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Field
Trial Title
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Before
Willingness to Pay for Workplace Peers
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After
Peers and Motivation at Work
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Trial Status
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Before
in_development
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After
completed
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Abstract
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Before
This study uses an incentivized choice experiment to measure workers' willingness to pay for higher-ability peers.
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After
This study comprises two experiments. The first experiment randomly assigns tea-harvesting workers to locations on fields to estimate peer effects in output. The second is an incentivized choice experiment that measures workers' willingness to pay for higher-ability peers.
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Trial Start Date
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Before
March 07, 2016
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After
February 02, 2015
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Last Published
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Before
March 07, 2016 01:06 AM
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After
September 02, 2018 02:30 PM
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Primary Outcomes (End Points)
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Before
Our key outcome of interest is the willingness to pay for higher-ability peers. See the pre-analysis plan for details.
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After
Our outcome for the peer effects estimation experiment is the daily log output of tea.
Our key outcome for the incentivized choice experiment is the willingness to pay for higher-ability peers. See the pre-analysis plan for details.
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Experimental Design (Public)
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Before
Our experiment consists of three steps:
1. Random assignment of workers to peers. This was implemented starting in late February.
2. A survey containing the incentivized choice experiment to elicit WTP. This begins on March 7, 2016.
3. Workers’s positions are changed to implement the choices for the 10% of people who chose faster peers. This happens immediately after the survey ends, and is done to make the elicited choices incentive-compatible.
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After
Our peer effects estimation experiment consists of two steps:
1. Random assignment of workers to peers.
2. Using randomly-assigned peers and daily output data to estimate a linear-in-means model of peer effects.
Our incentivized choice experiment consists of three steps:
1. Random assignment of workers to peers. This was implemented starting in late February.
2. A survey containing the incentivized choice experiment to elicit WTP. This begins on March 7, 2016.
3. Workers’s positions are changed to implement the choices for the 10% of people who chose faster peers. This happens immediately after the survey ends, and is done to make the elicited choices incentive-compatible.
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Randomization Unit
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Before
Individual.
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After
For the first experiment, randomization is within-individual at a level of cycle day.
For the second experiment, randomization is at the individual level.
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Planned Number of Clusters
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Before
983 workers
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After
999 workers
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Planned Number of Observations
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Before
983 workers
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After
999 workers
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Intervention (Hidden)
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Before
Previous work by the study team (Brune, Chyn, and Kerwin 2015) established that there are positive peer effects among tea pluckers at the Lujeri tea estate in Malawi's Southern Region, with the effects concentrated at the bottom of the ability distribution. Pilot work suggests that workers value these peer effects and are willing to pay for them. Our study extends the ongoing work reported in Brune et al. (2015), using a survey of the same sample of workers to elicit incentivized choices about whether they prefer higher-ability peers (faster pluckers). Workers are offered six different choices over potential peers that might want to switch places to work next to. One of those choices, selected at random, is implemented for 10% or workers. Workers know these probabilities ex ante and thus the optimal response is to truthfully reveal their preferences. We use the elicited choices to estimate the mean willingness to pay for faster peers, and the marginal willingness to pay for an improvement in peer quality.
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After
There are two experiments in this study:
1. Our first experiment randomly assigns tea-harvesting workers to locations on fields to estimate peer effects. To conduct our study, we partnered with Lujeri Tea Estates, a large agricultural firm in Malawi. Our sample is a group of roughly 1,000 employees who hand-pick ("pluck") leaves from tea bushes (hereafter, we refer to these workers as pluckers). Workers temporarily store plucked leaves in baskets and empty their baskets at a central weighing station. There is no explicit cooperation involved in this process, and pay is a constant piece rate for each kilogram of plucked tea.
Production at the firm is organized by assigning workers to "gangs" which are each managed by a supervisor. The size of a gang is typically around 45 pluckers, but the sizes range from 29 on the low end to 76 on the high end. Each gang is responsible for plucking tea from a pre-determined set of fields over the course of a harvesting "cycle" (7 to 12 calendar days). In our analysis sample, there are 78 fields for the 22 gangs we study.
On each tea field for a gang, the supervisor assigns workers to pluck tea from a specific set of plots (between 1 and 3 per harvest cycle day, depending on the characteristics of the field). The assignment of workers to plots for given field is done at the beginning of the main season and generally remains in place throughout the season. Each field has between 30 and 120 plots, and workers must finish plucking their assigned plots before moving on to other plots. At the completion of a harvesting cycle (most commonly 6 work days, or 7 calendar days, since Sunday is a day off), the gang returns to the initial field for a new round of plucking - unlike other crops that are harvested once or a few times, tea bushes grow continuously throughout the season.
We designed our experimental intervention to randomly assign workers to plots on tea fields to generate exogenous variation in exposure to workplace peers. To implement this, we obtained the roster of workers in each gang and a "plucking program" for each gang. The plucking program is a predetermined list of which field (or fields) a gang works on during each day of its cycle and the number of pluckers that should be assigned to each field. In the simplest case, there is one field on each cycle day with all the pluckers working on it. We use this information to generate randomly-ordered lists of pluckers for each day of a gang's harvesting cycle. On cycle days where a gang works on multiple fields, we also randomly determine which workers are on each field.
We used these randomized lists to determine the order in which supervisors assign pluckers to plots on each field. The random assignment took advantage of the usual assignment process in which pluckers stand in a queue and receive plot assignments in the order that they are standing. The supervisor makes the assignments by ``snaking'' back and forth across the field and taking the next plucker from the queue for each plot. Our random assignment scheme altered this system by giving the supervisors a randomly-ordered list to use in this snake pattern. Each gang supervisor assigned workers using the randomly generated list of worker assignments in February 2015. We verified compliance with these assignments by having our project managers visit each gang in the week after randomization. In addition, project staff confirmed compliance with the assignment via random spot checks several weeks after the initial assignment. As a result of our intervention, workers are assigned randomly to plots within a field for different cycle days.
2. The incentivized choice experiment uses a survey of the same sample of workers to elicit incentivized choices about whether they prefer higher-ability peers (faster pluckers). Workers are offered six different choices over potential peers that might want to switch places to work next to. One of those choices, selected at random, is implemented for 10% or workers. Workers know these probabilities ex ante and thus the optimal response is to truthfully reveal their preferences. We use the elicited choices to estimate the mean willingness to pay for faster peers, and the marginal willingness to pay for an improvement in peer quality.
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