Entry into and Escape from Poverty: The Role of Female Labor Supply in Rural India

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This paper investigates the factors influencing poverty transitions among rural households. There is a higher likelihood for the poor rural household in escaping poverty and lower likelihood for non-poor households to fall into poverty over time, with the increase in average completed years of education and mean labor hours supplied by female members in the household. However, the contribution of female labor supply and education toward changes in poverty risks is low due to the low-wage cycle prevalent among women. Further, higher maximum educational attainment of households and a higher level of assets ensures a higher probability of escaping poverty and a lower probability of falling into poverty over time. However, there is a higher likelihood for a non-poor household to enter poverty over time and a lower likelihood for a poor household in escaping poverty over time with an increase in dependency ratio and household size. Efforts need to be made to transform a woman’s role from an “income buffering” to an “income generation” role. Women’s economic participation and empowerment are powerful tools for poverty reduction at the household level.

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Fig. 1

Source: Author’s own calculation from IHDS-II (2011–2012) & IHDS-I (2004–2005)

Fig. 2

Source: Author’s own calculation from IHDS-II (2011–2012) & IHDS-I (2004–2005)

Fig. 3

Source: Author’s own calculation from IHDS-II (2011–2012) & IHDS-I (2004–2005)

Fig. 4

Source: Author’s own calculation from IHDS-II (2011–2012) & IHDS-I (2004–2005)


  1. 1.

    China has 64% of its women working, one of the highest rates in the world (Dwivedi 2017). In the USA, it is over 56%. Further, Nepal and Bangladesh do much better than India; only Pakistan has a lower rate than India (ibid).

  2. 2.

    Similar results are also found in a region-level panel data analysis for India by Lanjouw and Murgai (2009). They find that expansion of non-farm sector leads directly to poverty reduction and also indirectly by putting pressure on agriculture wage rates, on which most of the poor are dependent upon.

  3. 3.

    Weights are numerical values that are used in surveys to multiply by response values, in order to account for missing observations (missing in terms of either non-responses or pre-arranged sample design). In the case of sample designs, weights estimate the totals or means for data, based on a selected subset of the entire population (Knaub 2007).

  4. 4.

    The National Sample Survey came into existence in 1950. It is a “multi-subject integrated continuing sample survey program launched for collection of data on the various aspects of the national economy (covering both rural and urban areas) required by different agencies of the Government, both Central and States. NSS can be classified under four categories: household surveys on socio-economic subjects; Surveys on land holding, livestock and agriculture; Establishment surveys, and enterprise surveys; Village surveys” (NSS report).

  5. 5.

    It is described as the ratio of probability density function (pdf = f(x)) to cumulative distribution function (CDF = F(x)) of a continuous random variable. Thus, IMR = f(x)/F(x).

  6. 6.

    Poverty line cut-offs are based on Tendulkar poverty lines for both years 2005 and 2012. Poor (BPL) and non-poor (APL) have been classified based on the cut-offs.

  7. 7.

    Ratio of number of dependents (those less than 15 years of age and those aging more than 60 years) to number of members in the working age-group (15–59 years) in a household.

  8. 8.

    Number of members residing in the same household.

  9. 9.

    In such non-poor households, women may be out of workforce due to less pressure to work and low returns for women (relative to men) in the job market. But when they decide to enter workforce, they have less experience, low pay and the low-wage cycle perpetuates which keeps their earnings relatively low as compared to men.

  10. 10.

    Women are often used as a reserve army of labor and may be compelled to increase their labor hours or enter workforce when the non-poor household is under financial crisis.


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See Tables 7, 8, 9 and 10.

Table 7 Sample size of panel data.
Table 8 Rural panel (individual level) data.
Table 9 Percentage distribution of rural women across socio-economic variables (panel data).
Table 10 Percentage distribution of rural households across socio-economic variables (panel data).

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Sebastian, N. Entry into and Escape from Poverty: The Role of Female Labor Supply in Rural India. Ind. J. Labour Econ. 63, 719–740 (2020). https://doi.org/10.1007/s41027-020-00242-5

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  • Poverty transition
  • Female
  • Labor supply
  • Rural
  • India
  • Panel

JEL Classification

  • J220
  • R23
  • J16
  • I32