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Are you open? The openness index measures countries' exposure to international trade

How much do countries rely on international trade? A common measure is the openness index, which adds imports and exports in goods and services and divides this sum by GDP. The larger the ratio, the more the country is exposed to international trade. Looking at the map, it’s quite apparent that the largest economies are the least open by this definition. But this is quite natural: Because they are so large, much of their trade is internal. For small economies that cannot produce everything they need, more trade has to be external. The extreme case here is Hong Kong, with a ratio of 440%. Indeed, it doubles as a trading hub, with more than its GDP being reexported.

Note: As of this writing, the last year available for this indicator is 2010. It is taken from the Penn World Tables, which are updated every few years.

How this map was created: Go to GeoFRED and select region type “Nation.” Look for “openness” in the dropdown menu.

Suggested by Christian Zimmermann

Just one word: Plastics Relative importance weights of the components of industrial production: Part 2

Actually, this post is not about just one word. There are at least four: plastics, yes, but also textiles, electricity, and ice cream.

As we discussed in the previous post, many sectors of the economy, with their specific products and processes, contribute to the nation’s overall industrial production. This graph traces the relative contributions of four more components from FRED’s 322 series in this category.

Over the past 45 years shown in the graph, the production of plastics has grown in importance pretty consistently; someone in, say, 1967 who invested in that industry might have seen a nice return. With some peaks and valleys, electric power generation has become demonstrably more important, too. And its growth has been largely countercyclical—that is, it revs up through each postwar recession. Textile mills, on the other hand, have been declining in importance in U.S. industrial production for the entire time this data series has been calculated.

And what to make of ice cream? The previous post traced the progress of cheese, another wonderful edible good. And just like cheese, ice cream is a very small part of U.S. industrial production but its degree of importance has remained deliciously tried and true (though these series are seasonally adjusted, which matters most for ice cream).

How this graph was created: Search for “Relative Importance Weights”: As noted above, you’ll find 322 series to choose from. Check the measures you want and click “Add to Graph.”

Suggested by George Fortier.

View on FRED, series used in this post: RIWG22111S, RIWG313S, RIWG3261S, RIWN31152S

Newspapers are still more important than cheese Relative importance weights of the components of industrial production: Part 1

Many sectors of the economy, with their specific products and processes, contribute to the nation’s overall industrial production. The Board of Governors of the Federal Reserve System provides data on these components in their G.17 Industrial Production and Capacity Utilization release. As they state, these values are “estimates of the industries’ relative contributions to overall growth.” The graph above covers four specific components on the smaller end of the scale: newspaper publishing, cheese, tobacco, and fruit and vegetable processing. (FRED offers 322 series in this category.) Again, to be clear, these data measure the raw volume of goods that contribute to industrial production—not to health, wealth, or quality of life.

Over the past 45 years, the contributions of these components have changed—drastically, in some cases. From the late 1970s through the late 1980s, for example, newspaper publishing enjoyed prominence at the top of this list. But its contribution to this index has never been lower than it is today. Tobacco’s contribution surged to the top in the early 1990s and again in the early 2000s and is now neck and neck with fruits and vegetables. Cheese continues its quiet but rock-steady course at the bottom of this list.

How this graph was created: Search for “Relative Importance Weights”: As noted above, you’ll find 322 series to choose from. Check the measures you want and click “Add to Graph.”

Suggested by George Fortier.

View on FRED, series used in this post: RIWG3114S, RIWG3122S, RIWG51111S, RIWN311513S

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 workforce in the 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


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