Title  Political Information and Electoral Choices: A Premetaanalysis Plan 

Post date  03/08/2015 
C1 Background and Explanation of Rationale 
Civil society groups and social scientists commonly emphasize the need for high quality public information on the performance of politicians as an informed electorate is at the heart of liberal theories of democratic practice (Fearon, 1999). The extent to which performance information in effect make a difference in institutionally weak environments is, however, an open question. Specifically when does such information lead to the rewarding of good performance candidates at the polls and when are voting decisions dominated by nonperformance criteria such as ethnic ties and clientelistic relations? This project covers seven separate studies (in Benin, Mexico, Brazil, India, Burkina Faso and two in Uganda) that address the above questions using interventions that provide subjects with information about key actions of incumbent political representatives. A subset of studies employ a factorial design in which information is provided through a public mechanism. Details of individual studies are registered separately. 
C2 What are the hypotheses to be tested? 
Primary:
Secondary outcomes:
Mediators:
Substitution effects:
Context heterogeneous effects:
Design Heterogeneous Effects:

C3 How will these hypotheses be tested? * 
1. Regression controlling for a predefined set of controls (see detailed pre analysis plan) with clustering at the constituency level or equivalent and weighting by inverse propensity weights when relevant. Let \(Q_j\) denote the quality of candidate j and \(P_{ij}\) denote the prior of voter i regarding j. Define \(L^+\) as the set of treatment subjects for whom \(Q_j>P_{ij}\) or \(Q_j=P_{ij}\) and \(Q_j \geq \hat{Q}_{j}\). These are subjects that receive good news  either the information provided exceeds priors or the information confirms positive priors. Let \(L^\) denote the remaining subjects. Let \(N^+_{ij}\) denote the difference \(Q_j  P_{ij}\), defined for all subjects in \(L^+\) and standardized by the mean {and standard deviation} of \(Q_j  P_{ij}\) in the \(L^+\) group in each country (or relevant locality). \(N^+_{ij}\) is therefore a standardized measure of "good news'' with mean 0 {and standard deviation of 1}. Let \(N^_{ij}\) denote the same quantity but for all subjects receiving bad news. Then the two core estimating equations are: \(E(Y_{ij}  i \in L^+) = \beta_0+ \beta_1 N^+_{ij} + \beta_2 T_i + \beta_3 T_i N^+_{ij} + \sum_{j=1}^k(\nu_k Z_i^k + \psi_k Z_i^kT_i) \label{eq.main1a}\) \(E(Y_{ij}  i \in L^ ) = \gamma_0+ \gamma_1 N^_{ij} + \gamma_2 T_i + \gamma_3 T_i N^_{ij} + \sum_{j=1}^k(\nu_k Z_i^k + \psi_k Z_i^kT_i) \label{eq.main1b}\) where \(Z_1, Z_2,...,Z_k\) are prespecified covariates, also standardized to have a 0 mean. 2. Primary analysis pools data; secondary analysis uses Bayesian hierarchical regression to estimate average treatment effects for each site as well as population parameters. 
C4 Country  
C5 Scale (# of Units)  not provided by authors 
C6 Was a power analysis conducted prior to data collection?  For individual studies 
C7 Has this research received Insitutional Review Board (IRB) or ethics committee approval?  Individual studies will receive approval 
C8 IRB Number  not provided by authors 
C9 Date of IRB Approval  not provided by authors 
C10 Will the intervention be implemented by the researcher or a third party?  Mixed; generally by researchers in partnership with third parties 
C11 Did any of the research team receive remuneration from the implementing agency for taking part in this research?  No 
C12 If relevant, is there an advance agreement with the implementation group that all results can be published?  Yes 
C13 JEL Classification(s)  not provided by authors 