Corruption and conflict

1. Week 3: Finishing Corruption + Conflict
Arthur Blouin
October 4, 2022
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2. Does Monitoring reduce corruption?
Olken (2007) ,“Monitoring Corruption: Evidence from a Field
Experiment in Indonesia”, Journal of Political Economy
I Randomized Control Trial
I 608 Indonesian Villages
I Each village was building a village road as part of a
nationwide infrastructure building project
I He randomly told some village leaders that their road project
would definitely be audited by the government
I Otherwise the probability of receiving an audit was 4%
I Also tries to increase ‘grass-roots’ monitoring by randomizing
invitations to villagers to participate in one of two ways:
1. community meetings;
2. anonymous comment forms
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4. Assumption needed in RCT: Balance of Treatment
The one (and only!!) assumption that we need to interpret RCT
estimates as causal is that treatment assignment is balanced.
I Helps us to know that nothing went wrong in the
randomization
I If everything is randomly assigned there shouldn’t (in theory)
be any differences between control and treatment
I If sample sizes are small, of course unlucky assignments can
happen
I Balancing therefore also serves as a check on sampling
I The following type of table should appear in any RCT paper:
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6. Olken (2007): Main estimating equation
I Auditjk means that subdistrict j in stratum k received an audit
I Invitationsijk means that village i in subdistrict j in stratum k
received an invitation to attend a meeting
I InvitationsandCommentsijk means that village i in subdistrict
j in stratum k received an invitation to attend a meeting and
fill out an anonymous comment form at the meeting
I Because this is an RCT we don’t need controls
I However, measurement error might arise from engineers who
sample roads
I Also estimates versions of the specification with engineer fixed
effects
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8. Olken (2007): Where is the missing money coming from?
Missing money could come from inflating quantities or prices:
I Could be that they reported putting in 10lbs of sand when
they only put in 5lbs
I Olken hires engineers to take samples from the roads to see
exactly how much sand was put into the road and compares it
to claims made
I Could be that they reported that sand costs $2/lb when it
really only costs $1/lb
I Monitors check with suppliers to verify the price of materials
I Quality probably isn’t a big issue when we’re dealing with the
price of sand, cement, etc.
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10. Olken (2007): What about the invitations to participate?
Olken sends out invitations to the villagers to participate in the
monitoring process. We want to know that:
1. Invitations actually caused people to get more involved in the
monitoring process
2. People participating in the monitoring process reduced
corruption
I Did people discuss any issues related to the road building at
the meetings?
I Did people discuss issues related to corruption? (maybe they
were just had noise complaints)
I Did the discussions result in any action? (agreeing to replace a
supplier or village official, agreeing that money should be
returned, agreeing to an internal village investigation, asking
for help from district project officials, etc.)
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13. Olken (2007): Takeaways
I Monitoring seems to help in reducing corruption
I Even grass-roots monitoring, which is relatively inexpensive to
put in place, is somewhat helpful
I But this only helped with labour costs, not at all with materials
I most of the corruption was in materials
I Drawbacks of the study:
I Not many, it’s a pretty amazing study
I No long run effects (this is a cheap-shot: the paper does more
than enough as it is)
I e.g. How does monitoring impact selection of leaders? How
does monitoring impact culture of corruption?
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14. Should we be concerned about a ‘Culture of Corruption’?
Bureaucrat is corrupt if:
1 − p
p
(b − d) > w − v
I While changes in p may be the most practical, being able to
change d is probably the most interesting.
I This loosely captures a preference for honesty - can this be
changed by people’s environment?
I Hard question to tackle: how do you separate the
environmental effect (e.g. p, w, etc) from the impact of the
environment on preferences?
I Large implications:
I If environment impacts preferences, the effect of one bad
policy doesn’t necessarily go away when the bad policy is
reversed - means that history matters
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15. Culture of Corruption
Fisman and Miguel (2006) “Cultures of Corruption: Evidence
From Diplomatic Parking Tickets”
I They look at diplomats in NYC who can’t be ticketed for
parking illegally (diplomatic immunity) but who do so anyway
I This way they get around the problem of having to disentangle
‘institution effects’ from ‘preference for doing the right thing’
I They take advantage of the fact that if environment impacts
preferences, history matters
I Question: Are diplomats from corrupt countries more corrupt
even when they face the same punishment for corruption?
I Our Becker model with exogenous d would predict that
corruption should be the same for everyone with no punishment
I They show that diplomats from highly corrupt countries are
more likely to be corrupt in NYC
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16. Fisman and Miguel (2006), Culture of Corruption
Very clever idea, but right off the bat, some things to keep in mind
as we go through this:
I Are they picking up differences in culture? Or are the most
corrupt people in the country more likely to choose power jobs
in countries where power can be abused easily?
I If corruption is high in the home country, maybe diplomats
won’t be shamed when they get home, but diplomats from
non-corrupt countries will be (i.e. punishment is not really the
same)
I Are corrupt countries just ones that have less respect for the
US?
I Still kind of makes their point that stuff other than
enforcement matters for corruption...I’m actually not sure how
they’d feel about this interpretation.
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17. Fisman and Miguel (2006), Culture of Corruption
Data and Context:
I parking violations data exist at the individual diplomat level
for all U.N. mission diplomats present in New York City
(numbering over 1700 at any given point in time)
I In NYC diplomats cars are ticketed, but diplomats don’t face
any punishment if they don’t pay the ticket
I Is there any measurement error here? Yes - parking cops would
know that diplomat cars wouldn’t pay tickets and likely ticket
them less as a result.
I Should we be concerned? Unless parking cop knows what
country diplomat is from, and ticket cars from corrupt
countries less, there’s no real reason to worry (how plausible?)
I Between 1997 and 2002 diplomats accumulated 150,000
unpaid tickets resulting in $18million in unpaid fines
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18. Fisman and Miguel (2006), Culture of Corruption
In 2002 there was a law change:
I City has the right to tow diplomatic vehicles
I UN parking permits could be revoked for unpaid tickets
I 110% of the fine would be deducted from US aid to the
diplomat’s home country
I This caused a substantial drop in parking violations
I Focus is on 1997-2002 (the 0 enforcement period)
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19. Empirical Strategy
I log(diplomats) is to control for the fact that more diplomats
→ more tickets (tickets per diplomat makes more sense as a
main measure)
I Corruption is the main variable of interest: index of diplomats
home country corruption
I X is a vector of controls - here we have multiple observations
for the same diplomat, so don’t we want diplomat FE??
I NO! diplomat fixed effects would control for variable we’re
most interested in!
I We can’t even use country fixed effects - that would also
control for the treatment (which is bad).
I We can use region fixed effects, and that is included in X
I also in X: GDP/capita, gov’t wage in home country, length of
time in NYC, etc.
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21. Fisman and Miguel (2006), Culture of Corruption
Maybe missing out on a key part of the puzzle?
I Most obvious thing is that corruption is confounded with
some other variable (spurious correlation?)
I Feelings of home country to U.S?
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23. Fisman and Miguel (2006), Culture of Corruption
So, the results from this study are a bit ambiguous.
I It seems like stuff other than just enforcement is important
I It’s unclear what or why
I It seems like home country feelings to the US is the most
robust result, but that’s based on a small sample size
I Don’t address the possibility that there is a different selection
mechanism into jobs with power in different environments
I Overall, we can only say: interesting but nothing really
conclusive here
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24. On Selection Into ‘Power Jobs’ and Corruption
Another way d might be endogenous is selection
I The main weakness with the empirical approach that
attributes persistence of environment to culture is the
selection
I If the institutional environment is one where punishment for
corruption is low, will all of the most corrupt people will want
those jobs?
I If the institutional environment is one where you can’t ever
get away with corruption, it shouldn’t attract corrupt people -
there’s no economic advantage to being corrupt in this case.
I We are just starting to get more research on this question
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25. Is Corruption About Selection Effects?
Hanna and Wang (2014) “Dishonesty and Selection into the
public service”
I Sample of students in India in their final year of college (high
corruption country)
I Use a lab experiment to figure out who is most likely to cheat
I Ask people about their career aspirations
I Do the biggest cheaters aspire to work in the government?
I Big drawback of a narrow focus - how does this compare to
countries that aren’t corrupt?
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26. Hanna and Wang (2014): Measuring Corruption
They have a pretty clever way of measuring corruption in the lab:
I Have a person roll a dice 42 times and report what they rolled
I The higher the dice rolls, the more money they get
I They have the opportunity to cheat, nobody is going to check
what they report
I We expect them to roll 7 1s, 7 2s, 7 3s, etc. - a uniform
distribution.
I They measure how significantly people deviated from the
uniform distribution.
I They don’t know for sure that people lied, but people with
greater deviations from the uniform distribution are much
more likely to have lied
I Dishonesty as measured by the dice task is rampant.
I About 34 percent of the students reported points that were
above the 99th percentile of the theoretical distribution
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27. Hanna and Wang (2014): Measuring Corruption
But is this measure of “corruption” related to actual corruption?
I This is a legitimate concern because what they’re measuring
isn’t really corruption, it’s lying (certainly related)
I We think of corruption as ‘abusing public power for private
gain’. People here are not stealing from a public good, they’re
stealing from private agents (the researchers)
I They check to see if their measure is related to corruption
among real bureaucrats
I They looked at attendance records among government nurses
I They argue that low attendance is a form of corruption -
calling in sick when you’re not really sick.
I If this is related to their measure, they’ll argue that their
measure is at least picking up something close to a preference
for corruption
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29. Takeaways from corruption research
I Corruption seems pretty economically important
I In terms of managing corruption, the Becker model of
monitoring/punishment is definitely very important (we have
fantastic research on this) but isn’t the whole story
I Culture may be important, but results aren’t quite there yet
I Selection into positions of power in countries that can’t
manage corruption is probably important.
I It seems like less honest people are more likely to want
government job
I Is this so that they can be corrupt though? To know this we
need to compare estimates in corrupt countries to estimates in
countries that aren’t corrupt
I Making progress on this question, but not quite there yet.
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30. Conflict
Large Development literature on conflict. Two main questions,
competing theories
I Why do people engage in conflict?
I A risk neutral agent that benefited financially could never earn
enough to offset the nontrivial chance of death
I Group gain versus personal loss?
I How does violence impact economic activity? (surprisingly not
so obvious)
I Conflict Bad: Paul Collier et al (2004): conflict is largely
responsible for the growing gap between rich and poor
countries
I Conflict “Good”: Acemoglu/Robinson (2006); Ferguson
(2002); Tilly (1975); Tilly (1992); Slater (2005); Weinstein
(2005): wars serve a critical role in the development of strong
and capable government institutions
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31. Why do people engage in conflict?
Grossman (1991): An unnecessarily complicated model with a
simple set-up and intuition
I Workers can choose to work in two sectors: violent or
productive
I The violent sector produces some constant returns associated
with pillaging (i.e. exog. to development).
I Wages in the productive sector depend on how rich a country
is
I Workers maximize earnings.
I As development occurs, more and more people find it profitable
to work in the productive sector instead of the violent one.
I As people select out of the violence sector, the odds of conflict
go down.
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32. Why do people engage in conflict?
A clear prediction:
I As productive employment becomes more profitable, conflict
decreases.
I Is it really that simple? the Grossman model makes (at least)
one heroic assumption:
I are returns to pillaging/violence really independent of
economic development?
I If returns to conflict are increasing in wealth, then the
prediction of the model predictions are ambiguous.
I Take the extreme: in a society with no capital and no formal
wages, what is there to pillage?
I So, let’s look at the evidence: does economic growth make
wars more or less likely in the development context?
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33. Case Study: Nduma Defense of Congo-Renouvelé (NDC-R)
Government of DRC has withdrawn from many areas of the
country
I Has allowed armed groups to set up their own ‘monopolies of
violence’ and in many places these groups have more state
legitimacy than the national government
I NDC-R has 2,075 combatants and administrators, controls
108 villages, accounting for 45,747 inhabitants. They own 32
spears, 50 arrows, 4 pistols, 15 grenades 20 shotguns, 17
RPG, 31 PKM, 7 mortars, 1702 AK7 and 94 saltelite phones.
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34. Case Study: Nduma Defense of Congo-Renouvelé
(NDC-R) con’d
Government of DRC has withdrawn from many areas of the
country
I To subsidize all of this, they collect monthly head tax in cash
from households. They conduct a census yearly, coerce village
chiefs to implement the tax, and conduct random audits to
combat tax evasion.
I They issue mining permits, collect mining output tax, collect
a weekly agriculture tax, set up tolls on roads. They run
state-level monopolies on beer, liquor and cigarettes.
I Sex offenders, tax evaders, spies and thieves are physically
sanctioned, often in public. The group has a logo, anthem
and a system of formal, sealed correspondence.
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35. What happens when the productive sector gets more
profitable?
Sanchez de la Sierra 2018 “On the Origins of the State:
Stationary Bandits and Taxation in Eastern Congo. JPE.
Suppose we have an armed actor. They can be ‘roving’ or
‘stationary’. Once they become stationary, they will attempt to
form a monopoly of violence.
I If we can explain when armed actors choose to become
stationary, we may explain early state formation and
institutions development.
I We would like to know: (a) when do bandits choose to
attempt a monopoly over violence in a particular location? (b)
what explains the degree and sophistication of taxation?
I Monopoly of violence is more likely in locations where taxation
is more profitable
I Monopoly of violence is more likely in locations where taxation
is easier to implement
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36. Empirical Strategy and Specification
I Empirical challenge: need variation in profitability of a
location, and ease of implementation of taxation
1. variation in profitability from variation in international mineral
prices - specifically gold and coltan
2. variation in the ease of taxation from the types of mines most
prevalent in a village.
I Coltan: huge and difficult to conceal, and ∴ easy to tax.
I Gold: smaller, easy to conceal, and difficult to tax.
Yit = βt + αi + γcCi pUS
c,t + γg Gi pUS
g,t + X0
β + it
I We have time fixed effects, village fixed effects, whether the
village is a coltan village (Ci ) or a gold village (Gi ), the price
of gold (pUS
g,t ) and coltan(pUS
c,t ) and some controls.
I This is a (complicated) difference in differences empirical
strategy. We have 2 treatment groups (gold and coltan
groups) and are exploiting pre/post increases in the price of
the relevant commodity.
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37. Relevant prices over the study period
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38. Violence in the relevant regions
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39. We care about more than violence
I the results from these figures suggest that violence is more
likely when the product of a location becomes more profitable.
This is what we might expect under the ‘violence to capture a
location’ theory
I Note that a competing theory would suggest that as labour
becomes more valuable, workers opt out of the ‘violence’
industry and opt into a legitimate industry, resulting in less
violence as prices rise
I Sanchez de la Sierra can go further, and discuss the
REASONS behind the violence, and document that this is a
fight to act as a state
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40. State-like behaviour in the relevant regions
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41. State-like behaviour in the relevant regions
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42. Takeaways
Regardless of ‘official’ statehood, we should be concerned about
which ‘state’ is most legitimate on the ground
I Here we see parallel states fighting for the right to act as the
‘missing’ state
I Opportunity for taxation in the productive sector induces
fighting for control of the region
I Some evidence on both questions:
I Profit opportunity seems to increase violence
I Also leads to potentially better institutional environment
(though here it may be development → institutions; not
violence → institutions.
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43. Is profit the only reason for violence?
A lot of violence seems to stem from hatred or exclusion and not
necessarily profit. Both the Grossman theory and the Sanchez de la
Sierra evidence frame the problem as one of economics only.
Reasonable?
I Think of Myanmar a few years ago: genocide of Rohingya
people
I It’s very hard to rationalize this as economic gain. The
Rohingya are historically discriminated against, not major
economic actors.
I Confusing lack of action from (former Nobel Peace prize
winner!!) Aung San Suu Kyi
I Do we have evidence of non-economic reasons for civil
violence?
I Evidence from Rwandan Genocide
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44. A different view: The Unexpected Consequences of
Drought
Ted Miguel (2005) “Poverty and Witch Killings” Review of
Economic Studies
I Can droughts cause witch killings?!?
I Witch killing is still a legitimate issue
I about 2100 people accused of witchcraft between 2000 and
2012
I In Tanzania alone 23 arrests for Witch killing even though
most go unpunished.
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45. Miguel (2005)
Witches in Tanzania:
I Witches harness occult powers to cause harm to others
I Powers over health, weather, crops
I Allows individuals to attribute misfortune to human malice
I Witchcraft beliefs strong among Sukuma in western Tanzania
I 64 percent follow traditional religions
I Government figures indicate there were over 3,000 witch
murders from 1970 to 1988 in the region
I In 67 villages over 11 years, 65 witch murders and 73 witch
attacks
I Witches are poor, elderly women (50-70 years old)
I Murdered by relatives or kin
I Crack-down in the 1970s failed, no serious apprehension
efforts now
I “In the Sukuma community, if you kill a witch it is not really
considered a crime.” (BBC 2002)
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46. Miguel (2005)
Witches Around the World:
I African countries
I South Africa (Northern Province)
I Congo, Ghana, Kenya, Mozambique, Uganda, Zimbabwe, etc.
I Other regions
I Witch killing in poor Andean villages
I Witch killing in tribal areas in India
I Early modern Europe (16th-18th centuries)
I Extreme precipitation and temperature set off witch killing
(Behringer 1999, Oster 2002)
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47. Narrative put forth in the paper
Theory of Witch Killing:
I “Witches” are typically older women - people who we might
expect to consume more food than they produce
I Killing the least productive members of society allows more
people to be fed, and so a belief in witches might arise as an
equilibrium response to drought
I The elderly are likely to have the lowest future production
I Elderly women politically marginalized and socially isolated
I Other explanations include scapegoating, cultural norms
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48. Data: Miguel (2005)
Data:
I Survey Data from Meatu, Tanzania
I Collected in 2001-2002 by NGO enumerators
I Representative sample of nearly 1300 households
I Demographics, socioeconomic status, consumption expenditure
I Very poor area: average annual per capita consumption 200
U.S. dollars
I Village Council survey (67 villages)
I Years of extreme rainfall (drought or flood), 1992-2002
I Village rainfall reports and measured rainfall are highly
correlated (for the subset of villages with both)
I Witch and non-witch murders, recall data from 1992-2002
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49. Data: Miguel (2005)
Victim Characteristics:
I 96 percent are female, average age 58 years
I Victims come from relatively poor households in terms of
assets and livestock
I Most victims are killed during the pre-harvest ‘hungry’ season
of the year (mainly April to July)
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50. Are Killings Motivated by Economic Conditions?
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51. Weather Events and Witch Killings
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52. ‘Rational’ murders or scapegoating?
I Witches are supposed to be able to cause both extreme
rainfall and disease
I Under the scapegoat theory they should be blamed for both
I If murders are to save food, we should only expect murders
when there’s a shortage of food
I Table 4, column 4 estimates a model with both extreme
rainfall (which reduces income/food) and disease (which
doesn’t)
I We only find an effect on the rainfall variable, consistent with
‘rational’ murders and inconsistent with scapegoating (if
anything there are fewer witch killings when there’s a disease
epidemic).
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53. Comments of the results?
I He kind of sneaks in the intuition of an IV here without really
saying so
I He wouldn’t have been able to just regress poverty on witch
killings - frequent and accepted murders might reduce income
so he’s got a reverse causality problem.
I So he uses extreme weather: poor harvest reduces income, and
is exogenous!
I This is exactly the IV intuition! He’s (almost) using rainfall as
an instrument for poverty
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54. Effect of War on Economic Outcomes?
War has an economic effect via capital destruction, but also can
impact a number of other things:
1. It has an impact on education and work experience (Blattman
and Annan, 2010)
2. It can actually spur economic growth (Rogall and
Yanagizawa-Drott, 2019)
3. It can lead to more pro-sociality (Bauer, Blattman, Chitylova,
Henrich, Miguel, Mitts, 2016)
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55. 1. Effect of War on Education, Work Experience
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56. 2. Effect of War on Income
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57. 3. Effect of War on Pro-sociality
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58. There’s lots we still don’t know!
Very active research question in development:
1. What leads to better / worse ‘bargaining’ outcomes between
groups
2. Peace / reconciliation interventions (I’ve done some work on
this)
3. Why does war lead to things like pro-sociality? What are the
limits of these effects?
Hard to study so lots of unanswered questions.
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