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The FRED® Blog

The components of GDP growth What's driving the second quarter's 4.1% gain?

On July 27, the Bureau of Economic Analysis released its “advance” estimate of GDP growth, which was 4.1%, the strongest since 2014. So, what contributes to GDP growth? We answer this question with a FRED pie chart, which shows the components of GDP that contributed to that 4.1% growth rate and how much they each contributed. (By the way, this 4.1% number is valid at the time of this writing, but is subject to revision; see this blog post on GDP revisions.) As a simple exercise, consider this scenario: If the investment component made up 41% of GDP and it had grown 10%, then investment’s contribution would account for all of the second quarter’s 4.1% GDP growth. But this is imaginary, and economic data are rarely that simple anyway. In fact, for this quarter, investment didn’t grow at all; it was actually slightly less than zero. As was imports. (Both values were –0.06%.) Luckily, no component had a significant negative impact, which a pie chart can’t represent.

So what did drive GDP growth last quarter? Consumption of services (34.8%), consumption of goods (29.6%), and exports (26.7%) each contributed about a third to the increase. Government expenditures (8.8%)  complete the circle. More details can be found in this release. As it turns out, the only significant drag came from the reduction in non-farm inventories, while the biggest drivers were housing and utilities, health care, food services and accommodation, investment in structures and intellectual property, and (the biggest of all) exports of goods, which exactly matches the reduction in inventories.

How this graph was created: Search for “GDP contributions” and click on a relevant series. Scroll to the bottom of the page to find the release, then check the relevant series and click “Add to Graph.” From the “Edit Graph” panel, open the “Format” tab and choose graph type “Pie.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: A020RY2Q224SBEA, A822RY2Q224SBEA, DGDSRY2Q224SBEA, DSERRY2Q224SBEA

Is the little house on the prairie getting even smaller? The downward trend of U.S. farm income

Living on the farm is always subject to the vagaries of nature. If you’re farming to earn income, life is also subject to changes in the marketplace and in the policy realm. This graph follows the fortunes of farmers who own their farms. The proprietors’ income series shown here, for farms, is adjusted for inflation and tracks revenue that farm owners receive from their investment in land, machinery, and structures as well as the fruits of their own labor. (NOTE: When you want to divide national income into labor income and capital income, you’re left with a chunk you can’t attribute: proprietors’ income. That’s because they earn both kinds of income but don’t pay themselves a salary.)

The graph shows that proprietors’ income in the agricultural sector is quite volatile. Moreover, recessions have been particularly tough on farmers—their income almost reaches zero in 1983:Q3! But clearly other shocks also affect their income. In fact, one senses there’s a long-term downward trend here. It’s possible that the conditions of a relatively small number of (smaller?) farms may be driving this trend. Even if average farm size has grown over time, it seems that average farm income has not.

How this graph was created: Search FRED for “farmer income” and choose the relevant series. From the “Edit Graph” menu and then the “Customize data” feature, search for and add “CPI” and apply the formula a/b*100.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: B042RC1Q027SBEA, CPIAUCSL

Clocking the sales of cars and homes Stalled in the Great Recession, sales are speedy today

If you’re in the market for a big ticket item, like a new car or a new home, FRED has some data for you.

The line in red shows the median number of months it takes to sell a new home. At the height of the previous recession, it took 14 months or more for half of the home sales and 14 months or less for the other half. It’s no secret that the previous recession was special in this respect, and we see that this statistic has recovered very nicely since then.

In blue, we see a similar but not identical concept for cars: Dividing the inventory by the monthly sales gives the average number of months it takes to sell cars in current inventory. Strictly speaking, the two measures are not directly comparable, but they are relatable. Indeed, it’s no surprise that cars tend to sell faster than homes: Cars are generally a smaller investment, are less heterogeneous, and depend less on location. Yet in recent years it looks like the two measures have become much closer, largely because homes are now selling unusually fast.

How this graph was created: Search for and select “car sales ratio” and click on “Add to Graph.” From the “Edit Graph” panel, open the “Add Line” tab; search for and select “median months house on market.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: AISRSA, MNMFS

Antebellum “free” banking and the era of Bitcoin The past and present of unregulated currency

Smack in the middle of summer, you may find yourself with more free time, a freewheeling attitude, and maybe a wild inclination to pick up a new hobby, like spikeball… Or maybe even try out the hot new investment—cryptocurrency!

In short, cryptocurrency is a digital asset that is not regulated by a central authority, in the way money is regulated by the Federal Reserve System in the United States. No governing authority determines how much, by whom, or when crypto is produced or exchanged. Instead, the beauty of virtual currency is the “peer to peer network” and blockchain technology that makes it easier to transfer funds and more difficult to forge transactions.

The lack of collateral behind today’s cryptocurrencies is reminiscent of the pre-Civil War era of “free banking.” Back then, anyone with sufficient funds was able to open their own bank and issue their own notes, similar to the freedom available to a programmer who adds to the supply of crypto through mining. U.S. states that were successful at free banking used secure government bonds as backing. On the other hand, states that allowed low-security bonds and risky mortgages helped coin the term “wildcat banking”; these cases involved defaulted loans and bank notes that declined up to 60% in worth.

Bitcoin, one of the many types of cryptocurrency on the market today, is revered for its lack of regulation; however, this “freedom” also contributes to its notoriously volatile reputation. The above graph depicts Bitcoin’s price fluctuations (for example, from $20,000 in December 2017 to around $7,300 in mid-July 2018). In fact, a logarithmic scale is needed to best capture these fluctuations. (That is, the units are in U.S. dollars, but the distances between the lines can be interpreted as percentage differences; see an earlier post for more on logarithmic scale.)

At the CoinDesk Consensus, President James Bullard of the St. Louis Fed stated that “cryptocurrencies are creating drift toward a non-uniform currency in the U.S., a state of affairs that has existed historically but was disliked and eventually replaced.” Historically, investing in non-government-backed, non-uniform forms of currency has been risky. That said, blockchain technology also didn’t exist in pre-Civil War America.

How this graph was made: Search for “Coinbase,” select “Coinbase Bitcoin,” and click “Add to Graph.” From the “Edit Graph” panel, choose the “Format” tab and select the checkbox for “Log scale.” For graphs depicting rapid growth, consider using the log feature, available in every series on FRED: This helps to highlight small fluctuations in data points, linearizing the output.

Suggested by Elizabeth Tong and Christian Zimmermann.

View on FRED, series used in this post: CBBTCUSD

The long and the short of the workweek Weekly hours of work by sector

Not everyone has the same workweek. One factor that determines your working hours is the sector you work in. As the graph above shows, there are substantial differences among sectors, due to both regular hours and overtime. Indeed, in mining and logging, the average workweek is over 47 hours long. At the other extreme, workers in the leisure industry on average work only 25 hours. The latter may be a special case, though, because of the prevalence of part-time work. Generally, the service sector has an average in the 30s and the goods-producing sector has an average in the 40s.

But are these differences caused by the specific time period chosen in the bar graph? Let’s see. The second graph looks at four sectors over several decades, and it’s clear that the differences have been there for a long time and seem to be getting even starker.

Maybe these differences are caused by varying reliance on overtime. Unfortunately, we have overtime hours for only manufacturing, which are visible in the last graph. Manufacturing overtime seems to have been trending up slightly over the past several decades, but this is just one of many contributing factors that might explain the workweek differences among sectors. Indeed, manufacturing overtime is only about four hours while the difference in weekly hours between manufacturing and professional and business services is six to seven hours.

How this graph was created: Go to the release table for weekly hours by sector, select “Average Weekly Hours,” select the series you want, and click “Add to Graph.” In the date range fields, select May 2018 and June 2018 for the most-recent data. From the “Edit Graph” panel, go to the “Format” tab and change “Graph type” to “Bar.” For the second graph, use the same release table and set of weekly hours; select the series you want, and click “Add to Graph.” For the third graph, use the same release table but select “Average Ovetime Hours” and the manufacturing sector series.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: AWHMAN, AWOTMAN, CES1000000007, CES2000000007, CES3100000007, CES3200000007, CES4000000007, CES5000000007, CES5500000007, CES6000000007, CES6500000007, CES7000000007


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