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U.S. car production: right on track Passenger car production mirrors the pattern of railroad construction, 20 years delayed

The U.S. has always been preoccupied with travel. Lewis and Clark’s 1804-06 expedition revealed that no single waterway spanned the continent, which led Thomas Jefferson to push for a railroad network to connect the east and west coasts. Over the next 80 years, the U.S. experienced its railroad boom. The next 20 years brought about an automobile boom…and the first coast-to-coast journey by car. This post won’t cover travel by canoe, but it will look at railroads and cars.

The graph depicts miles of railroads and the number of passenger cars built throughout America’s early industrial history. The timing differs—the first U.S. railroad was chartered in 1815, and the first U.S. gas-powered auto was invented in 1893—but the patterns of growth for these two industries are strikingly similar. Both modes of transportation experienced an initial spike (railroads in the mid-1850s and cars in the 1890s), followed by a slight decline and then a higher peak (railroads in 1887 and cars in 1907).

The obvious difference is that 20-year delay in passenger car peak production, due to the pace of technological change in the U.S. Labor patterns and manufacturing itself changed during the early 20th century with the introduction of the assembly line and shifting consumer tastes, which led automobile production to rise. Conversely, railroad construction slowed as fewer locations in the U.S. were left unconnected.

Economic conditions over the years have also affected these two variables in major ways: Railroad construction, which made up 15% of U.S. capital formation in the 1880s, declined precipitously during the Depression of 1882-85. Likewise, car production plummeted after the Panic of 1907. The Great Depression also had far-reaching effects on both railroad construction and passenger car production: In 1933, both variables saw a decline of about 85% from the prior year, showing the susceptibility of the construction and manufacturing sectors to economic downturns.

How this graph was created: Search FRED for “miles of railroad built” and select the relevant series. Click “Edit Graph” and select “Add Line.” Search “passenger cars built” and click “Add.” Under the “Format” tab, change the y-axis position of “Passenger Cars Built for the United States” to “Right.” Adjust the end date to 1934.

Suggested by Maria Hyrc.

View on FRED, series used in this post: A01154USA471NNBR, A02F2AUSA374NNBR

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.



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