Chapter

Chapter 6B. India

Editor(s):
Kalpana Kochhar, Sonali Jain-Chandra, and Monique Newiak
Published Date:
February 2017
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Author(s)
Sonali Das, Sonali Jain-Chandra, Kalpana Kochhar and Naresh Kumar 

Among emerging market and developing economies, India has one of the lowest female labor force participation rates—typically measured as the share of women who are employed or seeking work as a share of the working-age female population. India’s rate, at about 33 percent in 2012, is well below the global average of about 50 percent and the east Asia average of about 63 percent. India is the second-most populous country in the world, with an estimated population of 1.26 billion at the end of 2014. Accordingly, a female labor force participation rate of 33 percent implies that only 125 million of the roughly 380 million working-age Indian females are seeking work or are currently employed.

Moreover, at 50 percent, India’s participation gender gap is the one of the widest among Group of Twenty economies. Furthermore, female labor force participation has been on a declining trend in India, in contrast with most other regions, particularly since 2004–05. Drawing more women into the labor force, along with other important structural reforms that could create more jobs, would be a source of future growth for India as it aims to reap the “demographic dividend” from its large and youthful labor force.1

It has long been understood in the literature that gender equality plays an important role in economic development. Various studies highlight how lower female labor force participation or weak entrepreneurial activity drag down economic growth and that empowering women has significant economic benefits in addition to promoting gender equality (Duflo 2012; World Bank 2012). The World Economic Forum’s 2014 Global Gender Gap Report finds a positive correlation between gender equality and GDP per capita, the level of competitiveness, and human development indicators. Seminal work by Goldin (1995) explored the U-shaped relationship between female labor supply and the level of economic development across countries. Initially, when the income level is low and the agricultural sector dominates the economy, women’s participation in the labor force is high, due to the necessity of working to pay for basic goods and services. As incomes rise, women’s labor force participation often falls, only to rise again when female education levels improve and, consequently, the value of women’s time in the labor market increases. This process suggests that, at low levels of development, the income effect of providing additional labor dominates a small substitution effect, whereas as incomes increase, the substitution effect comes to dominate.2Gaddis and Klasen (2014) explore the effect of structural change on female labor force participation using sector-specific growth rates. They find a relationship consistent with a U pattern but small effects from structural change.

Against this backdrop, this analysis revisits the determinants of female labor participation in India, analyzes how labor market rigidities affect female labor force participation, and examines the drivers of formal and informal sector employment. It assesses whether India’s largest public employment program, resulting from the enactment of the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) in 2005, has resulted in higher female labor force participation.3 Launched as one of the world’s largest employment programs, the MGNREGA offers 100 days of guaranteed wage employment in every financial year for all registered unskilled manual workers (both women and men). The MGNREGA includes multiple provisions that are supportive of women in the workplace. These include requiring that at least 33 percent of participating workers are women; stipulating that equal wages be paid for men and women; and providing for facilities, such as worksite childcare, that reduce barriers to women’s participation (Government of India 2014). The MGNREGA includes other provisions aimed at making work attractive for women, such as the stipulation that work is to take place within five kilometers of an applicant’s residence.

Indian Female Labor Force Participation

The main data set used in this analysis is detailed household-level data from five employment and unemployment surveys conducted by India’s National Sample Survey Office (NSSO) covering the years 1993–94, 1999–2000, 2004–05, 2009–10, and 2011–12. Following detailed data gathering and organization, the analysis presents stylized facts from all five survey rounds; the empirical estimation of the determinants of labor force participation is conducted on the most recent round of the survey (68th round, July 2011 to June 2012).4 The employment and unemployment surveys of the NSSO are the primary sources of data on various labor force indicators at national and state levels. NSSO surveys, with large, nationally representative sample sizes, have been conducted every five years all over the country. The survey period spans more than a year, and the sample covers more than 100,000 representative households in each of the five surveys. The number of households surveyed in the latest round of the survey (68th round) was 101,724 (59,700 in rural areas and 42,024 in urban areas), and the number of persons surveyed was 456,999 (280,763 in rural areas and 176,236 in urban areas). This makes India’s NSSO surveys among the world’s largest employment surveys.

According to NSSO definitions, individuals are classified into various activity categories on the basis of activities that they pursue during specific reference periods. Three reference periods are used in NSSO surveys: (1) one year, (2) one week, and (3) each day of the reference week. The activity status determined on the basis of the reference period of one year is known as the “usual activity status” of a person, the status determined on the basis of a reference period of one week is known as the “current weekly status” of the person, and the activity status determined on the basis of the engagement on each day during the reference week is known as the “current daily status” of the person.

Under the usual activity status a person is classified as belonging to the labor force if he or she had been either working or looking for work during the longer part of the reference year. For a person already identified as belonging to the labor force, the usual activity status is further divided into “usual principal activity status” and “usual secondary activity status.” The activity status on which a person spent relatively longer time during the 365 days preceding the date of the survey is considered the usual principal activity status of the person.

A person whose usual principal status is determined on the basis of the major time criterion may have pursued some economic activity for 30 days or more during the reference period of 365 days preceding the date of survey. The status in which such economic activity is pursued during the reference period of 365 days preceding the date of survey is the “subsidiary economic activity status” of the person. In case of multiple subsidiary economic activities, the major activity and status based on the relatively longer time spent criterion are considered.5

In this context, the labor force is measured through the usual principal activity status, which is more suitable to the study of trends in longer-term employment. Generally, government programs and policies are focused toward generating more stable jobs and encouraging a shift from informal-sector to formal-sector jobs.

Moreover, a reference period of just one day or one week may capture well the employment intensity for that particularly short period but may not reflect the overall pattern and level in terms of months or days worked throughout the year. Therefore, each of the smaller reference periods, except the long (one-year) reference period, may not be completely representative of the employment patterns and incidence for the concerned year, and moreover, may not be suitable for comparison across reference periods of varying lengths over time.

The following stylized facts emerge from the household survey data:

  • Female labor force participation varies widely between urban and rural areas. It is much higher in rural areas than it is in urban areas (Figure 6.19). Over time, the gap between urban and rural areas has narrowed moderately, with most of the convergence being driven by the fall in participation rates in rural areas. As a result, taken together, female labor force participation rates nationwide have fallen since the mid-2000s.

  • There is a wide range of female labor force participation rates across Indian states (Figure 6.20), with states in the south and east of India (such as Andhra Pradesh, Tamil Nadu, and Sikkim) generally displaying higher participation rates than those in the north (such as Bihar, Punjab, and Haryana).

  • There is a growing gap between male and female labor force participation rates (Figure 6.21). These gender gaps are particularly pronounced in urban areas, where they are wider and average 60 percentage points. In rural areas, participation gaps between males and females average about 45 percentage points.

  • There is a U-shaped relationship between education and female labor force participation rates (Figure 6.22). With increasing education, labor force participation rates for women first start to decline and then pick up among highly educated women (particularly university graduates) who experience the pull factor of higher-paying white-collar jobs. There is still an education gender gap in India, but it has been narrowing over time. As the gender gap in education closes further, particularly at higher education levels, female labor force participation can be expected to rise. In addition to raising labor input, the resulting accumulation of women with the requisite skills, knowledge, and experience for labor force participation should boost potential output.

  • Income has a dampening effect on female labor force participation, with participation rates higher among low-income households, due largely to economic necessity (Table 6.2).6 With rising household incomes, participation rates for women start to drop off.

Figure 6.19.Female Labor Force Participation Rate in India

Sources: NSS Employment and Unemployment Surveys; and IMF staff calculations.

Figure 6.20.Female Labor Force Participation Rates Across Indian States

(Percent, 2011/12)

Sources: India’s National Sample Survey Office; and IMF staff calculations.

Figure 6.21.Urban and Rural Labor Force Participation in India

Sources: India’s National Sample Survey Office; and IMF staff calculations.

Figure 6.22.Urban Female Labor Force Participation in India by Education Level

(Percent)

Sources: India’s National Sample Survey Office; and IMF staff calculations.

Table 6.2.Determinants of Labor Force Participation in IndiaDependent variable = 1 if in labor
FemaleMale
AllUrbanRuralAllUrbanRural
Predicted wage0.0010.153**0.0380.270**−0.0220.410***
(0.041)(0.064)(0.051)(0.091)(0.129)(0.119)
Married−0.452**«−0.787***−0.374***2.651***2.647***2.659***
(0.038)(0.057)(0.050)(0.061)(0.076)(0.081)
Children−0.155***−0.167***−0.077*−0.115**0.041−0.204***
(0.035)(0.052)(0.045)(0.046)(0.067)(0.063)
Illiterate−1.221***−0.845***−1.315***−1.813***−1.163***−2.026***
(0.067)(0.105)(0.086)(0.103)(0.156)(0.129)
Some education0.631***0.493***0.706***1.663***1.432***1.774***
(0.064)(0.100)(0.083)(0.062)(0.085)(0.083)
Post-secondary education1.240***1.271***1.034***1.071***1.299***0.859***
(0.073)(0.106)(0.108)(0.075)(0.097)(0.107)
log(Expenditure per capita)−1.126***−2.461***−0.8410.159−0.0930.513
(0.384)(0.510)(0.565)(0.562)(0.682)(0.927)
log(Expenditure per capita)0.0450.141***0.035−0.037−0.019−0.067
squared(0.026)(0.033)(0.039)(0.036)(0.043)(0.063)
log(SDP per capita)1.0900.5461.3510.2260.1380.276
(0.038)(0.051)(0.048)(0.054)(0.071)(0.074)
Observations133,22052,50980,711133,94753,89053,890
Source: Authors’ calculations.Robust standard errors in parentheses, clustered at household level, * p<0.1, ** p<0.05, *** p<0.01.

Labor Market Flexibility

It has been widely noted that relatively inflexible labor markets have weighed on employment generation in India (Dougherty 2009; Kochhar and others 2006), affecting firm hiring decisions (Adhvaryu, Chari, and Siddharth 2013) and resulting in lower productivity (Gupta, Hasan, and Kumar 2009). Moreover, there is considerable cross-state heterogeneity in labor market rigidities.

To gauge the differences in flexibility of labor markets in Indian states, the analysis uses a state-level index produced by the Organisation for Economic Co-operation and Development (OECD). The OECD’s Employment Protection Legislation index is based on a survey of labor market regulations. The index covers 21 of India’s 29 states, which comprise 97.5 percent of India’s 2011–12 NSSO-measured population of 1.21 billion.7 The index is constructed by counting amendments to regulations that are expected to increase labor market flexibility. These includes amendments to four key pieces of labor market regulation: the Industrial Disputes Act, the Factories Act, the Shops Act, and the Contract Labor Act.

With the Industrial Disputes Act, for example, the index would take a higher value for states that require a shorter amount of time for employers to give notice to terminate an employee; have made amendments allowing certain exemptions to the Act; have lowered the threshold size of the firm to which chapter V-B applies;8 exclude the complete cessation of a certain function from the definition of retrenchment; have instituted a time limit for raising disputes; or have instituted other amendments to the procedures for layoffs, retrenchment, and closure that should ease planning for firms. The OECD’s Employment Protection Legislation index also captures differences in the ease of complying with regulations (for example, rules on dealing with inspectors, registers, and filing of returns). As in Dougherty 2009, the index is scaled, taking values from 14 to 28, by its maximum value, thus ending with a variable that ranges from 0.5 to 1.

The analysis uses three alternative classifications to identify which workers in the sample are in the formal or informal sector and creates an indicator variable equal to one when the conditions for each of these classifications hold. The employment and unemployment survey conducted in the 68th round of the NSSO, from July 2011 to June 2012, asked workers for information on various characteristics of the enterprises in which they were employed (for example, type of enterprise9 and number of workers in the enterprise), and questions on the conditions of employment of the regular wage and salaried employees (for example, whether an individual has a job contract or is eligible for paid leave).

Categorization of formal sector jobs is based on these questions about conditions of employment. Because there is no explicit question on the existence of informality, its existence is inferred using three different methods. The first categorization of formality refers to jobs where the worker has a formal contract or is eligible for paid leave. The second categorization variable indicating formal employment is based on the location of the workplace. For example, workers who work on “the street with a fixed location” would be classified as informal sector employees. The third categorization of formality comes from India’s Ministry of Statistics and Programme Implementation (2014), which classifies workers in either proprietary or partnership enterprises (small firms, usually owned by individuals, family members, or their close associates) as employed in the informal sector.

Labor force participation rates can also be influenced by wage differentials facing women. As expected, wages in the informal sector are lower than in formal sector jobs. The NSSO survey data contains information on wage and salary earnings, from which a daily wage can be calculated for about 15,000 female workers and 54,000 male workers. In the sample, the daily wage for women in formal jobs is over four times as high as for women in informal jobs. Notably, there is a gender wage gap in both the formal and informal sectors, with male workers earning a higher wage on average in both sectors.

The empirical analysis asked the following three questions: What are the determinants of female labor force participation in India in both urban and rural areas? Is female labor force participation higher in Indian states with less stringent labor market regulations? Do these factors affect whether employment occurs in the formal or informal sectors?

Following is a summary of the answers to those questions, based on the analysis:

  • Married women are less likely to be in the labor force, whereas married men are more likely to be in the labor force.

  • Both women and men with young children are less likely to be in the labor force.

  • Illiterate individuals of both sexes are less likely to be in the labor force, and the probability of being in the labor force increases with higher levels of education for both sexes.

  • Consistent with the stylized facts, females in households with higher spending per capita, which is a proxy for income, are less likely to be in the labor force. However, this negative effect is nonlinear and decreases as income increases. This nonlinear relationship between income and participation appears to be driven by urban females.

  • The chance of being employed in the formal sector, as opposed to the informal sector, also increases in more flexible state labor markets.

  • Female labor force participation is higher in states with relative higher spending on social services and education.

  • Poor infrastructure has a dampening effect on female labor force participation. Women living in states with greater access to roads are more likely to be in the labor force, and those in states with less reliable state power utilities are less likely to be in the labor force.

  • In rural areas, both women and men who hold an MGNREGA card are more likely to be in the labor force; the probability is higher for women than for men, possibly due to the female-friendly provisions of the Act.

Conclusions

Female labor force participation in India is lower than it is in many other emerging market economies and has been declining since the mid-2000s. Moreover, there is a large gap in the labor force participation rates of men and women. This gender gap should be narrowed to fully harness India’s demographic dividend. In addition, related literature also finds that greater economic participation of women leads to higher economic growth.

A number of policy initiatives could be used to address this gender gap in Indian labor force participation. These include increased labor market flexibility (which could lead to the creation of more formal sector jobs) allowing more women—many of whom are working in the informal sector—to be employed in the formal sector. In addition, supply-side reforms to improve infrastructure and address other constraints to job creation could also enable more women to enter the labor force. Finally, higher social spending, including investment in education, could also lead to higher female labor force participation by boosting the number of women with the requisite skills, knowledge, and experience.

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A version of this analysis was published as Das and others 2015.

The demographic dividend refers to the potential benefits to a country from an increase in the working-age population relative to the number of dependents, with the latter defined as those less than 15 years old or over 65 years old. The falling fertility rate in India will result in an increase in the working-age population share in that country, as well as in its share of the population, through approximately the next 35 years.

The income effect is the change of hours of work of an individual with respect to a change in family income. The substitution effect is the change in hours of work of an individual with respect to a change in his or her wage, holding income constant.

The Act took effect in February 2006 and was implemented in phases across the country. During Phase I it was introduced in 200 of the country’s most underdeveloped districts; during Phase II it was implemented in an additional 130 districts, during 2007–08; and during Phase III it was implemented in the remaining rural districts of the country, beginning on April 1, 2008. All rural districts in India are now covered under the MGNREGA.

Labor force participation rates based on usual principal activity status are presented throughout, unless otherwise specified.

The Report of the Committee of Experts on Unemployment Estimates submitted to the Planning Commission in 1970 states that “In our complex economy, the character of the labor force, employment and unemployment, is too heterogeneous to justify aggregation into single-dimensional magnitudes.”

This analysis uses monthly consumption per capita as a proxy for household income.

The 21 states covered are: Andhra Pradesh (which refers to the undivided state comprising the present states of Andhra Pradesh and Telangana), Assam, Bihar, Chhattisgarh, Delhi, Goa, Gujarat, Haryana, Himachal Pradesh, Jharkhand, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh, Uttarakhand, and West Bengal.

Chapter V-B of the Act requires firms employing 100 or more workers to obtain government permission for layoffs, retrenchments, and closures (as of 1984).

This includes proprietary, partnership, government/public sector, public/private limited company, cooperative societies/trusts/other nonprofit institutions, employer’s households (such as private households employing maid, servant, watchman, cook), and others.

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