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Are household debt and student debt exploding?

On the importance of properly deflating

The graph above shows two series related to household debt that have received a great deal of attention lately: consumer credit (mostly lines of credit and credit cards) and student loans. These series show stark increases especially in recent years. But one has to be careful before jumping to conclusions, as the eye may be deceived here. First, the student loans shown here are only those that come directly from the federal government, and that specific program was introduced in 1994. So part of the increase is simply this program ramping up. But more importantly, one has to consider the important factors for the time period shown here: overall prices increased, population grew, and real incomes increased as well. Thus, it could be that these graphs simply show the increases in these three factors and nothing else.

To make things clearer, we need to divide by a measure that also increases along with these three factors and thus represents the size of the economy over the years. One popular candidate for this is nominal (that is, not real) GDP. It accounts for price, population, and productivity growth. The graph below is the same as the above, except that both series are divided by nominal GDP. The new graph still shows an increase for both series, but it’s not as dramatic. It also has the advantage of providing a frame of reference for the numbers: Total outstanding consumer credit currently amounts to about 20% of national income, and student debt is 6%. Whether this is excessive is open to debate. But one should focus on the data in percentages, not in billions of dollars.

How these graphs were created: Search for “consumer credit” and click on the desired series. Once you have the graph, go to the “Edit Graph” section and open the “Add Line” panel. Search for “student loans” and take the series with a longer time range. Apply formula a/1000 so that the units match. You have now the first graph. For the second, add a series to each line by searching for “GDP” (do not take real GDP) and apply formulas a/b and a/b/1000, respectively.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: FGCCSAQ027S, GDP, HCCSDODNS

Good news for women in the labor force?

Assess the data before jumping to conclusions

If you’re looking at gender disparity in the U.S. labor force, FRED’s got your back! In a previous blog post, we analyzed the demographics of the activity rate decline. Today, we look at the gender disparity story told by two different labor market indicators: the employment and activity rates (the latter is also known as the labor force participation rate). Although these two indicators may both provide information about employment, they actually measure different aspects of the labor market.

The employment rate is the ratio of people who are currently employed to the working age population. The activity rate is the ratio of people who are employed or actively seeking employment to the working age population. Hence, the activity rate measures the overall willingness of the working age population to work. The Y-axis in the top graph represents the differences of activity and employment rates by gender. Starting off at 60% in 1955, it’s striking how the gaps for both rates declined at a similar pace until they stabilize at approximately 15% currently. This seems to suggest that, as women have joined the workforce, they’re finding employment at rates similar to men. It’s also important to note that the gender gap in employment reached its lowest level during 2007-2009 recession. Does that imply that women’s jobs are more recession proof?

Before trying to come to any conclusions on (i) the cause of the narrowing of the gaps and (ii) the steady-state that began around 2000, let’s look at the individual series for men and women. It could even be the case that both male and female employment have been declining, but that male employment has been declining at a faster rate. This is illustrated clearly by the employment rate during the 2007-2009 recession. The two graphs below show the evolution of both rates by gender. By graphing these series, we can see that the female employment rate has indeed been catching up with the male employment rate since 1977, while the male employment rate has been fluctuating around 85-90%. However, the decrease in the gender gap during the recent recession seems to be attributed to the fact that the recession has hit men harder, as there is a sharper decline in male employment than female employment.

Similarly, we can see that the female activity rate has increased from 1955 to 2000, while the male activity rate has been on a slow decline since 1955. However, the female activity rate has also been on a slight decline since 2000. Hence, we cannot conclude that more women have been joining the workforce in recent years; in fact, the opposite is true. Much of the decline since 2000 can be explained by the aging of the Baby Boomers into their retirement years and the new trend of lower participation among teens and young adults. (For more insights on this issue, see this recent speech by Janet Yellen.)

How these graphs were created: For the first graph, search for “Employment Rate: Aged 25-54: Males for the United States” and select “Add to Graph.” From the “Edit Graph” section, add “Employment Rate: Aged 25-54: Females for the United States” as another line and apply the formula “a-b.” To include the differences in activity rate, add “Activity Rate: Aged 25-54: Males for the United States” and repeat the same procedure. Expand the years to 1955 to see the earliest possible data. For the second graph, search for “Employment Rate: Aged 25-54: Males for the United States” and choose the seasonally adjusted monthly series. From the “Edit Graph” section, add the seasonal adjusted monthly series for “Employment Rate: Aged 25-54: Females for the United States.” Similarly, search for the seasonally adjusted monthly series for “Activity Rate: Aged 25-54: Males for the United States” and add “Activity Rate: Aged 25-54: Females for the United States” for the last graph.

Suggested by HeeSung Kim.

View on FRED, series used in this post: LRAC25FEUSM156S, LRAC25MAUSM156S, LREM25FEUSQ156S, LREM25MAUSM156S

The house price tumble in pictures

FRED recently added county-level data on house prices. As with a lot of regional data, it’s best to look at it on our mapping tool, GeoFRED, which lets you visualize the distribution and the evolution of economic and socio-demographic statistics. One particularly interesting thing you can do with these new data is to replay the housing crisis of 2007-2008. The map shows the change in house prices that took place in 2006: The darker areas show the counties where growth was the highest, mostly the West and Florida. The maps below* show the change in 2007, when the West Coast and Florida were suddenly the areas with the lowest growth, and in 2008, when this downturn expanded to most of the West. If you go to GeoFRED, you can cycle through more years and see how crisis unfolded.

* Note that the colors in these maps denote lower values.

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 Christian Zimmermann.

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