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Abstract
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Despite extensive research on the impact of various bonus structures, little is known about why firms choose different structures and the extent to which they understand the implications of choosing different contracts in practice. This project aims to determine the optimal incentives for motivating workers and to assess whether the incentives that managers and workers believe to be effective are actually optimal in practice.
We explore four key research questions:
(1) Which incentive schemes do managers and workers perceive to work best and why?
(2) What is the extent of disagreement between managers and workers about their preferred contracts?
(3) What incentives work best in practice?
(4) How well do managers' and workers' predictions align with actual outcomes? What leads individuals to be able to better predict what contracts will work well?
The study will consist of two main parts: a baseline survey and a randomized controlled trial (RCT). The baseline survey will determine managers' and workers' preferred contracts and the extent of their agreement. The RCT will test the impact of different contract structures on worker output and the sophistication of managers and workers in understanding these impacts. The intervention will involve randomizing different contract schemes -- derived based on managers and workers preferences -- across retail mobile money agents in Ghana and evaluating their performance effects using detailed administrative transaction data.
We implement an at-scale nationwide randomized experiment in partnership with the largest mobile money service provider in Ghana that (i) provides mobile money financial services nationwide with 95 percent market share spanning both rural and urban areas, (ii) has a multi-layered management hierarchy, where managers at different levels are in charge of different tasks such as commercialization, sales, and incentives design across the country, and (iii) employs incentive contracts to motivate workers, who in this context are retail agents acting as representatives of the service provider to directly handle mobile money transactions with customers. Their operations are akin to tellers in retail banking branches more commonly seen in developed countries (Annan JPE Forthcoming). These retail agents are compensated according to a formula, which effectively stipulates the relationship between output and rewards for the agents. The service provider unilaterally determines the contract. Thus, agents take the contract as given and exert optimal efforts.
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After
Despite extensive research on the impact of various bonus structures, little is known about why firms choose different structures and the extent to which they understand the implications of choosing different contracts in practice. This project aims to determine the optimal incentives for motivating workers and to assess whether the incentives that managers and workers believe to be effective are actually optimal in practice.
We explore four key research questions:
(1) Which incentive schemes do managers and workers perceive to work best and why?
(2) What is the extent of disagreement between managers and workers about their preferred contracts?
(3) What incentives work best in practice?
(4) How well do managers’ (and workers’) predictions align with actual outcomes? What leads individuals to be able to better predict what contracts will work well?
The study will consist of two main parts: a baseline survey and a randomized controlled trial (RCT). The baseline survey will determine managers’ and workers’ preferred contracts and the extent of their agreement. The RCT will test the impact of different contract structures on worker output and the sophistication of managers and workers in understanding these impacts. The intervention will involve randomizing different contract schemes -- derived based on managers preferences -- across retail mobile money agents in Ghana and evaluating their performance effects using detailed administrative transaction data.
We implement an at-scale nationwide randomized experiment in partnership with the largest mobile money service provider in Ghana that (i) provides mobile money financial services nationwide with 95 percent market share spanning both rural and urban areas, (ii) has a multi-layered management hierarchy, where managers at different levels are in charge of different tasks such as commercialization, sales, and incentives design across the country, and (iii) employs incentive contracts to motivate workers, who in this context are retail agents acting as representatives of the service provider to directly handle mobile money transactions with customers. Their operations are akin to tellers in retail banking branches more commonly seen in developed countries (Annan JPE). These retail agents are compensated according to a formula, which effectively stipulates the relationship between output and rewards for the agents. The service provider unilaterally determines the contract. Thus, agents take the contract as given and exert optimal efforts.
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Trial End Date
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June 30, 2025
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December 31, 2025
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Last Published
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September 26, 2024 12:30 PM
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April 26, 2025 01:33 PM
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Intervention (Public)
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Intervention:
(i) Conduct baseline surveys with managers and agents to elicit their most preferred incentive scheme across a set of bonus schemes and rank all incentive schemes in terms of revenue maximization.
(ii) Randomize the implementation of different contract schemes, derived based on managers’ and agents’ preferences: status quo vs. manager-favorite vs. agent/worker-favorite.
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After
Intervention:
(1) Conduct baseline surveys with managers and agents to elicit their most preferred incentive scheme across a set of bonus schemes and rank all incentive schemes in terms of revenue maximization.
(2) Randomize the implementation of different contract schemes, implemented strictly according to managers’ rankings – i.e. how managers perceive the various contracts to work, from worst to best.
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Intervention Start Date
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December 01, 2024
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May 03, 2025
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Intervention End Date
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March 31, 2025
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August 02, 2025
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Primary Outcomes (End Points)
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##Administrative firm data to estimate treatment effects:
*worker output: revenues
*types of transactions (withdrawals or deposits or others), number of transactions and customers, and average transaction amounts
*worker inputs: the number of working days per week, opening hours per working day (inferred from daily transaction records)
##Endline surveys:
*hours and days of operation, liquidity, worker hiring, advertising or marketing campaigns
*service quality: agent presence, failed transactions, customer service, misconduct, and overcharging
*non-mobile money lines of business, coordination between agents
*qualitative discussions about:
(i) possible mechanisms,
(ii) what managers/workers will consider optimal contract in words (this may/may not include the 5 options we presented or not),
(iii) why they chose/rank the contracts the way they did.
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##Administrative firm data to estimate treatment effects:
*worker output: (i) total performance (i.e. value of transaction), (ii) MTN revenues = profits, (iii) Agent revenues (total payment to agents, under status quo contract and assigned contract), and (iv) MTN cost ratios.
*noncompliance (invalid transactions), types of transactions (withdrawals or deposits or others), number of transactions and customers, and average transaction amounts.
*worker inputs: the number of working days per week, opening hours per working day (inferred from daily transaction records).
##Endline surveys:
*hours and days of operation, liquidity, worker hiring, advertising or marketing campaigns
*service quality outcomes, relevant for consumer protection: agent presence, failed transactions, customer service, misconduct or overcharging, and physical & verbal transparency.
*non-mobile money lines of business, coordination between agents
*qualitative discussions about:
(i) possible mechanisms,
(ii) what managers/workers will consider optimal contract in words (this may/may not include the 5 options we presented or not),
(iii) why they chose/rank the contracts the way they did.
*psychological wellbeing: (i) mental health (Kessler Psychological Distress Scale (K10)), (ii) subjective wellbeing (rating aspects of own life/others’ lives), (iii) cognition (recalling words), violence (gender relations), and (iv) preferences (risk and time)
## Process Data
Fraction of agent who agree to participate in assigned contract
Fraction of, and associated administrative data, of agents who opt out of the assigned contract during the experiment
## Separate Work: Climate Data
*Weather data: (i) realizations (temperature, precipitation), forecasts (temperature, precipitation)
*Measures of adaptation to unexpected weather
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Experimental Design (Public)
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Before
Research questions:
(1) Which incentive schemes do managers and workers perceive to work best and why?
(2) What is the extent of disagreement between managers and workers about their preferred contracts?
(3) What incentives work best in practice?
(4) How well do managers' and workers' predictions align with actual outcomes? What leads individuals to be able to better predict what contracts will work well?
Our study is composed of two main parts. The baseline survey will provide answers to the first two research questions: managers' and agents' preferred contracts and the extent to which they agree with each other. The RCT will provide answers to the last two research questions: the actual impact of various contracts and whether managers and agents are sophisticated about these impacts.
To implement both the survey and RCT feasibly, we will focus on a small set of bonus schemes. These schemes represent key structures emphasized in the literature and are widely used in practice. Additionally, we have confirmed with the service provider that these schemes are logistically implementable. Our selected schemes include:
• Individual Performance-Based Schemes:
1. Status Quo Linear Contract: Workers receive bonuses for every unit of output.
2. Threshold Contract: Workers receive a flat payment upon reaching a predetermined threshold.
3. Pseudo-Franchising Scheme: Akin to two-Part Tariff, workers receive a boost in the per-unit commission in exchange for an upfront fee.
4. Multi-Tasking Contract: Both core and allied services are included in the bonus calculation. [core: cash in and outs; allied: airtime purchase, customer registration]
• Relative Comparison Schemes:
5. Tournament Scheme: Workers compete in tournaments where top performers in each locality receive an additional bonus.
• Control Scheme:
6. No Bonus Contract: This contract, strictly dominated by any other contract, is included to check whether respondents pay attention and understand the options presented.
#### Perceptions of incentive contracts [RQ1-2]
To answer the first two research questions, we will first conduct baseline surveys with managers and agents to elicit their most preferred incentive scheme and rank all incentive schemes in terms of revenue maximization.
#### Performance under incentive contracts [RQ3]
For the third research question of estimating the causal effects of various contract schemes, we will conduct a large-scale nationwide randomized controlled trial (RCT) using the same study sample. The RCT will involve randomizing the implementation of different contract schemes, derived based on managers’ and agents’ preferences.
The randomization will occur at the local TSC level, meaning every manager and agent within a TSC receives the same treatment. This level of randomization is chosen because TSCs manage AADs and ensure the appropriate deployment of incentives in the field. TSC managers also influence higher-level decisions, bridging the information gap between lower-level and senior managers.
Specifically, one-third of the TSCs (n=~22) and communities (n=133) will be assigned to the control group, maintaining the status quo of a linear contract. Another third (n=~22 TSCs and n=133 communities) will be assigned to the "manager-favorite" contract, implementing the scheme preferred by a majority of managers. The final third (n=~22 TSCs and n=133 communities) will be assigned to the "agent-favorite" contract, implementing the scheme preferred by a majority of agents.
For fair comparisons, the contracts will be designed to ensure budget neutrality, such that the average total bonus payment is constant across communities.
#### Connecting perceptions and performance under incentive contracts [RQ4]
By linking the administrative firm data to survey responses, we can answer the fourth research question: whether managers and workers accurately assess the effects of different contract structures.
###Heterogeneity and nature of selection:
(1) Test whether managers can predict which contracts will work best for agents within versus outside their TSC.
(2) Assess heterogeneity based on (i) managers’ and agents’ reported preferences at baseline and (ii) features of the environment agents operate in to characterize selection.
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After
Research questions:
(1) Which incentive schemes do managers and workers perceive to work best and why?
(2) What is the extent of disagreement between managers and workers about their preferred contracts?
(3) What incentives work best in practice?
(4) How well do managers' and workers' predictions align with actual outcomes? What leads individuals to be able to better predict what contracts will work well?
Our study is composed of two main parts. The baseline survey will provide answers to the first two research questions: managers' and agents' preferred contracts and the extent to which they agree with each other. The RCT will provide answers to the last two research questions: the actual impact of various contracts and whether managers are sophisticated about these impacts.
To implement both the survey and RCT feasibly, we will focus on a small set of bonus schemes. These schemes represent key structures emphasized in the literature and are widely used in practice. Additionally, we have confirmed with the service provider that these schemes are logistically implementable (we have verified this through a technical pilot). Our selected schemes include:
• Individual Performance-Based Schemes:
(1) Simple Linear Contract (Status Quo): Workers receive bonuses for every unit of output.
(2) Threshold Contract: Workers receive a flat payment upon reaching a predetermined threshold.
(3) Pure Franchising Scheme: Akin to two-Part Tariff, workers receive a boost in the per-unit commission in exchange for an upfront fee.
• Relative Comparison Schemes:
(4) Tournament Scheme: Workers compete in tournaments where top performers in each locality receive an additional bonus.
• Control Scheme:
(5) Flat Bonus/week Contract: This contract is included (i) to check whether respondents pay attention and understand the options presented and (ii) to evaluate the implications of the provider MTN switching to non-performance-based compensation schemes.
#### Perceptions of incentive contracts [RQ1-2]
To answer the first two research questions, we will first conduct baseline surveys with managers and agents to elicit their most preferred incentive scheme and rank all incentive schemes in terms of revenue maximization.
#### Performance under incentive contracts [RQ3]
For the third research question of estimating the causal effects of various contract schemes, we will conduct a large-scale nationwide randomized controlled trial (RCT) using the same study sample. The RCT will involve randomizing the implementation of different contract schemes, implemented strictly according to managers’ rankings or preferences – i.e. how managers perceive the various contracts to work, from worst to best.
The randomization will occur at the local market (community) level, meaning every agent (and manager assigned) within a community receives the same treatment.
Specifically, the communities will be randomly assigned to the various contracts in proportions that reflect the managerial rankings. For example, if 30% of the time managers rank the Threshold Scheme to maximize revenues, then 30% of communities will be assigned to the Threshold. Similarly, if 10% of the time managers rank the Flat Bonus/week Scheme to maximize revenues, then 10% of communities will be assigned to the Flat Bonus, etc. However, for statistical power, in practice we only preserve the managerial rankings and then unequally assign communities according to 25.0% (for the best or top ranked contract), 22.5%, 20.0%, 17.5%, and 15.0% (for the worst or bottom ranked contract).
For fair comparisons, the contracts will be designed to ensure expenditure neutrality for the firm, such that the average total bonus payment is constant across schemes.
#### Connecting perceptions and performance under incentive contracts [RQ4]
By linking the administrative firm data to survey responses, we can answer the fourth research question: whether managers and workers accurately assess the effects of different contract structures.
###Analysis #1: Predicting Optimal Contracts: Managers’ vs Agents’ Preferences and Rankings?
We will combine `descriptive analysis’ and `machine learning’ techniques to examine views about (i) the value of monetary and non-monetary incentives, (ii) adequacy of the current agent compensation scheme, (iii) their ‘most preferred’ compensation scheme if they had to choose, (iv) the compensation schemes that would ‘increase revenues = profit for MTN’ if they had to predict. We will use this to assess whether managers preferred scheme ‘is always’ the profit-maximizing to MTN. This exercise will enable us to identify the most important factors (demographic and environmental) associated (i) with managers’ or workers’ preferences, and (ii) with the disagreement between managers and workers about their preferred contracts, while controlling for market and firm-level characteristics.
###Analysis #2: Which Contracts are `Best’? How Do they Compare with Managerial Predictions?
In assessing which contracts are best, and in connecting this to managerial (and agent) predictions, we will focus on two conditions: IR (Individual Rationality) and IC (Incentive Compatibility).
• IR: We will look at (i) uptake vs decline to participate rates across contracts (extensive margin), (ii) stayers vs droppers and how long agents stayed across contracts conditional on taking up the scheme (intensive margin), and (iii) the reasons for declining/dropping off.
• IC: We examine (i) worker output, (ii) worker inputs, and (iii) worker noncompliance outcomes overall and for only agents who stayed throughout the intervention’s period, while accounting for differential attrition.
• IR + IC jointly: We will look at (i) worker output, (ii) worker inputs, and (iii) worker noncompliance outcomes overall and for only agents stayed throughout the intervention’s period, while accounting for attrition.
###Analysis #3: Heterogeneity (Mediation Analysis) and Nature of Selection?
(1) Assess heterogeneity of IC outcomes based on:
*Market conditions: (i) level of past economic activity and output/payoffs both at worker level and community level; (ii) variance in past economic activity and output/payoffs (at worker and community level); (iii) rural/urban; (iv) number of agents in a community and within the agent firm; (v) whether the retail business is operated by owner/non-owner.
*Manager characteristics: (i) managerial level, (ii) experience.
*Agent characteristics: (i) gender, (ii) experience, (iii) household income.
(2) Assess selection based on differences in uptake/staying in assigned contracts (IR outcomes) and treatment effects separately for:
*Managers (if they were assigned their preference vs not),
*Agents (if they assigned their preference vs not), and
*Both mangers and agents (if both assigned their preference vs not).
Since the number of experimental communities relative to the total number of communities covered by the “typical” manager is small, we expect selection on effects on the manager side to minimal.
We will analyze whether uptake and staying are related to the market conditions and agent characteristics in (i)
(3) Test whether managers can predict which contracts will work best for agents within vs outside their territories, and see if it relates to manager characteristics
(1)-(3) help understand what leads managers and workers to be able to better predict what contracts will work well, while revealing potential correlation in preferences and information gaps between agents, lower-level and senior managers.
###Analysis #4: How Far Away are We from the IR Constraint?
As a follow-up, we plan to implement a willingness-to-accept (WTA) exercise that will elicit, from the agents that declined to participate or dropped off from their assigned contracts during the intervention, (i) why they declined/dropped and (ii) how much they need to be paid to be moved back to their assigned schemes.
###Analysis #5: Changes in Fraud of Agents and Consumer Protection Outcomes?
Look at whether there is differences in noncompliance (invalid transactions), service quality outcomes, misconduct or overcharging based on assigned contract (and demographics/market condition).
###Analysis #6: Changes in Psychological Wellbeing?
Evaluate whether there are differences in mental health or distress, subjective wellbeing, cognition, (domestic) violence, and risk/time preferences based on assigned contract (and demographics/market condition).
###Analysis #7 (Separate Work): Which Contracts Constrain/Promote Adaptation to Climate Shocks?
Evaluate whether there are differences in the effects of unexpected weather on firm outcomes (sales revenue, labor supply, the number of customers, etc) with and without weather forecasts based on assigned contract (and demographics/market condition).
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Randomization Unit
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At local TSC-manager level.
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At local market (community) level. Stratified by baseline (i) revenue x (ii) number of agents x (iii) commercial zones.
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Planned Number of Clusters
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~65 TSCs
400 communities
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Surveys: ~706 communities
RCT: ~473 communities
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Planned Number of Observations
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15 agents/community x 4000 communities = 6,000 agents or workers
65 TSC managers and all other managers = ~650 managers
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Surveys:
*~9 agents/community x 706 communities = ~6,025 agents or workers
*lower-level, middle-level, senior & other managers = ~456 managers
RCT:
*~473 communities = ~3,397 agents
*lower-level, middle-level, senior & other managers = ~456 managers
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Sample size (or number of clusters) by treatment arms
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15 agents/community x 4000 communities = 6,000 agents or workers
65 TSC managers and all other managers = ~650 managers
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*~473 communities (containing ~3,397 agents), assigned to 5 different contracts in proportions that reflect managerial rankings.
*lower-level, middle-level, senior & other managers = ~456 managers
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Keyword(s)
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Finance, Firms And Productivity, Labor
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Behavior, Finance, Firms And Productivity, Labor
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