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The impact of social distancing on leisure and hospitality

State-level data from the BLS

The FRED Blog has discussed the impact of the COVID-19 pandemic on national retail sales and employment. And Leibovici, Santacreu, and Famiglietti index the contact-intensity of a range of occupations and estimate the economic impact of their reduced activity. Their work ranks several leisure and hospitality occupations in the high-contact category.

Today, we look at the impact that social distancing has had on employment specifically in the leisure and hospitality industry.

The GeoFRED map above shows the percent change in employment levels in the leisure and hospitality industry by U.S. state between May 2019 and May 2020. Note that the data are seasonally adjusted. That means they discount regularly occurring increases and decreases in activity due to seasonal demand, such as winter skiing in Colorado or summer vacationing in Florida.

The number of employees in the leisure and hospitality industry decreased in all 50 states during May compared with a year ago. That decrease ranged from 18% in Oklahoma to 62% in New York. The median value was 38%.

And to learn about how closing restaurants and hotels spills over to total employment, read the work of Garriga and Sanchez.

How these maps were created: The original post referenced an interactive map from our now discontinued GeoFRED site. The revised post provides a replacement map 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 Diego Mendez-Carbajo.

Constructing a bilateral real exchange rate

How to create new series on FRED

FRED lets you create commonly used data series that are not predefined. For example, you can normalize current account balances or government budget balances by GDP and you can deflate nominal data with a price index.

One popular variable that you can create is a bilateral real exchange rate index. While a nominal exchange rate is the relative price of 2 monies (e.g., the relative price of a euro in terms of U.S. dollars), a real exchange rate is the relative price of consumption baskets in two countries. A consumption basket is a set of goods and services that represent the purchases of a typical consumer in country in a given year. Thus the real exchange rate is the price of European goods in terms of U.S. goods. One converts a nominal exchange rate into a real rate by multiplying by the ratio of the national price levels:

U.S. goods per euro area goods basket = (USD per euro) * (euro price level) / (U.S. price level)

FRED has many kinds of broad, real effective exchange rates. Here is a list of FRED’s real U.S. exchange rates. These are effective or trade-weighted real exchange rates. They are weighted averages of bilateral real exchange rates. Currencies of the countries that the U.S. trade with the most receive the highest weights in the formula. Real effective exchange rates provide a look at changes in the overall value of foreign consumption baskets in terms of the U.S. consumption basket. When a real effective exchange rate rises (falls), the average foreign consumption basket becomes more (less) expensive in terms of U.S. consumption.

But what if you want to see the price of foreign consumption in terms of U.S. consumption for a particular country or area? For example, what if you want to see the real exchange rate for the dollar per euro, as we detailed at the start of the post? You can construct such a bilateral real exchange rate yourself in FRED using monthly price and exchange rate data from the U.S. and the euro area. The following instructions give you the graph at the top of this post.

1. Search for “euro” in the FRED search box and select “U.S. / Euro Foreign Exchange Rate.” The default graph will be a daily exchange rate (DEXUSEU).


2. Because consumer price series are monthly (or quarterly), use the orange “Edit Graph” button on the right hand side to change the frequency to monthly and the aggregation method to “average.” This series is series “a” in the graph. Keep the editing box open.

3. Add the U.S. and euro area CPI series using the “customize data” area.

a. To add the U.S. CPI data, type “cpi” directly under the text that says “You can begin by adding a series to combine with your existing series.” Click on the first series in the popup list called “Consumer price index for all urban consumers.” Click “Add” on the right-hand side of the box and the U.S. CPI series became series “b” in the graph. Note that the graph itself has not changed.

b. To add the euro area CPI data, type “euro cpi” directly under the text that says “You can begin by adding a series to combine with your existing series.” Click on the first series in the popup list called “Harmonized Index of Consumer Prices: All Items for the Euro Area.” Click “Add” on the right-hand side of the box and the euro CPI series became series “c” in the graph.

4. Now that we have defined our exchange rate and price series, we use them to construct a real exchange rate by typing the following formula in the “Formula” box near the bottom of the editing box: a*c/b. Then click “Apply” to the right of the box. The picture in the graph will finally change to the bilateral real exchange rate, i.e., baskets of U.S. goods per basket of euro area goods.

5. To change the real exchange rate to an index, select “Index (Scale value to 100 for chosen date)” from the “Units” box at the bottom of the editing box and then type “1999-01-01” in the date box. Close the edit box with the X in the upper right-hand corner.

6. To see a long span of the data series that you have created, select “Max” from the data range choices, i.e., “1Y | 5Y | 10Y | Max”, at the top of the graph.

Suggested by Chris Neely.

View on FRED, series used in this post: CP0000EZ19M086NEST, CPIAUCSL, DEXUSEU

When initial claims, unemployment, and payroll employment clash

Not long ago, the FRED Blog discussed several details about the construction and interpretation of the data for initial weekly claims for unemployment benefits. As of May 30, FRED shows that the four-week moving average was 2.3 million new claims. Yet, the FRED graph above shows that for the entire month of May 2020, there was a decrease in the number of persons unemployed. And there was also a simultaneous increase in the level of payroll employment. How is all this possible?

First of all, data related to the labor market come from different sources: The U.S. Employment and Training Administration reports the number of initial weekly claims for unemployment benefits; and the U.S. Bureau of Labor Statistics, through the Current Employment Statistics (Establishment Survey), reports the payroll employment and unemployment figures.

Also, the data series have similar names but represent different concepts. Even if you file an initial claim for unemployment benefits, for example, it does not necessarily mean that you will be counted as unemployed.

Finally, keep in mind that changes in the number of persons listed on payrolls do not correspond to changes in the number of persons employed or unemployed. The FRED graph below shows that during May, June, July, and October of 2019 there were simultaneous increases in the level of payroll employment and increases in the number of persons unemployed.

How these graphs were created: Search for and select “All Employees, Total Nonfarm” anuse the “Edit Graph” menu to add two more lines: “Unemployment Level” and “Employment Level.” Next, change the units of any of the three series to “Change, Thousands of Persons” and click on “Copy to all.” Lastly, change the graph format to “Bar,” edit the colors to taste, and change the date range to match the time periods of each graph.

Suggested by Diego Mendez-Carbajo.

View on FRED, series used in this post: CE16OV, PAYEMS, UNEMPLOY


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