gender gap

Page 127 to 195

Draft for CommentsDecomposition of Gender Wage Gap in India: An Econometric Analysis

S.Madheswaran

Institute for Social and Economic Change, Bangalore

Email: [email protected]

Basudeb Guha Khasnobis

World Institute of Development Research, Helisinki

Email: [email protected]_______________

This paper is a part of the research project on the Gender wage Gap and its Impact on poverty: Evidence from India sponsored by WIDER, Helsinki, Finland. We are thankful to Jeemol Unni, Alakh Sharma, G.K.Karanath, M.R.Narayana and K.S.James for their valuable comments and suggestions.

Draft for Comments

Decomposition of Gender Wage Gap in India: An Econometric Analysis

S.Madheswaran

Institute for Social and Economic Change, Bangalore


Basudeb Guha Khasnobis

World Institute of Development Research, Helisinki


Womens status in the labor market is inferior to mens in most countries of the world including India, according to key indicators such as occupational distribution, earnings, nature and terms of employment and unemployment. Women and men typically perform different tasks and are located in different industries and occupational sectors. The principle of equal pay for work of equal value has gained wide acceptance and is reflected in several International Labour Organization conventions. Yet, gender gaps in earnings remain among the most persistent forms of inequality in the labor market. With this background, the present study focuses on wage differentials between males and females and estimate the extent of discrimination against females in the urban labour market. This study has used nationally representative employment surveys to investigate the magnitude of the gender pay gap in India. The National Sample Survey Organisation (NSSO) conducted employment surveys during January-December 1983, July 1993June 1994, July and 1999June 2000. We have decomposed the gross wage differentials using Oaxaca, Reimer, Cotton and Neumark method. Of these estimates, Neumark method seems to be the least objectionable one, because the calculated standard error is smaller for this estimates compared to other methods. The raw wage differential is declining from 0.40 to 0.26 from 1983 to 1999-2000 for the regular workers. The endowment effect and treatment (discrimination) component is narrowing down for the regular workers over the period of time from 1983 to 1999-2000. It is worth noting that the decline in endowment difference has largely contributed to decline in raw wage differentials. It is also interesting to note that the ratio of treatment component to raw wage differentials (in percentage terms) is widening for the regular/salaried workers. The discrimination coefficient in regular labour market varies from 62.8 percent in 1983, 68.6 percent and 80.5 percent in 1999-2000. This result implies that among regular workers, the proportion of educated workers is increasing, but the nature of education differs greatly and hence their returns also vary greatly. Educated people, as captured by the NSS in broad categories can have greatly diverging wages. The divergence in the wages of regular worker with education is so great (due to the divergence in education levels). The picture is different in casual labour market. The difference in raw wage differentials is declining very steeply from 1983 (0.70) to 1993 (0.55) and it is stable in 1999-2000. Unlike in regular labour market, the endowment difference is widening up in casual labour market. This may be due to skill requirement for the changing labour market scenario. The treatment component in the casual labour market declines and also the ratio between treatment component and raw wage differentials (in percentage) is narrowing down. The extent of discrimination coefficient in the casual labour market is 74.9 percent in 1983, 73.7 percent in 1993-94 and 61.3 percent in 1999-2000. On the whole, the discrimination against female in the Indian labour market persists.

Key words: gender wage Gap, Decomposition, India .

JEL Classification: J31,J711 Introduction

The presence of gender discrimination in labour markets has attracted the attention of economists for several reasons. Non-discriminatory treatment of workers of different sexes, races, or religions can be regarded as a worthy social goal in itself. The elimination of discrimination can also improve both efficiency and growth. Gary Becker (1975) posits a model in which male and female labour is perfect substitutes. Employers hiring decisions are based on workers perceived productivity and their characteristics. Suppose employers prefer to hire men over women, the cost of hiring an additional woman is the wage rate plus an additional factor that Becker calls the discrimination coefficient the loss of utility experienced by the employer when he hires an additional woman. In equilibrium, the wages of men will equal those of women plus the discrimination coefficient. Consequently, a sub-optimal number of women will be employed. If all firms are equally discriminatory, the economy wide impact is a gain for men at the expense of women, with the firms profits (and hence their savings, investment, and growth) also reduced.

The empirical studies show that the observed earnings differences between workers were associated with differences in education and experience; earnings differences might also arise by personal characteristics of the individuals such as the sex and the community to which he/she belongs. In all societies, women, on the average, earn less than men. This disadvantageous position of women is manifested in many spheres political, social, familial, economic, etc. The economic discrimination is probably the root of the other discriminations. This widespread discrimination against women has been a matter of great concern all over the world. The principle of equal pay for work of equal value has gained wide acceptance and is reflected in several International Labour Organization conventions. Yet, gender gaps in earnings remain among the most persistent forms of inequality in the labor market. Social scientists including economists have contributed a lot to our understanding of the earnings differentials during the last three decades. The importance of this issue has spurred much research activity there are literally hundreds of studies based on data from developed countries. With respect to the gender wage gap, a substantial literature now exists on countries in Latin America and East Asia. The volume edited by G. Psacharopoulos and Z. Tzannatos (1992) contains 21 studies of 15 different Latin American countries. Correcting for selectivity biases, they find that, on average, discrimination accounts for about 88% of the male advantage in pay. Horton (1996) provides a seven-country study of women in East Asian labour markets. Generally, differences in returns to male and female characteristics account for at least half the gap between male and female earnings, although this differential appears to be narrowing over time. Almost all the economic studies have attempted to estimate the extent of wage discrimination against women in the developed countries, whereas few systematic studies have been conducted for third world countries (Gunderson 1989, De Beyer and Knight 1989, Terrell 1992, Cohen and House 1993 and World Development Report 1995, Appleton, Hoddinot and Krishnan, 1999).

One important issue is what factors explain the low wage of women in both formal and informal employment? Wage received by individuals obviously varies depending on the level of human capital investment, which determines productivity. Wages of workers tend to be higher in the formal sector than in the informal because the former use higher capital investment per worker and modern technologies, which in turn requires the use of more skilled labor. Workers in the informal sector in contrast work with very little capital and simple technologies that require relatively less skilled labor, and hence low productivity. In other words wage differences exist partly because of skill differences i.e., varying levels of human capital endowments, generally measured in terms of the level of schooling, types of training and work experience; and partly due to differences in the level of investment and technology in the formal and informal sectors. In an economy where there are no barriers to mobility between the formal and the informal sectors, one would not expect to find any difference in wage between individuals with the same level of human capital investment. As long as individuals with a given level of education are able to move freely between the two sectors, they will always choose to work in the formal sector where the wages are higher. The distinction between formal and informal sectors in explaining wage differential assumes importance only because there are barriers to mobility between the two sectors. If these workers had identical endowments in terms of human capital then this differential in wage can occur only because of the existence of barriers to mobility between the two sectors. But some writers have noted that part of the wage inequality may be due to womens preferences; despite low income they may prefer to remain in informal employment because they derive non-pecuniary benefits such as flexible hours and forms of work that enable them to combine earning opportunities with household responsibilities.

Wage differences among both men and women can thus be explained in terms of their human capital endowments as well as their access to formal sector employment. There is however overwhelming evidence to suggest that gender bias also plays an important role in the determination of womens wages. This can happen if women are discriminated in the labor market and consequently they tend to receive lower returns to their labor, even though they possess equal endowments in terms of human capital as men. Further, if women face greater barriers to accessing jobs in the formal sector than men then it is an additional source of gender bias. There are indications that this is indeed happening in most developing countries. Women face a variety of barriers in accessing jobs in the formal sector, partly attributed to unhelpful attitudes and preferences of employers. More frequently differential access to formal sector jobs is derived from occupational segregation in the sense that women are denied access to what are considered as male occupations. Discrimination in the labor market based on gender considerations occurs in many subtle ways in spite of national legislations banning it. It mainly takes the form of paying lower wages for women even though they are equally qualified as men. Often this takes place in a subtle manner. For instance, employers could insist on higher qualifications for women for the same job and wage. Work experience of women may be rewarded unequally.

Even though differences in human capital endowments are the major determinant of wage disparities among women, labor market imperfections also explain why a disproportionate number of women are in low wage employment in developing countries. It is believed that such gender-based discrimination explains much of wage inequalities between men and women. It would also seem to explain why more women relative to men are in informal employment. What is perhaps interesting to note is that such gender bias seems to exist within the formal and informal sector, presumably because male wageworkers are engaged in more productive activities, to which women have little access? In this context, the following questions arise. Are there any significant earnings differentials between males and females? What extent is the wage variations explained by differences in human capital endowments and other characteristics?

The paper is organized as follows. Section 2 reviews the empirical literature related male-female wage differentials in developing countries including India. Section 3 outlines data sources and method for decomposing wage differentials. The empirical resu;ts are reported in section 4. Section 5 concludes the paper.

2. Review of Literature

Since there are excellent surveys available on discrimination (Cain, 1986; Spasford and Tzannatos, 1993), our review is selective and very brief. Mere observed earnings differentials between any two groups male Vs female cannot be the discrimination in the labour market. Earnings differentials may partly be due to differences in human capital between the groups. The earnings differentials can be interpreted as a result of one of the following forms of discrimination (Schmidt, 1984): (a) direct discrimination, (b) discrimination through occupational segregation and (c) societal discrimination.

Direct discrimination, which is sometimes called pay or wage discrimination, occurs when the wage differentials between equally capable males and females in the same job are not based on productivity differences between them; instead, sex of the employee determines the wages; and thus women workers are paid less.

The basic argument in the second form of discrimination is that the exclusion of females from certain occupations which are highly rewarded results in higher pay for males and lower average pay for females.

A third version of the discrimination hypothesis relates to the fact that women have traditionally been expected to perform roles as wives and mothers and this subordinates their own career goals to the interests of their husbands and children. This might have been the source of difference in education and training between them, which influences their earnings. In other words, past discrimination in areas like education, training, health and other non-market activities results in discrimination in the labour market. These pre-market discriminations with respect to investment in human capital are not included in our study. The present study focuses its attention on the economic analysis of discriminatory behaviour and its effect on the victims of discrimination in the market.

2.1 Empirical Research on Labour Market DiscriminationIn this section we survey the empirical evidences on the relative earnings position of women and men, with special emphasis on the role played by the asymmetric distributions of men and women across occupations. We then divide the empirical research into (i) average female-male earnings differentials (ii) empirical evidence on the relative role of discrimination and endowments in sex-related earnings differentials and (iii) empirical evidence on the role of occupational structure. Since the empirical literature in this field is numerous, we confine ourselves to the developing countries.

(i) Average Male-Female Earnings DifferentialsTable 1 presents unadjusted average female-male earnings ratios for a number of countries. It shows that earnings differentials between women and men are substantial in most of them. In Latin American and Caribbean countries, the average female-to-male earnings ratio in the late 1980s ranged from 0.55 in Jamaica to 0.97 in Paraguay, with most falling in the 0.6 to 0.8 range. Except for Costa Rica, in all the countries in the region for which data are available for two points in time, there is clearly a rising trend in the ratios. From the social and economic standpoints this is encouraging, especially when it is borne in mind that some of this increase was taking place during a recessionary period.

As is evident from Table 1, the Latin American ratios are not dissimilar to those in other parts of the world; also differentials are not necessarily lower in the industrialized countries. In the United States, for example, white female full-time workers have typically earned between 58 and 65 percent of male salaries since 1955. However, the ratios do seem to point to a more rapid improvement in the relative earnings position of women in Latin America than in both other developing and the more industrialized economies presented in the table.

The variations in the average ratios reflect a number of factors, many of which need to be held constant in order to make meaningful comparisons. For example, some of the figures are calculated with monthly earnings and some with weekly earnings, so that variations in the number of hours worked can affect the issue. The fact that more women tend to work part time will lower the ratio. Again, some of the ratios are calculated for the entire non-agricultural labour whereas others are limited to manufacturing; where manufacturing is primarily a private sector activity and a significant portion of other formal non-agricultural employment is public, the manufacturing ratios may be smaller, reflecting the fact that higher sex-related wage differentials are commonly found in the private sector than in the public sector, when productivity-related variables are held constant (see, for example, Chapman and Harding ,1986) for Malaysia and House (1983) for Cyprus).

Table 1: A Survey of Female-Male Earnings Ratio in Different Countries

CountryYearPay periodRatioCountryYearPay periodRatio

Latin American and Caribbean RegionKenya1980Monthly0.80

Argentina1985Weekly0.8519840.83

Bolivia1989Weekly0.6019880.80

Brazil1980Monthly0.61Malaysia1976Weekly0.65

Chile1979Monthly0.511991Monthly0.54

19870.471981Monthly0.64

Colombia1978Weekly0.55Singapore1980Daily0.89

19880.8519820.81

Costa Rica1980Monthly0.7019850.77

19890.6919880.76

Ecuador1966Monthly0.48Swaziland1980Monthly0.44

19870.6419830.64

El Salvador1970Hourly0.8219870.65

19800.81Tanzania1978Monthly0.89

19870.9019810.92

Haiti1987Hourly0.87More Developed Countries

Jamaica1989Weekly0.55Belgium19850.62

Paraguay19830.8119880.64

19890.97France19700.87

Peru1986Weekly0.7319810.80

Uruguay1979Weekly0.52Germany (F.R)19700.69

Venezula1987Weekly0.7019820.73

19890.7819890.73

Other Developing CountriesHongkong19820.76

Cyprus1975Weekly0.4719860.77

19820.5819890.73

19890.60Japan19710.52

Egypt1971Weekly0.6119820.53

19770.6319890.50

Korea (R.of)19800.44

19830.47

19890.53

Nether lands19800.78

19880.77

Switzerland19800.68

19880.67

United Kingdom19700.70

19820.69

19840.70

United States19800.59

19870.65

Source: Terrell (1992); Psacharopoulos and Tzannatos (1992); Usha (1981); Various Year Book of Labour Statistics (Geneva, ILO)

Differences in the relative levels of human capital (mainly education and experience) between men and women may also account for some of the wage gap, and changes in the levels over time can explain narrowing or widening of the gap. For example, a study by Gindling (1990) on the Costa Rican labour market found that the decline in the female-male earnings ratio during the 1981-82 recession was due primarily to the entrance of women with less education than the average level possessed by previously employed women. Similarly, a recent study by Polachek (1990) finds that the narrowing of the wage gap in the USA between the 1970s (when the female-male wage ratio was around 58-59 percent) and the 1980s (when it rose to 65 percent) can be largely explained by a slowdown in the number of new female entrants into the labour market in the 1980s, resulting in a rise in the average level of experience of the women in employment. Finally, a significant portion of these differentials is usually attributed to discrimination, some of which may be overt wage discrimination in the labour market and some of which is considered pre-or non-market discrimination.

(ii) Empirical Evidence on the Relative Role of Discrimination and Endowments in Sex-Related Earnings DifferentialsThe econometric literature groups the determinants of the wage gap between the sexes into two broad categories. One is overt labour market discrimination, which is usually defined as different payment rules for men and women with the same productivity characteristics or valuation in the labour market of personal characteristics of the workers that are unrelated to productivity (Ehrenberg and Smith, 1991, p.531). The second determinant is the differences in the endowments of men and women. This term usually refers to the amount of human capital possessed (e.g. education, training, experience); however, job characteristics (e.g. private vs public employer, size or location of firm) are also sometimes included. Where women have acquired less human capital before entering the market, this may reflect pre-or non-market discrimination.

A number of studies have used decomposition methodology to determine the relative importance of two sources in explaining female-male earnings differentials in many countries: for example, Chapman and Harding (1986), House (1983), Gannicott (1986), Gunderson (1989), Miller (1987a), Daniels (1991), Gill (1991), Gindling (1991), Tenjo (1991), Velez and Winter (1991, Winter (1991) and Ying Chu Ng (1991) and collection papers in Psacharopoulos and Tzannatos (1992). The main findings of a number of studies are reported in Table 4.5. In order to have comparison, we consider the decomposition method calculated from earnings functions.

As the decompositions in Table 2 indicate, in all but one country study that of Tanzania in 1977 (Knight and Sabot, 1982) more than one-half of the female-male earnings differentials is explained by differences in the coefficients for men and women. What is also evident from Table 2 is that the differences in these coefficients account for a much larger proportion of the earnings differentials in Latin America (more than three-quarters) than in the more industrialized countries (from a little more than one-half to about two-thirds). Recently, a variant of decomposition suggested by Reimer (1983, 1985), Cotton (1988) and Neumark (1988) become popular. These methods were developed to overcome some flaws in the standard decomposition. A few empirical studies exist in this tradition. The results of these studies are given in Table 3.

In most countries, it is the difference in the return to an additional year of schooling or experience, rather than the difference in the female-male levels of education and experience, that accounts for a lions share in the observed earnings gap. This reflects the fact that since the 1960s the gap between mens and womens average number of years of education has narrowed substantially in most countries. Then the intriguing question is why women are getting lower returns on education and experience. On the basis of growing evidence, we feel that a key in answering this question lies in the relative distribution of men and women across occupations. The fact that there are many occupations in which women are not found means that they are crowding in a few, hence depressing the wages in these occupations. In the following section we present evidence on the importance of the occupational structure in explaining earnings differentials and the extent of sex segregation among occupations.

Table 2: Survey of Empirical Findings on Sources of Earnings Differentials by Sex (Oaxaca Method)

CountryYearFemale-Male Earnings RatioPercentage earnings differential explained by1

Human Capital EndowmentReturns to Endowments (Discrimination)

Industrialised Countries:

CanadaaTaiwanbUSA

Manufact.cManufact.dServicesdUKo1970

1980

1982

1970

1981

1981

1990

19770.60

0.64

0.64

0.59

0.59

0.75

-

-36.7

35.3

40.1

45.0

31.3

39.5

32.0

30.063.3

64.7

59.9

55.0

68.7

60.5

68.0

70.0

Latin American and Caribbean:

Argentina2e

Chile3fColombia3gHaiti3hVenezula3i1985

1987

1988

1987

19890.65

0.47

0.85

0.87

0.7822.0

-14.9

12.3

4.1

14.078.0

114.9

87.7

95.9

86.0

Other Developing Countries:

Cyprus4jMalaysiakMalaysiarTanzania1IndiamPakistann1979

1979

1991

1977

1981

19930.55

0.71

0.56

0.76

0.64

-37.5

14.4

53.0

102.9

61.8

59.662.5

85.6

47.0

-2.9

38.2

40.4

ChinapChinaq1973

1987-

0.2249.0

-12.151.0

112.1

Note: 1) The figures correspond to the decomposition method that uses the female mean characteristics as the base. Except for Canadian study, none of these results is corrected for selectivity bias; 2) The earnings function includes dummy for industrial categories, marital status, employee status, and foreign nationality in addition to education and experience; 3) The earnings equation includes only education and experience; 4) In addition to education and experience, the earnings equation includes dummies for firm size, industry, occupation, region, union membership, public sector and English speaking university.

Sources: a) Gunderson (1979); Miller (1987); b) Ganniocott (1986); c)Hodson and England (1986); d) Montomery and Wascher (1987) and Gerhart (1990); e) Ying Chu Ng (1991); f) Gill (1991); g) Velez and Winter (1991); h) Daniels (1991); i) Winter (1991); j) House (1983); k) Chapman and Ross Harding (1986); l) Knight and Sabot (1982); m) Usha (1981); n) Ashraf and Ashraf (1994); o) Dolton (1986); p) Ofer and Vinkour (1981); q) Meng and Miller (1995); r) Lee and Nagaraj (1995)

Table 3: Review of Studies Using Cotton, Neumark and Reimer Approaches

StudyApproachCountrySample and YearMale Treatment AdvantageFemale Treatment DisadvantageProductivity Differentials

Ashraf & Ashraf (1993)*CottonPakistanPIDE, Socio-economic household survey of Rawalpindi city, 197519.5028.5320.52

Ransom and Megdal (1993)NeumarkUSAData from several national survey & information from academic institution 1965-19850.016

0.013

0.009

0.0170.087

0.081

0.050

0.0620.199

0.158

0.144

0.148

Oaxaca and Ransom (1994)Cotton

NeumarkUSACPS, 19880.0447

0.10160.1246

0.10700.0471

0.1053

Miller (1994)CottonAustraliaNational Survey 1973 & 19890.2023

0.05880.2328

0.07290.0324

0.0122

Stelcner (1991)CottonBrazilCensus Data from public use sample, 19800.097

-0.007

0.056

0.0560.473

-0.038

0.479

0.201-0.235

-0.166

-0.018

0.109

Cotton (1988)CottonUSA1 % sample of public use sample, 1980 census0.04810.006070.1050

StudyApproachCountrySample and YearDifferences in CharacteristicDifferences in ParametersDue to Sample Selection

Kidd and Viney (1991)*ReimerAustraliaSpecial supplementary Australian Bureau of Statistics, 198217.2754.3328.40

Miller and Rummery (1991)ReimerAustraliaAustralian Longitudinal survey, 19850.0510.1100.111

Note: * Figures are in percentage

Indian Studies:

Of the other recent analyses that have used the Oaxaca-Blinder method to decompose wages of males and females in the Indian labour market, three stand out in particular (see for brief review.Das, 2006). They are:

Duraisamy and Duraisamy (1996) decomposed wages of workers with post-secondary education in different scientific disciplines from data sets of 1961-81. overall, they found that womens earnings were about 21 per cent less than mens and about 67-77 per cent of the differential is explained by discrimination.

Kingdons (1997) analysis of earnings in urban Lucknow (where female labour force participation is about 11%) from a 1995 data set where she includes both self-employed and wage workers (corrected for selectivity) shows that about 45 per cent of the earnings differential is explained by discrimination.

Kingdon and Unni (1997) decomposed wages for wage earners in urban districts of MP and Tamil Nadu using the NSS 43rd Round (1987-88). Based on OLS, after standardising by male and female means, they find that that the average discrimination was between 75% - 78% in the urban labour market in the two states. Their findings indicate that women suffer high levels of wage discrimination in the Indian urban labour market, and education contributes little to this discrimination: the wage-disadvantage effect of womens lower years of education than men is entirely offset by the wage-advantage effect of womens higher returns to education than mens. They conclude that this wage disadvantage contributes to lower educational attainment among women. Thus, contrary to human capital explanations of low education contributing to low labour force participation, their findings lend credence to the fact that the opposite may in fact be true that labour market discrimination contributes to low educational attainment.

(iii) The Role of Occupational Structure

(a) Importance of Occupational Structure in Earnings FunctionStudies incorporating the occupational structure in estimating earnings functions have shown that the distribution of men and women across occupations can explain much of the female-male earnings differential. Perhaps the most dramatic effect was found in Malaysia by Chapman and Harding (1986). They calculated that, if Malaysian women had the same occupational structure as men in 1979, the wage gap would, ceteris paribus, have been reduced by 61.6 percent. Houses (1983) study of Cyprus shows that the differential occupational structure between the sexes and the difference in returns for men and women within the same occupation accounted for 16 per cent of the earnings gap. In fact, out of ten sets of variables, the set of occupational variables was the second most important factor (after potential experience) in explaining the wage gap. In Tanzania, Knight and Sabot (1982) find that the differences in the distribution of men and women across five occupational categories explained 22 percent of the wage gap. In India, Usha (1981) finds that out of 38.2 percent of unexplained differentials, 12.86 percent is the contribution of occupation variables. The study by Oaxaca (1973) for United States suggest that equalizing the occupational distribution of men and women with the same education and years of experience would reduce the earnings gap by 22.5 percent.

Studies on gender differences in labour market outcomes typically focus on wages or occupations. The interaction between occupational attainment and the wage differential has, however, received little attention in the gender discrimination literature. Brown, et al., (1980) proposed an integrated approach to study wage and job discrimination simultaneously. Following them, few studies estimate earnings differentials, which is shown in Table 4. From the table it is clear that wage discrimination is more pronounced than job discrimination. Further men have better advancement opportunities than women occupations (Gindling 1991, Tenjo 1991, Duncan 1991).

Table 4.: Review of Studies Using Brown et.al Approach

StudySample, Year and CountryGross Earnings DifferentialsIntra-Occupational DifferenceInter-Occupational Difference

Wage ExplainedWage DiscriminationJob ExplainedJob Discrimination

Miller, (1987)General Household Survey, 1980, UK0.49500.1908 0.2420

(0.4328)0.1340 -0.0718

(0.0622)

Dolton and Kidd, (1994)Survey of UK Graduates, 1997, UK0.20830.1169 0.0201

(0.0574)0.0254 0.0320

(0.0622)

Kidd, (1993)Family Survey Australian Bureau of Statistics, 1982, Australia0.19330.0965 0.1734

(0.2699)-0.0282 -0.0484

(-0.0766)

Banarjee and Knight (1985)Survey of migrant Workers in Delhi, 1976, India (Caste discri.)0.17470.0039 0.0919

(0.0958)0.0620 0.0169

(0.0789)

Cohen and House (1993)Khartoum employment Survey, 1991, Sudan0.14400.0910 0.0300

(0.1210)-0.0820 0.1050

(0.0230)

Gindling (1991)Household survey of employment & unemployment 1989, Costa Rica0.0946 (0.0723) (0.0223)

Reilly (1991)National Survey of Ireland, 1982, Ireland0.0012 (0.1286) (-0.1274)

Hawke (1991)*CPS, 1987, US157.0522.30 163.65

(185.95)0.1300 -29.05

(-28.93)

Hawke (1995)*Income Distribution Survey, 1986,

Australia109.2735.29 74.30

(109.60)8.01 -8.41

(-0.33)

Meng and Miller, (1995)TVP Sample Survey, 1986-87, China0.2247-0.0495 0.2219

(0.1724)0.0227 0.0296

(0.0524)

Note: 1) Figures in Parantheses represent totals

2) * Indicate Figures are in percentage

(b) The Extent of Sex SegregationThere is evidence in the Third World for females crowding in the low paying occupations of the service and professional categories (Terrell, 1992). Although women represent a large proportion of the relatively high-paying professionals occupation, most professional women tend to be in one of two categories nurses or teachers which are at the low end of the wage scale. For example, in India in 1971, 90 percent of women professional workers were nurses or school teachers as compared to 57 per cent of professional women were in those two categories as compared with 40 per cent of the man in 1979 (House, 1983).

The sex segregation studies generally calculate the index following the popular Duncan and Duncan Index (1995). Boulding, et al., (1976) applying the Duncan index to occupational data for the 1960s and found that occupational segregation was highest in Latin America (with an index of 0.49), followed by North Africa and the Middle East (0.39), Europe and North America (0.37), Africa (0.30) and Asia (0.28). More recently, Tzannatos (1991) calculated the same Duncan index for five Latin American and Caribbean countries using 1980-82 data and compared their value with the 1960s figures. There was a slight improvement in three countries (Ecuador, Mexico and Venezuela) and a worsening in two (Jamaica and Peru). He also showed that the improvement in two of the three countries was due to an increase in the number of occupations in which women were found, rather than an improvement in the sex ratio, which had actually worsened. Hence it is not clear whether there has been any improvement in sex segregation in Latin America from the 1960s to the 1980s.

In USA, on the other hand, the Duncan index appears to have been fairly stable in the 1960s and 1970s (Albelda, 1986; and Duncan, 1991). Recent work by Claudia Goldwin, Francine Blau and June ONeil, as reported in the New York Times (Oct 18. 1992, p. B10), indicates that female-male wage differentials in USA had narrowed substantially by 1990. They claim that one of the main reasons for this outcome is the fact that :sex segregation by occupation, which hadnt budged for a century, is beginning to break down. Thus, occupational segregation per se is a problem worthy of study; it has consequences for earnings differentials and so it is interesting to analyse.

3.Data Sources and Econometric Methodology

3.1 Data Sources

The National Sample Survey Organisation (NSSO) conducted employment surveys during Januray December 1983, July 1993June 1994 and July 1999June 2000. In this study we have used these three rounds of data for the empirical analysis. We confine ourselves to Urban data. The same analysis will be carried out for the rural data and comparison of rural and urban estimates will be attempted in our next paper. These survey data are used to derive national level estimates on labor force participation, occupational distribution and wages. The sample of households is drawn based on a two-stage stratified random sampling procedure. The first stage units are the census villages and urban blocks and the second stage comprises the households in these villages and urban blocks. The first stage units are selected circular systematically with probability proportional to the population and the villages and urban blocks are selected in the form of two or more independent subsamples. In the second stage, the households are arranged by means of livelihood (main occupation), and area of landholding in rural areas and monthly per-capita consumption expenditure in urban areas. The samples are selected circular systematically with a random start. The entire survey is divided into four sub-rounds of three months duration and equal number of sample villages and urban blocks were allocated to each sub-round. The survey details and the aggregate estimates are given in NSSO. The individual-level sample used is restricted to those workers in wage employment and aged between 15 and 65 years old.

The sample of individuals was divided into two mutually exclusive categories using current weekly status: (i) non-wage earners (i.e., non-participants in the labour market, the self-employed and the unemployed) and (ii) wage earners. The survey provides information on the activity status, wages/salary, days worked besides individual characteristics such as age, educational level, region of residence, etc., Household level information about the area of landholding and ownership of homestead are also available. Information on whether or not the household received income from different sources such as cultivation, wage/salary, interest and dividend, etc., were also collected in a companion survey on consumer expenditure. The nominal weekly wages include payment in cash and kind. The wage distribution was trimmed by 0.1% at the top and bottom tails. The nominal wages were converted to 1983 prices using industrial workers (CPIIW) for urban wages (Labour Bureau, various years). The employment surveys do not have data on the hours worked.

3.2 Econometric Methodology

There are three different approaches in the empirical literature to study discrimination. The first approach consists of running a regression of earnings upon the characteristics of all (male and female) workers including sex as one of the regressors. However this approach yields biased result because it assumes that the wage structure is same for both males and females. This approach also constrains the values of the coefficients on the other explanatory variables, such as education and experience, to be the same for males and females. The second approach is the decomposition technique. Using this approach one can partition the observed wage gap between an `endowment component and a `coefficient component. The later is derived as an unexplained residual and is termed as `discrimination coefficient. This approach was first developed by Blinder (1973) and Oaxaca (1973), and later extended to incorporate selectivity bias (Reimers, 1983, 1985) and the index number problem by Cotton (1988) and Neumark (1988). The third one is the expanded approach, which is proposed by Brown, et.al., (1980) incorporating occupational distribution in the earnings estimation. In this approach both wage discrimination and job discrimination can be calculated. In this chapter we use the second and third approaches. Although these techniques are well established, we discuss briefly each method.

Standard Decomposition Methods

1. Oaxca Decomposition Method:The decomposition method enables us to separate the wage differential into differences that can be explained by differences in characteristics and those that cannot be explained by differences in characteristics. The gross wage differential can be defined as

= (1)

Where Ym and Yf represents the wages of Male individuals and individuals belonging to the female categories respectively. In the absence of labour market discrimination, the male- female differential would reflect pure productivity differences (Q):

(2)

Where the superscript denotes the absence of market discrimination. The market discrimination coefficient (D) is then defined as the proportionate difference between G+1 and Q+1

(3)

Equations (1)-(3) imply the following logarithmic decomposition of the gross earnings differential

ln(G+1) = ln(D+1) + ln(Q+1)

(4)

This decomposition can be further applied within the framework of semi-logarithmic earnings equations (Mincer, 1974) and estimated via OLS such that

(Male Wage equation) (5)

(Female wage equation) (6)

where denotes the geometric mean of earnings, the vector of mean values of the regressors, the vector of coefficients and ( is the error term. Within this framework, the

gross differential in logarithmic term is given by

(7)

The Oaxaca Decomposition simply observes that equation (7) can be expanded. In other words, the difference of the coefficients of the two earnings functions is taken as a priori evidence of discrimination. If, for the given endowment, females were paid according to the male wage structure in the absence of discrimination, then the hypothetical female earning function would be given as

(8)

Subtracting equation (8) from equation (7) we get

(9)

Alternatively, the decomposition can also be done as

(10)

In equations, (9) and (10) above, on the right hand side, the first term can be interpreted as endowment differences. The second term in these equations has been regarded in the literature as the discrimination component. This study basically focuses on the discrimination component. Studies use either of these alternative decomposition forms (equation 9 or 10) based on their assumptions about the wage structure that would prevail in the absence of discrimination. Some authors take the averages of the estimated of the two equations (Greenhalgh, 1980). This kind of a problem is called the index number problem.

4.2.3.2 Decomposition Approach Of Cotton/ Neumark

To solve the index number problem, i.e., whether equation (9) or equation (10) should be used to calculate the discrimination coefficient, Cotton (1988) and Neumark (1988) have proposed an alternative decomposition. They argue that the appropriate decomposition depends on the type of discrimination hypothesized. In particular, employers may practice nepotism toward men or discrimination against women. Under nepotism, women are paid the competitive wage, but men are overpaid. In such a situation, the coefficients from the womens earnings functions provide an estimate of the non-discriminatory wage structure. Under discrimination, employers pay men competitive wages but underpay women. In this case, the male coefficients should be taken as the non-discriminatory wage structure. In reality, employers may practice both nepotism and discrimination. Neumark proposes a general model of discrimination in which employers may have different preferences (nepotistic or discriminatory) toward different types of workers (e.g., older uneducated women, etc.). Useful results can be obtained, given the restriction that employer preferences are homogeneous of degree zero within each type of labour; that is to say, employers care only about the proportion of each type of labour employed. With such a restriction, Neumark shows that the non-discriminatory wage structure can be estimated from an earnings function estimated over the pooled sample (that is, both men and women.)

When applied to our scenario, it can be interpreted as implying that discrimination not only lowers the salaries of female individuals, but also raises the salaries of male individuals. Equations (9) and (10) reveal only the relative pay effects of labour market discrimination and Cotton suggests that they mask overpayments to male individuals and underpayments to SC/St individuals, as compared to the non-discriminatory wage. Therefore, the discrimination component (unexplained) should comprise two parts one, representing the amount by which male characteristics are overcompensated relative to their marginal product. The other representing the amount by which female characteristics are under compensated (female disadvantage i.e. the cost of belonging to the female group).

The Cottons Decomposition is written as

(11)

where (* is the reward structure that would have occurred in the absence of discrimination. The first term on the RHS of equation (11) above is skill differences between female and male, while the second term represents the overpayment to males due to favoritism, and the third term the underpayment to females due to discrimination. The decomposition specified in equation (11) above cannot be made operational without some assumption about the salary structures for female and male in the absence of discrimination. The theory of discrimination provides some guidance in the choice of the non-discriminatory wage structure. The assumption is operationalised by weighting the NSC and female wage structures by respective proportions of NSC and female in the labor force. Thus, The estimator (* used above is defined as

(12)

where Pnsc and Psc are the sample proportions of male and SC/ST populations and and the male and female pay structures respectively.

Another versatile representation of nondiscriminatory or pooled wage structure is a proposed by Neumark (1988) and Oaxaca and Ransom (1994) , which can be written as

(13)

Where is a weughting matrix. The weighting matrix is specified by

where X is the observation matrix for the pooled sample Xm is the observation matrix for the male sample. The interpretation of as weighting matrix is readily seen by noting that

XX=XmXm+XfXf

(14)

Where Xf is the observation matrix of the female sample, Given and equation (13), any assumption about * reduces to an assumption about .

4.Empirical Results

4.1 Regular-Casual worker dichotomy in India

The dual labour market model supposes the existence of two distinct sectors of economic activity usually classified as the organised and unorganised sectors. The organised sector offers more stable jobs with higher pay, better working conditions and promotional opportunities whereas the unorganised sector is associated with unstable jobs and low or even flat returns to schooling, poor pay, bad working conditions and few opportunities for advancement (Dickens and Lang, 1985; Taubman and Wachter, 1986). Thus the dual labour market approach argues that there are two distinct types of jobs with separate wage equations and independent normally distributed unobservables. Tendulkar (2003) and Das (2003) argue that in the Indian context the organized and unorganised dichotomy generally used to analyse labour market outcomes is better represented using a typology reflecting the employment status of the individual. There are two reasons why this is a desirable strategy. First, the NSS surveys do not report whether the individual is employed in the organised or unorganised sector; they do however report whether the worker has a regular or casual job or is self-employed, unemployed or not participating in the labour market. The 1999 survey reported data on the type of enterprise that can be used to classify it as belonging to the organised or unorganised sector. This classification reveals that about 57% of regular workers were employed in enterprises that were either public, semi-public or otherwise in the registered or organised sector but only 10% of casual workers were so employed. Second, in the dual labour market literature workers in the unorganised sector are engaged in economic activities with low productivity resulting in low incomes, less stable employment contracts (this includes the self employed) and fewer social security benefits. There is an increasing awareness that the type of work contract is a better indicator of the informality of an individual's employment rather than whether or not the workplace is in the organised sector. For instance, a worker with a temporary contract with no provisions for social security should be considered as belonging to the unorganised sector even though he works in a large factory. In the Indian context this translates directly into the regular worker - casual worker dichotomy.

Regular wage employment is often considered to be the most preferred category of work (Das, 2003). Tendulkar (2003) refers to "workers having regular, contractual hired employment" as the "labour aristocracy because of the privileged service conditions this segment enjoys including high wages" (pp.2). Though these high wages reflect at least in part the returns to the higher skill endowments of these workers, redundancy (especially in the public sector) suggests the presence of rents. Regular workers are also covered by labour market regulations that confer some measure of employment security and social security benefits. Casual workers can be considered a subset of the informal labour market - they are generally engaged in economic activity with low wages, unstable employment contracts and little or no social security benefits. These workers are also much more likely to be poor than an individual with regular wage employment thereby fitting the description of poor and marginal workers competing in a distinct labour market (Heckman and Hotz, 1986). This is the approach followed in this paper- regular wage employment is taken as analogous to the primary labour market and casual wage employment to the secondary labour market in the dual labour market literature.

Under the assumption that there are indeed two distinct types of jobs with separate wage equations and independent normally distributed unobservables the process of wage determination for each of these types of jobs can be separately analysed. Heckman and Hotz (1986) give a comprehensive critique of the inadequacy of tests for labour market segmentation and there is no attempt to formally test segmentation between the two types of wage employment. The focus of this paper is on the process of wage determination for each of these workers and the wage gap between males and females.

4.2. Structure of Education and Labour Market in India

The educational levels for the labour force by sex reflect the high proportion of illiterate workers both in rural and urban areas. This continues to be the scenario in spite of the improvements noted over the years. The gap among male and female graduates and above category in urban areas is gradually shrinking. This positive outcome is reflected in direct beneficial increases in womens organised sector jobs even displacing men in these sectors. Except from this minuscule inversion, not much other change seems to be visible (Srivastava, 1999; Divakaran, 1996).

In the labour force, 17per cent rural males and 43 per cent urban males are educated above secondary levels, while the corresponding female proportions are 5 per cent and 32 per cent respectively (NSS, 1999-2000). The proportion of illiterate labourers by sex reflects the high magnitude of gender disparity (Table 5).

Table 5: Percentage Distribution of Labour Force by Educational Status

1987-881993-941999-200

MaleFemaleMaleFemaleMaleFemale

Rural

Illiterate 48.382.343.278.039.673.9

Literate but up to primary29.612.028.214.227.415.7

Middle11.63.213.94.416.05.8

Secondary8.42.011.32.813.53.6

Graduate & above2.10.42.80.63.41.0

Total100100100100100100

Urban

Illiterate 19.651.817.845.915.941.2

Literate but up to primary30.519.025.319.021.917.0

Middle16.47.317.68.918.89.7

Secondary21.812.324.714.026.415.7

Graduate & above11.79.614.512.216.916.4

Total100100100100100100

Note: Figures relate to usual status of individuals and population aged 15 years and above.

Source: NSSO, different years.

At higher educational levels, women are outperforming men (Rustagi, 2003) and yet the gender disparity in the educational status of the labour force is more skewed as compared to the overall population due to the association with income status. Women belonging to relatively better-off households pursue higher education but may abstain from entering the labour force, whereas for the poor illiterate women there is no choice but to work on whatever terms and conditions. The comprehension regarding this vulnerability dimension provides the employers and contractors with opportunities to pay lower wages. The necessity to survive explains womens labour supply being characterised by low literacy, employed in low technology, manual, low-skill requiring jobs which are inevitably low paid.

Among educated and qualified women, not all seek employment. Of those who do, most of them enter the traditional stereotypical jobs such as teaching, nursing, administrative and clerical jobs or repetitive monotonous manual chores for which women are considered well-suited. The differentiation begins prior to entry into labour markets as women are prone to choose subjects which land them into such professions and courses which are women-oriented (Kabeer, 1994; Tinker ed., 1994).

Pre-entry differentiation is visible from the subjects and professional courses women pursue, not so much due to their capabilities but more due to the socially accepted appropriateness of it. These compulsions affect the women and girls themselves, their parents, teachers, and other agents who have a role in their educational endeavors howsoever remote that may be. Vocational training instructors and policymakers often make provisions for girls to pursue the stereotypical skills that are influenced by their notions of what is meant for women (Raju, 2004; Duraisamy and Duraisamy, 1999).

The change in trend in the status distribution of the total workers in the economy as a whole was not very striking (Table 6). In fact it was what was generally expected in a liberalising economy. The proportion of self-employed workers declined and the regular workers were stable. There was casualisation of the work force. However, the picture changed as we disaggregated the data. Taking all non-agricultural workers, the percentage of self-employed workers was stable, indicated that the decline for the economy as a whole was perhaps an agricultural phenomenon. The percentage of regular workers was also stable while casualisation took place.

Table 6: Status Distribution of Workers by Gender and Location (percentages)YearAll workers all-India

RegularCasualEmployer + OAWUnpaidTotal SE

1999-0013.9933.1731.3121.5352.84

1993-9413.2132.0231.5623.2154.77

1987-8813.9130.3542.4013.3455.74

198313.4629.0136.5321.0057.53

Non-agriculture Rural +-Urban Male+Female

1999-0034.2420.8434.0910.8244.92

1993-9435.0219.9734.1910.8245.00

1987-8834.4621.7037.116.7243.84

198336.5218.8634.3810.2344.61

Non-agriculture Rural + Urban, Male

1999-0035.8521.3835.607.1742.77

1993-9437.3019.1636.127.4143.54

1987-8837.5219.3838.434.6743.11

198339.6617.0136.466.8843.33

Non-agriculture Rural + Urban, Female

1999-0027.6718.6327.9125.7453.69

1993-9426.1423.1326.6424.0950.73

1987-8823.4330.1132.3514.1146.46

198324.5125.9826.4123.1049.51

Non-agriculture Urban, Male

1999-0044.2215.6732.897.2340.12

1993-9445.9014.3032.357.4539.80

1987-8847.3613.1234.574.9539.52

198347.9813.8231.526.6938.21

Non-agriculture Urban, Female

1999-0039.8816.1926.1417.6943.93

1993-9438.4820.9328.8711.4240.28

1987-8838.3621.3528.8711.4240.28

198337.0124.2922.7016.0038.69

Source: Computed from Employment and Unemployment Surveys, various Rounds, obtained from CDs, New Delhi: National Sample Survey Organisation.

Further dis-aggregation of the non-agricultural workforce revealed a striking gender differential, the proportion of female self-employed and regular workers increased considerably and there was no casualisation of the workforce. Almost the exact opposite was true for the male non-agricultural workforce. When the data was then disaggregated by location, in urban areas the male and female non-agricultural workforce showed an increase in self-employment. The difference by gender among regular and casual workers remained, with female regular workers increasing while the male workforce was casualised.

Structure of Wages in India Among Various Segments in the Workforce

While the growth impetus produced positive impact on some sections of the workforce, it is possible that certain segments were left out of the growth process leading to increasing inequalities among workers (Unni, 2006). The average real wage earnings of all segments of the workers in rural and urban areas rose over the period of study. Thus, at the aggregate level there has been no decline in the real wages of workers even in the informal sector. Of course there are large variations in the absolute levels and rate of increase of the various segments which leads to increasing inequality in earnings among workers and we shall discuss this below.

The average daily real earnings were the highest for the urban male regular workers in non-agriculture (Table 7). This is obviously because the majority of these workers were likely to be in the formal sector. The urban women in regular work received the next highest real wages, but these were considerably below the male wages. Casual workers in non-agriculture, all of whom would be informally employed mainly in the informal sector received the lowest real wages. A section of these workers were also likely to work for the formal sector, but without any of the benefits accruing to regular workers in that sector. Casual workers in the rural areas were doubly disadvantaged and had real wages slightly below that of the urban casual workers. As expected the women casual workers in rural had the further disadvantage of gender and received the lowest real wages within the non-agricultural sector.

Real wages in agriculture were considerably lower than the non-agricultural sector, with womens wages being the lowest. Workers in the informal sector were thus relatively dis-advantaged in terms of real wages. This is further compounded by the fact that they were not likely to get work throughout the full year, particularly for the casual workers.

Table 7: Average Real Daily Wage Earnings of Regular and Casual Workers, All India,

15 to 59 years.Regular Workers

Real WagesRural MaleRural Female

Industry19831987-881993-941999-200019831987-881993-941999-2000

Agriculture (0)17.3226.0527.4744.1821.9219.0322.8331.05

Min. & Qua.(1)54.7469.1675.9992.7433.1942.5541.2829.16

Manufactur.(org) (2)34.8740.6939.6655.3510.4416.8317.1121.70

Manufactur.(Inorg)(3)43.7951.2757.2762.8020.5624.6233.4131.47

E,G & W (4)52.4464.6277.04124.170.0080.7262.00138.50

Construction (5)32.0456.4362.4365.8316.5632.6568.5769.46

Trade (6)21.4632.1529.9241.0310.5632.9229.0237.76

Transport (7)41.9157.3359.0070.9929.8147.8946.4457.92

Services (8)58.7982.6588.98101.4240.8569.2563.8558.46

Services (9)50.1468.8773.09112.3731.4450.7343.30108.71

1-945.4862.4166.5590.8226.8546.4440.5691.31

All (0-9)36.9853.8658.4880.2124.9638.5334.8971.83

Real WagesUrban MaleUrban Female

Agriculture (0)30.6247.6451.4092.9123.8132.9246.6638.92

Min. & Qua.(1)60.0979.9794.58159.5537.5054.8364.1192.75

Manufactur.(org) (2)47.5551.5556.5962.1119.3822.5730.0033.10


E,G & W (4)70.5186.64100.17149.9770.7275.8989.42127.79

Construction (5)55.5567.7970.6680.3940.6147.7838.2284.38

Trade (6)35.5638.9743.3059.1535.3431.7443.0878.36

Transport (7)58.0871.7374.2796.5955.2381.5677.01115.27

Services (8)91.25107.31124.98158.2375.0093.17105.35164.45

Services (9)60.4880.3787.56132.0443.0262.7863.07101.92

1-958.2772.5579.20106.5546.0360.1462.7397.33

All (0-9)57.6972.3578.12102.3442.2260.0762.3184.58

Casual Labourers Wages

Real WagesUrban MaleUrban Female

Agriculture (0)20.7722.6025.529.9812.2313.1816.4919.43

Min. & Qua.(1)31.9330.1729.647.3213.4020.2722.5934.44

Manufactur.(org) (2)26.5831.1733.9238.3911.8914.3116.0722.26


E,G & W (4)26.5129.2239.0944.770.0018.2323.170.00

Construction (5)30.3533.6437.6241.9017.2720.5624.8430.30

Trade (6)23.8326.7328.6733.9214.9314.8121.3128.54

Transport (7)27.5330.6434.6538.6620.4026.1119.9330.37

Services (8)26.7330.2628.5739.9413.6432.1931.4329.70

Services (9)22.1829.6428.1633.7113.0616.2119.3117.57

1-927.6030.9133.7939.3513.1516.6819.5124.68

All (0-9)26.4829.4532.3838.1412.8915.5018.4923.05

Note: Real wages are computed at 1993-94 prices. Industry level wages of rural casual workers was not available in the published data.

Source:National Sample Survey Organisation, Employment and Unemployment Surveys, various issues, Jeemol Unni (2006)

Relative Wage Earnings

While the wage earnings increased for all segments of the workers the relative growth of wages varied across segments leading to inequalities. There was considerable gap in the wage earnings of regular and casual workers, signifying gap between the formal and informal sectors, by location urban and rural and by gender (Table 8). Male regular and salaried workers in urban areas had wage earnings that were 2.4 times higher than that of casual workers in 1993-94 and this wage gap rose to 2.7 times in 1999-00. In the non-agricultural sector alone the wage gap rose from 2.3 to 2.7 times. The wage gap among female regular salaried and casual workers in non-agriculture was even greater and rose from 3.2 times in 1993-94 to 3.9 times in 1999-00.

Table 8: Relative Wages of Regular Salaried and Casual Labourers, All India, 1983-2000Real WagesUrban MaleUrban Female

Industry19831987-881993-941999-

200019831987-881993-941999-

2000

Agriculture (0)1.472.112.023.101.952.502.832.00

Mining & Quarrying (1)1.882.653.203.372.802.712.842.69

Manufactur.(org) (2)1.791.651.671.621.631.581.871.49


Electricity, Gas, Water (4)2.652.972.563.35-4.163.86-

Construction (5)1.832.011.881.922.352.321.542.78

Trade and Hotels (6)1.491.461.511.742.372.142.022.75

Transport (7)2.112.342.142.502.713.123.863.80

Business Financial Services (8)3.413.554.373.965.502.893.355.54

Social Personal Services (9)2.732.713.113.923.293.873.275.80

Non-Agriculture2.112.352.342.713.273.613.223.94

All (0-9)2.182.462.412.683.283.883.373.67

Rural MaleRural Female

Agriculture1.041.301.271.731.951.431.511.73

Non-Agriculture1.892.222.212.422.292.852.323.89

All2.042.412.522.802.202.802.283.88

Note: Figures refer to regular wage earnings per worker/casual wage earnings per worker.

Source: Jeemol Unni (2006)

The relative wage gap between regular and casual workers varied across the industry groups in urban areas. The wage gap was higher among the service sectors, being the greatest in business and financial services, compared to manufacturing. In fact when the manufacturing industries are divided into organic and inorganic industries, we found that the relative wage gap actually declined in the organic or traditional industries and increased in the inorganic or modern industries (Table 8). It was mainly in the inorganic and modern industries that the benefits of growth of the sector were distributed to workers in terms of employment growth and higher wage earnings in the unorganised sector. The exceptions were manufacture of food and paper products and printing and publishing. It appears that even in this sector some sections of the workers, the casual workers were likely to have gained relatively less. This may be due to the low skill levels (see Jeemol Unni, 2006).

Unni (2006) has calculated the coefficient of variation at the one-digit industry group level showed increasing variation among the non-agricultural sectors in rural and urban area in the successive period implying divergence in wage earnings among industry groups. The increasing inequality across industry groups in non-agriculture was more marked for the male workforce in urban areas and for the female workforce in the rural areas. The variation across industries is much larger for the regular salaried workers than for the casual workers. Further while the variation rose almost consistently over the last two decades among the regular workers that among casual workers remain constant. The variation across industry groups is much larger when the regular and casual workers are taken together. It appears that the casual workers benefited less from the processes of growth than the regular workers.

Further this finding corroborates with recent work of Kijima (2006). He has shown that the wage inequality in urban India started increasing before 1991. The increase in the wage inequality was mainly attributable to increase in the returns to skills. The accelerating skill premium was due to increase in the demand for skilled labour. The demand shift is attrbited to skill-biased technological changes within industries.

Age, Education and Occupation Earnings Profile by Gender

The daily wage rate is obtained by dividing the total wages and salaries (in cash and in kind) received for the work done in the reference week by the total number of days reported working in that week. It is likely that daily wages rate affected by variation in hours of work. As w e have mentioned before, the survey did not collect data on hours of wok but collected detailed information on the intensity of work (half or full day) for each activity in a day and for all the seven days in the reference period. This information is used to arrive at the days of work in a particular activity in the survey reference week.The Employment Unemployment Survey of the National Sample Survey (NSS), 1999-00, included a question on the registration of the enterprise that allowed us to identify the informal enterprises, defined as proprietors and partnership. Workers in these enterprises were treated as workers in the informal sector. We have used this definition to separate the formal and informal workers. We have argued that informal workers are heterogeneous and broadly consist of both wage and self-employed workers. Among the wage and salaried workers, or employees there are both informal and formal sector workers. The NSS data separates the employees into regular and casual workers. It has been argued that all regular workers are not formal sector workers and all casual workers are not in the informal sector (Unni and Rani, 2003).

Table 9: Average Wage Rates Per Day (Rs.) for Regular and Casual Workers in Formal and Informal Sectors, 1999-00

RegularCasualAll

FormalInformalFormalInformalFormalInformal

All19082535716870

Urban20986635819476

Rural15473585612762

Male19687586017573

Female15656363713446

Source: Computed from raw data of NSS, 55th Round, Employment and Unemployment Survey,

1999-2000.

We report the daily wage rates per day of the regular and casual workers in the formal and informal sectors in Table 9. This is also presented separately for urban and rural areas and for men and women. Currently minimum wages are around Rs.75-100 per day in most industries and in most states. In 1996 the National Centre for Labour (NCL) suggested that Rs.125 was required to meet satisfactory standards of living. Only the regular workers in the formal sector received above minimum wage rates on average. The NCL norm of Rs.125 per day was also achieved on average by the regular workers in the formal sector. none of the other segments of workers were able to reach close to this norm. The regular workers in the informal sector received wages relatively close to the bottom of our norm, Rs.75 per day. There was hardly any difference in the daily wage rates of the casual workers in the formal and informal sectors.

While wage rates are important, a minimum standard of living is really assured only with some minimum days of work in the year as well. The earnings per week based on the norm of Rs.100 to 125 per day workers out to be about Rs.500-600 using a minimum days work norm of 250. The NSS also provided earnings per week for the number of days worked in the week. This was divided by the number of reported workers to arrive at the wages per week for a worker. This implicitly includes the days for which work was obtained. Here against the cut-off of Rs.600 was reached on average only by the regular workers in the formal sector (Table 10). Like the wage rates per day regular workers in the informal sector obtained wage earnings only closer to the lower limit of Rs.500.

Table 10: Average Earnings in the Week Per Workers for Regular and Casual Workers in Formal and Informal Sectors, 1999-00

RegularCasualAll

FormalInformalFormalInformalFormalInformal

All1211528312281056424

Urban13345553613401226472

Rural981466280320792366

Male12555583393491102449

Female980354200215821279

Source: Same as for Table 9.

Appendix Table-1 summarizes the mean earnings for various age groups. In almost all age groups female workers receive lower than male workers. The table also translated to a graphic profile (Appendix Figure 1). Age-Experience earnings profile increase with age up to a peak point after which they either levels off or decline. The wage ratio of female to male has also been calculated, which is less than one and it implies that there is a discrimination against female workers in the regular/salaried and casual labour market. We have also calculated the mean earnings by levels of education and occupation, which is reported in Appendix Table 2 and 3. It is interesting to note that the earnings differentials by education persist over a period of time (Appendix Figure 2). In all the occupation, females earn less than males except clerical occupation (Appendix Figure 3). The earnings ratio between Male and Female by age, Education and occupation show that male workers earn more than their female counterparts with some exceptions.

4.3 Determinants of Wages: Earnings Function Results

To estimate the earnings differences attributed to discrimination, we estimated the earnings function separately for both Males and Females in Regular/salaried and Casual labour market- for three periods, namely 1983, 1993-94 and 1999-2000, which is reported in Table 11, 12 and 13. The natural logarithm of daily wage rate is used as the dependent variable and level of education, religion, marital status, sector, tenure, union status, occupation and region. The age and age square is positive and negative respectively in all the equations, which is proxy for the experience in the labour market. Both the coefficients are statistically significant at 1 percent level in all the equations. This implies that the age-earnings profile exhibits the standard non-linear relationship.

The private rate of return per year of education at different education levels can be computed using the coefficients from the wage equations. These serve as useful indicators of the productivity of education and also the incentive for individuals to invest in their human capital (Psacharopoulos and Patrinos, 2002). If the returns to education are different for different groups participating in the labour market this will affect the perceived economic benefits of education among these groups (Kingdon, 1998). The NSS surveys do not report the number of years of schooling, only the maximum level of schooling completed that allows the construction of the education dummy variables used in the wage regression models. Since education policy is a subject under state jurisdiction the schooling systems (at least until the secondary school) vary somewhat across states. In general, most states follow five years of primary, three years of middle, four years of secondary (including higher secondary) schooling and three years (four if a technical degree) of graduate education (Duraisamy, 2002).Table 11: Earnings Function for Males and Females - Regular and Casual Workers: OLS Results

Dependent Variable: Natural log( daily wage rate)

Variables1999-2000

Regular WorkersCasual Workers

MalesFemalesMalesFemales

Coeff.t-valuesCoeff.t-valuesCoeff.t-valuesCoeff.t-values

age0.0644931.680.0322226.870.05373820.430.0288256.5

agesq-0.00062-25.13-0.00026-4.32-0.00061-18.31-0.00034-5.89

Bprim0.0645473.760.152553.340.0906785.280.1790574.7

Prim0.0912615.930.2076654.840.16288610.250.1687954.43

Middle0.19124113.620.2141085.520.17905511.490.1200632.43

Secon0.35746225.630.54105614.360.2012989.490.2306463.17

Hsc0.4811431.30.6154315.270.1850485.060.695255.94

Grad1.03060447.891.20124819.090.7443844.410.551391.42

GradOther0.70625547.260.8308322.130.2633814.820.6601394.97

Muslim-0.05655-5.54-0.15604-4.55-0.00825-0.57-0.05785-1.44

Married0.0857188.290.041762.120.0398832.58-0.039675-1.57

South0.109949.82-0.25419.080.1345337.32-0.06576-1.59

North0.02242.13-0.120554.25-0.07445-4.06-0.13106-3.05

West0.0426853.99-0.03989-1.420.0144920.74-0.14626-3.37

SC-0.07777-7.41-0.0284-2.01-0.05551-2.37-0.004985-0.17

OBC-0.08865-11.28-0.18047-7.96-0.03238-2.41-0.10709-3.74

Public0.29178736.830.39764118.350.0277120.840.3646875.07

Admn0.37180223.290.535478.50.0991090.950.0731080.26

Clerical-0.07209-7.640.0651582.88-0.11826-2.330.3081712.27

Service-0.08741-7.34-0.21314-6.41-0.07772-2.5-0.25175-4.95

Farmers-0.05992-2.170.0025130.03-0.08295-3.29-0.03106-0.62

Prodn-0.02359-2.62-0.05232-1.50.1195835.720.2132834.45

permanent0.20602324.770.34822415.760.0345111.68-0.019905-0.72

unionmem0.23091329.470.38112317.410.2125659.530.1353472.06

constant2.68810272.132.91560132.642.78745556.062.95220432.01

F-Value1617.64373.6382.9623.22

R20.55560.58140.18610.1867

Adj.R20.55520.57980.18380.1787

N31081648287342452

Note: Heteroscedasticity Corrected Results

Table 12: Earnings Function for Males and Females - Regular and Casual Workers: OLS Results


Variables1993-1994




age0.06919822.920.030444.940.06272515.50.037977.92

agesq-0.00069-18.64-0.00029-3.55-0.00074-14.4-0.00046-7.27

Bprim0.0347591.450.0025420.040.0104310.410.0494111.13

Prim0.0574612.640.0512550.890.124145.170.0307971.7

Middle0.1548287.710.3522936.270.119454.690.0416871.73

Secon0.32783616.140.59725811.230.0986712.580.0862341.89

Hsc0.44609719.890.69859912.190.2209912.970.5004541.97

Grad0.84952227.671.19171813.160.7319162.240.4466640.94

GradOther0.66014630.150.82272415.630.0290960.270.0099910.04

Muslim-0.07312-4.38-0.049078-1.99-0.01088-0.47-0.04178-0.99

Married0.0775154.920.0162562.410.0442021.8-0.03255-1.11

south0.02241.51-0.20044-5.770.1350125.030.0201560.42

north0.002990.21-0.13463-3.780.0380021.330.0283340.55

west0.17544411.360.061081.660.0565921.740.0010830.02

sc-0.07886-4.71-0.0094-2.230.0328451.550.1050283.48

Public0.18781814.540.2859658.580.3262510.981.7673032.52

Admn0.35497514.730.4920685.210.2923821.66-0.02193-0.05

Clerical-0.05308-3.710.0263230.85-0.01985-0.22-0.18883-0.93

Service-0.04948-2.65-0.23946-4.93-0.07411-1.34-0.36274-4.47

Farmers-0.15201-3.57-0.03244-0.26-0.044-1.01-0.24823-3.17

Prodn0.0672824.66-0.11092-2.230.1884584.73-0.18262-2.37

permanent0.35447423.930.64969917.430.016791.08-1.97143-1.58

unionmem0.1642612.580.23997.250.1576035.210.2644124.4

constant1.83500633.682.2129618.741.86489723.962.250219.51

F-Value586.56194.0036.8827.47

R20.34910.47460.1020.0594

Adj.R20.34850.47210.09920.0514

N25180496474952744

Table 13: Earnings Function for Males and Females - Regular and Casual Workers: OLS Results


Variables1983




age0.0878156.940.05209713.060.05679219.990.027647.4

agesq-0.00091-47.2-0.00053-10.12-0.00067-18.31-0.00035-6.98

Bprim0.0243321.840.1295822.480.073843.690.0254681.69

Prim0.12759611.560.1409873.250.1481398.330.01241.65

Middle0.25636524.220.47404410.360.1814188.540.0644681.07

Secon0.56179553.460.84751121.720.2298436.990.207211.96

Grad1.12952757.51.27113516.570.3525411.920.9831772.88

GradOther0.87010570.451.05990525.190.4236644.231.6701413.95

Muslim-0.03528-3.82-0.10842-2.860.0249011.43-0.17217-5.13

Married0.11020512.750.0769083.640.1238186.88-0.01165-0.47

south0.1629917.8-0.22695-7.69-0.13249-5.75-0.27224-5.65

north-0.04843-5.55-0.11476-3.85-0.07472-3.09-0.15172-2.89

west0.0377294.14-0.01623-0.52-0.038-1.51-0.19581-3.82

sc-0.03019-3.15-0.123973-4.000.0121330.720.0653372.38

Admn0.38023522.080.3642283.130.5100843.62-0.31893-1.28

Clerical0.0187452.230.1380825.230.2342044.290.1545850.79

Service-0.00583-0.52-0.41587-10.870.074021.81-0.44154-5.48

Farmers-0.18567-7.85-0.11209-1.190.0016730.05-0.2741-3.5

Prodn0.0682697.83-0.32224-8.130.2225227.31-0.29336-3.81

constant0.70408124.781.15693414.520.9971717.661.59960815.69

F-Value1591.00270.7780.9812.8

R20.45750.4920.17980.0803

Adj.R20.45730.49020.17760.074

N35860533270372804

As we have mentioned, the rates of return to education can be interpreted based on the coefficient of education in the wage equation. The rates of return to education are tends to rise with levels of education both in the regular and casual labour market for both males and females. A review of various studies in different countries by Psacharopoulos & Patrinos (2002) suggests that returns are highest for primary education (about 26.6% world-wide and 20% in non-OECD Asian countries) and are decreasing in education level. This empirical regularity has been called into question by several studies, especially in African and some Asian countries (see for example, Bennell (1995; 1996a; 1996b) for a cross country review; Siphambe (2000) for Botswana, Glewwe (1991) for Ghana, Sahn & Alderman (1988) for Malaysia, Moll (1996) for South Africa, Gindling, Goldfarb & Chang (1995) for Taiwan and Hawley (2004) for Thailand). In particular, Kingdon (1998) finds in her review of other empirical work on the returns to education in India (mainly computed from specialized surveys in urban areas of a particular state or city) that the rate of return to education, tends to rise with education level.

The estimates in Table 11, 12 and 13 suggest that the findings of this paper are consistent with other work on the Indian labour market and that the conventional pattern of returns does not necessarily hold for India. The estimates for the private rate of return to education at different levels in the Indian studies indicate that returns to primary and middle education for men and women are lower than thode for secondary an higher levels. This result is consisten with earlier studies in India (Duraisamy, 2006).

Estimates of returns to education are often used to inform education policy decisions on the allocation of public investment on different levels of education. The finding of relatively low returns to lower levels of education does not, however, necessarily imply that educational policy in India should not emphasise primary and middle schooling. First, even within the sample of wage workers though regular workers have relatively low returns to primary education, casual workers reap some benefit from primary education. Second, the estimates reported in this paper are private rates of returns that overlook the social benefits of primary education, especially for female workers, such as political awareness and health outcomes (Kingdon, 1998). A vitally important indirect benefit of primary education is its role as an input for further education. As a result investment at this levels could influence the rates of return at higher levels. Appleton, Hoddinott & Knight (1996) find that in Cote dIvoire and Uganda though the direct private returns to primary education are low the value of primary education as an input to post-primary education was quantitatively important. Third, using rate of return calculations to direct investment in education implicitly assumes that there are capacity constraints at each level of schooling and that, given the existing returns to education, the role of investment is to choose which schools (primary, middle, secondary or graduate) to build to meet the excess demand. In countries where poor school quality rather than capacity constraints on education are the main problem and rates of return to higher education levels are less relevant (Glewwe, 1996). In this context the deepening of schooling by increasing quality rather than broadening by increasing quantity is a more appropriate strategy (Behrman and Birdsall, 1993). There was a rapid increase in schooling infrastructure in India after the 1950s - the gross enrolment ratio for boys in primary school rose from 61% to 104% between 1950-51 and 1999-2000 and for middle school from 21% to 67% (Government of India, 2002). There is some evidence that this led to a decline in quality (Duraisamy, 2002). In summary, these estimates of rates of return should be interpreted carefully as private rates of return for a sample of adult wage workers. Primary and middle education serve as necessary inputs to higher levels of education and as such it is necessary to understand the reasons for low returns rather than simply directing public investment according to the highest rates of return (Glewwe, 1996).

The next important findings of the paper is rates of return to female education is higher compared to males with exception in casual labour market. The study by Psacharopoulos (1995) and Schultz (1988, 1989) show that the rates of return to female education measured in years of schooling are higher than for male; but rates of return to primary, secondary and university education in dummy form do not reveal the same pattern; the results are mixed. An issue in the literature regarding the returns to education for men relative to women is whether female estimates have been adjusted for selectivity bias, i.e. by taking into account the prior decision of a woman on whether to participate or not in the labour force (Heckman, 1979). Again Psacharopoulos (1995) suggested that selectivity correction does not in fact influence much the rate of return estimate for females, and the returns experienced by females, whether corrected or not, exceed those for males by more than one percentage point.

The basic framework suggests at least four reasons why there might be gender difference in the observed private rate of return to education in the labour market at a point of time. Firstly, there might be gender differences in the private, risk-free interest on funds for investing in the schooling of boys is lower than that for girls because of greater public subsidies for the former, in equilibrium one would expect to observe lower rates of return to schooling investments in males than in females (Behrman and Deolalikar, 1995). Second, the usual semilog wage relation under a Mincerrian interpretation yields estimates of the private rate of return to the time spent in school, but does not incorporate other costs for girls attending school past puberty than for boys because of the types of cultural norms (Alderman et.al., 1993), the private pecuniary rate of return to the time spent by females in schooling as usually calculated need be greater in equilibrium than to the time spent by males to compensate for the greater disutility costs. Thirdly, there might be risk aversion on the part of providers of the resources for schooling even though the expected rates of return did not differ by gender, so that risk premia differ by gender. For example, there might be higher dispersion of returns for investing in schooling of females than that of males because, due to gender roles, the former may have a wider dispersion of possible time use (e.g. more time in household production in addition to options of working in family enterprises and in the labour market). If there were well-functioning capital and insurance markets for investments in schooling and no transaction costs of using such markets, such risks could be aggregated and effectively eliminated. But such markets are notoriously imperfect in all societies, including India, because of collateral problems. Therefore most financing of schooling is by households (e.g. through foregone labour in addition to any direct monetary expenses) without the possibility of much diversification of investments to lower risks at the household level. Fourthly, there may be different selectivity regarding female versus male labour market participation in wage labour markets that is not controlled well in standard estimates (Schultz 1989, 1993). For example, even if women have traits that are less rewarded by labour markets, gender differentials in paid labour force participation may result in those (relatively few) women who do participate being more capable on average than the larger numbers of participating men. This may occur particularly if women with the marketable traits are more likely to participate in the labour market but, because of household responsibilities or cultural norms, fewer women than men actually participate in the paid labour market. Thus, the estimated private rates of return to female schooling may be higher than to male schooling because of such selectivity (or equivalently, omitted variables bias). Apart from all these reasons, possibly market may be in disequilibrium, earnings of both males and females are influenced by disequilibrium perhaps women workers may differently influenced.

Ethnicity is also often a source of exclusion in India this translates into exclusion on the basis of religion and is largely applicable to the Indian Muslims. Certain other religious minorities, such as Sikhs, Jains and Buddhists, are historically entrenched in the predominantly Hindu society while others, such as Christians and Zoroastrians, have established also their group status in society. On the other hand, Muslims are the largest and heterogeneous minority within India and have historically been viewed as separate and this makes them more likely to be excluded (Das, 2003).

Mutually exclusive dummy variables for religion affiliation (relative to all other individuals belonging to other religions) are included in order to capture possible post-employment discrimination. This could take the form of low wages either due to lack of opportunities to rise or because of crowding into certain occupations within an industry (Nayak, 1994). The result shows that belonging being Muslim significantly decreases the wage received by regular workers. Though the Muslim coefficient is negative, which is not significant in casual labour market. The disadvantage faced by Muslims in the regular labour market has increased over time. Our result is contrary to the previous

Documents

gender gap