A history of tax exemptions for couples
FRED’s recent addition of data from the Internal Revenue Service is a gold mine of interesting factoids. The data cover different tax returns and drill down to particular line items. There are even time series on amounts for exemptions, deductions, and credits. The graph shows one such exemption, the personal exemption for married couples, in three versions: the nominal value as written in the tax code (blue), the real value after adjusting for inflation using the consumer price index (red), and the real value adjusting for the nominal increase in incomes using personal income per capita (green).
Move the glider to look at different time periods and you’ll notice the exemption was quite high (even in nominal terms) in the first years after the income tax was introduced, which is one factor explaining why only a minority of households were paying any tax in the first years. That eroded substantially after WWII, when the exemption was small. It has increased recently in nominal terms and keeps up with inflation but not with the increase in incomes. Indeed, it’s now trending, in real terms as deflated by income, to the lowest it has ever been. In terms of 1982-84 prices, it’s now at about $2300, compared with about $2000 at its lowest point.
How this graph was created: The exemption is among the most popular in the data release, so click on it and you have the blue line. From “Edit Graph,” use the add line feature to search for the same exemption and add to the line CPI (using the longer series) and apply formula a/b*100. Again add a line with the same exemption, add to it personal income per capita (make sure not to use the real series) and apply formula a/b*14000 (with 14000 being the factor needed to make the line roughly match the $2000 exemption in 1982-84, which is the base year for the CPI).
Suggested by Christian Zimmermann.
View on FRED, series used in this post:
The annual inflation rate of the U.S. declined to (roughly) zero at the beginning of 2015, and it has remained close by ever since. But is inflation equally low in all regions? To find out, we look at data series in FRED that track overall inflation for the U.S. and inflation for each of the four Census regions: the Northeast, Midwest, South, and West. As the graph shows, all U.S. price growth over the past eight months has come from the West; since January, inflation in the West has been at least a full percentage point above that of the other three regions. If the West were excluded from the picture, then August’s national inflation rate of 0.20% would instead have been –0.19%.
In a recent On the Economy blog post, we showed that the lion’s share of this difference between inflation in the West and inflation in the other three regions is explained by differences in prices for energy and shelter. Energy inflation explains between 42% and 70% of the gap, depending on the region, and shelter inflation explains between 37% and 51% of the gap.
Does it matter that shelter inflation is a key driver of these regional differences? If shelter inflation in the West is driven by low interest rates, then one implication for monetary policy is that normalization (or “liftoff”) could push inflation in the West down to the levels observed in the other three regions.
How this graph was created: Search for “CPI” and select the series “Consumer Price Index for All Urban Consumers: All Items” (monthly, not seasonally adjusted). Change the units from “Index 1982-1984=100” to “Percent Change from Year Ago.” Then add the four regional CPI series to the graph by searching for the following series IDs: CUUR0100SA0, CUUR0200SA0, CUUR0300SA0, and CUUR0400SA0. Also change the units for each of these to “Percent Change from Year Ago.” Finally, restrict the sample to start in January 2014 by using the settings above the graph on the right.
Suggested by Alejandro Badel and Joseph McGillicuddy
One popular measure of the price level is the consumer price index (CPI), which measures the average change over time in the prices paid by urban consumers for a market basket of goods and services. This index can be broken down into smaller component indexes, each representing a different subset of goods and services. So changes in the aggregate price level can be traced back to changes in the price levels of the underlying components. As described in a recent Economic Synopses essay, we have developed a “heat map” that visually represents CPI data in FRED: specifically, the relative inflation levels of various CPI components over the past 10 years. The heat map shown here lists the components in order according to their weight in the overall index as of July 17, 2015.
How this heat map was created: We used the FRED Add-In for Microsoft Excel (view instructions for installing the Add-In here) to download the FRED data: year-over-year percent change in each CPI component index over the past 10 years. We normalized each value by subtracting the series mean and dividing by its standard deviation calculated over the past 10 years to take into account differences in long-term trends and volatility across series. Each colored box in the heat map corresponds to the normalized inflation value for a given CPI component for a particular month. Blue represents an inflation value below the long-term trend of the index, and red represents an inflation value above the long-term trend. The darker the color, the greater the difference between that particular inflation value and the long-run average for the component index in terms of standard deviations.
Because we’re comparing series against their long-run averages, it’s possible for a “blue” series to have a higher inflation rate than a “red” series. For example, for June 2015, owners’ equivalent rent is red, with an inflation value of 2.95 percent; water, sewer, and trash is blue, and yet has a higher inflation value of 4.65 percent. The reason is that the June 2015 owners’ equivalent rent inflation is above its 10-year average of 2.16 percent; and the June 2015 water, sewer, and trash inflation is below its 10-year average of 5.11 percent.
An Excel file containing a version of this heat map can be found here, and anyone who downloads the FRED Excel Add-In has the ability to easily update the heat map when new data are released. Simply select the tab containing the raw data and press the “Update Data” button from the FRED Excel Add-in. (More instructions and details are provided in the Excel file.)
Suggested by Joseph T. McGillicuddy and Lowell R. Ricketts.
View on FRED, series used in this post: