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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: The original post referenced an interactive map from our now discontinued GeoFRED site. The revised post provides a replacement map 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 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


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