Indicators For Upcoming Economic “Crash”

SOURCE: U.S. Bureau of Labor Statistics - Current Employment Statistics

Total # of People Employed from 2007 thru 2026 so that we can include 2008-2009 Recession Period, Pre-COVID, COVID, and recent data

Data through September 2026:

Last 13 recent months of data - values are in thousands
series_id year_month year period value monthly_change
CES0000000001 2025-09-01 2025 M09 158548.0 76.0
CES0000000001 2025-10-01 2025 M10 158408.0 -140.0
CES0000000001 2025-11-01 2025 M11 158449.0 41.0
CES0000000001 2025-12-01 2025 M12 158432.0 -17.0
CES0000000001 2026-01-01 2026 M01 158592.0 160.0
CES0000000001 2026-02-01 2026 M02 158436.0 -156.0
CES0000000001 2026-03-01 2026 M03 158650.0 214.0
CES0000000001 2026-04-01 2026 M04 158798.0 148.0
CES0000000001 2026-05-01 2026 M05 158861.0 63.0
CES0000000001 2026-06-01 2026 M06 158892.0 31.0
CES0000000001 2026-07-01 2026 M07 158882.0 -10.0
CES0000000001 2026-08-01 2026 M08 159015.0 133.0
CES0000000001 2026-09-01 2026 M09 159044.0 29.0

Now, let’s look at difference or change in total employment numbers on a monthly basis (“monthly_change”)

From above, due to the extreme values, it is hard to visually compare. So, let’s compare “normal” employment periods which exclude extreme events. So let’s combine a data set containing only 2017 thru 2019 data with another data set contaning 2024 thru 2026 data.

shape: (69, 6)
series_id year_month year period value monthly_change
str date str str f64 f64
"CES0000000001" 2017-01-01 "2017" "M01" 145628.0 220.0
"CES0000000001" 2017-02-01 "2017" "M02" 145846.0 218.0
"CES0000000001" 2017-03-01 "2017" "M03" 145970.0 124.0
"CES0000000001" 2017-04-01 "2017" "M04" 146175.0 205.0
"CES0000000001" 2017-05-01 "2017" "M05" 146380.0 205.0
"CES0000000001" 2017-06-01 "2017" "M06" 146583.0 203.0
"CES0000000001" 2017-07-01 "2017" "M07" 146772.0 189.0
"CES0000000001" 2017-08-01 "2017" "M08" 146919.0 147.0
"CES0000000001" 2017-09-01 "2017" "M09" 147008.0 89.0
"CES0000000001" 2017-10-01 "2017" "M10" 147149.0 141.0
"CES0000000001" 2017-11-01 "2017" "M11" 147372.0 223.0
"CES0000000001" 2017-12-01 "2017" "M12" 147523.0 151.0
… … … … … …
"CES0000000001" 2025-10-01 "2025" "M10" 158408.0 -140.0
"CES0000000001" 2025-11-01 "2025" "M11" 158449.0 41.0
"CES0000000001" 2025-12-01 "2025" "M12" 158432.0 -17.0
"CES0000000001" 2026-01-01 "2026" "M01" 158592.0 160.0
"CES0000000001" 2026-02-01 "2026" "M02" 158436.0 -156.0
"CES0000000001" 2026-03-01 "2026" "M03" 158650.0 214.0
"CES0000000001" 2026-04-01 "2026" "M04" 158798.0 148.0
"CES0000000001" 2026-05-01 "2026" "M05" 158861.0 63.0
"CES0000000001" 2026-06-01 "2026" "M06" 158892.0 31.0
"CES0000000001" 2026-07-01 "2026" "M07" 158882.0 -10.0
"CES0000000001" 2026-08-01 "2026" "M08" 159015.0 133.0
"CES0000000001" 2026-09-01 "2026" "M09" 159044.0 29.0

From above, we see it is now easier to visually compare. There was a little bit of up-tick for beginning of 2026, but in recent months, jobs added are still low compared to pre-COVID.

Let’s look at private sector employment

Last 13 recent months of data
series_id year_month year period value monthly_change
CES0500000001 2025-09-01 2025 M09 135005.0 68.0
CES0500000001 2025-10-01 2025 M10 135018.0 13.0
CES0500000001 2025-11-01 2025 M11 135090.0 72.0
CES0500000001 2025-12-01 2025 M12 135083.0 -7.0
CES0500000001 2026-01-01 2026 M01 135263.0 180.0
CES0500000001 2026-02-01 2026 M02 135115.0 -148.0
CES0500000001 2026-03-01 2026 M03 135317.0 202.0
CES0500000001 2026-04-01 2026 M04 135467.0 150.0
CES0500000001 2026-05-01 2026 M05 135528.0 61.0
CES0500000001 2026-06-01 2026 M06 135554.0 26.0
CES0500000001 2026-07-01 2026 M07 135582.0 28.0
CES0500000001 2026-08-01 2026 M08 135671.0 89.0
CES0500000001 2026-09-01 2026 M09 135717.0 46.0

We recovered after the huge job losses in February 2026. But recent months trend does not look favorable.

Average of monthly change from 2017 thru 2019 of all non-farm employment:

Show code 
(
    GT(
        df_all
        .filter(
            pl.col("year").is_in(["2017","2018","2019"])
        )
        .select(
            pl.col("monthly_change").mean().alias("avg_value")
        )
    )
    .fmt_number(columns="avg_value", decimals=2)
    .cols_align(align="center")
    .show()
)
avg_value
177.39

Average of monthly change from 2024 thru 2026 of all non-farm employment:

Show code 
(
    GT(
        df_all
        .filter(
            pl.col("year").is_in(["2024","2025","2026"])
        )
        .select(
            pl.col("monthly_change").mean().alias("avg_value")
        )
    )
    .fmt_number(columns="avg_value", decimals=2)
    .cols_align(align="center")
    .show()
)
avg_value
66.27

On average, during the few years before COVID, 177 thousand jobs were added. From 2024 thru 2026, only 66 thousand jobs were added on average.

Average of monthly change from 2017 thru 2019 of all private sector employment:

Show code 
(
    GT(
        df_private
        .filter(
            pl.col("year").is_in(["2017","2018","2019"])
        )
        .select(
            pl.col("monthly_change").mean().alias("avg_value")
        )
    )
    .fmt_number(columns="avg_value", decimals=2)
    .cols_align(align="center")
    .show()
)
avg_value
165.67

Average of monthly change from 2024 thru 2026 of all private sector employment:

Show code 
(
    GT(
        df_private
        .filter(
            pl.col("year").is_in(["2024","2025","2026"])
        )
        .select(
            pl.col("monthly_change").mean().alias("avg_value")
        )
    )
    .fmt_number(columns="avg_value", decimals=2)
    .cols_align(align="center")
    .show()
)
avg_value
59.12

On average, during the few years before COVID, 166 thousand jobs were added. From 2024 thru 2026, only 59 thousand jobs were added on average.

Let’s take population into account of employment-eligible persons (16 years old and over) using series ID “LNS12300000”

SOURCE: U.S. Bureau of Labor Statistics - National Unemployment

Data through September 2026:

Last 5 recent months of data
series_id year_month year period value
LNS12300000 2026-05-01 2026 M05 59.2
LNS12300000 2026-06-01 2026 M06 59.0
LNS12300000 2026-07-01 2026 M07 58.9
LNS12300000 2026-08-01 2026 M08 59.1
LNS12300000 2026-09-01 2026 M09 59.2

When taking into account population level, we are at a downward trend. Pre-COVID, the trend was upwards.

Let’s look at a few more metrics

Layoffs and Discharges

Hires

Job Openings

Layoffs and discharges don’t look too alarming, but the number of hires and job openings are concerning.