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Social Connections in Hiring Decisions: Lab-Experimental Evidence from Tanzania
Last registered on February 15, 2021

Pre-Trial

Trial Information
General Information
Title
Social Connections in Hiring Decisions: Lab-Experimental Evidence from Tanzania
RCT ID
AEARCTR-0007205
Initial registration date
February 15, 2021
Last updated
February 15, 2021 1:45 PM EST
Location(s)
Primary Investigator
Affiliation
CERGE-EI
Other Primary Investigator(s)
PI Affiliation
University of Dar es Salaam, Tanzania
PI Affiliation
University of Copenhagen, Denmark
Additional Trial Information
Status
In development
Start date
2021-02-16
End date
2021-04-30
Secondary IDs
Abstract
Small- and Micro-Entreprises (SMEs), which make up the vast majority of businesses in developing countries, typically do not hire many workers, and if they do, they often hire workers from kinship or other social networks of the entrepreneur. These patterns can both exclude workers who are not part of such networks from access to jobs, as well as preventing businesses from hiring the most skilled and productive workers.

We propose a lab experiment with 300 SME entrepreneurs in Dar es Salaam, the capital of Tanzania, to cleanly answer two questions related to this topic. First, is a worker more (or less) productive when working for a socially/kinship related boss? This will answer the question whether there is an efficiency-based rationale for entrepreneurs to hire socially connected workers. Furthermore, if so, what are the mechanisms that induce workers to be more (or less) productive in such circumstances? Second, turning to the entrepreneurs themselves, we will test whether SME owners face social pressure to hire kin or other socially connected persons.
External Link(s)
Registration Citation
Citation
Chegere, Martin, Paolo Falco and Andreas Menzel. 2021. "Social Connections in Hiring Decisions: Lab-Experimental Evidence from Tanzania." AEA RCT Registry. February 15. https://doi.org/10.1257/rct.7205-1.0.
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Experimental Details
Interventions
Intervention(s)
This is a lab experiment without a typical "treatment". The experimental design of the lab-sessions is explained in more detail in the section "Experimental Design" below (as well as in the attached PAP (Pre-Analysis Plan)).
Intervention Start Date
2021-02-16
Intervention End Date
2021-04-30
Primary Outcomes
Primary Outcomes (end points)
Worker output in the real effort task. Bosses decision whether to "hire" worker in second round of game.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
1. Worker rate set by Bosses.
2. Tips paid by Bosses
Secondary Outcomes (explanation)
Re. Scondary Outcome 1: In some randomly selected sessions, instead of the Worker's and Bosses' payoff rate for each gram of beans sorted being set exogenously by the experimenter, Bosses will receive the combined rate, but, before the start of the game, need to decide which share of the rate to give to the worker. We are interested whether bosses matched with socially connected workers decide to pay a higher or lower piece rate to the workers.
Re. Scondary Outcome 2: In other randomly selected sessions, Bosses receive a small extra pot of money before the game, out of which they can "tip" workers after they did the real effort task (solely based on bosses own decision). We want to study whether - conditional on the output of the matched worker - bosses tip workers more or less if they have a social tie to the worker.
Experimental Design
Experimental Design
We plan to invite 300 SME entrepreneurs in Dar es Salaam (Tanzania) to a lab experiment, conducted in 30 sessions with around 10 entrepreneurs each. Entrepreneurs are instructed to bring along to the session a person who “either works for them, or that the entrepreneur can see hiring temporarily or permanently, if s/he needs a worker in his/her business”. We refer to the invited entrepreneurs as “Bosses”, and the persons they bring along as “Workers”. Thus, in each lab-session we plan to have 10 Bosses and the 10 Workers they bring along.

During the sessions, the "Workers" carry out a simple real-effort task, which is to sort beans into different cups depending on their colour. Workers have 8 minutes each time for the tasks, and the output is measured in grams of beans in cups without any bean of the wrong colour. Workers receive a pay-off proportional to their output. Each worker is matched with a "Boss", and Bosses also receive an additional pay-off proportional to the worker’s output. This captures the basic idea that entrepreneurs’ pay-off increases with the productivity of their workers.

A randomly chosen share of the Workers in each session are matched with the Bosses with whom they came to the session, while the other half is matched randomly with one of the remaining bosses. We can think of workers randomly matched with the Boss they arrived with as being randomly “treated” with having a social connection to their boss.

In a second round of the game, bosses and workers are matched anew, using the same method as in Round 2 (thus, they may not be matched to the same person again). Bosses have to decide if they want to let their matched Worker do the real effort task and receive pay-out based on the output produced, as in Round 2, or if they would rather not hire and instead take a fixed outside sum of money (with the worker also receiving a small fixed outside sum instead of a pay-off linked to output). The main variation that we introduce here is that in randomly selected sessions, Bosses can do this decision "anonymously", without the worker knowing who the boss is to whom they are connected. Thus, we can test, if bosses are relatively less likely to hire socially connected workers if they can "hide" this decision from the worker (and potentially his/her acquaintances).
Experimental Design Details
Not available
Randomization Method
Randomization done by computer at the start of each session
Randomization Unit
Workers, who are randomly matched to bosses twice in the session they attend, for the first and second round of the game.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
No clusters.
Sample size: planned number of observations
300 workers
Sample size (or number of clusters) by treatment arms
300 workers
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
University of Dar es Salaam
IRB Approval Date
2020-08-24
IRB Approval Number
N/A
Analysis Plan

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