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How much do we spend on new houses? The highs and lows in the numbers and values of new construction

Do we spend more on new houses than we used to? It can feel like it, especially because houses have become larger and available land has become more scarce. For a quantitative answer to this question, we can use FRED’s data on the number of new houses being sold across the U.S. and the median value of those houses. Multiplying these two indicators yields the total value of all houses sold in a given period. (Well, at least approximately: The mean would give us a better measure, but if the price distribution of new houses doesn’t change too much, this method will do.) Now, prices and incomes have generally increased, so we want to divide the total value of all houses sold by nominal GDP. The result is the series that we display in the graph above, with data normalized to 100 for the start of the sample.

What do we learn? 1) We spend relatively less on houses now, but we’re getting back to the trend. 2) There are strong seasonal factors in the sales volume of new houses. 3) Recessions are really bad for new house sales. 4) The U.S. spent historically high amounts for new houses just before the previous recession and then they dropped to historical lows. 5) Although this recent drop was extraordinarily severe, from a value of 183 to a value of 28 in the matter of a few years, the movements are also very large in other years and some values have doubled within a business cycle. After all, construction is known to be a very volatile sector, and this is especially true for new construction.

How this graph was created: search for “new houses sold, select the series and open the graph. Click on “edit graph” and add a series to the line by searching for median value, then again by searching for “nominal GDP.” Apply formula a*b/c. Finally, change the units ate the very bottom of the form to “Index” setting 100 on 1963-01-01.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: GDP, HSN1FNSA, MSPNHSUS

Take it easy! The Ease of Doing Business Index ranks regulatory environments around the world

In an increasingly competitive global economy, many in the private sector wonder whether their businesses would be better off if they were located somewhere else. FRED has data that can help shed some light on which countries foster the best business conditions: The World Bank’s Ease of Doing Business Index ranks 190 countries according to a combination of 10 factors, including cross-border trade, tax payment, electricity access, property registration, construction permits, and other issues related to how well the rules and regulations benefit private enterprise.

Despite its appearance as a simple ranking, the Ease of Doing Business Index is fairly intricate. It provides overall rankings as well as rankings in several categories. For example, New Zealand was ranked 1st overall in 2016, but was 55th in terms of trading across borders. Afghanistan was ranked 183rd overall, but was 42nd in ease of starting a business. It also tries to measure how far each country is from the ideal, with minute changes sometimes causing large moves in the rankings.

At its core, the index is about administrative hurdles and costs and thus doesn’t capture some factors that are relevant to the private sector, such as market size, labor force quality, and corruption. But the index still reflects these factors indirectly, because of how closely they’re tied to the indicators that are measured. For example, although macroeconomic stability isn’t explicitly incorporated into a nation’s ranking, it still impacts the time and cost of getting credit or starting a business, which is part of the ranking process.

A final consideration is the fact that data can be collected only from the formal economy. Many nations have large informal sectors and thus can be ranked lower in the index than they might be otherwise: The data disproportionately represent the more easily measured transactions in the formal economies of developed nations without taking into account the similarly efficient transactions in the informal economies of other nations. But, of course, the size of a country’s informal sector is likely correlated with the (lack of) ease of doing business in its formal sector.

How this map was created: In GeoFRED, select “Build New Map.” In the tools menu, click “Data” and search for “ease of doing business.”

Suggested by Maria Hyrc and Christian Zimmermann.

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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