background for fred finan’s march 13 lecture: “the ...webfac/eichengreen/e191... · 3/6/2012...

Announcements

Research Question Assignment

Introduction to Corruption

Experimental Design

How to Find and Use Data

Summary

Reminders for Next Week

Background for Fred Finan’s March 13 Lecture:“The Economics of Corruption”

Econ 191: Background Lecture 5

Dawn Powers

March 6, 2012

Dawn Powers Background for March 13: “Economics of Corruption”

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Outline

1 Announcements

2 Research Question Assignment

3 Introduction to Corruption

4 Experimental Design

5 How to Find and Use DataIntroduction to STATA


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Announcements

Hand in your literature reviews.Dawn will grade all assignments from here on out.Dawn will email research question feedback (and grades) thisweek.


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If you did not receive an “A” on the research questionassignment, you must turn in a revision of it by April 3.


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Let’s review what makes a good research question:Is it well-defined– i.e. does it answer the 5 “W’s?”

E.g.1 Bad: What happens to a native population when there is a lot

of immigration?

2 Better: What happens to a native population’s fertility rate

when there is an exogenous shock to immigration?

3 Best: What happened to the fertility rate of Costa Rican

women after the Nicaraguan earthquake of 1972 caused

massive Nicaraguan emigration to Costa Rica?

(Ramos-Chaves, 2010)


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Let’s review what makes a good research question:Is it well-motivated? I.e.

Is it interesting to you, but also other people?Might it have actionable policy implications?


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Let’s review what makes a good research question:Is it innovative?

E.g.1 Bad: Does an increase in transfer payments to the poor

increase their consumption and investment?

2 Better: Do different size increases in transfer payments to the

poor cause their consumption and investment to increase

differentially?

3 Best: Do different size increases in transfer payments to the

poor cause their consumption and investment to increase

differentially, depending also on whether the transfer is given

at planting vs. harvest season?


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Let’s review what makes a good research question:Is it falsifiable?

Can you actually investigate and verify/reject the hypothesis?E.g.

1 Bad: How much better would the economy be if a Republican

had been elected in 2008?

2 A little better: What predictions would the Solow model have

for economic growth, given differential tax rate levels?

3 Best: What is the difference in economic performance

between Democratic and Republican congressional districts

with tight races in 2008, where it was unclear who would win?


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Let’s review what makes a good research question:Does data exist that can help us test our question, or can weconstruct a model that predicts an answer to our question?

Empirical Example: Jayachandran (2006) found a panel dataset of 257 districts in India from 1956-1957, that containedinformation on individuals’ wages, access to credit, “controlvariables,” and crop yield (explained on next slide).Theoretical (Model) Example: Lundberg and Pollak (1996)constructed optimization problems for a husband and wifeusing specific, relevant assumptions about their relativebargaining powers. They solved these optimization problems topredict how an increase in the wife’s outside options wouldchange the distribution of income within the home.


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Let’s review what makes a good research question:Is the project reasonable for us to complete in one semester?


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Recall also what makes a good research question “economic”:1 Does it ask how an individual/group would solve a particular

problem, or respond to a particular incentive?E.g. If the Albanian central government begins to allowindividual districts to use their own poverty targeting schemes,how does the population of transfer payment recipientschange? (Alderman 2002)

OR2 Does it ask how a particular variable would respond to an

exogenous (external and independent) shock?E.g. Does consumption in subsistence-level Indian villages withrainfed agriculture correlate almost perfectly with rainfall? (Inother words, is there informal insurance against drought?)(Townsend 1994)


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Other thoughts:Remember, you can either try to creatively extend existingwork, or come up with your own question.

Creative extension: Same data set, slightly different question;or same question, different data set.

For empirical Papers, it is common to both:First think of a question, and then search for a dataset thatcould answer it–ORFirst come across a dataset, and think of a question you couldanswer with it.Come talk to us in office hours if you unsure that yourapproach is “correct.”


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Finally, here are some more examples of research questions wediscussed in the Jan. 24 lecture:

E.g. Do poor people in India work for lower wages if they haveinsufficient access to credit? (Jayachandran 2006)E.g.2. Do husbands treat wives with outside incomeopportunities better than wives without outside incomeopportunities? (Lundberg and Pollak 1996)E.g.3. Are there more terrorist attacks in conflict areas withless employment? (Berman 2010)E.g.4. Do fast-growing economies tend to slow down, andwhat are the policy implications? (Eichengreen et al. 2010)


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Next Week’s Topic“The Economics of Corruption”

Next week, Professor Finan will discuss:Benjamin A. Olken and Rohini Pande (2011), “Corruption inDeveloping Countries.”Claudio Ferraz and Frederico Finan (2010), “ElectoralAccountability and Corruption: Evidence from the Audits ofLocal Governments.”Claudio Ferraz and Frederico Finan (2008), “Exposing CorruptPoliticians: The Effect of Brazil’s Publicly Released Audits onElectoral Outcomes.”


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Next Week’s Topic“The Economics of Corruption”

This week, Dawn will discussDefinition and types of corruptionWhy is studying corruption important?How to design an experiment, as in Ferraz and Finan (2008)and (2010)How to find and use data (including an introduction to STATAusing data from Ferraz and Finan 2010)


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Motivation“The Economics of Corruption”

DefinitionCorruption: The abuse of public authority or trust for privatebenefit (IMF “Good Governance” factsheet, 2011)


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Types of CorruptionBribery: Payment given personally to a government official inexchange of his use of official powers.

Example: In India, money was given to agents in the RegionalTransport Offices to obtain a driver’s license without passingthe requisite or of vehicular driving test (Mullainathan,Bertrand, and Djankov 2006).


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Types of CorruptionTrading in influence: A government official sells his/her(incomplete) influence over the decisionmaking process in aninstitution where he or she is not an employee.

E.g. Lewis Lucke, a former USAID official, was paid$30,000/month by a US and a Haitian business enterprise touse his influence to secure reconstruction contracts in Haitiafter the earthquake.


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Types of CorruptionPatronage: Favoring supporters.Nepotism: Favoring relatives.Cronyism: Favoring personal friends.Example: In Indonesia, businesses controlled by family and

allies of former President Suharto would perform better thanbusiness controlled by Suharto’s opponents (Fisman 2001).


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Types of CorruptionElectoral fraud: Interfering with an election to change theelection result.

E.g. The majority won 99% of parliamentary seats in the 2010Ethiopian election.

Embezzlement: Theft of public funds by a public official.E.g. Village heads in Indonesia, given the same (relative)budget from the central government, built roads of varyingquality, indicating various levels of embezzlement (Olken2009).


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Kickbacks: Payment to a public official from the winner of acorrupt but successful bid for a public project.

E.g. The 2010 Commonwealth Games in New Delhi, held inthe developing world for the first time, saw corrupt winningbids for contracts to construct game venues.

Organized crime: Enterprises run with the purpose of engagingin illegal activity, usually for monetary gain.

E.g. The underreporting of cultural objects and antiquesexports is more common in more corrupt countries, andespecially common in corrupt countries that have a higherendowment of export-restricted items (Fisman Wei 2007).


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Why do we care about corruption?Significant economic costs:

1 The annual total of bribes paid worldwide is US $1 trillion(WB 2004)

2 Widespread corruption can cause the growth rate of a countryto be 0.5 to 1.0 percentage points lower than that of a similarcountry with little corruption (WB 2012)

3 15% of all companies in industrialised countries have to paybribes to win or retain business. In Asia this figure is 40%. Incountries of the former USSR, this figure is 60% (UN WorldDevpt Report 1997).


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Why do we care about corruption?Significant social costs:

1 ~50% of national funds allocated to Ghanaian clinics andhospitals do not reach them (TI 2006 Global CorruptionReport).

2 Bribes accounted for 24% of household earnings in Mexico in2005 (Transparencia Mexicana 2006)

3 Over half of survey respondents paid bribes to access servicesin East Africa, even when these services were funded by theirtaxes (TI Kenya 2009)

4 From 1991-1995, only 13% of Ugandan education funds(excluding teacher salaries) reached schools (WB 2004).

5 86% of Nicaraguan parents surveyed paid mandatory’contributions’ to teachers (CIET Int’l).


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Why do we care about corruption?Models suggesting we may not need to care:

Bardhan (1995): As long as the allocation of production andconsumption is unaffected, the allocation of the producersurplus (bureaucrat vs. treasury) will not decrease efficiency ofthe transaction in the short-run.Lien (1986): If bribing is competitive, then the lowest-cost firmwill be able to provide the largest bribe. Thus, the allocationof production will still be efficient.Theory of the second-best approach: With excessive taxes andoverly restrictive regulation,“speed money” or “greasepayments” may also improve economic efficiency.


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Why do we care about corruption?Models suggesting we do need to care:

Banerjee (1993)’s “red tape” model: If bureaucrats are able toaffect the level of corruption, they will increase the level of redtape.


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Why do we care about corruption?Q: Why do these models give different results?

A: Different assumptions: Exogenous vs. endogenouscorruption

1 Exogenous corruption: The level of corruption assumed in the

model is pre-determined independently of anyone or anything

in the model.

2 Endogenous corruption: The level of corruption can be

affected by actors in the model–here, the bureaucrat.


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Why do we care about corruption?Q: Why do these models have different assumptions?

A: As in solving systems of equations, decreasing the numberof “unknowns” (endogenous variables) makes a model easier tosolve, though such a model may (or may not) be less realistic.


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Experimental Design

Definitions:Experiment: Systematic gathering of information with thepurpose of testing a hypothesis.Experimental design: The systematic way in which suchinformation is gathered.


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Experimental Design

Experiments manipulate a variable of interest, and try to determinehow that manipulation influences other variables.

E.g. Muralidharan and Sundararaman (2010):300 schools in Andhra Pradesh, India are divided into 3groups: 100 schools provide teacher bonus pay based onclassroom performance (i.e. individual incentives); 100 schoolsprovide teacher bonus pay based on school performance (i.e.group incentives); and 100 schools comprise the control group(no teacher bonus pay).(PL: In the long run, student performance was significantlybetter in schools that gave teachers individual incentives,rather than group or no incentives.)


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Types of Experiments

Controlled experiment: Compares a sample receiving“treatment” to an otherwise identical sample not receivingtreatment. The researcher actively intervenes in dataproduction here.

E.g. Muralidharan and Sundararaman (2010)E.g.2. Miguel and Kremer (2004): Researchers randomlyassigned schools to the treatment or control group, and gavedeworming pills to treatment schools. (PL: School absenteeismin treatment schools decreased by 25%.)


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Controlled experiment:2 types:

1 Lab experiments: Controlled experiments conducted in a

laboratory setting.

• E.g. Ultimatum games: In the XLab at Berkeley, researchers

give Players 1 and 2 $X dollars. P1 decides how many dollars

out of $X he will offer to P2. P2 accepts or rejects P1’s

proposal. If P2 rejects, neither player receives anything.

2 Field experiments: Controlled experiments conducted in the

field.

• E.g. Gine (2007): In the Phllippines, researchers randomly

chose half of 169 microfinance groups to switch from group

lending to individual lending. (PL: No increase in default

occurred. Perhaps group lending was successful because it

weeded out weak borrowers.)


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Controlled experiment:Q: Why does randomization allow us to posit that thetreatment caused an outcome?

Let’s think about why causation is hard to establish. What arethe possible drivers of a positive correlation between variablesA and B (Corr(A,B) >))?

1 A causes B (causality)

2 B causes A (endogeneity/ reverse causality)

3 A causes B and B causes A simultaneously (simultaneity)

4 Some other factor C causes both A and B

(omitted/confounding variables or selection bias)

5 A and B are purely coincidentally correlated.


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A: Even though we are not able to collect data on all thefactors that affect A (the treatment) and B (the outcome),randomization allows us to randomly distribute these factorsbetween the treatment and control group, giving us theexpectation that the only difference between the treatmentand control groups is the treatment (and its effects).


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In Ordinary Least Squares (OLS) regression, we wouldtranslate Ferraz (2008) to the following simplified regression:

Yi = a + bTi + dXi + ei

whereYi : Whether the mayor of municipality i was reelected

(the outcome)Ti : Whether the mayor’s municipality i was audited

(the treatment)


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In Ordinary Least Squares (OLS) regression, we wouldtranslate Ferraz (2008) to the following simplified regression:


whereXi : Municipality characteristics that affect Yi , but not

through Ti (the “controls,” e.g. urban vs. rural)ei : Random error term for municipality i


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If we have data on Xi , then using conditional expectations, wecan calculate the expected outcome for different cases:1. E (Yi |Ti = 1,Xi = 1) = a + bE (Ti |Ti = 1,Xi = 1)

+dE (Xi |Ti = 1,Xi = 1) + E (ei |Ti = 1,Xi = 1)


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If we have data on Xi , then using conditional expectations, wecan calculate the expected outcome for different cases:1. E (Yi |Ti = 1,Xi = 1) = a + b + d , since

E (ei |Ti = 1,Xi = 1) = 0 by assn


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If we have data on Xi , then using conditional expectations, wecan calculate the expected outcome for different cases:1. E (Yi |Ti = 1,Xi = 1) = a + b + d . Similarly,


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If we have data on Xi , then using conditional expectations, wecan calculate the expected outcome for different cases:1. E (Yi |Ti = 1,Xi = 1) = a + b + d . Similarly,2. E (Yi |Ti = 1,Xi = 0) = a + b


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If we have data on Xi , then using conditional expectations, wecan calculate the expected outcome for different cases:1. E (Yi |Ti = 1,Xi = 1) = a + b + d . Similarly,2. E (Yi |Ti = 1,Xi = 0) = a + b3. E (Yi |Ti = 0,Xi = 1) = a + d


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If we have data on Xi , then using conditional expectations, wecan calculate the expected outcome for different cases:1. E (Yi |Ti = 1,Xi = 1) = a + b + d . Similarly,2. E (Yi |Ti = 1,Xi = 0) = a + b3. E (Yi |Ti = 0,Xi = 1) = a + d4. E (Yi |Ti = 0,Xi = 0) = a


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To find out the effect of being randomly assigned an audit, youwould subtract (4) - (2) and/or (3) - (1).


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However, if we do NOT have data on Xi , the municipality“control” variables, then using conditional expectations, theexpected outcome difference b/t treatment and control groupsis:E (Yi |Ti = 1)− E (Yi |Ti = 0) =

[a + b + dE (Xi |Ti = 1) + E (ei |Ti = 1)]−[a + 0 + dE (Xi |Ti = 0) + E (ei |Ti = 0)]


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However, if we do NOT have data on Xi , the municipality“control” variables, then using conditional expectations, theexpected outcome difference b/t treatment and control groups:E (Yi |Ti = 1)− E (Yi |Ti = 0) =

[a + b + dE (Xi |Ti = 1) + E (ei |Ti = 1)]−[a + 0 + dE (Xi |Ti = 0) + E (ei |Ti = 0)]

where the d terms are the average values of the controlvariable for audited and unaudited muncipalities, respectively.


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b + d [E (Xi |Ti = 1)− E (Xi |Ti = 0)]


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b + d [E (Xi |Ti = 1)− E (Xi |Ti = 0)]where the first term, b, is the true effect of Ti on Yi , and thedifference b/t the d terms is the “omitted variable/selectionbias” term.


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A: Randomization causes the control variables to be equal inexpectation between the treatment and control groups,leading the omitted variable/selection bias term to equal 0,so that the coefficient b on the treatment variable gives usthe true effect of treatment on the outcome variable Yi :E(Yi |Ti = 1)− E(Yi |Ti = 0) =

b + d [E(Xi |Ti = 1)− E(Xi |Ti = 0)]b + 0


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Natural experiment: Exploits the constancy of some variablein the model (e.g. location, individual ID, etc.) to isolateassociations between the non-constant variables in the model.The researcher does not actively manipulate the variables here.

E.g. Bundervoet, Verwimp, Akresh (2010): Because districtboundaries in Burundi did not change, researchers couldcompare health outcomes before and after war in affected andunaffected regions.

(Note: The “affected” and “unaffected” regions in Burundiwere somewhat akin to “treatment” and “control” groups, butbecause they were not randomly assigned, we do not use thetreatment/control terminology.)


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Natural experiment:Q: Why does having constancy of the unit of observation overtime (or space, or some other dimension) allow us to arguethat an event (or program, or law, etc.) caused an outcome?

Use the same model as in the controlled experiment:Yi = a + bTi + dXi + ei

E (Yi |Ti = 1)− E (Yi |Ti = 0) =b + d [E (Xi |Ti = 1)− E (Xi |Ti = 0)]true effect + “omitted variable/selection” effect


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Use the same model as in the controlled experiment:Yi = a + bTi + dXi + ei

However, assume that we cannot randomize who gets audited,but that some municipalities get audited and others do not.As long as the audit selection criteria is not correlatedwith the outcome measure, we can argue that the effectof the audits on reelection is causal, not correlative.


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Extend the data to two time periods (or locations, etc.):Yit = a + bTit + dXit + eit

t = 0: Pre-program/pre-event period (“baseline”)t = 1: Post-program/post-event period (“follow-up”)


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As with the controlled experiment, in period t = 1, theestimated treatment effect is:

E (Yi1|Ti1 = 1)− E (Yi1|Ti1 = 0) =b + d [E (Xi1|Ti1 = 1)− E (Xi1|Ti1 = 0)]true effect + “omitted variable/selection” effect fort=1


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However, in period t = 0 (before the event), the differencebetween the two groups is:

E (Yi1|Ti1 = 1)− E (Yi1|Ti1 = 0) == d [E (Xi0|Ti1 = 1)–E (Xi0|Ti1 = 0)]= The omitted variable bias term for t = 0


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We can take the difference between the treatment effect int = 1 and the difference between the two groups in t = 0,which we just computed, to eliminate the omitted variable biasand deliver the true effect:= E (Yi1|Ti1 = 1)–E (Yi1|Ti1 = 0)

–E (Yi0|Ti1 = 1)–E (Yi0|Ti1 = 0)= b + d [E (Xi1|Ti1 = 1)–E (Xi1|Ti1 = 0)]

–d [E (Xi0|Ti1 = 1)–E (Xi0|Ti1 = 0)]= True effect of Ti on Yi + {(OVB in t=1) - (OVB in t=0)}


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A: As long as OVB is constant before and after treatment, thedifference-in-difference estimator will give us the true effect:= E (Yi1|Ti1 = 1)–E (Yi1|Ti1 = 0)

–E (Yi0|Ti1 = 1)–E (Yi0|Ti1 = 0)= b + d [E (Xi1|Ti1 = 1)–E (Xi1|Ti1 = 0)]

–d [E (Xi0|Ti1 = 1)–E (Xi0|Ti1 = 0)]= True effect of Ti on Yi + {(OVB in t=1) - (OVB in t=0)}= Difference-in-difference estimator


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5-Minute Break

5-minute break


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Introduction to STATA


Internet exercise


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Outline

1 Announcements

2 Research Question Assignment

3 Introduction to Corruption

4 Experimental Design

5 How to Find and Use DataIntroduction to STATA


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Open Miguel and Kremer (2004) data. (Modified to fit in SmallSTATA.)


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Pass Around Attendance Sheet

Pass around attendance sheet


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A good research question for this class is:Well-definedWell-motivatedInnovativeFalsifiable, in theory and practiceEconomic


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There are many reasons relationships between two variablescan be endogenous. Two ways to fix this, and posit a causalrelationship, are via:

Controlled (lab or field) experimentsNatural experiments


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Next week is Guest Lecture 1: Fred Finan, “The Economics ofCorruption”=> Finish reading these papers!

Revise research question assignment for April 3.


background for fred finan’s march 13 lecture: “the ...webfac/eichengreen/e191... · 3/6/2012...

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