Representation and Accountability II

Carolina Torreblanca

University of Pennsylvania

Global Development: Intermediate Topics in Politics, Policy, and Data

PSCI 3200 - Spring 2026

Agenda

  • Ferraz and Finan (2011)
  • Chong et al. (2015)
  • Statistical Power

Back to Electoral Accountability

Recap: Mandate and Accountability

  • Mandate view: elections select good policy (prospective)
  • Accountability view: elections hold governments accountable (retrospective)
  • In both views, the vote is the key mechanism
  • Last week: accountability beyond elections (Björkman and Svensson)
  • Today: back to electoral accountability

Two Sides of Accountability

For electoral accountability to work:

  • Politicians must have incentives to behave well (fear of losing office)
  • Voters must have information to evaluate politicians

Today we test both sides

Corruption in the Global South

  • The global cost of corruption is at least $2.6 trillion, or 5% of global GDP (UN, 2018)
  • Corruption costs developing countries an estimated $1.26 trillion per year
  • Enough to lift 1.4 billion people above the poverty line for six years (WEF, 2019)

Can Accountability Curtail Corruption?

Today’s question: Can electoral accountability reduce corruption in the Global South?

  • We test two sides of the same coin:
    • Politician incentives: does the threat of losing office discipline politicians? (Ferraz and Finan, 2011)
    • Voter information: does giving voters information about corruption change behavior? (Chong et al., 2015)

Assumptions

For electoral accountability to discipline corruption, we need:

  1. Voters dislike corruption
  2. Voters can observe corruption (or its consequences)
  3. Voters will punish corrupt politicians at the ballot box
  4. Politicians anticipate this punishment and restrain themselves

Ferraz and Finan (2011)

1. The State of the World

  • Brazil is one of the most decentralized countries in the world
  • Local governments receive ~$35 billion/year from the federal government
  • Mayors have wide discretion over how to spend these resources
  • Common corruption schemes: diversion of funds, phantom firms, fake receipts, rigged procurement

2. The Research Question

  • Do reelection incentives reduce corruption among local politicians?
  • In 1997, Brazil introduced a constitutional amendment allowing mayors to run for a second consecutive term
  • First-term mayors can be reelected; second-term mayors cannot
  • This creates variation in reelection incentives

3. The Hypotheses

  1. Mayors who face reelection incentives (first-term) will be less corrupt than those who do not (second-term)
  2. This effect will be stronger where voters have more access to information (e.g., local media)
  3. This effect will be stronger where judicial punishment is less likely (so electoral accountability is the main constraint)

4. The Mechanism

“A corrupt mayor who faces the possibility of reelection can exploit this information asymmetry to increase reelection chances by refraining from rent-seeking and behaving as a noncorrupt mayor”

  • Corrupt politicians face a trade-off: steal now or behave and win reelection
  • If reelection is valuable enough, they restrain themselves
  • Second-term mayors face no such trade-off → they are “lame ducks”

5. Data and Measurement

How do you measure corruption?

  • In 2003, Brazil’s Controladoria Geral da União (CGU) launched an anti-corruption program
  • Randomly selected municipalities are audited for their use of federal funds
  • 10-15 CGU auditors spend ~1 week inspecting accounts, documents, and public works
  • Reports are public: posted online and sent to prosecutors

Main measure: Share of audited resources found to involve corruption

5. Data and Measurement

Three types of corruption identified in audits:

  1. Fraud in procurement: phantom firms, rigged bids, kickbacks
  2. Diversion of funds: money simply “disappears” from accounts
  3. Overinvoicing: goods and services purchased at inflated prices

6. Research Design

Key feature: the CGU audit lottery randomly selects which municipalities get audited . . .

Comparison: first-term mayors (can be reelected) vs second-term mayors (cannot)

\[r_i = \beta I_i + \mathbf{X}_i \varphi + \mathbf{Z}_i \gamma + \varepsilon_i\]

  • \(r_i\): corruption level in municipality \(i\)
  • \(I_i\): first-term mayor

6. Research Design

Is this an experiment?

  • No! Whether a mayor is in their first or second term is not randomly assigned
  • Potential confounds: ability, experience, selection
  • So how can we argue the results are causal?
  • THINK! mandate view says elections select good politicians
  • Second-term mayors have already won reelection
  • Should they be better or worse than first-term mayors?

7. Findings

7. Findings

  • First-term mayors misappropriate 27% fewer resources than second-term mayors
  • Lame-duck mayors steal approximately R$150,000 (~US$55,000) more
  • Robust to RDD, matching, alternative corruption measures

7. Findings

  • If experience, ability, etc, were confounding the result, what sign should the results have?
  • Is this an over or an underestimation?

8. Implications

  • Electoral accountability can work!
  • This is the politician incentive side of the story

Chong et al. (2015)

The Other Side of the Coin

  • F&F showed that reelection incentives discipline politicians
  • But the accountability view also assumes voters use information to punish
  • What if we directly give voters information about corruption and hold incentives fixed?
  • What should we expect to happen?

1. The State of the World

  • Mexico: elected mayors manage federal infrastructure funds (FISM)
  • Single-term limits: mayors cannot be reelected (unlike Brazil)
  • Mexico’s federal auditor (ASF) produces public audit reports
  • But most voters have never seen them: only ~10% knew FISM existed

2. The Research Question

  • Does providing voters with information about incumbent corruption change electoral outcomes?

“Retrospective voting models assume that offering more information to voters about their incumbents’ performance strengthens electoral accountability”

3. The Hypotheses

Two competing predictions:

“Inspire the fight”

  • Corruption information → voters punish incumbent party → support challengers → turnout increases

“Quash the hope”

  • Corruption information → voters lose faith in all politicians → turnout decreases, challenger support decreases

4. The Mechanism

Why might information backfire?

  • Voters may already suspect corruption; information confirms rather than surprises
  • If corruption is perceived as systemic, voters conclude no candidate can credibly withdraw from it
  • Voting may be seen as pointless

5. Data and Measurement

The intervention: door-to-door distribution of flyers, one week before 2009 municipal elections

5. Data and Measurement

Outcomes (from official electoral data, precinct-level):

  • Turnout: total votes / registered voters
  • Incumbent party votes: votes for incumbent’s party / registered voters
  • Challenger votes: votes for any challenger / registered voters

6. Research Design

  • Field experiment: 12 municipalities, 2,360 voting precincts
  • Flyers distributed door-to-door one week before elections
  • Three arms:
    • Treatment: flyer with % of funds spent corruptly
    • Placebo: flyer with % of funds spent (no corruption info)
    • Control: no flyer
  • Placebo is the reference group. Why?

6. Research Design

\[Y = \beta_0 + \beta_1 \, CorruptionInfo + \beta_2 \, NoInfo + M_j + \epsilon\]

  • Omitted category: placebo
  • \(\beta_1\): effect of receiving corruption info relative to placebo
  • \(\beta_2\): effect of receiving no flyer relative to placebo
  • \(M_j\): municipality fixed effects

6. Research Design

Does the effect depend on how much corruption there is?

\[\begin{aligned} Y = \beta_0 &+ \beta_1 \, Corr \times Low + \beta_2 \, Corr \times Med \\ &+ \beta_3 \, Corr \times High + \beta_4 \, NoInfo + M_j + \epsilon \end{aligned}\]

  • \(Corr\): received corruption info flyer (= 1 or 0)
  • \(Low\), \(Med\), \(High\): municipality corruption level (0–33%, 33–66%, 66–100%)
  • Does the effect of treatment depend on how much corrupiton is reported?

7. Findings

7. Findings

  • Corruption information decreases turnout by 2.5%
  • Decreases incumbent party votes by 0.43pp
  • Decreases challenger votes by 0.86pp
  • Effects are larger where corruption is higher (>66%: turnout drops 7pp)

Information here “quashed the hope”.

8. Implications

  • “Information is clearly necessary to improve accountability, [but] corruption information is not sufficient because voters may respond to it by withdrawing from the political process”

Taking Stock

Putting It Together

F&F (2011) Chong et al. (2015)
Country Brazil Mexico
Tests Politician incentives Voter information
Term limits 2 terms 1 term
Finding Reelection incentives reduce corruption 27% Corruption info decreases turnout and challenger votes

Electoral accountability requires both incentives and information — but information alone may not be enough

Statistical Power

A Closer Look at the Findings

Chong et al. find that for medium levels of corruption (33–66%), the effect of corruption information on turnout is:

\[\hat{\beta} = -0.30, \quad SE = 0.44\]

No stars! Recall: *, **, *** indicate \(p < 0.10\), \(p < 0.05\), \(p < 0.01\)

This coefficient is not statistically different from zero

So… is the effect zero?

A Closer Look at the Findings


Can we conclude the effect is zero?

  • We failed to reject the null hypothesis
  • But failing to reject \(\neq\) accepting the null!
  • Maybe the true effect is -0.30 and we just couldn’t detect it
  • This is where statistical power comes in

Statistical Power

Recall the interpretation of p-values:

  • The probability of observing a test statistic at least as extreme as the one you observed if the true parameter value is zero
  • Or, the probability of rejecting the null hypothesis when the null was true
  • This is called a “Type I” error: a false positive
  • We also have “Type II” errors: a false negative

Statistical Power

Power is the probability of correctly accepting the alternative hypothesis

  • The probability of a true positive

    • Equals (1 - probability of type II error)

    • The common threshold in the discipline is 80% power

You can check out the EGAP power calculator to understand better

Why Does Power Matter Here?

  • Chong et al. had 2,340 precincts powered to detect ~2.5pp effects overall
  • But splitting by corruption level → smaller subgroups → less power
  • A non-significant sub-group result does not mean the effect is zero! Means we dont know if it was zero or not

Discussion

Which is “worse” in research?

  • Type I error (false positive): concluding there is an effect when there isn’t one
  • Type II error (false negative): missing a real effect because your study couldn’t detect it