Table 1

Descriptive Statistics

Panel A: Time‐Varying Worker Characteristics
Sample
Full SampleAt the Time of Microcredential AwardWorkers Without Any Microcredentials
(1)(2)(3)
Mean (SD)MedianMean (SD)MedianMean (SD)Median
Number of microcredentials2.1803.18200
(3.81)(4.33)(0)
Number of completed projects42.44195.08017.972
(64.79)(18.03)(49.58)
Dollars earned11,193.823,165.92,272.3505,449.62195
(22,862.06)(10,244.44)(15,803.23)
Months active22.8617.819.79.9613.693.7
(20.29)(21.75)(19.15)
Freelancer rating3.023.432.573.063.313.59
(1.66)(2.07)(1.45)
Average project value403.59146.04673.23157.98601.28168.7
(1,335.32)(2,386.92)(2,459.63)
Panel B: Time‐Invariant Background Characteristics
ShareShareShare
Male70%70%68%
College degree or more74%72%58%
Top‐5 countries
India27%India24%India22%
Bangladesh12%Bangladesh12%United States15%
Philippines11%Philippines9%Ukraine7%
Pakistan10%United States8%Philippines6%
United States7%Pakistan7%Pakistan6%
Panel C: Sample Sizes
Share with at least 1 project won45%32%19%
Number of projects442,203233,79120,827
Number of workers46,79133,09113,700
  • Notes: Column 1 presents the descriptive statistics for the full sample, Column 2 presents the descriptive statistics at the time of microcredential completion for the subsample of workers who take tests, and Column 3 presents the descriptive statistics for workers who have not completed any tests. In Columns 1 and 3, worker characteristics are measured at time of project start, and one observation corresponds to a project completed by a worker. In Column 2, time‐varying characteristics are measured at time of microcredential completion. In Panel B, worker home countries are self‐reported and verified by the platform. Education is reported by workers themselves. Worker gender is inferred by workers’ self‐reported first name using the Python library SexMachine (https://github.com/ferhatelmas/sexmachine/, accessed January 30, 2024).