E-governance, Accountability, and Leakage in Public Programs. Experimental Evidence from a Financial Management Reform in India

E-governance, Accountability, and Leakage inPublic Programs. Experimental Evidence from a

Financial Management Reform in India

Clement Imbert (Warwick)

with Abhijit Banerjee (MIT), Esther Du�o (MIT), RohiniPande (Harvard), Santhosh Mathew (MoRD)

December 15th 2017

Stockholm Institute of Transition Economics

Motivation: Designing Program �nancing mechanisms

I Decentralized implementation, but centralized �nancing of

public programs is common practice, world over.

I Agency problem: How to best meet local body �nancing needs

while ensuring accountability of program administrators?

I Historically, governments rely on cash-based management

system with two features:

I Fund �ows based on anticipated not realized expenditures.I Signi�cant oversight on requested budget by superiors

I Accountability mechanism may itself create corruption:

I Delay between fund transfer and justi�cation of fund usagecreate leakage opportunities

I Control over fund �ow at each level can be an additionalsource of corruption.























Motivation: Leveraging Digital Financial Services

I The advent of digital �nancial services has altered thelandscape government transfer schemes.

I Several studies on bene�ts of using smart cards and mobilepayments to release funds to citizens

I Digital �nancial services enable real time fund release:I More e�cient expenditure-based fund �ow.

I They increase transparency of �nancial transactions:I Should reduce corruption at ground level,

I They limit monitoring role of intermediariesI Ambiguous e�ect on corruption / service delivery,

I They require well-functioning IT systemsI Else public service delivery may su�er.

The reform

I We study a demand-based, centrally-funded social program:I Funds �ow from central to state government, then from state

to local implementing agency (Gram Panchayat or GP)

I State fund �ow in the status quo:I Funds released in tranches based on anticipated expenditure.I Three tiers need to approve for release: block/district /State.

I Reformed state fund �ow:I Electronic fund release system based on ongoing expenditures:I GP directly access funds from state pool of MGNREGS funds.

I Key trade-o�:I Does transparency and greater accountability lower corruption

across the board?I Or by reducing the power of intermediaries does the reform

increase local incentives for corruption?

I What is the e�ect on program outcomes?

The reform









The reform









Preview

I Randomized experiment in 12 districts of Bihar (3,000 GP):

I 38% lower outlays from State (30% reduction in GP accountbalance and 24% reduction in spending)

I No decline in actual employment, wages paid or asset built asmeasured by an independent survey.

I Direct evidence that corruption went down: decline in fakebene�ciaries and local bureaucrats assets.

I Signi�cant downside for program bene�ciaries: 38% increase inpayment delays.

I Backlash: Reform was rolled back after seven months due tointense lobbying by district o�cials!

I Eventually, the central government imposed a similar �nancialsystem across India (including Bihar):

I Natural experiment: �nd similar, persistent 18% reduction inprogram expenditures.

Preview









Preview









Preview









Preview









Preview









Related literature

I Growing literature on administrative reforms in setting withweak capacity, with a signi�cant focus on e-governance

I E-procurement (Lewis-Faupel et al 2015)I E-transfers to bene�ciaries via smart cards (Muralidharan et al

2014, Barnwal 2014) and mobile phone (Aker 2014).

I The industrial organization of corruption:I The empirical literature has emphasized information disclosure,

monitoring and incentivesI Less tested theoretical literature on role of task organization

and administrative structure (Shleifer and Vishny, Banerjee..)

I Measuring corruption impacts (Olken and Pande 2012)I Forensic methods: Expenditure tracking surveys (Reinikka and

Svenson..) and local politician asset growth (Fisman et al)

Road Map

1. Introduction

2. Design of Financial Reform

3. Experimental Results

4. Epilogue

MGNREGS

I India's federal workfare program � MGNREGS:I centrepiece of India's rights-based social spending programsI 90% center-funded; GP implements village works.I In 2013 the program had roughly 50 million bene�ciary

households and cost 0.5% of GDP.

I Two common concernsI Funds related: Irregular funds �ow. Under-utilization of

available funds co-exists with unmet demand for funds.I Signi�cant leakage and rationing: Bihar one of the worst

implementers Details

I Key e-governance reforms:I Everywhere: online data entry, publicly available. Complete

records of people, day worked, payments.I Some states (but not Bihar): e-wage payment to bene�ciaries

(Muralidharan et al, 2014).

MGNREGS









MGNREGS









Initial reforms: Audit and e-governance

I In 2010-11, India's federal vigilance authority launched a

MGNREGS corruption enquiry in neighboring state of Odisha

I In response several states - including Bihar - tightened audits:I June 2011: the Bihar rural development department began

requiring weekly audits of ongoing and completed worksI November 2011: Clari�ed that public database be used to

sample projects and provide documentation to audit teamI Between June 2012-13: 64% of GPs in our sample districts

were audited atleast once.

I In 2010, Bihar introduced an e-platform - Central Planning

Scheme Monitoring Scheme (CPSMS) - to monitor account

balances, and give districts access to a state pool of funds.





















Fund Flow in Control

STATE POOL (Central Bank of India)

DISTRICT

BLOCK

PANCHAYAT

FUN

D TRANSFERFU

ND

REQ

UES

T CPSMS

Fund Flow in Treatment (Labor Payments only)

STATE POOL (Central Bank of India)

PANCHAYAT

FUN

D TRANSFERFU

ND

REQ

UES

T CPSMS

Parsing the intervention

I 1. Transparency impact:I Status quo: data entry of worker details lags by months.I Reform: realtime worker entry ⇒ audit possible soonerI Data on government audits reveals that detection of

malfeasance is 5 pp larger (or double) in T than in C.

I 2. Distribution of bargaining power among o�cials:I Status quo: block and district o�cers can exploit approval

powers to extract rents.I Reform: District o�cers have no role. Block o�cers have

some role as IT infrastructure are typically at block-level.

⇒ Level and distribution of corruption a�ected; e�ect di�erent

than in the standard "over�shing model" (Olken and Barron)Model

Sample

12 Districts

69T 126C Blocks

1002T 2029C GP

Data sources

I O�cial data on program implementationI CPSMS: �nancial transactions from all GP savings account.I MIS: monitoring system of the Ministry of Rural Development.I NREGA public data base: Job cards: employment and

payments reported on nrega.nic.in.

I Independent surveyI 10,000 households in 195 blocks, May � July 2013.I 4,165 MGNREGS projects randomly sampled from nrega.nic.in.

I CensusI Bihar data for India's Socio economic census (2013-2014): we

have the basic identifying information (and nothing else).

I On politicians and bureaucrats:I Mukhiyas interview in each surveyed village.I A�davit Asset declaration of all MNREGS employees.

Timeline

I July 2012: Randomization of blocks into treatment:

Infrastructure preparation

I Sept 1st 2012: Launch of expenditure based fund �ow system

in treatment blocks

I Sept 18th: State Pool runs dry.I Dec 11th: State Pool replenished.I Dec 15th-end Dec: Strike of GP Personnel

I April 1st 2013: Intervention is rolled back.

I May 15th - July 15th 2013: Endline survey

Timeline




in treatment blocks




Timeline




in treatment blocks




Context: Program Take-up

Context: Fund shortage and low spending overall

Road Map

1. Introduction

2. Design of Financial Reform

3. Experimental results

4. Epilogue

Decrease in Spending

Estimated e�ect = Rs 230,000 per GP for total of 4.1 million USD.Spending Results

Decrease in GP Account Balance

I Over the course of the project, the state credited USD 6.3

million less to GPs in the treatment group

Slight increase in employment (Independent Survey)

I No change in wage payments or projects carried out. Results

Where is the missing money?

I Politician survey: Results

I 20% of GP heads claimed corruption was a big issue inMGNREGS implementation, and 27% when prompted .

I Corruption complaint decline by 12 percentage points intreatment blocks.

I Checking o�cial reports against census data:I Two types of corruption: �Ghost workers� and �Ghost days�.I We match o�cial records by name with census. Details

I The match rate is 3-5% better in treatment blocks. Results

I Look at personal assets of bureaucrats involved:I Loss for block and GP o�cials (average e�ect insigni�cant).I E�ect also for district o�cials (non experimental).I Together, account for 83% of missing money. Results






















Did public service quality deteriorate?

I Signi�cant implementation problems related to IT and

increased demands on banks re. processing bills

I Data entry requirements were slow to be implemented locally.I Limited GP infrastructure implied o�cials had to wait to claim.I Separate requirement for entering worker details in MGNREGA

data base was not eliminated creating double entry burden.I Increased very signi�cantly payment requests at the state level:

the bank handled status quo blocks requests �rst !

I Increase in payment delays:I Politician Survey: heads of GP complain about delays.I Household survey and administrative data con�rm it.I No e�ect on illegal advance payment or on consumption.

Results

Did public service quality deteriorate?

I Signi�cant implementation problems related to IT and

increased demands on banks re. processing bills

I Data entry requirements were slow to be implemented locally.I Limited GP infrastructure implied o�cials had to wait to claim.I Separate requirement for entering worker details in MGNREGA

data base was not eliminated creating double entry burden.I Increased very signi�cantly payment requests at the state level:

the bank handled status quo blocks requests �rst !

I Increase in payment delays:I Politician Survey: heads of GP complain about delays.I Household survey and administrative data con�rm it.I No e�ect on illegal advance payment or on consumption.

Results

Road Map

1. Introduction

2. Context and Intervention

3. Experimental results

4. Epilogue

Epilogue

I The reform reduced dormant funds and reduced leakage (due

to decline in expenditure, not increase in actual delivery).

I This was done on a very large scale, in di�cult circumstances.

Implementation issues increased payment delays

I However... at the end of the �scal year system wasdiscontinued.

I Combination of concerns on delays and reduced fund �ow, andcomplaints from district o�cials,

I Di�cult for state o�cials to disentangle whether loweredexpenditure meant more unmet demand or less leakage.

I Locally, no constituency was in favor: people see no bene�ts,o�cials see reduction in bribes.

I But the central exchequer (and taxpayers) did bene�t!

Epilogue










Epilogue










Scale-up

In April 2013, the federal Ministry of Rural Development introduced

a closely related Electronic Fund Management System (EFMS):

I Similar to CPSMS: cuts out district from the fund �ow.

I Integrated: data entry in nrega.nic.in triggers payments.

I Goes further: payments directly to workers instead of GP.

We estimate the e�ect of EFMS on MGNREGS expenditures:

I exploit the roll-out of EFMS across Indian districts

(2012-2015) and implement a Di�-in-Di�.

I We �nd evidence of a 18% drop in labor expenditures.

I E�ects persist after two years.

I Similar e�ect for material expenditures.

Results

Scale-up

In April 2013, the federal Ministry of Rural Development introduced

a closely related Electronic Fund Management System (EFMS):

I Similar to CPSMS: cuts out district from the fund �ow.

I Integrated: data entry in nrega.nic.in triggers payments.

I Goes further: payments directly to workers instead of GP.

We estimate the e�ect of EFMS on MGNREGS expenditures:

I exploit the roll-out of EFMS across Indian districts

(2012-2015) and implement a Di�-in-Di�.

I We �nd evidence of a 18% drop in labor expenditures.

I E�ects persist after two years.

I Similar e�ect for material expenditures.

Results

APPENDIX

Leakage and rationing in MGNREGS

I Survey and admin (nrega.nic.in) based forensics:I Niehaus and Sukhtankar (2013) search for 1499 reported

bene�ciaries in the state of Orissa. 50% were ghost workers;I Imbert and Papp (2014) compare survey estimates (NSS) to

admin data: 40%-60% leakage in 2007-2008 to 20% in 2011.

I Signi�cant cross-state variation in MGNREGS implementation:low employment generation in Bihar

I 2009-10 NSS data: 35% of Bihar households were demandrationed; only 10% of households received MGNREGS work.

I We surveyed 350 local politicians: 48% cited corruption as animportant reason for poor implementation

Back

Leakage and rationing in MGNREGS

I Survey and admin (nrega.nic.in) based forensics:I Niehaus and Sukhtankar (2013) search for 1499 reported

bene�ciaries in the state of Orissa. 50% were ghost workers;I Imbert and Papp (2014) compare survey estimates (NSS) to

admin data: 40%-60% leakage in 2007-2008 to 20% in 2011.

I Signi�cant cross-state variation in MGNREGS implementation:low employment generation in Bihar

I 2009-10 NSS data: 35% of Bihar households were demandrationed; only 10% of households received MGNREGS work.

I We surveyed 350 local politicians: 48% cited corruption as animportant reason for poor implementation

Back

Model

I 3 layers: P (panchayat), B (block) and D (district).I Layer P actually operates the programI Can skim o� an amount s if he is puts a non-contractable

non-pecuniary e�ort cost 1

2cs2.

I Expected Penalty for skimming : πT s.I Need to get B and D to sign o� on claimI B and D can commit to a price pi , for i = B,D. for approving

every rupeeI B and D choose pB and pD non-cooperatively to maximize

earning.

Status quos maximizes

(1− πT )s − pi s − p−i s −1

2cs2,

Solutions:

pD = pB =(1− πT )

3Amount skimmed under the status quo is

s =(1− πT )

3c.

Under the status quo B (and D) therefore earn an amount

Y BT (πT ) =(1− πT )2

9c,

while P earns

Y PT (πT ) =(1− πT )(1 + 2πT )

9c.

Reform�Case 1: pD = 0

pB =(1− πN)

2,

and

s =(1− πN)

2c

which together imply that

Y BN(πN) =(1− πN)2

4c

while

Y PN(πN) =(1− πN)(1 + πN)

4c.

Skimming and revenues could be going up or down.

Reform�Case 2: pD = pD , extracted with probability α

I Note that without cap, we are back to case 1 (district o�cial

will just optimize over αpD), but with πN > πT .

I With a cap: for α small enough that this cap binds, The

pBchosen will be

pB =(1− πN − αp̄D)

2,

and therefore

s =(1− πN − αp̄D)

2c

Results

I

Y BN(πN) =(1− πN − αp̄D)2

4c

and

Y PN(πN) =(1− αp̄D)2 − (πN)2

4c.

I Clearly for πN < 1− αp̄D (which is the only case that makes

sense), an increase in πN reduces s, Y BNand Y PN , while a fall

in α increases all three.

I FinallyY BN(πN)

Y PN(πN)=

1− αp̄D + πN

1− αp̄D − πN

I Ratio can also go up or down.

Back

Decrease in spending (CPSMS)

Daily GP Spending

Before Set-up Intervention Period After

Sep'11- Jul'12- Sep'12- Jan'13 - Apr'13-

Jun'12 Aug'12 Dec'12 Mar'13 Jan'14

Treatment -0.502 0.0472 -1.039*** -1.267*** -0.345

(0.729) (0.291) (0.315) (0.280) (0.895)

Obs 3,025 3,025 3,025 3,025 3,025

Control 14.37 4.122 5.394 4.146 16.03

Source: CPSMS Debit Data.

S.e. are clustered at the block level

Total Estimated e�ect = Rs 230,000 per GP

Total Estimated e�ect = 4.1 million USD

Decrease in spending (nrega.nic.in)

Annual GP Spending

Apr'12 - Mar'13 Apr'13 - Mar'14

Expenditure items Labour Material Labour Material

-2.270*** -1.077** -0.277 0.313

(0.760) (0.526) (0.725) (0.534)

Observations 2,947 2,947 2,954 2,954

Control 13.83 7.717 13.61 8.373

Source: nrega.nic.in. Annual Expenditures from MIS data.

S.e. are clustered at the block level

Estimated e�ect = Rs 330,000 per GP

Back

Slight increase in employment (Household Survey)

Set-up Intervention After

Jul-Aug '12 Sep'12-Mar'13 Mar-Jun'13

A: Participation

Treatment -0.00753*** 0.00760* 0.00325

(0.00279) (0.00449) (0.00495)

Observations 390 390 390

Mean in Control 0.0114 0.0302 0.0326

B: Days Worked

Treatment -0.145** 0.355* 0.454

(0.0573) (0.205) (0.545)


Mean in Control 0.209 0.947 1.789

Source: Survey of 9436 households in 390 GP (May-July 2013)

s.e. clustered at block level. District FE and HH Controls

No change in payments received

Set-up Intervention After


C: Payments

Treatment -16.06** 12.90 -4.990

(6.892) (19.29) (32.45)


Mean in Control 22.17 79.62 99.83



No change in projects built

Number Registered Fraction Found

All On-going All On-going

Treatment 0.0494 -0.210 0.0172 0.0125

(0.263) (0.413) (0.0179) (0.0204)

Observations 390 390 3,872 3,241

Mean in Control 13.82 11.62 0.850 0.847

Source: MIS and MGNREGS Asset survey (May-July 2013)

Standard errors are clustered at the block level

Back

Issues with MGNREGS implementation (GP head survey)

Panel A: Lack of funds from government

Treatment -0.000833

(0.0498)

Mean in Control 0.718

Panel B: Corruption in the administration

Treatment -0.121**

(0.0572)


Panel C: CPSMS fund-�ow creates delays

Treatment 0.185***

(0.0539)


Source: Survey of 346 GP heads (May-July 2013)

s.e. clustered at block level. District FE and Ind Controls

Back

Matching Process: The Data

I NREGA Job Cards DataI 18,513 villages across 195 blocks within 12 districtsI Registration number, name, husband/father name, age, etc.

I SECC Census DataI 16,480 villages across 195 blocks within 12 districtsI Name, father name, age, etc.

I Goal is to determine for each household in the job cards data

whether there is a matching household in the census data

Matching Process: Step 1

I Step 1: Pair villages

I Within the same district and block, pair each village in the jobcards data with the top-matched village in the census data byname

I Match within panchayat or block if matched village not foundI 83.6% of villages paired with corresponding villageI 15.9% of villages paired with corresponding panchayat, to be

matched against all villages that panchayatI 0.5% of villages paired with corresponding block, to be

matched against all villages within that block

Matching Process: Step 2

I Step 2: Search for matches

I Individuals are designated as matched by geography, gender,and closeness of name

I Closeness of name is calculated using a language-speci�cdistance calculator, adapted to take into considerationabbreviations, missing middle names, etc.

I Households with one member are designated as matched if asingle matching individual is found

I Households with two or more members are designated asmatched if a household with two matching individuals is found

I We match either all households in the data base or householdsdesignated as working during the treatment period.

Back

Ghost Busters

I Overall we match a bit over 50% of working households during

the intervention period.

I Low, but similar to another calculation for leakage: number of

working household estimated from our survey, divided by

NREGA count: 60%.

I Program reduce fraction of ghost working household by 5%.

All job cards Intervention period Post intervention

(as of April 2014) (July 2012-March 2013) (Apr 2013 - March 2014)

(1) (2) (3)

Treatment 0.0187** 0.0181** 0.0107(0.00741) (0.00766) (0.00696)

Observations 3,095 2,868 2,922Mean in Control 0.644 0.673 0.698

Treatment 0.0135** 0.0126 0.0104(0.00613) (0.00764) (0.00732)

Observations 3,093 2,836 2,906Mean in Control 0.243 0.282 0.286

Panel A: Match Rate for job cards with one member only

Panel B: Match Rate for job cards with two members or more

Job cards with at least one working member

Note: The unit of observation is a Panchayat. The dependent variable is the fraction of job cards from nrega.nic.in matched by name with households from the SECC census. A job card with two members or more is matched when at least to members have been matched by name with a census household. The nrega.nic.in data was extracted from the nrega.nic.in server, it covers the period from July 2011 to March 2014. Treatment is a dummy which is equal to one for the blocks selected for the intervention. All specifications include district fixed effects.

Back

Impact on Functionaries' Wealth

I Beginning in 2012, Functionaries who had worked on

MGNREGS were required to report their assets

I Examine functionaries declared assets (2012-13 (before and

during) and 2013-14 (just after))

I This data is self reported (�rst and second round): some

caution needed

I It has been used before for elected o�cial and some evidence

that it has bite (Fisman, Schulz, Vig, 2015, 2016)

Decline in assets of block and GP o�cials in the middle ofthe distribution

Kolmogorov smirno� test of stochastic dominance= p=0.057

Average Treatment E�ect

2012-13 2013-14

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

Panel A: Movable Assets

Treatment -0.117 -0.119 -0.0345 -0.0321(0.0968) (0.0972) (0.0753) (0.0741)

Observations 2,453 2,453 1,734 1,734

Panel B: Total Assets

Treatment -0.0754 -0.0659 -0.102 -0.115(0.130) (0.128) (0.103) (0.102)

Observations 2,455 2,455 1,737 1,737

Functionary Controls No Yes No Yes

E�ect at the Median

2012-13 2013-14

(5) (6) (7) (8)

Panel A: Movable Assets

Treatment -0.101* -0.088* -0.073 -0.057(0.053) (0.046) (0.062) (0.053)

Observations 2,453 2,453 1,734 1,734

Panel B: Total Assets

Treatment -0.117 0.005 -0.137* -0.193***(0.073) (0.068) (0.074) (0.069)

Observations 2,455 2,455 1,737 1,737

Functionary Controls No Yes No Yes

I Mean point estimates implies that the drop in asset accounts

for 45% of the missing expenditures Back

Increased delays in payments

Set-up Intervention Period After


A: Payment delays

Treatment -44.32 44.79*** 4.384

(27.67) (11.66) (7.657)


Mean in Control 78.13 58.38 35.82

B: Illegal advances

Treatment -0.0775 -0.0778 0.0358

(0.129) (0.0631) (0.0719)


Mean in Control 0.382 0.286 0.374



Back

EFMS E�ect on labour expenditures

EFMS E�ect on material expenditures

Back

Economy & Finance

E-governance, Accountability, and Leakage in Public Programs. Experimental Evidence from a Financial Management Reform in India