Compare Employment and Wage Growth with BLS

Employment can expand while real purchasing power stagnates, which is why job counts and wages should be analyzed together. The BLS Labor Statistics Scraper can collect total nonfarm employment and average hourly earnings as separate official series without forcing them into the same unit.

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Keep a short data dictionary beside the analysis and review it when BLS changes a definition or release practice.

{"seriesIds":["CES0000000001","CES0500000003"],"startYear":2022,"endYear":2026,"maxResultsPerSeries":65}

Transform each series independently. For employment, calculate absolute or percentage change in payroll levels. For wages, calculate monthly or annual percentage growth. If the analysis claims real wage growth, join a clearly named CPI series and document the deflation formula. Never describe nominal earnings growth as improved purchasing power without that step.

Use series_id, year, and period as the join keys and keep the original value beside every derived field. That makes it possible to reproduce a chart when BLS revises seasonal factors. Annual averages can simplify long-horizon comparisons, but mixing annual and monthly records in one calculation will duplicate periods and distort results.

Segmented research requires more care. Industry payroll and earnings series may use different scopes or publish on different schedules. Build a small mapping table with series title, unit, seasonal-adjustment status, population, and intended chart label. Validate new series against their BLS pages before adding them to an automated Task. The connector solves collection; the mapping protects the meaning of the comparison as the dashboard grows.