Communicating Complex Incentives

Last registered on December 17, 2020


Trial Information

General Information

Communicating Complex Incentives
Initial registration date
December 06, 2019

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
December 06, 2019, 10:21 AM EST

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

Last updated
December 17, 2020, 8:12 AM EST

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


Primary Investigator

RWTH Aachen University and University of Essex

Other Primary Investigator(s)

PI Affiliation
LMU Munich
PI Affiliation
LMU Munich
PI Affiliation
University of Groningen

Additional Trial Information

In development
Start date
End date
Secondary IDs
Incentive schemes are extensively used in firms and often contain various dimensions for a multi-tasking job. While the bonus structure might become more complex in design for an increasingly multifaceted task, recent evidence suggests that complexity leads to inefficient choices. It is important to understand how these complex bonuses can be communicated to ensure that they are effective and do not entail adverse effects. In a setting with a highly complex incentive system, we study two different ways of communicating such incentives and analyze the effects on effort choices and performance.
External Link(s)

Registration Citation

Czura, Kristina et al. 2020. "Communicating Complex Incentives." AEA RCT Registry. December 17.
Experimental Details


Our interventions are implemented in the HR management app that our partner organization is using. We implement different information pages within this app to better communicate the performance of loan officers given their incentive schemes. For example, we provide information on the exact bonus attainment, how it is calculated and on which dimensions loan officers have to improve their performance to reach the next bonus level.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
We will analyze changes in performance in the incentivized dimensions: number of clients, increase in number of clients, number of loans disbursed, loan collection/ repayment performance and cross-selling products, as well as bonus attainment.
Primary Outcomes (explanation)
Primary outcomes are measures of performance on which the bonus payment is based. We will use data on these five dimensions in the following way: 1) binary variable for bonus attainment, 2) performance index based on all five dimensions, 3) each dimension as an outcome variable.

Secondary Outcomes

Secondary Outcomes (end points)
We will use the following secondary outcomes to assess the channels through which our intervention can affect performance: understanding of the incentive structure, effort, management practices, work satisfaction and work related stress.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We partner with a microfinance institution in Northern India to conduct a randomized controlled trial to examine how complex incentives can be communicated effectively in a field setting. This setting has three main advantages: first, it provides an environment with complex tasks and corresponding incentives. Second, the focus on a microfinance institution can also help understand how services can be provided more effectively, potentially increasing the overall effectiveness of microfinance. Third, we target our intervention at a rather low educated population who, in the face of automation, will also increasingly be confronted with complex tasks.
Experimental Design Details
In our partner organization, about 2700 loan officers work in 425 different branches. Loan officers face various tasks such as recruiting new clients, managing and helping existing clients, disbursing new loans, collecting regular instalments, and cross-selling utility products.

The wage consists of a monthly base salary and monthly high-powered bonus payments subject to performance on various dimensions. First of all, the bonus structure depends on branch characteristics (which types of loans are issued, how many clients the branch is handling) as well as loan officers’ caseload (in terms of the number of clients). The total bonus payment consists of the sum of five different dimensions that are measured and incentivized independently of each other: Bonus on net client increase, Bonus on collection rate, Bonus on utility products (phone, TV, etc.), Bonus on home improvement loan, and Bonus on sanitation loan.

To attain the first two bonus payments, a specific threshold must be met. The remaining bonus payments are piece rates. Currently, a large fraction of loan officer (approx. 70%) does not achieve any bonus payments. Most frequently, loan officers attain the bonus on net client increase, collection rate and utility products.

Experimental design:
We have three treatment groups, 1) control, 2) breakdown and 3) target, that receive different information on their bonus calculations in the HR management app that our partner organization is using. In control, loan officers receive the same information as they did before. Their total bonus amount is communicated to them by the end of the month in the payment slip which they can access via the HR app. Different from our treatment, this total bonus is only a lump-sum number. Therefore, loan officers in Control do not get information on i) the different categories of the bonus and ii) the composition of their bonus payment. However, they can access information regarding their current performance in the HR app independently. More specifically, this performance information contains the total number of clients, total outstanding, collection rate and the number of utility product sold. For this, they need to be able to calculate their bonus payments on their own which implies knowing all information that feeds into the bonus calculation, the different bonus thresholds and where to access information on their performance in the app. To capture any effects from administering surveys on bonus payments and incentives, we will have a "pure control" group that receives the same information as the control group, but for which we will not collect any survey data. In our second treatment group, the breakdown treatment, we provide loan officers the breakdown of their total bonus in the five above-mentioned categories in the internal HR app that is already in use. The information displays both the loan officers’ last month’s performance and how much bonus they received in each category in the last month. In our third treatment group, the target treatment, we provide loan officers with the exact bonus threshold for each category in addition to the breakdown information. More specifically, loan officers receive information that highlights the gap/excess between their performance and the required threshold for each bonus category for the last month. It also gives the information on how to precisely calculate bonus payments in all dimenstions. Similar to the breakdown treatment, all information will be communicated to loan officers via the internal HR app. We will assess the effects of our communication intervention based on survey data and administrative data. Survey data: First, a baseline survey will be administered before the start of the intervention. Second, a knowledge test of the current incentive scheme and general questions regarding financial literacy and cognitive capacity will be implemented. To assess potential learning from this test, we randomize individuals in a detailed and a less detailed version that arguably limits the scope for learning. Third, high-frequency work-stress questions will be administered on a weekly basis via the HR app during the intervention. Lastly, endline data will be collected after six months. Administrative data records on performance will complemented the survey data.
Randomization Method
Randomization was done in office by a computer
Randomization Unit
The unit of randomization is the branch.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
188 branches
Sample size: planned number of observations
887 loan officers
Sample size (or number of clusters) by treatment arms
1) control group: 49 branches
2) breakdown: 50 branches
3) target: 49 branches
4) pure control group: 37 branches
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
see pre-analysis plan
Supporting Documents and Materials

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Institutional Review Boards (IRBs)

IRB Name
Ethics Commission, Department of Economics, University of Munich
IRB Approval Date
IRB Approval Number


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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

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Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials