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Government spending on police

State and local expenditures data from the BEA

As police presence, tactics, and department funding are being discussed, the FRED Blog offers some data to add to the conversation.

The graph above shows a category of government expenditures, “public order and safety,” as listed in the national income and product accounts from the Bureau of Economic Analysis. One series (in blue) is total government, and one series (in red) is state and local government.

These expenditures, which include both police and fire departments, are a small part of government spending, but they have continually grown. This isn’t surprising, as the data aren’t adjusted for inflation, population growth, or economic growth.

The second graph shows state and local expenditures specifically for police as a share of all state and local government expenses. For 2018, police expenditures were 4.79%, which is at the higher end of the range. The low, in 1980, was 4.25%.

How many people are in the police force? The Current Population Survey helps us here, making the distinction between those who patrol and those in supervision and detective roles. As of 2019, the total was 766,000, with a slight increase in the former and stable if not decreasing numbers in the latter. Note that these numbers include both public and private police forces.

This fourth graph shows how much police are paid. The Current Population Survey doesn’t provide averages, but rather medians: So, half are paid more and half are paid less than the values shown. These values aren’t inflation-adjusted and do not include benefits. Overtime pay is included, though.

The final graph is the same as the previous one, but the wages are adjusted for inflation. There’s quite a bit of fluctuation, likely due to changes in overtime. And there’s a slight upward trend, which can come from higher hourly pay, more overtime, or a combination of the two.

How these graphs were created: First graph: Search FRED for “public order and safety” and click on the series encompassing all government levels. From the “Edit Graph” panel, use the “Add Line” tab to search for and select the state and local government series with the same keywords. Second graph: Start with the graph for state and local police expenses. From the “Edit Graph” panel, add a series through the “Customize data” search bar (different from the Add Line tab): Search for and select state and local government expenses and apply formula a/b*100. Third graph: Search for “employed police” and select the series. Fourth graph: Search for “median police earnings” and select the series. Fifth graph: Start with the fourth graph. Use “Customize data” to add the CPI for both series and apply the formula a/b/*255.651 (the last number being the average value of the CPI in 2019).

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CPIAUCSL, G160081A027NBEA, G160841A027NBEA, G160851A027NBEA, LEU0254491000A, LEU0254491900A, LEU0254544400A, LEU0254545300A, SLEXPND

Monetary policy tools today: Paying interest on all those reserves

As the school year winds down, the FRED Blog offers some advice to new graduates: Learning about monetary policy is a lifelong endeavor, because its tools can change even if your textbook doesn’t. (See our “textbook lag” posts, part I and part II.)

One way monetary policy tools have changed is that, effective March 26, the Board of Governors of the Federal Reserve System reduced reserve requirement ratios to zero percent: In response to the COVID-19 pandemic, the Board eliminated reserve requirements for all depository institutions to facilitate lending to households and businesses.

As the FRED graph above shows, since 2008, the volume of excess reserves has vastly outpaced the volume of required reserves. In fact, the total amount of bank reserves held at Federal Reserve Banks is at an all-time high.

Another recent change in the policy environment is described in a Page One Economics essay, “A New Frontier: Monetary Policy with Ample Reserves.” The Federal Open Market Committee (FOMC) adjusts the interest rate on excess reserves (IOER) to adjust the federal funds rate. if your textbook was published before 2008, it’s not likely to include this monetary policy tool.

Now, take your tassel from your graduation cap and bookmark the FRED Blog in your browser to keep on learning.

How this graph was created: Search for and select “Total Reserve Balances Maintained with Federal Reserve Banks.” From the “Edit Graph” panel, use the “Add Line” feature to search for and add “Reserve Balances Required; Reserve Balance Requirements.” Use “Format” to select “Graph type: Area” and choose your colors.

Suggested by Diego Mendez-Carbajo.

View on FRED, series used in this post: RESBALNSW, RESBALREQW

National income’s connection to life expectancy

Tracking countries with high, middle, and low income

There is a strong positive correlation between life expectancy and national income: That is, higher (lower) life expectancy for a country’s population is associated with higher (lower) GDP for that country. The FRED graph above provides the supporting evidence.

The red, green, and purple lines plot life expectancy at birth for high-, middle-, and low-income countries, respectively, since 1960. We can see the relationship between life expectancy and national income through (1) the comparison of income groups at any point in time and (2) the time trend of each individual income group.

In any given year, life expectancy is always highest for high-income countries and lowest for low-income countries. Over time, the group average for life expectancy increases for all three income levels and their national incomes also rise.

This graph also shows that the life expectancy gap between high- and low-income countries narrows over time:

  • In 1960, the average life expectancy for high-income countries was 68.5 years, while the average for low-income countries was 39.3 years, a gap of 29.2 years.
  • In 2018, this gap shrank to 16.9 years, with an average life expectancy of 80.7 for high-income countries and 63.8 for low-income countries.

This global increase of life expectancy over the past 60 years, especially for low-income countries, has been a significant achievement in human history. However, there’s a bit of country-specific variation, even within the high-income group. The U.S. is good example.

The blue line shows life expectancy for the U.S., which is always included in the high-income group over the sample period. U.S. life expectancy was slightly higher than that of high-income countries overall in the 1960s, was about even with them in the 1970s and 80s, and started to lag behind in the 1990s and even declined in recent years. The 2018 data show that life expectancy in the U.S. is 2 years lower than the average for all high-income countries. In short, U.S. life expectancy has increased, though its rate of increase for the past half century is lower than life expectancy for other high-income countries.

How this graph was created: Search for and select one of the “life expectancy and income” series for income groups (high, middle, low), then use the “Edit Graph” panel’s “Add Line” feature to search for the rest, plus the life expectancy total for the U.S.

Suggested by YiLi Chien.

View on FRED, series used in this post: SPDYNLE00INHIC, SPDYNLE00INLIC, SPDYNLE00INMIC, SPDYNLE00INUSA


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