Gender, Labour and Self-Employment in

IndiaAshwini Deshpande

Delhi School of EconomicsUniversity of Delhi

INDIA.

1983 1993-94 1999-00 2004-05 2009-100

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RURAL LFPRs by gender (age>=15)

Male Female All

RURAL LFPRs (1983-2010)

1983 1993-94 1999-00 2004-05 2009-100

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Urban LFPRs by gender (age>=15)

Male Female All

URBAN LFPRs (1983-2010)

Demand-based explanations: Low prody agriculture + excess supply of lab. Women’s paid lab needed only when men’s lab exhausted.

Supply-side explanations: Socially ordained division of lab: women in reproductive activities within the household

Discrimination-based explanation: employers discriminate against female workers, both in terms of hiring and wages.

Measurement issues: lot of women’s work not counted as “productive” plus women often deny involvement in productive work.

Why are female LFPRs low?

Share of women in regular wage/salaried employment lower than that of men (rural: 4% female WF in RWS (vs. 9) & urban: 39 vs. 42) (2009-10)

Correspondingly, share of women in casual workers & self-employment higher than men.

Rural: 79% women in agri; Urban: 53% women in tertiary

Employment Status and Sectoral Distribution

2009-2010: NSS 66th round emp. survey Urban: RWS average daily wage: Rs.

364.95. Rural: Rs. 231.59 Rural Male: Rs. 249.15; Rural Female:

155.87 => ratio of 0.63.Urban Male: 377.16; Urban female: 308.79Þ Ratio of 0.82

* Casual labour: 0.67 (Rural) and 0.58 (Urban)

Raw Daily Wage Gaps

Blinder-Oaxaca Decomposition (Khanna 2012): Strong evidence of labour market discrimination

Male-Female Wage Gaps

0 10 20 30 40 50 60 70 80 90 1000

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Gender Wage Gap (log)

QR Coef. OLS Coef.

Percentiles

Log

Wag

e G

ap

This plots the log of the gender wage gap at each of the 99 percentiles using NSS 2009-2010 wage data for those reporting regular wage/salaried employment (Khanna 2012).

The “Sticky Floor” effect is evident: wage gaps are much larger at the bottom of the distribution and decline almost monotonically till the top of the wage distribution.

The average gap (given by the OLS coefficient) is instructive, but misses out on this nuanced picture.

Quantile Regressions

1 2 3 4 5 6 7 8 90

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total_differentialcharacteristicscoefficients

QR Decompositions: if men were paid like women

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-0.1

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Total CharecteristicsCoefficients

QR Decompositions: if women were paid like men

Registered SSI sector: 3rd MSME Census (2001-2)

Women- owned enterprises 10.01% Women -managed enterprises 8.36%

States with higher than all-India proportions States with higher than all-India proportions

Arunachal Pradesh 24.8 Arunachal Pradesh 20.08

Assam 14.11 Assam 13.46

Karnataka 14.49 Karnataka 12.87

Manipur 16.69 Kerala 19.76

Meghalaya 33.59 Manipur 16.01

Mizoram 25.35 Meghalaya 33.02

Sikkim 25.17 Mizoram 26.71

Tamil Nadu 14.83 Nagaland 14.04

Sikkim 17.01

Tamil Nadu 13.33

Self-Employment

Overlap of ownership and management

65% of women-owned enterprises are managed by women

2% of male-owned enterprises are managed by women

Percent women employed by gender of ownership and management

In women-owned enterprises 57.56 In women-managed enterprises 68.48

In men-owned enterprises 6.17 In men-managed enterprises 6.1

Ownership and Management by women

owned managed

Manuf of wearing apparel 40.3 48.76

Mauf of food prodt and bev 13.06 12.09

Manuf of textiles 7.3 7.25

Manuf of fabricated metal prodts, except machinery 4.51 3

Manuf of chemicals and chemical products 3.73 3.25

Manuf of other non-metallic min products 3.69 2.64

Manuf of furniture 3.47 2.97

Retail trade, repair of household goods 2.92 2.51

Manuf of wood and straw products 2.68 2.3

Industry divisions of women ents

Women are not a homogenous category. For example: overlap of caste and gender. Earlier evidence: greater taboos on upper

caste women, who were materially more prosperous – trade-off between prosperity and immurement.

LFPRs among SC-ST women higher than UC. Now: trade-off vanishing. Dalit women worst

off: triple burden of gender, caste and class.

Caste and Religion

With greater legal differentiation, fewer women work, own or run businesses (WBL, 2012)

South Asia (except SL) one of the 3 regions where explicit legal gender differentiation in accessing institutions and in using property is most common.

Moreover, benefits such as paternity leave absent.

Legal and institutional barriers

Lack of autonomy in interacting with government institutions

Access to judicial system Getting a job: differences in work hours,

restrictions by industry, poor anti-discriminatory laws, with even poorer implementation

Benefits (e.g. maternity leave): India: employer pays (rather than the government), raising the cost of hiring women.

Accessing institutions

India: gender disc in lab mkt => wage gaps and differential access to wage employment, sometimes exclusion of women.

Discrimination inefficiencies and lower growth (Esteve-Volart, 2004).

Individuals belonging to a group which is discriminated against face higher interest rates in credit markets.

=> lab mkt disc credit market discrimination

Gender discrimination lowers growth

Esteve Volart (2004): An increase of 10% in the F/M ratio of

managers would increase PC NDP by 2% An increase of 10% in F/M ratio of total

workers would increase PC NDP by 8%. => Gender inequality in the access to

working positions is a bigger break on growth than gender inequality in the access to managerial positions.

INDIA: 1961-1991