Our empirical analysis was performed based on data from a company with new recruits to identify clear-cut peer effects with differential observability. To achieve the objectives of this study in the most cost-effective manner, a new internship scheme was set up, and a company was commissioned to run the scheme in Shenzhen of Guangdong Province, China. The experience of internship was a strict requirement for graduation at many Chinese universities, although the evaluation of one’s performance at the job was immaterial. Therefore, even a very small company found it easy to hire university students as summer interns, and the interns usually did not have any connection with the company after the period of their internship. Therefore, observing interns’ behaviors of a small company is ideal for us to identify their peer effect, since other effects (e.g. career concern) are weak or do not exist at all.
The internship involved a data input job of real survey questionnaires collected for another project. The company we commissioned is a small IT company in Shenzhen whose main business is software design and data processing. As a part of our agreement, the company allowed us to send two research assistants (RAs) to work as “managers” of the company and run the internship program during our study period.
Our experiments lasted for one month. Our investigation required workers who would take the job seriously and were happy with short-term jobs. The internship opportunity provided by our project was well appreciated by the Student Service Centre of Shenzhen University, which helped us advertised it in its intranet. All the interns were officially hired by the commissioned company, which also promised to issue internship certificates to all interns after they have completed the job.
Over 200 applications were received for the internship job, from which we randomly selected 40 candidates. Moreover, through random draws, the participants were divided into two groups, each consisting of 20 workers. The first group worked in the morning from 8:30 to 12:00, while the second group worked in the afternoon from 1:30 to 5:00. The participants worked from Monday to Friday for 3.5 hours per day. For the first 10 days (two weeks), a team-based performance pay was used, and worker pay was determined with a formula, which increases with total quantity and decreases with the average error rate (a measure of quality). Every day, a 5 min job review was conducted by two managers (our RAs) on the previous day’s work. For the first three days, the individual quantity of output was made public along with the group mean quality of output. The information on individual quality of output was only known by the person himself. For the next seven days, the same undertaking was realized for the morning team (control group), but both individual quantity and quality data were made public for the afternoon team (treatment group). Accordingly, the morning team only had observability on their quantity of output, whereas the afternoon team had full observability on both the quantity and quality of the output for each of its team members.