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Extending the Coverage of Minimum Wages in India: Simulations from Household Data

There is a debate in India about the possible extension of minimum wages to all wage-earners. This study provides some benchmark figures on the effects of either making the national minimum wage floor compulsory or extending the coverage of state-level minimum wages. Using the 2004-05 Employment- Unemployment Survey along with the Consumer Expenditure Survey, it estimates that the extension of minimum wages at existing levels could improve the earnings of 73 to 76 million low-paid salaried and casual workers. It also shows that if an extended minimum wage is perfectly enforced, it would substantially reduce inequality, poverty and the gender pay gap, even if there are some disemployment effects.


Extending the Coverage of Minimum Wages in India: Simulations from Household Data

Patrick Belser, Uma Rani

There is a debate in India about the possible extension of minimum wages to all wage-earners. This study provides some benchmark figures on the effects of either making the national minimum wage floor compulsory or extending the coverage of state-level minimum wages. Using the 2004-05 Employment-Unemployment Survey along with the Consumer Expenditure Survey, it estimates that the extension of minimum wages at existing levels could improve the earnings of 73 to 76 million low-paid salaried and casual workers. It also shows that if an extended minimum wage is perfectly enforced, it would substantially reduce inequality, poverty and the gender pay gap, even if there are some disemployment effects.

The authors would like to thank all the participants at the workshop on “Inequality and Poverty”, Château de Penthes, Geneva, on 7-8 May 2009. Our sincere thanks to Duncan Campbell, Frank Hoffer, David Kucera, Sangheon Lee, Martin Oelz, Catherine Saget and Manuela Tomei for their insightful comments on an earlier draft of the paper. The responsibility for opinions expressed in this article rests solely with the authors, and does not necessarily represent the view of the International Labour Office.

Patrick Belser ( and Uma Rani ( are at the International Labour Office, Geneva.

1 Introduction

uring the decades before the global economic crisis, the Indian economy expanded at a rate of 5.5% to 6.0% per annum. It is often hoped that such economic growth will eventually lead to improvements in the quality of working conditions in the form of higher wages (Van der Hoeven 2001). However, despite some positive changes, India’s pattern of economic growth has been associated with continuously high levels of inequality and poverty. Income inequality (Gini coefficients) in the past decade has increased in urban India by 3.6 percentage points and in rural India by 1.3 percentage points (Rani 2008). While the top 20% of both the urban and the rural population succeeded in increasing their consumption levels, there was stagnation among the vast majority and hence an increase in inequality (Sen and Himanshu 2005; Ravallion and Datt 2002). This reflects, at least in part, a growing gap between the wages of skilled and unskilled workers as well as an increase in geographic inequality, as measured by the diverging average per capita consumption levels across the different states and across urban and rural areas (Deaton and Dreze 2002).

As the benefits from growth have been unequally distributed, economic development has also reduced poverty less than many had expected. Although poverty trends are somewhat contentious and based on a very low national poverty line (Patnaik 2007), the Government of India estimates that poverty (headcount rate) has declined from 36.0% in 1993-94 to 27.5% in 2004-05, leaving about 302 million people under the poverty line. The World Bank’s estimate – as measured by a revised benchmark of $1.25 per day – establishes the poverty rate at 42% in 2005, equivalent to 456 million people (World Bank 2008). This estimate is close to the poverty estimate obtained by the Tendulkar Committee (41.8% for 2004-05) with a new methodology.

This scenario raises the question of whether a statutory national minimum wage could be one of the mechanisms in India to ensure that economic growth translates into less poverty and inequality. Currently, such a legally binding national minimum wage does not exist. Instead, there exist a number of compulsory state-level minimum wages for a select number of occupations, as well as a non-binding national minimum wage floor. In addition, the central government also sets some compulsory rates, mostly for state-owned enterprises. This garmented system – which results in more than 1,000 minimum wage rates for different categories of workers1 – does not convince everyone, and there seems to be a widespread view in India on the need to improve the coherence of this system.

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There have been many discussions and arguments about the minimum wage over the years.2 Recently, the Indian National Trade Union Congress (INTUC) also called for a national “decent minimum wage” in 2007 to be fixed for all industries, based on the needs of workers. One important discussion has revolved around the question of what the appropriate level of the minimum wage is to prevent labour “exploitation” and provide a decent standard of living. Another debate concerns the way to increase compliance by elevating the minimum wage to a fundamental right, even equating non-compliance with a form of forced labour. Finally, in India, policymakers have discussed for years the possibility of simplifying and extending the coverage of minimum wages to the whole labour force. This paper provides a contribution to this last issue, leaving aside other important policy debates. We also do not discuss whether the existing levels of minimum wages in India are appropriate for leading a decent life. Although we are aware of the literature which considers that minimum wages in India are perhaps too low, we believe that this subject needs a separate investigation.

Who would benefit from a compulsory national minimum wage and what would be the impact on poverty and inequality? What could be the potential effects on employment if the minimum wage is made compulsory? These are the main questions that we explore in the present paper. We consider two different policy options – making the national minimum wage floor compulsory across the country or, as an alternative, extending the coverage of existing state-level minimum wages to all workers. The latter option is perhaps more attractive in a large country such as India, where the costs of living as well as economic conditions differ widely across states. At the outset, we would also clarify that we are not proposing to have a single national minimum wage floor or only state-level minimum wages. Our proposition is that there should be a national minimum wage floor or state-level minimum wages, and that no worker should be paid below the minimum wage that has been set. Other occupational wages could exist, which should be set above this basic minimum wage. We make a modest attempt to provide some benchmark figures on the possible effects of making the national minimum wage floor compulsory or extending the state-level minimum wages to all workers.

While there is a fairly large literature on the effects of minimum wages on poverty and inequality in both developed and developing countries,3 we are not aware of many studies which simulate ex ante such impacts in developing countries (one exception is Bird and Manning 2008). Also, contrary to most research on minimum wages, we look at the effects of an extension in the coverage of minimum wages and do not analyse the impact of an increase in the level of minimum wages. We hope that this paper will contribute to the ongoing debate and be useful for policymakers in considering whether to expand the minimum wage coverage to all workers at the prevailing levels set in India.

It is useful to note that the debate about minimum wage coverage is by no means restricted to India. Two International Labour Organisation (ILO) reports show that there have recently been clear indications of a more vigorous use of minimum wage policies in both developed and developing countries, and that this “revival” follows a period in which income inequality increased in about two-thirds of the countries for which data exists (ILO 2008a, 2008b). Debates are also ongoing at the ILO about the need to extend minimum wages to domestic workers, who are excluded from coverage in most countries.

2 Methodology and Data

What would happen in India if the national minimum floor wage was made compulsory, or if the mandatory state-level minimum wages were extended to all workers instead of covering only workers in so-called “scheduled” employment? More precisely, whose wages would increase and by how much could inequality and poverty be reduced? We provide some elements of a response by simulating these effects.

First, we provide some descriptive statistics on the proportion of wage-earners 4 in India who earn less than the national minimum floor wage. We consider that these are the workers who would benefit from the extension of a mandatory minimum wage. We also provide some information on the characteristics of workers whose wages are below either the national minimum wage floor or the state-level minimum wages. To do this, we employ the following simple bivariate probit model for the population in the age group 15-64 years.

MWi = D0 + E1(Ei) + E2(Hi) + Hi (1)

The dependent variable in the model MW indicates whether a worker receives the minimum wage (either national minimum wage floor or statutory state-level minimum wage) and is a binary variable taking the value “1” if the worker is paid below the minimum wage and “0” otherwise. E refers to a number of employment characteristics of each worker (age, experience, sex, education levels, occupation and industry categories, and size of the firm), and H refers to household characteristics of the worker (caste, region).

Second, we simulate the possible effects of a national mandatory minimum wage and of extended state-level minimum wages on wage inequality. The wage distributions of workers engaged in regular salaried and casual wage employment are examined together in one category. The wage dispersion or inequality for wage workers is measured using Gini coefficients. The Gini coefficient varies between zero (indicating no inequality) and one and is defined as follows:





where n is the number of individuals in the sample, w is the arithmetic mean wage, wi is the income of individual i, and wj is the income of individual j.

Third, we simulate the poverty-reducing effect of a national mandatory minimum wage and of extended state-level minimum wages. An advantage of using the binary model is that income and expenditure distribution data typically contain non-eligible errors (Gaiha 1988). The problem is especially severe as income accrues individually, but expenses and poverty are measured at the household level. The use of per capita expenditure as the dependent variable therefore infers a precision which cannot be taken as granted. In such cases, it can be safer to analyse the probability of expenditure falling within a specified interval. For these reasons, we focus on probit models and explore whether

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extending minimum wages reduces the probability of a household being poor. We estimate the equation of the form

Pi = D0 + E1(MWi) + E2(Xi) + E3(Hi) + Hi (3) where the dependent variable Pi is whether a worker is in a household below the poverty line or not, and it is a binary variable. The explanatory variables include a dummy variable for minimum wage MWi, which indicates whether a worker is paid below the minimum wage or not. Other explanatory variables include the vector Xi of individual specific human capital variables (age, agesquared, gender, dummies for education, industry and occupation categories, size of the firm); and Hi includes the household specific variables (region, caste).

Finally, we simulate how our results would be affected by adverse employment effects of different magnitude. This is particularly difficult in light of the wide range of estimates and lack of consensus about the overall effects of minimum wages on employment. Based on our reading of the literature, we assume two different elasticities of labour demand and apply them to our simulations. In our simulations, we assume that higher labour costs reduce the number of days worked by minimum wage workers, and therefore also reduces to some extent the ability of the minimum wage to reduce poverty and inequality.

Simulating the possible effects of a national mandatory minimum wage on wage inequality assuming employment effects of different magnitude is a straightforward exercise. We assume that the number of days worked by minimum wage workers is reduced by an amount that depends on the elasticity of labour demand and compute the new Gini coefficients. For simulating the effects on poverty, the ideal method would be to impute the extension of minimum wages to all salaried and casual workers into income data. One could then re-estimate the new poverty rates and run simulations with different employment effects. However, for this analysis, we use monthly per capita consumer expenditure as a proxy for income, and we therefore assume that the number of days worked has (been) reduced and then simulate the povertyreducing effect of an extended mandatory minimum wage.

To run our simulations we use the Employment-Unemployment Survey along with the Consumer Expenditure Survey under taken by the National Sample Survey Organisation (NSSO), which cover all the major Indian states. Although these surveys are undertaken every five years, we use one single round corresponding to the year 2004-05. The Employment-Unemployment Survey provides information on the characteristics of all household members (including sex, age, caste, educational level) as well as on the number of days worked and wages of both casual and salaried workers.5 The Consumer Expenditure Survey provides – for the same households – the monthly per capita consumption expenditure, which we use as a proxy for income to classify households into below and above the poverty line. We use both the national and state level poverty lines for the analysis, and we construct dummy variables for both these indicators.

The variables included in the probit models are age, age squared, dummy for female, dummy for those living in urban areas; caste groups with dummies for scheduled castes (scs), scheduled tribes (sts), Other Backward Classes (obcs) and forward castes (Hindu). The Employment-Unemployment Survey allows

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us to classify individuals into different categories depending on their levels of education6 and their status in employment. Regarding the latter, the data allows for three major categories – self-employed, salaried and casual labour. The self-employed comprise own-account workers, employers and unpaid family workers, while salaried and casual workers are two separate categories of wage-earners. Salaried workers include regular salaried and wage employees, while casual workers often work on a daily basis. The survey also provides information on the number of workers in an enterprise and we have used this information to construct the firm size variable. We distinguish between tiny firms (less than six workers), small firms (six to nine workers), medium firms (10 to 20 workers) and large firms (more than 20 workers).

To facilitate the analysis, we also aggregate the three-digit occupation groups classified under the National Occupation Classification (NOC) into seven occupation categories7 and aggregate the five-digit industry groups classified under the National Industrial Classification (NIC) into six industry groups with similar qualitative characteristics.8 The service sector categorisation is based on capital and skill requirements. We define low pro ductive services as sectors that are largely low-skilled, whereas high productive services comprise modern skills and capital- intensive services.

Finally, we use two minimum wage indicators – one is the minimum wage set at the national level (national minimum wage floor); and the other is the minimum wage set at the state level. Though there are a number of minimum wages for different occupational categories differing across regions, we use the average minimum wages at the state level and national minimum wage floor with no variation across economic activities or occupations. For 2004-05, the national minimum wage floor was set at Rs 66 per day, while the state-level minimum wages that we have used are provided in Appendix I (p 55). The only selection criterion is age, and we have included all persons aged 15-64 years in the sample.

3 Results
3.1 The Beneficiaries

Who and how many workers could potentially benefit from extending mandatory minimum wages? By definition, minimum

Table 1: Employment by Activity Status, All India (2004-05)

Male Female Total
Self-employed 58.1 63.7 60.2
Salaried 9.0 3.7 7.1
Casual labour 32.9 32.6 32.8
Total 20,16,65,800 11,61,51,000 31,78,16,800
Self-employed 44.8 47.7 45.4
Salaried 40.6 35.6 39.5
Casual labour 14.6 16.7 15.0
Total 7,07,47,100 1,96,83,000 9,04,30,000
Self-employed 54.6 61.2 56.8
Salaried 17.2 8.4 14.3
Casual labour 28.1 30.3 28.9
Total 27,24,12,900 13,58,34,000 40,82,46,800

Source: Computed from the raw data provided by the National Sample Survey Organisation (NSSO), Employment-Unemployment Survey, 2004-05.

wages can only apply to wage-earners, that is, people in paid em higher probability for such workers to earn below minimum
ployment. So we first provide some information on the total wages. Being a woman and living in a rural area also increases
number of wage-earners in India. In Table 1 (p 49), we see that the chances of receiving less than the minimum. Strikingly, for
there are about 408 million people employed in India, of whom 232 women this is mostly due to differences among salaried workers,
million (56.8%) are self-employed. This leaves a total of 175 million where being a woman increases the probability of not receiving
wage-earners (43.2%), Table 2: Proportion of Workers Below Minimum the minimum wage by 17%.
of whom 58 million Wage Pay, All India Looking at industry groups, we see that for salaried workers
(14.3%) are salaried Male Female Total National minimum wage floor the probability of receiving below minimum wages is much
workers and 117 million Salaried workers higher in agriculture than in any other sector, while casual
(28.9%) are casual work-Rural 27.4 50.1 31.6 workers seem to be particularly at risk in the construction sector.
ers. Among all wage-Urban 17.9 36.0 21.3 In terms of occupation, service workers are most frequently un
earners, a majority of Total 21.5 41.3 25.3 derpaid, whether they are salaried or casual workers. Produc
two-thirds are male and Casual workers tion workers have a higher probability of receiving minimum
another majority of twothirds live in rural areas. Rural 55.0 45.9 51.9 Urban 38.0 50.7 40.8 Total 52.7 46.3 50.6 wages as they are often unionised. Firm size matters too. Salaried workers working in tiny enterprises, when compared to
The wage-earners in ruState-level minimum wages those in large enterprises, have a higher probability of earning
ral areas are mainly cas-Salaried workers Rural 22.2 42.2 25.7 below minimum wages.
ual workers, while a majority of salaried workers Urban 24.9 40.6 28.2 3.2 Effects on Wage Inequality
are found in urban areas. Total 23.8 41.2 27.2 Casual workers By how much could an extended mandatory minimum wage in
Not all wage-earners Rural 55.2 47.4 52.7 support of low-paid workers reduce overall wage inequality? At
are paid the minimum Urban 49.7 54.9 50.8 the existing wages that workers earn, the Gini coefficient for
wage. Overall, no less Total 53.7 48.7 52.3 wage inequality is 0.499. When we impute the prevailing
than 73 million work-The national minimum wage is Rs 66 per day for 2004 and the state-level minimum wages are provided in Appendix II. national minimum wage floor for all workers who earn below
ers, equivalent to 42% Source: Computed from the raw data provided by the minimum wage, and assume no significant disemployment
of all wage-earners, re-NSSO, Employment-Unemployment Survey, 2004-05. effects, we find that inequality would decline by 9 percentage
ceive wages that are below the national minimum wage floor of points to 0.410. The decline is much steeper in rural than in urban
Rs 66 per day (2004-05). This includes more than half of all cas areas, indicating that a larger proportion of those in rural areas
ual workers (58.6 million earners) and one-fourth of all salaried currently earn below the minimum (Table 4, p 51). If we impute
workers (14.5 million workers). Unsurprisingly, female workers the state-level minimum wages for all workers who earn below
and those residing in rural areas are more likely to earn below the minimum wage, then the inequality further declines by an
minimum wages (Table 2). As already pointed out, the national other percentage point to 0.398. These are considerable effects,
minimum wage floor is only indicative. Therefore we also look and bring down the level of wage inequality in India.
at the state-level minimum wages. We find a similar result – 76 When we impute the prevailing minimum wage for all workers
million workers are paid less than the minimum wages, includ who earn below minimum wage, we find that in agriculture and in
ing 27.2% of salaried workers and 52.3% of casual workers. the low-productive service sector the wage inequality would de-
These high proportions may result from a variety of reasons, but cline by more than 15 percentage points. This is followed by the
presumably include a large number of workers in “schedules” manufacturing sector, where wage inequality would decline by 10
that are not covered by the minimum wage legislation. percentage points (Table 5, p 51). The decline in wage inequality in
Who is paid below the minimum wage? That is, who would mining, electricity, gas and water, and the high-productive service
benefit from enforcing mandatory minimum wages? Table 3 (p 51) sector is comparatively low or minimal, implying that the extent of
gives the marginal effects from the probit model for both national minimum wage coverage in these sectors is quite high. The results
and state-level minimum wages for salaried and casual labour. are very similar when we impute the state-level minimum wages
The results for national and state-level minimum wages are in for all workers earning below the minimum wage.
most cases in the same direction, and the only difference is in the Full coverage of minimum wages would not only reduce ine
size of the effects. As expected, the illiterate and those with no qualities across and within sectors, but would also reduce the
more than middle-level education are more likely to earn below gender wage gap. We estimated two earnings functions with the
the minimum wage among both salaried and casual labour. log of actual daily wage earnings, adjusted national minimum
Everything else held constant, there is a marginally higher proba wage daily earnings for 2004-05, and the exponential of the sex
bility for ST and oBC to earn below minimum wage compared to coefficient provides an estimate of women’s adjusted relative
upper castes. But there is a higher probability for SC salaried wage. The analysis was done for salaried and casual workers sep
workers to earn minimum wages, which may be because many arately. Both the earnings functions are controlled for age, expe
work in formal enterprises at the lower cadre (Class IV rience, schooling, occupation, industry, caste, size of the firm,
employees), which is to a large extent due to the reservation region and state dummies. The results reveal that, if all workers
policy to support such groups. This becomes evident in the case receive at least minimum wages, among salaried workers, the
of casual workers where, in the absence of such a policy, there is a gender wage gap would narrow from 0.84 to 0.90, that is, by
50 may 28, 2011 vol xlvI no 22 Economic Political Weekly
Table 3: Probit Estimates of Workers Receiving Below Minimum Wages, Ages 15-64 Years, India (2004-05)
Salaried Casual Labour
Model 1 Model 2 Model 1 Model 2
Marginal Effects Marginal Effects Marginal Effects Marginal Effects
Predicted outcome 0.1527 0.2142 0.4536 0.5234
Age -0.0203*** (0.001) -0.0219*** (0.001) -0.0012 (0.001) 0.0053*** (0.001)
Age squared 0.0002*** (0.000) 0.0002*** (0.000) 0.0000** (0.000) -0.0000* (0.000)
Sex (male)
Female 0.1674*** (0.006) 0.1571*** (0.006) -0.0645*** (0.006) -0.1138*** (0.006)
Living in urban areas -0.0262*** (0.004) -0.0111** (0.005) -0.0159*** (0.006) 0.0162*** (0.006)
Level of education (Reference group: above secondary school)
Illiterate 0.2723*** (0.011) 0.2895*** (0.011) 0.1723*** (0.017) 0.1061*** (0.016)
Literate 0.2021*** (0.012) 0.2093*** (0.012) 0.1095*** (0.017) 0.0586*** (0.017)
Primary school 0.1633*** (0.009) 0.1817*** (0.010) 0.0581*** (0.017) 0.0382* (0.017)
Middle school 0.1236*** (0.008) 0.1345*** (0.008) 0.0363** (0.018) 0.0353** (0.017)
Secondary school 0.0764*** (0.007) 0.0924*** (0.008) 0.0103 (0.020) 0.0160 (0.019)
Caste (Reference group: forward castes/Hindus)
Scheduled caste -0.0201*** (0.006) -0.0530*** (0.007) 0.0327*** (0.009) 0.0226*** (0.009)
Scheduled tribe 0.0499*** (0.006) 0.0777*** (0.007) -0.0053 (0.007) 0.0571*** (0.007)
Other backward classes 0.0569*** (0.005) 0.0898*** (0.005) -0.0147** (0.007) 0.0397*** (0.007)
Industry categories (Reference group: mining, electricity, gas and water)
Agriculture 0.3581*** (0.027) 0.3664*** (0.025) 0.0395** (0.020) -0.0200 (0.020)
Manufacturing 0.1549*** (0.019) 0.2324*** (0.020) 0.0255** (0.020) -0.0090 (0.020)
Construction 0.0880*** (0.025) 0.1384*** (0.027) -0.1267*** (0.018) -0.1436*** (0.019)
Low-productive services 0.1652*** (0.020) 0.2199*** (0.020) 0.0668*** (0.021) -0.0090 (0.021)
High-productive services 0.0594*** (0.013) 0.0766*** (0.015) -0.0766*** (0.021) -0.0832*** (0.022)
Occupation categories (Reference group: professionals)
Administration -0.0080 (0.010) 0.0072 (0.012) -0.0117 (0.034) 0.0161 (0.034)
Clerical 0.0072 (0.007) 0.0228** (0.008) 0.0245 (0.032) 0.0686* (0.031)
Sales 0.0877*** (0.011) 0.1282*** (0.012) 0.0082 (0.027) 0.0495* (0.026)
Service 0.1005*** (0.009) 0.1312*** (0.011) 0.0718*** (0.026) 0.1079*** (0.025)
Farmers 0.0589*** (0.010) 0.0695*** (0.011) 0.0095 (0.023) 0.0304 (0.023)
Production workers 0.0435*** (0.007) 0.0654*** (0.008) -0.0253 (0.023) 0.0077 (0.023)
Size of the enterprise (Reference group: large enterprises)
Tiny 0.1485*** (0.005) 0.1855*** (0.006)
Small 0.1067*** (0.008) 0.1322*** (0.009)
Medium 0.0564*** (0.008) 0.0820*** (0.008)

Text in parentheses is reference category; figures in parentheses are standard errors; *** denotes significance at the 1% level; ** denotes significance at the 5% level; * denotes significance at the 10% level.

Model 1: National minimum wage floor; Model 2: State-level minimum wages.

6 percentage points. Among casual workers, the wage gap would narrow from 0.74 to 0.92, by 18 percentage points. Thus, we find that if minimum wages were to expand to all workers, the impact would be enormous for female casual workers.

3.3 Effects on Poverty Reduction

Has the minimum wage the ability to help workers who live in poor households? In the literature, it is often argued that minimum wages benefit workers in the formal economy who usually live in non-poor families. However in India, as in other developing countries, a relatively high proportion of poor, low-skilled people in both rural and urban areas are wage-earners. Our analysis of Indian data for 2004-05 shows that about 30% of salaried workers and 40% of casual workers who earn below minimum wages belong to poor families (and that among the wage-earners belonging to poor families, about 50% earn below the minimum wage). If these poor workers were to receive at least the minimum wage, it would presumably help them and their families move out of poverty.

In our simulations, we can estimate the potential impact of extending the coverage of the national minimum wage floor on workers’ probability of being poor (that is, living in poor households). Our findings show that for salaried workers, the fact of

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Table 4: Wage Inequality, by Sector

Sector Actual Wage Adjusting for Adjusting for National Minimum State-level Wage Floor Minimum Wage

Rural 0.482 0.357 0.357

Urban 0.486 0.432 0.413

All 0.499 0.410 0.398

Source: Computed from the raw data provided by the NSSO, Employment-Unemployment Survey, 2004-05.

Table 5: Wage Inequality by Industry Groups

Industry Groups Actual Wage Adjusting for Adjusting for National Minimum State-level Wage Floor Minimum Wage

Agriculture 0.307 0.097 0.170

Mining, electricity, gas and water 0.437 0.415 0.405

Manufacturing 0.456 0.366 0.350

Construction 0.297 0.219 0.229

Low-productive service sector 0.410 0.248 0.267

High-productive service sector 0.395 0.375 0.363

Source: Computed from the raw data provided by the NSSO, Employment-Unemployment Survey, 2004-05.

being paid below the minimum wage currently increases the probability of being poor by 9% to 10% compared to otherwise similar workers (Table 6, p 52). For casual workers, not receiving the national minimum wage raises the probability of living in poverty by 7% to 8%. These results indicate that the enforcement of national minimum wages would reduce the probability for wage-earners of being poor by anywhere between 7% and 10%.9 Similarly, complete coverage of state-level minimum wages would reduce the probability for wage-earners being poor by 3% to 6%. The marginal effects of the probit estimates also bring out that the minimum wage is the third most important factor in reducing the poverty risk for the wage-earner household after education and location, if extended to all workers. Clearly this is a significant effect and strongly suggests that minimum wages, whether national or state level, may help in lifting a significant number of low-income families out of poverty.

3.4 Employment Effects

Our simulations have not yet discussed any potential employmentdisplacing effect of minimum wages. The implicit assumption in our analysis, so far, has been that minimum wages redistribute incomes without hurting employment. This is not entirely unrealistic. One recent view is that standard neoclassical economics has probably overstated the adverse effects of minimum wages on employment. Based on an assessment of recent academic literature, more than 650 economists, including five Nobel Prize winners and six past presidents of the American Economic Association, issued a statement in which they consider that higher minimum wages “can significantly improve the lives of low-income workers and their families, without the adverse effects that critics have claimed”.10 A recent ILO publication (2008a: 44-45) also considers that if set at a reasonable level, minimum wages “can increase the number of workers with access to decent wages and reduce the gender pay gap with little or no adverse impact on employment levels”. Keynesian arguments suggest that minimum wages may even have positive employment effects if they contribute towards raising household consumption and overall aggregate demand (Herr, Kazandziska and Mahnkopf-Praportnik 2009: 25).

As there is no consensus within the literature, we do not wish to discard the possibility that minimum wages reduce labour demand and the total number of days worked by low-paid workers. The extent to which labour demand may fall depends on the elasticity of labour demand. Fields and Kanbur (2007) try to address this theoretically and show how poverty effects of a minimum wage increase depends on four parameters – “how high the minimum wage is relative to the poverty line, how elastic the demand for labour is, how much income-sharing takes place, and how

Table 6: Probit Estimates of Workers in Households Below the Poverty Line, Ages 15-64 Years in India (2004-05)

Salaried Casual Labour
Model 1 Model 2 Model 1 Model 2
Marginal Effects Marginal Effects Marginal Effects Marginal Effects
Predicted outcome 0.0782 0.0835 0.3385 0.3276
Age 0.0043*** (0.001) 0.0036*** (0.001) 0.0047*** (0.001) 0.0065*** (0.001)
Age squared -0.0000*** (0.000) -0.0000*** (0.000) -0.0001*** (0.000) -0.0001*** (0.000)
Sex (male)
Female -0.0139*** (0.003) -0.0023** (0.003) -0.0063 (0.006) -0.0247*** (0.006)
Not receiving minimum wages 0.0948*** (0.004) 0.0647*** (0.004) 0.0807*** (0.005) 0.0286*** (0.005)
Living in urban areas 0.0999*** (0.003) 0.1027*** (0.003) 0.3608*** (0.006) 0.3503*** (0.006)
Level of education (Reference group: above secondary school)
Illiterate 0.2224*** (0.011) 0.2154*** (0.010) 0.2035*** (0.017) 0.1993*** (0.016)
Literate 0.1699*** (0.011) 0.1655*** (0.011) 0.1424*** (0.019) 0.1449*** (0.019)
Primary school 0.1343*** (0.008) 0.1281*** (0.009) 0.0934*** (0.018) 0.1033*** (0.018)
Middle school 0.0848*** (0.006) 0.0962*** (0.006) 0.0559*** (0.018) 0.0751*** (0.018)
Secondary school 0.0493*** (0.006) 0.0523*** (0.006) 0.0208 (0.020) 0.0502** (0.020)
Caste (Reference group: forward castes/Hindus)
Scheduled caste 0.0308*** (0.006) 0.0229*** (0.006) 0.1607*** (0.009) 0.1397*** (0.009)
Scheduled tribe 0.0642*** (0.005) 0.0613*** (0.005) 0.1004*** (0.008) 0.0876*** (0.007)
Other backward classes 0.0503*** (0.004) 0.0468*** (0.004) 0.0588*** (0.007) 0.0336*** (0.007)
Industry categories (Reference group: mining, electricity, gas and water)
Agriculture 0.0639*** (0.017) 0.1200*** (0.020) 0.1127*** (0.020) 0.0845*** (0.019)
Manufacturing 0.0449*** (0.011) 0.0565*** (0.012) 0.0213 (0.021) 0.0056 (0.020)
Construction 0.0371** (0.017) 0.0618** (0.019) 0.0502* (0.020) 0.0489 (0.019)
Low-productive services 0.0641*** (0.013) 0.0798*** (0.014) 0.0890*** (0.022) 0.0773*** (0.022)
High-productive services 0.0378*** (0.008) 0.0446*** (0.009) 0.0029 (0.022) -0.0199 (0.021)
Occupation categories (Reference group: professional)
Administration -0.0327*** (0.006) -0.0257*** (0.007) 0.0504 (0.036) 0.0241 (0.035)
Clerical 0.0175*** (0.006) 0.0258*** (0.006) -0.0542* (0.032) -0.1056 (0.029)
Sales 0.0355*** (0.008) 0.0449*** (0.008) 0.1062*** (0.029) 0.1132*** (0.029)
Service 0.0387*** (0.007) 0.0584*** (0.008) 0.0520** (0.028) 0.0504** (0.027)
Farmers 0.0701*** (0.010) 0.0842*** (0.010) 0.1381*** (0.024) 0.1234*** (0.023)
Production workers 0.0461*** (0.006) 0.0599*** (0.006) 0.1211*** (0.024) 0.1056*** (0.024)
Size of the enterprise (Reference group: large enterprises and formal)
Tiny 0.0471*** (0.004) 0.0522*** (0.004)
Small 0.0229*** (0.006) 0.0301*** (0.005)
Medium 0.0011 (0.005) 0.008 (0.005)

Text in parentheses is reference category; figures in parentheses are standard errors; *** denotes significance at the 1% level; ** denotes significance at the 5% level; * denotes significance at the 10% level. Model 1: National minimum wage floor; Model 2: State-level minimum wages.

may 28, 2011 vol xlvI no 22

sensitive the poverty measure is to the depth of poverty” (146). Available estimates of the so-called “wage elasticity of employment” vary widely, depending on methodologies and counterfactuals. While many find an elasticity around zero (as indicated above), others have estimated much more sizeable values.

For the purpose of our simulations we assume two different possibilities – an elasticity of -0.20 and a larger one of -0.5. While the former is taken from the review by Cunningham (2007), according to whom most countries in Latin America were found “experiencing a job loss of 2% for a 10% increase in the minimum wage” (44), the second estimate is probably an upper-bound. Our simulations are based on the hypothesis that in a country such as India the employment effect is more likely to occur through an adjustment of the number of days worked by the employees – especially for casual workers – rather than through an increase in unemployment (see, for example, Neumark and Wascher 2008: 77). Disemployment effects in developing countries are unlikely to result in open unemployment; as more likely, the displaced workers will move from wage employment into self-employment.

Implications of the two different employment effects on inequality estimates are shown in Table 8. We show the Gini coefficient for wages in the two different scenarios that would result from extending the coverage to workers who are currently paid below the minimum wage. We compare these estimates with the actual Gini coefficient and the coefficient that we obtained from above by assuming no adverse employment effects. We see that in the presence of adverse employment effects, the ability of the minimum wage to compress the wage distribution is reduced. But, in spite of these adverse effects, it is to be noted that the new level of inequality after implementation of the minimum wage is still at least 5 percentage points lower than the existing Gini coefficient for wage inequality of 0.499. Table 7: Impact of Minimum Wage on Inequality with Employment Effects, by Sector

Sector Actual Wage Adjusting for National Elasticity of -0.2 Elasticity of -0.5 Minimum Wage Floor

Rural 0.482 0.357 0.378 0.410 Urban 0.486 0.432 0.443 0.459 All 0.499 0.410 0.426 0.450

Source: Computed from the raw data provided by the NSSO, Employment-Unemployment Survey, 2004-05.

Table 8: Impact of Minimum Wage on Poverty with Employment Effects

Sector Actual Wage Elasticity of -0.2 Elasticity of -0.5
Salaried workers 0.091 0.087 0.084
Casual workers 0.079 0.068 0.051

Source: Computed from the raw data provided by the NSSO, Employment-Unemployment Survey, 2004-05.

Similarly, we estimate the potential impact on workers’ probability of being poor (that is, living in poor households), with the number of working days reduced for the employees. Earlier we found that if minimum wages were extended to all workers, then the probability of being poor would fall by 7% to 9% depending on whether the worker was salaried or casual. However, if the number of working days were reduced, as a result of compulsory minimum wages, the beneficial effects of minimum wages on the probability of being poor would also be reduced. The effects on salaried workers and casual workers depend upon the elasticity of labour demand. Our simulations in Table 8 show that, even under a scenario with a high elasticity of -0.5, minimum wages

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would still be able to reduce the probability of poverty by 5% to 8% for casual and salaried workers, respectively.

3.5 Enforcement

The paper has produced simulations of the effects of a binding minimum wage under the hypothesis that it would be perfectly enforced. We are aware that the extent to which these benefits can be achieved in practice depends much on the actual degree of compliance. We are also aware of the difficulties with implementation and of the fact that “simply legislating a minimum wage will not make it happen” (Murgai and Ravallion 2005: 2). In practice, compliance is always less than perfect in both developed and developing countries. In developed countries, the proportion of workers paid less than the national minimum wages ranges from about 1.3% of all wage-earners in the UK to 2.6% of all hourly paid workers in the US (Bureau of Labor Statistics 2009; Metcalf 2008). Non-compliance in developing countries, as measured by the proportion of wage-earners who are paid below the minimum wage, is comparatively higher. In Brazil, for example, Lemos (2005) estimated that in 2000 the proportion of workers earning below the minimum wage was 13.7% in the private sector and 4.6% in the public sector. Even at this level of less-thanperfect compliance, however, a majority of workers in India would be able to benefit from expanded minimum wages.

To ensure a high rate of compliance requires a coherent enforcement strategy based on provision of information, effective labour inspections and sanctions in case of violations. Lack of clear information available to employers and workers about the level of minimum wages, and about possible sanctions in case of violation, also reduces the likelihood of compliance.11 Another mechanism could be greater involvement of worker’s organisations and nongovernmental organisations (NGOs) to ensure that the implementation machinery is effective (Labour Bureau 2005).

Along with a better “enforcement” strategy, the option of government acting as the “employer of last resort” could also be considered. Murgai and Ravallion (2005) suggest that minimum wage legislation in poor countries can only be made really effective if the government acts as the “employer of last resort” and commits to employ the entire excess supply of unskilled workers at the stipulated minimum wage rate. Indeed, from the perspective of workers, the supply of labour at a wage below the minimum depends on whether such jobs are available. If the sub-minimum wage jobs are available, labour will continue to be supplied at sub-minimum wages. Thus, programmes such as those under the 2006 National Rural Employment Guarantee Act (NREGA) and the Employment Guarantee Scheme (EGS), which was implemented in Maharashtra, can play a key role in fostering compliance with a mandatory minimum wage. At the same time, these benefits must be balanced against the need for these programmes to target the poorest segment of the population and avoid drawing in a large proportion of the labour force.

4 Conclusions

In India, the Minimum Wage Act of 1948 is perceived as being of great importance, particularly to unorganised casual workers, who – as our paper calculates – account for two-thirds of all wage-earners and a total of about 117 million workers. In this paper we show that if minimum wages are extended to all workers and, if fully implemented, it would have a significant impact on inequality and poverty in India. Indeed, our simulations show that minimum wages can play an important part in reducing inequality and poverty even in the presence of some adverse employment effects. This finding is supported by others in the literature. For example, Lustig and McLeod (1997) using cross-country data on developing countries find that a higher minimum wage does lower poverty, even though it reduces employment.12 In India, the large potential impact can be easily explained by our finding that an extension of either system of minimum wages could potentially improve the wages and the lives of about 73 to 76 million low-paid workers.

At the same time, the paper leaves many questions unresolved and open for future investigation. One important question that requires further consideration concerns the possibility of some employment substitution between men and women. We have seen earlier that the minimum wage would increase the wages of women more than of men and, hence, reduce the gender wage gap. This may lead to two possible outcomes. On the one hand, there could be a significant decline in women’s employment, especially where men can replace women workers. This is surely a cause for worry, especially where the female worker is the main earner of the household.13 The second situation is where men are unlikely to replace women workers due to gender segregation of work, like transplanting in agriculture or assembly work in the manufacturing sector.

Another question concerns the effect of minimum wages on prices. Our paper assumes no explicit effect of higher minimum wages on prices and inflation. In the empirical literature, the effects on prices are generally argued to be of a small magnitude.14 In the case of India, although our simulations suggests that complete coverage could benefit a very large number of workers, the total amount of transferred resources remains relatively limited. In terms of gross domestic product (GDP), a back-of-the-envelope calculation suggests that even with full compliance, and assuming a full employment situation, a minimum wage would hardly transfer more than about 1.5% of India’s GDP to low wage employees. Yet Ghose (1997) points out that in the case of agriculture, procurement prices fixed by the government are based on production costs, including wage costs. We recognise that this could lead to more substantial effects on prices, if the extension of minimum wages is not accompanied by some reform in the system of procurement.

A related shortcoming is that our simulations concern mainly wage-earners and leave aside the 227 million self-employed. It could be argued – as in Bird and Manning (2008) – that selfemployed households receive no direct benefits from minimum wages, but pay a cost in terms of higher prices of goods and services produced by minimum-wage earners. While the empirical literature shows that the costs in terms of higher prices are generally marginal and spread over a large number of people, this possible side-effect of minimum wages certainly deserves further attention in the Indian context.

Finally, our simulations also ignore the possible benefits of lower inequality on overall aggregate demand in India. In principle, according to Keynesian arguments, partial-equilibrium analysis of minimum wages is unsatisfactory. The minimum wage is a redistributive tool which transfers resources from high-income groups to low-income groups. Because the relatively poor have a higher propensity to spend their money than the relatively rich (see, for example, Stiglitz 2009), this fall in inequality can be expected to increase aggregate demand and employment. In other words, even if minimum wages force some companies to undertake layoffs in the short term, they can be expected to ultimately lead to higher private consumption and the creation of new jobs elsewhere in the economy. We have not tried to simulate these second-round effects.

In spite of these limitations, our paper provides some of the first benchmark figures on the potential benefits of minimum wages. Our simulations highlight the fact that by providing an effective backstop for wages, a minimum wage can compress inequality and, in particular, reduce the distance between the low paid and those in the middle of the wage distribution. An important effect of an extended minimum wage would be a sharp reduction in the gender pay gap. We find that if all workers receive at least minimum wages, the average wages of women compared to men would increase from 84% to 90% for salaried workers and from 74% to 92% for casual workers. This effect does not arise because women are over-represented among workers with sub-minimum wage. From our data set, we calculate that women represent about one-third of all wage-earners and also about one-third of the sub-minimum wage population. Rather, our strong results stem from the fact that, even among sub-minimum wage workers, women are paid lower wages than their male counterparts. Lifting all wages to the mandatory minimum would eliminate inequality among the lowest paid.

Significant impacts can also be expected on the number of wage-earners who live in poverty. Although the literature has often questioned the relevance of minimum wages to the poverty debates, our analysis shows that – among the sub-minimum wage population – more than one-third of wage-earners actually live in poverty. Hence, the minimum wages is a policy tool that can reach directly into poor households. With the actual number of days worked kept constant, we find that the payment of a national minimum wage would reduce low-paid workers’ probability of being poor by 8% to 9%. In case of a large decline in days worked, this effect is reduced, but remains positive and significant.

For all these reasons, an extension in the coverage of minimum wages, either through a national minimum wage floor or through state-level minimum wages, might bring worthwhile social benefits to India. Although we recognise that a minimum wage is not ideally targeted nor necessarily the most costeffective way to achieve poverty reduction, we consider that the combined effects on inequality, poverty and the gender pay gap at low fiscal costs make it a useful instrument. In a country such as India where the majority of wage workers have no access to social security benefits – and where many types of incometransfer programmes remain unrealistic – extending minimum wages to the whole labour force would be a step towards more social justice.

may 28, 2011 vol xlvI no 22

Notes Bureau of Labor Statistics (2009): Characteristics of Patnaik, U (2007): “Neo-liberalism and Rural Poverty

1 India is not alone in having multiple minimum wage rates. For example, in Argentina, there are dozens of minimum wages set for agricultural workers, while one minimum wage is set for all other economic activities (Kristensen and Cunningham 2006). In Mexico, wages are set separately for three regions and 88 occupations (Gindling and Terrell 2004). In Costa Rica, there were more than 500 wages set by industry and occupational categories; from 1988 to 1999, the structure of minimum wages was simplified and gradually reduced to 19 minimum wages based on skills (El-Hamidi and Terrell 2002). See also Marinakis and Velasco who discuss the multiple minimum wages in the Latin American context (2006).

2 GoI (2007) provides a detailed account and discussions on the measures taken to make minimum wages compulsory for all occupations in India.

3 See, for example, the reviews in Neumark and Wascher (2008) on the US, Vaughan-Whitehead (2010) on Europe, and Cunningham (2007) on Latin America

4 Wage-earners include both those who are covered and entitled under a specific schedule of employment and those who are not covered under any schedule of employment.

5 The survey provides daily wages for casual workers and monthly incomes for salaried workers.

6 We created dummies for six education categories: illiterate; literate; primary; middle; secondary and higher secondary; and above secondary education (reference group).

7 The seven occupation groups for which we created dummies are administration, clerical, sales, service workers, farmer, production workers and professionals (reference group).

8 The six industry groups for which we created dummies are agriculture, manufacturing, construction, low-skilled services (comprising trade, hotels and restaurants, transport, and personal services), high skilled services (comprising banking and insurance, communication and storage, real estate, business services and public administration) and mining, electricity, gas and water (reference group).

9 These figures, when compared to developed countries, might be similar, but one needs to be cautious while making such comparisons. This is because these poverty effects are only for the wage-earners who comprise 45% of the workforce and the remaining are self-employed. In advanced countries, wage-earners would comprise more than 80% of the workforce.

10 See

11 In this context, it is interesting to note that a recent evaluation study on the implementation of the Minimum Wages Act, 1948 in the stone-breaking and stone-crushing industry in Karnataka in 200708 found that, among employers, only 30% said they were aware of the Minimum Wages Act and 27% of the prescribed/statutory minimum wages to be paid to workers. Among workers, only 8.4% were aware of the Minimum Wages Act and 18.5% of any inspection authority (GoI 2009b).

12 See also Saget (2001).

13 In households where women have entered the labour market as an additional worker to meet the subsistence needs of the household (that is, poverty-induced), their exit from the labour market could in theory be compensated as long as the male earners are able to get both minimum wages and minimum days of work required for the households.

14 See Lemos (2004) for an international survey of the literature; Dube, Naidu and Reich (2007) for a case study.


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Appendix I: State-Level Minimum Wages (in Rs)

States Minimum Wages
1 Andhra Pradesh 77
2 Arunachal Pradesh 40
3 Assam 47
4 Bihar 59
5 Goa 121
6 Gujarat 74
7 Haryana 88
8 Himachal Pradesh 65
9 Jammu and Kashmir 66
10 Karnataka 79
11 Kerala 200
12 Madhya Pradesh 66
13 Maharashtra 66
14 Manipur 63
15 Meghalaya 70
16 Mizoram 84
17 Nagaland 66
18 Orissa 52
19 Punjab 89
20 Rajasthan 74
21 Sikkim 66
22 Tamil Nadu 89
23 Tripura 66
24 Uttar Pradesh 86
25 West Bengal 66
26 Andaman and Nicobar Islands 108
27 Chandigarh 109
28 Dadra and Nagar Haveli 66
29 Daman and Diu 66
30 Delhi 111
31 Lakshadweep 82
32 Pondicherry 66
33 Chhattisgarh 86
34 Jharkhand 66
35 Uttaranchal 78

Source: Labour Bureau, 2004; from the table “Number of Scheduled Employments in Central Sphere/State/UTs and Range of Minimum Wages as on 31-12-2004”, at http://

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