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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

Where health is lacking

Mapping public health issues with GeoFRED

GeoFRED maps can help us understand a lot of things, including trends in regional socioeconomic data, which could ultimately provide insights for policy recommendations. In this post, we look at two important indicators of health throughout the United States: premature deaths and preventable hospital admissions. High levels of premature deaths indicate issues with public health. (See a previous blog post for some background on this concept.) The South has a comparatively higher concentration of high rates in this area.

The maps show a correlation between areas that suffer from high rates of premature death and areas that have a high rate of preventable hospital admissions, which is defined as stays in acute-care hospitals that could have been taken care of in ambulatory or ordinary inpatient settings, adjusted for socioeconomic factors. Examples are pneumonia, diabetes, and dehydration. A high rate of these admissions indicates that more people are lacking appropriate health options, likely leading to more preventable deaths.

While regional trends and correlations do not indicate causation, a review of interconnected socioeconomic patterns over several years can be useful for understanding persistent problems in certain areas. Refer to GeoFRED for related maps on race, income inequality, homeownership, burdened homeowners, and disconnected youth.

How these maps were created: Premature Death: From GeoFRED, click on “Build New Map.” Under the “Tools” menu, select “County” in the region type search bar. For the first map, enter “premature death” in the data search bar and then select “Age-Adjusted Premature Death Rate”; for the second map, enter “preventable hospital admissions” in the data search bar and then select “Rate of Preventable Hospital Admissions.”

Suggested by Samantha Kiss and Christian Zimmermann.



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