Targeted Aid and the Threat of Capture in World Bank Projects
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Targeted Aid and the Threat of Capture in World Bank Projects Matthew S. Winters University of Illinois at Urbana-Champaign [email protected] 14 November 2009
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Targeted Aid and the Threat of Capture in World Bank Projects. Matthew S. Winters University of Illinois at Urbana-Champaign [email protected] 14 November 2009. Corruption in World Bank Projects. - PowerPoint PPT Presentation
Transcript of Targeted Aid and the Threat of Capture in World Bank Projects
Targeted Aid and the Threat of Capture in World Bank Projects
Matthew S. WintersUniversity of Illinois at Urbana-Champaign
14 November 2009
Corruption in World Bank Projects
• March 2008: World Bank and Indian government announce investigation into five Bank-financed health projects ($569 million)– Second National AIDS Control Project: due to bid-
rigging, faulty HIV-test kits may have been distributed to clinics and blood banks
– Orissa Health Systems Development Project: uninitiated/incomplete hospital rehabilitation leaky roofs, crumbling ceilings, ungrounded neo-natal equipment
Corruption in World Bank Projects• “I hadn’t worked on one project during those
twelve years [at the World Bank] that did not reek of corruption” (Berkman 2008: 121)
• This is extreme, but is it correct?• And what factors make a project more or less
susceptible to capture or corruption?
• Key hypothesis: more specifically targeted projects are less likely to suffer from capture
Corruption in World Bank Projects• “I hadn’t worked on one project during those
twelve years [at the World Bank] that did not reek of corruption” (Berkman 2008: 121)
• This is extreme, but is it correct?• And what factors make a project more or less
susceptible to capture or corruption?
• Key hypothesis: more specifically targeted projects are less likely to suffer from capture
Corruption in World Bank Projects• “I hadn’t worked on one project during those
twelve years [at the World Bank] that did not reek of corruption” (Berkman 2008: 121)
• This is extreme, but is it correct?• And what factors make a project more or less
susceptible to capture or corruption?
• Key hypothesis: more specifically targeted projects are less likely to suffer from capture
Corruption in World Bank Projects• “I hadn’t worked on one project during those
twelve years [at the World Bank] that did not reek of corruption” (Berkman 2008: 121)
• This is extreme, but is it correct?• And what factors make a project more or less
susceptible to capture or corruption?
• Key hypothesis: more specifically targeted projects are less likely to suffer from capture
Targeted Aid
• Insofar as international donors want to constrain governments into using aid for poverty alleviation, we generally think about donor conditionality (e.g. Svensson 2000)
• But an alternative might exist in which domestic constituencies constrain the government and/or make the government a better aid monitor
Targeted Aid• If a donor targets aid at a particular group in
society – defined geographically or socially – and that group is aware of this, then the group can organize around the aid project, possibly constraining the government and preventing capture or corruption
• A Project-Level Hypothesis: aid projects with more well-defined constituencies should be less subject to capture
Database of Capture in World Bank Projects
• Based on World Bank’s Implementation Completion and Results Report (ICR)– Compiled by World Bank operations team “using input
from the implementing government agency, co-financiers, and other partners/stakeholders”
– Reviewed by Internal Evaluations Group and submitted to Board
– Each finished report available to public since August 2001; some earlier reports also available
• Coded for all publicly-available ICRs as of the end of 2005
Outcome Variable: Capture• If aid funds do not reach their intended
destination (as cash or as goods and services), I refer to them as having been captured (Reinikka and Svensson 2004)– Corruption: kickbacks, bribery, bid-rigging,
embezzlement– Discriminatory government policies in selecting
program recipients– Reallocation of funds to other purposes
• Purposeful act – not waste or inefficiency due to incompetence
Coding ICRs for Capture• Code “Yes” if:
Direct mention of corruption (bid-rigging, kickback schemes, etc.)
Political interference in allocation decisionsNegative descriptions of financial management,
procurement practices or audits• But code “No” if:
Government takes quick action ICR describes bureaucratic incompetenceThe problem is a lack of counterpart fundingThe problem is the reallocation of other resources in
budget
ICRs• Project Lending –
investment projects with a clear constituency (perhaps national)
• Non-Project Lending – budgetary support, structural adjustment, technical assistance, etc.
Evidence of Capture
Country Characteristics and Capture
Difference-in-means tests do not take account of repeat country-year observations.
Key Explanatory Variable: Level of Targeting
Code as a scale (but order is not clear)Code as categorical (highly targeted, partially
targeted, not targeted)Code as dichotomous (highly targeted vs. not)
Geographic vs. Non-Geographic vs. Nationwide
Capture No Capture Total
Geographical Targeting
52(20 percent)
208(80 percent)
260
Other Targeting 30(21 percent)
113(79 percent)
143
Nationwide 40(21 percent)
155(79 percent)
195
Total 122(20 percent)
476(80 percent)
598
Pearson chi-squared p < 0.97
Single Cities/Single Regions vs. Other Projects
Capture No Capture Total
Strict Geographical Targeting
13(13 percent)
88(87 percent)
101
Other Targeting or Nationwide
109(22 percent)
388(78 percent)
497
Total 122(20 percent)
476(80 percent)
598
Pearson chi-squared p < 0.04
Concentrated Projects vs. Other Projects
Capture No Capture Total
Strict Geographical Targeting or
Business/Industry
18(12 percent)
138(88 percent)
156
Other Targeting or Nationwide
104(24 percent)
338(76 percent)
442
Total 122(20 percent)
476(80 percent)
598
Pearson chi-squared p < 0.01
Capture No Capture Total
Strict Geographical Targeting or
Business/Industry
11(17 percent)
55(83 percent)
66
Other Targeting or Nationwide
62(27 percent)
164(73 percent)
226
Total 73(25 percent)
219(75 percent)
292
Capture No Capture Total
Strict Geographical Targeting or
Business/Industry
7(8 percent)
83(92 percent)
90
Other Targeting or Nationwide
41(20 percent)
169(80 percent)
210
Total 48(16 percent)
252(84 percent)
300
High Corruption Countries (control of corruption < median)
Low Corruption Countries (control of corruption > median)
Capture No Capture Total
Strict Geographical Targeting or
Business/Industry
9(16 percent)
46(84 percent)
55
Other Targeting or Nationwide
61(27 percent)
161(73 percent)
222
Total 70(25 percent)
207(75 percent)
277
Capture No Capture Total
Strict Geographical Targeting or
Business/Industry
9(10 percent)
77(90 percent)
86
Other Targeting or Nationwide
39(20 percent)
155(80 percent)
194
Total 48(17 percent)
232(83 percent)
280
IDA Projects
IBRD and Blend Projects
Logistic Regressions Predicting Capture
Concentrated Targeting -0.83***(0.25)
-0.75***(0.26)
-0.84***(0.25)
Control of Corruption -0.81**(0.36)
-0.75**(0.37)
WGI Average -1.34***(0.43)
Log(GDP Per Capita (PPP)) -0.37**(0.19)
-0.37*(0.21)
-0.30(0.19)
Freedom House Score 0.07(0.05)
0.07(0.05)
0.16**(0.06)
Log (Total Project Amount) 0.13(0.09)
0.16*(0.08)
Log (WB Project Amount) 0.14(0.09)
Indicator for IBRD Loan 0.09(0.38)
0.05(0.39)
0.06(0.37)
Indicator for Blend Loan -0.49(0.30)
-0.54*(0.30)
-0.42(0.31)
N 569 545 569Robust standard errors clustered on country.
Summary
• Original dataset of capture in 598 World Bank-funded investment projects
• At project level, very preliminary evidence that more specific targeting reduces capture, even controlling for some other relevant factors
Future Directions
• Code additional years of data; test theory on new years using induced coding of explanatory variable
• Validity checks of outcome variable codings• Fuller models of factors predicting capture • Selection model accounting for World Bank’s
strategic use of targeting
Extra Slides
Cross-Project Data on Corruption
• Despite certain instances of corruption getting a lot of attention in the media, a broad search does not yield much information– Lexis-Nexis search on “World Bank AND project
AND corruption” for the years 1997, 1998 and 1999
– 956 news stories (including duplicates)– < 10 mention a specific project
Capture and Project Ratings
Capture and Project Characteristics
Capture and Source of World Bank Funds
Countries with High Rates of Capture
Country N Capture Pecent
Turkmenistan 1 1 100
Swaziland 1 1 100
Nigeria 2 2 100
Haiti 1 1 100
Bolivia 5 4 80
Uzbekistan 4 3 75
Madagascar 6 4 67
Bangladesh 9 5 56
Countries with Low Rates of Capture
Country N Capture Pecent
Bosnia and Herzegovina
11 0 0
West Bank and Gaza
10 0 0
Colombia 8 0 0
Romania 8 0 0
Jordan 5 0 0
Panama 5 0 0
Poland 5 0 0
