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Supervisor Discretion or Data-Driven Decision Making? Evidence from a Tanzanian Factory

Work & Education Tanzania
Disabled Factory Workers Stack Colorful Buckets in a Warehouse

Factory Workers Stack Colorful Buckets in a Warehouse in Dodoma, Tanzania. Photo Credit: Mitchell Maher / International Food Policy Institute via Flickr

Study Context

Managers in firms often have discretion in which applicants they hire or which workers are selected for a promotion. This degree of informality is especially prevalent in developing countries. What is unknown is whether this discretion is efficient or inefficient. On one hand, managers may have valuable private knowledge about worker quality that is not captured in publicly observable data. On the other hand, supervisors may have personal preferences and biases that are not aligned with the firm’s goals.

In Tanzania, researchers are partnering  with a large manufacturing firm to study how introducing more data-driven processes to firm decision making affects productivity, labor supply, and worker morale.

Study Design

Support from the Development Economics Challenge funds will enable the research team to conduct interviews and surveys with 120 supervisors at the firm. Currently, supervisors have almost complete discretion in how they allocate tasks among workers, with allocation to higher difficulty tasks serving as a de facto promotion due to higher production bonuses, on average. Theoretically, since a Supervisor’s bonus is also tied to productivity, they should be incentivized to promote their most productive employees to the higher difficulty tasks. However, it is unclear whether Supervisor discretion is efficient in this setting.

The interviews will collect information from supervisors about their beliefs and how they relate to worker performance and their selection of workers for task upgrades. Combined with rich administrative data from the factory on worker attendance and productivity, the interview and administrative data will inform the design of a randomized evaluation comparing supervisor discretion to more data-driven approaches to decision making.

Results and Policy Lessons

Results forthcoming.


2023 — 2023

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