decentralizing corruption? irrigation reform in...
TRANSCRIPT
Motivation Context and data Estimation strategies Results Conclusions
Decentralizing Corruption? Irrigation Reform inPakistan’s Indus Basin
Freeha Fatima Hanan G. Jacoby Ghazala MansuriWorld Bank
ABCDEWashington DC
June 20-21, 2016
Motivation Context and data Estimation strategies Results Conclusions
Centralization vs. Decentralization
• Public service delivery by centralized bureaucracyI Lacks accountabilityI Prone to corruption
• Can locally elected bodies (‘decentralization’) performbetter and be less corrupt?• Or, does elite capture (perverted accountability) lead to
rent-seeking in a different guise?• And, to what extent does elite capture depend on local
context–i.e., the ‘power structure’?
Motivation Context and data Estimation strategies Results Conclusions
Empirical Questions
• Decentralization empirics have been hampered byI Lack of objective measure of rent-seekingI Lack of large-scale ‘experiment’ in decentralization
• RCTs (Alatas et al. 2013, Beath et al. 2013)I Provide evidence on how elite capture varies with
(experimentally induced) institutions of local governmentI But not on the broader question of how local governance
compares to centralized bureaucracy.
Motivation Context and data Estimation strategies Results Conclusions
Irrigation Reform Experiment in the Indus Basin
• Pakistan’s Indus basin watershed: World’s largest canalirrigation system, lifeblood of the rural economy• System is managed by provincial irrigation department
bureaucracy, long criticized for its corruption.• But, over last decade, management of several large
sub-systems in Punjab was transferred to elected farmer’sorganizations (FOs).• Administrative data of discharge-gauge readings taken
along the entire canal system of Punjab from 2006-2014.I Provide objective measure of rent-seeking both before and
after reform (i.e., lack of bribery; lack of corruption).I Allow decentralization to be evaluated using alternative
panel data strategies.
Motivation Context and data Estimation strategies Results Conclusions
Status Quo Ante
• Each outlet along a channel supplies a watercourse alongwhich several dozen farmers take turns irrigating.• In theory, outlet dimensions are calculated to deliver a
specific discharge proportional to command area ofwatercourse.• In reality, farmers, especially those at the head of the
channel, illicitly enlarge their outlets or even break into thechannel itself to increase their water allocation. outlet tampering
• Typically, farmers bribe irrigation officials to avoid takingaction or prosecuting the offense.
Motivation Context and data Estimation strategies Results Conclusions
The Reform
• Punjab irrigation system divided into 17 Circles, eachmeant to establish an Area Water Board (AWB).• Five AWBs initially sanctioned to form FOs (FO Circles);
reform subsequently stalled leaving 12 non-FO circles.• FOs tasked with maintaining channels, monitoring
rotational system to ensure equitable allocation, mediatingdisputes, and collecting water taxes.• Distributary-level FO is run by 9-member management
committee (MC) elected from among outlet-level chairmen.• MC tenure is 3 yrs. (again, in theory!) reform timeline
Motivation Context and data Estimation strategies Results Conclusions
Canal Discharge Data
• Since 2006, Program Monitoring and Implementation Unit(PMIU) has maintained (online) daily records of:I Actual discharge at the head and tail of each channel (resp.
highest and lowest outlets) system schematic
I Authorized (design) discharge, which accounts for thenumber of off-taking outlets and for conveyance losses(hence is a declining function of distance to head)
system schematic
• Note: design discharge at the tail is never zero; allsanctioned outlets are entitled some canal water.
Motivation Context and data Estimation strategies Results Conclusions
Metrics
• Construct aggregate discharge for the economically mostimportant kharif (summer) season.• Let d index days and t index year and define
Y jit =
∑d∈t Qj
id∑d∈t Q̄j
id
• where Qjid is daily discharge at position j = H(ead),T (ail)
of channel i and Q̄Tid is the corresponding authorized daily
discharge.
Motivation Context and data Estimation strategies Results Conclusions
Irrigation System Overview
• Consistently underprovides relative to design Y T ,Y H < 1• Shortfall greater at the tail than head Y T < Y H
• Y H − Y T > in FO vs. non-FO Circles: theft or selection?0
.2.4
.6.8
1
2006 2008 2010 2012 2014 2006 2008 2010 2012 2014
FO Circles Non-FO Circles
Head TailActual/authorized discharge
Motivation Context and data Estimation strategies Results Conclusions
Water TheftSuggestive evidence of attribution problem
• More upstream outlets→ relatively less water at tail• High CV of head discharge→ relatively less water at tail
.2.4
.6.8
actu
al/a
utho
rized
tail
disc
harg
e
0 1 2 3 4 5log(no. of outlets at top 75% of channel)
.2.4
.6.8
-4 -3 -2 -1 0 1log(coeff. of variation in kharif head discharge)
95% CI Fit conditional on actual/authorized head discharge
Motivation Context and data Estimation strategies Results Conclusions
Pipeline Strategy
Regression model for actual/authorized tail discharge:
Y Tit =
∑j
αjTjit +
∑t
βtY Hit + γc t + µi + εit
• Uses variation in timing of FO operationalization (T jit )
across FO channels. reform timeline
• Includes relative discharge at the head of channel (Y Hit )
• Includes channel fixed effects (µi )• Includes Circle-specific trends (γc t)– cf., ‖ trends assump.
Motivation Context and data Estimation strategies Results Conclusions
Spatial Matching Strategy
Letting b index GIS ‘buffer’, GIS buffer regression model is:
Y Tibt =
∑j
αjTjibt +
∑t
βtY Hibt + φbt + µi + εibt
where φbt is a buffer-year fixed effect.• Spatial matching estimator is akin to triple-differencing
I αj identified off of within buffer variation in outcomedifferences pre/post reform between FO and non-FOchannels.
I Unlike pipeline strategy, does not compare early to latereformers across FO Circles.
Motivation Context and data Estimation strategies Results Conclusions
Baseline Treatment Effect Estimates
Spatial Matching
Pipeline 40 km buffer 60 km buffer
(1) (2) (3) (4) (5) (6)
1st FO tenure (T 1) -0.0474*** -0.00780 -0.0509*** 0.00578 -0.0550*** -0.00290(0.0106) (0.0140) (0.0105) (0.0147) (0.00974) (0.0138)
2nd FO tenure (T 2) -0.00953 -0.0245 -0.00631 -0.0235 0.00700 -0.000416(0.0251) (0.0320) (0.0248) (0.0340) (0.0217) (0.0284)
Administrated (T M ) 0.0303 0.0333 -0.0107 -0.0239 -0.000346 0.00286(0.0187) (0.0269) (0.0196) (0.0277) (0.0184) (0.0258)
T 1× log(# outlets) -0.0225*** -0.0299*** -0.0266***(0.00443) (0.00522) (0.00471)
T 2× log(# outlets) 0.00907 0.0119 0.00937(0.0124) (0.0136) (0.0125)
T M× log(# outlets) 0.00184 0.0120 0.00614(0.0108) (0.0102) (0.00946)
F -test βt = 0 123.0*** 110.4*** 65.24*** 55.48*** 71.00*** 64.20***F -test γc = 0 22.11*** 21.75***R2 (within) 0.339 0.330 0.869 0.868 0.855 0.854
Mean Y T 0.597 0.582 0.732 0.732 0.726 0.726Observations 9,320 8,296 222,104 209,847 650,454 613,830Number of clusters 407 405 756 749 925 918
Motivation Context and data Estimation strategies Results Conclusions
Accountability of FOs
• Summary: Water theft increases after canal managementis transferred from Irrigation Dept. to FOs (in first tenure).• ⇒ Rent-seeking is more restrained under bureaucracy
than under local governance (perhaps bureaucrats havecareer concerns and thus cannot be too profligate).• Are some FOs more accountable to tail-enders than
others?I Narrowly: consider variation in tail representation on MCI Broadly: consider variation in land inequality along channel
Motivation Context and data Estimation strategies Results Conclusions
Tail Representation and FO Performance
• First tenure FOs with a president owning land at the tail (20%) did notperform better than other FOs relative to pre-reform/non-FO.
• First tenure FOs with a higher proportion of the MC owning land at thetail did not perform better than other FOs relative to pre-reform/non-FO
Spatial Matching
Pipeline 40 km buffer 60 km buffer
President MC President MC President MC
1st FO tenure (T 1) -0.0481*** -0.0496*** -0.0478*** -0.0467*** -0.0517*** -0.0506***(0.0110) (0.0121) (0.0112) (0.0120) (0.0102) (0.0113)
2nd FO tenure (T 2) -0.0133 -0.0235 -0.0133 -0.0231 -0.00408 -0.0135(0.0270) (0.0313) (0.0257) (0.0275) (0.0223) (0.0248)
Administrated (T M ) 0.0307 0.0313 -0.0109 -0.0126 -0.000678 -0.00238(0.0189) (0.0190) (0.0195) (0.0199) (0.0183) (0.0187)
T 1× Owns land at tail 0.00365 0.0115 -0.0167 -0.0258 -0.0172 -0.0260(0.0164) (0.0327) (0.0204) (0.0400) (0.0199) (0.0381)
T 2× Owns land at tail 0.0269 0.0645 0.0356 0.0609 0.0531 0.0757(0.0305) (0.0595) (0.0373) (0.0580) (0.0324) (0.0584)
R-squared 0.346 0.346 0.869 0.869 0.855 0.855Observations 9,127 9,127 222,104 222,104 650,454 650,454Number of clusters 1,038 1,038 756 756 925 925
Motivation Context and data Estimation strategies Results Conclusions
Power Structure
• FOs perform equally poorly whether or not they nominallyrepresent tail-enders⇒‘voice’ is window dressing.• Is there a deeper determinant of FO performance?• Literature (e.g., Mansuri and Rao, 2013) suggests that
where inequality is higher, local government is more likelyto serve interests of wealthy and powerful.• How to measure inequality in this setting?
I Of what asset? Land (available in Ag. censuses)I Over what population? {Cultivators} ∪ {Landowners}I With what statistic? Top shares (to get at concentration)
Motivation Context and data Estimation strategies Results Conclusions
Land Inequality and FO PerformanceBased on spatial matching with 40km buffer
Top 5%’s share Top 10%’s share Gini coeff. Proportion landless
(1) (2) (1) (2) (1) (2) (1) (2)
First FO tenure (T 1) 0.00616 0.0613** 0.0391 0.0907** -0.0343 0.00612 -0.0462*** 0.0192(0.0288) (0.0292) (0.0374) (0.0377) (0.0469) (0.0488) (0.0155) (0.0213)
Second FO tenure (T2) 0.0455 -0.0180 0.0683 -0.00367 -0.0376 -0.127 -0.0101 -0.0639(0.0686) (0.0909) (0.0780) (0.0927) (0.114) (0.142) (0.0380) (0.0646)
Administrated (T M ) -0.00491 -0.0197 -0.00471 -0.0209 -0.00506 -0.0165 -0.00427 -0.0179(0.0220) (0.0330) (0.0221) (0.0329) (0.0221) (0.0331) (0.0222) (0.0335)
T 1×inequality -0.129*** -0.110** -0.157*** -0.137** -0.0495 -0.00310 -0.116* -0.108*(0.0488) (0.0492) (0.0547) (0.0562) (0.0750) (0.0773) (0.0624) (0.0640)
T 2×inequality -0.0983 -0.0835 -0.116 -0.0918 0.0534 0.113 0.0358 0.0282(0.112) (0.119) (0.117) (0.124) (0.203) (0.219) (0.255) (0.253)
T 1×log(no. outlets) -0.0280*** -0.0277*** -0.0295*** -0.0285***(0.00686) (0.00684) (0.00678) (0.00673)
T 2×log(no. outlets) 0.0227 0.0231 0.0220 0.0223(0.0217) (0.0215) (0.0218) (0.0217)
T M×log(no. outlets) 0.00850 0.00910 0.00683 0.00813(0.0128) (0.0128) (0.0128) (0.0129)
R-squared 0.858 0.858 0.858 0.858 0.858 0.858 0.858 0.858Observations 151,572 148,127 151,572 148,127 151,572 148,127 151,572 148,127Number of clusters 621 618 621 618 621 618 621 618
marginal effect by level of inequality
Motivation Context and data Estimation strategies Results Conclusions
Land Inequality and FO performance: Robustness
All Censuses Recent Censuses
T 1×inequality Cult. ∪ LO Cultivators LO Cult. ∪ LO Cultivators LO
Top 5% share -0.110** -0.148*** -0.0855 -0.126** -0.165*** -0.0909(0.0492) (0.0541) (0.0543) (0.0527) (0.0579) (0.0564)
Top 10% share -0.137** -0.181*** -0.130** -0.161** -0.195*** -0.132**(0.0562) (0.0619) (0.0617) (0.0626) (0.0686) (0.0662)
Observations 148,127 145,778 137,006 139,365 134,211 129,856No. of clusters 618 609 588 595 584 569
Motivation Context and data Estimation strategies Results Conclusions
Summary and Implications
• Transferring canal management from irrigation departmentbureaucrats to FOs increased water theft⇒ elite captureis a real concern (contra Atalas et al. 2013).• Indeed, local institutions perform worse precisely where
there are strong elites (i.e., concentrated landholdings).• While decentralization in the Indus basin did not deliver on
its promise of a more equitable canal water distribution, itmay be premature to entirely foresake irrigation reform.• Designing representative institutions that mitigate elite
capture could tip the balance in favor of local control.
Schematic of Irrigation System
Head outlets and watercourses
Tail outlets and watercourses
Dis
trib
uta
ry
Minor canal
Design discharge
Distance from head0
= PMIU discharge gauge
Branch canal
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Example of GIS Buffer around Treated Channel
Area Water Boards
Indus basin irrigation system in Punjab
GIS Buffer and Spatial Matching
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Marginal Effect of 1st FO Tenure by Land Inequality
-.15
-.1-.0
50
.05
ave.
mar
gina
l effe
ct o
f 1st
FO
tenu
re
.2 .3 .4 .5 .6 .7 .8 .9 1
0.0
2.0
4.0
6Fr
actio
n
.2 .4 .6 .8 1land ownership share of top 10% (first tenure FO channels)
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Timeline of FO Operationalization
Number of Proportion of channels in Circle within regime
Circle Channels FOs 2006 2007 2008 2009 2010 2011 2012 2013 2014
LCC East 235 85 0.97 0.97 0.97 0.97 0.97 0.73 0.85 0.86 0.91LCC West 198 73 1.00 1.00 0.97 0.97 0.98 0.98 0.98 0.98 0.74Bahawalnagar 146 69 1.00 1.00 1.00 1.00 1.00 0.97 0.97 0.97 0.57LBDC 237 60 1.00 1.00 1.00 1.00 1.00 1.00 0.98 0.98 1.00Derajat 222 120 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
• Red = pre-reform (T 1 = T M = T 2 = 0)• Blue = first FO tenure operational (T 1 = 1)• Yellow = administrator control (T M = 1)• Green = second FO tenure operational (T 2 = 1)
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