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Is corrup)on good for your health?
Guilherme Lichand Marcos Lopes Marcelo Medeiros
Harvard University CEPESP/FGV PUC-‐Rio
FighBng CorrupBon in Developing and TransiBon Countries – SITE Conference, September, 1st
Every dollar that a corrupt official or a corrupt business person puts in their pocket is a dol lar stolen from a pregnant woman who needs health care. In the developing world, corrup&on is public enemy number 1”
“
Even in the case of peQy bribery or extorBon, it is relevant to ask, what is the alterna&ve?”
“
What are the causal effects of corrup)on?
• Experiments: • Bertrand, Djankov, Hanna and Mullainathan (2004); Zamboni and Litschig (2013)
• Natural experiments: • Reinikka and Svensson (2004); DiTella and Schargrodsky (2003)
This paper
• Assesses the impacts of the introducBon of a random-‐audits program in Brazil on the incidence of corrupBon within Health transfers;
• Assesses how such impacts were transmiQed to Health outputs and outcomes.
Preview of findings
• Random-‐audits program significantly reduced procurement problems; • Outputs and outcomes become worse.
• Mismanagement increased one-‐to-‐one with the decrease in corrupBon.
• Sharp decrease in spending points to procurement staff being paralyzed by the risk of misprocuring.
Audit reports
[Ghost firm] “When evalua+ng the procurement process to purchase two mobile health units in 2002 by the municipal government of Dourados, auditors found evidence of fraud. The only public outbidder, Santa Maria Comércio e Representações Ltda., did not legally exist according to the local branch of the Federal Revenue Secretariat in Cuiabá, State of Mato Grosso do Sul. Despite this fact, the municipal government has concluded the public bid and paid the company the agreed-‐upon amount.”
Audit reports
[Over-‐invoicing] “When analyzing a procurement process to purchase medical supplies in 2004, auditors found that the municipal government of Poloni had paid higher prices for medica+on than the one agreed upon the public-‐bid contract. For example, according to receipt number 115655 (Procurement number 2004/01696), the correct price 150 mg of the medica+on Rani+dine was R$ 0.18 per tablet, but the municipality paid R$ 0.28 per tablet. No further documenta+on was presented by the municipal government, and the outbidder Empresa Soquímica Laboratórios Ltda. embezzled the resources.”
What is corrup)on?
Table A1 – List of irregularities
Panel A: Corruption Category Irregularity
Procurement Irregular receipts Procurement Evidence for ghost firms Procurement Contracts not signed or falsified signatures Procurement Favored vendor Procurement Lack of publicity Procurement Documents set with different dates Procurement Other procurement problems Procurement Irregular class Procurement No realization
Resource diversion Over-invoicing Resource diversion Off-the-record invoice
Timeline
Challenges • An anB-‐corrupBon program, right at the mid-‐point of mayors’ term, but… • What about pre-‐treatment data? • What about a control group?
• Novel dataset with: • Pre-‐treatment data:
• Auditors follow transfers’ paper trail, usually 3 years (but someBmes more) prior to the Bme of the audit.
• Very detailed informaBon about each transfer: • In parBcular, extent to which there are heterogeneous opportuniBes for embezzlement.
Procurement-‐intensity
• We code the share of acBons listed under Health Ministry’s descripBon of the transfer involving procurement-‐related terms, such as:
• AcquisiBon; • Purchase; • ModernizaBon; • Enlargement; • ConstrucBon.
• High procurement-‐intensity transfers have higher opportuniBes for embezzlement.
Differences-‐in-‐differences
Corruption
Low High Difference
[Low - High] procurement- intensity
procurement- intensity
Before 2003
0.17 0.37 -0.19*** [0.02] [0.03] [0.04]
After 2003
0.12 0.12 0.00 [0.01] [0.01] [0.01]
Difference [Before - After]
0.05** 0.25*** -0.20*** [0.03] [0.03] (0.03)
Iden)fica)on assump)on • Iden&cal poten&al (reported) outcomes
• Sufficient condiBons:
Ø Parallel trends (between treatment and control) for actual outcomes;
Ø Parallel trends for misreporBng by auditors for reported outcomes.
Corrup)on: Before vs. aFer
18.2%
22.3% 21.6%
37.1%
28.0%
30.6%
14.1% 12.3%
14.3% 15.2%
12.1%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Differences-‐in-‐differences
Corruption
Low High Difference
[Low - High] procurement- intensity
procurement- intensity
Before 2003
0.17 0.37 -0.19*** [0.02] [0.03] [0.04]
After 2003
0.12 0.12 0.00 [0.01] [0.01] [0.01]
Difference [Before - After]
0.05** 0.25*** -0.20*** [0.03] [0.03] (0.03)
Corrup)on
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
45.0%
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Low procurement-‐intensity High procurement-‐intensity
Regression framework
𝐶𝑜𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛↓𝑗,𝑚,𝑡 =
𝛽𝑃𝑜𝑠𝑡𝑃𝑟𝑜𝑔𝑟𝑎𝑚↓𝑡 𝑥 𝑃𝑟𝑜𝑐𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑣𝑒↓𝑗 +𝑃𝑟𝑜𝑐𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑣𝑒↓𝑗 + 𝛼↓𝑚 + 𝛼↓𝑡 +𝛾𝑋↓𝑗,𝑚,𝑡 + 𝜖↓𝑗,𝑚,𝑡
Results: Corrup)on (1) (2) (3) (4) (5)
Municipal-level
[2001-2004] [2001-2004] [1997-2007] [1997-2007] [1997-2007]Post-2003 x -0.178*** -0.169*** -0.167*** -0.196*** -0.195***
High procurement intensity [0.0387] [0.0339] [0.0330] [0.0382] [0.0382]High procurement intensity 0.214*** 0.174*** 0.135*** 0.135*** 0.135***
[0.0394] [0.0405] [0.0384] [0.0384] [0.0389]Procurement intensity 0.0610** 0.0765*** 0.0752*** 0.0728**
[0.0304] [0.0290] [0.0289] [0.0294]ln(transfer amount) 0.00379 0.00386 0.00338 0.00295
[0.00478] [0.00367] [0.00372] [0.00368]Second-half of the term x 0.0421** 0.0434**
High procurement intensity [0.0208] [0.0207]Second-half of the term -0.239*** -0.279***
[0.0487] [0.0462]Municipality fixed-effects Yes Yes Yes Yes YesYear fixed-effects Yes Yes Yes Yes YesTerm fixed-effects No No Yes Yes YesDraw fixed-effects No No No No YesObservations 1,932 7,072 10,538 10,538 10,538R-squared 0.555 0.243 0.196 0.196 0.201
Robust standard errors clustered at the municipal level in brackets*** p<0.01, ** p<0.05, * p<0.1
Transfer-level Transfer-level Transfer-level Transfer-level
Diff-‐in-‐diff coefficient by year
Diff-‐in-‐diff coefficient by draw
Health outputs and outcomes
𝑍↓𝑗,𝑚,𝑡 = 1/𝑛↓𝑗 ∑𝑖↑▒𝑌↓𝑖,𝑗,𝑚,𝑡 − ¯ˉ𝑌 ↓𝑖,𝑗,𝑡=0 /𝜎↓𝑖,𝑗,𝑡=0 ⇒𝐸[𝑍↓𝑗,𝑚,𝑡=0 ]=0,𝑉𝑎𝑟[𝑍↓𝑗,𝑚,𝑡=0 ]=1
High procurement-‐intensity Low procurement-‐intensity
-‐ Hospital beds; -‐ ImmunizaBon; -‐ % adequate water; -‐ % adequate sanitaBon.
-‐ Coverage of Family Health program; -‐ Medical consultaBons.
Regression framework
𝑍↓𝑗,𝑚,𝑡 =
𝛿𝑃𝑜𝑠𝑡𝑃𝑟𝑜𝑔𝑟𝑎𝑚↓𝑡 𝑥 𝑃𝑟𝑜𝑐𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑣𝑒↓𝑗 + 𝜃↓𝑗 + 𝜃↓𝑚 + 𝜃↓𝑡 +𝜂𝑋↓𝑚,𝑡 + 𝜐↓𝑗,𝑚,𝑡
Z-‐scores
-‐1.5
-‐1
-‐0.5
0
0.5
1
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Low procurement-‐intensity High procurement-‐intensity
Results: Z-‐scores
(1) (2) (3) [2001-2004] [1997-2007] [1997-2007]
Post-2003 x -0.309*** -0.494*** -0.495***
High procurement-intensity [0.107] [0.0948] [0.103]
High proc. intensity x 0.00140 Audit within 100 km in previous year [0.0977]
Audit within 100 km in previous year -0.109
[0.0897] Municipality fixed-effects Yes Yes Yes Year fixed-effects Yes Yes Yes Term fixed-effects No Yes Yes Observations 1,870 2,699 2,699 R-squared 0.556 0.513 0.514
Mechanism
(1) (2) (3) Corruption Mismanagement Compliance
Post-2003 x -0.150*** 0.153*** -0.00262
High procurement-intensity [0.0346] [0.0446] [0.0294]
Audit within 100 km in t-1 x -0.0424** 0.0247 0.0177 High proc. intensity [0.0187] [0.0253] [0.0199]
Audit within 100 km in t-1 0.136** -0.116 -0.0198
[0.0652] [0.0734] [0.0314] Municipality fixed-effects Yes Yes Yes Year fixed-effects Yes Yes Yes Observations 7,072 7,072 7,072 R-squared 0.245 0.251 0.301
Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1
Mismanagement categories (1) (2) (3) (4) (5) (6)
Resource diversion
Health council
problems
Performance problems
Infrastructure and stock problems
Human resources problems
Documentation problems
Post-2003 x -0.0614** 0.0289* -0.0404 0.156*** 0.0169 0.0906**High procurement-intensity [0.0260] [0.0162] [0.0324] [0.0280] [0.0147] [0.0360]
High proc. intensity x -0.0328* 0.0042 0.0188 0.0106 0.0079 0.0414*Audit within 100 km in t-1 [0.0175] [0.00875] [0.0250] [0.0251] [0.0147] [0.0235]
Audit within 100 km in t-1 -0.0346 -0.0142 -0.0215 0.0398 -0.00578 -0.0915**[0.0385] [0.0221] [0.0456] [0.0482] [0.0222] [0.0424]
Municipality fixed-effects Yes Yes Yes Yes Yes YesYear fixed-effects Yes Yes Yes Yes Yes YesObservations 7,072 7,072 7,072 7,072 7,072 7,072R-squared 0.237 0.125 0.136 0.21 0.124 0.165
Robust standard errors clustered at the municipal level in brackets*** p<0.01, ** p<0.05, * p<0.1
Spending
8
9
10
11
12
13
14
15
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Low procurement-‐intensity High procurement-‐intensity
Spending
ln(transfer amount) (1) (2) (3) Post-2003 x -0.490*** -0.455*** -0.562**
High procurement-intensity [0.133] [0.133] [0.272] Number of investigations 0.0234*** 0.0253***
[0.00749] [0.00866] ln(supply of funds by transfer group) -0.0279 [0.0277] Municipality fixed-effects Yes Yes Yes Year fixed-effects Yes Yes Yes Observations 7,072 7,072 5,222 R-squared 0.416 0.419 0.446
Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1
A simple decomposi)on
𝑑ln (𝐶↓𝑡 ) /𝑑 𝑡 =𝑑𝑙𝑛(𝑌↓𝑡 /𝑋↓𝑡 )/𝑑 𝑡 +𝑑𝑙𝑛(𝑋↓𝑡 /𝑇↓𝑡 )/𝑑 𝑡 −0.2 =𝑑𝑙𝑛(𝑌↓𝑡 /𝑋↓𝑡 )/𝑑 𝑡 − 0.5 ⇒𝑑𝑙𝑛(𝑌↓𝑡 /𝑋↓𝑡 )/𝑑 𝑡 =0.3
“Big ideas”
• Procurement mistakes might be interesBng to incorporate in a model
Ø Capacity-‐building intervenBons; Ø SelecBve monitoring: larger transfers, where incenBves to embezzle are much higher;
Ø InteracBon with asymmetric punishments.
• “Cap and trade” for corrupBon
Ø Different ciBes have different costs of reducing corrupBon.
