Giovanna Prennushi, Lead Economist, World BankHDCP-IRC Workshop

July 13, 2007 -- Casteggio, Italy

Poverty impact analysis: integrating quantitative and qualitative information

(Preliminary Findings, Do not Quote)Deepa Narayan, Giovanna Prennushi, Soumya Kapoor

based on:Moving out of Poverty in Andhra Pradesh,

India

Outline of Presentation

I. Objectives, Sampling and Methods

II. Preliminary Findings

III. Challenges and Lessons

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?

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

AP districts covered:

Adilabad

Anantapur

Srikakulam

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 LifeResult: "pseudo"-panel data based on recall

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

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)

Methods: Ladder of LifeStep

Community 15 (Adilabad)

Community 28(Anantapur)

Community 56 (Srikakulam)

1 Landless laborers Landless agricultural laborers

Landless labor

2 Laborers with 1-2 acres Small farmers and weavers with 1-5 acres of land

Small farmers with 1 acre and mason workers

3 Small farmers with 3-4 acres

Small farmers with 5-10 acres and occupational communities

Medium farmers with 2-4 acres

4 Medium farmers with 5-6 acres

Medium farmers with 10-30 acres

Government employees and farmers with 2-8 acres

5 Big farmers with 10+ acres of land

Farmers with 30-60 acres and government employees

Big farmers with 10-20 acres and traders

6 Big farmers and landlords with large tracts of land

Landlords with 60-120 acres

Note: The thicker line indicates the position of the community poverty line.

Community Mobility Matrix: Village 28, Anantapur District

NOW 1 2 3 4 5 6 Total

1

23,24,25,29,46,47,53,54,55,56,57,59,65,66,67,68,72,73,74,81,89,91,96,110,121,122,124,125,126,127,128,129,134,135,136,137,138,144,145,146,147

44 80,140

44

2

2,4,7,10,18,22,28,31,33,35,40,45,48,50,51,52,58,60,61,63,64,69,70,71,75,77,82,88,90,93,95,97,98,99,100,103,104,105,106,107,108,109,111,113,114, 117,118,119,123, 133,139,141,142, 143

116

55

3

3,6,19,20,26,30,32,43,49,62,76,78,79,94,102, 112,131,132, 148

130

20

4

1,5,8,21,42,83,92,101, 115,149, 150

11

5

9,13,14, 27,34,36,41,84,87

12, 16,17

12

6

11,15,37,38,39,85,86,120

8

10 YEARS AGO

Total

41 55 22 11 10 11 150

NOW

1 2 3 4 5 Total

1

8,14,20,22,27,30,35,

42,54,63,74,15,19,65,

32,

7,9,12,17,21,23,24,

29,33,34,36,38,41,44,

47,52,55,58,70, 49,

77,78,82,83,3,48,49,5

3,84,31,62,60,57,

54,56

50

2

32,73,79,80,6,28, 6, 81 5,18,51,50,68,75,16,

11,40,43,45, 46,18,72

1,25,64,71,76,

39,69

29

3

59, 37, 2,5, 13,26, 67,

66, 75

9

4

4,61

2

5

10 1

10 Y E A R S A G O

Total

23 49 9 8 2 91

Community Mobility Matrix: Village 56, Srikakulam District

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

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

ago years 10Poor HHs

CPL crossing Movers

ago years 10Poor HHs

Poor theamong Movers

HHs no. Total

nowpoor HHs -ago yrs 10poor HHs

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.

District

Poor in

1995

Poor in

2005

Change in poverty MOP MPI

Adilabad 0.53 0.48 -0.06 0.20 0.34

Anantapur 0.80 0.76 -0.04 0.10 0.25

Srikakulam 0.49 0.35 -0.14 0.44 0.62

AP Sample Average 0.63 0.56 -0.07 0.22 0.38

Change in poverty, 1995-2005, by cluster

-100 -50 0 50 100

Srikakulam

Anantapur

Adilabad

VelagaddaGobburu

SiddigamKusimi

DonubaiHaddubangi

M RajupalemNadimivalasaSanthavurity

CheepiKistupuram

NarisingupuramMakarajola

KothugumadaNimmada

Venkaiahpeta

MallapuramUdegolam

KundirpiYatakal

BasapuramYenumaladoddi(K)

ParasalaCharupalliJanthaluruMalayanar

MulakaleduBK Samudram

YerravankapalliRavuludikiDonnikota

SiddarampuramG.Venkatapalli

SanapaKairevu

MakodikiChinnavaduguruYenumaladoddi

RampuramB. Yaleru

ToshamAshepalli

JaminiDoulatabadShantapurBusimettaLingapur

ThimmapurKoritikal

VenkatapurWankidi

AndugulapetKuntala ( K)

DaboliUmri (K)

Kolhari

Starting Poor v/s Ending Poor

Starting Poor Ending Poor

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

1995 2005 Median number of groups per community

2 19

Median no. groups dealing with finance

0 12

Share of HH belonging to at least a group

8% 40%

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

Average number of groups a HH had/has joined

1995 2005 Chronic Poor 0.11 0.55 Fallers 0.00 0.30 Movers 0.14 0.78 Always Rich 0.18 0.78

Impact of Self-Help Groups (2) Women in particular, but also men, tell us that SHGs had

a big impactFactors responsible for progress in the community

FGDfpfactor1 | Freq. Percent Cum. --------------+----------------------------------- Water | 14 25.00 25.00 Sanitation | 1 1.79 26.79 Roads | 4 7.14 33.93 Housing | 3 5.36 39.29 Social | 2 3.57 42.86 AG | 2 3.57 46.43 Econ Opp | 5 8.93 55.36 Education | 6 10.71 66.07 SHG | 19 33.93 100.00 --------------+----------------------------------- Total | 56 100.00

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."

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

(1) (2)

moverm moverm

educ 0.012 0.036 [0.09] [0.28]

healthshock -0.415 -0.35

[2.33]* [2.03]*

land95 0.031 0.03 [1.63] [1.41]

assets95std 0.104 0.091

[0.91] [0.76]

econstrength95std -0.123 -0.088 [1.01] [0.61]

locdemocracy95wm 0.304 0.361

[1.94] [2.41]*

responsivenessKI95 0.414 0.332 [2.36]* [1.78]

collaction95std -0.101 -0.097

[0.75] [0.61]

fairlaw95 0.701 0.715 [3.27]** [3.06]**

divisions95 -0.165 -0.178

[1.98] [1.72]

fairtreatsch95 -0.162 -0.175 [2.85]** [2.61]*

Adilabad -1.095 -1.031

[3.39]** [3.19]**

Anantapur -1.392 -1.319 [5.11]** [4.04]**

totgroups95 0.038

[0.34]

groupch 0.001 [0.01]

vilgroups95 -0.028

[0.74]

groupchc 0.001 [0.10]

Constant -0.79 -0.627

[1.20] [0.77]

Observations 526 481 Linearized t-statistics in brackets

* significant at 5% level; ** significant at 1% level

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

Challenges and LessonsSome 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

Challenges and LessonsSome 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