Objective:

The goal of this analysis was to accurately identify users who earned income from gig work in 2025, and to classify those users as part-time or full-time gig workers using transaction-level financial data. The approach prioritizes precision, transparency, and behavioral validity, rather than relying on self-reported employment status.

Data Sources:

We used the following primary datasets:

Existing tables used:

• external_transactions
• external_bank_accounts

Derived Tables:

• Canonical table of income transactions: income_transactions_2025
• Monthly aggregation of gig vs non-gig: income_transactions_2025
• Users grouped by financial institution: gig_worker_profile_2025
• User level final table, combines bank accounts for users with multiple: gig_worker_profile_2025_user

These tables can be found in dwh_dev.priyanka All analyses are restricted to calendar year 2025.

Step 1: Defining Income Transactions

To ensure we were only analyzing true income, we applied the following criteria:

Income Qualification Rules

A transaction is classified as income if:

• amount_cents < 0 aka (money flowing into the account)
• personal_finance_category.detailed = 'INCOME_WAGES'
• destination does not include pending
• Only transactions that occurred in 2025