ECIALLCIV: US Employment Cost Index YoY Growth from FRED (2002–2026)

The US Employment Cost Index dataset serves year-over-year growth in total compensation for all civilian workers, quarterly — the BLS reference measure of labor cost pressure. Unlike average hourly earnings, the ECI holds the occupation and industry mix fixed, so it measures the price of labor rather than shifts in who is employed. Growth peaked at 5.1% in Q2 2022 — the fastest in the index’s modern history — bottomed near 1.4% in 2009, and ran at 3.4% in early 2026 against a 2019 average of 2.75%.

Dataset: US Employment Cost Index Growth (2002–2026) · Updated 2026-04-01

Source: US Bureau of Labor Statistics, Employment Cost Index · FRED series ECIALLCIV

Macro Takeaway

Dataset Overview

Dataset Variables

The CSV and Excel files contain the following columns.

A value of 3.43 means total compensation costs rose 3.43% over the previous four quarters. Note: the chart above displays the underlying index level from FRED; the CSV serves the year-over-year growth rate.

Download the Complete Dataset

The full dataset is available in CSV and Excel formats.

FRED Direct CSV Access

The underlying data is available from FRED under series code ECIALLCIV:

https://fred.stlouisfed.org/graph/fredgraph.csv?id=ECIALLCIV

Direct CSV Access — Eco3min Structured Dataset

https://eco3min.fr/dataset/employment-cost-index.csv

This URL returns the complete dataset in CSV format. It can be used directly in pandas, R, curl, or any data tool.

Using the Dataset in Python

import pandas as pd

url = "https://eco3min.fr/dataset/employment-cost-index.csv"
df = pd.read_csv(url, parse_dates=["date"])

print(f"Latest ECI growth: {df['eci_yoy'].iloc[-1]:.2f}% YoY")
print(df.tail())

Using the Dataset in R

library(readr)

url <- "https://eco3min.fr/dataset/employment-cost-index.csv"
df <- read_csv(url)

tail(df)
summary(df$eci_yoy)

Both examples load the dataset directly from the URL — no download or API key required.

Methodology

The ECI measures the change in the cost of labor — wages, salaries, and benefits — for a fixed basket of occupations and industries, surveyed quarterly by the BLS from ~27,000 establishment observations. Total compensation for all civilian workers (ECIALLCIV) is the headline aggregate; wages-only and private-industry variants exist.

This dataset serves the year-over-year growth rate computed from the index (December 2005 = 100). The underlying index begins in 2001, so the YoY series begins in 2002. The BLS headline is often quoted as the seasonally adjusted 3-month change; the YoY transform is smoother and the standard for historical comparison.

This dataset is updated daily (Mon–Sat, 08:00 UTC) via automated pull from the FRED API; new observations appear with each source release.

Data Quality & Provider Notes

• Release latency. Published ~1 month after quarter end, on a fixed BLS calendar (late January, April, July, October).
• Revisions policy. Minimal — the ECI is rarely revised outside periodic rebasing and seasonal updates, a notable advantage over compensation measures derived from NIPA data.
• Composition-free design. Fixed employment weights are the series’ defining feature; it is the only major wage measure immune to workforce mix shifts.
• Alternative measures. Average hourly earnings (monthly, mix-sensitive) and the Atlanta Fed Wage Growth Tracker (median, person-level) complement it at higher frequency.
• Known gaps. None; the current-methodology civilian series is continuous from 2001.

Common Pitfalls When Using the ECI

1. Mixing the two headline conventions. The BLS press release leads with the quarterly (3-month) seasonally adjusted change; this dataset serves YoY. A ‘0.9% quarterly’ and a ‘3.4% annual’ print describe the same release.
2. Comparing nominal ECI to a real benchmark. ECI growth is nominal; deflating by CPI is required for purchasing-power statements — see the real wage series for that transform.
3. Treating wage growth as inflation one-for-one. Compensation growth of 3.4% with 2.8% productivity growth implies far less price pressure than the same wage number with zero productivity — the unit-labor-cost distinction.
4. Ignoring the quarterly cadence. The ECI lags monthly wage measures by up to three months; it confirms trends rather than catching turns.

Historical Regimes

2002–2007 — The 3–4% norm. Compensation growth ran near 3–4% through the housing-era expansion, drifting down as the cycle aged.

2008–2014 — Disinflation floor. Growth collapsed to the series low near 1.4% (late 2009) and stayed below 2% for five years — the longest stretch of wage stagnation in the modern record.

2015–2019 — The slow rebuild. A gradual climb to a 2.75% average in 2019 as the labor market tightened past full employment — slower than most Phillips-curve models predicted.

2020 — The composition stress test. While average hourly earnings spiked ~8% on layoff mix effects, the ECI barely moved — the episode that cemented its status as the clean wage measure.

2021–2022 — The surge. Growth accelerated to the modern-record 5.1% (Q2 2022) as quits hit 3.0% and job-switcher premiums exploded — the wage leg of the inflation episode.

2023–2026 — The long glide. A slow deceleration to 3.4% by early 2026 — most of the way back toward, but not yet at, the pre-pandemic norm.

Related Macroeconomic Datasets

The ECI is the wage input; productivity and unit labor costs convert it into inflation-relevant terms.

Macroeconomic Dataset Hub

This dataset is part of the Eco3min macro-financial data repository.

Sources

• US Bureau of Labor Statistics — Employment Cost Index
• Federal Reserve Bank of St. Louis — FRED series ECIALLCIV

Dataset Reference