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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.

Is college still worth it? Re-examining the college premium

A recent symposium held by the Center for Household Financial Stability at the St. Louis Fed looks at the question of whether the college premium is still increasing and positive, using new data from the Fed’s Survey of Consumer Finances. On an absolute level, college graduates earn more than high school graduates, as shown in the graph above. This is consistent with the understanding that the benefits of a college education are greater than the costs.

If we look at the college premium, we can see that it has always been positive, indicating that there is a positive benefit of graduating with a bachelor’s degree. This graph shows that, at the end of the first quarter of 2018, college graduates received weekly wages that were 80 percent higher than those of high school graduates.

However, there’s more to this story. Recent research shows that the college premium may or may not be very strong depending on birth year, family, and other inherited characteristics. When looking at the wealth premium instead of just the income premium, the college premium was weak for all races and ethnicities in the 1980s cohorts, whereas the college premium exists for cohorts in earlier decades. A potential reason for this result is the high and rising cost of college. Over the past decade, we see an increase in the dollar amount of total outstanding student loans per total number of college graduates in the labor force, reaching almost $27,000 per college graduate available for work at the end of the first quarter of 2018. High levels of student debt may affect the ability to accumulate wealth, resulting in the declining college wealth premium. This is just one of the reasons for further investigation into the college premium, rising tuition costs, and how education influences economic well-being.

How these graphs were created: For the first graph, search for “wages bachelor’s degree” and select the quarterly data series to add to the graph. From the “Edit Graph” panel, go to “Add Line” and search for “wages high school” and select the corresponding series. To create the second graph, use the same steps to get to the “wages bachelor’s degree” series. Then under the “Customize data” section, search for “wages high school” and select the series. Then enter in the formula (a/b) – 1 to get the college premium. For the third graph, search for “student loans” and select the series for outstanding student loans. From the “Edit Graph” panel, go to “Customize data,” search for “bachelor’s labor force level” to add to the graph. Then in the formula bar, divide line 1 by line 2 and adjust units to show dollars (i.e., enter a/b*1000000).

Suggested by Suvy Qin and Christian Zimmermann.

View on FRED, series used in this post: LEU0252917300Q, LEU0252918500Q, LNS11027662, SLOAS

Why is it so difficult to live where you work? Housing costs and homeownership in economic centers

In some areas of the U.S., housing has become so expensive that people find it difficult or impossble to afford housing anywhere near where they work. The recent focus on the homeless population in Los Angeles highlights the most extreme form of this situation: Many of the homeless in that area are not only employed, but also are experiencing homelessness for the first time. Unaffordable housing and long commutes are particularly burdensome for low-income individuals, but these issues have consequences for all Americans. (Check out a previous FRED blog post on the distribution of commute times in the U.S. for more information.)

The maps in this post show U.S. county-level data from 2016 for two concepts: the homeownership rate and burdened households. Both depict a spatial representation of affordable housing in the U.S. Homeownership is clustered away from urban centers. Counties such as Los Angeles, Suffolk, and Cook (home to the cities of L.A., Boston, and Chicago) report homeownership rates of 48, 37, and 54 percent, respectively, significantly lower than the national average of 70 percent.

Burdened households lack access to affordable housing, which the U.S. Department of Housing and Urban Development defines as “housing for which the occupant(s) is/are paying no more than 30 percent of his or her income for gross housing costs, including utilities.” The maps show that the least affordable housing, represented by low homeownership rates and a high density of burdened households, lies in urban areas rich with economic opportunity.

Living close to work has a significant beneficial impact on employment and happiness. If they can choose to, individuals are likely to live closer to where they work; and workers with accessible jobs are more resistant to joblessness and long periods of job searching. Proximity matters the most for low-income residents, who are more constrained by housing and commuting costs. Hence, accessibility to employment increases the chances not only of working but also of escaping welfare.

More affordable housing has the potential to increase efficiency and optimization, key concepts in the study of economics: Low-income residents might gain greater economic mobility, and more high-skilled, talented individuals might move into urban areas to help maximize the economic potential of those areas. Also, the average American might simply be able to cut down on time spent in traffic getting to work.

How these maps were created: From GeoFRED, click “Build New Map.” From the “Tools” menu, select “County” as “Region Type” and expand the “Data” selection. Under “Data,” search for “Homeownership Rate” and then “Burdened Households.”

Suggested by Elizabeth Tong and Christian Zimmermann.



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