Federal Reserve Economic Data

FRED currently has more than 822,000 data series, and you can leverage this wealth of information to create new data. For example, you can reasonably approximate the value of the exchange rate between the Swedish krona and the euro by using two different, yet related, data series.

The FRED graph above uses quarterly exchange rate data from the Organisation for Economic Co-operation and Development. We start with the exchange rate between the Swedish krona relative to the US dollar. Next, we customize the data by adding a second series to Line 1 in the graph: the exchange rate between the US dollar and the euro. Last, we combine both series by applying the formula a/b, which stands for the transformation krona per dollar / euro per dollar.

The resulting series is a very close approximation of the quarterly average exchange rate between the Swedish krona and the euro reported by the Central Bank of Sweden. Låt oss fundera med FRED®!

Coda: Coincidentally, you can create that very same data approximation by using data about Sweden’s GDP reported by Eurostat, the European Union’s statistical authority. The quarterly GDP figures available in FRED are reported both in the domestic currency (the krona) and in the currency of the Eurozone (the euro). We customized the data by applying this formula: GDP measured in krona / GDP measured in euros. You can see the result side by side with our earlier work here.

How this graph was created: Search FRED for and select “National Currency to US Dollar Exchange Rate: Average of Daily Rates for Sweden.” From the “Edit Graph” panel, use the “Edit Line 1” tab to customize the data by searching for and selecting “Currency Conversions: US$ Exchange Rate: Average of Daily Rates: National Currency:usd for the Euro Area (19 Countries).” Next, create a custom formula to combine the series by typing in a/b and clicking “Apply.”

Suggested by Diego Mendez-Carbajo.

While much of the future depends on things that are impossible to forecast or time (for example, a pandemic), particular dynamics in some economic data have allowed some success in predicting a recession in the short run.

The FRED graph above shows data from Marcelle Chauvet and Jeremy Piger; the data set is based on economic data that tend to lead business cycle indicators—that is, they provide insight before the other data do. Judging from the graph, every recession (shaded in gray) has been preceded by a small increase in this computed probability of recession. There have been errors, but those errors have always predicted a recession that did not happen.

At the time of this writing, these data do not seem to exhibit any noticeable increase, which implies the data are not signaling a significant risk of recession. Hence, if a recession occurred soon, that would mark the first time this indicator would fail in this way. Another indicator, the Sahm Rule, is aligned with this assessment. But who knows? Abnormal things have happened in the data in the past few years.

How this graph was created: Search FRED for “recession probability” and select the series.

Suggested by Christian Zimmermann.

The Current Population Survey (CPS) from the US Census is one of the oldest, largest, and most well-recognized surveys in the United States. Every month, the survey is used to collect information from a probability-selected sample of about 60,000 households. The US Bureau of Labor Statistics then uses those data to calculate the median usual weekly earnings of wage and salary workers.

The Federal Reserve Bank of Atlanta also uses CPS data, and the FRED graph above shows monthly values of the wage growth tracker the Atlanta Fed produces with the data. The households sampled in the CPS are interviewed at regular intervals, and the Atlanta Fed measures the growth rate of typical labor earnings by comparing the wages of about 2,000 anonymous individuals who respond to the same survey 12 months apart.

The wage growth tracker series report the median, or typical, percent change from a year ago in all the hourly wages used in this calculation. That’s why it is called “unweighted.” The wage growth tracker reports both a 3-month moving average (the blue line) and a 12-month moving average (the red line) because the survey respondents change from month to month. A moving average smooths out data outliers that are likely to distort the visualization of trends and cycles by adding all the data points over the stated number of months and dividing them by that number. The longer the length of the moving average, the smoother the data are. (Also notice that the 3- and 12-month moving average calculations are labeled by the last month of those time frames. So, while the data start in January 1997, the earliest data points are in March and December 1997.)

Now, what do the data show? First, the tracked wage growth rates have not been smaller than zero between 1997 and the time of this writing. Perhaps this isn’t surprising because the CPS reports non-inflation-adjusted, or nominal, earnings. Second, the downward trend in tracked wage growth recorded between 1997 and 2011 reversed after that later date. Last and most startling, the slowdown in tracked wage growth that followed the 2001 and the 2007-2009 recessions (the shaded areas in the FRED graph) did not materialize after the 2020 recession. Instead, tracked wage growth has accelerated noticeably since 2021 and only recently seems to have plateaued. That reflects the currently resilient conditions of the overall labor market and the upward pressure on nominal wages resulting from the recent bout of above-average inflation.

Don’t lose track of the FRED Blog, as we’ll continue to explore this rich trove of wage growth tracker data over the next several months.

How this graph was created: Search FRED for and select “12-Month Moving Average of Unweighted Median Hourly Wage Growth: Overall.” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “3-Month Moving Average of Unweighted Median Hourly Wage Growth: Overall.”

Suggested by Diego Mendez-Carbajo.