Objectives:
1. Analyze unemployment rate, participation rate, and payroll figures.
2. Relate labor market health to monetary policy expectations.
Theory & Concepts:
Unemployment rate vs. U‑6 (broad underemployment measure).
Non‑Farm Payrolls (NFP): structure, seasonal adjustments, revisions.
Labor force participation and average hourly earnings as leading indicators.
The fundamental analysis of the foreign exchange market necessitates taking into account the crucial role played by indicators related to the labour market. These metrics provide an up-to-date assessment of economic slackness, wage pressures, and, consequently, the inflationary climate that influences the policies pursued by central banks.
Among the most frequently cited indicators, the U-3 unemployment rate stands out. Defined by the International Labour Organization (ILO), this rate represents the proportion of civilians actively seeking employment but unable to secure a job. However, it may underestimate the full extent of labour market challenges, failing to include discouraged job seekers or underemployed individuals working part-time or with fewer hours than they desire.
The U‑3 formula:
Unemployed Persons: those without a job, actively looking, and available to work.
Labor Force: all employed + all unemployed.
U‑3 is the “headline” unemployment rate published by the Bureau of Labor Statistics (BLS). It measures the share of the labor force that is jobless and seeking work:
- A rising U‑3 signals a weakening labor market (more people unable to find jobs).
- A falling U‑3 signals a tightening market (employers picking up more workers).
Numerical example
Survey data for a given month (hypothetical):
1. Plug into the formula
2. Interpretation
5 percent of the labor force is unemployed and actively looking for work.
Use:
- Core gauge of labor‑market health for policymakers and markets.
- Tracked alongside job‑growth numbers and wage data to assess cyclical conditions.
Limitation:
- Doesn’t count discouraged workers who stopped looking, or underemployed part‑timers (they fall in U‑6).
- Can understate slack in the labor market, especially in downturns when many give up the job hunt.
To address this limitation, the U-6 unemployment rate incorporates these additional groups into its calculation. Interactive Brokers reports that the U-6 figure can be several percentage points above the U-3 level, reaching as high as 15% in times of severe economic downturns.
The U‑6 formula:
- Unemployed: as in U‑3, actively looking for work.
- Marginally Attached: persons not in the labor force who want and are available for work, and have looked in the last 12 months but not in the last 4 weeks (includes “discouraged workers”).
- Part‑Time for Economic Reasons: employed part‑time because full‑time work is unavailable or hours have been cut.
U‑6 captures all U‑3 unemployed plus people who are underemployed or have given up searching recently. It reflects total labor slack:
- A higher U‑6 (versus U‑3) indicates more hidden underutilization—underemployed or discouraged workers.
- U‑6 tends to exceed U‑3, especially during downturns when many cut hours or stop looking.
Numerical example
Survey data (hypothetical):
1. Sum the “numerator” components:
8+2+5=15 million
2. Adjusted “denominator”:
160+2=162 million
3. Compute U‑6:
So the U‑6 rate ≈ 9.3 %, meaning about 9.3 % of the labor force plus marginally attached are unemployed, underemployed, or discouraged.
Use:
- Gives a fuller picture of labor‑market slack for policymakers and researchers.
- Highlights underemployment and discouraged‑worker effects beyond headline U‑3.
Limitation:
- Includes marginally attached who haven’t looked recently—some debate whether to count them.
- Part‑time for economic reasons lumps very different situations (temporary schedule vs. chronic underemployment).
- Still misses truly long‑term discouraged or out‑of‑labor‑force individuals who haven’t searched in over a year.
Another crucial indicator is the labour force participation rate, which gauges the proportion of the working-age population that is either employed or actively seeking employment. An increase in this metric amid declining unemployment rates suggests a strong foundation for the labour market, whereas a decrease may imply underlying weaknesses as certain workers leave the labour force.
The LFPR formula:
- Labor Force = Employed + Unemployed (actively seeking work)
- Civilian Non‐Institutional Population = Total civilian population aged 16 + , excluding those in prisons, mental institutions, or on active military duty.
The LFPR measures the share of the eligible population that is either working or looking for work:
- A rising LFPR suggests more people are entering or re‐entering the job market (e.g. students graduating, retirees delaying retirement).
- A falling LFPR can signal discouraged workers leaving the labor force, demographic shifts (aging population), or structural changes (automation, caregiving).
Numerical example
Census‐style survey data for a given month (hypothetical):
Compute LFPR:
62.3 percent of all civilians aged 16 and over are participating in the labor market.
Use:
- Gauges overall labor‐market engagement (beyond just unemployment).
- Helps distinguish whether changes in the unemployment rate reflect job gains/losses or shifts in people entering/leaving the labor force.
Limitation:
- Doesn’t capture underemployment or hours worked—someone may be employed but working far fewer hours than desired.
- Can hide demographic effects (e.g. aging baby‐boomers lowering the rate even if youth participation is stable).
- Moves slowly—structural and demographic trends can swamp cyclical signals
Every month, attention turns to the Non-Farm Payrolls report, considered the most significant economic event in the United States calendar. This report, based on surveys conducted among businesses and governmental agencies, covers approximately 131,000 entities, representing roughly 80% of all non-agricultural jobs in the nation.
The primary focus of the report is on the seasonally adjusted change in non-farm employment, which is subject to two critical methodological considerations.
Firstly, it is annually compared to the Quarterly Census of Employment and Wages, which can result in revisions to hundreds of thousands of employment figures. Secondly, a statistical model is employed to estimate the net new jobs based on unreported businesses, known as the «birth-death» adjustment.
Traders calculate the Non-Farm Payroll (NFP) surprise by comparing the actual number of newly reported jobs with a consensus forecast. The difference between the two is then standardized by the variability of survey responses, yielding a z-score that reflects the unexpected nature of the data release.
Of equal importance are wage dynamics, which serve as an indicator of underlying inflationary pressures. The Bureau of Labor Statistics releases data on average hourly earnings (AHE) for non-farm private employees, reflecting changes in compensation per hour and forming a crucial input into consumer price projections.
AHE, being influenced by shifts in the composition of industrial sectors and occupational structures, necessitates the use of the employment cost index (ECI) by economists to monitor changes in the aggregate labor costs borne by employers, encompassing wages, salaries, and fringe benefits. This analysis employs a standardized framework of job attributes, enabling the isolation of variations in pure costs.
In the historical context, persistent increases in the ECI often precede periods of monetary tightening by central banks. The rationale behind this relationship lies in the fact that rising unit labor expenses directly impact firms' pricing strategies and contribute to underlying inflationary pressures.
In order to anticipate non-farm payrolls (NFP) and wage surprises before their official release, market participants closely monitor a range of key indicators in the labor market.
One such indicator is the Initial Jobless Claims report, which is released every Thursday. This report provides information on the number of individuals who have first filed for unemployment benefits. It serves as an early indicator of layoffs and trends in employment. A consistent decline in this metric typically indicates a reduction in unemployment and a tightening of labor conditions in the coming months.
Additionally, the Job Openings and Labor Turnover Survey (JOLTS), released with a one-month delay, provides detailed data on job openings, hiring, and separations across the economy. This data directly measures labor demand and can be used to forecast future wage pressures. A higher proportion of job vacancies relative to the number of unemployed individuals suggests upward pressure on wages, potentially leading to higher-than-expected Actual Hourly Earnings (AHE) figures.
Certain companies also keep an eye on the ADP National Employment Report. This report, derived from payroll data processing, provides an early glimpse into upcoming NFP (non-farm payrolls). However, analysts continue to debate the predictive accuracy of this indicator.
The Phillips Curve serves as a theoretical framework for interpreting these empirical cues. The curve suggests an inverse correlation between unemployment and inflation. Early iterations of the curve viewed unemployment gaps as the sole determinant of inflation. More recent versions, known as New Keynesian Phillips Curves, also consider forward-looking inflation expectations and real marginal costs. These reflect price rigidity and the role of anticipations in wage negotiations.
Empirical data suggests that the conventional Phillips Curve relationship may have lost some of its relevance in developed economies. Despite continued large fluctuations in unemployment, inflationary pressures appear to be dampened by anchored inflation expectations and the increasing integration of global markets.
However, sectoral analyses reveal vulnerabilities in labour-intensive service sectors, where local labour market strength can directly translate into wage increases and consumer price hikes. To anticipate and model these unforeseen developments in the labour market, economists and analysts employ mixed-frequency econometric models like Bayesian vector autoregressive (BVAR) and mixed-frequency vector autoregressive models (MVAR), which integrate high-frequency data on weekly unemployment claims, monthly employment figures, and quarterly gross domestic product (GDP) to improve the accuracy of short-term forecasts for key indicators like non-farm payroll employment and wage growth.
The European Central Bank employs sophisticated models to anticipate the repercussions of shifts in the labour market on economic activity.
These models, known as autoregressive distributed lag models (ARDL), incorporate lagged values of key indicators such as aggregate hours worked, employment conditions index, job openings, labour turnover, and unemployment claims. These models have demonstrated remarkable accuracy in predicting unexpected changes in aggregate hours worked more than 70% of the time in backtesting scenarios.
To further refine these forecasts, machine learning techniques like random forests and gradient boosting are employed, capturing intricate interactions between macroeconomic data, financial market indicators, and survey-based information.
Unforeseen fluctuations in the labour market can result in rapid adjustments of exchange rates and interest rates. In the past, an unanticipated increase of one standard deviation in non-farm payrolls (+1σ) was associated with a 0.4–0.6 percentage point rise in the USD index futures. This was reflected in the adjustment of federal funds futures, indicating tighter monetary conditions.
Strategies based on market events often involve placing bracket orders around buy and sell prices to capture volatility and mitigate slippage risks. This can be accomplished through the use of automated straddle trades. Alternatively, a market fade approach can be employed, involving shorting the market after a significant volume depletion when the price moves beyond the intraday weighted average price (VWAP).
In order to manage risk effectively, traders can employ dynamic position sizing strategies when the Z-score surpasses a threshold of ±1.5. This involves reducing position sizes, while simultaneously tightening stop-loss levels by 0.5 times the average true range (ATR). This approach helps preserve capital during times of extreme market volatility, allowing traders to seamlessly transition between trend-following and mean reversion strategies, thereby mitigating potential losses.
Professional analysts utilize a comprehensive approach to labor market analysis, integrating a wide range of data sources. This includes broad and narrow indicators of unemployment, establishment surveys, detailed wage metrics, advanced forecasting models, and leading indicator nowcasts. By leveraging this extensive data set, analysts can create a robust analysis of the labor market, providing not only directional views on currency markets but also identifying opportunities for event-driven volatility and anchoring forex positions with the most up-to-date and granular labor market information.
