agents and Tour<br>Operators|Finance and<br>professional<br>services (admin and<br>support)|17|All except Trainee<br>and Watchman<br>specified by year; 4<br>years|62|
|**_Total_**||**_446_**||**_2003_**|

# **2 Data and Legislated Wages’ Trends**

We use a series of repeated cross-section data from the Mauritian labor force survey, known as the Continuous Multi-Purpose Household Survey (CMPHS), conducted between 2004 and 2014. The CMPHS was launched by Statistics Mauritius in April 1999. Since then it has been conducted monthly with the exception of 2000 when the survey was suspended to avoid overlapping with the Housing and Population Census.

We restrict our sample to include only employed individuals and those with non-zero reported earnings. Self-employed, own account and unemployed workers who have worked before are included in our analysis as the uncovered sector in our estimation of minimum wage effects. For this estimation, only workers who are in the covered RO sectors and the self-employed, employers and unemployed in or last employed in the RO covered sectors are considered.

We assign minimum wages to workers covered by 30 categories of remuneration orders and estimate employment and hours of work effects through the use of data related to two groups 7
of workers: Those in the covered and uncovered sectors.<sup>6</sup> The covered sector is defined as the sector wherein wage-earning workers are covered by remuneration orders and thus have a minimum wage that is applicable to them. The uncovered sector includes self-employed workers in those remuneration order covered categories of workers and unemployed workers who have previously worked as wage-earning employees in the covered sector.

In real terms, legislated minimum wages have fallen over time for most of the ROs. Table 5 in the Appendix shows the real hourly legislated minimum wage for each of the 30 ROs over the 2004 – 2014 period. The log of the real minimum wage for each RO, defined as the lowest stipulated wage rate for each particular RO, is shown relative to its value in 2004 for the 2004 – 2014 period in Figure 2.<sup>7</sup> It is clear that for most of the ROs, the real minimum wage was lower in 2014 than in 2004. For those that have higher real minimum wages in 2014, only two of the minimum wages are more than 20 percent higher than their level in 2004. However, of the three key RO categories in terms of numbers of covered workers employed (distributive trades, construction and manufacturing), both distributive trades and the manufacturing ROs (comprising both the factory and export enterprises ROs) have seen legislated minimum wages increase over the period.

> 6 Additional details concerning the assignment of workers to specific RO categories are presented in Appendix Two.

> 7 Note four ROs with a small number of identified workers are omitted from this figure. These ROs are: banks fishermen, cinema employees, salt workers and travel agent employees.

8
<!-- Start of picture text --> SS 5 5 =<br><= / 7 — ee gee<br><!-- End of picture text --> <u>SS 5 5 =</u> ~~<= / 7 — ee gee —®—~~ <mark>Baking</mark> ~~—®—~~ Blockmak ~~ing@—Ca~~ tering ~~—®—~~ <mark>Cleaning</mark> ~~—®—~~ Distributive ~~tra@-Do~~ mestic ~~—®—~~ Electrical ~~—©—~~ <mark>Export</mark> ~~—@®—~~ <mark>Factory</mark> ~~—®—~~ Fiel ~~d-~~ c ~~rop—@ L~~ ight metal ~~—@®—~~ <mark>Livestock</mark> ~~-—®—~~ Office atte ~~ndarts~~ Newspapets <mark>Nursing</mark> ~~—@®—~~ <mark>Printing</mark> ~~—@®—~~ Pre ~~-~~ pri ~~mary¥@—P~~ rivate sec ~~ondaf~~ Public tran ~~spert@—~~ Road haul ~~age—@—~~ Security ~~—®—~~ Sugar ~~- ag—~~ Sugar ~~-~~ n ~~onag@—Ta~~ iloring ~~—®—~~ <mark>Tea</mark>
<!-- Start of picture text --> /<br>GA". Oe Ps t — 2h Sow KE<br>AS AS LOSSPRR<br>NSS BER SSEEERYT<br>SS et ee ee (EE<br>% {><br><!-- End of picture text --> <!-- Start of picture text --> —— —e— eo — eo —e eo<br>® 0 —e— —o- —9o— -@- —e—<br>oe ee —e— ee oe ee<br>—e— oe eo =<br><!-- End of picture text -->
**Figure 4: Minimum wage-employment elasticities for low and middle income countries**

<!-- Start of picture text --> 1<br>.5<br>Elasticity 0-.5<br>-1<br>-1.5<br>Indonesia Honduras Brazil Puerto Rico Indonesia Brazil Indonesia Brazil China Indonesia Indonesia Thailand South Africa Indonesia Brazil Brazil Indonesia Indonesia Brazil Costa Rica Ghana Puerto Rico Indonesia Indonesia Czech Republic Ghana Colombia Honduras South Africa Mexico Puerto Rico Indonesia<br><!-- End of picture text --> _Source: Neumark and Wascher (2007) and DPRU (2014) calculations Note:_ The upper dotted line is the median elasticity (-0.08); the lower dotted line is the mean elasticity (-0.11).

Based on the sample of 59 developed and 32 developing country estimates reviewed by DPRU (2016), 81 percent of the elasticities were negative while 19 percent were positive. Further, the absolute value of these coefficients is on average small. This suggests that, in general, increases in wages will have either benign or only slightly negative employment effects.

With respect to the impact on hours worked, Gindling and Terrell (2007) note that the expected sign of this impact is ambiguous both in theory and in the literature. Higher costs of employment could result in cost-minimizing employers reducing the number of workers employed at a fixed monthly or weekly rate and increasing the hours of the fewer workers that they do employ. However, employers may also respond to increases in wage rates by either reducing employment at the extensive (reducing the number of employees) or intensive margin (reducing the total hours worked). The regulatory-induced costs of firing workers would be one reason for example, where employers may choose to keep the wage bill and headcount of employees constant – but reduce the number of hours required.

11
# **4 Effect of Changes in Minimum Wages on Labor Supply**

## **4.1 Identification strategy**

The methodology we employ here is to use individual-level pooled cross-sectional data (for the years 2004 to 2014), to estimate how changes in minimum wages have affected the employment of workers and number of hours worked holding constant other factors that might affect wages. The approach is similar to a number of approaches in the literature including most notably that of Gindling and Terrell (2007), who estimated employment effects for a similarly complex system of minimum wages in Costa Rica.

We estimate an equation of the following form: EEiiii � αα � ββββββMMMMiiii � XXiiii γγ � δδVVVVzzii � DDss � DDii � εεiiii ... (1) Where EEiiii = 1 if individual i is employed in the covered sector at time t EEiiii = 0 if individual i is an own-account worker, an employer or unemployed worker who has worked before at time t.

XXiiii = individual characteristics (gender, experience, education, and interactions).

- ββββMMMMiiii = logarithm of real hourly minimum wage that applies to individual i at time

- t.<sup>9</sup>

DDDD , DDDD = sets of dummies for industry/occupation and year VVVVzzii = value added of sector z at time t.

The coefficient ββ is an estimate of the impact on employment in the covered sector of changes in the minimum wage.<sup>10</sup> The minimum wage is assigned to each worker based on their RO category. We were unable to assign specific wages based on job title and years of experience. We have taken the minimum wage for each RO category to be the lowest stipulated wage in each RO for that category of RO workers.

> 9 Minimum wages are set in either hourly, daily or monthly terms. Where they are not in hourly terms, they have been standardized to a real hourly minimum wage using 8 hours as the reference number of hours for a workday and 168 hours as the reference number of hours for a work month. All real wages are in 2012 prices.

> 10 Note that this specification including self-employed and unemployed workers who have worked before in the covered sectors assumes that workers who lose their jobs in the covered sectors either become unemployed or self-employed in those covered sectors they left.

12
The dummy variables for each RO category are included to control for RO category specific fixed effects and for the endogenous correlation of employment and minimum wages across RO categories. Value added for each broad industry category is included to control for changes in demand over time. To control for endogenous changes in yearly average minimum wages as well as other year-specific factors, a dummy variable is included for each year.

We estimate equation (1) with OLS and test for an employment effect of legal minimum wages in the covered sector. We then proceed to use equation (1) to consider the effect of minimum wages on the number of hours worked per week in the covered and uncovered sectors by substituting the log of hours worked for the EMP variable.

Further, to test the exogeneity of the minimum wage variable, we use one-year lagged minimum wages as an instrumental variable and obtain two-stage least squares estimates for the employment and hours worked regressions.

# **4.2 Challenges and limitations**

One of limitations in our estimation strategy is that we are unable, due to data limitations, to assign the exact minimum wage schedule, within the broader RO category to each worker in the sample. We have thus taken the minimum wage for each RO category to be the lowest stipulated wage in each RO for that category of RO workers. We have not assigned specific minimum wages for specific job titles and years of experience.<sup>11</sup> Further, we do need to be partially concerned with the problem of endogeneity with respect to the minimum wage variable in our analysis. The variation in minimum wage shifts over time in our sample is not as significant and we also do not observe structural changes in the minimum wage system for Mauritius. This may mean that endogeneity may remain a problem. We take account of this however, by obtaining two-stage least squares estimates which use lagged minimum wages as an instrument for minimum wages in addition to our OLS estimates.

With respect to the unemployed workers included as part of the uncovered sector in our analysis, we are limited in that we do not have complete occupation data for all years in the period. We have the required industry data for all individuals used in the analysis. However, for the unemployed who have worked before, we have occupational data about their last job for only eight of the eleven years we consider.<sup>12</sup> These workers however do not constitute a large number of individuals in the sample and are more likely than not to be workers in the occupational classes covered by ROs. The implication of this minor data limitation is thus that, in the years 2004, 2006 and 2007, for which we do not have occupational data for the

> 11 See Table 1 above which shows the number of wage rates there were in 2016. Ideally, we would want to assign each worker their very specific wage rate to fully exploit the variation in minimum wages in the complex remuneration orders system to obtain estimates that do not raise any endogeneity concerns with regard to the minimum wage variable.

> 12 In Appendix Two we explain how we have used occupational and industry codes to assign workers and unemployed workers who have worked before to remuneration order categories.

13
unemployed, the number of unemployed who had worked before is possibly slightly overstated.<sup>13</sup> We also excluded a number of RO categories for which we identified only a very small number of covered workers (that is, fewer than 50 covered workers identified in any year). These categories are banks fishermen, cinema employees, salt workers and travel agent employees.

# **5 Results**

For our OLS regressions, we present three results for both the employment and hours of work regressions. These include an overall result for all workers in the sample, and individual results for males and females within the sample.

We find a negative employment effect arising from the minimum wage for all three of these groups. All are significant, with the coefficient on the log minimum wage variable being significant at the 1 percent level for females, the 10 percent level for males and the 5 percent level for all workers.

> 13 Essentially, we are assuming that, for those three years, all of the unemployed, as identified by their industry only, were in occupational classes that are covered by the remuneration orders, and not in the higher occupational classes not covered by remuneration orders in those industries. Where remuneration order categorization was based on occupation and not industry, we were however unfortunately not able to assign unemployed workers for those remuneration order categories at all for these three years. This was true for only two remuneration order categories: tailors and office attendants. These are however not categories with a significant number of identified remuneration order covered workers.

14
# **Table 2: Estimates of the Effects of Minimum Wages on Employment**

|VARIABLES|All|Males|Females|
|---|---|---|---|
|Log-MW|-0.0572**|-0.0766*|-0.106***|
||(0.0251)|(0.0400)|(0.0353)|
|Constant|0.889***|1.039***|0.699***|
||(0.130)|(0.192)|(0.226)|
|Observations|92,871|58,070|34,801|
|R-squared|0.163|0.163|0.182|

_Notes:_

1. *** p<0.01, ** p<0.05, * p<0.1

2. Data in all regressions are weighted using sample weights.

3. Explanatory variables in the regressions also include: years of education, age, age squared, age cubed, gender, dummies for remuneration order categories, dummies for years, and value added by broad industry.

4. The covered sector is wage-earning workers in the private sector. The uncovered sector are employers and ownaccount workers in the RO sectors wage-earning employees are covered in, as well as the unemployed who have worked before in those sectors.

5. Reported significance levels are based on robust standard errors.

The results suggest that a 10 percent increase in the minimum wage is associated with a decline in employment in the covered sector by 0.57 percent. For males, the effect associated with a 10 percent increase is a decline of employment of 0.77 percent, while for females the effect associated with a 10 percent increase is a decline of female employment in the covered sector of 1.06 percent. The overall effect is driven by the female effect, which is significant at the 1 percent level.

For the hours of work OLS regressions, in the covered sector, significant effects are found for both males and females. The results indicate that a 10 percent increase in minimum wages is associated with a 2.3 percent increase in average work hours for males in the covered sector but a 1.8 percent decline in average work hours for females in the covered sector.

15
**Table 3: Estimates of the Effects of Minimum Wages on Hours Worked**

|VARIABLES|Covered –<br>All<br>Log-hours|Covered -<br>Males<br>Log-hours|Covered -<br>Females<br>Log-hours|Uncovered -<br>All<br>Log-hours|Uncovered -<br>Males<br>Log-hours|Uncovered -<br>Females<br>Log-hours|
|---|---|---|---|---|---|---|
|Log-MW|-0.0429|0.232***|-0.181***|0.419***|0.414***|-0.0631|
||-0.0376|-0.0389|-0.0575|-0.0953|-0.0989|-0.343|
|Constant|3.647***|2.232***|5.138***|0.454|1.049**|1.648|
||-0.165|-0.167|-0.334|-0.453|-0.466|-1.972|
|Observations|67,548|41,345|26,203|17,719|13,436|4,283|
|R-squared|0.333|0.157|0.358|0.16|0.077|0.191|

_Notes:_

1. *** p<0.01, ** p<0.05, * p<0.1

2. Data in all regressions are weighted using sample weights.

3. Explanatory variables in the regressions also include: years of education, age, age squared, age cubed, gender, dummies for remuneration order categories, dummies for years, and value added by broad industry.

4. The covered sector is wage-earning workers in the private sector. The uncovered sector are employers and ownaccount workers in the RO sectors wage-earning employees are covered in, as well as the unemployed who have worked before in those sectors.

5. Reported significance levels are based on robust standard errors.

In the uncovered sector, a significant overall effect is found for the sector with a 10 percent increase in the minimum wage being associated with an increase in average number of hours worked of 4.2 percent. The effect is driven by an increase in average work hours among male workers in the uncovered sector.

As we have noted, we test for endogeneity in our OLS regressions and obtain two-stage least squares estimates making use of lagged minimum wages as an instrument for minimum wages as well. The results of the second stage regressions are shown in Table 4 below.

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**Table 4: Two Stage Least Square Estimates of the Effects of Minimum Wages on Employment and Hours Worked**

|VARIABLES|Employment<br>All<br>Employment|Working Hours<br>Covered<br>Log-hours|Working hours<br>Uncovered<br>Log-hours|
|---|---|---|---|
|Log-MW|0.113***|-0.0632|0.471***|
||(0.0317)|(0.0558)|(0.149)|
|Constant|0.383***|3.718***|1.070*|
||(0.147)|(0.224)|(0.566)|
|Observations|85,876|62,442|16,438|
|R-squared|0.163|0.340|0.167|

_Notes:_

1. *** p<0.01, ** p<0.05, * p<0.1

2. Data in all regressions are weighted using sample weights.

3. Explanatory variables in the regressions also include: years of education, age, age squared, age cubed, gender, dummies for remuneration order categories, dummies for years, and value added by broad industry.

4. The covered sector is wage-earning workers in the private sector. The uncovered sector are employers and ownaccount workers in the RO sectors wage-earning employees are covered in, as well as the unemployed who have worked before in those sectors.

5. Reported significance levels are based on robust standard errors.

In all the first-stage regressions, lagged minimum wages were positively correlated with actual minimum wages. Using a standard Hausman test for the endogeneity of the minimum wage variable in the hours equations, we find that endogeneity of the minimum wage variable is not a problem. However, for the employment equation, endogeneity remains a problem.<sup>14</sup> Thus, while we are reasonably confident that our hours results do not suffer from endogeneity bias, it is this 2SLS estimate which must be considered for reporting the impact of minimum wages on employment.

The result suggests that changes in employment in the covered sector are in fact significantly positively associated with changes in minimum wages, with a 10 percent increase in minimum wages being associated with a 1.13 percent increase in employment in the covered sector.

The elasticity of 0.113 is within the range we presented in Figure 4 above which showed a number of elasticities found for LMI countries. The elasticities presented in the figure range from a negative value of -1.3 to a positive value of 1. The result is thus in line with the existing literature which has found that increases in minimum wages have either benign or only slight employment effects.

> 14 The chi-square Hausman endogeneity test statistics are 29.95, 1.07 and 0.0046 respectively for the employment, hours worked (covered sector) and hours worked (uncovered sector) regressions. The test statistic for the employment regression suggests endogeneity at the 1 percent level of significance.

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# **6 A Note on Compliance**

Enforcement of, and compliance with, minimum wage legislation is a substantial contributing factor that must be considered in discussions of minimum wage effects. The international literature on minimum wages implicitly assumes that wage laws are adequately or perfectly enforced. However, high rates of non-compliance with minimum wages have been found in many developing countries. Concerns over the economic effects of minimum wage laws on employment are understandably prominent in the active labor market policy agenda, but the enforcement of these laws should not be ignored. Preliminary research in this area provides unambiguous evidence that a significant proportion of covered workers continue to earn subminimum wages and that factors such as levels of unemployment, firm size, informality, intensity of inspection and the level of fines for non-compliance all influence compliance rates to varying degrees.<sup>15</sup> A recent study by Rani, Belser, and Oelz (2013) explores issues of minimum wage coverage and the gaps in minimum wage compliance in 11 LMI countries.<sup>1617</sup> The work shows that of the 746 million workers identified in all countries, 51.8 percent were wage earners, 43.6 percent were covered by wage legislation, and 8.1 percent were excluded or unprotected. Of the 326 million covered workers, an average of 33.3 percent were being paid less than what was stipulated by legislation, and the extent to which these workers were being underpaid averaged 40 percent less than the legislated minimum wage (Rani, Belser, & Oelz, 2013).

Bhorat _et al._ (2013) suggest that there are four sets of variables that are important in understanding the factors influencing minimum wage violation in the developing world. There are, firstly, institutional factors such as the penalty structure for non-compliance, the complexity of the wage schedule and the resources allocated to enforcement services. All play a role in shaping the levels of violation to some extent, but their importance will vary based on the specific country and labor market context. Secondly, the individual characteristics of inspectors, including their level of education, can influence the extent to which they are effective at achieving compliance. Thirdly, firm characteristics such as size, distance from the enforcement agency, the number of previous violations, and the level of foreign ownership will impact the levels of enforcement and violation. Finally, local labor market characteristics such as the unemployment rate, the average wage rate relative to the minimum wage, and the levels of unionization also play a role.

In the context of estimating an employment effect of minimum wages, we thus emphasize that, to accurately measure the impact of imposed minimum wages on employment and work

> 15 See for example Bhorat _et al_ . (2012), Rani, Belser and Oelz. (2013) and Almeida _et al._ (2009 and 2012).

> 16 Coverage gaps represent the proportion of wage earners that not are covered by minimum wage legislation in the labor market. Compliance gaps represent the proportion of wage earners who are covered by minimum wage legislation but still make sub-minimum wages.

> 17 Brazil, Costa Rica, India, Indonesia, Mali, Mexico, Peru, the Philippines, South Africa, Turkey, Vietnam.

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hours, minimum wages must be binding on all workers in the covered sectors in the private sector.

To ascertain whether this is the case for Mauritius, in the first instance we look for spikes in the wage distribution at or around the minimum wage. We thus plot the kernel density estimate of the log wage less the log minimum wage for each worker for the covered and uncovered sector workers separately below.<sup>18</sup> A zero indicates that a worker is earning the legal minimum wage. These densities are shown below for both the covered (that is, waged workers covered by ROs) and the uncovered (that is, employers and self-employed workers in the RO covered sectors) sectors for the period 2004 – 2014. They are shown for 2004 and 2014 separately in the Appendix.

In both 2004 and 2014, and over all years, clear upward spikes can be seen at the level of zero for wage-minimum wage differential for the covered sector. This cannot be seen clearly for the uncovered sector. Thus, there is evidence that there is considerable compliance with minimum wages in the covered sector but also that such compliance is imperfect.<sup>19</sup> This should be borne in mind in interpretation of the results of our estimation of employment effects of Mauritius minimum wages.

> 18 It should be noted that this is a broad indication of compliance as we have only assigned one possible minimum wage per remuneration order (that is, the lowest stipulated wage for that remuneration order). In actuality, there are multiple specified wage rates by occupation and years of work experience as shown in the table above. We have been unable to allocate these specific wage rates to workers due to inadequate occupational data and no years of work experience variable, and thus provide no indication of compliance with these very specific wage rates.

> 19 We emphasize again here that we have been unable to check the specific compliance of individual wage rates, but that this refers to compliance with the lowest wage specified for each remuneration order category.

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**Figure 5: Wage-minimum wage differential kernel density estimate for the covered sector (2004 – 2014)**

<!-- Start of picture text -->
-4  -2<br>Log(W)-Log(MW)<br>.8<br>.6<br>.4<br>Density - 2004-2014<br>.2<br>0<br><!-- End of picture text -->

<!-- Start of picture text --> Log(W)-Log(MW)<br><!-- End of picture text -->

**Figure 6: Wage-minimum wage differential kernel density estimate for the uncovered sector (2004 – 2014)**

<!-- Start of picture text -->
-6  -4  -2<br>Log(W)-Log(MW)<br>.6<br>.4<br>Density - 2004-2014<br>.2<br>0<br><!-- End of picture text -->

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# **7 Conclusion**

In this paper, we have considered the effects of minimum wages on employment and hours worked in Mauritius. Using data from labor force surveys conducted between 2004 and 2014, we assigned minimum wages to workers covered by 30 categories of remuneration orders and estimated how changes in these minimum wages affected employment and hours of work over the period 2004 – 2014.

Our 2SLS estimates suggest a slightly positive effect of minimum wages on employment in the covered sector where remuneration order specified minimum wages are applicable. The employment elasticity of 0.113 is within the range of elasticities found in previous studies of employment effects of minimum wages in LMI countries and confirms the findings of those studies that increases in minimum wages have benign effects on employment.

We also find that a 10 percent increase in minimum wages was associated with a 2.3 percent increase in average work hours for males in the covered sector but a 1.8 percent decline in average work hours for females in the covered sector. This suggests that industries in which females are more likely to be represented, such as domestic work and services, may be responding to increased minimum wages by decreasing the number of hours worked by female employees rather than firing workers, despite the overall positive employment effect.

In the uncovered sector, a significant hours of work effect, driven by an increase in average male work hours, is found for the sector overall with a 10 percent increase in the minimum wage being associated with an increase in average number of hours worked of 4.2 percent. It seems then that increases in the minimum wage have the effect of increasing average work hours for males in both the covered and uncovered sectors.

We caution, however, that while the overall estimated employment effects seem to be benign, as noted by Bhorat, Kanbur and Stanwix (2015), this may be the result of context-specific factors which interact with the minimum wage to ensure that the effects are on average very small. Ultimately, the impact of any enforced change in wage levels on any particular sector depends on a range of factors. These include: the level of the minimum wage relative to average wages, the size of the wage increase, the sector under consideration, the timing of wage changes, the change post-law in worker productivity levels, and finally, enforcement and compliance.

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