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Field
Trial Status
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Before
on_going
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After
completed
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Field
Abstract
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Before
Existing research demonstrates sizable negative effects of high temperatures on labor productivity across skill types. In this study, we use experimental variation in room temperature to examine how the effects of incentive structure and work arrangements vary with higher costs of effort induced by higher temperatures. Motivated by findings that higher temperatures make coordination more difficult, our first objective is to first address how higher temperatures differentially affect team based production. Second, we test a potential intervention for overcoming the negative productivity impacts of high temperature. Specifically, we test the effectiveness of bonus rates that reflect the increasing cost of effort over time in high temperatures for overcoming the negative productivity impacts of warmth in both individual and team-based production.
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After
Existing research demonstrates sizable negative effects of high temperatures on labor productivity across skill types. In this study, we use experimental variation in room temperature to examine how the effects of work arrangements vary with higher costs of effort induced by higher temperatures. Motivated by findings that higher temperatures make coordination more difficult, our objective is to investigate how higher temperatures differentially affect team based production.
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Field
Trial Start Date
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Before
October 19, 2018
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After
October 03, 2022
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Field
Trial End Date
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Before
July 31, 2019
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After
November 15, 2022
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Field
Last Published
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Before
May 21, 2019 02:58 PM
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After
May 28, 2024 02:13 PM
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Field
Study Withdrawn
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Before
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After
No
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Field
Intervention Completion Date
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Before
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After
November 15, 2022
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Field
Data Collection Complete
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Before
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After
Yes
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Field
Intervention (Public)
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Before
We will study our research question on computer science students hired to complete a computer programming task. We will randomize the temperature of the room they perform the task in, the incentive bonuses they are offered, and whether the task is completed individually or in teams.
Our study has 8 treatment groups as summarized below:
1. Individual based production, low room temperature, constant bonus rate
2. Individual based production, low room temperature, increasing bonus rate
3. Individual based production, high room temperature, constant bonus rate
4. Individual based production, high room temperature, increasing bonus rate
5. Team based production, low room temperature, constant bonus rate
6. Team based production, low room temperature, increasing bonus rate
7. Team based production, high room temperature, constant bonus rate
8. Team based production, high room temperature, increasing bonus rate
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After
We will study our research question on computer science students hired to complete a computer programming task. We randomized the temperature (control: 24 degrees Celsius or warm:29 degrees Celsius) of the room they perform the task in and whether the task is completed individually or in teams.
Our study has 4 treatment groups as summarized below:
1. Individual based production, control room temperature
2. Individual based production, warm room temperature
3. Team based production, control room temperature
4. Team based production, warm room temperature
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Field
Intervention Start Date
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Before
October 19, 2018
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After
October 03, 2022
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Field
Intervention End Date
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Before
December 15, 2018
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After
November 15, 2022
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Field
Experimental Design (Public)
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Before
To test our research questions, we will hire undergraduate computer science students enrolled in 4-year college programs in Dhaka to complete a computer-programming job that asks programmers to implement five feature additions to an existing Java-based script. Participants will be given four and a half hours to complete the task. All features can be implemented independently of each other and are designed to be of similar difficulty. Layered onto this program is task tracking that allows us to track effort over time by collecting data on key strokes, pixels scrolled, and clicks participants make each minute. The program also has a tool that automatically checks the functionality of features upon submission, and programmers can submit each feature multiple times until they have confirmation that they have completed it correctly. The number of times each feature is submitted to be evaluated by the programmer is also stored by the program.
We chose the population of Dhaka computer science undergraduate students for our study for several reasons. First, Dhaka experiences very high temperatures in summer; the average maximum temperature in September is 32 degrees Celsius and the average minimum is not much lower at 26 degrees Celsius. Second, the IT sector contributes significantly to Bangladesh’s economy; in 2017, it was estimated that the IT sector in Bangladesh was worth $600 million and accounted for about 250,000 jobs . Ensuring IT worker productivity is maintained as temperatures rise is, therefore, likely to be important to the continued economic development of the country.
Our randomized control trials involve a series of treatments that will be randomly assigned across session-room temperatures. To generate temperature variation in our study, we will randomly assign the air conditioner temperature in each room that participants are working in to be 23 degrees or 29 degrees Celsius (73.4 degrees Fahrenheit or 84.2 degrees Fahrenheit). We will run two session-rooms at a time with 10 participants per room for both the individual sessions and the team sessions. We have selected the temperature values 23 degrees or 29 degrees to generate sufficient temperature variation across participants, and to ensure that we are not subjecting participants to uncomfortably high temperatures.
In addition to temperature variation, we will vary how the bonuses are allocated across sessions. In half of all sessions, participants will receive a constant bonus rate for each feature successfully implemented as determined by the automated testing tool. The bonus will be the will be equal to 9% of their total salary, which is 13USD/1100BDT, allowing them to earn an additional 45% of their salary in bonuses. In the other half of all sessions, participants receive an increasing bonus rate for each feature successfully implemented. The bonus will increase for each feature implemented by 3 percentage points, and will start at 3% of salary. Therefore, participants in both bonus structure treatments can earn the same total amount in bonuses. This treatment is intended to establish whether cost of effort increases faster when temperatures are higher such that workers benefit more from an increasing than a constant pay-for-performance rate over time when they are in warmer temperatures.
Lastly, we will randomly vary whether the task is to be completed in pairs of two programmers or individually. Within the team sessions, pairs will be randomly assigned and will be instructed to work on the task according to a pair programming format where members take turns being the driver (typing) and navigator (coming up with solutions). With 10 participants per session room, each session room assigned to the team treatment will have 5 teams. This treatment is intended to establish whether the impacts of higher temperature differ when team work is required relative to when it is not. In the team sessions, bonuses will be paid for joint performance such that if a team successfully implements a feature, both team members will receive an equivalent bonus. Both team and independent sessions will be randomly assigned the different incentive designs.
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After
To test our research questions, we will hire undergraduate computer science students enrolled in 4-year college programs in Dhaka to complete a computer-programming job that asks programmers to implement five feature additions to an existing Java-based script. Participants will be given four and a half hours to complete the task. All features can be implemented independently of each other and are designed to be of similar difficulty. Layered onto this program is task tracking that allows us to track effort over time by collecting data on key strokes, pixels scrolled, and clicks participants make each minute. The program also has a tool that automatically checks the functionality of features upon submission, and programmers can submit each feature multiple times until they have confirmation that they have completed it correctly. The number of times each feature is submitted to be evaluated by the programmer is also stored by the program.
We chose the population of Dhaka computer science undergraduate students for our study for several reasons. First, Dhaka experiences very high temperatures in summer; the average maximum temperature in September is 32 degrees Celsius and the average minimum is not much lower at 26 degrees Celsius. Second, the IT sector contributes significantly to Bangladesh’s economy; in 2017, it was estimated that the IT sector in Bangladesh was worth $600 million and accounted for about 250,000 jobs . Ensuring IT worker productivity is maintained as temperatures rise is, therefore, likely to be important to the continued economic development of the country.
Our randomized control trials involve a two treatments that will be randomly assigned across session-rooms. To generate temperature variation in our study, we will randomly assign the air conditioner temperature in each room that participants are working in to be 24 degrees or 29 degrees Celsius. We will run two session-rooms at a time with at most 10 participants per room for both the individual sessions and the team sessions. We have selected the temperature values 24 degrees or 29 degrees to generate sufficient temperature variation across participants, and to ensure that we are not subjecting participants to uncomfortably high temperatures.
Lastly, we will randomly vary whether the task is to be completed in pairs of two programmers or individually. Within the team sessions, pairs will be randomly assigned and will be instructed to work on the task according to a pair programming format where members take turns being the driver (typing) and navigator (coming up with solutions). With 10 participants per session room, each session room assigned to the team treatment will have at most 5 teams. This treatment is intended to establish whether the impacts of higher temperature differ when team work is required relative to when it is not. In the team sessions, bonuses will be paid for joint performance such that if a team successfully implements a feature, both team members will receive an equivalent bonus.
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Field
Randomization Method
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Before
Treatment conditions will be randomized across sessions days using coin flips
Participants will be randomly assigned to session days using Stata's generate uniform distribution number assignment command (gen uniform())
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After
Team and individual conditions was randomized across session days using Stata's generate uniform distribution number assignment command (gen uniform())
Room temperature was randomized across rooms within session days using Stata's generate uniform distribution number assignment command (gen uniform())
Participants were randomly assigned to session days using Stata's generate uniform distribution number assignment command (gen uniform())
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Field
Sample size (or number of clusters) by treatment arms
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Before
25 individuals in Individual Production, Constant Bonus, Cool Temp; 25 individuals in Individual Production, Increasing Bonus, Cool Temp; 25 individuals in Individual Production, Constant Bonus, Warm Temp; 25 individuals in Individual Production, Increasing Bonus, Warm Temp; 25 teams in Individual Production, Constant Bonus, Cool Temp; 25 teams in Individual Production, Increasing Bonus, Cool Temp; 25 teams in Individual Production, Constant Bonus, Warm Temp; 25 teams in Individual Production, Increasing Bonus, Warm Temp
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After
50 individuals in Individual Production, Control temp; 50 individuals in Individual Production, Warm temp; 50 individuals in Team Production, Control temp; 50 individuals in Team Production, Warm temp
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Field
Keyword(s)
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Before
Education, Environment And Energy, Firms And Productivity, Labor
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After
Education, Environment And Energy, Firms And Productivity, Labor
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Field
Building on Existing Work
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Before
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After
No
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