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Is GDP a good measure of well-being? Mapping out health and income

GDP has been used as a measure of economic well-being since the 1940s: It measures the total economic output by individuals, businesses, and the government and is a tangible way to quantify the state of the economy. However, some economists have questioned how well GDP measures well-being: For example, GDP fails to account for the quality of goods and services, the depletion of natural resources, and unpaid jobs that are nevertheless important (e.g., household chores). Although this criticism may be well founded, GDP is highly correlated with other measures of well-being, such as life expectancy at birth and the infant mortality rate, both of which capture some aspects of quality of life.

The map above shows a version of GDP per capita for each nation—specifically, GDP per capita adjusted by purchasing power parity (PPP). Currencies differ in their purchasing power (i.e., the number of units of a currency it takes to buy the same basket of goods across countries), so it’s hard to compare the GDPs of different countries at face value and current exchange rates. Thus, we use PPP-converted GDP per capita, which equalizes the purchasing power of different currencies by accounting for the differences in the prices of goods across countries. People in countries with higher levels of per capita GDP have, on average, higher levels of income and consumption. As expected, the map shows that developed countries (e.g., the U.S., Canada, most of Western Europe, and Australia) have higher levels of PPP-converted GDP per capita.

The infant mortality rate is the number of deaths of infants under one year old per 1,000 live births, which can be interpreted as an index for the general health of a country. As the second map shows, infant mortality is the greatest in African countries, some Latin American countries, and parts of Asia such as India, Pakistan, Indonesia, and Papua New Guinea. If we look back at the first map, we see that the GDPs of these countries are among the lowest. Similarly, we also see that low infant mortality rates in the advanced countries correspond with high GDPs.

Life expectancy at birth reflects the average number of years a newborn is expected to live, holding constant the current mortality rates. Life expectancy reflects the overall mortality level of a population and is another indicator for the general health of a country. The last map shows that life expectancy is the greatest in the U.S., Canada, Chile, parts of Europe, Australia, and other developed countries that are in the top GDP bracket; countries with lower life expectancies, such as the countries in Africa and Asia noted above, have very low GDPs.

These maps reveal the high degree of correlation between GDP and other measures of well-being. So, although GDP is an imperfect measure and doesn’t capture every aspect of a country’s quality of life, it’s still a reasonable proxy of the overall well-being of an economy.

How these maps were created: Go to GeoFRED, click on “Build New Map.” From the left corner, click on “Tools” and expand the “Choose Data” option. Under “Data,” search for “Purchasing Power Parity Converted GDP Per Capita.” From the given options, select “Purchasing Power Parity Converted GDP Per Capita (Chain Series).” For the second graph, under “Data,” search for and select “Infant Mortality Rate.” For the third graph, search for and select “Life Expectancy at Birth, Total.”

Suggested by Maximiliano Dvorkin and Asha Bharadwaj.

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

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