Wage Differential and Segmented Informal Labour Markets in India– Selectivity Corrected Estimation of Multinomial Logit Model Panchanan DasProfessorDepartment of EconomicsUniversity of CalcuttaIndiaEmail: daspanchanan@ymail.com

Paper prepared for presentation at the

“5th Conference of the Regulating for Decent Work Network”

At the International Labour Office Geneva, Switzerland

3-5 July 2017

Abstract

This paper estimates the effects of some household and person specific factors on job selection as well as on wage differences among the workers working under different types of job contract with Indian data based on the theoretical structure of labour market segmentation.The unit level data from 61st

round (2004-05) and 68th round (2011-12) on employment and unemployment in India have been used in this study. The labour marketis segmented into four parts: self-employment, informal wage employment, formal wage employment and other workers along with persons available for employment. The participation equation is estimated by assuming that employment condition in terms of job contract is endogenous and job selection is non-random.Sample selection bias is corrected for estimating wage equation in different types of employment byusing multinomial logit model.The study investigates how the non-random selection of jobs of different types of contracts affects the estimation of the wage differential in the form of unequal pay for roughly equal productive jobs. The study observes that the share of self-employment, in which own account workers have been dominating, declined without any significant rise in wage employment.Wage workers have been dominated by casual employees which are informal in nature.Women were absorbed mostly in household base domestic activities and their share in such activities increased significantly between 2004-05 and 2011-12.There was a substantial shift of paid employment from formal to informal both for men and women, andfrom self-employment or wage employment towards domestic activities particularly for women workers.The informalisation of wage employment and the incidence of being in the labour force increased while the probability of entering into formal wage employment declined significantly in 2011-12 as compared to 2004-05.Marital status of a person increases the chance of entering into the formal wage employment. The effect of education on entering into the formal job market is positive and the effect increases with the level of education.The gender wage gap is much higher in informal wage employment as compared to formal wage employment and it appears in the Indian labour market mainly because of discrimination that cannot be explained by the factors relating to workers’ productivity. The incidence of wage discrimination is higher among informal workers and majority of them come from the vulnerable social groups in terms of castes or gender.

Key words: Job Selection, Labour Market, Informalisation, Multinomial LogitJEL Classification: I 21, I38, O53, J24, J64

Wage Differential and Segmented Informal Labour Markets in India– Selectivity Corrected Estimation of Multinomial Logit Model Panchanan Das

1. Introduction

One of the most disconcerting developments under neo-liberal reforms in the transitional

developing economies, and indeed throughout the world,has been a sharp increase in

informalisationof employment through labour market segmentation.Traditionally, labour market is

segmented broadly into formal and informal parts based on different contractual arrangements and

behavioural rules. The informal jobs include part-time, fixed-term and temporary employment mostly

on casual basis without any written job contracts and social security benefits. The formal jobs, on the

other hand, are permanent in nature with higher pay and social security benefits. It is well documented

that wage gap appears between formal and informal workers and the gap has increased significantly

after the initiation ofmarket based reforms in the transitional developing economies. Labour market is

segmented also within the informal parts based mainly on the type of job contracts and method of

payment. Informal jobs are not homogeneous and the degree of heterogeneity depends on the nature

of contract between the workers and the employing entity. This would cause different wages for

different tier jobs and even for different workers within the same tier in informal employment based

on gender, age, and ethnic origin. This paper focuses on wage differential between men and women

workers with roughly similar productivity characteristics in a fragmented informal labour market

based on types of job contracts.

If informal jobs are becoming more segmented, the most vulnerable groups among workers,

particularly women workers, would be affected badly more. Women tend to be more vulnerable than

men, experiencing lower participation rates and earing less even when they do enter the labour market

(Standing 1999). Several possible explanations have been offered in the literature about the more

concentration of women in informal jobs than men. Women, sometimes, are forced to accept informal

jobs so that they can manage to perform unpaid household activities along with the paid work.In this

context, the marital status and number of dependent children affect the decision to work outside the

household for women1.

A vast literature has emerged on earnings inequality in the labour market and most of them have

focussed on wage inequality between formal and informal workers. A very few attempts have been

made to look into wage differences within the informal part of the labour market by taking the

selection problem into account.Deininger et al. (2013), for example,estimated the gender wage gap in

1 Women workers in many countries including India are encouraged to participate to the labour force through protective legislation. Generous maternity leave rules allowing long periods of leave, more flexibility and monetary benefits to women with children.

India by controlling for selection into labour market participation and observed that the share of the

gap due to different returns to job characteristics is higher among casual workers than among non-

casual workers. They ignored the selection into multiple employment status.Our study takes care of

this gap in the literature.

This paper attempts to analyse the nature of segmentation of jobs in the informal part of the labour

market and the associated wage differential by using household and personal level information from

61st and 68th round survey on employment and unemployment situation in India. The nature of jobs

has been changed towards informalisationrapidly after initiating pro-business market openness and

deregulation of the labour market in India. This stylised fact has motivated to analyse job selection by

individuals in a segmented labour market on the basis of types of job contract and the wage

differential among workers with same level of education by the types of contract. In this study we

define informalisation of employment in terms of job contracts. A worker with no written job contract

or written contract for shorter period (up to three years) is treated as informal worker irrespective of

the sector in which he or she is employed. This is because a worker who has no written job contract is

highly vulnerable and is subject to high job insecurity. It is very much easier for an employer to deny

any kind of employment benefit to such workers or even an employer can execute easily hire and fire

policy to those workers. Labour market flexibility, a part of the structural adjustment programme of

the union government of India, allows the firms to fix job conditions by following their profit

maximising rule. Profit maximising firms have taken this opportunity to reduce their labour costs

either by displacing labour or by increasing working hours per worker. Labour market reforms in this

direction enhances the peripheral segment of the labour market by increasing informalisation of

employment.

The basic objective of this paper is to estimate the effects of some household and person specific

factors on job selection as well as on wage differences among the workers working under different

types of job contract with Indian data based on the theoretical structure of labour market segmentation

(for example, Maloney 1999, Gunther and Launov2012).The participation equation is estimated by

assuming that employment condition in terms of job contract is endogenous and job selection is non-

random selection using multinomial logit model.Sample selection bias is a common feature in

estimating wage equation and the bias is corrected for estimating wage equation in different types of

employment. We then investigate how the non-random selection of jobs of different types of contracts

affects the estimation of the wage differentialin the form of unequal pay for roughly equal productive

jobs by following Peterson et al, (1997). The wage differential is decomposed by following methods

originally developed by Oaxaca (1973) and Blinder (1973) and subsequently refined by Oaxaca and

Ransom (1994).

The rest part of the study is organised as follows. Section 2 describes the data and construction of

variables used in this study. Section 3 analyses how the Indian labour market is segmented on the

basis of type of job contracts and activity status of the working age population and wage differential

among them by levels of education. Section 4 deals with econometric methodology used in this

study.Section 5 interprets the empirical results based on multinomial regression model. Section 6

summaries and concludes.

2. Data

We have used unit level data from 61st and 68th round survey on employment and unemployment

situation in India (Schedule 10) for the period 2004-05 and 2011-12 provided by the National Sample

Survey Office (NSSO). These surveys are the primary sources of data on various indicators of labour

market in India. The cross-sectional survey is roughly representative of the national, state, and the so-

called “NSS region” level. It gathers information about demographic characteristics of household

members, weekly time disposition, and their main and secondary job activities. The principal job

activities are defined for all household members as self-employed, regular salaried worker, casual

wage labourer and so on. In this study we have taken usual principal status of employment2to examine

employment status of a person within the age group 15-60 years.

In schedule 10 of these survey rounds, activity status is classified into 13 groups consisting mainly

different forms of self-employment, wage employment and other activities. Self-employed are those

who operate their own farm or non-farm enterprises or are engaged independently in a profession or

trade. The self-employed are further categorised into own-account workers, employers, and unpaid

workers in household enterprises. Wage employment is divided into regular wage employment and

casual employment. Regular wage workers are those who work in other’s farm or non-farm

enterprises of household or non-household type and get salary or wages on a regular basis, not on the

basis of daily or periodic renewal of work contract. This category not only includespersons getting

time wage but also persons receiving piece wage or salary and paid apprentices, both full time

andpart-time. On the other hand, a person working in other’s farm or non-farm enterprises, both

household and non-household type, and getting wage according to the terms of the daily or periodic

work contract is a casual wage labour.

We have categorised the working age people, excluding students and disable to work, into four

segments based on type of job contracts and principal usual activity status: informal self-employed,

informal wage employed, formal wage employed, and the persons available for employment. In the

sample used in this study, wages are observed only for wage workers and if we ignore the individuals

2 Wage information for the regular salaried workers and casual workers are only available from surveys but as for the self-employed category it is not easy to separate out the wage component.

with no wage earnings the sample becomes non-random or incidentally truncated and here the

problem of sample selection bias will arise. How to resolve this problem is discussed in section 4.

3. Labour market segmentation and wage differential: some observed facts

3.1 Segmentation by activity status

We have estimated a summary distribution of working age population separately for men and women

by principal usual activity status as recorded in schedule 10 of 61 st and 68th survey round and the

results are displayed in Table 1. It is observed that the worker population ratio (self-employment3 and

wage employment taken together)for men was little less than four times the ratio for women in 2011-

12. Labour market participation declined during these two survey rounds and the rate of decline was

significantly higher for women than for men (Table 1).

The share of self-employment, in which own account workers4 have been dominating,declined by 5

percentage point without any significant rise in wage employment. The dominance of own account

workers and unpaid family workers in self-employment among men and women respectively is not

surprising in India. During the past few decades governments both at the national and subnational

level have supported small business start-ups to reduce unemployment. However, the falling share of

it without rising share of wage employment may be an indication of failure of the government policies

in generating employment. The share of the self-employed as employer is significantly small, even

too small among the women, and remained constant over these two survey periods.

Wage workers are classified in the survey into regular paid employees and employees with casual

payment. Wage workers have been dominated by casual employees which are informal in nature. In

India, just above 38 percent of men and 11 per cent of women in the working age population group

were in wage employment, both regular and casual employment taken together, in 2011-12. While the

men’s share in wage employment increased marginally, the women’s share declined significantly in

2011-12 in comparison to the figures appeared in 2004-05. Wage employment on regular basis

increased slightly to 15.5 per cent and 3.8 per cent for men and women respectively in 2011-12. The

incidence of casualisation of employment was significantly more in the private sector jobs than in

public sector jobs as expected, but what is concerning is the share of casual workers increased in the

public sector particularly for men in 2011-12.A significant transformation occurred from self-

employment or wage employment towards domestic activitiesfor women workers during this period.

Women are more likely to stay away from gainful employment for biological or other reasons 3 The classification of the different types of self-employment is rather problematic. By definition, self-employment is highly informalised.

4 Own account workers enjoy very limited access to social protection

connected with religious, social and ethnic factors. Women were absorbed mostly in household base

domestic activities and their share in such activities increased from 55 per cent in 2004-05 to 61 per

cent in 2011-12.

Table 1 Distribution of working age people by activity status

Activity type Men Women2004-05 2011-12 2004-05 2011-12

Self-employment of which 43.8 38.9 16.8 11.2Own account worker 31.8 29.6 4.7 4.0Employer 1.4 1.4 0.2 0.1Unpaid family worker 10.6 8.0 12.0 7.1Wage employment of which 37.0 38.3 14.5 11.3Regular salaried employee 14.1 15.5 3.4 3.8Casual wage labour in public sector 0.1 0.6 0.04 0.3Casual wage labour in private sector 22.8 22.2 11.1 7.2Others* of which 19.2 22.8 68.7 77.5Domestic duties 0.5 0.5 54.8 60.9

Note: * includes unemployed, students, household domestic workers, pensioner, disabled and others.Source: Author’s calculation with unit level data from 61st and 68th NSS round

3.2 Segmentation by job contracts and incidence of informalisation

One of the important parameters determining the nature of employment is the type of job contract. In

this study we analyse labour market segmentation and informalisation of employment both for self-

employed and wage workers. Formal workers must have written job contracts for longer period. So,

the workers with no written job contract, or job contract for shorter period may be treated as informal

workers. Employment without any written contract is the extreme form of informalisation. Workers

have no job security without any written contract, because they have no proof of their contractual

status and are in a weak position to exercise any demand for compensation.Informal employment is

identified not only on the basis of type of job contract but also on some other official norms, legal

aspects, income taxation, social security benefits including paid leave, sick leave payment, and so on 5.

There are varying degrees of informalisation of employment between fully regulated and protected

formal employment and fully unregulated and unprotected informal employment (ILO 2002).Thus,

labour market is segmented not only between formal and informal part but also between different

forms of informal employment.

5 Informal employment comprises all jobs outside the framework of regulations (Hussmanns 2004). Thus, being in informal employment depends on the legal and institutional framework of an economy.

Table 2 presents the distribution of wage worker by types of job contacts both for men and women

prepared from unit level information in 2004-05 and 2011-12.The labour market in India is dominated

primarily by the paid workers without any written job contracts. In 2011-12 around 80 per cent of

men and 75 per cent of women who were in paid job had no written contract at all. The incidence of

informalisationof this extreme type increased markedly during 2004-05 and 2011-12,both for men and

women.The results shown in Table 2 suggest a substantial shift of paid employment from formal to

informal both for men and women. The share of informal workers in terms of without any written

contract grew among all paid workers and it has grown very rapidly in the formal sector of the

economy. Informalisation of employment increased not only because of casualization of the

workforce, but also because of more precarious employment conditions of the regular salaried

workers without any job contracts(Srivastava, 2016). The survey data also suggest (we have not

reported the results here) that the increase in the share of workers with no written job contract was not

appeared only among casual workers but among regular salaried workers as well.

Table 2 Distribution of wage workers by type of job contracts

Type of job contract Men worker Women worker2004-05 2011-12 2004-05 2011-12

No written job contract 74.9 80.1 72.3 74.9Written job contract up to 1 year 1.7 2.6 1.5 3.2Written job contract for more than 1 year to 3 years 1.6 1.7 2.6 2.0Written job contract for more than 3 years 21.8 15.6 23.7 19.9

Source: As for Table 1

3.3Informal employment by workers’ education

The incidence of informalisation of employment has not been restricted only to illiterate or less

literate workers. A significant part of the educated workers are also forced to accept jobs with no

written job contracts or without any social security benefit. Table 4 presents the distribution of

workers who have no written job contracts by workers’ level of education. The share of informal

workers was notably high among the workers with middle school level of education as well as among

illiterate workers both for men and women. The share of informal women workers was significantly

high among the illiterates as compared to the share for men. Although the incidence of informalisation

of employment declined at lower levels of workers’ education, it jumped up remarkably among

workers with higher education level during the period between 2004-05 and 2011-12. Both for men

and women workers, informalisation increased at the highest rate for graduate and post-graduate

workers. Perhaps, this is one of the disconcerting outcomesof neo-liberal reforms in India. Workers

with adequate human capital, at least in terms education, have somehow managed to get a job, but

without any job security or in indecent job conditions.

Table 4 Distribution of workers with no written job contract by level of education

Workers’ education Men Women2004-05 2011-12 2004-05 2011-12

Not literate 21.0 19.8 48.0 36.2Below primary 12.8 11.1 10.1 9.4Primary 18.2 16.1 10.3 13.1Middle school 22.0 20.1 10.5 12.1Secondary 11.3 12.8 5.8 5.9Higher secondary 5.6 7.0 3.6 5.8Graduate and diploma 7.4 10.5 9.1 13.7Postgraduate and above 1.7 2.7 2.6 3.8

Source: As for Table 1

3.4 Wage differential by employment condition

A sizable literature, both theoretical and empirical, has grown in documenting wage differential

among different types of workers.The empirical literature points out that the distribution of earnings

among workers of all categories has grown wider. The growth of international trade, declining

unionisation and real minimum wage, and skill-biased technological change may be responsible for

rising inequality globally (Levy and Murnane 1992, Acemoglu2002). Skill-biased technological

change has been an important determinant of rising wage inequality (Johnson 1997). Technological

change of this type has enhanced employment and wages of skilled workers while depressing the

employment opportunities and earnings of the less-skilled. In India, increasing trade openness is

associated with increasing labour productivity and rising wage inequality among skilled and unskilled

workers in the organised manufacturing sector (Galbraith et al. 2004, Dutta 2005, Das 2007).

In this study, we focus on wage differential between informal and formal workers, and also between

the workers with different conditions of employment based on the nature of job contract. We have

calculated ratio of mean wages for workers with no written job contract or job contract for shorter

period (less than 3 years) to mean wages for workers with job contracts for longer period (more than 3

years) at different levels of workers’ education. This ratio measures the extent of wage differential

between informal workers of different types in terms of types of job contract and formal workers with

job contract for longer period at each level of workers’ education. Normally, the informal segment of

the labour market is characterized by lower wages.

Table 5 shows the ratio of mean wage forinformal workers to the mean wage of formal workers

separately for men and women workers in 2004-05 and 2011-12. As expected, wages in informal

employment are lower than in formal employment (the wage ratio less than unity). But, the extent of

wage differential is different at different level of education as well as at different employment

condition (Table 5). The average wage for illiterate men workers with no written job contract was less

than half the wage for similar worker with written job contract for more than 3 years. For illiterate

women workers, on the other hand,the wage differential between formal and informal workers of any

type was significantly less. The wage difference between formal and informal employment in the

employment condition with no job contract or job contract for less than one year were roughly the

same for men workers at different education level up to secondary education. There was a significant

variation in formal-informal wage differential by workers’ education in employment with job contract

for more than one year and up to three years. The wage gap in this employment condition was the

least among workers with primary level of education and the gap reduced significantly in 2011-12

compared to the wage gap in 2004-05. The informal-formal wage differential was significantly less

among workers with education level graduate and above than at lower education, particularly for men

workers.

Table 5Wage differential by types of job contract at different education of workers

Job contract

Education level

Men WomenNo written contract

For 1 year or less

More than 1 year to 3 years

No written contract

For 1 year or less

More than 1 year to 3 years

2004-05Illiterate 0.45 0.44 0.43 0.74 1.02 0.85Below primary 0.45 0.74 0.58 0.73 0.51 0.94Primary 0.47 0.32 0.67 0.49 0.69 0.37Middle school 0.45 0.41 0.57 0.55 0.20 0.67Secondary 0.44 0.34 0.57 0.52 0.44 0.36Graduate 0.60 0.64 0.91 0.60 0.42 0.582011-12Illiterate 0.47 0.59 0.39 0.58 0.63 0.85Below primary 0.43 0.67 0.58 0.92 1.75 1.30Primary 0.47 0.47 0.89 0.48 0.76 0.00Middle school 0.41 0.47 0.50 0.78 0.63 0.69Secondary 0.45 0.47 0.57 0.55 0.86 0.46Graduate 0.62 0.84 0.74 0.60 0.81 0.56Note: The reference group is wage employment with job contract 3 years or moreSource: As for Table 1

While job security and social security are very low in the informal part of the labour market, informal

jobs may offer some favourable conditions like flexibility in working hours to workers, and these

aspects may influence wages differently for men and women. The wage differential between informal

and formal workers among women was less than those among men. In other words, informalisation of

employment at least in terms of type of job contracts had penalise more the men than the women in

pay differential6.Even, in some few cases, women workers earned more in informal employment than

in formal employment. As shown in Table 6, for women workers with below primary level of

education got higher pay in informal employment (with job contract one year or less and job contract

up to three years) than in formal employment (job contract for more than three years) in 2011-12.

4. Methodology

We decompose the wage gap by using the methodology developed originally by Oaxaca (1973) and

Blinder (1973), and extended further byOaxaca and Ransom (1994) and Oaxaca and Ransom

(1999).To find out the reference wage, we estimate a wage regression given in equation (1) with the

pooled sample of both men and women workers separately for formal wage workers, informal wage

workers and for other workers including persons available for employment from 61 st and 68th survey

rounds data. We have taken as control variables in equation (1). Then we estimate the similar wage

equation separately for men and women workers in each segment (j=1, 2, 3) of the labour market.

ln w ij=β0 j+β1 j D2011+β2 j Drural+β3 j D female+β4 DTE+∑k=1

4

γ k DEduk

+θ1 j age+θ2 j age2+φ j unemprate+εij (1)

The explanatory variables include year dummy to locate the time effect, rural dummy for

differentiating mean wage between rural and urban workers, female dummy for gender differential,

dummy variable for workers with technical education, education dummies, along with age of workers

and regional unemployment rate.To capture demand side effects of the labour market, we use the

regional unemployment rate that characterises the state of the local labour market. We construct the

regional unemployment for different education groups in order to identify the impact of labour

demand.Even if workers may accept a job for which they are overqualified, the unemployment rate

among people of the same education level will impact their decision to participate in a particular

segment and their wages.

The wage gap between men and women workers in each of the three segments of employment is

decomposed in the following way:

ln { w̄F−ln { w̄ ¿M=( X̄F' −X̄ M

' ) β̂+ X̄ F' ( β̂F− β̂ )+ X̄ M

' ( β̂− β̂ M )¿ (2)

6 The results shown in Table 6 partially support the findings of Gong and Van Soest (2002) who studied the urban labour market in Mexico,and found that the differences between formal and informal wages is small for women.

The first term in the right hand side accounts for wage differences by gender (F and M) in each

segment of employment by workers’ characteristics capturing the endowment effect.The last two

terms account for differences by gender in presence of labour market segmentation associated with

the given characteristics. The sum of the last two terms is the adjusted wage gap that takes into

account the observable differences in characteristics between men and women in each segmentof the

labour market.

The OLS estimate of equation (1) is based only on the sample from restricted population in which

persons have wage income by ignoring the part where wage information is missing. The use of the

sample from restricted population creates sample selection bias. To correct for the bias we follow a

two-step methodologydeveloped in Dubin and McFadden (1984). In step one, we estimate the

selection equation in a frame of multinomial logit. In step two, we estimate the wage equation after

correcting the bias. The estimated selection equation is used to analyse the informalisation of

employment in a segmented labour market and the estimated wage equation is used to analyse wage

differential.

In the selection equation, the dependent variable is a categorical variable, employmentstatus based on

types of job contract and principal activity status (informal self-employed, informal wage employed,

formal wage employed, and other workers including the persons available for employment). As

employment status is likely to be endogenously determined, the dependent variable is a stochastic

event the outcome of which depends on its density function. In this study, we estimate the probability

that a person to be either in formal employment or in informal employment of a particular type by

taking into account that multiple potential outcomes exist in the labour market. Persons have different

probabilities to be in employment with a particular work status depending on their preferences as well

as on demand constraints and employers behaviours that may cause job rationing and labour market

segmentation. As there are more than two categories of employment, the multinomial logit model may

be appropriate for predicting the occupational choice of the individuals. For a limited dependent

variable with hcategories the multinomial regression model estimates h-1 logit equations.

Whether a person wants to participate in the labour market as worker in a particular segment depends

on the level of utility derived from that segment of the market by that person. Let yij¿

be the utility of

individual i to be in employment category j, and zi be the vector of observed individual characteristics

determining the choice of occupation by individual i, βj is the coefficient vector attached in

employment category j, uijis random error.The vector zi includes workers’ education, age, sex, marital

status, social status, dependency ratio in the household, and the regional unemployment rate.

The utility function is stochastic and a linear function of the observed individual characteristics:

y ij¿ =zi

' β j+uij (3)

Now, the utility, y ij¿

, is a latent variablewhich we do not observe. What we observe is a

polychotomous variable, y, with values 1 to 4 corresponding to four types of employment category

based on type of job contract and workers’ activity status. An individual iparticipates in employment

category j for which y = jwheny ij

¿ >maxj≠k

yik¿

.

If the errors are independent and identically distributed, the probability that the

response to the j th outcome is

P ( y= j )=P j=exp ( zi

' β j)

∑j=1

4

exp ( zi' β j )

(4)

The model described in equation (4), however, is unidentified in the sense that there is more than one

solution to βj that leads to the same probabilities for y=1, y=2, y=3 etc. To identify the model, we

have to set arbitrarily βj =0 for any value of j.If we arbitrarily set β1 =0,the remaining coefficients β2,

β3and β4 will measure the change relative to the y = 1 group.

5. Empirical findings

5.1 Selection in multiple potential employment status

We have estimated the multinomial logit equation to analyse the effects of supply side and demand

side variables on the probability of being in each employment status. We have segmented the labour

market into four parts: self-employment, informal wage employment, formal wage employment and

other workers along with persons available for employment. In our estimation self-employment is

taken as a referent group, the predicted value of probability of being in this group is 0.59 (Table

6).Since the parameter estimates are relative to the referent group, the standard interpretation of the

multinomial logit is that for a unit change in the predictor variable, the logit of a particular outcome in

the labour market relative to the referent group is expected to change by its respective parameter

estimate given the variables in the model are held constant. Table 6 presents the estimated results of

multinomial logit for the selection equation as shown in (4).

The estimated coefficient for dependency ratio suggests that the number of dependent family member

in a household increases the probability of selecting informal wage employmentcompared to be in

informal self-employment. But, the dependency ratiohas no significant effect on formal wage

employment.The probability of entering into informal wage employment and that of being in the

labour force decreases, but formal wage employment increases at a diminishing rate with age of a

person. The coefficient of D_2011 measures the type of change of employment status in 2011-12 as

compared to 2004-05. The estimated coefficient suggests that informalisation of wage employment

and the incidence of being in the labour force increased while the probability of entering into formal

wage employment declined significantly during this period.The coefficient for female suggests that

the probability for being in informal wage employment for women relative to men is lower, while the

probability of being in other works or available for employment for women is higher than men

relative to informal self-employment given all other predictor variables in the model are held constant.

There is no significant gender difference in formal wage employment as compared to self-

employment. Marital status of a person, on the other hand, increases the chance of entering into the

formal wage employment while reduces the chance of being informally employed and also being

available for employment.

Table 6 Multinomial logit estimation for employment status of a person

CovariatesInformal wage employment

Formal wage employment

Available for employment

Intercept 1.68*** -7.88*** 0.22**

dependency ratio 0.08*** 0.00 -0.18***

age -0.01*** 0.27*** -0.14***

age2 -0.0002*** -0.0030*** 0.0005***

D_2011 0.27*** -0.07*** 0.09***

rural -0.59*** -0.65*** -0.62***

female -0.21*** -0.02 0.77***

married -0.13*** 0.06** -1.08***

technical education -0.08*** 1.20*** 0.88***

below primary -0.28*** -0.31*** -0.39***

primary -0.43*** 0.05 -0.35***

middle school -0.61*** 0.56*** 0.04secondary school -0.79*** 1.49*** 0.85***

graduate and above -0.36*** 2.40*** 1.79***

unemployment rate 3.77*** 4.58*** 16.32***

Probability of being in informal self-employment = .59

Note: *** significant at 1 % level, ** significant at 5 % level, the rest are insignificant

Source: As for Table 1

Estimated coefficients shown in Table 6 suggest that education lowers the probability of being in

informal employment and raises the chance of entering into formal wage employment relative to be in

informal self-employment. The effect of education on entering into the formal job market increases

with the level of education. We find that regional unemployment rate increases the probability of a

person to be remained in the labour force at a significantly higher rate. The higher regional

unemployment rates also increase the probability of entering into the job market both in informal and

formal parts, although at a lower rate.It may happen because, as it becomes tougher to find a job,

people tend to search jobs more intensively both in informal and formal segments in the labour

market.

5.2 Estimating wage differential

The wage equation shown in (1) is estimated by applying OLS after correcting self-selection bias for

each type of employment status. The estimated results are shown in Table 7. The mean wage is higher

for formal wage employees than for informal employees whatever may be the level of education and

other characteristics of the workers. The weekly wage used here is in nominal Rupees in logarithmic

form. The positive coefficient of the year dummy, D_2011, indicates inflationary factor for nominal

wage in 2011-12 as compared to 2004-05. The inflationary effect on wage is higher for informal

workers than for formal wage worker. The mean wage in rural areas is lower than in urban areas for

each type of employment status. Women workers earn lower wage than men workers both in informal

and formal activities. The gender wage gap is much higher in informal wage employment (30 percent)

as compared to formal wage employment (18 percent). The gender gap is relatively small among

those who are not recognised as employed but are available for employment. Return to education

increases with education both in informal and formal employment and it is the highest at workers’

education graduate and above. Workers, both men and women, with education got higher wages than

workers with no education. The skill premium due to technical education is highly significant both in

formal and informal employment may be because of skill biased technological change that appeared

after liberalisation. Regional unemployment rate enhances wage rate both in formal and informal

employment.

Table 7 Estimated wage equation by employment type

Covariates Informal wage

employment

Formal wage

employment

Available for employment

Intercept 5.11*** 5.12*** 4.71***

D_2011 0.97*** 0.84*** 0.91***

rural -0.22*** -0.21*** -0.28***

female -0.30*** -0.18*** -0.09**

technical education 0.52*** 0.40*** 0.39***

below primary 0.05*** -0.29*** -0.15middle school 0.20*** -0.01 0.21**

secondary 0.47*** 0.34*** 0.48***

graduate and above 1.06*** 0.69*** 0.87***

age 0.04*** 0.07*** 0.09***

age2 0.00*** 0.00*** 0.00***

unemployment rate 1.66*** 0.78*** 1.23***

Number of observation (n) 45,417 12,009 2,062 F(11, n-12) 3931.4 701.45 112.7Prob> F 0.000 0.000 0.000R2 0.4878 0.3914 0.3768Adj R2 0.4877 0.3909 0.3735

Note: *** significant at 1 % level, ** significant at 5 % level, the rest are insignificant

Source: As for Table 1

5.3 Wage gap decomposition: endowment effect and discrimination

The estimated wage regression is used to decompose the wage gap between men and women

workers in formal and informal employment by applying the methodology based originally on Blinder

(1973) and Oaxaca (1973). We have measured wage differential in logarithmic scale. Table 8 reports

the mean predictions of log wages by men and women workers in different types of employment, and

their differences in the upper panel.The gender wage difference is higher in informal employment

than in formal employment.

In the lower panel of Table 8, the wage gap is decomposed into two parts.The first part

reflects the mean increase in women’s wages if they had the same characteristics as men.This is the

endowment effect measuring the impact of differences in education, work experience, and other

characteristics of workers. The second part quantifies wage discrimination showing the change in

women’s wages when applying the men’s coefficients of the estimated wage equation to the women’s

characteristics. The endowment effect accounts for a little of the gender wage gap both in informal

and formal employment. The wage differential appears in the Indian labour market mainly because of

discrimination that cannot be explained by the factors relating to workers’ productivity. The incidence

of wage discrimination is higher in informal employment than in formal employment.

Table 8 Oaxaca- Blinder decomposition of gender wage gap

Mean wage (in log)

Informal wage employment

Formal wage employment

Available for employment

Men worker 6.50*** 7.67*** 6.87***

Women worker 6.09*** 7.49*** 6.89***

Wage differential 0.42*** 0.18*** -0.02DecompositionExplained 0.12 0.01 -0.11***

Unexplained 0.30 0.17*** 0.09**

Note: *** significant at 1 % level, ** significant at 5 % level, the rest are insignificant

Source: As for Table 1

One might be interested to find out how much of the gender wage gap is due to differences in

education and how much is due to differences in work experience or other factors affecting wage rate.

In analyzing wage discrimination in a segmented labour market in India it might be informative to

determine how much of the unexplained gap is related to differing returns to education and how much

is related to differing returns to work experience. We can easily locate the contributions of the

individual predictors to the explained part, or the endowment effect, and to the unexplained part,

discrimination, because the total effect of the explained component is a simple sum over the

individual contributions. In this study the contributions of the individual predictors to different parts

of the wage gap is estimated for different types of wage employment.

The computed contributions of the predictors to the components of the wage gap

decomposition are shown in Tables 9 for different types of employment. As wage discrimination is a

part of wage gap that could not be explained by the predictors of wage, the major part of the

discrimination is associated with the intercept term. The contribution of the intercept component,

measuring the average effect on discrimination whatever may be the covariates of wages, is the

maximum (.29) among informal workers majority of whom come from the vulnerable social groups in

terms of castes or gender. Education, in all cases, increased the endowments effect, while it reduced

discrimination in wage differential between men and women workers.

Table 9 Factors’ contributions to components of gender wage gap

Explained UnexplainedInformal wage employment

Formal wage employment

Available for employment

Informal wage employment

Formal wage employment Others

Intercept 0.289*** 0.194 -0.610D_2011 0.049*** -0.027*** 0.056*** -0.033*** 0.023 0.048rural 0.016*** -0.003 -0.008 -0.040*** 0.090*** -0.075**

technical education 0.004*** -0.005 -0.012** 0.000 -0.012 -0.016below primary 0.001*** 0.000 -0.002 -0.004 0.004 -0.011middle school 0.020*** 0.000 0.012** 0.002 0.021*** -0.011graduate and above 0.000 -0.017 -0.066*** -0.015*** -0.068*** -0.071*

secondary 0.044*** 0.008 -0.007 -0.016*** -0.013 -0.070*

age -0.093*** 0.204*** -0.225*** 0.039 -0.164 1.712***

age2 0.077*** -0.153*** 0.184*** 0.046 0.131 -0.717***

unemployment rate 0.004*** -0.002 -0.043*** 0.028*** -0.025 -0.086*

Note: *** significant at 1 % level, ** significant at 5 % level, * significant at 1 % level, the rest are insignificantSource: As for Table 1

6. Conclusions

The faster growth of the informal labour market in India may be the direct and indirect outcome of

integration of the domestic economy into the global market. The global competition puts pressure to

minimise production costs in the tradable sectors leading to demand-led growth in informal sector

employment. The fall in public sector employment in the process of privatisation coupled with rural-

urban migration has forced the workers ready to work under indecent conditions of informal

employment.

This paper explores how informality shapes labour market outcomes for men and women by looking

into segmentation of employment in terms of job contract and type of employment status, and traces

its transformation after one and a half decade of market integration by applying multinomial logit for

correcting selectivity bias with survey data in India. The paper also looks into the gender wage

differential among workers under different employment conditions and decomposes the differential to

analyse the extent to which the wage gap can be accounted for by productivity differences as reflected

in human capital endowments. Labour market is segmented not only between formal and informal

part but also between different forms of informal employment. We have segmented the labour market

into four parts: self-employment, informal wage employment, formal wage employment and other

workers along with persons available for employment.

We observe that the men’s share in wage employment increased marginally, but the women’s share

declined significantly. A significant transformation occurred from self-employment or wage

employment towards domestic activities for women workers. Women were absorbed mostly in

household base domestic activities and their share in such activities increased significantly. The

labour market in India is dominated primarily by the paid workers without any written job contracts.

The study observes a substantial shift of paid employment from formal to informal both for men and

women.

The incidence of informalisation of employment has not been restricted only to illiterate or less

literate workers. A significant part of the educated workers are also forced to accept jobs with no

written job contracts or without any social security benefit. The share of informal workers was

notably high among the workers with middle school level of education as well as among illiterate

workers both for men and women. The incidence of informalisation of employment jumped up

remarkably among workers with higher education level during the period between 2004-05 and 2011-

12

The informalisation of wage employment and the incidence of being in the labour force increased

while the probability of entering into formal wage employment declined significantly in 2011-12 as

compared to 2004-05. The probability for being in informal wage employment for women relative to

men is lower, while the probability of being in other works or available for employment for women is

higher than men relative to informal self-employment given all other predictor variables in the model

are held constant. The number of dependent family member in a household increases the probability

of selecting informal wage employment compared to be in informal self-employment. Marital status

of a person increases the chance of entering into the formal wage employment. The effect of

education on entering into the formal job market is positive and the effect increases with the level of

education.

The mean wage is higher for formal wage employees than for informal employees whatever may be

the level of education and other characteristics of the workers. The extent of wage differential is

different at different level of education as well as at different employment condition. Informalisation

of employment at least in terms of type of job contracts had penalise more the men than the women in

pay differential. Return to education increases with education both in informal and formal

employment and it is the highest at workers’ education graduate and above. The gender wage gap is

much higher in informal wage employment as compared to formal wage employment and it appears in

the Indian labour market mainly because of discrimination that cannot be explained by the factors

relating to workers’ productivity. The incidence of wage discrimination is higher among informal

workers majority of whom come from the vulnerable social groups in terms of castes or gender.

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