Grouping state-level economic activity
More than half of the data in FRED can be displayed in choropleth maps, which is a type of data visualization where geographical areas are colored differently according to the range of their data values. The contours of the geographical areas in FRED maps represent political and statistical boundaries. Political boundaries shape the counties, states, and nations we’re familiar with. But what is a statistical boundary? Who draws them? How did those boundaries come to be?
Statistical boundaries are groupings of smaller geographies, such as counties, or states, that are drawn by data collection organizations like the US Census and the US Bureau of Economic Analysis (BEA) to facilitate the presentation and analysis of statistical and economic information.
The FRED map above shows data on the number of people residing in the US during 2022. The BEA organizes them into eight regions: New England, Mideast, Great Lakes, Plains, Southeast, Southwest, Rocky Mountain, and Far West.
Each region represents groupings of states with similar economic and social conditions. This classification was drawn after the 1950 Census to group states with similar economic and social indicators. You can learn more about the process of drawing BEA regions here.
Economic and social conditions change over time, so certain statistical boundaries are periodically revised. Research can be used to draw alternative boundaries for regional economic data geographies. You can read about an alternative definition of economic regions here.
Where can you go next using FRED maps? Read this short essay to learn more about data geographies in FRED.
How this map was created: In FRED, search for “Resident Population in the Plains BEA Region.” Click on “View Map.”
Suggested by Diego Mendez-Carbajo.
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.
The recession-predicting dataset of Chauvet and Piger
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.