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Labor force participation rates across the OECD Who's working depends on where you look

One critical element for the growth of an economy is an active working-age population: Growth can be hampered when (i) the overall population is aging and a larger share of the population is retired or (ii) a larger share of the working-age population simply isn’t working. The graph above shows, for four countries, the share of the population that’s 25 to 54 years of age—i.e., prime working age—with a job. The remainder of that population is either unemployed or not looking for a job.

This graph reveals some stark contrasts. Japan and the U.K. show steady increases, which helps counter the effects of their aging populations, a condition that’s of particular concern in Japan. Spain shows a very rapid increase, which demonstrates that such a statistic need not move in a sluggish way. The U.S., however, shows no significant movement in the 1990s and a decline since then. We know this isn’t due to an increase in unemployment, which is at its lowest rate in a long time.

To be fair, the increases in other countries are partly due to increases in women’s labor force participation. The U.S. experienced a surge in women’s participation much earlier and has apparently reached its plateau. Much of the decrease in U.S. labor force activity, as it turns out, has to do with men: Even a quick look at the graph below shows the steady decline in their activity. Understanding why this is happening is a topic of much current investigation.

How these graphs were created: For the first, search for “Participation Rate” and then use the sidebar to narrow down the choices. Then select the desired series (annual, in our case) and click on “Add to Graph.” For the second, searching for “United States Participation Rate” gives your the right options. Choose the annual series again, and click on “Add to Graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: LRAC25FEUSA156N, LRAC25MAUSA156N, LRAC25TTESA156N, LRAC25TTGBA156N, LRAC25TTJPA156N, LRAC25TTUSA156N

A healthy appetite for health care? How supply and demand may affect the costs and consumption of health care services

Health care has improved considerably in the past couple of decades, in terms of both quality and access. Yet, with health care costs on the rise in recent years, it’s also a topic of many heated discussions. Supply factors could be behind the increase in costs for health care services, but would also have a negative impact on their demand. On the other hand, higher demand for health care services would increase both the price and quantity consumed.

With FRED’s personal consumption expenditures price index data, we use the graph above to show the ratio of the price index for health care services to the overall price index for all goods and services in the economy. (The base year is set to 1999.) We can see that health care services are about 10 percent more expensive today, relative to all other goods and services, than they were 18 years ago.

The graph below shows, in billions of chained 2009 dollars, the amount spent on health care as a share of total consumption spending. (It’s important to keep in mind that the series displayed here mute the effect of changes in the price levels, as prices are “fixed” to the levels in 2009.) We can see an increasing trend for the past 18 years, indicating that the amount of health care consumed, as a share of total expenditures, has also been rising. This also implies that consumer spending on health care has been increasing more than consumer spending on other types of goods.

These graphs suggest that some demand factors could be behind the increased cost of health care, as both the price and the consumption of health care services, relative to other components of consumption, have increased. Some possible demand factors could be related to longer life spans, the demand for newer and more expensive procedures, and so on. Our analysis here is stylized, but further research should look at this issue more closely to try to illuminate the supply and demand factors behind the rising cost of health care.

How these graphs were created: For the first graph, search for “Personal Consumption Expenditures: Services: Health care (chain-type price index)” and select the quarterly, seasonally adjusted series. From the “Edit Graph” section, under “Units,” select “Index (Scale value to 100 for chosen date)” and set the date to 1999-01-01. Then use the “Add Line” option to add the quarterly and seasonally adjusted series for “Personal Consumption Expenditures (chain-type price index).” Apply the same adjustment to set the index to 100 for 1999-01-01. Then apply the formula a/b. Set the starting date for the graph to 1999-01-01. For the second graph, search for “Real Personal Consumption Expenditures: Services: Health care” and select the quarterly, seasonally adjusted series. Then, from the “Edit Graph” section, use the “Add Line” option to search for “Real Personal Consumption Expenditures,” quarterly, seasonally adjusted. Then apply the formula a/b.

Suggested by Maximiliano Dvorkin and Asha Bharadwaj.

View on FRED, series used in this post: DHLCRG3Q086SBEA, DHLCRX1Q020SBEA, PCEC96, PCECTPI

Who’s hiring? Hiring rates differ across economic sectors

As we’ve often discussed on this blog, the U.S. labor market is pretty active, with high turnover. Turnover differs quite a bit across sectors, though. In the graph above, which covers hiring rates, we did something that’s usually not recommended: cramming seven(!) series in a single graph. Yet, FRED’s features still allow us to distinguish what’s happening. Hover over a legend and you’ll see the relevant line light up, making it easier to find. Hover over the graph and you’ll see a list of data points for a particular month. Or you can draw a bar graph, which we did below.

These two graphs highlight the large disparities across sectors: Construction, hospitality, and professional services have hiring rates on the order of 5-7% of their workforce every month. The rate for government is below 2% (except for a spike related to the decennial census), and the rate for manufacturing isn’t far from that. Once you drill down into sub-sectors, you can find even more disparity, from 9.2% (June 2001) for arts, entertainment, and recreation to 0.6% (January 2012) for the federal government (but 14.7% in May 2010 with the aforementioned census).

By the way… If we would have made this graph using the separation rate, it would have looked roughly the same. Indeed, imagine the labor market as a bathtub: The hiring rate is the faucet that pours water in, and the separation rate is the drain that pulls water out. With the pool of employed workers roughly equal from month to month, the hiring and separation rates must roughly match or else the bathtub would either overflow or empty itself. So, currently, a higher hiring rate is simply a sign of a higher turnover rate; both the faucet and the drain are larger.

How these graphs were created: Search for “JOLTS hiring” and use the left sidebar to filter results: We used only seasonally adjusted national series that show the rates of hiring. Select the series you want and then click “Add to Graph.” For the second graph, start with the first and select the last year of data (“1Y”); then go to “Edit Graph”/”Format” and change the graph type to “bar.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: JTS2300HIR, JTS3000HIR, JTS4000HIR, JTS540099HIR, JTS6000HIR, JTS7000HIR, JTS9000HIR

Markets in the shadow of the eclipse Could the solar eclipse affect stock market activity?

Some researchers have studied the possible effects that astronomical and meteorological conditions have on the stock market. So, what should investors do about the total solar eclipse today? First, the bad news: As you’re reading this, it’s already too late to react. Second, the good news: The graph above depicts the weekly returns of the Wilshire Index, with colored vertical lines depicting three annular solar eclipses visible from the U.S. during the sample period. (The gray bars represent recession periods.) At least for the annular variety, there’s not much to write home about regarding the stock market. The weekly returns were -1.2%, -2.1%, and -0.1%, respectively, which are all well within normal fluctuations for weekly data on stock markets. And the fact that all three are negative isn’t worrisome in a statistical sense. But is there a reason to come to a different conclusion for a total eclipse? Well, a total eclipse is sufficiently rare that the evidence will be anecdotal. So we probably need to check FRED again in a century or two to find more conclusive evidence.

How this graph was created: Search for “Wilshire” and click on your preferred index. In the “Edit Graph” section, change the frequency to “week ending Saturday” to get more data points on the graph. (Daily data are automatically aggregated once there are too many points.) Change units to “Percent Change.” To add the vertical lines, click on the “Add Line” tab, expand “Create user-defined line,” and click on “Create line.” Here you have the opportunity to enter two dates and one value for each. To make the line vertical, use the same date for the start and the end. In this case, the three eclipses were on 1984-05-30, 1994-05-10, and 2012-05-20. Then put values that are just within the limits of the graph—in this case -19.9 and 9.9.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: WILL5000INDFC

Leaves fall, prices rise Tracing the timing of school supply price hikes

It’s backpack season, and many Americans are starting a new school year. A recent FRED Blog post covered the impact of seasonality. This post covers education-related data—specifically, the cost of books and supplies. The top graph shows the monthly changes in the price of educational books and supplies over the past ten years, using the consumer price index, which measures inflation by reporting the percent change over time, on average, of a variety of goods and services paid for by U.S. urban consumers. In the past decade, educational supply costs have tended to rise most significantly in August and January, coinciding with the beginning of academic semesters for most students.

Seasonal adjustment accounts for these variations by using data from previous years to smooth seasonal eccentricities. The graph above shows the difference between the raw and adjusted CPI indices, showing that level-differences have increased over the past several decades. However, the difference between indices may not show the whole story, as CPI is used to report inflation as a percent change. The next graph shows the difference in the monthly percent change in the price of books and supplies between the raw and adjusted data, showing that the seasonal difference in the monthly price change has actually decreased over time.

The seasonal changes, besides decreasing, have also scattered across the school year. In the early 1970s, prices consistently rose each October and remained fairly stable during the remaining months. In the next decade, spikes emerged at the beginning of the calendar year as well, presumably as consumers spent more on supplies for the second semester of the school year. In recent years, the autumn spike in prices has begun earlier, shifting from October to August and September, likely reflecting changing market strategies and school schedules.

The crucial question remains: When can I find the best deals on school supplies? You may not want to ask your parents for the answer. Older folks may advise against buying in October, based on their experiences of cost increases, and propose an August shopping trip instead. However, today’s students may want to make the most of the price decreases of the early summer and late fall, with back-to-school shopping trips in July and December.

How these graphs were created: For the first graph, search for “CPI US Books and Supplies” and select the not seasonally adjusted, monthly series. From the “Edit Graph” section, adjust the units to “Percent Change.” From the “Format” tab, change the graph type to “Bar.” Finally, adjust the graph to display data from the last 10 years using the “10Y” button in the top right corner of the graph page. For the second graph, search for the same series: “CPI US Books and Supplies,” not seasonally adjusted, monthly. From the “Edit Graph” section, add the seasonally adjusted data to line 1 by searching for “CPI US Books and Supplies adjusted” in the box below “Customize Data.” Click “Add.” In the formula tab, type “a-b” and click “Apply.” For the last graph, follow the steps for the previous graph, but change the units for both series to “Percent Change.” In the format tab, select “Bar” as the graph type.

Suggested by Maria Hyrc.

View on FRED, series used in this post: CUSR0000SEEA, CUUR0000SEEA


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