Chapter 6

LABOUR MARKET STRUCTURE AND DETERMINANTS OF EARNINGS

6.1 The Proposition

The discussions in Chapter 2 (Sections 2.1 and 2.2) reveal that the segmented labour

market theory focuses mainly on the socio-institutional factors influencing job access

patterns and wages. According to this theory, given a structured labour market, the

mechanisms which determine wages are quite different in primary and secondary

segments. In the primary segment, wages are determined within the structured internal

labour markets by rules, customs and procedures are unresponsive to economic factors, and

wages are attached to jobs rather than personal productivity or ability of the individual

worker. In the secondary segment, on the other hand, wages are detennined by aggregate

market supply of and demand for a particular category of workers. In this segment,

variations in earnings are not caused due to variations in productivity-related

characteristics across workers, because employers at the time of recruitment, do not give

weightage to the productivity-related characteristics such as level of education of the

secondary segment worker.

So far as the role of education is concerned, the segmentation theorists argue that

education is important at the 'port of entry' into the primary segment of the labour market

(Reich, et.al., 1973; Carnoy and Rumberger, 1976). In fact, employers use education as a

'screening device' while making recruitments to primary segment jobs. Since wages are

attached to jobs rather than to individuals, job entry becomes crucial in determining one's

wages in the primary segment of the labour market. However, the educational level of an

individual is not important in gaining access to secondary segment jobs. Apart from this,

the theory argues that, given a segmented labour market, the socio-economic background

of the worker, to a large extent, influences his/her level of earnings. Since there is a

correlation between socio-economic backgrounds and educational levels of employees

(Bowles, 1972), there can be a correlation between education and earnings, particularly in

the primary segment of the labour market. To be specific, the segmentation theorists claim

that the returns to human capital such as education, experience and training in the primary

and secondary segments are different, and in the secondary segment, there is little or no

returns to schooling (Dickens and Lang, 1985 ). It has also been found in many empirical

studies that, if the labour market is segmented, the determinants of earnings and the size of

estimated coefficients (of common explanatory variables) in the secondary segment

earnings equation are significantly different from those in the primary segment earnings

equation (see Section 2.3, Chapter 2).

Given the theoretical propositions of the segmented labour market theory, most of the

empirical studies attempting to test for existence of segmented labour markets use

multivariate analysis to examine the determinants of earnings in both primary and

secondary labour market segments (see Section 2.3, Chapter 2). Specifically, these studies

use Ordinary Least Squares method of estimating separate earnings functions for primary

and secondary segments. Moreover, in most of such studies modified Mincerian type

human capital earnings functions are estimated separately for each segment of the labour

market. In the present study, we have used the above mentioned methodology for

estimating earnings functions separately for each labour market segment. Specifically, in

this chapter the attempt is to : (i) identify the determinants of earnings in the whole sample

and in each labour market segment; and (ii) examine the significance of education as an

explanatory variable in the earnings equations for both primary and secondary segments.

6.2 The Model

It has already been mentioned in Chapter 2 (Section 2.3) that almost all empiricists use

the 'human capital model' to estimate earnings functions in different labour market

segments. Their specification of earnings function is based on the Mincerian earnings

model (1962), though their actual earnings functions are the modified version of the

Mincerian type. This modified version which is often termed as the exploratory approach.

besides considering basic human capital variables such as years of schooling and years of

196

I I

labour market experience, includes any such variable that may be expected to influence

earnings (Andrisani, 1973; Blaug, 1974; Osterman, 1975; Rosenberg, 1975; Camoy and

Rumberger, 1976; Camoy, Girling and Rumberger 1976; Psacharapoulos, 1977; Mac

Nabb, 1987; Liu, 1975; Velloso, 1975; Toledo, 1979; Lobo, 1977; Uthoff, 1986; Singh,

1984; Baneljee and Knight, 1985; Snooks, 1983; Gasper, 1995; Ammugan and

Nagarajan, 1994). According to the exploratory approach, variables relating to worker's

personal characteristics, socio-economic background, occupation and labour market

conditions, and geographical conditions are considered in the earnings functions.

Moreover, almost all the empirical studies use the ordinary least squares (OLS) method of

estimating earnings functions for different labour market segments.

The general model of earnings function used in this study is as follo\vs :

Yi = / (HP, QS, FB, SB, OL, GL), where,

Yi refers to the monthly total earnings of a worker from the cunent job held;

HP refers to the vector of human capital variables other than QS and other personal characteristics of the individual worker;

QS refers to the vector of variables relating to quality of schooling of the individual worker;

FB refers to the vector of variables related to the family background of the individual worker;

SB refers to vector of variables representing the social background of the individual worker;

OL refers to the vector of variables pertaining to occupation and the labour market; and

GL refers to variables explaining the geographical condition of residence of the worker.

197

Given the general earnings function, our specific model in this study is the ordinary

least squares with continuous regressors. The specific model is :

, Yj = b0 + ~1bj Xjj + Uj,

where, j stands for an individual, i for a variable, Yi is the continuous dependent variable,

Xj (i=I...n) are n independent variables, continuous or dummy, b0 and bj are

parameters to be estimated, and Uj are random, unobserved disturbances with zero mean

and constant- i.e. unknown variance.

In this study, the total sample size is 413, and we have divided these san1ple workers

into primary independent, primary subordinate and secondary segments on the basis of

'years of schooling' of the sample workers and 'protection and autonomy' of jobs. The

number of the primary segment workers, which is the sum of the workers in the primary

independent and primary subordinate segments, is 116, and the secondary segment has 297

workers.

Given the sample size and the size of individual labour market segments, we have tried

to estimate separate earnings functions for each labour market segment and for the entire

sample. Accordingly, we have estimated earnings functions like the one mentioned above

for each group of workers. Therefore, the earnings function which has been estimated for

the entire sample workers is :

H n In Y· = b + L bW X· + u·

I 0 j =l' IJ J w

where, In Yi is the log of total monthly earnings before tax on the current job of the

sample worker.

The earnings function which has been estimated for the secondary segment workers is :

s I;

ln Yi = b0 + t: M XiJ. + UJ·· t: 1 I

s where, In Yi is the log of total monthly earnings before tax on the current job of the

secondary segment worker.

198

Similarly, the earnings function which has been estimated for the primary segment

workers is:

P n In Y· = b + '\' bfx·· + U· 1 0 f;'l I lJ J'

p where, In Yi is the log of total monthly earnings before tax on the current job of the

primary segment worker.

In our mode!, we have specified the earnings functions in the semi-logarithmic fom1,

that is, the logarithm of earnings is regressed on the potential explanatory variables.

Because, this has some definite advantages. Often, income distributions are found to be

approximately log-normally distributed. Therefore, the semi-logarithmic form of earnings

function provides a better fit than other functional forms involving the same explanatory

variables. Moreover, the human capital theoretic reasoning itself argues for semi­

logarithmic form since the investment cost of schooling and post-schooling are treated in

time- equivalent values. Moreover, use of semi-logarithmic form enables in interpreting

the regression coefficients as the percentage effect of a unit change in the explanatory

variables on earnings (Mincer, 1974; Becker and Chiswick, 1966; Snooks, 1983; Fields,

1980; Blaug, 1987).

6.2.1. Definition of Variables

We have used several relevant variables in our model, and these variables are measured

in various ways. The point to be noted here is that the number and the type of variables

used in different regression equations are different. However, all or most of the basic

independent variables given in Table 6.1 have been chosen to estimate alternative earnings

equations separately for individual labour market segments, and for the entire sample

population of workers.

199

Table 6.1. : Definitions of Variables

Sl. No. Variable Name

1. Age (E2)

2. sex (E3 l

3. Caste (ES)*

4. Religion (E6)*

5. Marital status (EB)

6. Family size (E10)

7. Place of origin (E15)

8. Parents' education (PAEDU)

9. Father's occupation (FAOCC)*

200

Value Labels/Description

measured in years

0 male 1 female

1

2

scheduled caste backward caste

3 higher caste (Dummy variable::; C1 ~ 1 if

backward caste, 0 if otherwise; C2 = 1 if higher caste, 0 if otherwise; excluded category is Scheduled Caste)

1 Hindu 2 Muslim 3 Christic;.n

(Dummy variables : R1 = 1 if Hindu, 0 if otherwise; R2 = 1 if Muslim, 0 if otherwise;

0 1

excluded Christian)

unmarried married

category

measured in total numbe-c of members in the family, including the respondent

0 rural 1 urban

measured in years of schooling of both father and mother of worker.

measured in seven categories:' 0 unclassified workers 1 unskilled manual worker 2 cultivator 3 petty shopkeeper 4 skilled worker 5 clerical & related worker

is

6 administrative & professional (Dummy variables F1 = 1 if

cultivator, 0 if otherwise; F2 1 if manual worker, 0

10 Average annual family income (E21)

11. Place of schooling (E22)

12. Educational achievement (E25A) of the worker

13. Education squared

14. Vocational educational (E26) achievement

15. Vocational education squared (VEDNQ)

16. Mode of entry to Ist job (FJMODE)

17. First job of the worker (E29)*

201

if otherwise; F3 1 if petty shopkeeper, 0 if otherwise; F4 = 1 if skilled worker, 0 if otherwise; FS = 1 if clerical and related worker, 0 if otherwise; F6 = 1 if administrative or professional worker, 0 if otherwise; excluded category is unclassified workers)

measured in thousands of rupees and includes income of all the earning members in the family, except that of the individual worker

0 no schooling 1 rural 2 urban

measured in years ·of schooling

square of E25A

measured in years of vocational schooling

square of E26

0

1

informal channel of entry or selection formal channel of entry or selection

measured in seven categories & ranked on the basis of mean earnings of the last month of the 1st job

1 unskilled manual 2 skilled 3 clerical & related 4 sales 5 supervisors 6 technical & Professional 7 administrative &

managerial Jl = 1 (Dummy variables

unskilled manual if otherwise; J2

worker, 1

skilled worker, 0 otherwise; J3 1

if 0

if i.f if

18. Average number of hours worked daily (E54)

19. On-the-job training facility on the current job (E57)

20. Job changes or relative stability of employment (V4)

21. Labour market structure (V6)

22. Labour market experience (LMEX)

23. Labour market experience squared (LMEXQ}

24. Farm size (Pll}

25. Education-experience interaction (EDEXP}

clerical and related worker, 0 if otherwise; J4 1 if supervisor, 0 if otherwise; JS 1 if technical or professional worker, 0 if otherwise; J6 1 if sales worker, 0 if otherwise; excluded category is proprietors)

measures in number of hours worked daily

0 no 1 yes

1 first job is current job 2 changed job twice 3 changed job thrice 4 changed job 4 times 5 changed job 5 times 6 changed job 6 times

0 secondary segment 1 primary segment (i.e. primary independent segment plus primary subordinate segment)

measured in actual years of labour market experience in all the jobs held by the individuals worker

Square of LMEX

measured in terms of total annual turnover of the firm in rupees (in lakhs)

years of education multiplied by years of labour market experience

* Later in the multiple regression analysis we have used dummy varia.bles (i.e. derived regressors} instead of these basic variables.

The dependent variable 'In Y' is the log of total monthly earnings before ta'\ on the

current job held by the worker. It also includes other types of payments such as overtime

202

wages, incentive bonus etc. The variables considered in different regressions can broadly

be grouped into the following categories :

Human Capital Variables : This category of variables includes investment in the form of schoo_ling and post- schooling investment in the form of labour market experience. It also includes investment in on-the-job training of the worker, either by the worker himself or by the firm. In order to account for the parabolic effect of schooling on the earnings profiles of the worker over the years, we have considered 'education squared' as an independent variable in the earnings equations. Moreover, the post-schooling investment component ofhuman capital is measured in tenns of years of experience in the labour market In our sample, in many cases, employment is not continuous and there are periods of job search and unemployment. Therefore, instead of following the Mincerian way of calculation of experience in the labour market, we have considered in the model the actual years of experience in the labour market as indicated by the respondents. Besides, as age is proxy for labour market experience of the worker, we have preferred actual years of labour market experience as an independent variable in the model. In fact, it is better to discard age of the worker in favour of ·actual years of expcrien::e in the labour market', as age itself is negatively correlated with length of schooling (Mincer, 1972; Blaug, 1987). Besides, in our model, the parabolic effect of declining 'experience-earnings' after a given years of labour market experience is observed by treating experience in quadratic form (i.e. experience plus experience squared) (Mincer, 1974).

Other Personal Characteristics and Social Background Variables : This category of independent variables includes sex, caste, religion and marital status of the worker. These, variables do influence the earnings structure of workers in the labour market. Therefore we have considered these variables in our model. Besides, 'marital status' is a good indicator of the stable behaviour of the \Vorker, because married worked are considered to be responsible and stable workers, at least by the employers, and therefore marital status exerts considerable effect on their earnings.

Family Backgrotmd Variables : It has been observed in empirical literature that family background greatly influences one's educational attainment and occupational attainment, and ultimately one's earnings level (Behrman, et.al, 1980; Leibowits, 1974; Perl, 1973; Fishlow, 1972; Mincer & Polacheck, 1974). This category of independent variables includes family size, parents education measured in terms of years of schooling of both father and mother (i.e. mother's schooling plus father's schooling), fathers occupation and average annual family income of the worker, excluding the income of the worker.

Occupation and Labour Market Related Variables : It is a \Vel! known fact that the mode of entry to the first job and the first job itself are important detem1inants

203

of the current job of the worker, and ultimately his/her earnings level. Therefore, we have considered 'mode of entry to the first job' and 'the first job' of the respondent as independent variables in the model. Besides, to capture the effects of structural factors in the labour market on one's earnings level we have included -labour market structure (i.e. segment of employment), which captures the effects of a whole lot ofjob characteristics. So far as the firm level variables are concerned, we have included size of the firm measured in average annual turnover of the fitm (in lakhs of rupees) as an mdependent variable in various regression ec;uations.

Quality of Schooling Variables : In our sample, all the educated workers have their schooling from government schools. However, we have used ·location of schools' (rural or urban schools) as an independent variable in the analysis.

Geograp!Iical Location Related Variables : The place of one's origin - i.e. rural or urban - affects one's accessibility to an environment of schooling and job market. Such an accessibility determines one's capacity to face the challenges in education and employment, which ultimately determines one's earnings potentiai (Birdsall & Behrman, 1984). Therefore, to capture the dfect of one's place of origin on one's earnings, place of origin of the worker is included as an indepcnuent variable in the analysis.

6.3 Empirical Analysis

The present study is based on a total sample of 413 workers of 11 manufacturing fim1s

in Delhi, and after grouping these workers, on the ba<>is of 'years of schooling' of workers

and 'protection and autonomy of employment' we have derived three distinct labour

market segments - i.e. primary independent, primary subordinate and secondary. After

dividing the sample workers, it was found that only 16 workers fail into the primary

independent segment, 100 workers into the primary subordinate segment, and 297 workers

into the secondary segment of the labour market. Given the small size of the primary

independent segment, we have clubbed this segment with the primary subordimte sep'1ent

for the purpose of empirical analysis, which raises the number of workers falling into the

primary segment of the labour market to 116. Then we estimated separate ean1ings

functions for each labour market segment and for the entire sample population of workers

in an attempt to find out the best statistical explanation of variations in earnings between

individuals.

204

In each case, we have estimated the equation by a stepwise regression procedure.

Perhaps, it is pertinent here to mention that to estimate the regression equations we have

used the Statistical Package for Social Sciences (SPSS) computer programme. While

estimating the earniqgs equation by a stepwise regression procedure, the computer

programme enters an independent variable in each step on the basis of highest partial

correlation coefficient, and it accepts or rejects an independent variable on the basis of a

partial F-test with 95 percent confidence limit. However, before reaching at the final

regression equations for each labour market segment and for the sample population of

workers, we have checked for high correlation among independent variables and selected

the explanatory variables of each equation carefully. Before running the regressions, we

have calculated the arithmetic mean and standard deviation of all independent variables

and the simple Pearson's correlation coefficients between all pairs of variables, including

the dependent variable. However, some of the variables used in our correlation analysis

are categorical. In this case, one can also calculate the correlation matrix using polychoric

or polyserial correlation method. Table 6.1 shows the list of independent variables which

we used in different alternative regressions. However, some regressions do not include all

the independent variables listed in Table 6.1. Depending on the nature of the individual

labour market segment, we have dropped a few independent variables in various specific

regressions. For example, in the equation for the secondary segment we have not included

variables such as 'on-the-job training' 'years of vocational schooling' as none of the

workers in this segment has on-the-job training or vocational schooling. For each

individual labour market segment and for the entire sample population of workers we have

estimated several alternative regressions, and finally reached at 3 regre~sion equations -

one each for the primary labour market segment, secondary labour market segment, and the

entire sample population - which provide best statistical explanations of the variations in

earnings. We have estimated several alternative regressions before finally selecting the

three equations because of the fact that many of the independent variables considered in

different regression equations are highly correlated with each other. Running of alternative

regressions helped to differentiate and establish the actual contribution of the independent

variables to earnings variations among individuals. All the regression models attempted

205

here are highly significant, and confirm to the partial F-test with 95 percent confidence

limit. In the following sections, the attempt is to examine the determinants of earnings in

different labour market segments and in the entire sample population of workers in the

manufacturing sector i~ Delhi.

6.3.1 Determinants of Earnings in the Secondary Segment

Initially, in an attempt to explain total earnings of a worker in the secondary segment,

we ran several alternative regressions which included 16 original variables and 18 dummy

variables. As has already been mentioned in Table 6.1 we have used dummy variables for

'caste', 'religion' 'father's occupation' and 'first job' ofthe worker. This is to be noted here

that while using dummy variables in multiple regression we always get rid of at least one

subcategory of the original variables, and the effect of this subcategory i:o, then c~ptured in

the constant term of the regression equation. In other words, while using dummy variables,

we must drop one sub-group to avoid 'singularity' of the moment-matrix of independent

variables (Blaug, 1987). It is to mention here that we have chosen the method of not

constraining the constant term of the regression equation to zero. Since we have not

constrained the constant term to equal zero, the regression coefficients associated with

dummy variables must be interpreted as giving the percentage difference in earnings of an

individual belonging to a particular category rather thar1 to the excluded category, after

holding all other variables constant. However, our final regression equation for the

secondary segment included 7 independent variables : (i) labour market experience; (ii)

labour market experience squared; (iii) religion; (iv) marital status; (v) parents' education;

(vi) average annual family income; and (vii) first job ofthe worker.

The correlation analysis for the secondary segment reveals that 'age of the \vo:~.;:er' is

highly correlated with log earnings (see Table 6.2). Age is also highly and significantly

correlated with total labour market experience of the worker, which in tum is highly

correlated with log earnings. Given the high and significant correlation coefficient

between 'age' and 'labour market experience', i.e .. 5647 we have dropped age in favour of

206

Table 6.2 : Correlation matrix for secondary segment

LOG EAR E2 AGEQ E3 E5 E6 E8 EJO El5 PAEDU FAOCC E21

LOG EAR 1.0000

E2 AGEQ .3329 .. .3172**

1.0000 .9883 .. 1.0000

EJ -.0203 -.0103 -.0164 1.0000

E5 .0291 .0578 .0511 .1164

1.0000

E6 E8 .3955 .. .3070•.

-.0652 .5404** -.0624 .4925•• -.0688 .1233 -.0923 .0371 1.0000 -.0071

1.0000

207

E10 E15 PAEDU FAOCC E2l .1812* .0520 .2377•• .2749 .. .23JO• • .1265 -.0439 -1046 -.0110 -.0173 .0946 -.04R6 -.1119 -.0168 -.0280

-.0972 .6293*' 3029•• .305J.·· .3004'" -.0869 .1299 .1278 .1676• .04-0 .1084 -.0174 .1343 .1851• .0033

-.2163•• .1494• -.1442 .0465 -.0247 1.0000 -.1369 .0370 .0436 .231J9H

1.0000 .3033•• .. 3543** .21 os· • 1.0000 ,6031 .. .4588·.

1.0000 .46J2H 1.0000

Table 6.2 (Contd.)

E22 E25A EDUQ FJMODE E29 E54 LMEX LMEXQ PI\ --·----------LOG EAR -.0130 -.0155 .0110 .0573 .4580** -.1838* .479JH .4049.0 -.0689 E2 -.0936 -.0862 -.0684 -.0041 -.0606 .0838 .5647*• .5205** .0982 AGEQ -.1084 -1138 -.0949 -.0119 -.0504 .0634 .5713*" .55J()U .0851 E3 .4912** .1419* .1491 -.0169 -1124 -.2242'* -.0544 ·.05.3,1 -.0890 E5 .1379 .2019** .2\\6H .0538 ·.0910 . 0262 -.onn -.0776 -.1016 E6 -.0274 -.1075 -.11 I 3 .1090 .3929 .. -.1283 ()()(,'.) 0083 -.1480 E8 .1659• .0944 .0666 .0495 -.0782 -.0361 ·.4122*. -.2832 .. .0617

E10 -.0980 .0550 . 0671 -.0410 .0199 .0420 .1255 .0624 -.0481 E15 . 7431** .0941 .0743 .1266 -0063 -.1867* -.010:i .0140 -.0182 PAEDU .3040** .3294** .3360*. .2367+* .I.'S 16* -.2161** - 05) 1 -.0535 -1722•

FAOCC . 3584 .. . ?857 .. .2fi21U .0675 . 1940* • -.2332 .. .0194 .0161 -.1544*

E21 .1734* .1837~ .1905"' .0206 .1187 -.1534• .0680 .0·106 .0492

E22 1.0000 .4533 .. .2.9J3H. .1044 .f)174 .1357 -.0934 -.0959 -0205

E25A 1.0000 .9542* 1 .0753 -.0004 -.1477 -.li\3 ·. i 193 -.0842

EDUQ 1.0000 .0775 .0014 -.1582* -.0865 -.0854 -0857

FJMODE 1.0000 .I \JR .0244 -0239 -.0292 -.0237

E29 !.0000 -.3634•. .1 :1.11• 1463 -.216)H

E54 1.0000 -1)3!• - 1563• .4140**

LMEX .1.0000 .9410** -.0449

LMEXQ 1.0000 ·.0284

P11 1.0000 ----·-·---------··----------------------·-----------~---·--·-·---------·----·---·-···-------·------------------·--

Number d C<l:><:s : 297 2-tailcd Signif: • -- .01 ** -- .00 I

::'08

labour market experience in the regression equation for the secondary segment. Moreover,

age is a proxy fOi work experience, and if data on actual ye.ars of work experience are

available, it is better to use work experience than age of the worker in the earnings function

(Mincer, 1972; Blaug, i 987). Similarly, we have dropped ·age squared' in favour of

'labour market experience squared' in the equation. 'Marital status' of the worker is

positively and significantly correlated with log earnings, and it means that if the worker is

married, his/her earnings is higher than the worker who is unmarried.

It can be seen in Table 6.2 that the correlation coefficients between ·parent::' <:ducation'

and fathers' occupation', parents' education and 'average annual family income', and

'fathers' occupation' and 'average annuai family income' are high and significar1t. This

implies that if the worker has less educated parents, his/ber father is into low paid

occupation, and in tum his/her average family income is also less. Similarly, a worker

whose average family income is high has better educated parents and his/her father is also

into a relatively better paid occupation. The degree of correlation between 'j;,thers'

occupation' and 'parents' education' is .6031, which is also significant. Therefore, in our

final equation for the secondary segment 'parents' education' captures the effects of

'fathers' occupation' on earnings, and therefore 'fathers' occupation' does not figure in the

final equation. Moreover, given the positive and significantly high degree of conelation

between 'place of origin' of the worker and his/ber 'average a~mual family income', the

latter variable captures the effect of the 'place of origin' on earnings. It also implies that

workers having rural origin belong to relatively poor families in terms of average <L"L'l.ual

family income.

Table 6.3: Mean & standard deviation of earnings schooling and experience for the secondary segment

Statistics

Mean Standard deviation No. of cases

Log earnings

7.20 .41

297

Years of schooling

209

7.38 2.86

297

Years of labour market experience

4.77 3.38

297

In the secondary segment, the 'place of origin' of the worker and his/her 'place of

schooling' is highly and significantly correlated with each other. This simply means that

workers having rural origin have their education from rural government schools, and those

having urban origin ~ave their education from urban government schools. Besides, the

correlation coefficient between the 'place of schooling' and 'years of schooling' of the

worker is .4533 which is also significant thereby implying that workers in the secondary

segment who have their education from rural schools have less years of schooling than that

of workers schooled in urban government schools. Therefore, when years of schooling of

the worker is included as an independent variable in the equation, 'place of schooling' is

dropped from the equation on the basis of partial F-test. However, we will find latter that

the 'years of schooling' of the worker is also not included in the fmal equation for the

secondary segment, as it is not significantly correlated with log earnings. Same is ilie case

with ·education squared' as an independent variable in the equation.

In our regression equation for the secondary segment, we have included the ·first job'

of the worker as a regressor, because it is positively and significantly correlated with log

earnings and the explanatory power of this variable is also significant. It can be seen in

Table 6.2 that the 'first job' of the worker is negatively and significantly correlated ·with

'average number of hours worked daily' by the worker and the 'firm size'. This means that

workers in the low paid secondary segment jobs have to work for longer hours daily, and

they are mostly concentrated in relatively small firms, measured in terms of average annual

turnover of the firm. So the 'first job' of the worker as an independent variable in the

regression equation for the secondary segment captures the effects of the other two

correlated variables on earnings.

210

Table 6.4: Correlation matrix for final equation for secondary segment

LOG EAR LMEX LMEXO PAEDU E8 E21 Rl Ji

LOG EAR 1.000 .4793** .4049** .2377** .3070** .2330** -.3955"* -.4580 .. LMEX 1.0000 .9410 .. -.0531 .4122** .0680 -.0069 -.1531* LMEXQ 1.0000 -.0535 .2832""* .0406 -.0083 -.1463 PAEDU 1.0000 -.1442 .4588"'* -.1343 -.1516• E8 1.0000 -.0247 -.0071 -.0782 E21 1.0000 -.0033 -.1187

Rl 1.0000 .3929*•

J 1 1.0000 _____ , _____

Number of cases : 297 2 - tailed significance : * - .01 * * - .001

211

Table 6.5 Regression results for the secondary segment (Dependent variable : log earnings)

Vairable 8-Co-efficient Standard Error p - coefficient T- Value Significance of of 8-coefficient T-Value

Labour market .78168 .016305 .651242 4.794 .0000 experience

Labour market -.001947 .907824E-04 -.276137 -2.145 .0328 experience squared

Religion Hindu -.369436 .600168 -.274846 -6.140 .0000 Marital status .113702 .045546 .120298 2.496 .0131 Parents' education .014717 .004516 .153719 3.259 .0013 Average annual 2.637586E-06 1.19916E-06 .102341 2.200 .0286

family income First job

Unskilled manual -.209691 .038767 -.245828 -5.409 .0000

Constant 7.210028 Multiple R .72038 R-Square .51895 Adjusted R-square .50730 Standard Error .28480

F "' 44.53858 Regression 7 Residual 2.89 No. of cases 297

2l2

J

The final earnings equation for the secondary segment of the labour market includes

five original variables and two derived regressors or dummy variables. Thus, the final

equation for the secondary labour market segment which best fits the data is as follows (see

Table 6.5):

5 In Yi = 7.210028 + .078168 (labour market experience) - .369436 (religion

Hindu) - .209691 (first job : unskilled manual worker) + .014717 (parents' education) + .113702 (marital status) + 2.637586E-06 (average annual family income) - .001947 (labour market experience squared)

The above earnings equation explains nearly 52 (i.e. R2 = .51895) percent of earnings

variance in the secondary segment of the labour market, and all the B-coefficients are

significant at 5 percent level, and even some coefficients are significant at one percent

level. Among all independent variables in the regression equation for the secondary

segment, the explanatory power of the 'labour market experience' is the highest - i.e. it

explains nearly 23 per cent of earnings variance in the secondary segment (R2 =.22976).

·Labour market experience' of the worker also enters at a very early stage in a stepwise

regression procedure. 'Labour market experience' of the worker and 'religion' of the

worker, which has been divided into two dummy variables, together explain nearly 38

percent of earnings differentials in the secondary segment. In this case, R2 is .38355, and

R2 change is .15379, which implies that religion of the worker alone explains nearly 15

percent variance in earnings. When ·first job' of the worker, which is again divided into 6

dummy variables is brought in the equation, it along with earlier two variables explain

about 45 percent of variance in earnings (R2 = .44834). Here, R2 change is .6479. Thus,

the explanatory power of' first job' of the worker is relatively low in the equation. Then, in

the next step, inclusion of 'parents' education' as an explanatory variable in the equation

raises the value ofR2 marginally from .44834 to .48204. This variable together with earlier

three variables explain about 48 percent of earnings variations in the secondary segment.

The contribution of 'parents' education' alone in explaining the earnings variance in the

secondary segment is about 3.4 percent. 'Marital status' ofthe worker along \vith the earlier

four explanatory variables explain nearly 50 percent of earnings differentials in the

secondary segment (R2 = .50195). The individual contribution of 'marital status' as an

213

independent variable in explaining total earnings in this segment is only about 2 percent.

When the 'average annual family income' of the worker enters the equation, nearly 51

percent of earnings variance is explained (R2=.51 129). Finally, 'labour market experience

squared' when brough~ in the equation raises the value of R2 to .51895, and the earnings

equation for the secondary segment explains nearly 52 percent of earnings differentials.

However, it can be seen in Table 6.5 that the B-coefficient of 'labour market experienced

squared' is negative which captures the declining experience-earnings profiles after a

certain period of work experience of the secondary segment worker. Thus, we find that

total earnings in the secondary segment can be explained in terms of : (I) labour market

experience; (2) religion; (3) first job; ( 4) parents education; (5) average annual family

i~come; (6) marital status; and (7) labour market experience squared. It should be noted

here that in the secondary segment 'years of schooling' of the worker has no market

premium, though work experience has.

Before we move on to discuss our earnings equation for the primary segment of the

labour market, it is perhaps important here to mention that multicollinearity among the

independent variables is a problem in any regression equation. Since it is a property of the

sample data and not of the population, one cannot strictly speaking, test for its existence

(Blaug, 1987). But the correlation matrix of our best regression discussed above does not

show that multicollinearity affects our results (see Table 6.4). Multicollirearity manifests

in high R-square coupled with low t-values of almost all the coefficients. That is not the

case here.

6.3.2 Determinants of Earnings in the Primary Segment

In the primary segment of the labour market, we have explained total earnings in terms

of: (1) father's occupation; (2) labour market experience; (3) labour market experience

squared; (4) on-the-job training; (5) sex; (6) years of schooling squared; and (7) years of

vocational schooling squared. Initially, we regressed log earnings on 20 original and 16

dummy variables. We ran several alternative regressions to decide about the best

explanatory variables in the earnings equation for the primary segment. The final

214

l

regression equation which best fits the primary segment data includes six original variables

and one dummy variable - i.e. 'administrative or professional occupation' of the father of

the worker. Our final regression for the primary segment is significant, and the B­

coefficient of all the explanatory variables in the final regression are also highly

significant, with 95 percent confidence limit. We have also checked for the problem of

multicollinearity between independent variables, and our final correlation Table 6.8 does

not suggest existence of multicollinearity among explanatory variables. The final earnings

equation for the primary segment is as follows (see Table 6.9): p

In Yi = 6.852817 + .700172 (father's occupation : administrative/professional) + .092363 (labour market experience) - .002426 (labour market experience squared) + .288124 (on-the-job training) - .232952 (sex) + .002679 (years of schooling squared) + .022613 (years of vocational schooling squared).

We reached at the above final earnings equation for the primary segment after carefully

examining all the independent variables initially considered in the equation. Like the

secondary segment equation, we preferred to drop 'age' and 'age squared' in favour of

'labour market experience' and 'labour market experience squared'. If we examine the

correlation matrix for the primary segment of the labour market, we will find that ·sex of

the worker' is negatively and significantly correlated with log earnings (see Table 6.6).

This means that if the worker is a female, she earns less than her male counterpart in the

primary segment of the labour market. However, 'sex of the worker' is also negatively and

significantly correlated with 'marital status of worker'. The correlation coefficient between

these two variables is -.4882, which implies that most of the female workers in the

primary segment are unmarried. Besides, 'marital status' of the worker is significantly

correlated with many other independent variables (see Table 6.6), and the correlation

coefficient between 'labour market experience' and 'marital status' of the worker is .3821,

which is significant too. This simply means that, in the primary segment of the labour

market, most of the female workers are unmarried, and the married workers have relatively

longer labour market experience. Therefore, when 'labour market experience' is included

in the equation, the 'marital status' does not enter the equation as an explanatory variable

on the basis of partial F -test.

215

l

Table 6.6 : Correlation matrix for primary segment

LOG EAR E2 AGEQ E3 E5 E6 E8 EIO E15 FAOCC E21

LOG EAR 1.0000 .6449° 0 .6259° 0 -.3136•. .1194 -.0487 .3172•• .2240 .2309 .5337•• .5192°* E2 1.0000 .9912° 0 ·.2976° .0235 -.0238 .4849°. .0484 .0067 .2632" .3575° 0

AGEQ 1.0000 ·.2630° .0191 -.0261 .4418 .. .0365 .0240 .2649° .353o••

E3 1.0000 .1229 .1695 -.4882°. -.1440 .2229 -.0763 .0189

E5 1.0000 .0327 -.2060 -.0659 .2675• .0991 .1610

E6 1.0000 -.0149 1088 -1540 -.0371 .0450

E8 1.0000 .1656 -.3646•. .1002 .0943

EIO I 0000 -.1272 .1462 .2590°

E15 1.0000 .1314 .383&··

FAOCC 1.0000 .456o••

E21 1.0000

216

Table 6.6 (Contd.)

E22 E25A EDUQ E26 VEDNQ FJMODE E29 E54 E57 V4 PAEDU LMEX LMEXQ PI I LOOEAR .2264 .2339 .2426° .3321° 0 .4397° 0 -.0470 .4009° 0 -.0634 .5773° 0 .2746° .3168** .4932** .3679** .0193 E2 .0017 .0275 .0307 .2080 .3005* -.!351 .0691 .0947 .3964** .6577** -.0!31 .8200** .7139** -.0298 AGEQ .0204 .0323 .0352 .1993 .2917* -.1233 .0572 .0836 .3924* 0 .6475** -.0137 .8363** .7662** -.0562 E3 .2229 .1620 .1470 -.2270 -.3056** .3184** -.3115* 0 -.1892 -.1542 -.2114 .1538 -.1795 -.1354 .0960 E5 .2675* .1678 .2040 -.0321 -.0306 .1337 .1034 -.1651 .1184 -.0627 .3639** -.0590 -.0884 -.1469 E6 -.1540 .0529 .0566 -.0298 -.0209 .2041 .0190 -.0055 -.1153 .0134 1073 -0364 -.0403 .1139 E8 .3646** .3397** -.3323** -.3078°* .3590** -.2664* -.0515 .2993* -.2030 -.3713* 0 .2715* .382.1** .2572* .1057 EIO -.1272 -.1351 -.1338 .1204 .1470 -.1835 .1732 .0982 .0277 -.0625 -.0114 -.1220 -.IS68 -.0336 El5 .9654** .4813 .. .4771 .. -.1657 -.1301 .4274* 0 .1809 -.3487** .1021 -.0624 .5345** .0413 .0442 -.0966 FAOCC .1711 .0886 .0919 .3032** .3431** -.0069 .2240 .1868 .3528*. -.0428 .3041** .1250 .1149 .1610 E21 .3786** .2691* .2681* .2871* .3693** .2427* .2240 -.1653 .3193** .0770 .4668*• .1956 .1342 .2623* E22 1.0000 .4419 .. .4379** -.1774 -.1333 .3920** .2350 -.3154* 0 .1021 -.0417 .4963*• .0328 .0415 -.0759 E2SA 1.0000 .9987** -.4506** -.3748** .3473** .1700 -.4849** .0623 -.1984 .4673** -.0231 -.0144 -.1587 EDUQ 1.0000 -.4497** -.3739** .3471° 0 .0546 -.4834° 0 .0582 -.1979 .4731** -.0229 -.0152 -.1543

E26 1.0000 .9660** -.0831 .0715 .2422* .3549** .0969 .0515 .1826 .1697 .3001.

VEDNQ 1.0000 -.0927 .3096** .1665 .4253** .1329 .0931 .2514* .2300 .2722•

FJMODE 1.0000 .3511 •• -.2748* -.0095 -.1202 .4933 .. -.1086 -.0637 .1472

E29 1.0000 -.0184 .2841° -.2214 .3330*• -.1247 -.0914 .0906

E54 1.0000 -.0153 .1328 -.4151** .0382 .0175 .2246

E57 1.0000 .2016 .1861 .3969** .3463** .0764

V4 1.0000 -.2Wl .7485** .6396** .0202

PAEDU 1.0000 -.1013 -.0711 .0734

LMEX .1.0000 .9248** .0439

LMEXQ 1.0000 -.0019

PII 1.0000

Number of cases: 116 2-tailed Signif: • -- .01 •• -- .001

217

Table 6.7: Mean & standard deviation of earnings, schooling and experience for the primary segment

Statistics

Mean Standard deviation No. of cases

Log earnings

7.95 .59

116

Years of schooling

13. I I 2.20

I 16

Years of labour market expenence

6.3I 4.79

I I 6

It can be seen in Table 6.6 that 'parents' education', 'father's occupation' and 'average

annual family income' of the worker are highly positively and significantly correlated with

each other. Each of these variables is also positively and significantly correlated with log

earnings. This implies that as the parents' educational status increases, the occupational

status of the father of the primary segment worker increases, and so also his/her average

annual family income. It also means that workers having higher socio-economic

background earn more than the workers having lower socio-economic background in the

primary segment of the labour market. Among these three variables, 'fathers' occupation'

is highly positively correlated with log earnings, and this variable captures the effects of

other two variables in explaining the earnings variance in the primary segment. Therefore,

when 'father's occupation' as an explanatory variable is included in the regression equation

for the primary segment, the explanatory power of the model improves over the

explanatory power of other models which includes either of the other two highly correlated

variables, i.e. 'parents' education' and 'average annual family income'. Though 'years of

schooling' of the worker is not significantly correlated with log earnings, it is positively

and significantly correlated with 'father's occupation' implying thereby that as the father's

occupational status increases, the educational status of the worker also increases.

However, 'years of vocational schooling' is positively and significantly correlated with log

earnings. This means that every additional years of vocational schooling of the worker

increases earnings in the primary segment of the labour market.

218

Table 6.8 Correlation matrix for final equation for the primary segment

LOG EAR E3 E57 EDUQ LMEX LMEXQ VEDNQ F6 LOG EAR 1.0000 -.3 136 .. .5773** .2426* .4932 .. .3679•• .4397•• .7046•• E3 1.0000 -.1542 .1470 -.1795 -.1354 -.3056•• -.1600 E57 1.0000 .0582 .3969*• .3463•. .4253•• .4419•• EDUQ 1.0000 -.0229 -.0152 -.3739•. .2150 LMEX 1.0000 .9248•• .2514• .2610• LMEXQ 1.0000 .2300 .2309 VEDNQ 1.0000 .3768• F6 1.0000

Number of cases : 116 2 -tailed significance : • - .0 I • • - .00 I

219

Table 6.9: Regression results for the primary segment (Dependent variable: log earnings)

Vairable B-coefficient Standard Error of B-coefficient

Father's occupation Administrative or Professional .700172 .102529

Labour market experience .092363 .016286 Labour market experience squared -.002426 5.81015E-04 On-the-job training .288124 .105840 Sex -.232952 .088201 Years of schooling squared .002679 6.22593E-04 Years of vocational shoo ling squared .022613 .007282 Constant 6.852817 Multiple R .86407 R- square .74661 Adjusted R- square .73019 Standard Error .30812 F= 45.45971 Regression 7 Residual 108 No. of cases 116

220

~-coefficient T-Value Significance ofT

.408791 6.829 .0000

. 746214 5.671 .0000 -.534730 -4.175 .0001 .163690 2.722 .0076

-.136008 -2.641 .0095 .250173 4.303 .0000 .203380 3.106 .0024

We have dropped 'first job' of the worker as an explanatory variable from the regression

equation for the primary segment, though this variable is highly positively correlated to log

earnings. This we have done because of the fact that examination of sample data on first

and current jobs of workers in the primary segment of the labour market reveals that the

·first job' of most of the workers is also their current job. Therefore, to neutralize the effect

of current job on the earnings level of workers on the current job, we preferred to drop

'first job' as an explanatory variable from the equation. However, the effect of the first job

is captured by the 'years of vocational schooling' variable (see Table 6.6).

Thus, when log earnings is regressed on the final 6 original variables and one dummy

variable included in the equation for primary segment, the model explains nearly 75

percent of earnings variance (see Table 6.9). The B-coefficients of all the explanatory

variables in the equation are also highly significant at 5 percent level. Among all the

explanatory variables in the equation, the explanatory power of 'father's occupation' is the

highest having R-square value of .49647. This variable alone explains about 50 percent

earnings differentials in the primary segment. 'Father's occupation' along with 'labour

market experience' of the worker explains nearly 60 percent of variation in earnings in the

primary segment. In this case, R-square change is .1 0263, thereby implying that inclusion

of' labour market experience' in the equation increases the explanatory power of the model

by about 10 percent. When 'labour market experience squared' is brought in the equation,

the explanatory power of the model increases by around 5 percent (R2 change = .04624),

and the model explains nearly 65 percent of earnings differentials in the primary segment.

However, the negative B- coefficient of 'labour market experience squared' variable

implies that, after a certain years of labour market experience, every additional year of

labour market experience yields a negative return in the primary segment of the labour

market.

In the next step, with inclusion of ·on-the-job training' facility on the current job as an

explanatory variable in the regression raises the R2-value of the model to .68235, which

221

means that the contribution of this variable alone in explaining variations in earnings in the

primary segment is about 4 percent (i.e. R2 -change = .03701 ). 'Sex' of the worker along

with earlier variables explains 70 percent of variations in earnings. The individual

contribution of 'sex' as an explanatory variable in raising the explanatory power of the

model is marginal, i.e. only around 2 percent.

When 'years of schooling squared' is brought in the regression equation for the primary

segment, the explanatory power of the model increases by nearly 2 percent (i.e. R-square

change is .02382), and the model explains about 72 percent of earnings variance is the

primary segment. Finally, 'years of vocational schooling squared' along with the other six

variables increases the explanatory power of the model to about 75 percent, i.e. R2 =

.74661. Thus, our final regression model suggests that the major determinants of earnings

in the primary segment are 'father's occupation' ·labour market experience' ·sex' ·on-the­

job training' and 'general and vocational education' of the worker. It should be noted here

that education of the worker has a positive market premium in the primary segment of the

labour market, and not in the secondary segment. Moreover, 'on-the-job training' has also

a positive markeepremium in the primary segment of the labour market.

6.3.3 Determinants of Earnings in the whole Sample

We have also estimated several alternative regressions to identify the maJor

determinants of earnings in the entire sample population of workers. The final regression

model for the whole sample is highly significant, and the B-coefficients of the explanatory

variables in the fmal regression equation are also significant at 5 percent level. To begin

with, we regressed log earnings on 22 original variables and 16 dummy variables. But, our

final aggregate earnings equation includes 6 original variables and 3 dummy variables. The

aggregate earnings equation which best fits the sample data is as follows (see Table 6.13) :

w In Yi = 6.815261 + .277726 (labour market segment of employment) + .084892

(labour market experience) + .743984 (fathers' occupation administrative or professional) + .399225 (religion : Muslim) + .016425 (parents' education) - .002071 (labour market experience squared) -.173946 (first job : unskilled manual worker) + .374189 (on-the-job training)+ .112043 (marital status).

222

Before going to discuss about the final earnings equation for the entire population, it is

perhaps necessary here to mention the process of selection of the variables. Like the earlier

two earnings functions, we have also preferred here to drop 'age' and 'age squared' in

favour of'labour mark~t experience' and 'labour market experience squared'. It can be seen

in Table 6.10 that 'caste of the worker' is positively and significantly correlated with 'years

of schooling', thereby meaning that workers belonging to lower caste categories arc

relatively less educated than the higher caste workers. The, ·religion of the worker' is

positively and significantly correlated with log earnings, and as we can see this variable

remains as an important explanatory variable in our final earnings function. 'Marital status'

of the worker is positively and significantly correlated with log earnings and 'labour

market experience'. This implies that unmarried workers in the sample have less years of

labour market experience, and they earn less than the married workers in the small scale

manufacturing sector in Delhi. Similarly, the degree of correlation between 'place of

origin' of the worker and log earnings is also positive and significant, which means that

workers having urban origin earn more than the workers having rural origin, in the

manufacturing sector. However, the 'place of origin' of the worker variable is significantly

correlated with many other independent variables, particularly with 'parents' education.

'Parents' education' captures the effect of the 'place of origin' on the earnings of the

individual worker. (see Table 6.1 0). Table 6.10 shows that in our sample, workers having

rural background are a disadvantaged group in the small scale manufacturing sector in

Delhi. Their parents are less educated and the occupational status of their fathers is also

low. Moreover, these workers belong to poor households, having considerably low

average annual family income. So far as their labour market experience is concerned these

workers had access to their low paid first job through informal channels of recruitment.

And, they are mostly found working in the lower segment of the manufacturing labour

market of the small scale manufacturing sector. Thus, the 'labour market structure'

variable or the labour market segment in which the individual worker is employed captures

the influence of a whole lot of socio-economic variables 'on the earnings of the worker.

223

Table 6.10 : Correlation matrix for the whole sample

LOG EAR E2 AGEQ E3 E5 E6 E8 EIO El5 PAEDU FAOCC E21 LOG EAR 1.0000 .5285** .5258** -.0294 .1938** .1782** -.2070** .0962 .3953** .5780** .5425** .5723** E2 1.0000 .9849** -.0814 .1260 -.0247 -.4759** .0660 .1305* .1684** .2286** .3083** AGEQ 1.0000 -.0856 .1157 -.0235 .4247** .. 0389 .1346* .1633** .2404** .3224** E3 1.0000 .1374* .0703 -.2779** -.1273* .431 o•• .2776** .1395* .1730** E5 1.0000 -.0369 -.0682 -.1036 .2413** .2915** .2128** .1812*. E6 1.0000 -.0066 .0976 -.0475 .1297* .0743 .0595 E8 1.0000 -.2066*. -.2376*. -.1836** -.0079 -.0022 EIO 1.0000 -.1602* -.0607 .0357 .1259

El5 1.0000 .6196** .3764** .4952*•

PAEDU 1.0000 .5610** .632o••

FAOCC 1.0000 .5607••

E21 1.0000

224

Table 6.10 (Contd.)

E22 E25A F.OUQ F.26 VEDNQ EDEXP f-JMODE E29 E54 E57 V4 V6 LMEX LMEXQ PI\ LOG EAR .3490 .. .44!2•• .5048** . 5305** .5434 .. .5960• .4091** .6624** -.4007*. .4817** .2306** .5892** .4910 .. .3920** .1169 E2 .0946 .1648** .2028 .. .2757•• .3073 .. .6396• .1541• .2474*"' -.0804 .3123*. .3358** .2923** .6802** .6082*• .I 173 AGEQ .0956 .1529• .1929*• .2779•• .3171•• .6740* .1459* .2430** -.0909 .3380*. .3439** .2850** .7045** .6748 .. .0917 E3 .3941•• .2230** .2266** .0009 -.0701 .0101 .2790** .0040 -.2600** -.0524 -.1376* .1744** -.0806 -.0720 .0378 E5 .2493** .3299** .3269•• .1996•• .1747•• .1319* .2260** .2170** -.1766** .1069 -.1578* .2822** .0000 -.0095 -.0205 E6 -.0446 .0093 .0292 .0187 .0216 .0049 .1606* .1272* -.0992 -.0600 .1235 .0647 -.0024 -.0104 -.0127 E8 -.2401*• -.1581• -.1595• .0839 -.1225 .2319* -.1576* -.0251 .1315* .-.0861 .2930** -.0695 -.3779*• .2383 .. -.0213 EIO -.1421• -.0619 -.0787 -.0106 .0067 -.0606 -.1439* -.0298 .1011 -.0175 .0867 -.1010 .0161 -.0656 -.0661 E15 .8803•• .48oo•• .5336•. .2587** .2326** .3029* .5596*. .4691*. -.4379** .2192** -.1030 .5021** .1026 .1068 .0763 PAEDU .5860** .6725•• .7243*"' .5194** .4808** .3632* .7023** .6905** -.5399*. .3130*"' -.1265 .7176** .0757 .0693 .1325* FAOCC .3974•• .4025** .4065** .4442** .4458** .3211* .2936** .4442** -.2424** .3736** -.0642 .4072** .1403* .1373• .1043

E21 .4559•• .4635** .5107** .5261** .5459** .4067* .4943** .523 I** -.3629*. .3950*. -.0255 .5122** .2086** .1732** .2580~ ~

E22 1.0000 .6097** .5683** .2545** .2309** .3417* .5070** .4461** -.3952** .2049 .. -.1231 .4833 •• .0520 .0584 .OS 10

E25A 1.0000 .9638** .3891** .3452** . 5092* .5755"* .5710** -.4953 •• .2333** -.1733** .6916** .0651 .0610 .0957

EDUQ 1.0000 .3944** .3504** .5100* .6464** .629R** -.5398"* . 2583 .. -1702*• .7547 .. .0<>65 .0895 .1105

E26 1.0000 .9731 .. .3627* .4351** . 6563** -.2712** .4457** -.0627 .6591 •• . 1971** .1990** .2754" •

VEDNQ 1.0000 . 3874* .3 775 •• .63 13 •• -.2517*. .5011** -.0407 .5960** .2296** .2365** .2628**

EDEXP 1.0000 .3124** . 3534** -.3253 •• .4163 •• .3494** .4623** .8174** .7993** .0882

FJMODE 1.0000 .6504** -.4479** .2154** -.1266 .7132** .0760 .0745 .2279**

E29 1.0000 -.4922** .3943** -.0568 .8128** .1306* .1103 .1825*"

E54 1.0000 -.1672** -.1278* -.5176*. -.1640** -.1405* .167!**

E57 1.0000 .0381 .31 06** .3012** .3145** .1\80

V4 1.0000 -.1247 .5912** .4348 .. -.1155

V6 1.0000 .1782** .1575* .2417$$

LMEX 1.0000 .9094** .0345

LMEXQ 1.0000 .0247

PII 1.0000

Number of cases: 413 2-tailed Sign if: • -- .0 I ... -- .001

225

Table 6.10 also shows that the degree of correlation between 'place of schooling' of the

worker and log earnings is positive and high, meaning thereby that workers schooled is

rural government schools earn less than the workers schooled in urban government schools

in the manufacturing labour market. These workers are also relatively less educated.

Besides, 'years of schooling' of the worker is positively and significantly correlated with

log earnings. This means that, in our sample, workers having more years of schooling

earn more than the workers having less years of schooling. However, we will find that in

our final equation even ·year of schooling' is not considered, rather ·on-the-job training'

proves to be one of the explanatory variables of earnings variations in the whole sample.

Similarly, 'years of vocational schooling' and log earnings are also highly and significantly

correlated with each other, which means that workers having more years of vocational

schooling are paid more than the workers having less years of vocational schooling. The

positive and high correlation between 'labour market structure' and 'years of vocational

schooling' suggests that workers in the upper segment of the labour market have high

vocational educational status than the workers in the lower segments of the labour market.

In fact, in our sample, in the secondary segment of the labour market, workers have no

vocational schooling at all. Thus, 'labour market structure' also captures the influence of

vocational education of the worker on his/her earnings.

In the manufacturing labour market, 'average number of hours worked daily' by the

worker is negatively correlated with log earnings, which implies that workers who work for

more number of hours a day are paid less than the workers who work for less number of

hours a day (see Table 6.1 0). Similarly, workers who work for a longer duration in a day

are found in the lower segments of the labour market. Here too 'labour market structure'

captures the influence of· daily work duration' of the worker on his/her earnings. ·On-the­

job training' is paid in the manufacturing labour market and those workers working in the

upper segment of the labour market enjoy the on-the-job training facility. Thus, 'labour

market structure' as an explanatory variable in the earnings equation for the whole sample

226

capture the effects of many socio-economic, educational and labour market related

variables on the earnings of the worker.

Table 6.11 : ¥ean & standard deviation of earnings, schooling and experience for the wh~le sample

Statistics

Mean Standard deviation No. of cases

Log earnings

7.41 .58

413

Years of schooling

8.99 3.75

413

Years of labour market expenence

5.20 3.89

413

The final earnings equation for the whole sample explains total earnings in terms of

'segment of employment' of the worker, his/her 'fathers' occupation', 'religion', 'marital

status', 'parents' education' 'first job' 'on-the-job-training', and 'labour market experience'.

lbe equation is significant, and our final correlation matrix does not show existence of

multicollinearity among explanatory variables (see Table 6.12). This earnings equation

explains nearly 74 percent of earnings variations in the sample (see Table 6.13). As has

already been discussed earlier, 'labour market structure' or 'segment of employment' is the

most powerful explanatory variable in the earnings equation, and it alone explains nearly

35 percent of earnings variation in the whole sample (R2==.34715). This finding supports

our argument that in the small scale manufacturing sector in Delhi structural factors in the

labour market are very important in determining wages of a worker. \\'ben 'labour market

experience' is considered along with the 'structure of the labour market', the equation

explains nearly 50 percent of earnings variations (R2==.50100). Here, R-square change is

.15385, implying that the contribution of labour market experience alone to the explanatory

power of the equation is about 15 percent. When ·fathers' occupation' divided into 6

dummy variables is included in the equation, its explanatory power increases by around 10

percent and the equation explains 60 percent of earnings variations (R2==.60382). When

'religion' of the worker is brought in the equation, the equation explains nearly 66 percent

of earnings variations, the increase here is by 6 percent.

227

Table 6.12 Final correlation matrix for the aggregate equation for the whole sample

LOG EAR E8 E57 PAEDU V6 LMEX LMEXQ F6 R2 J\

LOG EAR 1.000 .2070** .4817** .5780** .5891 ** .491 o• • .3920** .5406** . \589* -.5381**

E8 1.000 .0861 -.1836** -.0695 .3779** .2383** .678 .0282 .0009

E57 1.0000 .3130** .3106** .3012** .3145** .4809** -.0563 -.1909**

PAEDU 1.0000 .7176** .0757 .0693 .3844** -.0506 -.4821**

V6 1.0000 .1782** .1575* .3044 •• -.1408* -.5282**

LME 1.0000 .9094** .2041 ** -.0001 -.1436*

LMEXQ 1.0000 .2151 ,.. .0006 -.1048

F6 1.0000 -.0600 -.2037**

R2 1.0000 -.2306**

11 1.0000

Number of cases : 413 2 -tailed significance : • - .0 I ** - .00 I

22R

Table 6.13 : Regression results for the whole sample (Dependent variable : log earnings)

Vairable

Segment of employment Labour market experience Father's occupation

Administrative or Professional Religion (Muslim) Parents' education Labour market experience squared First job unskilled manual worker On-the-job training Marital status Constant Multiple R R- square Adjusted R - square Standard Error F= Regression Residual No. of cases

B-coefficient

.277726

.084892

.743984

.399225

.016425 -.002071 -.173946 .374189 .112043

6.815261 .85975 .73917 .73334 .29736

126.89459 9

403 413

Standard Error ~-coefficient T-Value of B-coefficient

.051434 .217017 5.400

.010027 .572848 8.466

.088333 .256984 8.422

.059434 .185578 6.717

.003013 .215663 5.451 4.33721E-04 -.307955 -4.774

.037676 -.151196 -4.617

.093826 .121716 3.988

.038806 .085608 2.887

·----·-------·-----··

229

Significance ofT

.0000

.0000

.0000

.0000

.0000

.0000

.0000

.0001

.0041

In the next step, 'parents' education' along with the earlier 4 variables raises the

explanatory power of the equation to about 69 percent. In other words, other things

remaining the same, 'parents education' explains nearly 2 percent of earnings differentials

in the whole sample, R2-change is .02388. Inclusion of' labour market experience squared'

in the equation increases the explanatory power of the equation by 2 percent (R2-change =

.02252), and the equation explains nearly 71 percent of earnings variations in the sample.

'First job' of the worker divided into 6 dummy variables along with the earlier variables

explains about 72 percent of earnings variations among workers in the whole sample. Here,

the individual contribution of the ·first job' variable in explaining earnings differentials in

the sample is only one percent. When 'on-the-job training' is entered in the equation, the

R2 value increases to .73377, and finally, 'marital status' of the worker along with the

earlier 8 variables raises the R2-value to .73917. The contribution of 'marital status' in

explaining earnings variations in the whole sample is relatively very low, i.e. only 0.5

percent.

The final earnings equation for the whole sample suggests that structural factors in the

labour market and socio-economic backgrounds are most important determinants of

earnings of the sample workers, and the ·segment of employment' explains a little less than

half of the earnings variations in the sample. Besides, 'labour market experience' also

emerges as the next best determinant of earnings, and the effect of 'religion of the worker'

on his/her earnings level is significant but very low. Social background of the worker also

plays an important role in determining the earnings of an individual worker in the urban

manufacturing labour market in Delhi.

6.4 Some Findings

We ran several alternative regressions separately for each individual labour market

segment and for the whole sample. As can be seen in Table 6.14, finally we have selected

3 regressions, one each for the secondary segment, primary segment and for the whole

sample, as our 'best fits'. In this section, our discussions of the findings will focus on these

three regressions. All the regressions discussed here are highly significant and confirm to

230

the partial F-test, with 95 percent of confidence limit. Also Students' T-test confinn that

all the B-coefficients of these regressions are significant at 5 percent level, though some

are even significant at one percent level.

Our final regression equation for the secondary segment explains total earnings in

terms of five original variables. such as 'marital status', 'parents' education', 'average

annual family income'. 'labour market experience', 'labour market experience squared' and

two dummy variables such as 'religion' of the worker (i.e. Hindu) and first job (i.e.

unskilled manual worker). And, the equation explains about 52 percent of earnings

variations among individuals in the secondary segment (R2 = .51895). The results ofthis

regression suggest that the 'first job' of the worker is one of the important predictors of

earnings in the secondary segment. The ·first job' is divided into 6 dummy variables, and in

the secondary segment equation, the dummy variable representing the 'unskilled manual

worker' has a negative and large coefficient (see Table 6.14). So far as the manufacturing

urban labour market in Delhi is concerned, this finding suggests that workers who get into

low paid unskilled manual first jobs continue to earn 21 percent less in their current jobs

compared to the workers who get into relatively high paid first jobs. We have already

discussed in chapter 5 that job changes in the secondary segment of the manufacturing

labour market is quite frequent compared to the frequency of job changes by workers in the

primary segment of the labour market. The above finding therefore suggests that workers

having low paid first jobs are still caught up in the secondary segment of the labour market.

Thus, job history of the worker is one of the important determinants of earnings in the

manufacturing labour market in Delhi.

It can be seen in Table 6.14 that the coefficient of the religion dummy (i,e. Hindu) is

negative and large (i.e. B- coefficient = -.36946). This means that being a Hindu rather

than Muslim or Christian depresses the earnings by 36.9 percent in the secondary labour

market. This is perhaps a surprising finding. However, if we examine the characteristics of

the secondary labour market in our sample, we will find that most of the Hindus are

working in unskilled manual jobs which are lower paid jobs, and most of the workers

belonging to other religions such as Islam or Christianity are working in the skilled

231

Table 6. I 4: Regression result~ for ~econdary 11e2ment, primary segmenl nnd whole snmple (l>ependent variable: log earnin2s)

Vnrinble

Sex Religion

Hindi Muslim

Marital status Parents' education Father's occupation

Administrative or professional Average annual family income Years of schooling squared Y car~ of vocntional schooling squared First job

Unskilled manual worker On-the-job training Labour market experience Labour market experience squared Segment of employment or labour market structure Constant Multiple R R-square Adjusted R-square Standard Error F= Regression Residual No. of Cases

Secondary Segment

B-cocfficient

-369436

.I i3702

.014717

2.637586E-06

-.209691

.78168 -.00 I 947

7.210028 .72038 .51895 .50730 .28480

44.53858 7

289 297

T-Valuc -.6.140

-6.140

2.496 3.259

2.200

-5.409

.0000 -2.145

Note: All the 8-coefficicnts arc significant at 5 percent level.

232

Primary Segment Whole Sample (Aggregate/Equation)

B-coe fficient -.232952

.700172

.0026 7(1

.022613

.2R8124

.092363 -.002426

6.852817 .86407 .74661 .73019 .30812

45.45971 7

108 116

T-Value B-coefficient T-Value -·--2.641"·----·-·--------------- ---

.399225 6.717

.112043 2.887

.016425 5.451

6.829 .743984 8.422

·1.:10.\ 3.106

. 17.:19-16 -4.617 2.722 .374189 3.988

.000 .84892 .0000 -4.175 -.002071 --4.774

.277726 5.400 6.815261

.85975

.73917

.73334

.29736 ! 26.89459

Q

403 -113

occupations. This is perhaps one of the reasons explaining disadvantaged position of Hindu

workers in the secondary segment.

'Marital status' of the worker also emerges as one of the important predictors of

earnings in the secondary segment. Given the coefficient of .113702, it implies that being

married raises one's earnings by 11.4 percent in the secondary segment. This finding

somehow suggests that perhaps employers consider married persons as relatively stable

workers in the secondary segment, and it is this characteristics of this category of workers

which yields them relatively a higher market premium than the unmarried workers. Among

the family-background variables. ·parents' education' and ·average annual family income'

are two important determinants of earnings in the secondary segment. Given the

statistically significant and positive coefficients of these two predictors, it means that an

individuals earnings are higher, higher the educational attainments of his/her parents, and

also higher the average annual income of his/her family. Thus, family- background of an

individual influences, to a large extent, the level of earnings in the secondary segment. This

finding do support the claims of the segmented labour market theory that socio-economic

backgrounds of an individual rather than his/her productivity related characteristics are

important in determining earnings in a highly structured labour market, like the one we

have in our sample.

'Total labour market experience' of the worker is the most important predictor of

earnings in the secondary segment, the size of its B-coefficient is .078168. What it means

is that, given the semi-logarithmic form of the earnings equation, an additional year of

labour market experience gives a return of about 7.8 percent in the secondary segment of

the manufacturing labour market. Moreover, a negative coefficient of 'labour market

experience squared', suggests that, after a certain initial years of labour market experience,

an additional year of labour market experience exerts a declining effect on the level of

earnings of the secondary segment worker.

Thus, the earnings equation for the secondary segment suggests that socio-economic

backgrounds, such as religion, family income, marital status, parents' education', labour

233

market experience, a.'1d tirst job (unskilled manual) of the worker are important

determinants of personal earnings. One of the objective measures of the importance of an

independent variable in the final regression equation is the P-coefficient. If we examine

the P-coefficients of the regression equation for the secondary segment, we will find that

'labour market experience' contributes most to the explanation of the variations in earnings

in the secondary segment. In terms of relative importance of the explanatory variables,

'labour market experience' is followed by 'parents' education', 'marital status', 'average

annual family income', 'first job', and ·reiigiOn'. However, what is striking here is that

'years of schooling' of the secondary segment worker does not yield any returns in the

urban manufacturing sector in Delhi, and therefore it does not figure in the final earnings

equation. This finding of our regression analysis supports the proposition of the segmented

labour market theory that there are no returns to schooling, and if there is any, it is too low

in the secondary segment of the labour market. This finding also suggests that wage

setting mechanism in the secondary segment does not take into consideration the variations

in productivity related characteristics across individual workers. Moreover, our finding

also indicates that secondary segment workers do not. receive any on-the-job training

facility, which further supports the proposition of the segmented labour market theory.

Besides, our final earnings equation for the secondary segment explains only 52 percent

of the variations in personal earnings, and nearly 48 percent of variations in earnings can

not be explained by this equation. Perhaps, what it indicates is that the wage determination

process in the secondary segment is highly influenced by market forces such as demand for

and supply of labour in the manufacturing sector. Given the high rate of rural-urban

migration in the country, employers find surplus labour supply in the secondary segment of

the manufacturing labour market. Existence of this surplus labour supply depresses the

'reservation wage' level of the secondary segment workers in Delhi, and also employers

find it easy to obtain labour by offering low wages. It should also be noted here that

workers in the small scale manufacturing sector in Delhi, especially in our sample. are not

unionised, which indicates that these workers have no bargaining power in the market.

234

Regression analysis for the primary segment of the labour market reveals that 'sex',

'fathers' occupation, 'labour market experience', 'education', and 'on-the-job training' of

the worker are important predictors of personal earnings, and the earnings equation

explains about 75 pe~cent of variations in earnings. It should be noted here that human

capital variables such as education, experience and on-the-job training are valued in the

primary segment of the labour market. and education finds a place in the final earnings

equation. Among all the predictors in the primary segment earnings equation. the size of

the B-coefficient of 'labour market experience' is .092363, and it means that an additional

year of labour market experience in the primary segment raises the earnings of the worker

by around 9.2 percent. If we compare the coefficients of thjs variable in the earnings

equations for primary segment and secondary segment, we will find that returns to an

additional year of labour market experience in the primary segment is higher than the

returns to an additional year of labour market experience in the secondary segment of the

manufacturing labour market in Delhi. This indicates that workers in the secondary

segment have a relatively flat experience- earnings profile compared to the experience -

earnings profile of the primary segment workers. This finding also suggests that 'labour

market experience' has a relatively high market premium in the primary segment than in

the secondary segment.

Among the social background variables, 'fathers' occupation' (administrative or

professional) is an important predictor of personal earnings in the primary segment, and the

B-coefficient of this variable is . 700172. This implies that if the father of an worker in the

primary segment is in administrative or professional job, his/her earnings increases by

nearly 70 percent. The B-coefficient of 'sex' variable is negative and significant. It can be

seen in Table 6.14 that, in the primary segment, being a female rather than a male

depresses one's earnings by 23.3 percent, all other things remaining the same. This finding

perhaps indicates that in the primary segment employers prefer males to females as their

employees.

235

One of the important findings here is that both general and vocational education of the

worker do have market premium in the primary segment of the manufacturing labour

market. In our final equation for the primary segment, positive coefficients of 'years of

schooling squared' and 'years of vocational schooling squared' that the level of general and

vocational education of the worker positively influences the worker's earnings, and

perhaps, these are used as 'screening devices' in this segment. It also implies that earnings

in the primary segment are a parabolic function of level of general and vocational

education, and the parabolic effects of years of general and vocational schooling are

positive. In this segment, those workers who enjoy on-the-job training facility on the

current job earn nearly 28.8 percent more than those workers who do not enjoy this facility.

The above findings support one of the claims of the segmentation theorists that human

capital variables in general and education in particular yields a positive return in the

primary segment of the labour market. Another important finding here is that if we

compare the B-coefficients of 'labour market experience squared' in the primary segment

equation and in the secondary segment equation, we will see that both the coefficients are

negative and significant. But the size of the B-coefficient of this variable in the secondary

segment equation is smaller than that of the B-coefficient of the· primary segment equation.

This means that, in the secondary segment, declining returns to an additional year of labour

market experience begins at an early stage compared to the years of labour market

experience after which every additional·year of experience yields a declining return In the

primary segment.

A comparison of regression results for primary and secondary segments of the labour

market reveals that the earnings functions that 'best fits' the individual segments are

different, and the size of the coefficients of the common variables in these earnings

functions are also different, generally large in case of primary segment equation. This

finding strongly supports our conclusion that the urban small scale manufacturing labour

market in Delhi is divided into distinct segments, where workers face different earnings

functions. And, even if workers face the same earnings function in both primary and

secondary segments, the size of the coefficients of the common explanatory variables in the

236

equations differ between the individual segments of the labour market. We, therefore,

argue that a distinct low wage secondary labour market exists in the manufacturing sector

in Delhi where wage setting mechanism is greatly influenced by the market forces.

In the earnings equation for the primary segment, if we rank the seven determinants of

earnings in terms of their relative contributions in explaining total earnings (on the basis of

size of p-coefficients of these variables), we will find that ·labour market experience' heads

the list and it is followed by 'fathers' occupation' (administrative or professional), 'years of

general schooling squared', 'years of vocational schooling squared', 'on- the-job training',

'sex' and 'labour market experience squared'.

As can be seen in Table 6.14, the aggregate equation for the entire sample explains total

earnings in terms of: (I) ·segment of employment' (i.e. structure of the labour market); (2)

'labour market experience'; (3) 'fathers' occupation' (administrative or professional); (4)

'religion' (Muslim); (5) 'parents' education'; (6) 'labour market experience squared'; (7)

'first job' (unskilled manual); (8) 'on-the-job training'; and (9) 'marital status' of the

worker. The 'segment of employment' (i.e. whether a primary or secondary segment

employee) emerges ac; one of the powerful predictors of personal earnings in the sample

small scale manufacturing labour market in Delhi. The B-coefficient of this variable is

.277726, which implies that being employed in the primary segment rather than in the

secondary segment increases the earnings of a worker by 27.8 percent. This finding draws

our attention to the nature of jobs found in these two labour market segments. In the

primary segment, jobs are protected in terms of some form of written agreement of

employment, and the secondary jobs are unprotected. This very fact influences the level of

earnings of worker to a great extent, indicating the importance of institutional factors in the

process of earnings determination in the sample manufacturing labour market in Delhi.

Another important finding is that 'religion' of the worker also proves to be an important

predictor of personal earnings in the sample, the coefficient of which is .399255. This

means that being a Muslim can raise ones earnings by 39.9 percent. This finding is

somewhat not surprising because of the fact that most of the low paid secondary segment

237

workers in our sample are Hindus, and Muslims and Christians are either working in

skilled or technical jobs which are relatively higher paid.

The 'first job' (unskilled manual) as an important predictor explains substantially the

earnings variations ~ong individuals in the sample. This dummy variable has a negative

and significant B-coefficient, i.e. -.173946. This means that, in the sample, those who have

their first job as unskilled manual worker earn about 17.4 percent less. This finding

indicates the importance of ones initial job history in wage determination process in the

sample manufacturing labour market. Among the family-background variables, 'parents'

education' and 'father's occupation' (administrative or professional) prove to be important

determinants of earnings in the entire sample, An additional year of parents' schooling

raises ones earnings by 1.6 percent in the entire sample. Similarly, if one's father is

employed in administrative or professional occupation, he/she earns around 74.4 percent

more in the sample labour market. These findings do support the proposition of the

segmented labour market theory that social background plays an important role in the

earnings determination process in a highly segmented labour market. Moreover, being

married also increases ones earnings by 11.2 percent in the entire sample, which indicates

that in the sample labour market, the stability related characteristics of a worker are valued

by the employers. 'On-the-job training' as a predictor also plays an important role in

explaining total earnings in the entire sample. The B-coefficient ofthis variable is .374189,

and it means that, in the sample population of workers, those who enjoy the on-the-job

training facility on current job earn nearly 3 7.4 percent more than those who do not. Apart

from this, 'labour market experience' also exerts a positive influence on the level of

personal earnings of the worker in the sample manufacturing sector. The B-coefficient of

this variable is positive and significant, i.e .. 084892. This means that an additional year of

labour market experience raises ones earnings by 8.5 percent. We also find that the

parabolic effect of labour market experience on earnings is negative, thereby implying that

after a certain years of work experience, returns to every additional year of work

experience starts declining.

238

Ranking of the independent variables on the basis of size of the P-coefficients in the

aggregate earnings equation show that 'years of labour market experience' is the most

important predictor of personal earnings in the entire sample, and this is followed by

'father's occupation' (administrative or professional), 'segment of employment', 'parents'

education', 'religion' (Muslim), 'on-the-job training', 'marital status', 'first job' (unskilled

manual), and 'labour market experience squared'.

Our regression analysis in this chapter strongly indicates the existence of

compartmentalised labour market segments in the sample urban manufacturing sector in

Delhi. Moreover, our findings suggest that structural factors and socio-economic

backgrounds are important in determining earnings in the urban manufacturing labour

market, and the overall labour market outcomes of this sector reflects the existing socio­

economic inequality in our society. The functioning of this small scale manufacturing

labour market in Delhi also reproduces the existing socio- economic inequality. This is

demonstrated by the fact that socio-economically disadvantaged groups are found

concentrated in the vulnerable low labour market segments in the urban manufacturing

sector in Delhi. In other words, results of our analysis show that the relatively less

privileged groups in our society are a less fortunate lot in the urban manufacturing labour

market in India in general, and in Delhi in particular.

239

l