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They say nothing beats a home-cooked meal Comparing price inflation of food at home and away from home

The graph shows the evolution of two price indexes: food consumed at home and food served in a restaurant. It’s striking how the price of food served to you has kept increasing, while the price of food you prepare yourself has either increased more slowly or even decreased. In fact, the difference between these prices has increased by 61% over the sample period, meaning that the ratio of restaurant food prices to home food prices is 61% higher now than it was in 1953. What do we make of this? After all, the basic ingredient for both is the same: agricultural products. The difference is that restaurant meals also include a substantial service component: Other people prepare the food and serve it to you. While agriculture has benefited from big-time productivity enhancements, the same cannot be said for the manual labor provided in a restaurant. As real wages increase, the kitchen and wait staff become more expensive more quickly than the goods they prepare and serve, which is why our restaurant bills grow more quickly than our grocery bills. To be fair, we don’t usually pay ourselves to do our own grocery shopping, cooking, serving, and dishwashing. Or, for that matter, give ourselves a 20% tip.

How this graph was created: From the CPI release table, select the two series and click “Add to Graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CUSR0000SAF11, CUSR0000SEFV

Interest rates for future centenarians Discount rates for evaluation of future values

FRED recently added high quality market bond yield curve data from the U.S. Treasury. These are interest rates computed from high-quality commercial bonds to reflect the market’s thinking about how much it’s discounting future incomes with minimal risk. The U.S. Treasury needs these measures to evaluate the current value of liabilities in pension funds. You do this properly by using various maturities—from 6 months to 100 years in 6-month intervals. This produces an interesting yield curve. We focus here on the 100-year example. Obviously, there’s no commercial bond out there with a 100-year maturity right now. The calculation intrapolates for the various maturities and in this case likely extrapolates. We’re wondering, though, how a 100-year discount rate rate could be useful for pension liability pricing, as no employee alive today would reasonably expect to receive a pension distribution a century from now. However, this can be useful for other purposes, such as evaluating the usefulness of infrastructure with long lifespans or the impact of climate change. Note also that this 100-year rate has been decreasing significantly, just as all the others have, showing that the current interest rate environment has an effect far into the future.

How this graph was created: Search for “HQM bond” and, surprisingly, the 100-year rate is among the top choices.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: HQMCB100YR

Going postal Plotting your data to test seasonal adjustment

Ever take a statistics class? If so, do you recall your instructor telling you to “plot your data” and look at it before using it? It’s sound advice but is not always heeded, which can lead to foolish data-driven errors. While surfing through FRED (as I am wont to do; and who isn’t?!) I found such an error related to the difficulties in seasonal adjustment.

The graph shows two series: seasonally adjusted (red) and not seasonally adjusted (blue) U.S. postal employment. There’s a clear increase in employment each December to help cover the Christmas rush. Since the December rise is temporary and conveys no long-term information about employment patterns, we usually look only at the seasonally adjusted figures. But as we can see in the red line, the seasonal adjustment methodology only partially removes the seasonal pattern. In most cases, it does a good job removing the December effect between the early 1970s and now. But in the earlier years, the seasonal adjustment fails spectacularly, leaving the bulk of the December effect uncorrected. Clearly, the pattern of seasonality has changed across time and the methodology used to seasonally adjust should reflect that change—and there’s no way we would have known that had we not “plotted the data” before making the adjustment.

How this graph was created: Browse data by category. Under the category “Population, Employment, & Labor Markets” select “Current Employment Statistics (Establishment Survey).” Then select the subcategory “Government.” Scroll through the 23 series to find “All Employees: Government: U.S. Postal Service” and select the not seasonally adjusted version. From the “Edit Graph” option, use the “Add Line” tab to search for “postal service employees.” Select the seasonally adjusted version of the series “All Employees: Government: U.S. Postal Service” and click “Add data series.”

Suggested by Michael McCracken.

View on FRED, series used in this post: CES9091912001, CEU9091912001

Hyperinflation When prices go out of control

You might guess that hyperinflation refers to increases in the price level at very high rates. There’s no official threshold for “very high,” but the four cases shown in the graph are clearly examples of hyperinflation. The most spectacular case in FRED is Zimbabwe: The data are incomplete because in 2009 the country actually stopped reporting inflation, which likely rose several orders of magnitude higher that year. First, to be clear, Zimbabwe’s inflation rate of 24,000% means that prices were multiplied by 241 within a year. Peak hyperinflation in the other three countries has a factor of 42, 32, and 30. Second, why does hyperinflation occur? Generally, it’s because the government takes over the central bank and finances its operations by printing money. As people see this occurring, money loses value and the government has to print even more to stay afloat. This vicious circle can then be broken only by a radical change of practice: In Zimbabwe, that was abandoning the use of the local currency, which is indeed radical, as the country reported deflation in some years since going cold turkey. (Turkey, by the way, also has had periods of very high inflation, but not as dramatic as our examples. We reported on this earlier.)

How this graph was done: Search for “Zimbabwe inflation.” Once you have the graph, use the “Edit Graph” option to open the “Add Line” tab to search for other inflation rates. Repeat until satisfied.

Suggested by Christian Zimmermann.


Fan your forecasting flame with FREDcast FRED’s new forecasting game

On January 20th FRED’s newest data gizmo, FREDcast, is coming out of beta. FREDcast is an interactive forecasting game that allows users to enter forecasts for four different economic variables, track their forecast’s accuracy on the scoreboards, and compete with friends and other users in leagues. The game is designed for all levels of users, from high school students to professional forecasters. Just log-in to FREDcast using your FRED account and walk through the prompts to enter your forecasts for each variable. FREDcast forecasts are zero horizon, meaning users forecast economic data for the month (or quarter) in which they are in. For example, from January 1st to January 20th, users submit forecasts for the January unemployment rate, the January consumer price index (CPI), the January payroll employment, and quarter one real gross domestic product (GDP). Forecasts are due by the 20th of each month, and scores are released as the economic data come out. View exact release dates on FRED’s economic calendar.

The four FREDcast series are available in FRED. Below is a graph of each series in the appropriate units for FREDcast forecasts. All series in FREDcast are seasonally adjusted. From top to bottom: Real gross domestic product (GDP) is the only quarterly series, and the units are the percent change from the preceding period at a seasonally adjusted annual rate. Next is the unemployment rate, which is forecast as a monthly rate. Next are the consumer price index (CPI) and payroll employment. The inflation series used in FREDcast is the percent change in the CPI from one year ago, while payroll employment is the level change from the prior month measured in persons.

How these graphs were created: GDP: Search for real gross domestic product, and graph the series with the units “Percent Change from Preceding Period, Quarterly, Seasonally Adjusted Annual Rate.” Set the start date to 2006-07-01, and follow this path: Edit Graph > Format > Graph Type > Bar. Unemployment Rate: Search for unemployment rate, and graph the seasonally adjusted civilian unemployment rate. Set the start date to 2006-12-01. CPI: Search for consumer price index, and graph the series “Consumer Price Index for All Urban Consumers: All Items” with monthly, seasonally adjusted units. Set the start date to 2006-11-01, and follow this path: Edit Graph > Units > Percent Change from Year Ago. Payroll Employment: Search for payroll employment, and graph the series “All Employees: Total Nonfarm Payrolls” in seasonally adjusted units. Set the start date to 2006-12-01, and follow this path: Edit Graph > Units > Change, Thousands of Persons. Last, multiply the series by 1000 to get it in units of persons by entering a*1000 in the formula box and clicking “Apply.”

Suggested by Michael Owyang and Hannah Shell.

View on FRED, series used in this post: A191RL1Q225SBEA, CPIAUCSL, PAYEMS, UNRATE

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