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Field
Abstract
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Before
We study how public officials respond to information on their own outputs and citizen policy preferences across 74 local municipalities in Estonia. Using a citizen survey and Quadratic Voting for Survey Research (QVSR) methods, we capture the policy preferences of citizens in each municipality. We then randomize public officials into one of four treatment arms based on their municipality of work. The treatment arms vary the information provided to municipal public officials on local municipal performance (service-delivery and public-performance indicators) and citizens' policy preferences. Our first treatment arm strengthens awareness and use of a new local government dashboard. This treatment provides local public officials with a detailed diagnostic assessment of their municipality's performance, highlighting how they are doing and where they might improve, with a focus on outputs (service-delivery indicators) only. The objective is to see whether simply updating public officials with information on outputs is sufficient to change their behavior. Our second treatment arm promotes the dashboard among local citizens via Facebook adverts in order to increase citizens' awareness and usage of the dashboard. We also contact a representative sample of citizens in each treatment local government and introduce them to the dashboard and its functionality. Our third treatment arm combines the first two arms, allowing us to assess the existence of complementarities. Our fourth treatment arm is a control group, in which public officials are not provided training nor information on citizens' policy preferences. We assess the effects of the treatment arms on measures of citizen policy preferences; measures of local municipality efficiency (backlogs, service wait times, fiscal waste); population welfare outcomes (health indicators and education indicators); voter turnout; election outcomes; and budgetary outcomes. Together, these interventions will provide information into how information on publicly available information on public-sector performance and citizen preferences can impact provider behaviors and future performance.
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After
We study how public officials respond to information on their own outputs and citizen policy preferences across 74 local municipalities in Estonia. Using a citizen survey and Quadratic Voting for Survey Research (QVSR) methods, we capture the policy preferences of citizens in each municipality. We then randomize public officials into one of four treatment arms based on their municipality of work. The treatment arms vary the information provided to municipal public officials on local municipal performance (service-delivery and public-performance indicators) and citizens' policy preferences. Our first treatment arm strengthens awareness and use of a new local government dashboard (19 municipalities). This treatment provides local public officials with a detailed diagnostic assessment of their municipality's performance, highlighting how they are doing and where they might improve, with a focus on outputs (service-delivery indicators) only. The objective is to see whether simply updating public officials with information on outputs is sufficient to change their behavior. Our second treatment arm promotes the dashboard among local citizens via Facebook adverts in order to increase citizens' awareness and usage of the dashboard (17 municipalities). We also contact a representative sample of citizens in each treatment local government and introduce them to the dashboard and its functionality. Our third treatment arm combines the first two arms, allowing us to assess the existence of complementarities (18 municipalities). Our fourth treatment arm is a control group, in which public officials are not provided training nor information on citizens' policy preferences (20 municipalities). We assess the effects of the treatment arms on measures of citizen policy preferences; measures of local municipality efficiency (backlogs, service wait times, fiscal waste); population welfare outcomes (health indicators and education indicators); voter turnout; election outcomes; and budgetary outcomes. Together, these interventions will provide information into how information on publicly available information on public-sector performance and citizen preferences can impact provider behaviors and future performance.
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Field
Last Published
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Before
March 17, 2021 10:31 AM
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After
March 22, 2021 11:25 AM
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Field
Intervention (Public)
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Before
T1: providing a detailed diagnostic assessment of municipality performance to public officials, highlighting how they are doing and where they might improve, with a focus on outputs (indicators of local municipality performance) only. The objective of this treatment arm to see whether simply updating public officials with information on outputs is sufficient to change their behavior, actions, and performance.
T2: promoting the dashboard among local citizens via Facebook adverts and introducing a representative survey of citizens to the dashboard and its functionality. The objective of this treatment arm is to substantially increase awareness and usage of the dashboard among local citizens.
T3: T1 and T2 Combined
T4: Neither (control group)
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After
T1 (19 municipalities): providing a detailed diagnostic assessment of municipality performance to public officials, highlighting how they are doing and where they might improve, with a focus on outputs (indicators of local municipality performance) only. The objective of this treatment arm to see whether simply updating public officials with information on outputs is sufficient to change their behavior, actions, and performance.
T2 (17 municipalities): promoting the dashboard among local citizens via Facebook adverts and introducing a representative survey of citizens to the dashboard and its functionality. The objective of this treatment arm is to substantially increase awareness and usage of the dashboard among local citizens.
T3 (18 municipalities): T1 and T2 Combined
T4 (20 municipalities): Neither (control group)
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Field
Sample size (or number of clusters) by treatment arms
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Before
Municipalities will be randomized into the three treatment arms with equal probability (one-quarter): 18 in T1, 18 in T2, 18 in T3, 20 in T4.
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After
Municipalities will be randomized into the three treatment arms with equal probability (one-quarter): 19 in T1, 17 in T2, 18 in T3, 20 in T4.
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