Federal Reserve Economic Data

The FRED® Blog

The long and the short of the workweek

Weekly hours of work by sector

Not everyone has the same workweek. One factor that determines your working hours is the sector you work in. As the graph above shows, there are substantial differences among sectors, due to both regular hours and overtime. Indeed, in mining and logging, the average workweek is over 47 hours long. At the other extreme, workers in the leisure industry on average work only 25 hours. The latter may be a special case, though, because of the prevalence of part-time work. Generally, the service sector has an average in the 30s and the goods-producing sector has an average in the 40s.

But are these differences caused by the specific time period chosen in the bar graph? Let’s see. The second graph looks at four sectors over several decades, and it’s clear that the differences have been there for a long time and seem to be getting even starker.

Maybe these differences are caused by varying reliance on overtime. Unfortunately, we have overtime hours for only manufacturing, which are visible in the last graph. Manufacturing overtime seems to have been trending up slightly over the past several decades, but this is just one of many contributing factors that might explain the workweek differences among sectors. Indeed, manufacturing overtime is only about four hours while the difference in weekly hours between manufacturing and professional and business services is six to seven hours.

How this graph was created: Go to the release table for weekly hours by sector, select “Average Weekly Hours,” select the series you want, and click “Add to Graph.” In the date range fields, select May 2018 and June 2018 for the most-recent data. From the “Edit Graph” panel, go to the “Format” tab and change “Graph type” to “Bar.” For the second graph, use the same release table and set of weekly hours; select the series you want, and click “Add to Graph.” For the third graph, use the same release table but select “Average Ovetime Hours” and the manufacturing sector series.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: AWHMAN, AWOTMAN, CES1000000007, CES2000000007, CES3100000007, CES3200000007, CES4000000007, CES5000000007, CES5500000007, CES6000000007, CES6500000007, CES7000000007

Where health is lacking

Mapping public health issues with GeoFRED

GeoFRED maps can help us understand a lot of things, including trends in regional socioeconomic data, which could ultimately provide insights for policy recommendations. In this post, we look at two important indicators of health throughout the United States: premature deaths and preventable hospital admissions. High levels of premature deaths indicate issues with public health. (See a previous blog post for some background on this concept.) The South has a comparatively higher concentration of high rates in this area.

The maps show a correlation between areas that suffer from high rates of premature death and areas that have a high rate of preventable hospital admissions, which is defined as stays in acute-care hospitals that could have been taken care of in ambulatory or ordinary inpatient settings, adjusted for socioeconomic factors. Examples are pneumonia, diabetes, and dehydration. A high rate of these admissions indicates that more people are lacking appropriate health options, likely leading to more preventable deaths.

While regional trends and correlations do not indicate causation, a review of interconnected socioeconomic patterns over several years can be useful for understanding persistent problems in certain areas. Refer to GeoFRED for related maps on race, income inequality, homeownership, burdened homeowners, and disconnected youth.

How these maps were created: The original post referenced interactive maps from our now discontinued GeoFRED site. The revised post provides replacement maps 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 Samantha Kiss and Christian Zimmermann.

Is college still worth it?

Re-examining the college premium

A recent symposium held by the Center for Household Financial Stability at the St. Louis Fed looks at the question of whether the college premium is still increasing and positive, using new data from the Fed’s Survey of Consumer Finances. On an absolute level, college graduates earn more than high school graduates, as shown in the graph above. This is consistent with the understanding that the benefits of a college education are greater than the costs.

If we look at the college premium, we can see that it has always been positive, indicating that there is a positive benefit of graduating with a bachelor’s degree. This graph shows that, at the end of the first quarter of 2018, college graduates received weekly wages that were 80 percent higher than those of high school graduates.

However, there’s more to this story. Recent research shows that the college premium may or may not be very strong depending on birth year, family, and other inherited characteristics. When looking at the wealth premium instead of just the income premium, the college premium was weak for all races and ethnicities in the 1980s cohorts, whereas the college premium exists for cohorts in earlier decades. A potential reason for this result is the high and rising cost of college. Over the past decade, we see an increase in the dollar amount of total outstanding student loans per total number of college graduates in the labor force, reaching almost $27,000 per college graduate available for work at the end of the first quarter of 2018. High levels of student debt may affect the ability to accumulate wealth, resulting in the declining college wealth premium. This is just one of the reasons for further investigation into the college premium, rising tuition costs, and how education influences economic well-being.

How these graphs were created: For the first graph, search for “wages bachelor’s degree” and select the quarterly data series to add to the graph. From the “Edit Graph” panel, go to “Add Line” and search for “wages high school” and select the corresponding series. To create the second graph, use the same steps to get to the “wages bachelor’s degree” series. Then under the “Customize data” section, search for “wages high school” and select the series. Then enter in the formula (a/b) – 1 to get the college premium. For the third graph, search for “student loans” and select the series for outstanding student loans. From the “Edit Graph” panel, go to “Customize data,” search for “bachelor’s labor force level” to add to the graph. Then in the formula bar, divide line 1 by line 2 and adjust units to show dollars (i.e., enter a/b*1000000).

Suggested by Suvy Qin and Christian Zimmermann.

View on FRED, series used in this post: LEU0252917300Q, LEU0252918500Q, LNS11027662, SLOAS


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