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COMMENTARY

Economic & Political Weekly EPW february 25, 2012 vol xlvii no 8 13

Corruption in the MGNREGSAssessing an Index

Martin Ravallion

These are the views o the author, and need not

refect those o the World Bank or any member

country or aliated organisation. Useul

comments were received rom Jean Drze,

Rinku Murgai and Dominique van de Walle.

Martin Ravallion (Mravallion@worldbank.org) is director o the World Banks research

department and is based in Washington.

There is corruption in the

Mahatma Gandhi National

Rural Employment Guarantee

Scheme, no question about that.

But simple indices that claim to

measure corruption and make an

assessment o interstate levels o

corruption can end up oering usa wrong understanding.

Much concern has been expre-

ssed in Indias media about

corruption on the Mahatma

Gandhi National Rural Employment

Guarantee Scheme (MGNREGS) an

ambitious national eort, launched in

2005, to ght rural poverty by providing

unskilled work at low wages and on

demand. O course, corruption is hardly

unique to this scheme. However, the act

that MGNREGS is intended to ght poverty

adds to the indignation about corruption.

The relative perormance o Indias

states in terms o corruption on the

scheme is naturally o much interest.

Surjit Bhalla (2012) has created an index

o state-level corruption on MGNREGS.

He claims an overwhelming presence

o non-Congress ruled states in the top

hal o perormance (i e, the states with

less corruption). He points specically totwo Congress-led states, Andhra Pradesh

(AP) and Rajasthan, which have a high

value o his index.

To those who have studied MGNREGS,

Bhallas claims are surprising at rst

glance. To most observers (the author in-

cluded, based on my eldwork since

2005), the administrative processes in

AP and Rajasthan have appeared to be

quite good. So too have related peror-

mance measures. The gaps between

survey-based estimates o participation

in MGNREGS and the numbers recorded

in the ocial administrative data are

much lower or these states than or

India as a whole suggestive o lower

leakage although some non-Congress

states also do well by this measure, such

as Tamil Nadu (Imbert and Papp 2011).

The ability to meet the demand or work

also appears to be well-above average in

AP and Rajasthan, though (here too)

there are non-Congress states that alsodo well (again, Tamil Nadu is an exam-

ple) (Dutta et al 2012).

We need to take a closer look at Bhallas

corruption index to see why it is higher

in some states than others. His index is

the sum o (i) the participation rate or

the non-poor less that or the poor,

and (ii) the share o wage expenditureon the scheme going to the non-poor less

that going to the poor. So we can write

the Bhalla index or state i as:

CiBhalla =(P

iNon-poor P

ipoor)+(S

iNon-poor S

iPoor)

Component (i) Component (ii)

HerePiNon-pooris the participation rate

in MGNREGS or the non-poor (the pro-

portion o the non-poor who participate),

Pi

pooris that or the poor, while SiNon-poor

and Si

Poorare the shares o wage expen-

ditures going to the non-poor and poor in

state i respectively.1 The poor are de-

ned by Bhalla as those households with

consumption per person (as measured in

the National Sample Survey or 2009-10)

below the Tendulkar poverty lines pro-

duced by the Planning Commission, up-

dated or infation by Bhalla to 2009-10.

Confusing Mistargeting

with Corruption

Let us rst consider Component (i). Wecan all agree that a high participation rate

on MGNREGS or poor people, relative to

those less poor, is desirable. That is what

Component (i) measures. In act Compo-

nent (i) minus one is known as the

Targeting Dierential (TD) in the liter-

ature, and it is thought to be a relatively

good indicator o perormance in reducing

poverty (Ravallion 2009). The TD or

MGNREGS o 0.12 (on a scale rom 1 to +1)

is not high. (For example, ChinasDi Bao

programme a cash transer pro-

gramme targeted explicitly to those

with income below the (locally-deter-

mined) poverty lines has a TD o 0.22;

see Ravallion 2009.)

However, there is nothing corrupt

about people living above the Tendulkar

poverty line participating in MGNREGS.

The Act that created the scheme does

not bar those living above any poverty

line rom participating. Rather it says

that anyone who wants work at thestipulated wage rate should get it (up to

100 days per household).

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COMMENTARY

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The sel-targeting mechanism o a

scheme such as MGNREGS tends to mean

that amilies with a relatively high

consumption will be less likely to want

to do this kind o work at low wages.

But some people in amilies above the

poverty line may still want the work.

For example, they may have been hit by

a shock that will lower their incomes,

but this is not yet evident in their con-

sumption (possibly thanks to the

scheme). Or the amily as a whole may

have a consumption-expenditure perperson above the poverty line, but one

individual in the household needs help

rom the scheme.

There is unmet demand or work on

MGnREGS, as shown in Dutta et al (2012).

This creates scope or corruption through

the power o local ocials to decide who

gets work and who does not. However,

using the same NSS round as Bhalla,

Dutta et al show that, on balance, the

rationing process on MGNREGS generally

avours the poor, not the non-poor. O

course, there are some local exceptions

to this generalisation. But overall it is

the non-poor who are more likely to have

unmet demand or work on MGNREGS.

There are undoubtedly important

relative-poverty eects relevant to all

these calculations. The Tendulkar lines

were designed or making consistent

interstate comparisons nationally. So

they try to adjust or cost-o-living dier-

ences between states, but not dierencesin relative poverty. What it means to be

poor in a state such as Kerala (with only

11% living below the

Tendulkar line) may

well be understated by

the Tendulkar lines.

The nationally non-

poor in Kerala may

well be considered

poor in Kerala. And itshould not be orgo-

tten that MGNREGS is

implemented at the

state level.

The upshot o these

observations is that

many actors infuence

participation in a

scheme such as the

MGNREGS, besides the

average consumption o the amily rela-

tive to the Planning Commissions na-

tional poverty line. That does not mean

the scheme is corrupt in any meaning-

ul sense. Nor should Bhallas calcula-

tions convince us that there is a very

large amount o leakage to the non-

poor when we allow or deensible,

broader, concepts o what it means to

be poor.

Maybe Bhallas numbers are just

reminding us o the limitations o meas-

uring poverty by a households currentconsumption per person. There is no

doubt that consumption is hugely impor-

tant to economic welare in India, but it

can never claim to capture everything

that matters to welare, and that

matters to participation in a scheme

such as MGNREGS.

What Then Is Driving

the Bhalla Index?

We have seen that there are good rea-

sons to question the relevance to corrup-

tion o Component (i) in Bhallas index.

However, it turns out that this compo-

nent is not what is driving his index.

Indeed, it is easily veried that i there

were no dierences across states in the

TD then one would get pretty much the

same values or his index. The correla-

tion coecient between CiBhalla and the

Bhalla index one would obtain i the TD

was identical across all states is 0.98.

Observant readers o Bhalla (2012)may have already the main clue to what

is really driving the interstate dierences

in his index, namely, its high (negative)

correlation with the poverty rate

(r =.92). Figure 1 plots the Bhalla index

against the rural poverty rates across

states (using Bhallas estimates). Judged

by Bhallas index, corruption on

MGNREGS is pretty much a measure o

lack o poverty!In act this correlation is not surpris-

ing when we look more closely at the in-

dex. Consider now the second compo-

nent, which is simply 100 2Sipoor. By

denition we have:

PiPoor Wi

Poor

SiPoor = Hi. ( ). ( )Pi Wi

HereHiis the headcount index o poverty

in state i, Piis the participation rate in

the programme or the population as a

whole, Wipoor is the average o the wage

earnings rom the scheme received by

poor participants, and Wiis the overall

average or all participants. As we have

seen, the participation rate or the poor

is greater than that or the population

as a whole. This is true in every state.

While Bhalla does not give the wage

ratio (the last term in parentheses in the

above equation), it is possible to back it

out rom the numbers he does give. The

wage ratio or India as a whole is 0.90,and it does not vary much across the

states either. And the wage ratio turns

out to be negatively correlated with

pipoor/P

i (r = 0.64). So the two eects in

parentheses are partially osetting each

other. The main thing driving the dier-

ences between states in the share o

wage expenditure going to the poor is

thus the poverty rate; the correlation co-

ecient between SipoorandHi is 0.89. As

one would expect, the states with a low

share o wage expenditure going to the

poor when judged by a common national

poverty line are by and large the states

with low poverty rates.

Figure 1 also gives the value o the

index or each state iMGNREGS had the

same perormance attributes as the

all-India parameter values reported by

Bhalla. Then the onlyreason or dier-

ences in the index is the poverty rate,

and the index declines smoothly with

the latter. By comparing this version othe Bhalla index with his original we

see something new: the scheme is

The figure is based on Bhallas (2010) estimates for all data, based on the NSS

Employment-Unemployment Survey for 2009-10. (Bhallas estimates differ slightly from

those in Dutta et al (2012) due to a difference in how the Tendulkar poverty lines were

applied.) AP = Andhra Pradesh; Jhk = Jharkhand; MP = Madhya Pradesh;

Mah = Maharashtra; UP = Uttar Pradesh; TN = Tamil Nadu; WB = West Bengal.

Dashed line: Smoothed

scatter plot for Bhallas

data points

Unbroken line: Bhalla index

using instead the all-India

parameters for MGNREGS

Kerala

Karnataka

AP TN

Rajasthan

Gujarat

Mah

WB

UP JhK

Orissa

MP

Bihar

Chhattisgarh

80

60

40

20

0

-20

-40

-60

BhallascorruptionindexforMGNREGS

5 10 15 20 25 30 35 40 45 50 55 60

Headcount index of rural poverty (% below national poverty line)

Figure 1: Bhallas Corruption Index Plotted against the Rural Poverty Rate

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COMMENTARY

Economic & Political Weekly EPW february 25, 2012 vol xlvii no 8 15

actually working to bring down his in-

dex in poorer states, relative to what one

would expect i the scheme worked ex-

actly the same way everywhere. While

this is not a message Bhalla ound in his

data, it is there.

It is not the act thatAP and Rajasthan

are led by the Congress that leads to ahigh value o Bhallas corruption index,

but their lack o poverty relative to other

states. As is clear rom Figure 1, his index

is not in act any higher, or lower, orAP

and Rajasthan than one would expect,

once one controls or the poverty rate.

There is clearly corruption in MGN-

REGS, as in many public programmes,

and in countries at all stages o develop-

ment. But let us not pretend that Bhallas

index has taught us anything credible

about that problem.

Note

1 I write the index the way Bhalla describes it. Inhis table he multiplies it by minus one, but thisis potentially conusing so I will not do so here.

References

Bhalla, Surjit (2012): No Proo Required: Corrup-tion by Any Other Name, Financial Express,4 February.

Dutta, Puja, Rinku Murgai, Martin Ravallion andDominique van de Walle (2012): Does IndiasEmployment Guarantee Scheme GuaranteeEmployment?, World Bank.

Imbert, Clement and John Papp (2011): Estimat ing

Leakages in Indias Employment Guarantee inReetika Khera (ed.), The Battle for EmploymentGuarantee (New Delhi: Oxord University Press).

Ravallion, Martin (2009): How Relevant Is Tar-geting to the Success o the Antipoverty Pro-gramme?, World Bank Research Observer,24(3): 205-31.

Sukumar Muralidharan (sukumar.md@gmail.com) is a reelance journalist based

in New Delhi.

Turmoil in SyriaPrelude to Wider Battles in the

Arab World

Sukumar Muralidharan

The Syrian civil war is not merely

about that country any more. I it

continues or any urther length

o time, it could draw in virtually

every country o consequence in

the wider region. In this, it couldwell be the prelude to a civil war

involving the entire Arab world.

And that would be potentially

a atal challenge to the key

principles o western geopolitics

in the region: to keep Iran out,

Arab nationalism down and

Israel on top.

In early February o 2012, the United

Nations Security Councils eort to

stamp its approval on an Arab

League peace plan or Syria oundered

on the dual veto o Russia and China.

Fears were reely expressed that Syria

was sliding towards civil war. Usingterms rarely heard in recent diplomatic

exchanges, the US ambassador to the UN

vented her disgust at the collusion o

two global powers in the Syrian blood-

bath. The US Secretary o State Hillary

Clinton denounced the stance o the two

Security Council recalcitrants as a trav-

esty. Freedom-loving people every-

where, she declared, should join the

eort to sustain and arm the Syrian lib-

eration struggle.

By the standards o the last two dec-

ades, this has been a rare moment o dis-

cord among the Security Council gran-

dees. Yet, the breakdown o the oppres-

sive consensus that allowed the US and

its allies to interpret everyUN resolution

in a manner o their convenience was

long oretold.

Estimates o the number o casualties

caused by the year-long turmoil in Syria,

even as the Security Council broke up in

acrimony, stood between 6,000 and7,500. O these, between a quarter and a

th were thought to have been rom the

ranks o the state security orces. It was

disproportionate warare, but not quite

as bad as the US war in Iraq, which has

killed just under 5,000 US servicemen,

while claiming upwards o a hundred

thousand Iraqi civilians, not to mention

an untold number o those who could be

classied as insurgents. Yet the grim

statistics rom Syria would establish that

civil war is very much the accomplished

reality there, not merely the potential

outcome o a ailure to intervene.

A Conict without Witness

Syrias civil war is a confict without wit-

ness. Inormation has been sporadic and

images sparse, allowing no basis or aconsidered judgment. The global com-

munity sees images o a country in tur-

moil with ordinary people earul or

their lives and no longer sure o their

daily routines. The dominant global nar-

rative puts this down to the desperate

eort by the Syrian regime to suppress a

wholly legitimate upsurge o political

protests. Harsh repression has turned a

peaceul mass movement into an urban

guerrilla operation, with poorly organ-

ised but highly motivated groups o

partisans seeking to bring to account the

seemingly eternal dictatorship o the

al-Assad dynasty.

Inured to a high degree o control

over citizen loyalty and having or long

trusted in repression as a air alternative

to widening the circle o consent, the al-

Assad regime or its part has responded

to the new realities by striking heavy

hammer blows at civilian centres, oten

targeting entire urban populations orcollective punishment, as in the city o

Homs. The strategy has served the regime