Labour Market Momentum

Abstract

The Labour Market Momentum signal measures shifts in employment strength by combining jobless claims trends with nonfarm payroll growth. Weekly claims are inverted (higher claims = weaker momentum), while payroll growth is directly positive. The composite reveals cycles of Tightening, Neutral, and Softening labour dynamics.

1. Data Sources (Expanded)

This composite uses high-frequency unemployment insurance (UI) claims as a near-real-time labour stress gauge, and the establishment survey payroll series as the canonical measure of payroll employment growth. All series are obtained from FRED for consistent access and metadata; the original producers are the U.S. Department of Labor (Employment and Training Administration) and the U.S. Bureau of Labor Statistics (BLS).

ICSA — Initial Claims, Seasonally Adjusted (FRED: ICSA)

• Producer: U.S. Department of Labor (ETA), published weekly; distributed via FRED.
• What it measures: First-time filings for UI benefits — a timely indicator of changes in layoffs.
• Frequency & timing: Weekly observations; typically released with a short lag after the reference week.
• Construction notes: Seasonally adjusted; sensitive to holiday effects and administrative changes (eligibility, backlogs, program transitions).
• Revision risk: Weekly values can be revised as states update counts; month-end averaging reduces, but does not eliminate, noise.

CCSA — Continuing Claims, Seasonally Adjusted (FRED: CCSA)

• Producer: U.S. Department of Labor (ETA), published weekly; distributed via FRED.
• What it measures: Ongoing UI benefit claims (insured unemployment). These claims are based on the week of unemployment (not the week the initial claim was filed).
• Interpretation nuance: More affected by benefit duration, re-employment rates, and policy rules than initial claims; best read as persistence of labour market stress rather than the onset of stress.
• Revision risk: Similar to ICSA (administrative updates), plus sensitivity to policy/program parameters.

PAYEMS — Total Nonfarm Payrolls, Seasonally Adjusted (FRED: PAYEMS)

• Producer: BLS Current Employment Statistics (CES) establishment survey; monthly.
• What it measures: Payroll jobs at nonfarm establishments (level), widely treated as the baseline labour activity series.
• Frequency & timing: Monthly; subject to routine monthly revisions and annual benchmark revisions to align with more comprehensive UI tax records (QCEW).
• Revision risk: Monthly prints are revised in subsequent releases; the annual benchmark process can shift the historical level and near-term momentum.

2. Transformations

1. Compute year‑on‑year (YoY) percent change for all three series.
2. For PAYEMS, also compute the 3‑month difference to capture short‑term hiring momentum:

This highlights inflection points in employment growth.

3. Normalisation

4. Composite Construction

Jobless claims are inverted (rising claims = negative signal) before combining with payroll momentum:

5. Regime Mapping

6. Implementation (Python)

import pandas as pd
import numpy as np

def robust_z(s, win=36, min_win=18):
    x = pd.to_numeric(s, errors="coerce").astype(float)
    w = max(min_win, min(win, x.dropna().size))
    med = x.rolling(w, min_periods=min_win).median()
    mad = (x - med).abs().rolling(w, min_periods=min_win).median()
    return (x - med) / (1.4826 * mad.replace(0, np.nan))

d = df_labor.copy()

for col in ["ICSA","CCSA","PAYEMS"]:
    d[f"{col}_YoY"] = d[col].pct_change(12)

d["PAYEMS_3mDiff"] = d["PAYEMS"].diff(3)

d["Z_ICSA"]  = robust_z(-d["ICSA_YoY"])
d["Z_CCSA"]  = robust_z(-d["CCSA_YoY"])
d["Z_PAYEMS"] = robust_z(d["PAYEMS_3mDiff"])

d["Labor_Momentum"] = d[["Z_ICSA","Z_CCSA","Z_PAYEMS"]].mean(axis=1)

hi, lo = 0.75, -0.75

def _regime(v):
    if pd.isna(v): return np.nan
    return "Tightening" if v > hi else ("Softening" if v < lo else "Neutral")

d["Labor_Regime"] = d["Labor_Momentum"].apply(_regime)

df_sig_labor = d
display(df_sig_labor.tail())

7. Interpretation

The composite (Labor_Momentum) is an average of three robust z-scores. Positive values indicate labour strengthening (claims falling and/or payroll momentum rising); negative values indicate labour softening.

Reading the drivers

• Claims-driven moves: If Z_ICSA and Z_CCSA fall (more negative), the composite will typically weaken even if payroll momentum is stable. This tends to be the earliest warning channel.
• Payroll-driven moves: If Z_PAYEMS turns sharply lower while claims remain benign, the composite can weaken after the onset of a slowdown in hiring rather than layoffs.
• Divergences: A common transition pattern is claims rising first (softening signal) while payroll momentum remains positive, followed later by a weaker PAYEMS_3mDiff.

8. Model Interpretation (with Academic Context)

This signal is designed for state and momentum inference rather than point forecasting. It combines: (i) weekly UI claims (fast, noisy, layoff-sensitive) and (ii) payroll hiring momentum (slower, benchmarked, broader labour demand). Interpreting the output is best done by decomposing the composite into component contributions and by accounting for known data imperfections.

What the composite is (and is not)

• It is: a robust, standardized measure of recent labour-market momentum relative to the prior ~3 years (36-month rolling window).
• It is not: an unemployment rate proxy, nor a level estimate of labour tightness; it is a direction-and-extremeness metric.

How to interpret regimes

• Tightening: Historically, claims often lead the unemployment rate and broader cyclical turning points; a sustained tightening regime suggests labour is providing support to growth, but late-cycle overheating risks can rise if inflation is also firm.
• Softening: Sustained increases in initial claims have been documented as leading indicators of labour deterioration and recession risk; this regime is a “watch” condition for broader slowdown dynamics.
• Neutral: Treat as “insufficient evidence” rather than a view; use other macro blocks (inflation/credit/liquidity) for confirmation.

Why robust z-scores

Claims and payroll momentum can contain outliers (pandemic-period spikes, strikes, administrative distortions) and are revised. Median/MAD scaling reduces sensitivity to extreme points compared with mean/standard-deviation scaling, improving stability for regime mapping. The constant 1.4826 rescales MAD to be comparable to the standard deviation under Gaussian assumptions.

Data caveats that matter for interpretation

• Claims noise: Weekly claims can be volatile and revised; month-end aggregation helps but does not eliminate short-lived distortions.
• Payroll revisions: CES payroll employment is revised each month and benchmarked annually to QCEW (UI tax records). This can change recent momentum signals and may alter apparent turning points.
• Policy/administration dependence: Claims levels can shift with eligibility rules, processing backlogs, and program changes — interpret extremes with institutional context.

9. Limitations

• Weekly claims data are volatile; month‑end averaging helps but may still produce noise.
• PAYEMS subject to periodic revisions; re‑benchmarking can alter short‑term momentum.
• Fixed thresholds (±0.75) may require calibration for different employment cycles.