# Federal Reserve Economic Data: Your trusted data source since 1991

The FRED® Blog

## Cash vs. 1-month Treasury bills

Is it worth it to buy 1-month Treasury bills? The above FRED graph shows their returns in recent years: While they often get very close to zero, at least they’re positive.* But “positive” may not count for much since we have to account for inflation. So let’s redo the graph by subtracting inflation from the return.

This exercise isn’t as simple as it might appear: First, we must factor-in inflation over the life of the bill, which is shorter than the period in which inflation is typically reported. Second, the Treasury return that’s reported in the data is annualized, meaning the monthly return is compounded to an annual return.

So here’s what we need to do to the CPI:

1. Take the percent change from the previous month, to match the maturity of the (1-month) bill
2. Divide it by 100, to get rid of the % units
3. Add 1, to prepare for compounding
4. Take the power of 12, to compound for one full year (to match the annualized Treasury rate)
5. Remove 1
6. Multiply by 100 to express it back in % units
7. Subtract the result from the Treasury rate

The result shows that the real return on the 1-month Treasury bill is very often negative. But simply holding on to your money would have been worse, as money is notorious for earning no interest whatsoever.

*In December 2011, the nominal return actually hit 0.00%.

How these graphs were created: For the first graph, search for “one month Treasury” and select the monthly series. For the second graph, take the first, go to the “Edit Graph” panel, add the CPI series, change its units to “Percent Change,” and apply formula a-((1+b/100)^12-1)*100.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CPIAUCSL, GS1M

## The big February employment miss

The Bureau of Labor Statistics (BLS) released its most recent employment report on March 8: In February of this year, the nonfarm economy, on net, created only 25,000 private-sector jobs and 20,000 jobs overall. One part of this report is the establishment survey, which contributed some of the weakest numbers since the past recession.

Forecasters failed to predict these anemic jobs numbers. In fact, before the report’s release, consensus market expectations foresaw 180,000 jobs being created in February. Thus, consensus expectations “missed” the February payroll number by 160,000 persons on the downside. The household survey component of the report was stronger, with the unemployment rate declining by 0.2%. According to the BLS, this decline in unemployment “reflects, in part, the return of federal workers who were furloughed in January due to the partial government shutdown.”

Another well-known statistic used to predict the BLS jobs number is the ADP national employment report. It’s produced by the ADP Research Institute, which is part of ADP. (ADP is an American company that provides human resource and payroll management software and measures nonfarm private sector employment using anonymous data from its clients.) The ADP report, which is released monthly a few days before the BLS employment numbers come out, predicted an increase in private nonfarm payroll of 183,000 in February. Thus, the ADP report differed from the BLS number by 158,000. This difference was one of the largest in 14 years, relative to forecast errors in months outside of U.S. recessions.

FRED provides an integrated picture of all this in the graph above, which combines three monthly series: BLS total nonfarm payroll, BLS government employment, and ADP total private nonfarm payroll. All three are presented as their month-over-month changes in thousands of persons. We combine the three series by first taking the difference of the first two. This gives the change in private nonfarm payroll according to the BLS. Second, we construct a “forecast error” by subtracting the ADP number (which we use as our forecast) from the actual BLS private payroll number.

Excluding five months during the recession (the shaded section in the graph), the downside difference between the BLS and ADP numbers for February employment was larger than the downside difference of any other month since 2003.

How this graph was created: Search for “total nonfarm payrolls,” select the series “All Employees: Total Nonfarm Payrolls,” and click “Add to Graph.” From the “Edit Graph” panel, use the “Edit Line 1” feature to “Customize data”: In this field, enter “government employees.” From the list of options, choose “All Employees: Government” and click “Add.” Again under “Customize data,” search for and add “Total Nonfarm Private Payroll Employment.” The first two series are from the BLS and the third is from ADP. For each of these three series, adjust the units to “Change, Thousands.” Next, compute the series shown here by subtracting the other two. FRED denotes the variables for each series in order: So, enter “a-b-c” into the “Formula” box and click “Apply.”

Suggested by Bill Dupor.

View on FRED, series used in this post: CES9091000001, NPPTTL, PAYEMS

## The most constant economic series ever

Here at the FRED Blog, we often represent economic measures such as consumption or investment as a share of GDP. (For example, a recent post looked at the trade balance as a share of GDP.) We do this to account for general growth and inflation: Most macroeconomic measures grow over time because (1) the overall economy grows and (2) prices tend to increase. For many economic questions, what really matters is how economic measures relate to other measures, such as GDP. Now, when you add up all the components of GDP, you get GDP. This is exactly what we represent in the graph above, the share of GDP in GDP. This is an extremely important series to watch: If it deviates from its current trend, we know that something has gone terribly terribly wrong.

How this graph was created: Use the release tables on the percentage shares of GDP, select annual or quarterly (it doesn’t matter for this graph), and select GDP (the first line).

Suggested by Christian Zimmermann.

View on FRED, series used in this post: A191RE1A156NBEA