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Giovanna Prennushi, Lead Economist, World Bank HDCP-IRC Workshop July 13, 2007 -- Casteggio, Italy PowerPoint Presentation
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Poverty impact analysis: integrating quantitative and qualitative information. Giovanna Prennushi, Lead Economist, World Bank HDCP-IRC Workshop July 13, 2007 -- Casteggio, Italy. based on : Moving out of Poverty in Andhra Pradesh, India. (Preliminary Findings, Do not Quote)

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slide1

Poverty impact analysis: integrating quantitative and qualitative information

Giovanna Prennushi, Lead Economist, World Bank

HDCP-IRC Workshop

July 13, 2007 -- Casteggio, Italy

slide2

based on:Moving out of Poverty in Andhra Pradesh, India

(Preliminary Findings, Do not Quote)

Deepa Narayan, Giovanna Prennushi, Soumya Kapoor

slide3

Outline of Presentation

  • Objectives, Sampling and Methods
  • Preliminary Findings
  • Challenges and Lessons
slide4

Study Objectives

  • How and why do some households move out of poverty while others remain trapped in chronic poverty?
  • What role do self-help groups help people in moving out of poverty?
slide5

Sampling Framework

  • Survey used sample of an earlier study (the Mid-Term Assessment of the District Poverty Initiative Program) because it intended to create a panel
    • 60 clusters in 3 of the poorest districts of the state (Adilabad, Anantapur, Srikakulam) -- not representative of the state as a whole
    • 15 households per clusters: 10 visited earlier + 5 new
  • While the idea was to have program and control villages, by the time of the study the program had been extended to control villages
    • So no control villages
slide6

AP districts covered:

Adilabad

Srikakulam

Anantapur

slide7

Methods

  • Quantitative community-level interviews with key informants
  • Focus Group Discussions with men and women (separately) on "Ladder of Life"
  • In-Depth quantitative questionnaire on sample of households
  • Additional tools:
    • Individual life histories with some of the HH interviewed
    • FGDs on other topics (power and freedom, youth aspirations)
    • Community timeline with key informants

Key tool: Ladder of Life

Result: "pseudo"-panel data based on recall

(not as good as panel data, but second-best where there are no panel data)

slide8

Methods: Ladder of Life

  • FGD participants are asked to:

(a) identify steps in a "Ladder of Life"

    • As many steps as needed
    • Based on overall situation, not just assets or expenditures

(b) place all households in the cluster on a step in the ladder

(c) now (2005) and ten years earlier

Result: "pseudo"-panel data based on recall

(not as good as panel data, but second-best where there are no panel data)

slide12

Preliminary results

  • Poverty has declined across the study areas, but not uniformly
  • Self-Help Groups have played an important role according to participatory data but don't appear as significant in the quantitative analysis of moving out of poverty
slide13

Poverty indicators derived from the mobility matrix

1) Moving out of Poverty Index:

(MOP: Poverty Reduction)

2) Mobility of the Poor Index:

(MPI)

3) Change in the incidence of

poverty

slide14

Change in poverty, 1995-2005, by district

  • Overall, the share of households in poverty at the end of the period (2005) is lower than the share at the beginning (1995), indicating a reduction in the incidence of poverty
  • Significant "churning"
  • Srikakulam outperformed the other two districts in indicators of upward mobility of the poor (MOP, MPI), and had less poverty to begin with.
slide16

Impact of Self-Help Groups (1)

  • The number of groups and the share of households belonging to a group increased substantially in the sampled communities
slide17

On average, Movers belonged to more groups than the Chronic Poor...

Average number of groups a HH had/has joined

slide18

Impact of Self-Help Groups (2)

  • Women in particular, but also men, tell us that SHGs had a big impact

Factors responsible for progress in the community

slide19

Impact of Self-Help Groups (2)

  • Women in particular, but also men, tell us that SHGs had a big impact
    • "Before these groups, our women never went out to meetings, banks and discussions. They didn’t know anything except their households’ work. Now they are able to deal with all these things very easily. "
    • "Recently one husband thrashed his wife in a drunken state. Our group came to know about this and we all went to him and abused him and threatened him saying that if it happens again, we will take serious action against him. The power of women groups is up to that extent. "
    • "One year back we initiated a movement to eradicate the consumption of arrack shops. We went to the liquor shops and threw away all the liquor."
    • "We are no longer enslaved to the moneylenders."
slide20

Impact of Self-Help Groups (3)

  • Quantitative data don't reveal a significant impact
    • Dependent variable is a 0-1 dummy, equal to 1 for those who started poor and moved out of poverty
    • Explanatory variables capture initial conditions on:
      • household assets
      • strength of the local economy
      • functioning of local democracy
      • fair treatment across ethnic/caste groups
      • district dummies
      • groups in 1995
    • ... and the change in the number of groups as a policy variable
  • Groups are not significant
slide22

Impact of SHGs (4)

  • Why and how do groups matter?
    • SHGs emerge where there is strong social stratification, as a response to exclusion and disempowerment
    • Poor people are clearly empowered through the formation of groups
      • FGD participants mention increased self-reliance, freedom, dignity and agency of their members
    • Empowered poor people can progress in many ways, but they may not be able to escape poverty if there are few economic opportunities available
slide23

Challenges and Lessons

Some challenges are specific (but not unique) to this study:

  • The sample was not randomly selected
    • At the level of clusters, nothing we can do; results are simply not representative
    • At the level of households, we can correct approximately for over- and under-sampling since we know the distribution of all households in each cluster by mobility status
  • The fieldwork (esp. quantitative) was not top-notch
    • Researchers, organizations, and field workers who are skilled in participatory methods may not be very good at quantitative methods, and vice-versa
slide24

Challenges and Lessons

Some challenges are more general:

  • Different people in a community view things differently
    • Ex: perceptions about corruption, responsiveness of local government
    • Ex: male and female LoL results

Which perceptions do we rely on?

  • Different methods provide a rich picture, but yield different results

Key lessons:

  • Build team with both quant and participatory skills
  • Keep questions simple and focused