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Accountability and Information 1
Final Project Essentials: Fixed Effects
Logistics
Assignments
- Today
- Dunning et al. (2019)
- Data Assignment 1 (due by midnight)
- Tuesday (3/28)
- Foreign aid readings
- Github tutorial (maybe)
- April 9
- Final project proposal
Agenda
- Dunning et al. (2019)
- FINAL questions about Data Assignment 1
- Final Project Essentials: Fixed Effects
Dunning et al. (2019)
Barriers hindering accumulation
What is accumulation?
- Building knowledge across studies
What makes it difficult?
- Limited replication
- Heterogeneity of design and measurement
- Publication bias
Barriers hindering accumulation
Facilitating accumulation
What solutions are discussed by the authors?
- Pre-registration
- Harmonizing theory, measurement, and estimation
- Publication of null results
Facilitating accumulation
Models of political accountability
Accountability requires that voters:
- Observe credible signal of performance
- Attribution
- Benchmarking
- Update their beliefs
- Have credible alternatives
Models of political accountability
What does the literature say?
- Theory is mixed
- Partisan and sectarian attachments are strong
- Voters may be reluctant to update their beliefs
- Experimental results are mixed
- Demobilization
- Limited recall
- Ephemeral effects
Research Design
Intervention
- Information on political performance
- Legislative behavior
- Spending irregularities
- Budget allocation
- Candidate experience
Research Design
Accountability requires that voters:
- Observe credible signal of performance
- Attribution
- Benchmarking
- Update their beliefs
- Good news: \(Q > P \mid 1(P = Q, Q > med(Q) )\)
- Have credible alternatives
Ecological Validity
- How is information disseminated?
- What is the real world activity being tested?
Describing Information
- good news group is above the line
- correlation is positive but weak
Problems hindering accumulation
Research Design
Core hypotheses:
- Good news increases voter support for incumbents
- Bad news decreases voter support for incumbents
- Effect of information will increase with gap between Q and P
- Strongest for nonpartisan and non-coethnic voters
Findings
Why the null results?
- Source credibility?
- No…
- Lack of retention
- Lack of updating on politician performance
- Lack of updating about politician quality
- Lack of coordination (private treatment)
Statistical Power
Null results were not “foregone conclusion”?
- 80% power
- Change the vote of 5/100 voters
- Change turnout of 4/100 voters
Findings
Findings
Findings
Policy Implications
- Much of the work funded by donors is probably not having an impact on accountability
- Targeting of information provision needs to be rethought
- Public dissemination and coordination
Data Assignment Questions
Final Project Essentials
Unit Fixed Effects
What is a unit fixed effect?
- Simple linear models make comparisons both across and within units (when there are multiple measures)
- Fixed effects ‘absorb’ variation across units; now coefficients estimate only the variation within units
- This ‘controls’ for time-invariant heterogeneity
What are they used for?
- Reducing concerns about confounding
- Difference-in-differences
Unit Fixed Effects
Normal model
\[ Y_it = \alpha + \beta_1 X_{it} + \epsilon_{it} \] Unit fixed Effects model
\[ Y_it = \gamma_i + \beta_1 X_{it} + \epsilon_{it} \]
Example: Unit FEs with one binary predictor
- \(\alpha\): \(E[Y_i]\) when \(X_{i1} = 0\)
- \(\gamma_i\): \(E[Y_i]\) for every unit when \(X_{i1} = 0\)
Unit Fixed Effects
Unit fixed Effects model
\[ Y_it = \gamma_i + \beta_1 X_{it} + \epsilon_{it} \]
**How does the interpretation of \(\beta_1\) change?
- \(\gamma_i\): \(E[Y_i]\) when \(X_{it} = 0\)
- \(\beta_1\) Change in \(E[Y_i]\) when \(X_{it} = 1\)
- Each \(\gamma_i\) will absorb everything that doesn’t vary over time in that unit
Unit Fixed Effects
