Federal Reserve Economic Data

The FRED® Blog

Government employment in context

The graph above shows the number of people employed in the U.S. government (excluding armed forces and intelligence agencies, but including the postal service). This number has increased almost continuously: The few exceptions are immediately after World War II, in the early 1980s, and since the previous recession. Note also that small spikes occur every ten years, owing to the temporary hiring for the census.

But does this picture tell a true story of an ever-expanding government? The graph spans almost 80 years, and over that period the U.S. population has continuously expanded. So a more-realistic picture would need to calculate the share of government employment in total employment. This is shown in the graph below. The picture looks quite different now: The current share of government employment is actually very low, and one has to go back to 1960 to find a lower number! The highest point is in 1975, not 2010 as in the first graph. Clearly, context matters.

How these graphs were created: Search for “government employees” and select “All Employees: Government” (series ID: USGOVT) for the first graph. For the second graph, add the series “All Employees: Total Nonfarm Payrolls” to series 1 through the “Modify existing series” option. Use the “Create your own data transformation” option to apply the formula a/b*100 to express the result in percentages.

Suggested by Christian Zimmermann

View on FRED, series used in this post: PAYEMS, USGOVT

Gamble on gambling?

Several U.S. states have considered expanding gambling operations as a new source of revenue, especially since the past recession. Is this a good idea? Is it viable? Many have questioned this plan for various reasons, but this post specifically examines whether there is room for expansion in the gambling industry to shore up state budgets.

The graph above shows the share of gambling in total personal expenditures. While there has indeed been a rapid expansion of these expenditures up to the mid-1990s, the trend has flattened markedly since then. It even decreased during the past recession, showing that this industry is certainly not recession-proof. That may not bode well for states that have a balanced-budget mandate and need countercyclical sources of revenue: Gambling does not appear to be a source that states can depend on.

How this graph was created: Search for “gambling expenditures” and select the series shown above, which is nominal and has an annual frequency. Add it to the graph. Then use the “Add Data Series” option to add “personal consumption expenditures” to series 1 by selecting “Modify existing series.” (Be sure to choose the personal consumption series that is nominal and has an annual frequency.) Then select “Create your own data transformation” and add the formula a/b*100. The result is then expressed in percentages.

Suggested by Christian Zimmermann

View on FRED, series used in this post: DGAMRC1A027NBEA, PCECA

GDP revisions

Measuring GDP and its components is tricky business. GDP is supposed to measure all economic activity of a country, but of course not all activity is well monitored. So one has to work with proxies and estimates based on various indicators and surveys. Those efforts take time, and GDP estimates must be corrected after they’re first released. Everyone knows the initial GDP measure is imprecise and that revisions can be rather dramatic, yet the initial release receives the lion’s share of press coverage. Later revisions receive little attention, yet they matter quite a bit.

The latest GDP release included revisions going all the way back to 2012. ALFRED, a sibling of our FRED database, allows comparison of different historical “vintages” of data for many series included in FRED. The graph above shows the quarter-to-quarter growth rates for the two latest releases of U.S. real GDP. One can easily see that there have been some stark changes: In particular, the measure for the first quarter of 2015 was initially a much-discussed negative growth rate but was then revised to reveal a positive growth rate.

If these revisions average out to zero, we simply have an imprecise measure of GDP and that’s that. But if these revisions lean in one particular direction, then we may have some systematic bias in the initial estimates. One way to look for a significant bias is to examine the levels of GDP across data vintages. (The levels are the cumulation of the growth rates.) The graph below shows these levels, and one can see a difference between the two series all the way to the last data point.

How these graphs were created: For the first graph, search “real gross domestic product” on ALFRED. Choose “Series” instead of “Site” on the drop down menu (which is to the left of the search field). Click on the first choice and then on “% Chg” under “Units” below the graph. The second graph is simply the default version of the first graph, which has units in billions of dollars.

Suggested by Christian Zimmermann

View on FRED, series used in this post: GDPC1


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