Gender Differences in Correlates of Participation in the Labour Market in and around Delhi Mumbai and Kolkata

The processes that explain participation in the labour market has been historically different for men and women, both socially and culturally. However, the efficiency norm with which the market functions in the neoliberal regime is expected to alter these social and cultural norms. Other than that, the peri-urban context offers us a manageable spatial platform to observe outcomes that are at work in the larger economy, in the sense of seeking merit in terms of employing labour. The social differences, in terms of gender should be minimized, under conditions of perfect competition, controlling for skill and experience.

The hypothesis that the processes at work in the market regime for men and women are governed by economic factors does not get validated by Table 6. There are significant differences for men and women, in fact, that indicate that the labour market functioning in the neo-liberal regime has not changed the patriarchal norms that dominated in the closed economy era. In fact, such differences probably have been retrenched in some ways.

Three sets of indicators have been used for the logit analysis to explain participation in labour market for men and women. The first is location-related indicators, which in this case are the three spatial units used for this study throughout, i.e. urban core, peri-urban areas and residual states. City and rural-urban locations are two more spatial elements that have been brought into this analysis. The second set of indicators relate to individual characteristics like employment, social group, marital status and age. Age is treated as a proxy for experience. The third set of indicators, i.e. the household characteristics, include the monthly per capita consumption expenditure (MPCE) and household size.

The logit analysis reveals that while urban cores offer more opportunities both for men and women compared to residual states, the peri-urban areas are not significantly different form the latter for easier entry into the labour market for men, and are in fact a more difficult space for women compared to the residual states. Thus the opportunities created by the urban processes are largely limited to the urban core for both genders in terms of entry to the labour market, and the space that is undergoing a change exclusively to cater to urban expansion, is infact, a restrictive space for women.

Rural location expectedly makes for easier entry to the labour market for both men and women. Delhi, has a more restrictive labour market compared to both Mumbai and Kolkata, for both men and women, much more for the latter compared to Mumbai. The fact that Delhi has experienced a higher degree of opening up to private investment and has benefitted in terms of higher growth from globalization processes compared to at least Kolkata, has made no difference to the situation portrayed above.

The individual characteristics work very differently in explaining the probability of being a worker for men and women. Men have a lower probability of being a worker if they are more educated, except that the probability declines somewhat

Table 6 Results of logit regression explaining probability of participating in the labour market

Variables

Male

Females

в

Exp(P)

Sig

В

Exp(P)

Sig

Categorical Variables

Location (Reference: Residual States)

Spatial location

-

0.00

-

-

0.00

Urban core

0.29

1.33

0.00

0.37

1.45

0.00

Peri-urban

0.08

1.08

0.17

-0.17

0.85

0.00

Cities (Reference: Delhi)

Cities

-

0.00

-

-

0.00

Mumbai

0.50

1.65

0.00

1.26

3.53

0.00

Kolkata

0.27

1.31

0.00

0.28

1.33

0.00

Sector (Reference: Urban)

Rural

0.15

1.16

0.00

0.49

1.64

0.00

Educational Attainment: Reference: High School and Above

Educational attainment

0.00

-

-

0.00

Illiterate

0.68

1.98

0.00

0.26

1.29

0.00

Up to Primary

1.51

4.52

0.00

-0.07

0.93

0.19

Middle

0.59

1.80

0.00

-0.38

0.68

0.00

Secondary

0.04

1.04

0.45

-0.65

0.52

0.00

Social group(Reference: General Caste)

Social group

-

0.06

-

-

0.00

Scheduled tribes

0.25

1.26

0.03

0.62

1.85

0.00

Scheduled castes

-0.07

0.95

0.37

0.19

1.21

0.00

Other backward Castes

0.12

1.05

0.33

0.01

1.01

0.87

Marital Status (Reference: Never Married)

Marital status

-

0.00

-

-

0.00

Currently married

2.43

11.38

0.00

0.16

1.18

0.00

Divorced and Separated

-0.43

0.65

0.04

1.39

4.02

0.00

Continuous Variables

Age

0.11

1.11

0.01

0.001

1.01

0.00

Household size

-0.02

0.98

0.07

-0.05

0.95

0.00

MPCE in Rs. ‘00

-0.01

0.99

0.00

0.00

1.00

0.11

Constant

-3.09

0.05

0.00

-2.73

0.07

0.00

Source Calculated from NSSO Employment-Unemployment Round (68th), 2011-12

from having a primary school attainment to being an illiterate. The norm of being the family breadwinner probably works for all men, but the choices are more numerous to people with higher education. For women, the probability of being a worker is the most for an illiterate, for the same reasons. But this apart, having a high school and above attainment facilitates entry into the labour market more than the lower educational attainments. The drop in probability is the maximum for the secondary level educational status, after the probability increases with subsequently lower educational levels, like the men. The social group identity does not make any major difference to the probability of being a worker for men, except that STs have a higher likelihood compared to the general category. For women, though, lower the caste group, higher the probability of entry to the job market, with the rider that STs, have an even higher probability than the SCs when compared to the general caste. It is well documented that though lower castes are generally disadvantaged in the social structure, paradoxically, the cultural barriers for women from such households to enter the job market is less marked compared to those upper caste women (Beteille 1991; Agarwal 1994). The cultural restrictions on the never married girls/women are high that prevent them from working, controlling for education and though the domestic and extra-domestic responsibilities that make it difficult for a married woman to work, the probability of the latter of being a worker is higher compared to the former. While the divorced and separated men have the lowest probability of working, their counterparts among women have the higher odds ratio of doing so, having no male support for earning their living. A higher age facilitates entry to the job market for both men and women, though to a lower extent in the case of latter.

Household size, expectedly, makes no difference to men in being a worker, whereas for women the chances that they can join work becomes less with a bigger household size, due to an increase in the care-giving responsibility. Finally, while a man from a poorer household have a higher probability of working, the economic background of the household does not make a significant difference for women.

 
Source
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