The Evolution of Voluntary Cooperation in Firms: Evidence From a Field Experiment in a Large Retail Company

1) Introduction: What is the project about?
This project is about the drivers of cooperation in organizations and what its impact is and how it is exerted. Intuitively, we known that worker to worker helping behavior is very important for aggregate performance, yet we seldom understand its mechanisms, particularly in business contexts. In this project, I will be collaborating with three organizations that are implementing a practice/methodology called BAPP that is well suited to study cooperation among workers. A field experiment will be conducted in four upcoming implementations of the BAPP methodology.

2) What is the BAPP methodology?
The field experiment leverages a research collaboration that has been established with DEKRA Insight, a company specialized in workplace safety prevention. One of the services that DEKRA provides to its clients is the BAPP methodology (Behavioral Accident Prevention Process). BAPP methodology promotes cooperative behavior among the employees of a treated site (plant, store, warehouse, etc. // typically big, above 50 employees). This methodology work as follows:
i) A starting team of 10 employees is set up and trained by DEKRA consultants to become “observers”. The leader of the team is selected by consent: unlike the rest of the members of the team, the leader is 100% devoted to the project.
ii) During the first three months the team develops a list of risky behaviors for their site and are trained on how to provide constructive feedback to colleagues on safe working behavior. This feedback is known as an “observation”.
iii) In the 4th and 5th month, the group start executing observations in the site. Also, they are trained to invite and train colleagues that want to become observers themselves.
iv) From the 6th to 8th month, the focus is on ramping up the amount of observations and observers. After the 8th month, DEKRA consultants leave but monitor the progress at a distance. Success over time is attained when a large part of the employees in a site have become observers and each worker is observed at least once a month.
v) An observation consists on approaching the worker and, after his consent, observing its behavior for 10 to 20 minutes. Verbal feedback is then provided and a detailed feedback sheet is filled and uploaded to an online data storage system. This data upload typically takes 20% to 30% of the observation time. The identity of the observed worker remains anonymous.
vi) A BAPP site committee is formed among the starting team, plus the site manager. This committee meets monthly. They analyse performance and decide which will be the strategy to follow in terms of whom to observe and whom to invite to become observers. The new observers of the site do not form part of the monthly committee and they typically do not receive formal feedback on performance (informally communication might happen though).
This setting is well suited to study the drivers of cooperation in the field. Essentially, BAPP builds on and promotes the cooperative spirit of workers. It harnesses this behavior in a structured way so that it spreads and permeates the treated organization. Moreover, it requires that workers devote time and effort in order to provide a beneficial feedback to colleagues. At the target rates of observations, observers devote between 2 and 4 percent of their time to the BAPP methodology. Adding to this the training time, then the individual costs of participating in BAPP can be quite high.

3) What is the role of the Chilean Safety Association (ACHS) and Sodimac?
We are also collaborating with the Chilean Safety Association (ACHS), which is a private and non-profit organization that provides services in occupational safety and health in Chile. These services include prevention, medical treatments when accidents occur, and pensions and subsidies when a worker is impaired to continue working. Half of the Chilean firms and workforce are affiliated to ACHS. Firms are mandated to contribute a percentage of their payroll (on average 1.2 percent) to the ACHS or one of its competitors. The ACHS is mandated to devote at least 12% of its budget to prevention services.
The ACHS partnered with BST in 2012 to implemented BAPP in their affiliated firms. ACHS’s consultants have been trained to deliver the BAPP methodology.
ACHS has agreed to support the execution of research on BAPP in one of their clients. They support the idea of executing experiments to improve the state of safety prevention in Chile. Because of previous research, the natural candidate among their clients is Sodimac, a home improvement company. This company has already implemented BAPP in 5 of its 60 stores (these 5 stores were implemented in 2014). Currently, Sodimac is implementing BAPP in 4 new stores. One of them started the observations in June-2017, a second one started in mid July-2017 and the remaining two will start to execute observations in August-2017.

3) What will be the experimental treatments?
Three treatments will be performed:
Treatment 1: “Group Structure”.
Half of the site’s workforce will be randomly assignment to different equally sized groups, and then half of the starting observers (which also constitute the committee that meets monthly) will be randomly assigned to one of these groups. The observer should then execute observations and invite to become observers only the workers of its assigned group. The others observers will execute observations and invite in the remaining workforce of the site. Let’s use an example. Imagine a site with 200 employees and a starting group of 10 starting observers; well, the site would be divided into 5 groups of 20, and each the selected 5 observers will be in charge of a specific group.
The workers assigned to the groups will be informed of the name of other group members as well as the name of their assigned observer. They are also informed that if they become observers they will be executing observations within the group.
Treatment 2: “Communication”.
In the groups having the treatment 1, two communications elements will be introduced in order to boost the treatment 1. First, the observers of the groups (the starting observers plus the new ones that will be formed over time) will have a group meeting after the monthly site committee. This meeting is a kind of “group committee”, where performance will be discussed and tactics decided upon. Second, in each month, one of the new observers of the group will be invited to participate in the site committee.
Treatment 3: “Reputation”.
A public list will be created displaying the name of the all the observers and the amount of observations for that each one of them has executed. This list will be displayed in a public and highly observed space, such as a newsletter, a site wide e-mail, or by posting it in bulletin boards.

4) Sites and treatments
We will intervene the four BAPP implementations taking place in Sodimac. In all the sites, we will implement the treatment 1; in the site #2 we will implement the treatment 2; in the site #3 we will implement the treatment 3; and in the site #4 we will implement the treatments 2 and 3.

2017-06-15

2018-12-31

There are two main outcome variables we are interested in:
1) Number of observations per observer. We expect that the treatments will make the observers do a higher number of observations.
2) Workplace accidents. We expect that the workers assigned to the groups will experience a lower number of accidents.
3) Percentage of workers that become observers. We expect this percentage to be higher in our treatments conditions.

Our experimental design is as follows:
- We execute a field experiment in four stores of a large home improvement retail company.
- In these stores, the company is implementing a methodology designed to improve workplace safety. In this method, a group of ten workers are selected (forming the "site committee") and then they are trained to: i) provide 10 minutes feedback on safety behavior to fellow workers (becoming "observers"), ii) invite and train other workers of the store to become "observers". Which worker is observed is not structured nor directed by the standard implementation of the methodology: although some degree of structure might emerge over time in an implementation, there is a lot of random observations in the process (e.g., "just observing the worker at hand").
- The key issue in this methodology is that becoming an observer and executing observations is costly, while most of the benefits are accrued by fellow co-workers that receive the feedback. In essence, they execute a cooperative act: bear a personal cost in order to benefit a third party. As expected, the problem in this methodology is cooperation in large groups. As the number of observers in a site grow, the incentives and the commitment to execute the expected number of observations goes down.
- We designed three treatments that attempt to ameliorate the problem of cooperation in large groups:
1) Treatment 1. In each of the four sites, we created a structure of five groups where one tenth of the employees were randomly assigned to one of five randomly selected observers. The selected observers, as well as the enrolled ones, will be bound to the assigned group to execute their observations.
2) Treatment 2. In the sites 2 and 4, we allowed these groups to meet and also to interact with the rest of the "site committee".
3) Treatment 3. In the sites 3 and 4, we will publish a monthly list of the amount of observations executed by all the observers in the site.
- To execute these treatment we collaborate with the retail store company and with the consulting company in charge of executing the methodology.

- The assignment of treatments to sites was done by randomizing the treatment across the sites with a spreadsheet.
- The assignment of observers to groups is being done on the ground by the consultant executing the methodology. This randomization is being done by using a lottery box, where there are ten balls and only five indicate being selected to the treatment 1. The ten observers have to select a ball from the box.
- The assignment of the employees to groups was done using a Stata randomization command. We generated four blocks (stratas) in the assignment: gender, age, tenure and position.

- For assigning workers to groups, the unit was individual workers.
- For assigning observers to treatment 1, the observer was the unit.
When analyzing outcomes we need to consider these assignment rules. For example, we will analyze accident at the individual worker level, rendering the clustering of standard errors at the observer level a necessity. In contrast, to study observations we will compare observations between observers, generating the need to cluster standard errors within stores.

Yes

We have four sites (four stores), and thus, four clusters.

On average, in each store we have 10 observers and, on average, 250 workers. Five observers and 125 workers will be randomly matched in groups of 25 workers (the remaining observers and workers will have a standard implementation, without group structuring). Thus, we will have 20 observers in treatment and 20 observers in control, as well as 500 workers in treatment and 500 workers in control. We will observe at least a year of observations. Each observer is supposed to execute one observation a week. We will collect monthly information on accidents and observations. We will also collect historical information on accidents (prior to the treatment) in order to improve statistical inference.

As indicated above, we will have 20 observers and 500 workers in the treatment condition and 20 observers 500 workers in the control condition.

Our power calculations indicate that for accidents, the minimum detectable effect size is 20% of one standard deviation in the occurrence of an accident of a worker in a given month (i.e., likelihood of accident) (mean: 0.55% // sd: 7.4%). [Significance: 1.96 // Power: 0.8] For the number of observations per observer, the minimum detectable effect size is 75% of one standard deviation of the number of observations per observer per month (mean: 4.5 // sd: 3). [Significance: 1.96 // Power: 0.8]