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Employment in coal

Coal has been in the news lately—specifically, coal mining jobs. Let’s take a look at what FRED can show us about coal mining employment among U.S. states. For starters, half of U.S. states don’t mine coal. Of the 25 that do, 10 have only a handful of mines and many other states have so little employment in that industry that the Bureau of Labor Statistics doesn’t separate coal mining from other mining activities.* In the end, we have a precise data series for only three coal-producing states.

It turns out these three series are enough to show that coal mining employment has differed among states. Employment has trended down since the start of the series (in 1990) in both Kentucky and Pennsylvania. But Wyoming has no such downward trend; in fact, there’s been an uptick in recent years, except for a marked decrease in 2016. Why the difference? Coal from Pennsylvania and Kentucky has a much higher SO2 content, which is costly to remove during the burning process. So coal-fired power plants prefer to use the less-polluting coal from, say, the state of Wyoming (not to be confused with the coal-rich Wyoming Valley in Pennsylvania). But with the boom in shale gas extraction and the lower cost of natural gas (and other alternative fuels), even Wyoming coal is becoming less competitive. We’ll see what the future holds for this industry.

*Per the U.S. Energy Information Administration, the top five U.S. coal producers in 2015 were Wyoming, West Virginia, Kentucky, Illinois, and Pennsylvania. The BEA offers employment data for West Virginia and Illinois only for all forms of mining plus logging.

How this graph was created: Search for “coal employment” and select the three series. Click on “Add to Graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: SMU21000001021210001SA, SMU42000001021210001SA, SMU56000001021210001SA

Investing in FRED

FRED doesn’t provide advice on how you should invest your savings. Different circumstances warrant different portfolios; and, as we often hear, past performance is no guarantee of future results. But FRED can show how various forms of investment have performed over the past 40 years or so. Here, we compare stocks, gold, and real estate.

  • Stocks. There are many different stock indexes, but the Wilshire 5000 index is the most comprehensive. It shows the value of a portfolio of stocks with the dividends reinvested in that portfolio.
  • Gold. It doesn’t really matter which price index you use because they’re all very similar in the long run and there’s no dividend to account for.
  • Real estate. This is tricky, so we use two series: The Case-Shiller house price index captures the value of the house itself but not the (implicit) rent from it that could be reinvested in the same way dividends can be reinvested for stocks. The Wilshire index dedicated to real estate funds (REIT) does account for reinvestment.

The graph shows that, in the long-run, stocks and real estate are quite similar. But gold clearly lags and is similar to owning a house but not living in it or renting it out. Of course, in the short run, things can look different from this long-run picture and individual stocks or houses could perform differently from the big indexes.

How this graph was created: Search for and select the first series, add it to the graph, and use the “Edit Graph” button to add the three other lines individually. Restrict the sample period to start on 1975-01-01. Then, to have the lines start at the same point, select units “Index (scale value to 100 for date chosen)” choosing 1975-01-01 as the date. That will not work for the fourth series because it starts later. Apply formula a*1.27 so that it starts on the same level as the home price index. For longer time series, it’s a good idea to take logarithms so the data in the early years are distinguishable. Do this by choosing units “Natural Log” at the bottom of each line panel. Finally, in the “Format” panel, change the color of the gold price series to gold.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CSUSHPINSA, GOLDPMGBD228NLBM, WILL5000IND, WILLREITIND

The puzzle of real median household income

The graph above shows two often-reported series that look at a measure of income adjusted for inflation and population: real median household income and real per capita GDP. They should be similar, but there are quite a few differences. For example, median household income has stagnated for about two decades while per capita GDP has steadily increased. Let’s try to straighten out this puzzle.

The blue line in the middle graph shows that the number of people in each household has decreased. So the number of households in the nation has increased faster than population, which means that any measure divided by population grows faster than one divided by number of households. To see how much this matters quantitatively, we divide both income concepts by the number of households in the bottom graph. Obviously, they still don’t line up, but at least the gap is smaller. What explains the remainder?

First, the income definitions are different: Household income is based on a survey that asks people about only their income, not their employer-provided benefits and retirement contributions. In a previous post, we showed that these benefits have increased relatively more than wages. Real GDP includes all income in the economy. Second, if the distribution of income becomes more unequal, then the median decreases while the mean stays put. How much each of these contribute to the remaining gap can only be determined with a look at the microdata.

How these graphs were create: Top graph: Search for “real median household income,” click on the series, open the “Edit Graph” panel, then select “Index (scale value to 100 for chosen date)” for units, with 1984-01-01 as the date. Then add a line after searching for “real per capita GDP.” Choose the same units. Middle graph: Search for “civilian population,” open the “Edit Graph” panel, then search for “number of households,” and apply the formula a/b. Bottom graph: Repeat the procedure for the top graph for the first line. For the second line, use the “Add Line” feature, search for “real GDP,” then add the “number of households” series, and apply formula a/b. Finally, choose as units “Index (scale value to 100 for chosen date)” with 1984-01-01 in the bottom field, as the units pertain to the result of the formula.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: A939RX0Q048SBEA, CNP16OV, GDPC1, MEHOINUSA672N, TTLHH


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