1 Introduction

D

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:

QQ

*LQL ¦¦ ZL ZM

(2)

QYL M

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

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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.

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

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.

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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.

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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 http://www.epi.org/publications/entry/minwagestmt2006/.

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.

references

Bird, K and C Manning (2008): “Minimum Wages and Poverty in a Developing Country: Simulations from Indonesia’s Household Survey”, World Development, 36 (5), pp 916-33.

in India”, Economic Political Weekly, 42 (30), pp 3132-50.

Rani, U (2008): “Impact of Changing Work Patterns on Income Inequality”, Discussion Paper No 193 (Geneva: International Institute for Labour Studies).

Ravallion, M and G Datt (2002): “Why Has Economic Growth Been More Pro-Poor in Some States of India Than Others?”, Journal of Development Economics, 68, pp 381-400.

Saget, C (2001): “Poverty Reduction and Decent Work in Developing Countries: Do Minimum Wages Help?”, International Labour Review, 140 (3), pp 237-69.

Sen, A and Himanshu (2005): “Poverty and Inequality in India: Getting Closer to the Truth” in A Deaton and V Kozel (ed.), The Great Indian Poverty Debate (New Delhi: Macmillan).

Stiglitz, J (2009): “The Global Crisis, Social Protection and Jobs”, International Labour Review, 148 (1-2), pp 1-13.

Van der Hoeven, R (2001): “Labour Markets and Economic Reforms under the Washington Consensus: What Happened to Income Inequality?”, The Indian Journal of Labour Economics, 44 (3), pp 321-46.

Vaughan-Whitehead, D (2010): The Minimum Wage Revisited in the Enlarged EU, Edited Volume (Geneva and Edward Elgar, Cheltenham, UK: ILO).

World Bank (2008): “New Global Poverty Estimates: What It Means for India”, at http://www.worldbank.org.in.

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:// labourbureau.nic.in/wagetab.htm.

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