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Motivating Bureaucrats: Autonomy vs Performance Pay for Public Procurement in Pakistan
Last registered on February 10, 2017

Pre-Trial

Trial Information
General Information
Title
Motivating Bureaucrats: Autonomy vs Performance Pay for Public Procurement in Pakistan
RCT ID
AEARCTR-0000610
Initial registration date
January 24, 2015
Last updated
February 10, 2017 6:44 PM EST
Location(s)
Region
Primary Investigator
Affiliation
Columbia University
Other Primary Investigator(s)
PI Affiliation
London School of Economics
PI Affiliation
London School of Economics
PI Affiliation
Columbia University
Additional Trial Information
Status
On going
Start date
2014-07-01
End date
2017-04-30
Secondary IDs
Abstract
This project works directly with the government of Punjab, Pakistan to improve the effectiveness of public procurement. We develop two novel methods to measure procurement effectiveness and leakages based on detailed, item-level characteristics data and random field audits of purchases. We then implement a field experiment to study how these are affected by two policy reforms targeting bureaucrats’ extrinsic and intrinsic motivations to achieve value for money in procurement. The first treatment awards financial bonuses for good procurement performance. The second treatment empowers bureaucrats to exercise discretion in procurement to achieve better value for money by giving them a larger cash-in-hand budget, making pre-audit procedures simpler and more predictable, and releasing their procurement budget earlier. A third treatment combines the two to identify complementarities.
External Link(s)
Registration Citation
Citation
Bandiera, Oriana et al. 2017. "Motivating Bureaucrats: Autonomy vs Performance Pay for Public Procurement in Pakistan." AEA RCT Registry. February 10. https://doi.org/10.1257/rct.610-4.0.
Former Citation
Bandiera, Oriana et al. 2017. "Motivating Bureaucrats: Autonomy vs Performance Pay for Public Procurement in Pakistan." AEA RCT Registry. February 10. http://www.socialscienceregistry.org/trials/610/history/13916.
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Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2015-07-01
Intervention End Date
2016-06-30
Primary Outcomes
Primary Outcomes (end points)
Our main outcome variables are twofold.
1-Quality-adjusted prices paid
2-Quality, quantity and price discrepancies between products procured as self-reported and as revealed through audit.
Primary Outcomes (explanation)
Quality adjusted prices will be constructed from extremely detailed data on the goods being purchased, with a focus on “generic” goods where quality can be determined by using a relatively small number of attributes of a good. With this detailed data, we can adjust prices paid for the quality of the goods purchased to uncover differences in the prices different procurement officers pay for the same good. We are collecting this data through an online portal, the Punjab Online Procurement System (POPS), designed and maintained by us, into which procurement officers enter the full set of details of every purchase of a generic good they make.
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Drawing and Disbursing Officers (the government officers legally responsible for public procurement) are randomly allocated to 1 of 5 treatments, stratifying by district and by line department (Health, Higher Education, Communication & Works, Agriculture). Treatment arm 1: Financial bonuses for good procurement performance; data collected through POPS. Treatment arm 2: Increased discretion for bureaucrats; data collected through POPS. Treatment arm 3: Combined treatments 1 & 2. Treatment arm 4: Control group; data collected through POPS. Treatment arm 5: Pure control group; data not collected through POPS, but through field visits.
Experimental Design Details
Randomization Method
Computer program
Randomization Unit
Drawing and Disbursing Officer (the bureaucrat with the legal responsibility and authority for conducting public procurement)
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
1074 Drawing and Disbursing Officers (DDOs)
Sample size: planned number of observations
Approx 1074*50=53,700
Sample size (or number of clusters) by treatment arms
202 in Incentive payment arm
203 in Discretion arm
228 in Combined treatments arm
320 in Control group
121 in Pure Control group
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Using Bloom's (2005) expression for the MDE, our estimate of the intracluster correlation from baseline data of 0.067, and an estimate of an average of 75 observations per bureaucrat, we estimate that a sample of 572 bureaucrats, or 42,882 observations, will allow us to detect a 5% effect with 95% probability.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Research Ethics Committee, London School of Economics
IRB Approval Date
2015-01-22
IRB Approval Number
n/a
Analysis Plan
Analysis Plan Documents
Pre-Analysis Plan

MD5: 5e219b9afcc1426a455567ef8977f080

SHA1: d90690eab1b70ff7b93eb7e638409760c658c849

Uploaded At: February 10, 2017

Survey Instrument

MD5: 6a36e41e164d95873088a7f71c729895

SHA1: 40c2d6baa4290280b14bda4dba2856d923a02fce

Uploaded At: February 10, 2017

Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is data collection complete?
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)
REPORTS & OTHER MATERIALS