Corruption in the MGNREGS
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Transcript of Corruption in the MGNREGS
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7/28/2019 Corruption in the MGNREGS
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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 ([email protected]) 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
february 25, 2012 vol xlvii no 8 EPW Economic & Political Weekly14
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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7/28/2019 Corruption in the MGNREGS
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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 ([email protected]) 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