Outsourcing Property Tax Re-Assessments in the Greater Chennai Corporation

Last registered on September 19, 2022

Pre-Trial

Trial Information

General Information

Title
Outsourcing Property Tax Re-Assessments in the Greater Chennai Corporation
RCT ID
AEARCTR-0009965
Initial registration date
September 12, 2022

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
September 19, 2022, 4:06 PM EDT

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

Locations

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Primary Investigator

Affiliation
MIT

Other Primary Investigator(s)

PI Affiliation
Harvard University
PI Affiliation
MIT
PI Affiliation
Harvard University

Additional Trial Information

Status
In development
Start date
2022-07-15
End date
2027-08-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Strengthening the enforcement and collection of property tax has become a key priority for the Government of Tamil Nadu, India. This study aims to test whether outsourcing property assessments to third-party firms is a cost-effective way to increase property tax revenues for the Greater Chennai Corporation (GCC) and to determine the optimal contract for incentivizing third-party firms to complete assessments correctly and efficiently.

The GCC (municipal corporation for the capital city of Tamil Nadu) implemented a census of all properties between 2018-2022 and identified 250,000 properties that are under-valued or un-assessed and need to be re-assessed. We will implement a randomized evaluation comparing two different contracts for outsourcing the re-assessment of these properties to third-party firms: a fixed fee contract and a hybrid contract (fixed fee plus performance bonus). Additionally, we will compare the performance of third-party firms to tax assessors currently employed by the GCC, to measure the performance of outsourcing relative to in-sourcing. The interventions proposed in this study have the potential to raise property tax revenues for the GCC without imposing additional burdens on existing tax assessors and collectors.
External Link(s)

Registration Citation

Citation
Duflo, Esther et al. 2022. "Outsourcing Property Tax Re-Assessments in the Greater Chennai Corporation." AEA RCT Registry. September 19. https://doi.org/10.1257/rct.9965-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Impact evaluation of outsourcing property tax assessments to third-party firms under two types of contracts: a fixed fee contract and a hybrid contract (fixed fee plus performance bonus). The study will compare property tax assessments completed by third-party firms under these two contracts relative to each other and to assessments completed by tax assessors employed by the Greater Chennai Corporation (municipal corporation for the capital city of Tamil Nadu, India).
Intervention Start Date
2022-11-15
Intervention End Date
2023-12-31

Primary Outcomes

Primary Outcomes (end points)
The primary outcomes for the study are as follows: property tax demand generated, property tax revenue collected, accuracy of assessed property values, taxpayer satisfaction. We will also study firm characteristics for all firms that submit bids for each contract, and will measure firm outcomes (including hiring practices, training practices etc.) for the firms that are eventually awarded each contract.
Primary Outcomes (explanation)
Outcome data on property tax demand and property tax revenues will come from administrative data maintained by the GCC. Property tax demand is defined as taxes owed based on the assessed property value. Data on accuracy of assessed property values will come from independent property surveys of a randomly selected subset of properties: surveyors will record all the characteristics used to calculate assessed values to generate an independent measure of the property value. Data on taxpayer satisfaction will come from administrative records of complaints filed by taxpayers as well as independent surveys of randomly selected taxpayers implemented by the research team. We will obtain data on all firms that submit bids for each contract from the bid forms. We will obtain additional data on firm characteristics and other outcomes (including hiring practices, training practices etc.) for firms that were awarded each contract through firm surveys implemented once all the re-asessments have been completed.

Secondary Outcomes

Secondary Outcomes (end points)
We plan to test for heterogeneous treatment effects based on the following property characteristics:
1. Residential v.s. Commercial v.s. Mixed-use properties
2. Owner-occupied v.s. Tenanted properties
3. Property size

Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The study will implement a randomized evaluation of two contracts for outsourcing re-assessments to third-party firms: fixed fee and hybrid fee (fixed fee plus a performance bonus, defined as a percentage of the additional tax demand generated as a result of the re-assessment). Outsourced re-assessments will also be compared to re-assessments completed by tax assessors currently employed by the GCC. Each of the 200 wards in the Greater Chennai Corporation will be randomly assigned to one of the following arms:

[T1] Outsourcing re-assessments to firms compensated with a fixed fee
[T2] Outsourcing re-assessments to firms compensated based on a hybrid contract
[C] Re-assessment by existing tax assessors (In-sourcing)

Based on a property census implemented between 2018-2022, the GCC has identified a list of under-valued and unassessed properties. During the “Study Phase”, a randomly selected subset of these properties in each ward will be re-assessed by the relevant party as described above. We will then compare outcomes across the three arms to identify the “best performing” contract. In “Scale-up Phase One”, a randomly selected subset of properties in the control wards will be re-assessed by the third-party firms. Firms will be compensated according to the terms of the best-performing contract regardless of which contract they were originally hired under. By comparing the performance of firms that selected into the fixed and hybrid contracts while keeping the contract incentives constant, we will be able to measure selection effects. Finally, in “Scale-up Phase Two”, all remaining properties identified for re-assessment will be assessed by the third-party firms compensated as per the terms of the best-performing contract.
Experimental Design Details
Not available
Randomization Method
Computer random number generator
Randomization Unit
Wards and properties.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
200 wards. The GCC consists of three regions (North, Central, South), further divided into 15 zones. Stratifying by Zone x Region, each of the 200 wards will be randomly assigned to one of the following arms: Outsourcing with Fixed fee, Outsourcing with Hybrid fee, Re-assessment by government assessors (Control wards for the "Study Phase").
Sample size: planned number of observations
250,000 properties that have been identified for re-assessment by the GCC
Sample size (or number of clusters) by treatment arms
[T1] "Study Phase" Outsourcing with fixed fee – ~66 wards
[T2] "Study Phase" Outsourcing with hybrid fee – ~66 wards
[C] "Study Phase" Re-assessment by GCC assessors – ~68 wards
For "Scale-up Phase One" and "Scale-up Phase Two", half of these wards (~34 wards) will be assigned to the firm hired under the flat fee contract and the remaining will be assigned to the firm hired under the hybrid contract.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Massachusetts Institute of Technology COUHES Committee on the Use of Humans as Experimental Subjects
IRB Approval Date
2022-08-12
IRB Approval Number
2207000710
IRB Name
Institute for Financial Management and Research Human Subjects Committee
IRB Approval Date
2022-08-17
IRB Approval Number
IRB00007107