India: Selected Issues Paper

This Selected Issues paper examines how surges in global financial market volatility spill over to emerging market economies (EMs) including India. The results suggest that a surge in global financial market volatility is transmitted very strongly to key macroeconomic and financial variables of EMs, and the extent of its pass-through increases with the depth of external balance-sheet linkages between advanced countries and EMs. The paper also looks at food inflation, which has often been singled out as a key driver of India’s high and persistent inflation.

Abstract

This Selected Issues paper examines how surges in global financial market volatility spill over to emerging market economies (EMs) including India. The results suggest that a surge in global financial market volatility is transmitted very strongly to key macroeconomic and financial variables of EMs, and the extent of its pass-through increases with the depth of external balance-sheet linkages between advanced countries and EMs. The paper also looks at food inflation, which has often been singled out as a key driver of India’s high and persistent inflation.

Holding Up Half The Sky: Analysis of Female Labor Force Participation in India1

India stands out among G-20 peers as having one of the lowest rates of female labor force participation. A growing literature shows that boosting labor force participation of women leads to significant economic gains. This chapter finds that a number of policy initiatives can be used to tackle low female economic participation, including increased labor market flexibility, higher investment in infrastructure, and enhanced social spending.

1. While women’s economic participation is in itself an important social and development goal, it is also crucial for growth. Various studies have highlighted how lower female labor force participation or weak entrepreneurial activity drags down economic growth. The World Economic Forum’s 2014 Global Gender Gap Report finds a positive correlation between gender equality and per capita GDP, the level of competitiveness as well as human development indicators. Indeed, drawing more women into the labor force, along with other important structural reforms to create more jobs, could be a source of future growth in India as it aims to reap the “demographic dividend” from its large and youthful labor force. This chapter benchmarks India relative to other countries on female labor force participation. In addition, it uses individual-level survey data to examine participation choices and the key drivers of female participation in the labor force. The chapter also examines policy levers, including at the state level, which can be used to boost female labor force participation.

2. India has one of the lowest female labor force participation (FLFP) rates when compared with emerging market and developing countries. India’s FLFP rate is around 29 percent (of the female population aged 15 years and above) at the national level (with significant differences across Indian states)—a level much below that of other emerging market peers. FLFP rates have been falling in India, in contrast to most other developing countries and emerging market economies (text chart). As well, the gender gaps in participation (between males and females) are the second highest among G-20 economies, at 52 percent (with only Saudi Arabia having a larger gender participation gap).

A12ufig01

Female Labor Participation Rate

(In percent of female population ages 15+)

Citation: IMF Staff Country Reports 2015, 062; 10.5089/9781498316200.002.A012

Source: World Bank, World Development Indicators, 2013; Key Indicators of the Labour Market (KILM), ILO.

3. There is a growing literature on the determinants of FLFP as well as the economic implications of low female participation (summarized in IMF 2013). This literature highlights that gender gaps in labor force participation (FLFP), entrepreneurial activity, or education impedes economic growth (e.g. Cuberes and Teignier, 2014; Esteve-Volart, 2004, and Klasen and Lamanna 2008 among others). Cuberes and Teigner (2012) simulate an occupational choice model that imposes several frictions on female economic participation and their wages, and show that gender gaps in entrepreneurship and in labor force participation significantly reduce income per capita. For India, they find that income per worker is reduced by 26 percent. A number of recent empirical papers have examined low and declining female labor force participation in India. Klasen and Pieters (2012) find that for urban Indian women, participation in the workforce at lower education levels is dictated by economic necessity, and only for highly educated women there is a pull factor to enter the workforce. Esteve-Volart (2004) uses panel data on Indian states to show that the ratio of female to male workers (and managers) is positively correlated with growth and living standards. Finally, OECD (2014) finds that female economic participation (which includes labor force participation and participation in entrepreneurial activities) in India remain low and that raising FLFP through selected policies could boost economic growth by around 1½–2½ percent per annum.

Stylized Facts on Female Labor Force Participation

4. Household data from five rounds of the National Sample Survey Organization’s (NSSO) Employment and Unemployment Survey is used in the analysis. The surveys cover the years 1993/1994, 1999/2000, 2004/2005, 2009/2010, and 2011/2012. The estimation of determinants of labor force participation presented in this chapter focuses on the most recent round of the survey, 2011/2012, and females aged 18 to 60.

5. The following stylized facts emerge from the household survey data:

  • Female labor force participation rates vary widely between urban and rural areas. Labor force participation of women in rural areas is much higher than women in urban areas. Over time, the gap between urban and rural areas has narrowed slightly, with most of the change being driven by the fall in participation rates in rural areas.

A12ufig02

Female Labor Force Participation Rate in India

Citation: IMF Staff Country Reports 2015, 062; 10.5089/9781498316200.002.A012

Source: NSS Employment and Unemployment Suweys and IMF staff calculations.
  • There is also considerable diversity in female labor force participation rates across Indian states, with states in the South and East of India generally displaying higher participation rates than those in North India.

A12ufig03

Female Labor Force Participation Across States

Citation: IMF Staff Country Reports 2015, 062; 10.5089/9781498316200.002.A012

Source: NSS Employment and Unemployment Surveys and IMF staff calculations.
  • There is also a growing gap between male and female labor force participation rates. These are particularly pronounced in urban areas, where they are wider, at around 60 percentage points. In rural areas, participation gaps between males and females were around 45 percentage points.

A12ufig04

Urban: Labor Force Participation Rate

Citation: IMF Staff Country Reports 2015, 062; 10.5089/9781498316200.002.A012

Source: NSS Employment and Unemployment Surveys and IMF staff calculations.
A12ufig05

Rural: Labor Force Participation Rate

Citation: IMF Staff Country Reports 2015, 062; 10.5089/9781498316200.002.A012

Source: NSS Employment and Unemployment Surveys and IMF staff calculations.
  • There is a U-shaped relationship between education and labor force participation rates of women. With increasing education, labor force participation rates first start to decline and then pick up among highly-educated women, who experience the pull factor of higherpaying white collar jobs.

A12ufig06

Urban Female Labor Force Participation

Citation: IMF Staff Country Reports 2015, 062; 10.5089/9781498316200.002.A012

Source: NSS Employment and Unemployment Surveys and IMF staff calculations.

Empirical Methodology and Results

6. The following questions are analyzed:

  • What are the determinants of female labor force participation in India’s urban and rural areas?

  • Have states with less stringent labor market regulations seen higher job creation and greater female participation?

  • How do these factors affect whether employment occurs in the formal or informal sector?

7. A two stage estimation procedure is used. In the first stage, an individual’s expected wages are estimated as follows:

wi=θ1+θ2Zi+ηiE(w)=w^

where w is the log of daily wages and Z is a vector of individual and household characteristics variables including: age and age squared, dummy variables representing literacy, levels of educational attainment, marriage, children aged 0 to 4, and 5 to 16, and whether the individual lives a rural or urban area. In the main specification, the probability of being in the labor force is then estimated as follows:

Pr{Li=1}=α+β1w^i+β2EPL+β3Xi+νs+εi

where Li = 1 if individual i is in the labor force, w^ is the log of daily wages, EPL is the employment legislation index discussed below, and X is a vector of individual and household characteristics variables including:

  • Age, dummy variable representing whether the individual is married, or has children

  • Dummy variables representing literacy, and levels of educational attainment

  • Monthly household expenditure to capture the income level of the household

  • State-dummies to control for unexplained differences in labor force participation across states

We estimate weighted logit models to ensure the estimates represent the population, unlike previous studies using the NSS Employment and Unemployment surveys. Standard errors are clustered at household level. Other specifications focus on employed individuals only, to study the factors that increase the probability that an individual is employed in the formal sector instead of the informal sector.

8. It has been widely noted that relatively inflexible labor markets have weighed on employment generation in India. There is considerable cross-state heterogeneity in labor market rigidities. Accordingly, we use a state-level index to gauge the flexibility of Indian labor markets. This index is the OECD’s Employment Protection Legislation (EPL), which is based on a survey of labor market regulations. It counts amendments to regulations that are expected to increase labor market flexibility. This includes amendments to the Industrial Disputes Act (IDA),2 the Factories Act, the Shops Act, and the Contract Labor Act. For example, with respect to the IDA, the index would take a higher value for Indian states that: require a shorter amount of time to give notice to fire; have made amendments allowing certain exemptions to the Act, that have lowered the threshold size of the firm to which chapter VB applies; 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 EPL index also captures differences in the ease of complying with regulation (e.g. rules on dealing with inspectors, registers, filing of returns). As in Dougherty (2009), we scale the index, which takes values from 14 to 28, by its maximum value, thus ending with a variable that ranges from 0.5 to one.

9. The main regression results are as follows:

  • The baseline regressions (Table 1) show the impact of individual characteristics on the probability of being in the labor force. This probability rises when a woman is highly educated, but falls with rising number of children and marriage. Expected wages have a significant positive effect on the probability of being in the labor force. Rising households incomes have a dampening effect on participation.

  • More flexible labor markets are associated with higher female participation in the labor force. The coefficient of 3.25 (Table 1) implies that the probability of being in labor force increases by about 13.8 percentage points when EPL increases from 0.5 to 1 (and all other variables are at their means).

  • The chance of being employed in a formal job, as opposed to in the informal sector, also increases in more flexible labor markets. When categorizing labor as formal both by the location of employment and by whether an individual has an employment contract (Table 2), the results indicate a higher likelihood of being formally employed in states with higher EPL.

  • Poor infrastructure has a dampening effect on female labor force participation. Women in states with greater access to roads are more likely to be in the labor force (Table 3).

  • Higher social spending (as a share of net state domestic product (NSDP)) is associated with greater female labor force participation (Table 3).

Table 1.

Determinants of Female Labor Force Participation

article image
Source: IMF staff estimates.Note: Robust standard errors in parentheses, clustered at household level, *** p<0.01, ** p<0.05, * p<0.1. All specifications include state dummies.
Table 2.

Formal and Informal Employment

article image
Source: IMF staff estimates.Robust standard errors in parentheses, clustered at household level. *** p<0.01, ** p<0.05, * p<0.1. All specifications include state dummies.

Categorized as informal or formal based on location of work.

Table 3.

Infrastructure and Social Spending

article image
Source: IMF staff estimates.Note: Robust standard errors in parentheses, clustered at household level, *** p<0.01, ** p<0.05, * p<0.1. NSDP is net state domestic product. All specifications include individual and household control variables, log(NSDP), and predicted wages.

10. In sum, there is a large gap in the labor force participation rates of men and women in India. This gap should be addressed to fully harness India’s demographic dividend. A number of policy initiatives can be used to address this gap. These include increased labor market flexibility (which could lead to the creation of more formal jobs), allowing more women (many of whom are employed in the informal sector) to be employed in the formal sector. Investment in infrastructure to improve connectivity would also help foster higher female participation. As well, higher social spending, including investment in education, can boost female labor force participation.

References

  • Cuberes, D. and M. Teignier, 2012, “Gender Gaps in the Labor Market and Aggregate Productivity,” Sheffield Economic Research Paper SERP 2012017.

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  • Cuberes, D. and M. Teignier, 2014, “Gender Inequality and Economic Growth: A Critical Review,” Journal of International Development, Vol. 26, pp. 260276.

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  • Dougherty, S., 2009, “Labour Regulation and Employment Dynamics at the State Level in India”, Review of Market Integration, Vol. 1(3), pp. 295337.

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  • Esteve-Volart, B., 2004, “Gender Discrimination and Growth: Theory and Evidence from India,” STICERD - Development Economics Papers 42, Suntory and Toyota International Centres for Economics and Related Disciplines, London School of Economics.

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  • International Monetary Fund, 2013, Women, Work, and the Economy: Macroeconomic Gains from Gender Equity, IMF Staff Discussion Note SDN 13/10, September 2013.

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  • Klasen, S. and J. Pieters, 2012, “Push or Pull? Drivers of Female Labor Force Participation during India’s Economic Boom,” IZA Discussion Papers 6395, Institute for the Study of Labor (IZA).

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  • Klasen, S. and F. Lamanna, 2008, “The Impact of Gender Inequality in Education and Employment on Economic Growth in Developing Countries: Updates and Extensions,” Ibero America Institute for Econ. Research (IAI) Discussion Papers 175, Ibero-America Institute for Economic Research.

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  • OECD, 2014, Economic Survey of India, Chapter 2, “Raising the Economic Participation of Women in India – a New Growth Engine?,” November 2014.

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1

Prepared by Sonali Das, Sonali Jain-Chandra, and Naresh Kumar, based on a forthcoming IMF Working Paper.

2

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

India: Selected Issues Paper
Author: International Monetary Fund. Asia and Pacific Dept