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Public Attribution of Responsibilities in Decentralized Autocracies
Last registered on April 08, 2020


Trial Information
General Information
Public Attribution of Responsibilities in Decentralized Autocracies
Initial registration date
April 07, 2020
Last updated
April 08, 2020 11:54 AM EDT
Primary Investigator
Columbia University
Other Primary Investigator(s)
Additional Trial Information
Start date
End date
Secondary IDs
Correct attribution of responsibility for economic outcomes is one of the key assumptions underlying citizens’ ability to hold politicians accountable: Correct attribution allows citizens to use punishment and reward strategies to discipline politicians and to prevent them from introducing policies that contravene the preferences of the electoral majority (Ferejohn, 1986; Fiorina, 1981). Even in contexts with formal, legal mechanisms of accountability and a putatively free media, citizens often fail to correctly punish and/or reward politicians for economic performance. Among the reasons for the absence of correct blame attribution—even in its most propitious circumstances—scholars highlight scarce or biased information (Alcañiz and Hellwig, 2011), perceptual biases of voters (Bullock, 2011), and diffuse structures of responsibilities in multilevel governments (Malhotra and Kuo, 2008; Reuter and Beazer, 2016).

This study examines whether and how contents of media reporting affect citizens’ perception of public policy outcomes, responsibility for those outcomes and evaluation of the politicians in non-democratic context in 4 Siberian regions of Russia: Novosibirsk, Irkutsk, Kemerovo and Krasnoyarsk. I analyze data from an original survey experiment in which respondents are asked to watch video excerpts from "Rossia 1" news reports that aim to inform citizens about responsibility for recent policy outcomes. Respondents are then asked to evaluate the outcomes of various economic policies as well as performance of different levels of government. The design allows me to learn whether popularity of the government in countries without strong democratic traditions and vibrant media can be predicated on strategic and potentially biased framing of the news. One unique feature of this study is its on allocation of responsibility between multiple tiers of the government. This feature allows me to test ability of citizens to correctly attribute responsibility in multi-level government structures — important but empirically understudied aspect of political accountability in comparative context.
External Link(s)
Registration Citation
Syunyaev, Georgiy. 2020. "Public Attribution of Responsibilities in Decentralized Autocracies." AEA RCT Registry. April 08. https://doi.org/10.1257/rct.5693-1.0.
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Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
1. Beliefs about quality of road infrastructure and quality of natural disaster prevention policy
2. Responsibility attribution between three main levels of government in Russia
3. Beliefs about strength of bias of main TV news outlets in Russia
4. Evaluation of competence of three main levels of government in Russia
Primary Outcomes (explanation)
Detailed information on construction can be found in the accompanying Pre-Analysis Plan, Section 4
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The survey experiment will be conducted online on a sample of 4000 respondents residing in one of the four regions of Russia, Novosibirsk, Irkutsk and Kemerovo oblasts and Krasnoyarskiy Krai. In each region I aim to enroll around 1000 respondents in each region. The choice of regions for the study is primarily driven by (a) the overlap with the location of the initial study conducted last year in Novosibirsk region, (b) the fact that all for regions in this study were affected by large scale natural forest fires that happened in Siberia this summer, and (c) two out of four regions have at least some level of government (either municipal in Novosibirsk, or regional in Irkutsk) controlled by the Communist Party member, that in local elections, especially in Siberia, openly opposes rulling party, United Russia.

The treatment media reports will include one video excerpt per treatment per region. In each region the media reports are chosen from the previously aired news coverage of the local branch of Rossia-1 TV channel owned directly by the federal government. The main news broadcast on Rossia-1 TV channel, Vesti, airs at least 3 times every day and includes national economic, political and cultural news as well as coverage of local events specific to the region, where the viewer resides. In each of the four regions in the study Rossia-1 was in top-10 most cited local media outlets in 2018 according to the media research company Medialogia. In addition, Vesti consistently ranks among the top viewed TV broadcasts in Russia according to Medialogia reports.

For the intervention I plan to use news reports on three topics in each region: (D) Responsibility attribution for preventing and combating natural forest fires (as a part of overall natural disaster relief policy); (R) Responsibility attribution for road construction and repairs (as a part of overall transport infrastructure development); and (P) Coverage of the birthday of prominent Russian actor (as a placebo news report unrelated to domestic policy or government performance). For the forest fires coverage I will use the coverage from Vesti broadcast on visit of Prime Minister of Russia, Dmitriy Medvedev, to one of the study regions (Krasnoyarsk), where he states that primary responsibility for forest fires is on regional and municipal governments rather than on national government. For road construction/repairs, the selected intervention video again comes from _Vesti_ broadcast and covers the general assembly of all heads of regions in Russia where Prime Minister, Dmitriy Medvedev, again states that primary responsibility for poor quality of roads is on regional and municipal governments rather than on national government. Finally, the placebo video used in the study also comes from Vesti broadcast but unlike D and R videos will cover event unrelated to policy or government performance: Birthday of prominent Russian actor. All reports used in the study share the same length (around 1 minute) and quality (come from the same news broadcast) to assure symmetricity of presentation of information and only vary the contents.

The proposed intervention aims to induce shock to beliefs about allocation of responsibility for specific policy (infrastructure or natural disaster relief) between different levels of government without strongly affecting beliefs about policy performance or media bias. While this is a matter of empirical verification, several substantive factors make it plausible that proposed treatment video reports might primarily affect beliefs about responsibility allocation:

1. In both treatment video reports the statement about responsibility allocation is delivered by Prime Minister of Russia, as opposed to narrator, and are strongly highlighted as federal government position;

2. Both policies covered in treatment videos are highly visible with forest fires being one of the most discussed issues of the year in Russian media. This in turn implies that respondents in the study are likely to have strong prior beliefs about the policy outcomes;

3. Both treatment video reports feature blame-shifting by the federal government, and mention problems of respective policies in general. This, combined with (2) above, implies that respondents are unlikely to find the information on policy performance contained in the treatment reports novel;

4. The media outlet, Rossia-1 used in the study is one of the most popular TV channels in Russia, and majority of respondent in the study are expected to have prior experience and fairly stable beliefs about the bias of the news coverage by Rossia-1 channel prior to viewership of the treatment video reports.

Exact wording and example screenshots from the news reports used in ths study can be found in the accompanying Pre-Analysis Plan, Appendix.
Experimental Design Details
Randomization Method
Simple random assignment using PHP randomization code
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
4000 individuals residing in 4 regions of Russia
Sample size: planned number of observations
4000 individuals residing in 4 regions of Russia
Sample size (or number of clusters) by treatment arms
Each of three experimental groups will consist of roughly 1350 individuals residing in 4 regions of Russia
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB Name
Columbia University MS IRB
IRB Approval Date
IRB Approval Number
Analysis Plan
Analysis Plan Documents
PAP for Public Attribution of Responsibilities in Decentralized Autocracies

MD5: b644ed90ea40d5b3749fb5682b865cec

SHA1: 49769383117aafaa286f917904e9fbfead3663ec

Uploaded At: April 07, 2020

Post Trial Information
Study Withdrawal
Is the intervention completed?
Intervention Completion Date
January 31, 2020, 12:00 AM +00:00
Is data collection complete?
Data Collection Completion Date
January 31, 2020, 12:00 AM +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
4205 individuals in 4 regions of Russia
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
4205 individuals in 4 regions of Russia
Final Sample Size (or Number of Clusters) by Treatment Arms
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)