Navigating Performance Complexity in Public Service Delivery: Do Frontline Workers Distinguish Between Inputs, Outputs, and Outcomes?

Last registered on April 08, 2024
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Pre-Trial

Trial Information

General Information

Title
Navigating Performance Complexity in Public Service Delivery: Do Frontline Workers Distinguish Between Inputs, Outputs, and Outcomes?
RCT ID
AEARCTR-0007418
Initial registration date
April 06, 2021

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
April 06, 2021, 6:20 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
April 08, 2024, 7:48 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country
Denmark
Region

Primary Investigator

Name
Paw Hansen
Affiliation
University of Copenhagen, Department of Political Science
Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status
Completed
Start date
2021-04-06
End date
2021-04-27
Keywords
Education, Other, Welfare
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
We provide descriptions of fictive high schools to a large sample of Danish high-school teachers and ask them to rate the outcome attainment of the presented schools.

Within each description, we randomly assign a number of different pieces information about outcomes: socioeconomic mobility, wellbeing, and transition to higher education. In addition, we provide teachers with information about schools' "production attributes", i.e. the inputs and outputs preceding outcomes.

For organizational inputs, we use two sets of wordings related to student backgrounds—one tied to student origin (e.g., “Has a high proportion of students with a non-Western background.”) and the other linked to parents’ educational background (e.g., “Has a high proportion of parents without higher education.”).

For organizational outputs, we use two distinct sets of wordings—one focusing on past efforts (e.g., “Has implemented many initiatives to improve quality in the past year.”) and the other on potential future efforts (e.g., “Significant focus on developing the teaching in the future.”).
External Link(s)

Registration Citation

Citation
Hansen, Paw. 2024. "Navigating Performance Complexity in Public Service Delivery: Do Frontline Workers Distinguish Between Inputs, Outputs, and Outcomes? ." AEA RCT Registry. April 08. https://doi.org/10.1257/rct.7418-1.2
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
We provide descriptions of fictive high schools to a large sample of Danish high-school teachers and ask them to rate the outcome attainment of the presented schools.

Within each description, we randomly assign a number of different pieces information about outcomes: socioeconomic mobility, wellbeing, and transition to higher education. In addition, we provide teachers with information about schools' "production attributes", i.e. the inputs and outputs preceding outcomes.

For organizational inputs, we use two sets of wordings related to student backgrounds—one tied to student origin (e.g., “Has a high proportion of students with a non-Western background.”) and the other linked to parents’ educational background (e.g., “Has a high proportion of parents without higher education.”).

For organizational outputs, we use two distinct sets of wordings—one focusing on past efforts (e.g., “Has implemented many initiatives to improve quality in the past year.”) and the other on potential future efforts (e.g., “Significant focus on developing the teaching in the future.”).
Intervention Start Date
2021-04-06
Intervention End Date
2021-04-27

Primary Outcomes

Primary Outcomes (end points)
We have two primary outcomes: 1) The probability that schools with certain characteristics are chosen as highest performing, and 2) the rating respondents assign to each school.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We provide descriptions of fictive high schools to a large sample of Danish high-school teachers and ask them to rate the outcome attainment of the presented schools.

Within each description, we randomly assign a number of different pieces information about outcomes: socioeconomic mobility, wellbeing, and transition to higher education. In addition, we provide teachers with information about schools' "production attributes", i.e. the inputs and outputs preceding outcomes.

For organizational inputs, we use two sets of wordings related to student backgrounds—one tied to student origin (e.g., “Has a high proportion of students with a non-Western background.”) and the other linked to parents’ educational background (e.g., “Has a high proportion of parents without higher education.”).

For organizational outputs, we use two distinct sets of wordings—one focusing on past efforts (e.g., “Has implemented many initiatives to improve quality in the past year.”) and the other on potential future efforts (e.g., “Significant focus on developing the teaching in the future.”).

We have two primary outcomes: 1) The probability that schools with certain characteristics are chosen as highest performing, and 2) the rating respondents assign to each school.
Experimental Design Details
Randomization Method
Randomization is done by computer.
Randomization Unit
Approx. 9,000 high school teachers
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Approx. 9,000 high school teachers
Sample size: planned number of observations
Approx. 2,500 high school teachers x 3 matched pairs = 7,500 choices and 15,000 ratings.
Sample size (or number of clusters) by treatment arms
Approx. 1/3 in each arm = 833 high school teachers in each arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
Yes
Intervention Completion Date
April 27, 2021, 12:00 +00:00
Data Collection Complete
Yes
Data Collection Completion Date
April 27, 2021, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
1796
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
Final Sample Size (or Number of Clusters) by Treatment Arms
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials