Continuing on the theme of last week’s minimum wage increase in Florida, there are two interesting papers recently accepted for publication that both cover the 1966 Fair Labor Standards Act. This law extended the federal minimum wage to a number of previously uncovered. Crucially, the newly covered industries employed a large number of African-American workers.
The two papers agree on some points, such as that African Americans saw large wage gains following the increase. But was there a disemployment effect? Here is where the papers differ.
Ellora Derenoncourt and Claire Montialoux’s paper “Minimum Wages and Racial Inequality” is forthcoming in the Quarterly Journal of Economics. Here is what they find: “We can rule out significant disemployment effects for black workers. Using a bunching design, we find no aggregate effect of the reform on employment.”
So who is right? Let me clearly state here that both of these papers are very well done, both in their methods and in their assembling of historical data. But I think there is a key difference in the samples they analyze: Derenoncourt and Montialoux’s paper only includes workers aged 25-55. Bailey and co-authors use a broader age range, 16-64, which importantly includes teenagers (this is discussed in Section D of their online appendix).
Since teenagers and other young workers are the ones we suspect are going to be most impacted by the minimum wage (much of the literature focuses on teenagers), the exclusion of workers under 25 seems like a curious omission, and a reason I tempted to believe the results of Bailey and co-authors. But Derenoncourt and Montialoux do try to justify their choice of age group: 1. workers under 21 were subject to a different minimum wage; and 2. workers under 25 were subject to the draft for the Vietnam War.
So once again, you might ask, who is right? I will admit here that I don’t know. Standard economic theory suggests that disemployment effects will result from a legal minimum wage (I fully acknowledge the emerging literature on monopsony power, but I maintain this is still not the standard analysis), and especially so for teenagers and young workers. So I am skeptical of any analysis which excludes these workers, whatever other merits it may have.
Here’s my take: we probably can’t tell much about how the minimum wage will impact young workers today based on these studies. If Derenoncourt and Montialoux’s reasons for removing young workers are indeed sound, then we aren’t really testing the question most economists are interested in today (so I would caution against their attempt to apply the results to labor markets today). But that doesn’t mean these aren’t interesting papers to read on an important change in the history of minimum wage laws in the US!
Corporations raise money in various ways to invest in their operation. A company may sell common stock to the public; the shareholders are not guaranteed any particular return on their investment, but if the company does well, the share price and the dividends paid by the stock can be expected to go up.
Preferred stock falls in between common stock and bonds. Investors mainly buy preferred stock for its dividends. Typically, the price of the preferred stock doesn’t go up like common stock can, but the company cannot pay any dividends on the common unless all of the promised dividends on the preferred are paid up.
CORPORATE BONDS: INVESTMENT GRADE AND JUNK
Companies can sell bonds to raise money. Bonds are somewhat standardized securities, which are marketed to the broad investing community. The company is legally bound to pay the interest, and eventually the principal, of a bond. Bonds are senior to stocks in case of extracting value from a company that has gone bankrupt. Some bonds are more senior than others, depending on the “covenants” in the fine print of the bond description (debenture). For smaller, less stable companies, the only way they may get someone to buy their bonds is to agree to certain conditions that make it more likely the bond will be repaid. For instance, the company selling the bond might be restricted from issuing more than a certain amount of total debt relative to its earnings, or from taking on additional debt which might be senior to its existing debt.
Bonds are rated by agencies such as Moody’s and Standard and Poor’s. Large, stable companies get high ratings (e.g. AA), and can pay lower interest. You, the public, can buy into investment grade bonds through funds such as iShares iBoxx $ Investment Grade Corporate Bond ETF (LQD). This fund currently pays about 2.6%, but most of the returns in the past several years have been from an increase in the price of the fund shares. (For longer term bonds, the market price of a previously-issued bond increases as market interest falls, which it has in recent years).
The lowest investment grade is BBB. The bonds of shakier companies are rated at BB or lower, and have pay higher interest. This is called high yield debt or junk bonds. You can invest in junk bonds through funds such as JNK and HYG.
CORPORATE BANK LOANS
Companies also obtain loans from banks. Banks scrutinize the operations of the company to decide whether they want to risk their money in making a loan. Banks usually demand restrictions and guarantees to help ensure the loan will paid back. These restrictions are called covenants. Sometimes the payback of the loan is tied to a specified asset. For instance, if the income of a company falls below a certain level (which might imperil paying off the loan), the covenant may require the company to give ownership of some asset, like a building or a set of oil wells, to the bank, so the bank can sell it to pay back the loan immediately, before economic conditions worsen.
This graphic shows some of the conditions a company might have to sign to in order to get a loan from a bank:
Here is a summary of the differences between bonds and loans, courtesy of WallStreetMojo (slightly edited):
The main difference is that a bond is highly tradeable. If you purchase a bond, there is usually a market place where you can trade it. It means you can even sell the bond, rather than waiting for the end of the thirty years. In practice, people purchase bonds when they wish to increase their portfolio in that way. Loans tend to be the agreements between borrowers and the banks. Loans are generally non-tradeable, and the bank will be obliged to see out the entire term of the loan.
In the case of repayments, bonds tend to be only repaid in full at the maturity of the bond – e.g., 10, 20, or 30 years. With bank loans, both principal and interest are paid down during the repayment period at regular intervals (like a home mortgage).
Issuing bonds give the corporations significantly greater freedom to operate as they deem fit because it frees them from the restrictions that are often attached to the loans that are lent by the banks. Consider, for example, that lenders or the creditors often require corporates to agree to a variety of limitations, such as not to issue more debt or not to make corporate acquisitions until their loans are repaid entirely.
The rate of interest that the companies pay the bond investors is often less than the rate of interest that they would be required to pay to obtain the loan from the bank. Sometimes the interest on the loan is not a fixed percent, but “floats” with general short-term interest rates.
A bond that is traded in the market possesses a credit rating, which is issued by the credit rating agencies, which starts from investment grade to speculative grade, where investment-grade bonds are considered to be of low risk and usually have low yields. On the contrary, a loan don’t have any such concept; instead, the creditworthiness is checked by the creditor.
The rough equivalent of a junk bond in the world of corporate loans is called a “leveraged loan”. A leveraged loan is a type of loan that is extended to companies or individuals that already have considerable amounts of debt or poor credit history. Lenders consider leveraged loans to carry a higher risk of default, and so they demand higher interest on the loan. Leveraged loans and junk bonds play a key role in helping smaller or struggling companies achieve their financial goals. Leveraged loans are widely used to fund mergers and acquisitions.
Because the company itself is considered shaky, creditors typically require that the company offers some specific asset for collateral to “secure” the loan. Also, the loan is typically written to be “senior” to other debt, including bonds, in case of bankruptcy. Historically, the recovery rate for such senior secured loans has been about 80%, as compared to a recovery of about 40% for unsecured bonds, if the company goes bankrupt.
Typically, a bank would not want to take all the risk of such a loan upon itself. Therefore, for a leveraged loan, the bank arranges for a syndicate of multiple banks or other financial institutions to own pieces of the loan. You, too, can get a piece of this action by buying shares of the fund Invesco Senior Loan ETF (BKLN), which is currently yielding 3.2%.
S&P Global Market Intelligence offers a primer on leveraged loans, complete with tutorial videos. As shown below, the market for leveraged loans in the U.S. is now over $ 1 trillion:
John Steinbeck’s The Grapes of Wrathdetails the impoverished circumstances of the fictional Joad family during the Great Depression and the Dust Bowl. Initially, the Joads are tenant farmers in Oklahoma, but due to the consolidation and mechanization of agriculture during the 1920s, they are displaced from their farm and without many options. After receiving a leaflet that promises abundant jobs and housing, the family follows in the path of many of their neighbors that have already left for California in search of more opportunity. Yet the hardships continue for the Joads. The grandfather dies on the arduous trip and find that they have been misled about the availability of jobs and the conditions of the squalid camps.
According to Steinbeck, the introduction of the tractor and the power of the bank are responsible for their initial misfortunes. The tractor makes farming easier and more efficient, but leaves families without work including the Joads. In an encounter with a tractor driver, a tenant farmer without work asks, “what you doing this kind of work for—against your own people?” (pg. 25). The tractor driver is seen as treasonous because he improves his own standing while a hundred other people—his people— are left without a means to provide for their own families. But the tractor driver doesn’t revel in his improved circumstances, instead he is blunt about all of their predicaments, “crop land isn’t for little guys like us anymore” (pg.25). This assessment indicates that despite their divergent trajectories, neither the tractor driver nor the tenant farmers have any influence, but they are both pawns of a larger power. Steinbeck insinuates that both individuals—the tenant farmer and the tractor driver– is largely expendable. If the driver leaves another tractor driver would gladly accept the job; if that one left, still another one would come along. The greater enemy is the big-wigs in ‘the East’ who give orders to ‘the bank,’ who are ultimately responsible for displacing the farmers.
The increasing efficiency of agriculture and its effect on the fictional Joad family illustrates what many families have faced due to the increasing efficiency of manufacturing. For the Joads, there is a strong sense of alienation. Their family home is damaged by a tractor, the neighbors are leaving, and there is no work available. Similarly, as factories and plants that were economic drivers have shuttered in rust belt towns, other main street staples such as the barber shop, the diner, and the hardware store can’t afford to stay open. As a result, formerly vibrant communities are emptied. Individuals are faced with the reality that the relatively straight-forward path to the middle class afforded to their parents will not be the same for them as options diminish for blue-collar work. The next steps for people, specifically without a college education, may not seem clear or within reach.
The monsters outlined in the first section of The Grapes of Wrath— the bank and the tractor—could be subbed in for the current monsters in our current political and economic discourse—automation and trade. The novel picks up on some of our current anti-establishment rhetoric as individuals in ‘the East’ that run the bank profit handsomely while families such as the Joads have their lives uprooted. The bank and the people in the East create a new class of winners and losers as well. The winners in this case are the tractor drivers who can now afford to give their kids shoes for the first time; the losers are the tenant farmers who have no income for food. The income inequality between the tractor driver and the tenant farmers is a microcosm of increasing income inequality in the U.S. as a result of rapidly increasing productively for a small sector of the labor force. In Average is Over, Tyler Cowen illustrates how low-skilled laborers face a similar scenario to the tenant farmer of the 1920s: individuals who are a complement to innovative technology are richly rewarded, but unskilled labor that can be replaced by it will struggle to find work in the knowledge economy.
The Grapes of Wrath demonstrates how creative destruction brought about by innovation and technology is an enduring phenomenon. Yet the characterization of this trend in The Grapes of Wrath seems prescient given the sentiments of many Americans that computers, automation, and globalization are richly benefitting a small portion of Americans that can harness these technologies at the drastic expense of many Americans that have been automated or outsourced out of their jobs.
Hannah Florence is a student at Samford University, where she studies economics, political science, and data analytics. She is currently a Young Scholar for the American Enterprise Institute’s Initiative on Faith and Public Life. After graduation, she hopes to continue her public policy research as she begins a career in Washington, D.C.
Why can computers beat humans at chess but not predict election outcomes with great precision? Experts in 2020 mostly forecasted that Biden would win by a large enough margin to avoid the kind of quibbling and recounts we are now seeing. I don’t write this as a criticism of the high-profile clever Nate Silver, or any other forecaster. I’m thinking through it as a data scientist.
First, consider a successful application of modern data mining. How did AlphaZero “learn” to play chess? It generated millions of hypothetical games and decided to use the strategies that looked successful ex-post. AlphaZero has excellent data and lots of it.
If we think about actual election outcomes, there aren’t enough observations to expect accurate forecasts. If each presidential election is one observation, then there have only been about 50 since the founding hundreds of years ago. No data scientist would want to work with 50 data points.
You can’t say “in the years when ‘defund the police!’ was associated with Democrats, the GOP presidential candidate gained among married women”. There has only ever been one presidential election when that occurred. Judging by what I have been observing of the DNC post-mortem on Twitter in the past week, that might not happen again. See this tweet for example:
I know very little about political analysis. Only from what I know about data science, I would imagine that computers will get better at predicting the outcomes of races for the House of Representatives.
House representatives serve 2-year terms. There are over 400 House elections every 2 years.
Think about this over one decade of American history. There are actually more than 400 representatives in the house, but let’s imagine a “Shelter” of Reps with 400 members for ease of calculation.
In one decade, there are usually two presidential elections. That means we get 2 observations to learn from. In the same decade, there would be 400×5 “Shelter” elections. That yields 2,000 observations, which is considered respectable for the application of data mining methods.
One application of such a forecasting machine would be to determine which slogans are the most likely to lead to success.
I’m going to teach text mining in the upcoming week. Most of my students have never heard of it. We have spent the semester talking about what do to with structured data, which includes some of the basic concepts from traditional statistics.
I often ask them to think about what computers can do. We talk about why “data analytics” classes are happening in 2020 and did not happen in 1990. Hardware and software innovations have expanded the boundaries of what computers can do for us.
The gritty details of how text mining works can make for a boring lecture, so I’m going to use the following narrative to get intellectually curious students on board. It always helps to start with fighting Nazis. Alan Turing helps defeat the Nazis by using a proto-computer to crack codes. The same brilliant Turing was smart enough to realize that computer could play chess someday (acknowledgement for me knowing that trivia: Average is Over). Turing didn’t live to see computers beat humans in chess but, in a sense, it didn’t take very long. Only about 50 years later, computers beat humans at chess.
Maybe chess is exactly the kind of thing that is hard for humans and easy for computers. When we discuss basic data mining, I tell students to think about how computers can do simple calculations much faster than humans can. It’s their comparative advantage.
Could Turing ever have imagined that a human seeking customer service from a bank could chat with a bot? Maybe text mining is a big advance over chess, but it only took about one decade longer for a computer (developed by IBM) to beat a human in Jeopardy. Winning Jeopardy requires the computer to get meaning from a sentence of words. Computers have already moved way beyond playing a game show to natural language processing.
How computers make sense of words starts with following simple rules, just as computer do to perform data mining on a spreadsheet of numbers. As I explain those rules to my students this week, I’m hoping that starting off the lecture with fighting Nazis will help them persevere through the algorithms.
What is justice? It’s a lofty question, up there in the pantheon of “What is the meaning of life?” and “Who let the dogs out?”. There is no great answer for that, but, essentially justice is about doing the right thing. What is right? That’s a good question.
There are three taste buds to justice: merit, need, and equality. When people make an argument for something being right they will draw on one or more of these taste buds. These criteria become especially important when groups decide to allocate goods through non-market institutions.
Here is a favorite example from Peyton Young’s book Equity: In Theory and Practice. At the end of World War II, the United States was demobilizing soldiers in Europe. Some had to be retained to fight Japan while others could come home. Which soldiers should come home first?
After debate, the U.S. Army decided to survey thousands of soldiers in the United States to identify relevant factors. There were four important factors: length of time in the Army, age, amount of overseas service, and number of dependents. Then troops completed a pairwise-comparison of each criteria like in the picture below.
You can see among the transitive rankings (90 percent of those surveyed satisfied transitivity) the two most important features to those surveyed in the United States was overseas service and number of dependents.
But, it turns out there was an important write-in candidate among the soldiers: exposure to combat. A large swath of soldiers mentioned that this should be an important criteria but the Army hadn’t considered it in their survey.
The Army devised another survey that attempted to develop how much different criteria should be weighted. You can see a sample question below and the resulting points system (from a series of questions like the sample question).
What matters most to the troops: exposure to combat and number of dependents. Put another way, what matters most is merit and need. The right thing to do regarding who comes home first involves consideration of whether you merit coming home (exposure to combat elevates you over others) and need (a child needs their parent).
So I do not have a precise definition of justice. But, I have noticed that when people talk about doing the right thing they often rely one one or more of merit, need, and equality.
During some general reading on finance, I ran across the following two information-rich graphics from Hoya Capital on the U.S. prison population. On the first graph, the blue areas show the absolute numbers, and the green line shows the percent incarceration rate. A rate of 0.5% comes to 500 prisoners per 100,000 population.
This graph shows a huge rise in the state and federal prison population between 1980 and 2000. There seems general agreement that much of that increase in the prison population is due to mandatory sentencing laws, which require relatively long sentences. In particular, “three strikes and you’re out” laws may demand a life sentence for three felony convictions, if at least one of them is for a serious violent crime. Another factor was the increased criminalization of drug use (possession), in addition to drug dealing.
The graphic below shows the particular classes of crimes of which inmates of the state and federal prison systems have been convicted. The largest single category is violent crimes, but other types are significant, such as drug and property crimes, and “public order” crimes. Public order crimes include activities such as prostitution, gambling, alcohol, child pornography, and some drug charges. This graphic also includes the large number of people in local jails, most of whom are imprisoned awaiting trial or sentencing.
The total number of people under legal supervision in the U.S., including probation and parole, is over 6 million:
The U.S. has by far the largest official prison population in the world, and the highest incarceration rate. The following graph from Wikipedia depicts incarceration rates for several countries or regions as of 2009:
Most developed countries have incarceration rates of around 100-200 per 100,000, which is where the U.S. was in about 1970. The relatively high rate for Russia is attributed in large part to strict “zero tolerance” laws on drugs.
Again, the main driver for the high rates in the U.S. is the long sentences, driven by mandates. Wikipedia notes that there are other countries, including some in Europe, which have higher annual admissions to prison per capita than in the U.S. However, “The typical mandatory sentence for a first-time drug offense in federal court is five or ten years, compared to other developed countries around the world where a first time offense would warrant at most 6 months in jail… The average burglary sentence in the United States is 16 months, compared to 5 months in Canada and 7 months in England.”
Policy debates on this topic continue. Obviously, we want to protect society from dangerous predators, but the direct and indirect costs to society for this level of incarceration are high. It seems like an area which is ripe for reform of some kind, though I do not claim to have a novel proposal.
I share this tweet because it provides a good visual of a strange event last night. As results were coming in at night in the US, there was a sudden huge reversal. For months the markets had predicted a Biden win. Throughout the night there was some wild speculation in which some buyers were willing to bet Trump would win. Around the time respectable people start waking up in the US, the market flipped again. I hear some people saying on Twitter that they regret no buying during the night. Near 5am Eastern Time that Trumps chance of winning went back down under 50%. That is also when new information came in showing that Biden would likely win Wisconsin.
At the time I write this, votes are still being counted. It is expected that the ballots still to be counted will mostly give votes to Biden. The “blue wave” did not materialize in 2020. If Joe Biden wins the presidential election, it will not be with the overwhelming mandate that some expected.
Economists often promote using betting markets to get predictions of the future. There are lots of applications beyond politics. These high profile elections bring attention to betting markets. Maybe people will begin relying more on them in other fields. I think betting on temperature increases and rising sea levels would be interesting and useful.
One of the more interesting results from last night’s election comes out of Florida: voters appear to have narrowly approved an increase of the minimum wage in stages to $15/hour in 2026 (Florida has a 60% requirement for ballot measures to pass, and the current vote total is just above that threshold).
Florida is not the first state to approve such an increase to $15/hour: 7 states have already done so, though no state is yet at that level. California will hit $15 first in 2022. Several US cities, such as New York and Seattle, as well as the “city-state” of Washington, DC are already at $15, but these are generally very high wage cities.
What makes Florida the most interesting of the states to try very high minimum wages is that Florida is not a high wage state. Once the minimum wage is fully phased in (in 2026), the minimum wage will be about 75% of Florida’s median wage (it was $17.23 in 2019). That’s much higher than other states: California will be the next highest at about 66%, with Oregon next around 64%. Oregon will be close to $15, but perhaps a little below, as they index their minimum wage for inflation.
(To make these estimates I am using 2019 median wage data from the BLS OES wage data and assuming 2% annual wage growth. This may not be exactly right, but it’s probably close enough.)
Also important to note in Florida: the median wage is not $17.23/hour all over the state. Several MSAs in Florida currently have a median wage at or even below $15 (Sebring, Florida is the lowest at around $14/hour). There will be some wage growth over the next 6 years in those areas, but still this means that the minimum wage will be applicable to roughly half the labor force.
That brings up another interesting legal question: will the minimum wage apply to salaried workers making less than $30,000 per year? The way the law is written, probably not, but logic would seem to dictate that it should. Otherwise, what’s to stop an employer from hiring an employee on a $2,000/month contract, equivalent to $12/hour for a full time worker?
The minimum wage debate among economists consumes a vast literature, and I am no expert on it, and will make no attempt to summarize it here. But Florida seems to be breaking new territory. My little state of Arkansas currently holds the “record” for a US state starting in 2021, with a minimum wage of $11/hour which will be about 67% of the median wage (and about 78% of the median wage in Hot Springs, Arkansas). Florida’s experiment will certainly give economists a new experiment to study.
Arin Dube, one of the leading researchers of the minimum wage and a strong advocate of raising the wage, suggested in a recent policy paper that a good minimum wage for Florida would be around $9/hour, given their wage distribution. That was in 2014 dollars, so we can roughly adjust that up to $11-$12 in 2026 dollars. Florida voters have chosen to go well beyond that recommendation.
Joy: I’m not an expert in elections or social media (unless having a Twitter habit counts). I asked Kate Zickel who manages political online accounts professionally to write about the current election:
It’s no secret that social media platforms like Facebook and Twitter have had a tremendous influence on political elections in America since their infancy in the mid 2000’s. While the exact impression of these platforms can be difficult to measure, it’s clear that their impact in 2020 is greater by far than in previous election cycles.
Data from SocialBankers reports that “While President Donald Trump’s use of Twitter has been widely acknowledged, and certainly had a tremendous impact on the outcome of the 2016 elections, former Vice President Joe Biden has actually surpassed the President in many key engagement metrics.” This includes the nearly 30 million American voters that comprise the largest percentage of Twitter’s user base at nearly 20%.
The New York Times’ Ben Smith recently explained how media and tech companies have evolved back into their roles as information gatekeepers leading up from the 2016 election. Twitter, for one, recently began pinning notices to the top of all U.S. Twitter users’ timelines warning about misinformation on mail-in voting while Google said it’s been pushing to make its core search products including YouTube into hubs for authoritative information about electoral processes and results.
There is, however, a direct inverse relationship between broadcast ad spends and digital ad spend since the 2018 midterms. For the first time ever, spending on digital political advertising has slightly surpassed cable. Still, advertising spent on broadcast television — mostly at the local level — reigns supreme.
And while the influence of social media at the polls isn’t exactly new, the 2020 presidential election has set a new precedent in this era of information gatekeeping.