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Inflation decline opens regional inflation differentials

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

View on FRED, series used in this post: CPIAUCNS, CUUR0100SA0, CUUR0200SA0, CUUR0300SA0, CUUR0400SA0

Higher order moments of unemployment duration

The unemployment rate has steadily improved since its peak at the end of the Great Recession. The unemployment rate is a good summary of the state of the labor market, but unemployment duration also contains information about how easy it is for people to find jobs. The BLS measures duration by taking a cross-section of unemployed and asking them how long they’ve been unemployed. A persistent characteristic of the data is that some people find jobs much more quickly than others—and the longer someone is unemployed, the lower their chance of finding a job.

This dynamic leads to a distribution of unemployment duration that is “right skewed”: That is, the distribution has a long “tail” of workers who’ve been unemployed for a long time and a large number of job finders with very short spells of unemployment. We can use many measures to evaluate this skewness, but a simple one is the ratio of the mean duration to the median duration. When the mean is much larger than the median (a ratio greater than 1), then these very long durations of unemployment have increased the mean duration and the large number of short durations have decreased the median duration.

In recessions, the skewness of unemployment duration (green line) always falls because the inflow of newly unemployed with zero duration reduces the mean duration. In the aftermath of the Great Recession, unemployment duration has become increasingly skewed outward: The mean and median are still high relative to other expansions, but strikingly the skewness has continually risen. The force behind this skewness is the number of long-term unemployed, who are now a particularly prominent portion of the distribution.

How this graph was created: Search for “mean unemployment duration” and add this series to the graph: UEMPMEAN. Then use the “Add Data Series” feature to search for “median of unemployment duration” and select UEMPMED as a new line. For the third series, combine these two series to create a new series: First add UEMPMED again, then add UEMPMEAN through the “Modify existing series” option. Use the “Create your own data transformation” option and insert the formula b/a . Place this new series on the right y-axis by itself.

Suggested by David Wiczer

View on FRED, series used in this post: UEMPMEAN, UEMPMED

Which measures of inflation are relevant for policy?

The Federal Reserve has set a 2% inflation target. Does it meet that target? It depends on which inflation rate you consider. FRED offers many different series for the U.S. that reveal different views of inflation because they pertain to different groups of goods and services. The graph above shows eight series that receive a lot of attention in the context of policy: Three are above and five are below that 2% target. How are they different? Some look only at consumption expenses or production costs. Some include overall economic activity. Some exclude energy and food, price categories thought to be volatile and thus capable of clouding the picture of underlying inflation. (For example, removing the currently low prices of oil and its derivatives clearly leads to higher inflation numbers.) Some focus on prices that move slowly, which are thought to be good indicators of trend inflation. And one index considers only the median in the distribution of price changes. You can consider even more series in FRED. The point is that there’s a wide spread across those inflation rates, and determining which is the most relevant isn’t easy.

How this graph was created: One of the many way to graph these series is to search for “price” and restrict the choices with tags such as “nation,” “usa,” and “sa” (seasonally adjusted). These eight are likely to be at the top of these search results. Select the series you want and click the “Add to graph” button. Some series are indexes and others are inflation rates, so modify the units to show “Percentage change from year ago” for the series in index form. Finally, to add the black horizontal line at 2%, open the “Add series” panel and select “Trend series” from the pulldown menu. Once it’s added, modify it by choosing 2 for the initial and final values and change the color to black. Oh… We also removed the axis label because it became unwieldy with eight depicted series.

Suggested by Christian Zimmermann


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