Wage Inequality in India

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Economic & Political Weekly EPW decemBER 15, 2012 vol xlviI no 50 59

But this is hardly ever the case, and, indeed, a large part of the

workforce in the developing world is either unemployed or

engaged in extremely low-paid contractual employment.

Trade openness increases the elasticity of labour demand and

thus erodes the bargaining power of labour (Rodrik 1997).

Increasing trade openness in India is associated with increas-

ing labour productivity and also wage inequality among skilled

and unskilled workers in the organised manufacturing sector

(Galbraith et al 2004; Dutta 2005; Das 2007). One of the major

explanations put forward for this rising wage inequality is the

rise in relative demand for skilled labour due to skill-biased

technological change. Our objective is not to relate wage

inequality to the skilled-biased technological change as such

or to examine what impact trade openness has had on wage

inequality, but to explore different dimensions of wage

inequality as observed within and between different occupa-

tional groups, men and women workers in rural and urban

areas by taking sectoral divisions in India after one and a half

decade of economic reforms.

A few studies captured some aspects of wage inequality inIndia. Using employment and unemployment surveys 1993-94

and 1999-2000, Glinskaya and Lokshin (2005) investigated

wage differentials between the public and private sectors in

India, and found, by applying their own methodologies that

the public sector premium ranges between 62% and 102% over

the private formal sector. Galbraith et al (2004) estimated

Theil indices of pay, without specifying whether it covers total

wages or total emoluments, in the registered manufacturing

sector in India covering the period 1979 to 1997 and observed a

rising trend in pay inequality among workers in this sector

during the post-liberalisation period. This increase is driven

primarily by increases in inequality between industry groups

rather than by regional inequality. By using data on minimumdaily wages for the lowest paid unskilled workers in the organ-

ised sector for the periods 1985-86 and 1993-94, Acharyya and

Marjit (2000) illustrated the widening gap between the mini-

mum and maximum wage during this period. Dutta (2005)

observed that wage inequality in India increased significantly

during the 1990s.

This paper contributes to the literature on inequality by tak-

ing into account different dimensions of wage inequality as

observed in the Indian labour market during one and a half

decades of the post-economic reforms period in a comprehen-

sive way. We have, first, used Gini index to look at the extent of

wage inequality across sectors, gender and activity status in

India. But the conventional approach to decomposing the

inequality index simply by population subgroups in the shape

of within and between components fails to capture the

fundamental determinants of inequality. To locate the marginal

effects of the major determinants of wage, namely, education,

experience and other personal or household characteristics,

on total wage inequality as suggested in the literature on hu-

man capital theory,1 we carry out decomposition of inequality

by factor components. The factors affecting wages will also

determine wage inequality and one could identify a list of factors

which may explain wage gaps among workers.

The paper is organised as follows. Section 2 presents some

methodological issues in measuring wage inequality and the

wage regression model. Section 3 describes the data and the

sample used in this study. Section 4 provides the estimated

results of wage differentials in India. The estimate of the wage

regression model and the contributions of some major covari-

ates to wage inequality are examined in Section 5. Section 6

summarises and concludes.

2 Measuring Wage Inequality

2.1 Unidimensional Gini Index

The Gini index (Gini 1912), associated with Lorenz (1905),2 is

used in this study as a summary measure of wage inequality

both within and between groups of workers by sectors in

rural and urban India. The index is a fraction of the area

between the equivalence line3 L(p) = p and the Lorenz curve.

If the area between the line of perfect equality and Lorenz

curve is A, and the area under the Lorenz curve is B, then

the Gini index isA/(A+B). SinceA+B = 0.5, the Gini coefficient,G = A/(0.5) = 2A = 1-2B. In symbolic representation,

n n

| yi y

j|

i=1 j=1G = ...(1)2n2 y

Here, n is the number of wage earners andy is the mean wage.

The Gini index satisfies the Pigou-Dalton transfer principle

by which if income is transferred from a rich person to a poor

person the resulting distribution is more equal. It also follows

the principles of anonymity, scale independence and popula-

tion independence. The Gini index is able to provide a more

meaningful measurement of inequality between different

subgroups (Dagum 1997, 1980). It takes into account not only

the differences between means, but also differences betweenother characteristics of the distributions of subgroups of

the population.

Let a population ofn individuals, with wage vector (y1, y

2, ...,

yn) and mean wage incomey , is disaggregated in k subgroups,

with n =k

j=1

nj and subgroup meanyj.

The Gini index between subgroupsj and h can be expressed as

1 nj nhG

jh= | yji yhr| ...(2)n

jn

h(y

j+ y

h)i=1r=1

IfF(y) be the cumulative distribution function of wage, one

can calculate the expected wage difference between groups j

and h as

yd1

jh= dF

j(y) (y x)dF

h(x), for y

ji> y

hrand y

j> y

h0 0

yd2

jh= dF

h(y) (y x)dF

j(x), for y

ji< y

hrand y

j> y

h...(3)

0 0

The relative economic affluence is defined as

d1jh

d2jh

Djh

= ...(4)d1

jh+ d2

jh

If the population share and wage share in subgroup j

arenj

pj

=n

andpjyj

sj

=y

respectively, the contribution to total


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inequality attributable to the differences between the k popu-

lation subgroups is

k k

Gb

= Gjh

Djh

(pjs

h+ p

hsj) ...(5)

j=1 h=1 jh

The Gini index for subgroup j is given by n

j

nj

(yij yrj) i=1 r=1G

jj= ...(6)

2n2j

yj

The within group inequality index is the sum of Gini indices

for all subgroups weighted by the product of population shares

and wage shares of the subgroups:

Gw

= k

j=1G

jjp

jsj

...(7)

If subgroups are non-overlapping, total inequality can be

expressed as the sum of within group and between group indices.

The groups are non-overlapping means each individuals wage

income in one group is greater or lower than each individual

in the other groups. But, if the subgroups are overlapping,Dagum (1997) suggests another component of inequality meas-

uring the contribution of the intensity of transvariation. This

component is a part of the between-group disparities issued from

the overlap between the two distributions. The contribution of

the transvariation between the subpopulations to G:

Gt=

k

j=1k

h=1G

jh(1 D

jh) (p

js

h+ p

hsj) ...(8)

hk

Thus Gini index can be decomposed into three components:

within group inequality, between group inequality and ine-

quality due to group overlapping:

G = Gw

+ Gb

+ Gt

...(9)

2.2 Wage Regression Model

A simple way to look at the wage gap between two or more

groups of workers is to consider group dummies in a single

wage regression. The underlying assumption here is that wages

differ between groups by a fixed amount, while the individual

and other characteristics have the same effect on their wages.

A more flexible approach to investigate the earnings gap relates

to the human capital theory (Mincer 1958, 1974; Becker 1964),

where an individuals wage rate reflects the productivity

potential based on various human capital characteristics.

According to human capital theory, accumulation of human

capital through education enhances workers productivity

and their life cycle earnings. Mincer (1974) estimated the

statistical relationship between market wages, education and

experience. The Mincerian wage regression, however, dis-

regards the endogeneity of post-schooling human capital ac-

cumulation and treats schooling and training symmetrically.

Griliches (1977) pointed out several econometric problems

that arise in estimating the returns to schooling and, in

particular, those pertaining to the measurement of both

schooling and ability.

We assume the following log-linear wage regression model:

ln yji= xj

ij + j

i...(10)

Hereyjiis wage of individual i in groupj. Vectorxj

icontains a set

of explanatory variables (covariates) augmented of job attributes,

labour market features and demographic characteristics for

group j, jiis an identically independently distributed (i.i.d.)

idiosyncratic error term with mean zero and constant variance2.

As the mean of the residual term in the wage equation is

zero, the inequality index for it cannot be defined by the usual

process. Again, as the intercept component, representing the

effects of other factors, is constant, the inequality index for it

will be zero. To overcome such problems, we can proceed in

the following way. Let, and be the estimated wage using

the intercept and without using it respectively. Then the

contribution of the unobserved factors to total inequality is

I(y) I() and that of the intercept term isI() I(), whereI()

denotes the inequality index. By using the estimated wage

equation we can calculate the predicted contributions of the

major covariates to the expected overall inequality. We have

utilised the Shapley decomposition approach developed by

Shorrocks A F (1980), andAraar and Duclos (2008). This

approach is based on the expected marginal contribution ofcovariates to the total inequality and can be obtained in the

following manner. Ifyk

is the estimated wage after replacing

xk, the kth explanatory variable in the wage regression equation,

by its sample mean, inequality inyk, denoted byI(y

k), cannot

be attributed toxk

any more. This is because this replacement

would eliminate any differences inxk

among individuals. The

contribution ofxk

to total inequality,I(y) I(yk), obtained when

only one independent variable xk is replaced by its sample

mean in the wage equation, is the first round effect. By replacing

two variablesxk

andxlwith their sample means in computing

ykl

, one can obtain a second round contribution, I(yl) I(y

lk)

for k l, ofxk

to total inequality. In the same way, the third

round contribution can be obtained as I(yml) I(ymlk). Thisprocess continues until allxs are replaced by their sample means.

3 The Data

The data used in this study come from the NSS 61st round survey

(2004-05) on employment and unemployment. The survey is

based on stratified two-stage sampling. The 2001 Census villages

in the rural sector and urban frame survey blocks in the urban

sector are the first stage sample units. The final stage ultimate

sample units are households selected by simple random sam-

pling without replacement in both the sectors. The data set

covers geographical areas all over India, excepting for a few

regions.4 The cross-sectional survey is roughly representative

of the national, state, and so-called NSS region level. It gathers

information about demographic characteristics of household

members, weekly time disposition, and their main and sec-

ondary job activities. The principal job activities are defined

for all household members as self-employed, regular salaried

worker, casual wage labourer and so on.

The sample selected for analysis in this study consists of

96,162 persons working for wages. We define total wages as the

sum of weekly cash and in-kind wages from the principal activity.

Workers reported in the NSS schedule are of eight categories by

enterprise type: proprietary male, proprietary female, proprietary


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rural and urban locations in India. Wage inequality among

rural workers is higher in both the public and private sectors,

while it is lower in the informal sector as compared with the

inequality among urban workers. Within the rural economy,

inequality is much higher in the private formal sector. The con-

tribution of between sector inequality to overall inequality in the

countryside is more prominent than in urban areas (Table 2a).

A considerable wage differentiation persists between men

and women workers in the Indian labour market. The average

wage for men workers, consisting of three-fourths of the total

workers, is more than two and a half times the wage for women

counterparts (Table 3). Wage differentials among women are

higher than those among men workers. Wage inequality among

women is the highest in the public sector as compared to the

other sectors (Table 3a). On the other hand, wage differential

among men workers is relatively more in the private sector. In

the case of gender division of the workforce, the within group

inequality contributes significantly more to overall inequality.

In analysing wage inequality in the Indian labour market,

we have looked at the employment status of workers. As self-

employed workers do not earn wage income, we have ignored

them in analysing wage distribution. Workers with regular em-

ployment have a better chance of securing employment security,

work security and social security than the casual workers.7 The

extent of labour market flexibility is obvious in the distributional

pattern of wage workers. A majority of workers in India (58%)

are employed on a casual basis and the wage gap between casual

and regular workers is substantial as expected (Table 4).

Table 4a displays the composition of formal and informal

employment along with mean weekly wage and inequality

index of regular and casual workers. The wage rate for casual

workers is lower in the public sector than even in the informal

sector. However, the dispersion of wages among casual workers

is lower than that among regular workers (Table 4a). Wage

inequality among the former type of workers is relatively low

in the private formal sector. Although workers permanently

absorbed are paid better in the private sector, the pay inequalityamong them is the highest in that sector. The results in Table 4a

reveal that the major portion of wage inequality, particularly

for casual workers, is accounted for by inequality among indi-

viduals between sectors rather than within a particular sector.

5 Estimating a Wage Regression Model

Wage in the labour market induces the way through which

workers decide to provide their services. In India, as in other

less developed countries, the labour market is not well developed

and in many cases wages are determined not by the interaction

of demand and supply but by a variety of ways. Some workers

are paid wages on a daily basis, while some others who perform

similar kind of work are paid on a tenure basis. Althoughworkers in the formal sector are organised under trade unions,

those in the informal sector are unorganised. Thus the demand-

supply analysis in a competitive frame may not be appropriate

in understanding how wages and employment are determined

in the Indian labour market. Although, at least theoretically,

an individuals choice of job is based on the utility maximisa-

tion principle, the choices for a large section of the workforce

are highly restricted by various social and economic factors in

a third world economy and in many cases they are forced to

sell their capacity to labour without following the norms of

optimisation. As mentioned above, in India more than 60% of

the workers are absorbed in the informal sector.

India has a long history of wage determination through an

administrative process8 even in the organised sector. A serious

attempt was taken by the interim government in 1946 to deter-

mine wages and differentials in wage rates as between various

occupations in major industries. The Industrial Policy Resolu-

tion of 1948 emphasised fixation of statutory minimum wages

in organised industries. In the Indian labour market, labourproductivity had not so far been a potent factor in the determi-

nation of wages.Wage boards were set up through govern-ment initiatives for different industries where the government

was the dominant player. Because of the increase in workers

Table 3: Wage Inequality by Gender

Mean Wage* Employment Share Gini IndexMale worker 802 76 0.50

Female worker 310 24 0.58

Overall inequality 0.55

Contribution of within group inequality 0.37

Contribution of between group inequality 0.10

Contribution of group overlap inequality 0.08

* Weekly wages in rupees.

Source: As for Table 1.

Table 3a: Wage Inequality among Men and Women across Sectors

Mean Wage* Employment Share Gini Index

Male Female Male Female Male Female

Public sector 1,885 814 29 31 0.30 0.43

Private formal sector 1,506 1,390 16 14 0.43 0.09

Informal sector 587 534 54 55 0.38 0.38

Overall inequality 0.50 0.58Contribution of within group inequality 0.09 0.10

Contribution of between group inequality 0.44 0.60

Contribution of group overlap inequality -0.04 -0.11



Table 4: Wage Inequality by Types of Employment


Regular worker 1,308 42 0.45

Casual worker 282 58 0.36

Overall inequality 0.55

Contribution of within group inequality 0.25

Contribution of between group inequality 0.29

Contribution of group overlap inequality 0.01



Table 4a: Wage Inequality among Regular and Casual Workers across Sectors


Regular Casual Regular Casual Regular CasualWorker Worker Worker Worker Worker Worker

Public sector 1,766 345 35 2 0.36 0.35

Private formal sector 2,165 513 17 7 0.47 0.23

Informal sector 719 395 48 91 0.42 0.35

Overall inequality 0.45 0.36

Contribution of within group inequality 0.15 0.08

Contribution of between group inequality 0.25 0.56

Contribution of group overlap inequality 0.05 -0.27




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protests and labour militancy during the 1970s, wage bargain-

ing took place at the industry level through the government

controlled wage boards. Thus, it is hardly possible to explain

wage differences among workers of roughly homogeneous

type not only in the unorganised sector but also in the organ-

ised sector in terms of the demand-supply mechanism. In an

economy where the labour market is imperfect, and there is

distress selling of labour, a multiplicity of wage rates may exist

because of the lack of bargaining power of ordinary workers.

In estimating wage regression for the Indian labour market

and decomposing wage inequality based on the estimated

wage equation, we have drawn on human capital theory which

calls for inclusion of skill variables such as education, training

and experience into the model. By following Mincer (1974), the

wage equation is specified as

yi=

0+

1x

1i+

2x

2i+

3x

3i+

4x2

3i+

i...(11)

Herex1andx

2denote the year of schooling in general educa-

tion and technical education respectively,x3

represents work

experience proxied by age, and is the normally distributederror term with mean 0 and variance 2measuring the effects

of unobservable factors. The quadratic term in experience

allows for the possible diminishing return to human capital

accumulated through schooling. The intercept term measures

the initial ability. The coefficients 1 and

2act as the

marginal effects of schooling and technical know-how re-

spectively, 3and

4are those that correspond to the return to

experience and reflect concavity of the age earnings profile

when4

is negative.

Table 5 presents estimated coefficients of the wage equation

specified in equation (11) for different sectors. The estimated

results indicate that all explanatory variables are statistically

significant at less than 1% level and the coefficients have thedesired sign. The marginal effects of education and technical

knowledge on wage are higher in the private formal sector

than in the others. But experience has a stronger positive effect in

the public sector jobs. The diminishing return to human capi-

tal is more effective in the private formal sector. As the effects

of education, technical skill and experience are different in

different sectors, workers endowed with education and skill of

similar standard may receive different wage simply because

they are absorbed in different sectors.

On the basis of the estimated wage regression for different

sectors as shown in Table 5, total inequality is decomposed into

predicted contributions of the covariates used in equation (11).

The Shapley decomposition of

Gini coefficients on the basis of

the estimated model is shown

in Table 6. This decomposition

allows us to have a clear idea

on how each covariate contri-

butes to the total inequality.

There has been no mismatch

in the variation of Gini indices

of the actual wages reported

in Table 1 and that of the esti-

mated wages shown in Table 6 across different sectors. Wage

inequality is the highest in the private formal sector.

The entries in Table 6 are the predicted contributions ofthe major determinants of wage income used in this study to

the Gini index of the estimated wages. Education is found to

play a dominant role in determining total inequality in wages

and in the private formal sector the contribution of variation

in education level to the variation in wage is the highest. The

contribution of work experience is also more in this sector.

Technical knowledge contributes a little to total inequality

particularly in the public sector. The results presented in

Table 6 reveal that a considerable part of total inequality is

accounted for by unobserved factors. The contributions of

each covariate to total inequality are further decomposed

into marginal contributions and are shown by the round ef-

fects in Table 7. Round 1 effect of education (x1), for example,measures the contribution of education under the assump-

tion that all covariates present in the wage equation. Round 2

effect is its contribution after eliminating the effect of techni-

cal skill on wage. The Round 3 contribution is obtained after

removing the impact of technical skill and experience. In a

similar way the Rounds 4 and 5 contributions of education

Table 5: Estimated Results of Wage Regression

Sectors Variables Coefficient s t-statistic P>t

Public sector Intercept -2,402.44 -24.03 0

x1

167.40 75.57 0

x2

35.33 11.74 0

x3

85.50 17.19 0

x23 -0.54 -8.96 0

Private formal sector Intercept -2,146.96 -6.89 0

x1

178.86 17.93 0

x2

62.27 4.05 0

x3

75.30 4.26 0

x23

-0.55 -2.36 0.018

Informal sector Intercept -429.34 -23.04 0

x1

56.21 69.51 0

x2

47.22 20.17 0

x3

26.43 24.82 0

x23

-0.23 -16.36 0


Table 6: Absolute Contributions toTotal InequalityVariables Public Private Informal

Sector Formal Sector

Sector

Intercept 0 0 0

x1

0.098 0.182 0.129

x2

0.007 0.023 0.014

x3

0.104 0.130 0.099

x23

0.000 0.003 0.008

0.142 0.183 0.164

Total 0.351 0.521 0.414


Table 7: Marginal Contributions to Total Inequality

Sectors Variables Round 1 Round 2 Round 3 Round 4 Round 5

Public sector Intercept 0 0 0 0 0

x1

0.032 0.022 0.016 0.014 0.016

x2

0.003 0.001 0.001 0.001 0.001

x3

0.054 0.033 0.017 0.004 -0.004

x23 0.028 0.01 -0.004 -0.014 -0.02

Private formal sector Intercept 0 0 0 0 0

x1

0.052 0.038 0.03 0.029 0.033

x2

0.007 0.004 0.003 0.004 0.005

x3

0.069 0.04 0.019 0.005 -0.004

x23

0.036 0.011 -0.006 -0.017 -0.022

Informal sector Intercept 0 0 0 0 0

x1

0.04 0.028 0.021 0.019 0.022

x2

0.003 0.002 0.002 0.003 0.003

x3

0.067 0.038 0.014 -0.004 -0.016

x23

0.041 0.016 -0.00 4 -0.018 -0.027



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are obtained. Work experience has an inequality reducing

effect, although it is very marginal in all sectors only after

removing the impacts of all other covariates on wage at

Round 5.

6 Conclusions

Inequality in the labour market is a significant determinant of

disparities in living standards. This study is essentially an em-

pirical exercise in exploring inequality in the Indian labour

market. It comprehensively examines the structure of wage in-

equality and employment for different types of male and female

workers engaged in formal and informal sectors both in the

rural and urban economy in India with the NSS 61st round

(2004-05) household level information on employment and

unemployment in India. A substantial wage gap exists between

workers engaged in different sectors. Workers in the informal

sector are paid even less than one-third of the formal sector

wage. In India, the average wage in the formal private sector

job is higher than that in the public sector.

The wage differential is higher in rural as compared to urbanareas, and is also higher among women than among men

workers. By examining wages in public, private-formal and

informal sectors, it is observed that the differences in wages

among workers are the highest in the private-formal sector.

Wage inequality among regular workers is considerably higher

than that among casual workers. Women workers earn much

lower wages than their men counterparts and inequality

among the former is much higher than among the latter.

Surprisingly enough, wage inequality among women is the

highest in public sector jobs in the country.

Decomposition of wage inequality by sub-population reveals

that a significant part of wage inequality as observed in

India is accounted for by inequality between groups rather

than inequality within group for every type of working

people. In fact, wage inequality persists in India mainly

because of significant wage differences between sectors. But

gender inequality in wage earnings is explained more by

the within component than the between component of

total inequality.

Estimating results of the wage regression model suggest

that the effects of education, technical skill and experiences on

wage are different across sectors, and this is, probably, why

wage inequality persists among workers of a roughly homoge-

neous type between sectors. It is observed that education has

more effect on the expected wage and also on wage inequalityin the Indian labour market. The study also infers about the

presence of diminishing returns to human capital in deter-

mining wages. However, a significant part of wage inequality

in India is accounted for by factors which are not considered

in human capital theory.

Notes

1 Human capital is accumulated through educa-tion in enhancing productivity and increasinglife cycle earnings.

2 The Lorenz curve , L (p),plots the relationshipbetween the cumulative percentage of recipientunits, arranged in ascending order of income,and the cumulative percentage of income

they earn.3 If all had the same income, the cumulative per-

centage of total income held by any bottomproportionp of the population would also bep.

4 (i) Leh (Ladakh) and Kargil districts of Jammuand Kashmir, (ii) interior villages of Nagalandsituated beyond five kilometres of the bus route,and (iii) villages in Andaman and NicobarIslands which remain inaccessible throughoutthe year.

5 The informal sector consists of all private enter-prises owned by individuals or householdsengaged in the sale and production of goodsand services operated on a proprietary or part-nership basis and with less than 10 workers.

6 All public sector units as well as the private sec-tor units with employment of 10 or more work-ers using power and 20 or more workers w ith-out using power form the formal or organised

sector, while the rest fall into the private un-organised or informal sector.

7 Casual workers are informal workers consist-ing of those who have worked in unorganisedenterprises or households and workers in theformal sector without any employment or socialsecurity benefits provided by the employers.

8 For detail see Report of the National Commis-sion on Labour (2002).

References

Achary ya, R and S Marjit (2000): Globa lisationand Inequality: An Analytical Perspective,Economic & Political Weekly, 23 September,3503-10.

Araar, A and J Y Duclos (2008): An Algorithm forComputing the Shapley Value, PEP and CIRPEE-Tech. Note:Novembre 2008,http://132.203.59.36/DAD/pdf_files/shap_dec_aj.pdf

Atk inson, A (1997): Bringing Income Dist ribu-tion in from the Cold, Economic Journal , 7,297-321.

Becker, G (1964): Human Capital A Theoretica l

and Empirical Analysis with Special Reference toEducation(Chicago: Columbia University Press).

Dagum, C (1980): Inequality Measures betweenIncome Distributions with Applications,Econometrica 48, 1791-1803.

(1997): A New Decomposition of the GiniIncome Inequality R atio,Empirical Economics,22, 515-31.

Das, P (2007): Wage Inequality in ManufacturingIndustries 1979-2002: A Case of West Bengaland Gujarat,Indian Journal of Labour Economics,50(2), 303-16.

Das, P, B Dasgupta and P K Biswas (2009): Genderand Labour: Post-Reform Scenario in India inM Sanyal et al (ed.), Post Reform Developmentin Asia Essays for Amiya Kumar Bagchi(Hyderabad: Orient BlackSwan).

Dutta, P (2005): Accounting for Wage Inequality

in India, PRUS Working Paper No 29,PovertyResearch Unit at Sussex, Department of Eco-nomics, University of Sussex.

Galbraith, J, D Roy Chowdhury and S Srivastava(2004): Pay Inequality in the Indian Manufac-turing Sector, 1979-98, University of TexasInequality Project Working Paper No 28 .

Gini, C (1912): Variabilit e mutabilita, reprinted inPizetti E and T Salvemini (ed.),Memorie di me-todologica statistica (Rome: Libreria ErediVirgi lio Veschi), 1955.

Glinskaya, E and M Lokshin (2005): Wage Differ-entials between the Public and Private Sectorsin India, World Bank Policy Research WorkingPaper 3574, April.

Griliches, Z (1977): Estimating the Returns to

Schooling: Some Econometric Problems,Econometri ca, 45(1), 1-22.

Lorenz, M O (1905): Methods for Measuring theConcentration of Wealth, Journal of theAmerican Statis tical Assoc iation, 9, 209-19.

Mincer, J (1958): Investment in Human Capitaland Personal Income Distr ibution,Journal ofPolitica l Economy, 66(4), 281-302.

(1974): Schooling, Experience and Earnings(New York: Columbia University Press).

NCEUS (2008):Report on Definitional and StatisticalIssues Relating to Informal Economy, NationalCommission for Enterprises in the UnorganisedSector, Planning Commission, Government ofIndia, New Delhi.

Rodrik, D (1997): Has Globalisation Gone Too Far?unpublished paper, Institute for InternationalEconomics, Washington DC.

Shorrocks, A F (1980): The Class of AdditivelyDecomposable Inequality Measures, Econo-metrica, 48, 613-25.

Unni, J and U Rani (2008):Flex ibility of Labour inGlobalising India: The Challenge of Skills andTechnology(New Delhi: Tulika Books).

Williamson, J (1982): The Structure of Pay inBritain, 1710-1911,Research in Economic History,

7, 1-54.

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Wage Inequality in India

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