Abstract
Community based livelihood interventions, which focus directly on increasing income and employment, have become an increasingly important component of large-scale poverty reduction programmes. We evaluate the impact of a participatory livelihoods intervention – the Tamil Nadu Empowerment and Poverty Reduction (Pudhu Vaazhvu) Project (PVP) using propensity score matching methods. The paper explores the impact of PVP on its core goals of empowering women and the rural poor, improving their economic welfare, and facilitating public action. We find significant effects of PVP on reducing the incidence of high cost debt and diversifying livelihoods. We also find evidence of women’s empowerment, and increased political participation.
Acknowledgements
This research paper is an output of the Social Observatory Team of the World Bank and Pudhu Vaazhvu Project (PVP). Discussions with the PVP project team, led by the Additional Project Director RV Shajeevana, were critical to the design of this evaluation. Support from all Project Directors of PVP; and from Kevin Crockford, Samik Das and Makiko Watanabe from the World Bank task team is gratefully acknowledged. We also thank PVP for support during survey implementation and GfK Mode for implementing the survey. The authors have benefitted from guidance, discussions and detailed comments from Upamanyu Datta, and Vijayendra Rao. The findings, interpretations and conclusions of this article should not be attributed to the World Bank Group, it’s executive directors, or its member countries.
Notes
1. Coimbatore, Cuddalore, Kancheepuram, Nagapattinam, Namakkal, Ramanathapuram, Salem, Theni, Thiruvannamalai, Thiruvalur, Thiruvarur, Thoothukudi, Tirrupur, Tirunelveli, Vellore and Villupuram.
2. Examples of these safety nets and services include India’s National Rural Employment Guarantee scheme, old age and widow’s pensions, and housing schemes that are implemented by both the state and central governments.
3. For their specific disability related needs such as hearing aids, crutches, and so on.
4. Rosenbaum and Rubin (1983) show that instead of matching on the covariate vector X, if households are matched using a linear projection of X, outcome is still independent of the treatment status. The linear projection we use is the propensity score generated using logit regression.
5. Kancheepuram, Thiruvallur, Thiruvanamalai and Villupuram from north; Namakkal and Tiruppur from west; Thoothukudi and Tirunelveli from south; and Cuddalore and Nagapattinam from the coastal region.
6. Living Standard Measurement Survey.
7. High cost debt is defined by loans with an annual interest rate of more than 50 per cent.
8. Low security jobs include the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), agricultural labour and unskilled casual labour as primary income generation activities.
9. Our measure of housing quality focused on the materials used for the roof, wall and floors of the house, and the addition of rooms to the house. Expenditure on repairs and could include the latter, as well as more minor repairs.
10. ‘Ration shop does not open regularly. People in the village often have to buy food grains from market’.
11. ‘There are insufficient public water sources in your village, making public water availability difficult’.
12. ‘A woman in the village is beating her daughter-in-law’.
13. ‘A man drinks and creates a ruckus in your village’.
14. The low market turnover of land in South Asia has been well documented in literature (see for instance Rosenzweig & Wolpin, Citation1985; Rosenzweig, Citation1980; Binswanger & Rosenzweig, Citation1986).