The takeaway

Employment of software coders has decelerated sharply since 2022. Recent research suggests this job-specific shock isn’t caused by a slowing industry but likely from the emergence of AI tools.

What employment data show

Our FRED graph above shows annual employment data for 1991-2025 reported by the US Bureau of Labor Statistics: The solid blue line shows the year-over-year percent change in total employment. The dashed green line shows the same measure but specifically for computer systems design and related services.

Annual employment growth in the computer systems design industry has outpaced overall employment growth, except for two distinct periods:

• In 2002-2003, the industry reeled back from the dot-com buildup of the late 1990s and was exposed to the 2001 recession.
• After 2022, a different type of employment shock took place.

What recent research shows

Analysis by Leland D. Crane and Paul E. Soto at the Board of Governors of the Federal Reserve System finds that aggregate employment of software coders decelerated sharply after the broad public launch of generative AI tools in November 2022.

The research points out that more than 30% of national coder employment is concentrated in the computer systems design and related services industry, 40% of which consists of coders.

These researchers were able to establish a likely causal effect between the public launch of ChatGPT and employment by modeling a counterfactual employment scenario without any external shock and then comparing that with the actual data. This allowed them to tell the difference between an occupational shock particular to coders and a shock to the broader industry that would also impact related computer system designs jobs.

To learn more about this topic, check this earlier FRED Blog post on the sharp decrease in job postings in the broader tech industry.

How this graph was created: Search FRED for “All Employees, Computer Systems Design and Related Services”. Click the “Edit Graph” button and select the “Add Line” tab to search for “All Employees, Total Nonfarm.” Don’t forget to click “Add data series.” For both selected series, change the units to “Percent Change, Annual,” with the aggregation method as “End of Period.” Last, change the date range to start in 1991-01-01.

Suggested by Maria Goffinet, Elena Roussanova, and Diego Mendez-Carbajo.

The takeaway

The federal minimum wage is the lowest hourly rate employers are required to pay workers. It’s been $7.25 per hour for a while now. State minimum wages are a different story: Many are higher than the federal minimum, but the benefits can vary depending on cost of living in that location.

Mapping state minimum wages

Washington DC and 30 of the 50 US states have minimum wages higher than the federal minimum. The median minimum wage for US states, including DC, is currently $11.85.

Our FRED map above shows these minimum wages in 2026.

Gray shading indicates a state either follows the federal minimum wage of $7.25 or has chosen to set their minimum wage at the same rate.

Other colors indicate the state minimum wage is higher than the federal minimum, from the lowest in light yellow to the highest in dark green. As of 2026, the highest of these state minimum wages is $18.40 in DC and the lowest is $8.75 in West Virginia.

Adjusting for cost of living

States consider many factors when setting their minimum wage, including cost of living. Our FRED map above shows one such measure: regional price parities (RPPs).

RPPs assign values to the relative price level in each state compared with the national average, which has a value of 100. California is highest, at 110.72, which means prices there are 10.7% higher than the national average. Arkansas is lowest, at 86.94, with prices about 13.1% lower than average.

RPPs are determined by the average prices paid for a typical basket of goods and services, which is applied consistently across states and reflects what the average household would consume, not necessarily what the average minimum wage worker would consume. Note that state-level RPPs are an average for the entire state; but of course, cost of living can vary within a state. For example, Florida’s RPP is 103.41. Within the state, though, RPPs vary from 95.47 in the Tampa area to 103.56 in the Miami area.

Adjusting the minimum wage by RPPs provides a measure of real purchasing power across states. For example: DC has the highest minimum wage, but also a high cost of living. Its minimum wage value drops by about $1.66 to $16.74 once adjusted by its RPP. New Hampshire is one of the states with the lowest minimum wage ($7.25), which drops to $6.96 once adjusted by its RPP.

How these maps were created: First map: Search FRED for and select “State Minimum Wage Rate for Missouri” (series ID STTMINWGMO). Or any state, really. In the upper right, click “View Map” and then the blue “Edit Map” button. Click the light-yellow color (next to the less than or equal to 7.25) and change the color to gray (#999995) so all states with a minimum wage equal to or below $7.25 appear gray. Second map: Search FRED for and select “Regional Price Parities: All Items for Missouri” (series ID MORPPALL) and click “View Map.”

Suggested by Reagan Gilmore and Charles Gascon.

The name

In the 1980s, Texas had a banking crisis whose causes included shocks in oil prices and real estate investments. In response, Gerard Cassidy of the Royal Bank of Canada developed the Texas ratio metric to assess a bank’s credit risk in that state.

The definition

The Texas ratio measures a bank’s nonperforming loans divided by the sum of tangible equity capital and allowance for losses on loans and leases.

Nonperforming loans consist of the following:

1. Nonaccrual loans, where a lender stops adding expected interest to their reported income.
2. Loans with payments 90 or more days past due.
3. Real estate assets acquired through foreclosure.

Tangible equity capital represents the available capital cushion to absorb losses and is found by subtracting intangible assets from total bank equity capital. Allowance for loan losses represents funds set aside to cover expected loan losses.

The interpretation

The lower the ratio (that is, the closer to 0%), the smaller the risk of loan losses to a bank’s capital. The higher the ratio, especially if it exceeds 100%, the greater the risk of a bank being unable to cover its potential loan losses.

The graphed data

Our FRED graph shows the aggregated Texas ratio for all FDIC-insured commercial banks in the U.S. between the first quarter of 1984 and the first quarter of 2026. At the time of this writing, its value is 5.82%. That’s near the all-time low of 4.59% recorded during the second quarter of 2022.

Read more about the Texas ratio, including values by bank size, in Banking Analytics: Understanding Credit Risk with the Texas Ratio.

How this graph was created: Search FRED for and select “Balance Sheet: Loans and Leases in Nonaccrual Status, Millions of U.S. Dollars, Not Seasonally Adjusted.” Click on the “Edit Graph” button and under the “Customize data” section in the “Edit Line” tab, search for “Balance Sheet: Loans and Leases 90 Days or More Past Due, Millions of U.S. Dollars, Not Seasonally Adjusted” and click “Add.” Repeat for “Balance Sheet: Total Assets: Other Real Estate Owned, Millions of U.S. Dollars, Not Seasonally Adjusted,” “Balance Sheet: Total Liabilities and Capital: Total Equity Capital: Total Bank Equity Capital, Millions of U.S. Dollars, Not Seasonally Adjusted,” “Balance Sheet: Total Assets: Intangible Assets, Millions of U.S. Dollars, Not Seasonally Adjusted,” and “Balance Sheet: Total Assets: Total Loans and Leases: Less: Reserve for Losses, Millions of U.S. Dollars, Not Seasonally Adjusted.” Enter the formula 100 * (a+b+c) / (d-e+f).

Suggested by Steven Tian and Diego Mendez-Carbajo.