RoboMacro is an AI-powered macroeconomic research platform that provides daily research notes, economic calendars, central bank monitoring, quant strategy tools, and equity screeners. It covers 20+ countries including US, UK, Eurozone, Japan, and China — all 100% automated by AI with no human in the loop.

Who created RoboMacro?

RoboMacro was created by Chris Apostolou, a former economist and credit analyst with 9+ years of buy-side and sell-side experience in macroeconomic research and financial markets.

RoboMacro offers free access to most of its research content including daily macro notes, the payrolls dashboard with 100+ charts, economic calendars, and central bank monitoring.

What countries and regions does RoboMacro cover?

RoboMacro covers all major economies including US, UK, Eurozone (Germany, France, Italy, Spain, Netherlands), Japan, China, Canada, Australia, New Zealand, Switzerland, Sweden, Norway, South Korea, India, Brazil, Mexico, Turkey, South Africa, and more — over 20 countries in total.

What data sources does RoboMacro use?

RoboMacro pulls data from FRED (Federal Reserve Economic Data), OECD, World Bank, BLS (Bureau of Labor Statistics), and other official statistical agencies. Charts update daily or monthly depending on the data release schedule.

How often is RoboMacro content updated?

Daily research notes are published every business day. Economic calendar data and central bank hawk/dove scores are updated in real-time. Strategy tools and equity screeners update daily. The payrolls dashboard updates monthly when new BLS data is released.

Labor Market Observatory

US Payrolls & Labor Market

100 models covering NFP payrolls, sectoral employment, wage growth, hours & productivity, unemployment, labor force, JOLTS, demographics, and recession signals — built from publicly available FRED data.

US Payrolls & Labor MarketCharts

Headline

Sectors

Wages

Hours

Unemployment

Labor Force

FT / PT

JOLTS

Demographics

Signals

View

Smoothing

Cache: 31 Jul 2026

Showing 1–10 of 102 charts

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Headline Payrolls

1. Total Nonfarm Payrolls

Thousands, SA — All employees on nonfarm payrolls (FRED: PAYEMS)

Chart 1: Total Nonfarm Payrolls

Thousands, SA — All employees on nonfarm payrolls (FRED: PAYEMS)

Sector: Headline Payrolls. Frequency: Monthly. Source: FRED / BLS.

Data series (1): Total NFP (Thousands) (latest: 158,984 on 2026-06-01).

Frequency: MonthlySeries: 1Last data: 1 Jun 2026

Source: FRED / BLS

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Headline Payrolls

2. Total Private Payrolls

Monthly — Private sector payrolls excluding government (FRED: USPRIV)

Chart 2: Total Private Payrolls

Monthly — Private sector payrolls excluding government (FRED: USPRIV)

Sector: Headline Payrolls. Frequency: Monthly. Source: FRED / BLS.

Data series (1): Private Payrolls (Thousands) (latest: 135,613 on 2026-06-01).

Frequency: MonthlySeries: 1Last data: 1 Jun 2026

Source: FRED / BLS

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Headline Payrolls

3. Monthly Change in Nonfarm Payrolls

Thousands, SA — Net new jobs added each month (FRED: PAYEMS diff)

Chart 3: Monthly Change in Nonfarm Payrolls

Thousands, SA — Net new jobs added each month (FRED: PAYEMS diff)

Sector: Headline Payrolls. Frequency: Monthly. Source: FRED / BLS.

Data series (1): NFP Monthly Change (latest: 158,984 on 2026-06-01).

Chart type: bar chart.

Transform: month-over-month difference.

Frequency: MonthlySeries: 1Last data: 1 Jun 2026

Source: FRED / BLS

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Headline Payrolls

4. Total NFP vs Private Payrolls

Total nonfarm vs private-sector employment — government job contribution

Chart 4: Total NFP vs Private Payrolls

Total nonfarm vs private-sector employment — government job contribution

Sector: Headline Payrolls. Frequency: Monthly. Source: FRED / BLS.

Data series (2): Total NFP (latest: 158,984 on 2026-06-01); Private Only (latest: 135,613 on 2026-06-01).

Frequency: MonthlySeries: 2Last data: 1 Jun 2026

Source: FRED / BLS

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Headline Payrolls

5. Payrolls Recovery Since Feb 2020

Rebased to 100 at Feb 2020 — total vs private vs government

Chart 5: Payrolls Recovery Since Feb 2020

Rebased to 100 at Feb 2020 — total vs private vs government

Sector: Headline Payrolls. Frequency: Monthly. Source: FRED / BLS.

Data series (3): Total NFP (latest: 158,984 on 2026-06-01); Private (latest: 135,613 on 2026-06-01); Government (latest: 23,371 on 2026-06-01).

Rebased to 100 at 2020-02-01.

Frequency: MonthlySeries: 3Last data: 1 Jun 2026Rebased: Jan 2024 = 100

Source: FRED / BLS

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Headline Payrolls

6. Goods-Producing vs Service-Providing Employment

Structural shift toward services economy (FRED: USGOOD + SRVPRD)

Chart 6: Goods-Producing vs Service-Providing Employment

Structural shift toward services economy (FRED: USGOOD + SRVPRD)

Sector: Headline Payrolls. Frequency: Monthly. Source: FRED / BLS.

Data series (2): Goods-Producing (latest: 21,536 on 2026-06-01); Service-Providing (latest: 137,448 on 2026-06-01).

Frequency: MonthlySeries: 2Last data: 1 Jun 2026

Source: FRED / BLS

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Headline Payrolls

7. Establishment vs Household Survey Employment

Payroll survey (PAYEMS) vs household survey (CE16OV) — divergence monitor

Chart 7: Establishment vs Household Survey Employment

Payroll survey (PAYEMS) vs household survey (CE16OV) — divergence monitor

Sector: Headline Payrolls. Frequency: Monthly. Source: FRED / BLS.

Data series (2): Establishment (NFP) (latest: 158,984 on 2026-06-01); Household (CPS) (latest: 162,264 on 2026-06-01).

Frequency: MonthlySeries: 2Last data: 1 Jun 2026

Source: FRED / BLS

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Headline Payrolls

8. Temporary Help Services vs Payrolls MoM Change

Monthly — temp help leads NFP turns by 3-6 months. NFP MoM change as bars on RHS.

Chart 8: Temporary Help Services vs Payrolls MoM Change

Monthly — temp help leads NFP turns by 3-6 months. NFP MoM change as bars on RHS.

Sector: Headline Payrolls. Frequency: Monthly. Source: FRED / BLS.

Data series (2): Temp Help (Thousands) (latest: 2,499.2 on 2026-06-01); NFP MoM Change (latest: 158,984 on 2026-06-01).

Frequency: MonthlySeries: 2Last data: 1 Jun 2026

Source: FRED / BLS

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Headline Payrolls

9. Multiple Jobholders

Monthly — workers holding more than one job (FRED: LNS12026620)

Chart 9: Multiple Jobholders

Monthly — workers holding more than one job (FRED: LNS12026620)

Sector: Headline Payrolls. Frequency: Monthly. Source: FRED / BLS.

Data series (1): Multiple Jobholders (Thousands) (latest: 5.3 on 2026-06-01).

Frequency: MonthlySeries: 1Last data: 1 Jun 2026

Source: FRED / BLS

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Sectoral Employment

10. Education & Health Services Employment

Monthly — largest sectoral driver of recent job growth (FRED: USEHS)

Chart 10: Education & Health Services Employment

Monthly — largest sectoral driver of recent job growth (FRED: USEHS)

Sector: Sectoral Employment. Frequency: Monthly. Source: FRED / BLS.

Data series (1): Education & Health (Thousands) (latest: 27,973 on 2026-06-01).

Frequency: MonthlySeries: 1Last data: 1 Jun 2026

Source: FRED / BLS

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Showing 1–10 of 102 charts

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Source: FRED (Federal Reserve Economic Data), BLS, Conference Board, Kansas City Fed.

Payrolls Forecasting Lab

EXPERIMENT

10 independent models — time-series econometrics through gradient boosting — each forecasting Friday's BLS Employment Situation.

Jul 2026 NFP

BLS Employment Situation — Friday, August 7, 2026

5 days away

Consensus Forecast — Median of 10 Models

Nonfarm Payrolls

96k

80% model range: 61k → 285k

Unemployment Rate

4.2%

80% model range: 4.2% → 5.4%

Part-Time Workers

4695k

80% model range: 980k → 4990k

Avg Hourly Earnings YoY

3.55%

80% model range: 3.46% → 3.72%

NFP model disagreement (σ): ±109k · Consensus range (80%): 61k – 285k

Monthly Track Record — Last 4 BLS Releases

Genuine out-of-sample NFP predictions. Each cell shows the forecast and how far it landed from the actual print — “18k below” means the forecast was 18k under the actual. Colour shows accuracy (green = close, red = far); green-boxed cells beat the street consensus (Dow Jones / FinanceFlowAPI economist survey).

Model	Jul 2026 NFP Forecast ↑	Jun 2026 Actual: +57k	May 2026 Actual: +129k	Apr 2026 Actual: +148k	Mar 2026 Actual: +214k
STREETStreet Consensus Dow Jones / FinanceFlowAPI economist survey	—	+110k53k above	+85k44k below	+62k86k below	+60k154k below
MEDModel Ensemble (median)	+96k	+84k27k above	+87k42k below	+70k78k below	+49k165k below
ARNaive Autoregressive (AR)	+62k	+73k #316k above Beat street	+65k #564k below	+130k #118k below Beat street	+72k #4142k below Beat street
SARIMASARIMAX	+44k	+46k #211k below Beat street	+39k #790k below	+41k #7107k below	+44k #6170k below
VARVector Autoregression	+100k	-155k #8212k below	-46k #9175k below	+235k #587k above	-343k #10557k below
UCUnobserved Components	+90k	+121k #664k above	+114k #215k below Beat street	+82k #466k below Beat street	-41k #9255k below
MIDASAlmon Distributed Lag	+107k	+94k #437k above Beat street	+151k #422k above Beat street	+130k #218k below Beat street	+112k #1102k below Beat street
ENetElasticNet Linear	+389k	+122k #765k above	+110k #319k below Beat street	+103k #345k below Beat street	+96k #2118k below Beat street
RFRandom Forest	+66k	+54k #13k below Beat street	+36k #893k below	+58k #690k below	+77k #3137k below Beat street
XGBXGBoost	+91k	+119k #562k above	+41k #688k below	-7k #10155k below	+43k #7171k below
LGBMLightGBM + Regime	+274k	-1277k #101334k below	+134k #15k above Beat street	+36k #8112k below	+54k #5160k below
MLPNeural Network (MLP)	+246k	+414k #9357k above	+432k #10303k above	0k #9148k below	0k #8214k below
Models beating street consensus:		4/10 models	4/10 models	4/10 models	4/10 models

Error = actual − forecast. Green (#1–3) best; red (#8–10) worst. Green-boxed cells beat the street consensus (Dow Jones survey / FinanceFlowAPI). Click any model for the full deep-dive.