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Does Social Capital Build Women’s Assets?  Neha Kumar and Agnes Quisumbing, IFPRI Peter Davis (Bath) Data Analysis and Technical Assistance, Ltd. Data Analysis and  Technical Assistance Ltd
Collective action:  a definition  ,[object Object],[object Object],[object Object],[object Object],Page
Forms of collective action: Philippines Page
Forms of collective action:  Bangladesh Page
Key questions ,[object Object],[object Object],Page
Revisiting agricultural technology sites after 10 years ,[object Object],[object Object],[object Object],[object Object],[object Object],Page
Page
Treatment and control groups of the 3 interventions as of baseline round Intervention Treatment Comparison Microfinance (1994) Participants in microfinance NGOs in 7 villages Nonparticipants in microfinance NGOs in all villages (eligible nonparticipants)  Agricultural technology (1996) Households in villages where agricultural technologies disseminated  Households in villages where agricultural technologies not yet disseminated  Educational transfers (food/cash for education) (2000) Households in FFE unions Households in non-FFE unions
Survey Design in 1996/7 Page  -4 rd panel 1996/1997 -Coverage of 3 major agric. seasons -3 sites, 47 villages, 955 HHs IN EACH SITE Type of NGO village HH type Agricultural technology introduced Technology not yet introduced NGO member adopters A (n=110/site) NGO member likely adopters B (n=110/site) Others C1 (n=55/site) C2 (n=55/site)
For example, vegetable technologies were initially not disseminated to a village where pottery was the major industry Treatment village Comparison village
Casual observation suggests that there have been many changes over the past decade…
Some technologies have clearly taken off Page
And some individuals/households have clearly been able to move out of poverty, and others, not.
There have also been some more subtle changes…
Many households have moved out of poverty Poverty head count and transitions Agricultural technology (1996-2006) Poverty headcount Poverty in baseline survey 70% Poverty in 2006/2007 18% Poverty transitions Chronic poor 16% Falling into poverty 2% Moving out of poverty 54% Never poor 28%
Households’ assets have grown over time, though landholding sizes have gone down Page  Asset holdings Household assets Value (‘1000 taka, 2007 prices) 1996 2006 Average growth rate Total nonland assets 27.0 49.7 8.4 Consumer durables 8.13 15.8 9.4 Ag durables 4.8 1.5 -6.9 Nonag durables 1.2 4.4 25.8 Jewelry 2.5 11.1 35.2 Livestock 10.5 17.0 6.3 Total owned land (decimals) 148.5 117.4 -2.1
Asset growth over time , 1996 and 2006 (exclusively held assets) Page  1996 2006 % change H W H W H W Landholdings (decimals) Homestead 10.3 .3 10.9 0.6 5.8 44.5 Cultivated 85.9 1.9 67.9 3.2 -21.0 39.7 Other land 5.4 .1 5.0 0.2 -8.9 6.1 Total owned land 101.7 2.4 83.8 4.0 -17.6 39.2 Nonland assets (‘000 taka, 2007 prices) Consumer durables 2.2 .3 5.8 0.4 166.4 40.8 Ag durables 1.6 n.s. 0.6 n.s. -62.7 6.2 Nonag durables 0.5 n.s. 3.3 0.1 494.3 428.9 Jewelry n.s. 1.5 1.5 2.1 5262.2 38.5 Livestock 5.8 1.7 9.1 1.1 57.5 -31.9 Nonland assets (excl livestock) 4.4 1.8 11.2 2.6 155.3 42.1
Distribution of area of owned land across ownership categories ,[object Object],[object Object],Page
Distribution of nonland assets across ownership categories ,[object Object],[object Object],Page
Measuring long-term impact ,[object Object],[object Object],[object Object],Page
Supposed we observe an increase in outcome Y for beneficiaries over time after an intervention Page  Y 0 Y 1 baseline(t 0 ) follow-up(t 1 ) Intervention (observed)
To measure impact, we need to remove the counterfactual from the observed outcome Page  Y 0 Y 1 baseline(t 0 ) follow-up(t 1 ) Intervention (observed) Y 1 * Impact= Y 1 -Y 1 * (counterfactual) Comparison
Issues with single differencing, or why I like panel data ,[object Object],[object Object],[object Object],[object Object],Page
Constructing a Comparison Group ,[object Object],[object Object],[object Object],[object Object],[object Object],Page
Selection Effects and Selection Bias ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Page
Propensity Score Matching ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Page
Alternative definitions of the treatment/comparison groups ,[object Object],[object Object],[object Object],[object Object],Page
Results Page
Big picture story at the household level  (Kumar and Quisumbing 2010) ,[object Object],[object Object],[object Object],[object Object],Page
Impacts on nutrient intake and nutritional status ,[object Object],[object Object],[object Object],[object Object],Page
Impact of agricultural technology on men’s and women’s assets ,[object Object],[object Object],[object Object],Page
Impact of early adoption on differential growth of husband’s vs. wife’s assets (H-W) Page
Impact of NGO or program membership on differential growth of husband’s vs. wife’s assets (H-W) Page
Suggestive conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object],Page
Does social capital build women’s assets? ,[object Object],[object Object],[object Object],[object Object],Page
有難うございます ! Page

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Quisumbing social capital bangladesh_grips

  • 1. Does Social Capital Build Women’s Assets? Neha Kumar and Agnes Quisumbing, IFPRI Peter Davis (Bath) Data Analysis and Technical Assistance, Ltd. Data Analysis and Technical Assistance Ltd
  • 2.
  • 3. Forms of collective action: Philippines Page
  • 4. Forms of collective action: Bangladesh Page
  • 5.
  • 6.
  • 8. Treatment and control groups of the 3 interventions as of baseline round Intervention Treatment Comparison Microfinance (1994) Participants in microfinance NGOs in 7 villages Nonparticipants in microfinance NGOs in all villages (eligible nonparticipants) Agricultural technology (1996) Households in villages where agricultural technologies disseminated Households in villages where agricultural technologies not yet disseminated Educational transfers (food/cash for education) (2000) Households in FFE unions Households in non-FFE unions
  • 9. Survey Design in 1996/7 Page -4 rd panel 1996/1997 -Coverage of 3 major agric. seasons -3 sites, 47 villages, 955 HHs IN EACH SITE Type of NGO village HH type Agricultural technology introduced Technology not yet introduced NGO member adopters A (n=110/site) NGO member likely adopters B (n=110/site) Others C1 (n=55/site) C2 (n=55/site)
  • 10. For example, vegetable technologies were initially not disseminated to a village where pottery was the major industry Treatment village Comparison village
  • 11. Casual observation suggests that there have been many changes over the past decade…
  • 12. Some technologies have clearly taken off Page
  • 13. And some individuals/households have clearly been able to move out of poverty, and others, not.
  • 14. There have also been some more subtle changes…
  • 15. Many households have moved out of poverty Poverty head count and transitions Agricultural technology (1996-2006) Poverty headcount Poverty in baseline survey 70% Poverty in 2006/2007 18% Poverty transitions Chronic poor 16% Falling into poverty 2% Moving out of poverty 54% Never poor 28%
  • 16. Households’ assets have grown over time, though landholding sizes have gone down Page Asset holdings Household assets Value (‘1000 taka, 2007 prices) 1996 2006 Average growth rate Total nonland assets 27.0 49.7 8.4 Consumer durables 8.13 15.8 9.4 Ag durables 4.8 1.5 -6.9 Nonag durables 1.2 4.4 25.8 Jewelry 2.5 11.1 35.2 Livestock 10.5 17.0 6.3 Total owned land (decimals) 148.5 117.4 -2.1
  • 17. Asset growth over time , 1996 and 2006 (exclusively held assets) Page 1996 2006 % change H W H W H W Landholdings (decimals) Homestead 10.3 .3 10.9 0.6 5.8 44.5 Cultivated 85.9 1.9 67.9 3.2 -21.0 39.7 Other land 5.4 .1 5.0 0.2 -8.9 6.1 Total owned land 101.7 2.4 83.8 4.0 -17.6 39.2 Nonland assets (‘000 taka, 2007 prices) Consumer durables 2.2 .3 5.8 0.4 166.4 40.8 Ag durables 1.6 n.s. 0.6 n.s. -62.7 6.2 Nonag durables 0.5 n.s. 3.3 0.1 494.3 428.9 Jewelry n.s. 1.5 1.5 2.1 5262.2 38.5 Livestock 5.8 1.7 9.1 1.1 57.5 -31.9 Nonland assets (excl livestock) 4.4 1.8 11.2 2.6 155.3 42.1
  • 18.
  • 19.
  • 20.
  • 21. Supposed we observe an increase in outcome Y for beneficiaries over time after an intervention Page Y 0 Y 1 baseline(t 0 ) follow-up(t 1 ) Intervention (observed)
  • 22. To measure impact, we need to remove the counterfactual from the observed outcome Page Y 0 Y 1 baseline(t 0 ) follow-up(t 1 ) Intervention (observed) Y 1 * Impact= Y 1 -Y 1 * (counterfactual) Comparison
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 29.
  • 30.
  • 31.
  • 32. Impact of early adoption on differential growth of husband’s vs. wife’s assets (H-W) Page
  • 33. Impact of NGO or program membership on differential growth of husband’s vs. wife’s assets (H-W) Page
  • 34.
  • 35.

Notas del editor

  1. The little girl on the right is carrying her school materials. More girls are going to school.
  2. Our poverty rates lower than BBS, possibly b/c our hhs are not landless
  3. Asset levels have increased, but men’s more than women’s
  4. Wives’ share of land increased, but husbands’ increased more
  5. Husband share of nonland assets increased, and wife’s decreased