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The meaning and mechanics of “inflation shocks”

Measuring expected vs. actual inflation

With inflation in the news, we look to the FRED graph above to reveal how much realized inflation has differed from expected inflation. The graph shows one measure of realized inflation from the Bureau of Labor Statistics and measures of expected inflation from the Federal Reserve Bank of Cleveland:

  • The blue line shows the monthly year-over-year change in the consumer price index.
  • The red line shows one-year-ahead inflation expectations recorded over the course of the year.
  • The green line shows the one-year-ahead inflation rate that was expected for February 2022 as of February 2021.

The distance between the blue line’s realized inflation as of February 2022 (7.91%) and the green line’s expected inflation for February 2022 (1.67%) represents an “inflation shock.” These shocks are important for future transactions in the economy.

Let’s use a hypothetical example to explain the actual inflation shock: Suppose that back in February 2021 someone agreed to pay you some nominal amount in exactly one year. At the time, you both expected that the payment would allow you to purchase only slightly less goods and services in February 2022 than you could have purchased when you made the agreement in February 2021, given that some inflation is likely to occur. As noted above, that expected amount was 1.67% less (shown by the graph’s green line).

In reality, as of February 2022, the prices of goods and services had risen 7.91% instead of the expected 1.67% at the time the agreement was made. So the payment received can purchase much less goods and services than what was expected in February 2021: 6.24 percentage points less than what was expected, to be precise. (7.91 – 1.67 = 6.24)

These surprises in inflation change the real value of any nominal contract, including previously agreed-upon salaries, fixed-rate mortgage payments, and government bonds with no built-in inflation protection. To see how inflation surprises affect one of the largest borrowers in the world—the U.S. government—see our On the Economy blog post.

How this graph was created: Search FRED for “Consumer Price Index” and select “Consumer Price Index for All Urban Consumers: All Items in U.S. City Average.” From the “Edit Graph” panel, change “Units” to “Percent Change from Year Ago.” Next, use the “Add Line” tab to search for and select “1-Year Expected Inflation” and click “Add data series.” Change the “Edit Lines” tab to edit Line 2 and change the “Units” to “Percent.” Next, return to the “Add Line” tab to select “Create user-defined line? [+]” and click “Create line.” Set both “Value start/end:” and “to” to be equal to the value of “1-Year Expected Inflation” in February 2021 (i.e., 1.67%). Finally, change the dates at the top right of the graph to be “2021-2-01 to 2022-2-01.” Note that, at the time of this writing, the data in the graph matched the values described in the text; but inflation data are frequently revised, so discrepancies may arise.

Suggested by Yu-Ting Chiang and Jesse LaBelle.

Which U.S. states may feel the trade effects of the Russia-Ukraine war?

Although the U.S. is not feeling the direct effects of the Russian invasion of Ukraine, U.S. consumers may already be feeling the negative economic effects of higher gas prices. Are there other effects on U.S. producers that may take longer to manifest? We explore this question with two GeoFRED heat maps of the value of exports by U.S. state: one for exports to Ukraine and the other for exports to Russia.

The map of exports to Ukraine refers to data from 2017, the latest year with available data. Colored dark green, the top exporters to Ukraine were states with export values between $59.98 million and $221.69 million. These states are spread out across the U.S., with California and Washington on the West Coast, Texas in the South, Iowa and Illinois in the Midwest, and New York, New Jersey, and Pennsylvania on the East Coast. At over $221 million, Pennsylvania had the highest value of exports to Ukraine. Washington came in second, at about $122.2 million.

The map of exports to Russia also refers to data from 2017. Unsurprisingly, state-level exports to Russia tended to be larger than those to Ukraine. Top exporters to Russia had export values between $115.08 million and $607.46 million. Colored dark green, the top exporters to Russia include California on the West Coast, Tennessee and Florida in the South, Illinois and Ohio in the Midwest, and Pennsylvania, New York, and Massachusetts on the East Coast. Illinois had the highest value of exports to Russia of over $607 million, about 2.7 times the value of the top exporter to Ukraine.

Top exporters to both Ukraine and Russia are likely to feel the economic impact of the ongoing war more deeply than other states, due to trade disruptions. Four states that are among the top exporters for both countries—California, New York, Pennsylvania, and Illinois—may even suffer a twofold blow. The negative effects for any given state, however, depend on what proportion of its worldwide export value goes to Russia and Ukraine.

How these maps were created: The original post referenced interactive maps from our now discontinued GeoFRED site. The revised post provides replacement maps from FRED’s new mapping tool. To create FRED maps, go to the data series page in question and look for the green “VIEW MAP” button at the top right of the graph. See this post for instructions to edit a FRED map. Only series with a green map button can be mapped.
Suggested by Michael McCracken and Ngân Trần.

Income inequality across racial groups and the Gini ratio

Income among Black households and families is the least equal

The FRED Blog has used the yearly Current Population Survey conducted by the U.S. Census Bureau to illustrate measures of inequality and highlight the difference between households and families. (A household includes all people living in a housing unit; a family includes only those related by marriage, blood, or adoption.) This post compares inequality for both household and family incomes across racial and ethnic groups.

The FRED graph above describes household inequality with something called the Gini ratio. This Gini ratio is a statistical measure of how unequal incomes are within a group. A value of 1 indicates absolute inequality, where one household earns all the income and the rest of them earn nothing. A value of 0 indicates absolute equality, where all households earn the same income.

The data show very similar degrees of income inequality for Asian, White, and Hispanic households. Black households consistently record higher income inequality than all other racial and ethnic groups. That means that the inequality between the high-earning Black households and the low-earning Black households is more pronounced than between the high-earning and low-earning households in the other population groups.

For comparison, the second FRED graph shows the income Gini ratio for families. It tells the same story about income inequality across racial and ethnic groups as the first graph does. Overall, household income inequality is higher than family income inequality across all racial and ethnic groups, although the relative difference between households and families is smallest among Hispanics and largest among Whites.

How these graphs were created: First graph: From FRED’s main page, browse data by “Release,” search for “Income and Poverty in the United States.” Select the “Income Gini Ratio for Households by Race, Annual” table. Next, check the box to the left of each of the four series shown in the graph. Last, click on “Add to Graph.” Second graph: Select the “Income Gini Ratio of Families by Race of Householder, Annual” table and repeat the last two steps described above.

Suggested by Diego Mendez-Carbajo.



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