Quisumbing social capital bangladesh_grips

Does Social Capital Build Women’s Assets?

Neha Kumar and Agnes Quisumbing, IFPRINeha Kumar and Agnes Quisumbing, IFPRI

Peter Davis (Bath)Peter Davis (Bath)

Data Analysis and Technical Assistance, Ltd.Data Analysis and Technical Assistance, Ltd.

Data Analysis and Technical Assistance Ltd

Collective action: a definition

Collective action is voluntary action taken by a group to achieve common interests

Of interest to development practitioners because many development interventions are implemented through groups, particularly women’s groups

Collective action viewed as a way of accumulating social capital

Idea that social capital and group liability can substitute for physical capital

Forms of collective action: Philippines

Forms of collective action: Bangladesh

Key questions

To what extent can collective action, while acting as a substitute in the short run, actually help build assets owned/controlled by women in the long run?

Does implementation modality—targeting to women’s groups vs. targeting to households—make a difference to gender asset inequality?

Revisiting agricultural technology sites after 10 years

Panel data set based on 957 households surveyed in 1996/7 and 2006/7 in study sites examining impact of new agricultural technologies in rural Bangladesh

3 technologies/implementation modalities:1. improved vegetables for homestead production, disseminated through

women’s groups (Saturia)

2. fishpond technology through women’s groups (Jessore)

3. fish pond technology targeted to individuals (Mymensingh)

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

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)

-4 rd panel 1996/1997

-Coverage of 3 major agric. seasons

-3 sites, 47 villages, 955 HHs

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

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

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)

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

1996 2006

Distribution of nonland assets across ownership categories

1996 2006

Measuring long-term impact

We want to evaluate the long-term impact of the technology (and implementation modalities) on various outcomes (e.g., men’s and women’s assets).

How do we know measured impacts are due to the program?

If we want to claim that the impacts observed are causal, we need a way to attribute the observed effects to the program and not to other factors

Supposed we observe an increase in outcome Y for beneficiaries over time after an intervention

Y0

Y1

baseline(t0) follow-up(t1)

Intervention

(observed)

To measure impact, we need to remove the counterfactual from the observed outcome

Y0

Y1

baseline(t0) follow-up(t1)

Intervention

(observed)

Y1*

Impact= Y1-Y1

*

(counterfactual)

Comparison

Issues with single differencing, or why I like panel data

Problem with just doing a single difference Y1-Y1* is that

there may be underlying factors that may be affecting both treatment and comparison groups, but that we don’t observe

Double-differencing allows you to compare changes through time between the treatment and comparison groups

That is, look at

(Y1T-Y0

T) - (Y1C-Y0

C)

Constructing a Comparison Group

Suppose we want to measure the impact of agricultural technology on men’s and women’s assets

Q: Why not compare average value of women’s assets of technology adopters with average value of women’s assets for nonadopters?

A: On average, nonadopters may be different from adopters in ways that make them an ineffective comparison group

Need to correct for differences between treated and untreated households arising from ‘selection effects’

Selection effects bias impact estimates if not removed

Selection Effects and Selection Bias

Targeting or program placement bias– NGO may target closer villages for initial dissemination – Closer villages might be wealthier (have more assets) even

without the program

Self-selection bias– access to technology depends on whether household has land

or fishpond, has available labor, and whether woman joins the NGO

– households that participate may be wealthier, more “empowered” women would tend to join NGOs

Need to control for selection bias when constructing a comparison group

Propensity Score Matching

▪ Original studies had well defined control groups

▪ In this project, “Propensity Score Matching (PSM)" is used to match program participants with nonparticipating-control group

▪ PSM technique▪ probability of adoption of the technology, based on

observable characteristics

▪ statistical comparison group → participants matched to nonparticipants with similar values of propensity scores.

▪ compare change in outcomes over time for these 2 groups; i.e., “difference in difference” analysis

Alternative definitions of the treatment/comparison groups

Early vs. late adopters (A vs. B and C)

NGO members with technology vs. NGO members without technology (A vs. B)

NGO members vs. non-NGO members (A and B vs. C)

We focus on the first and third comparisons today

Results

Big picture story at the household level (Kumar and Quisumbing 2010)

Biggest gains to early adoption are in the individual fishpond sites, significant positive impacts on hh-level consumption, assets, calorie availability

Short-term positive impact of early adoption in vegetables site dissipated in long run; technology is divisible and easy to adopt

Short-term positive impact of group fishponds also dissipated over long run; income gains have to be shared by many families

However, the story is quite different when we look at indicators of nutritional status, as well as individually owned assets

Impacts on nutrient intake and nutritional status

In individual fishpond sites, aggregate nutrient availability increased; percentage of hh members consuming below RDA decreased; stunting decreased; BUT: children’s and women’s BMIs have decreased.

In group fishpond sites, later adopters did better in terms of nutrient intake, but early adopters realized improvements in long-run nutritional status of children. However, ZBMI and percentage of kids with ZBMI<-2 increased

In the homestead vegetables sites, despite reduction in hh food consumption (from expenditure data), there were improvements in nutritional status: increase in vit A and iron for men; reduction in proportion of hh members below iron and vit A RDAs; improvement in stunting rates (girls), women’s BMI and hemoglobin

Did emphasis on vegetables, and targeting to women, improve nutrition even if income gains were minimal in the vegetables sites?

Impact of agricultural technology on men’s and women’s assets

How have the agricultural technology programs contributed to: (1) asset growth of men and women; (2) reduction of the gender asset gap?

We use matching methods to examine impact of the agricultural technology program over time on men’s and women’s assets on average, and men’s and women’s assets within the same household.

We look at changes in husband’s assets relative to changes in wife’s assets within the same household, focusing on exclusively owned assets

Impact of early adoption on differential growth of husband’s vs. wife’s assets

(H-W)

Impact of NGO or program membership on differential growth of husband’s vs. wife’s assets

(H-W)

Suggestive conclusions

Implementation modalities matter: women’s assets increased more by programs that targeted technologies through women’s groups

Even when comparing an identical technology (polyculture fish technology), we find women’s assets increased more, relative to men’s, when women were targeted

Nevertheless, the bulk of the household’s assets are controlled by men

Intrahousehold impacts may be quite different from household-level impacts; looking at the household level, the individual fishpond program appears to be the big success, but looking at improvements in individual (women’s and children’s) outcomes, group-based programs were more effective

This reinforces the need to look within the household when evaluating impacts of programs and policies

Does social capital build women’s assets?

In the Bangladeshi context, yes. Delivering services and microfinance in groups

underlies success of the microfinance movement in Bangladesh, along many dimensions.

But we must remember that social capital is not costless, and the poor often face barriers to accumulating social capital.

Building women’s social capital may be one way to reduce gender asset inequalities and achieve broader development objectives.

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Quisumbing social capital bangladesh_grips

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