Paolo Casini and Lore Vandewalle - DSEdse.univr.it/ssef/documents/material2012/slides Lore Vandewalle.pdf · Public Good Provision in Indian Rural Areas: the Returns to Collective

Public Good Provision in Indian Rural Areas: the Returns to Collective Actionby Female Microfinance Groups

Paolo Casini and Lore Vandewalle

K.U.Leuven (LICOS) and University of Goettingen

June 21, 2012

Lore Vandewalle (Goettingen) Public Good Provision in Indian Rural Areas 1 / 24

Introduction Self-Help Groups

Self-Help Groups (1/2)

Self-Help Groups (SHGs) are the most common form of microfinance in India.

Financial benefits: SHGs enable the poor to

increase their capacity to save.pool their resources, create a group fund and give out small loans toone another.open savings accounts with commercial banks and apply for bank loans.

⇒ Financial intermediaries for 97 million households


Introduction Self-Help Groups

Self-Help Groups (2/2)

Non-financial benefits: Women are relatively disadvantaged: they arerestricted in their mobility and their public role is minimal.⇒ SHGs provide a platform that allows these women to meet

weekly, which can help building social capital.There is evidence of

mutual assistance.collective actions aiming to solve problems affecting the villages.→ Visit a government officer to request a solution

(suspension of alcohol licenses)→ Intervene directly in the village

(organizing anti-alcohol campaigns)


Introduction Research question and findings

Research question and findings

Research question:

What is the impact of the collective actions of SHGs on the public goodprovision by the elected local authorities?

Findings:

1 The local authorities deal with a larger variety of public goods.

2 They start taking care of problems close to the interest of SHGs.

⇒ Important evidence of non-financial benefits of microfinance:Problems that are close to the needs of women seem to findtheir way into the political agenda.


Introduction Literature

Literature

Impact of microfinance: a focus on the long-term, non-financial effect.(Banerjee et al. (2010) and Karlan and Zinman (2010) do not find significant

effects in the short-term; Chowdhury et al. (2004))

Provides evidence that men and women have diverging preferences forsome public policies and that women’s leadership biases policy choicesin favor of women.(Lott and Kenny (1999); Edlund and Pande (2002); Chattopadhyay and Duflo

(2004))

The theoretical framework is based on political economy models.(Besley and Coate (1998); Besley and Coate (2003) and Besley et al. (2004))


Background Gram Panchayat

Composition of the Gram Panchayat: The lowest electedauthority in rural India

Gram Panchayat

(10,000 villagers)

5 to 15 Villages 11 to 25 Wards

One Ward Member is elected per ward.

1/3 ward members are women (reservation).



What we expect to observe when a public good problemappears in the ward

- Head of the Gram Panchayat

- District magistrate

- Police officer

- …

Ward Member

SHGs Other Groups Villagers

Villagers can form groups

Officer



What we do observe when a public good problem appearsin the ward

- Head of the Gram Panchayat

- District magistrate

- Police officer

- …

Ward Member

SHGs Other Groups Villagers

Villagers can form groups

Officer


Background Data set

First-hand data

SHGs initiated by PRADAN, a Delhi based NGO that creates SHGsconsisting of women only.

We use two surveys:1 In 2006, Baland, Somanathan and Vandewalle collected information on

532 SHGs and their 8,589 members in the state of Odisha.2 In 2010 we re-visited the SHGs and villages to gather information on:

The collective actions undertaken by SHGs.The type of public goods that ward members dealt with.We interviewed all ward members elected in the past 20 years(1992, 1997, 2002 and 2007).

⇒ We have information on 425 SHGs and 462 ward members, covering147 wards.


Background Data set

Public good focus of SHGs and ward members

% that visited an officer or intervened directly

Ward SHGsMembers

(1) (2)

Village infrastructure 79.9 53.7Welfare schemes 64.5 25.9Alcohol problems 13.0 59.8School problems 12.3 16.5Dowry and child marriage 0.0 2.8Forest issues 33.1 55.1Other 4.8 3.5

Average number of different issues 2.2 2.3(conditional on at least one)Number of observations 462 425


Background Data set

SHGs focus on alcohol, school and forest issues

Alcohol issues:- In India, men are 9.7 times more likely than women to regularly

consume alcohol (Neufeld et al. (2005)).- Alcohol consumption reduces the budget available for primary

expenses (Mishra (1999); Banerjee and Duflo (2007)).- There is strong evidence that alcoholism triggers violence against

women (Rao (1997); Panda and Agarwal (2005); Babu and Kar (2010)).

School issues:- Related to free midday meals, sanitation and teacher quality.

In line with the common finding that women spend more time andresources on family welfare (Anderson and Baland (2002); Duflo (2011)).

Forest issues:- The livelihood of many households depend on it. 29.7% of the

SHGs received training from PRADAN to improve their forest-based income.


Background Data set

Observation: Ward members deal with a larger variety ofpublic goods once SHGs are created and/or active

% of ward members dealing with the issuebefore a once SHGs are presentfirst SHG all before SHGs since SHGsis created undertake undertake

actions actions(1) (2) (3) (4)

Alcohol problems 2.3 17.1∗∗∗ 2.1 23.1∗∗∗

School problems 5.4 15.0∗∗∗ 2.1 20.2∗∗∗

Forest issues 21.7 37.5∗∗∗ 17.9 45.4∗∗∗

Village infrastructure 72.1 82.9∗∗∗ 77.9 84.9∗∗∗

Welfare schemes 36.4 75.4∗∗∗ 62.1∗∗∗ 80.7∗∗∗

Average number of different issues 1.7 2.4∗∗∗ 1.8 2.6∗∗∗

(conditional on at least one)

Significance of the difference relative to column (1) *** p<0.01, ** p<0.05, * p<0.1


Background Data set

Structure of the data set


The Model

The model: Why do we observe an increase in theactivities of ward members?

We model in a political economy framework. SupposeTwo types of villagers: 1 (men) and 2 (women)

Two categories of public goods: g1 and g2 are the number of goods of type 1 andtype 2 respectively

A villager of type i has a (weak) preference for goods of type i , but alsobenefits/suffers an externality induced by goods of type −i

Ui (g1, g2, λi ) = log(gi ) + λi log(g−i )

λi ∈ [−1, 1]: the level of externality that good −i generates for type i

The ward member maximizes social welfare to decide about the optimal number ofgoods (g1, g2) to provide.

maxT ,g

W (T , g , λ, µ) =µU1(g , λ1) + (1− µ)U2(g , λ2)− kT

s.t. : g1 + g2 = T

T ≤ Tmax

gi ≥ 0 i = 1, 2


The Model

Predictions

The model compares the type of goods the ward member provides in twodifferent set-ups: in the absence and the presence of SHGs.Comparing the equilibria leads to two predictions:

Prediction 1: The active presence of an SHG leads to an increase inthe number of issues the ward member deals with.

Prediction 2: The ward member starts dealing with public goodspreferred by SHGs. These include goods that either exert a negativeexternality on other villagers or whose importance the ward member isnot aware of.

Model


Empirical Strategy and Results Prediction 1

Prediction 1: Number of issues the ward member dealswith

Tij is the total number of different issues dealt with by ward member i in ward j .We estimate the following Poisson model:

f (Tij |Aij ,Bij , ...) =exp−µij µ

Tij

ij

Tij !

assuming that the mean parameter µij is given by:

µij = E [Tij |Aij ,Bij , ...] = exp(Aijβ + Bijγ + Cijδ + νj + ǫij) (1)

Aij : Two dummies:SHG present = 1 if SHGs were present in ward j during the mandate of ward

member i , but did not undertake collective actionsSHG active = 1 if an SHG undertook collective actions in ward j during

the mandate of ward member iBij : Controls for the ward member’s characteristics

(education, land, age, gender, children, caste category)Cij : Dummies to control for the year in which the ward member was elected

(’97, ’02 or ’07)νj : Ward fixed effect


Empirical Strategy and Results Selection

Is the event ‘SHG becomes active’ exogenous?

The presence of SHGs is exogenous to the political economy at the wardlevel:

No government involvement in the selection of villages.

No evidence that the government opposed or supported the creationof SHGs.

PRADAN’s SHG program has no explicit socio-political agenda.

But, ward members sensitive to women’s issues might induce SHGs tobecome active. If this is the case, results are spurious when using both thedummies for presence and for activity.



Can we predict an SHG becoming active?

⇒ We check whether ward member characteristics predict SHGs

becoming active, by estimating the following OLS model:

(based on Della Vigna and Kaplan (2007))

activeij = issues(i−1)jβ + Bijγ + Cijδ + νj + ǫij

activeij : Dummy indicating that the first SHG became activeduring the mandate of ward member i in ward j .

issues(i−1)j : Number of issues discussed by the previous ward memberBij : Controls for the ward member’s characteristicsCij : Time trendνj : Ward fixed effect



We cannot predict an SHG becoming active

SHG became active during mandateNumber of issues discussed -0.0489by previous ward member (0.0468)

Male 0.1576*(0.0856)

Education level (years) -0.0046(0.0146)

Land (acres) -0.0175(0.0212)

Number of children -0.0361(0.0254)

Caste category: SC -0.2845(0.2263)

Caste category: OBC/FC -0.1158(0.0955)

Ward fixed effects yesElection year dummies yesN 219

Other WM characteristics included are his age and squared age. Standard errorsclustered at the ward level are given in parentheses. *** p<0.01, ** p<0.05, * p<0.1



The active presence of SHGs leads to an increase in thenumber of issues the ward member deals with

Number of issues dealt with by the ward member

Poisson OLS(1) (2) (3) (4)

SHG present 1.1699 1.1814 0.2777 0.2982(0.1194) (0.1224) (0.1825) (0.1805)

SHG active 1.6742*** 1.9682*** 1.0715*** 1.4380***(0.1666) (0.2558) (0.1988) (0.2524)

Male 0.8566*** 0.9961 -0.3056** -0.0119(0.0498) (0.0997) (0.1238) (0.1739)

SHG active×Male 0.7902** -0.5316**(0.0868) (0.2148)

Ward fixed effects yes yes yes yesN 442 442 448 448

Other ward member characteristics included are his education level, land, number of children, age,squared age and caste category. Other controls included are dummies for the election year. Standard errorsclustered at the ward level are given in parentheses. *** p<0.01, ** p<0.05, * p<0.1



Prediction 2: Extra issues discussed by the ward members

We run the following OLS regressions:

Tijt = Aijβ + Bijγ + Cijδ + νj + ǫijt

Tijt : Dummy indicating that ward member i in ward j dealt with issue t.Aij : Two dummies:

SHG present = 1 if SHGs were present in ward j during the mandate ofward member i , but did not undertake actions

SHG active = 1 if an SHG undertook collective actions in ward j

during the mandate of ward member iBij : Controls for the ward member’s characteristics

(education, land, age, gender, children, caste category)Cij : Dummies to control for the year in which the ward member was elected

(’97, ’02 or ’07)νj : Ward fixed effect

=⇒ The impact differs largely across problems.



Ward members start dealing with public goods preferred bySHGs

Probability, per issue, of ward members visiting an officer

Alcohol School Forest Village Welfareissues issues issues infrastructure scheme(1) (2) (3) (4) (5)

SHG present -0.0042 0.0476 0.0627 0.0392 0.1001(0.0609) (0.0548) (0.0757) (0.0806) (0.0911)

SHG active 0.2832*** 0.2277*** 0.2451*** 0.0566 0.2176**(0.0678) (0.0729) (0.0706) (0.1000) (0.1062)

Male -0.1077*** -0.0736* -0.0582 0.0849* -0.1500***(0.0392) (0.0433) (0.0432) (0.0495) (0.0523)

Ward fixed effects yes yes yes yes yesN 448 448 448 448 448

Other ward member characteristics included are his education level, land, number of children, age, squared age andcaste category. Other controls included are dummies for the election year. Standard errors clustered at the ward level are givenin parentheses. *** p<0.01, ** p<0.05, * p<0.1


Empirical Strategy and Results Robustness Checks

Robustness Checks

The results are robust to

1 using the presence of SHGs only: We compare the activities of wardmembers before and after an SHG was created.

Regressions

2 including a ward level linear trendRegressions


Conclusions

Conclusions

We examine the impact of collective actions undertaken by SHGs onthe variety of issues the ward members deal with.

The main caveat is that we can focus on the type of public goodsonly and not on the quality and the intensity of the work done.

Success where Success where Success overSHGs were active problem appeared all wards

(1) (2) (3)% success % success % success

(# of wards) (# of wards) (# of wards)Alcohol problems 90.9 (71) 36.9 (127) 30.4 (147)School problems 85.1 (38) 19.5 (105) 8.9 (147)Forest issues 91.5 (77) 39.9 (130) 31.0 (147)Village infrastructure 88.2 (98) 43.6 (144) 37.8 (147)Welfare schemes 88.4 (63) 37.8 (120) 22.0 (147)

⇒ Despite some limitations, we believe our data allow us to givereliable evidence about the positive impact of collective actionsby socially disadvantaged women.


Thank you


Appendix The Model

1. In the absence of SHGs

The ward member maximizes social welfare to decide about the optimalnumber of goods (g1, g2) to provide.

maxT ,g

W (T , g , λ, µ) =µU1(g , λ1) + (1− µ)U2(g , λ2)− kT

s.t. : g1 + g2 = T

T ≤ Tmax

gi ≥ 0 i = 1, 2

µ ∈ [0, 1] Weight on type 1 villagers.T ∈ [0,Tmax ] Total number of goods provided by the ward member.k > 0 Cost for providing a unit of the public good.

In equilibrium: No provision of goods preferred by women:

- that have a strong negative externality on men (λ1 ≤µ−1µ

).

- whose importance the ward member is not aware of.⇒ SHGs can have incentives to undertake collective actions.


Appendix The Model

2. In the presence of SHGs (1/2)

t = 1: The SHG decides whether to undertake collective actions.If yes, it communicates it to the ward member.

t = 2: The ward member decides the optimal number of goods.t = 3: The SHG observes this and chooses how much to provide.

We search which is the SPNE (g∗

1 , g∗

2 , g∗

2c) by backward induction:The SHG solves:

maxg2c

Uc2 (g1, g2, g2c , λ2) = log(g2 + g2c) + λ2 log(g1)− cg2c

with g2c the number of goods of type 2 provided by the SHG at a cost c per unit.

The ward member now solves:

maxT ,g1,g2

W (T , g , λ, µ) =µU1(g , λ) + (1− µ)Uc2 (g , λ)− kT

s.t. : g1 + g2 = T

T ≤ Tmax

gi ≥ 0 i = 1, 2


Appendix The Model

2. In the presence of SHGs (2/2)

In equilibrium:

Provision of goods preferred by women:- that have a negative externality on men.

• The ward member no longer controls the negative externality on men.

• Dealing with public goods is costly for SHGs. The ward member tries

to reduce the cost by providing more himself.

- whose importance the ward member was not aware of.The increase in goods preferred by women is not offset by a decreasein other goods.Back


Appendix Robustness Checks

Robustness Check 1: The results hold when using thepresence of SHGs only

Prediction 1 Prediction 2Issues (#) Alcohol School Forest Village Welfare(Poisson) issues issues issues infrastr scheme

(1) (2) (3) (4) (5) (6)SHG present 1.3790*** 0.1086* 0.1183** 0.1343* 0.0461 0.1462

(0.1385) (0.0581) (0.0572) (0.0699) (0.0815) (0.0903)Male 0.8719** -0.0974** -0.0671 -0.0517 0.0855* -0.1458***

(0.0540) (0.0429) (0.0445) (0.0445) (0.0492) (0.0524)Ward f.e. yes yes yes yes yes yesN 442 448 448 448 448 448

Standard errors clustered at the ward level are given in parentheses. *** p<0.01, **p<0.05, * p<0.1

Back


Appendix Robustness Checks

Robustness Check 2: The results hold when a ward-levellinear trend is included

We replace the election year dummies by ward-level linear trends.

Prediction 1 Prediction 2Issues (#) Alcohol School Forest Village Welfare(Poisson) issues issues issues infrastr scheme

(1) (2) (3) (4) (5) (6)SHG present 1.0770 0.0259 -0.1410 0.0800 0.0193 -0.0357

(0.1006) (0.1103) (0.1112) (0.1149) (0.1361) (0.1561)SHG active 1.5220*** 0.3458** 0.0346 0.2592* 0.0501 0.0721

(0.1606) (0.1519) (0.1742) (0.1530) (0.1728) (0.2109)Male 0.8700** -0.1175 -0.0464 -0.0689 0.0983 -0.1519

(0.0580) (0.0732) (0.0817) (0.0785) (0.0985) (0.1159)Ward f.e. yes yes yes yes yes yesWard-levellinear trend yes yes yes yes yes yes

N 448 448 448 448 448 448

Standard errors clustered at the ward level are given in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Other WM characteristicsincluded are his education level, land, number of children, age, squared age and caste category.

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Paolo Casini and Lore Vandewalle - DSEdse.univr.it/ssef/documents/material2012/slides Lore Vandewalle.pdf · Public Good Provision in Indian Rural Areas: the Returns to Collective