Month: March 2015

When I was graduating from high school in 1978, a number of my friends went to the hometown University of Minnesota. At the time, it was possible to pay tuition and a substantial share of living expenses with the earnings from a full-time job in the summer and a part-time job during the school year. Given the trends in costs of higher education and the path of the minimum wage since then, this is no longer true.

Here\’s an illustration of the point with the University of Minnesota, with its current enrollment of about 41,000 undergraduates,as an example. (The figure is taken from a presentation by David Ernst, who among his other responsibilities is Executive Director of the Open Textbook Network, which provides links to about 170 free and open-license textbooks in a variety of subjects.)

Just to put this in perspective, say that a full-time student works 40 hours per week for 12 weeks of summer vacation, and then 10 hours per week for 30 weeks during the school year–while taking a break during vacations and finals. That schedule would total 780 hours per year. Back in the late 1970s, even being paid the minimum wage, this work schedule easily covered tuition. By the early 1990s, it no longer covered tuition. According to the OECD, the average annual hours worked by a US worker was 1,788 in 2013. At the minimum wage, that\’s now just enough to cover tuition–although it doesn\’t leave much space for being a full-time student.

Murray Weidenbaum died last May at the age of 78. During Weidenbaum’s career, his jobs and affifiliations included: the New York State Department of Labor; the U.S. Bureau of the Budget; Ph.D. study at Princeton; jobs at General Dynamics and Boeing; the University of Washington; the Stanford Research Institute; director of a Presidential Committee on the Economics of Defense and Disarmament (we’re now up to the 1960s); a NASA Economic Research Program based at Washington University in St. Louis, which by the end of his career had turned into a University Professorship; Assistant Secretary of the U.S. Treasury for Economic Policy; Chairman of President
Reagan’s Council of Economic Advisers; positions on corporate boards (Centerre Bank, Hill and Knowlton, May Department Stores, Tesoro Petroleum, Beatrice Foods, the Harbour Group); affiliations with the American Enterprise Institute and the Center for Strategic and International Studies; and blue-ribbon commissions on everything from trade deficits to terrorism.

As an economist focused on defense spending and the costs of regulation, perhaps his best-known line was: \”Don’t just stand there, undo something.” David R. Henderson offers some memories of Weidenbaum in \”A Feel for Economics: Murray Weidenbaum 1927–2014,\” appearing in Winter 2014-15 issue of Regulation magazine. 

\”Murray was the ultimate economist, as two stories about him bear out: Although he was Jewish, his family celebrated Christmas, and he and his wife encouraged their son and two daughters to believe in Santa Claus. In time, his older daughter reached the age when she began to doubt Santa Claus’s existence and suspected that her parents were the real source of Christmas gifts. But on Christmas morning, she opened a gift and found an expensive item that she had wanted. “There must be a Santa Claus,” she said, excitedly. “Dad’s too cheap to spend that much money.” Murray delighted in telling that story.

\”The second story is about traveling light. While working at CSAB [the Center for the Study of American Business at Washington University], I was getting ready to go to a conference. One thing that always happened at conferences, before the Internet achieved its prominence, was that one returned home with copies of various papers. If you collected enough such papers, it was hard to take just a carry-on, and you had to check a bag. What to do? I told Murray that my packing for the conference included old underwear that I didn’t mind losing. On the trip, I would throw away the underwear instead of bringing it home, creating space for papers in my carry-on. As far as I knew, I was the only person who did this—until Murray told me (eyes twinkling) that he often did that very thing.\”

The US economy was in a continuous recession for 65 months from October 1873 to March 1879. Historians call is the \”Long Depression,\” because the Great Depression from 1929 to 1933 saw \”only\” 42 consecutive months of economic decline. For comparison, the more recent Great Recession lasted 18 months.

Samuel Bernstein offered one of the classic descriptions of the Long Depression in his 1956 article, \”American Labor in the Long Depression, 1873-1878\” (Science & Society, Winter 1956, 20:1, pp. 59-83, available through JSTOR). Precise government statistics are not available for this time period, of course, but estimates of the unemployment rate for the later part of this period often exceeded 20%, and some exceeded 30%. For those with a job, real wages fell by half. Even those real wages were often paid in the form of company scrip, which could only be used at the company store, and was worth substantially less than cash.

Bernstein quotes from a Bulletin of the American Iron and Steel Association in the first quarter of 1874, when the Long Depression had barely begun. The report stated \”that the manufacturing industries of the country are rapidly sinking; and the conclusion is equally inevitable that all branches of business will soon collapse under the dead weight of the paralysisw hich has seized manufacturers and driven the labor classes into idleness, unless means are devised to stimulate and encourage productive enterprises.\” Output fell sharply. Here\’s Bernstein:

\”From 1873 to 1878 production fell precipitously. Mills either closed or ran part time. At the end of the first year of the crisis the Bureau of Statistics in Pennsylvania reported:\” Probably never in the history of the country has there been a time, when so many of the working classes, skilled and unskilled, have been moving from place to place seeking employment that was not to be had–never certainly for so long a time.\” Estimates on the production of pig-iron, coal, on cotton consumption, railroad revenues, imports of merchandise and bank clearing showed a reduction of 32 percent between 1873 and 1878. Its magnitude was second only to that between 1929 and 1932, namely 55 percent.\”

\”We read in a London paper that the Chinese government have purchased machinery,and engaged experienced engineers and spinners in Germany to establish cotton mills in China, so as to free that country from dependence upon English and Russian imports. Though China is somewhat tardy in her action, we may be certain that she is thorough. … More than this, the time is  not far distant when the textiles from the Chinese machine looms, iron and steel and cutlery from the Chinese furnaces, forges and workshops, with everything that machinery and cheap labor can produce, will crowd every market. The four hundred millions of China, with the two hundred and fifty millions of India,–the crowded and pauperized populations of Asia,–will offer the cup of cheap machine labor, filled to the brim, to our lips, and force us to drink it to the dregs, if we do not learn wisdom. It is in Asia, if anywhere, that the world is to find its workshop. There are the masses, and the conditions, necessary to develop the power of cheapness to perfection, and they will be used. For years we have been doing our utmost to teach the Chinese shoemaking, spinning and weving, engine driving, machine building, and other arts, in California, Massachusetts, and other States; and we may be sure they will make good use of their knowledge; for there is no people on earth with more  patient skill and better adpated to the use of machinery than the Chinese. When the Chinese goernment is doing for China, Dom Pedro is doing for Brazil [this would be Dom Pedro II, the last ruler of the Empire of Brazil], though in a different form.\”

A readable overview of the 1873-1878 period is available here.  If you had to describe the causes of the Long Depression in modern terms, you might call it a combination of a tech boom-and-bust cycle, an industrial transformation, a banking crisis, and a a euro-style problem of currency arrangements that were not serving the economy well.

The tech boom of that time was the railroad mania, which lead to a cycle of overbuilding and then bust, which in turn dragged down other manufacturing industries. By the later 1870s, about half of all the railroad track in the country was owned by those who had received in after bankruptcy proceedings. At the same time, the transportation network established by rail was feeding into a growth of much larger companies that were finding cost savings by investing in equipment and economies of scale. At least some of the unemployment was what we would today call \”technological unemployment,\” which is labor that is displaced by rapid technological change and cannot soon find alternative places of employment. International trade and big business was often conducted on a gold standard, but the government continued to circulate large numbers of \”greenback\” paper currency that had been introduced during the Civil War. As firms and consumers went broke, and currency values fluctuated against gold, there were were banking crises and times when financial payments could not be made.

Thus, W.G.M. was correct in that 1879 essay to perceive a transformation of American production. I wonder how the 1879 argument would have differed if the writer had been able to see how little progress the economies of China and India had made even 100 years after the writing of the article in 1979! For me, an ongoing lesson is that when economic times are rough, blaming other countries is always an easy temptation.

Homage: I ran across a mention of the 1879 Atlantic Monthly article in the overview written by Prakash Lougani for the March 2015 issue of Finance & Development, and tracked down the original.

Rare earths had a moment in the media spotlight in late 2010 and early 2011. The story goes that in September 2010, a Chinese and a Japanese ship collided in waters claimed by Japan. Japan detained the captain of the Chinese ship. China appears to have responded (although China denied this) by placing an embargo on sales of rare earths to Japan. It came to public attention that over the last two decades, rare earth mining operations had been opening in China, but often closing in the rest of  the world, and more than 95% of rare earth production was happening in China.

A number of chin-stroking analyses and warnings followed. As one example, Katherine Bourzac wrote an article in the April 2011 issue of Techology Review called \”The Rare-Earth Crisis,\” and subtitled \”Today’s electric cars and wind turbines rely on a few elements that are mined almost entirely in China. Demand for these materials may soon exceed supply. Will this be China’s next great economic advantage?\” Here\’s a sample of Bourzac\’s argument:

But even without Chinese restrictions and with the revival of the California mine, worldwide supplies of some rare earths could soon fall short of demand. Of particular concern are neodymium and dysprosium, which are used to make magnets that help generate torque in the motors of electric and hybrid cars and convert torque into electricity in large wind turbines. In a report released last December, the U.S. Department of Energy estimated that widespread use of electric-drive vehicles and offshore wind farms could cause shortages of these metals by 2015.

What would happen then is anyone’s guess. There are no practical alternatives to these metals in many critical applications requiring strong permanent magnets—materials that retain a magnetic field without the need for a power source to induce magnetism by passing an electric current through them. Most everyday magnets, including those that hold notes on the fridge, are permanent magnets. But they aren’t very strong, while those made from rare earths are tremendously so. Alloys of neodymium with iron and boron are four to five times as strong by weight as permanent magnets made from any other material. That’s one reason rare-earth magnets are found in nearly every hybrid and electric car on the road. The motor of Toyota’s Prius, for example, uses about a kilogram of rare earths. Offshore wind turbines can require hundreds of kilograms each.

New mining activity, not only at Mountain Pass but also in Australia and elsewhere, will increase supplies—but not enough to meet demand for certain critical metals, particularly dysprosium, in the next few years. … Because rare earths make such excellent magnets, researchers have put little effort since the early 1980s into improving them or developing other materials that could do the job. Few scientists and engineers outside China work on rare-earth metals and magnet alternatives. Inventing substitutes and getting them into motors will take years, first to develop the scientific expertise and then to build a manufacturing infrastructure. …  Few experts express optimism that there will be enough rare-earth materials to sustain significant growth of clean energy technologies like electric cars and wind power, which need every possible cost and efficiency advantage to compete.

So what in fact happened after the rare earths crisis of 2010-11? For the basic storyline, here\’s a price chart for rare earths–which are usuall mined together and then separated into constituent parts–in dollars per ton, from the industry research and consulting company TRU Group.

Here are four basic questions about media bias:

\”First, is media news reporting actually slanted? …

Second, if reporting is biased, what is the reason? Is such bias driven by the
supply-side, as when reporting reflects the prejudices of an outlet’s owners or journalists? …

Third, what is the effect of media competition on accuracy and bias?  …

Finally, does media reporting actually matter for individual understanding and
action? Does it affect knowledge? Does it influence participation in the political
process? Does it influence how people vote?\”

The questions are posed by Andrei Shleifer in his paper on \”Matthew Gentzkow, Winner of the 2014
Clark Medal,\” in the Winter 2015 issue of the Journal of Economic Perspectives. As background, the Clark medal is given by the American Economic Association each year \”to that American economist under the age of forty who is judged to have made the most significant contribution to economic thought and knowledge.\” Shleifer is describing the academic work for which Gentzkow won the award last year. Shleifer argues: \”In a very short decade, economic research has obtained fairly clear answers to at least some of these questions.\”

(Full disclosure: My paid job has been Managing Editor of the JEP since the first issue in 1987. All papers in JEP from the first issue to the most recent are freely available on-line courtesy of the American Economic Association. Shleifer was Editor of JEP, and thus my boss, from 2003-2008.)

On the first question of the existence of media bias, how does one go beyond anecdotes about how different newspapers or TV channels covered certain stories to come up with a defensible quantitative way of detecting media bias? The modern approach has been to use text analysis. For example, have a computer search a dataset of all speeches given in Congress during the year 2005. Have the computer search for phrases that are much more commonly used by Republicans or by Democrats. For example, in 2005 Democrats were much more likely to refer to the \”war in Iraq\” while Republicans were more likely to refer to the \”war on terror.\” Now do a search on the text used by media outlets, and see if they are more likely to be using Republican phrases, Democratic phrases, or an even mixture of the two.

Gentzkow didn\’t invent this approach to meausuring media bias. For earlier work on the subject in the research literature, a starting point would be the article by Tim Groseclose and Jeffrey Milyo, “A Measure of Media Bias,\” in the Quarterly Journal of Economics in 2005 (120:4, pp. 1191–1237). But in work with co-author Jesse Shapiro, Gentzkow applied the approach to newspapers across the US and was thus able to provide hard evidence that many newspapers indeed exhibit partisan bias in how they report the news.

Does the bias of newspapers reflect their owners, or their customers? Here\’s how Shleifer describes it:

\”Gentzkow and Shapiro then collected data on the use of these highly diagnostic phrases in US daily newspapers and used these data to place news outlets on the ideological spectrum comparable to members of Congress. In addition to this large methodological advance in how to measure partisan newspaper slant, the paper used detailed information on newspaper circulation and voting patterns across space to estimate a model of the demand for slant and to show that—consistent with the theory—consumers gravitate to like-minded sources, giving the newspapers an incentive to tailor their content to their readers. They also show that newspapers respond to that incentive and that variation in reader ideology explains a large portion of the variation in slant across US daily newspapers. … [A]fter controlling for a newspaper’s audience, the identity of its owner does not affect its slant. Two newspapers with the same owner look no more similar in their slant than newspapers with different owners. Ownership regulation in the US and elsewhere is based on the premise a news outlet’s owner determines how it spins the news. Gentzkow and Shapiro produced the first large-scale test of this hypothesis, which showed that, contrary to the conventional wisdom and regulatory stance, demand is much more influential in shaping content than supply as proxied by ownership.

Does more competition in the media tend to increase or diminish this bias? This question is tough  to answer, but in a different paper by Gentzkow and Shapiro, they look at a closely related topic of how people of different political beliefs use the Internet. Specifically, do people tend to cluster at the websites that that match their ideology, or do they surf around? Shleifer describes the result:

One might worry that the increase in choice among news suppliers as a result of the Internet would allow news consumers to self-segregate, reading only news that confirms their preconceptions. Gentzkow and Shapiro test this claim using data from a panel of Internet users for which they have a survey-based measure of political ideology and tracking data on online news consumption. They find that ideological segregation is surprisingly low online. The average conservative’s news outlet on the Internet is about as conservative as usatoday.com; the average liberal’s is as liberal as cnn.com. Strikingly, the Internet is less ideologically segregated than US residential geography: two people using the same news website are less likely to have an ideology in common than two people living in the same zip code.

Finally, is it the case that people just choose the media outlets that reflect their bias, in which case the media bias doesn\’t affect their opinions or their voting patterns? Or is there reason to believe that the extent of media bias does affect opinions and voting patterns?

In one study, Gentzkow looked at historical data on how television coverage spread across the United States, and what changes in voting patterns followed. As Shleifer writes; \”He estimates a huge negative effect: the availability of television accounts for between one-quarter and one-half of the total decline in voter turnout since the 1950s. Matt argues that a principal reason for this is substitution in media consumption away from newspapers, which provide more political
coverage and thus stimulate more interest in voting.\”

In a different study, Gentzkow and co-authors look at the patterns of newspapers being born and dying from 1869 to 2004, and compare this with voting patterns. Shleifer writes:

They find that newspapers have a large effect in raising voter turnout, especially in the period before the introduction of broadcast media. However, the political affiliation of entering newspapers does not affect the partisan composition of an area’s vote. The latter result contrasts with another important finding, by DellaVigna and Kaplan (2007), that the entry of Fox News does sway some voters toward voting Republican. An interpretation consistent with these findings is that newspapers motivate but don’t persuade, while television does the opposite.

Research on media bias and its political effects is certainly not settled, but for what it\’s worth, I\’d sum up the existing evidence in this way. There\’s lots of political bias in the media, mainly because media outlets are trying to attract customers with similar bias. But in the world of the Internet, at least, people of all beliefs do surf readily between news websites with different kind of bias. The growth of television to some extent displaced the role of newspapers and lowered the extent of voting. For the future, a central question is whether a population that gets its news from a mixture of websites and social media becomes better-informed or more willing to vote, or whether it becomes a population that instead becomes expert at selfiesm, cat videos, World of Goo, Candy Crush, Angry Birds, and the celebrity-du-jour.

In the lingo of demographers and economists, the \”dependency ratio\” refers to the fact that the working age population from ages 18-64 produces most of the output in any economy, but a certain amount of the consumption is done by those under 18 and those over 65. Thus, there is an \”old-age dependency ratio,\” which is the population 65 and older divided by the population from 18-64, a \”youth dependency ratio\” which is the under-18 population 17 divided by the population from 18-64, and at \”total dependency\” ratio which is the sum of the under-18 and 65-and-over population, divided by the 18-64 population.

Sandra L. Colby and Jennifer M. Ortman from the US Census Bureau offer some projections about dependency ratios in the March 2015 report \”Projections of the Size and Composition of the U.S. Population: 2014 to 2060\” (P25-1143).

As the figure shows, the youth dependency ratio is expected to hover around 35%–in fact, to decline a bit–in the decades to come. However, the old-age dependency ratio is on the rise. It\’s now about 23%, but 2035 will be up to about 38%. Taking the two ratios together, the under-18 population plus the 65-and-over population is now about 60% of the size of the 18-64 population, but the ratio is headed for about 75% in the next two decades.

It\’s worth emphasizing that the old-age dependency ratio for a couple of decades in the figure can be estimated with a pretty high degree of accuracy. After all, anyone who is going to  be 21 or older in 2035 has already been born. Large fluctuations in death rates or immigration rates are the only factors that can move the old-age dependency ratio substantially.

The report also includes a breakdown of the growth of population by age that helps to clarify what is happening behind these ratios. By 2040, the under-18 population is projected to rise by a total of 5%; the 18-44 population by 12%; the 45-64 population by 10%; and the 65 and older population by 78%.

Most of the rise in the old-age dependency ratio happens by the early 2030s. Thus, one can think about the next two decades as a time of transition: transition in public policies affecting the elderly like Social Security and Medicare; transition in work patterns as we seek to encourage at least some of the elderly to stay in the workforce longer; transition is how we think about the design of public services and facilities everywhere from hotel rooms to park trails for a population with a larger share of the elderly; and transition in how we start building systems that can support families and communities in providing assistance and care for the elderly who need it.

An extraordinary shift happened in the US agricultural sector during the last century or so. Robert A. Hoppe lays out the facts in his report \”Structure and Finances of U.S. Farms: Family Farm Report,
2014 Edition,\” written as Economic Information Bulletin Number 132, December 2014, for the U.S. Department of Agriculture. Indeed, when I hear arguments about how difficult (impossible?) it will be for the US workforce to adjust to the coming waves of technology, my thought quickly jump to the shift in agriculture.

For example, back around 1910, about one-third of all US workers were in agriculture (blue line, measured on the right-hand scale).  It\’s now about 2%. The absolute number of jobs in agriculture declined, too, but the big change was that more than 100% of the job growth in the U.S. was in the non-agricultural sector. I haven\’t researched the point, but my guess is that many people around 1910 would have viewed these changes as somewhere between  impossible and inconceivable.

The total actual acres operated by US farms has barely budged in the last half-century. But as the agricultural productivity steadily rose, the number of farms sharply declined, especially during the half-century from about 1930 to 1980.

In the current U.S. farm sector, about 90% are small farms measuring less than $350,000 per year in \”gross cash farm income\” (this is the revenue for the farm before subtracting expenses, not the income to the farmer). These small farms represent about half the land operated, and one-quarter of the total value of production.

When one looks across various commodities, the share of small farms is bigger in some (poultry, hay livestock) than others (dairy, cotton). But interestingly enough, a substantial share of production in each area still comes from small and medium firms, not just from large ones–although average profits are smaller on small farms.

This ability of small and medium firms to compete with larger firms means that although farm sizes are growing over time, large firms do not have a dramatic cost advantage over smaller ones–at least in a number of crops. as Hoppe notes:

\”Extensive economies of scale do not exist in farming. Most cost reductions can be attained at a relatively small business size, compared with other industries, even though farming tends to be capital intensive in the United States. … Crop production requires local knowledge of soils, pests, and weather while livestock production requires knowledge of livestock and how they respond to local conditions. This knowledge takes
time to acquire and is not easily transferred to others.\”

In the Winter 2014 issue of the Journal of Economic Perspectives, Daniel A. Sumner explores variuls explanations for the growth of farm size. in \”American Farms Keep Growing: Size, Production, and Policy.\” (Full disclosure: I\’ve been Managing Editor of the JEP since the first issue in 1987. All JEP articles back to the first issue are freely available online courtesy of the American Economic Association.) Sumner focuses on the interaction of managerial capability and agricultural technology in leading to larger farms. He wrote:

Changes in farm size distributions and growth of farms seems closely related to technological innovations, managerial capability, and productivity. Opportunities for competitive returns from investing financial and human capital in farming hinge on applying managerial capability to an operation large enough to provide sufficient payoff. Farms with better managers grow, and these managers take better advantage of innovations in technology, which themselves require more technical and managerial sophistication. Farms now routinely use outside consultants for technological services such as animal health and nutrition, calibration and timing of fertilizers and pesticides, and accounting. The result is higher productivity, especially in reducing labor and land per unit of output. Under this scenario, agricultural research leads to technology that pays off most to more-capable managers who operate larger farms that have lower costs and higher productivity. The result is reinforcing productivity improvements.

Part of the mythology of US higher education is that it offers a meritocracy, along with a lot of second chances, so that smart and hard-working students of all background have a genuine chance to succeed–no matter their family income. But the data certainly seems to suggest that family income has a lot to do with whether a student will attend college in the first place, and even more to do with whether a student will obtain a four-year college degree.

Margaret Cahalan and Laura Perna provide an overview of the evidence in \”2015 Indicators of Higher Education Equity in the United States: 45 Year Trend Report,\” published by the Pell Institute for the Study of Opportunity in Higher Education and the and University of Pennsylvania Alliance for Higher Education and Democracy (PennAHEAD).

As a starting point, consider what share of high school graduates, age 18-24, are enrolled in college of any type (two-year or four year, public, private, or for-profit). The gap between the top quarter of the income distribution and the bottom quarter has narrowed a bit in the last 45 years (from 33 percentage points to 27 percentage points), but it remains substantial. Of course, if one took into account the fact that students whose families are in the bottom quarter of the income distribution are less likely to become high school graduates, the gap would be wider still.

Given this background, it\’s not surprising that that those from the top quarter of the income distribution are more likely to have a bachelor\’s degree by age 24. Indeed, the share of those completing a bachelor\’s degree by age 24 has risen substantially for students from families in the top quarter of the income distribution, and barely budged for those in the bottom two quarters.

What if we focus just on those who actually entered college? It turns out that if are someone from a family in the top-quarter of the income distribution who enters college, you are extremely likely to complete a bachelor\’s degree by age 24; if you are in the bottom of the income distribution, you only have about a 21% chance of having a bachelor\’s degree by age 24. (Frankly, I don\’t tust that most recent estimate of 99%. It just can\’t be true that almost all of those who start a bachelor\’s degree in the top quarter of the income distribution finish it. Here\’s another skeptic. But I do believe that the gap is a substantial one.)

The report offers a range of evidence that the affordability of college is a bigger problem for students from low-income families even after taking financial aid into account. Students from low-income families take out more debt, and are more likely to attend for-profit colleges. Indeed, a general pattern for higher education a whole is that even as the cost of attending has risen, the share of the cost paid by households, rather  than by the state or federal government, has been rising.

The effects of these patterns on inequality of incomes in the United States are clearcut: higher income families are better able to provide financial and other kinds of support for their children, both as they grow up, and when it comes time to attend college, and when it comes time to find a job after college. In this way, higher education has become a central part part of the process by which high-income families can seek to assure that their children are more likely to have high incomes, too.

This connection is perhaps underappreciated. After all, it\’s a lot easier for professors and college students to protest high levels of compensation for the top professionals in finance, law, and the corporate world who are in the top 1% of the income distribution, rather than to face the idea that their own institutions of higher education are implicated in perpetuating inequality of incomes across generations. Here\’s some discussion bearing on the point from \”Human Capital in the 21st Century,\” by Alan B. Krueger, appearing in the First Quarter 2015 issue of the Milken Institute Review. Krueger writes:

Moreover, changes in earnings associated with different levels of education – that is, human capital – have played an outsized role in raising inequality among the bottom 99 percent of Americans. 

Consider the following hypothetical calculation. If the top 1 percent’s share of income had remained constant at its 1979 level, and all of the increase in share that actually went to the top 1 percent were redistributed to the bottom 99 percent – a feat that might or might not have been achievable without shrinking the total size of the pie – then each family in the bottom 99 percent would have gained about $7,000 in annual income (in today’s dollars). That is not an insignificant sum. But contrast it with the magnitude of the income premium associated with educational achievement: The earnings gap between the median household headed by a college graduate and the median household
headed by a high school graduate rose by $20,400 between 1979 and 2013 according to my calculations based on the Bureau of Labor Statistics’ Current Population Survey. This shift – which took place entirely within the bottom 99 percent – is three times as great as the shift that has taken place from the bottom 99 percent to the top 1 percent in the same time frame. What’s worse, there are reasons to believe that the enormous rise in inequality that we have experienced will reduce intergenerational economic mobility and cause inequality to rise further in the future. …

If the return to education increases over time, and higher-income parents are more prone to invest in the education of their children than lower-income parents – or if talents are inherited from one generation to the next – then the gap between children of higher- and lower-income families would be expected to grow with time. Furthermore, if social networking and family connections also have an important impact on outcomes in the job market, and those connections are transmitted across generations, one would expect the … effect to be even stronger. There are, indeed, signs that the rise in income inequality in the United States since the late 1970s has been undermining equality of opportunity. For example, the gap in participation in extracurricular activities between children of advantaged and disadvantaged parents has grown since the 1980s, as has the gap in parental spending on educational enrichment activities. Furthermore, the gap in educational attainment between children born to high- and low-income parents has widened. The rising gap in opportunities between children of low- and high-income families does not bode well for the future.

The first US bank to install an automatic teller machine (ATM) was a branch of Chemical Bank on Long Island in 1969. After relatively slow growth during the 1970s, there were about 100,000 ATMs across the US by 1990, a total that has now risen to about 400,000. So here\’s the question: During the rise of ATM machines in US banking, did the number of bank tellers rise or fall?

I would have guessed \”fall,\” and I\’m not alone. In a June 14, 2011, interview, President Obama used ATMs a an example of technology displacing labor. He said (I\’ve added punctuation to the raw transcript):

There are some structural issues with our economy where a lot of businesses have learned to become much more efficient with a lot fewer workers. You see it when you go to a bank and you use an ATM, you don\’t go to a bank teller, or you go to the airport and you\’re using a kiosk instead of checking in at the gate. 

However, James Bessen collected the actual data on ATMs and bank tellers from an array of scattered sources. Overall, the story is that as the ATM machines arrived, the number of bank tellers held steady and even rose slightly. Bessen discusses the interaction between technology and employment in \”Toil and Technology,\” in the March 2015 issue of Finance & Development. Here is Bessen\’s figure showing the rise in ATM machines and the number of tellers employed.

Why did the number of bank tellers rise even as ATMs became prevalent? Bessen highlights two changes. One major change wass the spread of opening more bank branches. Bessen points out that you could now open a branch with fewer bank tellers than before; in addition, I\’d add that many states were relaxing their rules and allowing banks to open more branches both within and between states during the 1980s and 1990s in particular. The other major change was that the job of a teller changed. Banks began to offer more services, and tellers evolved from being people who put checks in one drawer and handed out cash from another drawer to people who solved a variety of financial problems for customers.

The broader point, of course, is that just looking at how technology can substitute for a certain job is only one part of the analysis. Other parts include how regulations that affect that industry and area of employment are changing, and how new technology may cause jobs to evolve and shift in a way that benefits workers. Bessen argues that the main problem is not the \”end of work,\” but instead the problem is that many workers have a difficult time obtaining the skills they need so that their work can complement the new waves of technology as they arrive. As a result, we observe a combination of stagnant wages for many workers who have been unable to update their skills as needed, combined with much higher wages for those who have the new skills (which contributes to wage inequality), all combined with employers who complain that not enough employees already have the skills the employer wants. As Besson writes:

New information technologies do pose a problem for the economy. To date, however, that problem is not massive technological unemployment. It is a problem of stagnant wages for ordinary workers and skill shortages for employers. Workers are being displaced to jobs requiring new skills rather than being replaced entirely. This problem, nevertheless, is quite real: technology has heightened economic inequality. … The information technology revolution may well be accelerating. Artificial intelligence software will give computers dramatic new capabilities over the coming years, potentially taking over job tasks in hundreds of occupations. But that progress is not cause for despair about the “end of work.” Instead, it is all the more reason to focus on policies that will help large numbers of workers acquire the knowledge and skills necessary to work with this new technology.

A short essay of mine titled \”The Blurry Line Between Competition and Cooperation,\” was published a month ago at the Library of Economics and Liberty website.  I argued that the rule-based competition in economic markets is inextricably intermingled with of cooperative behavior. Paul H. Rubin takes a stronger positino in his 2013 Presidential Address to the Southern Economic Association titled \”Emporîophobia (Fear of Markets): Cooperation or Competition?\” It is published last year in the April 2014 issue of the  Southern Economic Journal (80:4, pp. 875-889). Many readers will have access to the Southern Economic journal through a library or personal subscription, but an version of the paper is also available on SSRN here.

Rubin\’s argument is that both competition and cooperation are used in a metaphorical sense when discussing markets. He makes a case that if economists are choosing between these metaphors, cooperation is not only a metaphor with more positive connotations when explaining or defending markets to noneconomists, but also that \”competition\” itself is a poor metaphor for describing economic actions and decisions, and how the economy works. In one section of the paper, he offers six reasons why economists in the name of accuracy should stop referring to competition. Here is a sampling.

\”First, there is no economic act that is itself competitive.\” 

Rubin writes: \”In their economic lives, people produce goods and services and exchange these goods and services for others. Both the production of goods and the exchange of goods for other goods are
cooperative acts. There is no competition in these actions. The motive for some acts may be
competitive, but the actions themselves are cooperative. … Unless an agent is willing to
engage in illegal actions (for example, burning a competitor\’s factory) or willing to go outside
the market (e.g., complaining to the Federal Trade Commission about a competitor), any
competitive act is actually performed through cooperative behavior. \”

\”Second, theprototypical economy, the purely competitive economy, involves no competition.\”

Perfect competition as taught in the textbooks is made up of \”price-takers\” selling identical products, who can sell their complete output at a market price that they cannot affect. Indeed, one correspondent to my earlier piece pointed out that farmers, who are often viewed as in a real-world situation similar to the textbook version of perfect competition, often do not view themselves as competing with their neighbors, and instead often stand ready to share the risks and fixed costs of farming by helping neighbors where possible.

\”Third, in other market structures acts may sometimes be viewed as competitive, but not always.\”

What about market structures that are not perfect competition? Rubin writes: \”There may be competition to become the monopolist, but tlais is either competition through being a better cooperator or political competition, for example, by lobbying for exclusive licenses. … Again, motives may be competitive but the actions themselves are cooperative.\”

\”Fourth, principles of cooperation (through specialization and division of labor) are at least as important to economists as competition.\”

\”Adam Smith is the father of competitive analysis. But he is also the father of
cooperative analysis. Specialization is the mother of cooperation. The pin factory is a masterful
analysis of cooperation. Somehow we economists have made the competitive analysis in Smith
the basis for our discipline and have made cooperation into something of a stepchild.\”

\”Fifth, competition is a tool, not the end purpose of the economy.\”

\”The purpose of an economy is to generate consumer surplus, which occurs through cooperative acts such as transactions and exchanges. Competition is a powerful tool for improving the functioning of
transactions by making sure that in each case the transactors are the best possible partners and
that transactions take place on the best possible terms. That is the purpose of competition. In other
words, the competition that occurs in an economy is competition for the right to cooperate. The
gain comes from the cooperation, not from the competition. Of course, competition is essential,
since it leads to the optimum terms for cooperation and selects the best parties to cooperate, but
nonetheless competition is a tool whose function is to facilitate cooperation. Society is willing to
tolerate markets because of their cooperative benefits, not because they are competitive.\”

\”Sixth, competition is ubiquitous in human interactions, and so competition is not a way of
distinguishing market economies from other economies.\”

\”Economies based on custom also have competition. For example, more successful hunters in a hunter-gatherer economy reap benefits, including access to women. In an exploitive economy success may be measured by exploiting the population and rising through the oppressive hierarchy. This is much more \”competitive\” than the path to success in a market economy. The unique feature of an economy organized through markets is that the competition that exists is competition for the right to cooperate, but it is the cooperation that is the defining feature of the market economy.\”

Ultimately, Rubin\’s argument is that \”emporîophobia,\” his term for \”fear of markets,\” would be reduced if economists put the language of cooperation front and center in their vocabularies.  Here\’s Rubin\’s example of how economists might talk about Wal-Mart:

If we focus on competition rather than cooperation, then we think of winners and losers. We feel sorry for the losers and may view the winners as cheaters. At the least, there is a tendency to favor underdogs and the losers from competition may be viewed as underdogs. We may also believe that a world with winners and losers is in some sense unfair. By our emphasis on competition, economists must take some blame for this error. But if we think about cooperation, then the losers are those who are less successful at cooperating, Wal-Mart succeeds not because it has beat up its rivals and driven them out of business. It succeeds because it has done a better job of cooperating with consumers, by offering them stuff they want at the lowest possible prices. Of course, economists
know this, but since non-economists begin with the competition model, economists must be defensive and try to dissuade citizens of their prior beliefs. If the default way of thinking was cooperation, then the critics of markets would be on the defensive.

I\’m not fully persuaded by Rubin\’s argument, in large part because I agree with a clause in the preceding paragraph that \”non-economists begin with the competition model.\” As long as this is true, economists who speak too purified a language of cooperation are in real danger of sounding out-of-touch. Also, economists must then immediately confront the problem that bargaining positions in the economy are not always the same, and the \”cooperation\” of a minimum-wage worker taking what feels like the only available part-time job before the monthly rent becomes due doesn\’t look quite the same as the \”cooperation\” of a chief executive officer receiving a large annual bonus.

But precisely because \”non-economists begin with the competition model,\” it is useful for economists to be concrete and specific about the very specific sense in which they use the term \”competition.\” After all, having many firms \”competing\” to offer a mixture of prices and qualities that consumers prefer is quite a bit different from having firms \”competing\” to defraud customers. And in many economic contexts, the form of competition of which free-market economist speak approvingly quickly shades into cooperative behaviors.