conversableeconomist

How Cities Stopped Being Ladders of Opportunity

One of the archetypal stories of the American experience involves the person who moves from a rural area or a smaller metro area to a big city, and after starting off in a humble role and having some ups and downs, becomes a big success. But the role of cities as ladders of opportunity have changed dramatically in the last few decades. David Autor tells the story in \”The Faltering Escalator of Urban Opportunity\” (appearing as a chapter in Securing Our Economic Future, edited by Melissa S. Kearney and Amy Ganz, and published by the Aspen Institute Economic Strategy Group late last year). Autor begins: 
For much of modern U.S. history, workers were drawn to cities by opportunities for the more enriching work offered there and the higher pay that came with it. As the eminent urban economist Edward L. Glaeser observed, “…cities have been an escape route for the underemployed residents of rural areas, such as the African-Americans who fled north during the Great Migration” (Glaeser 2020). But an important aspect of this opportunity escalator has broken down in recent decades. The migration of less-educated and lower-income individuals and families toward high-wage cities has reversed course (Ganong and Shoag 2017): Since 1980, college-educated workers have been steadily moving into affluent cities while non-college workers have been moving out.

The theories about the reasons for this shift can be divided into \”push\” or \”pull\” categories. One set of theories is that those with less education are being  pushed from large cities–perhaps by very high housing costs. The other set of theories, which Autor emphasizes, is that the pull of labor market opportunities for lower-skilled workers has diminished sharply in big urban areas. 

In the initial decades following WWII, U.S. cities offered a distinctive skills and earnings escalator to less-educated workers. A likely reason why is that, in these decades, adults without college degrees performed higher-skilled, more specialized jobs in cities than their non-urban counterparts. Laboring in urban factories and offices, they staffed middle-skill, middle-pay production, clerical, and administrative roles, where they worked in close collaboration with highly educated professionals (e.g., engineers, executives, attorneys, actuaries, etc.). These collaborative working relationships often demanded specific skills and shared expertise, and likely contributed to the higher wages (and higher productivity) of urban non-college workers. These jobs were comparatively scarce in suburbs and rural areas, far away from the office towers and (at one time) bustling urban production centers. Urban labor markets accordingly provided an escalator of opportunity and upward mobility for immigrants, minorities, less-affluent, and less-educated workers.

In the decades since 1980, however, this distinctive feature of urban labor markets has diminished. As rising automation and international trade have encroached on employment in urban production, administrative support, and clerical work, the noncollege urban occupational skill gradient has diminished and ultimately disappeared. While urban residents are on average substantially more educated—and their jobs vastly more skill-intensive—than four decades ago, non-college workers in U.S. cities perform substantially less specialized and more skill-intensive work than they did decades earlier. Polarization thus reflects an unwinding of the distinctive structure of work for non-college adults in dense cities and metro areas relative to suburban and rural areas. And as this distinctive occupational structure has receded, so has the formerly robust urban wage premium paid to non-college workers.

Autor presents a body of detailed evidence on this shift, which I won\’t try to summarize here. But here\’s one figure, just showing how wage patterns by education level have shifted in urban vs non-urban areas in recent decades. For those with higher levels of education, urban wages have grown faster than wages in non-urban areas; for those with lower levels of education, urban wages have been growing more slowly than wages in non-urban areas.

The aftereffects of the pandemic are likely to strengthen this pattern. Autor writes:

The COVID-19 crisis may change this trajectory. It seems probable that employers will learn two durable lessons from the swift, disruptive, and yet surprisingly successful movement of knowledge work from in-person to online: a first is that online meetings are almost as good as—and much cheaper than—time-consuming, resource-intensive business trips; a second is that virtual workplaces can provide a productive, cost-effective alternative to expensive urban offices for a meaningful subset of workers. If these lessons take root, they will shift norms around business travel and remote work, with profound consequences for the structure of urban labor demand. Already, U.S. employers surveyed during the current pandemic project that the share of working days delivered from home will triple after the pandemic has passed (Altig et al. 2020). Most significantly, the demand for non-college workers in the urban hospitality sector (i.e., air travel, ground transportation, hotels, restaurants) and in urban business services (i.e., cleaning, security, maintenance, repair, and construction) will not likely recover to its previous trajectory.

When I think about the role of cities as ladders of opportunity, I find myself thinking about back-and-forth movements–that is, not just rural and smaller cities to big metro areas, but also the moves from big cities back to smaller ones. I can\’t point to systematic data to back this up, but my sense is that the economic role of US cities for many decades was that they were a hub for economic activity that reached out to smaller cities that were within perhaps 100-200 miles. Supply chains and also movements of people went back and forth along these urban hub-and-spoke connections. 

But in the last few decades, big cities seem to have lost some of their connectedness to the areas around them. Instead of having a manufacturing plant or a back-office operation in a location within a few hours drive from the city, that manufacturing plant or back-office operation may be in another country on another continent. For many economic purposes US cities now operate in global competition with other large cities around the world. It will be interesting to see if the post-pandemic shifts in urban labor markets include a shift of some high-skilled labor back to smaller cities and rural areas as well. 
conversableeconomist

The Broken Promises of the Freedman’s Savings Bank: 1865-1874

The Freedman’s Savings Bank lasted from 1865 to 1874. It was founded by the US government to provide financial services to former slaves: in particular, there was concern that if black veterans of the Union army did not have bank accounts, they would not be able to receive their pay. In terms of setting up branches and receiving deposits, the bank was a considerable success. However, the management of the bank ranged from uninvolved to corrupt, and together with the Panic of 1873, the combination proved lethal for the bank, and tens of thousands of depositors lost most of their money.
Luke C.D. Stein and Constantine Yannelis offer some recent research on lessons the grim experience in \”Financial Inclusion, Human Capital, and Wealth Accumulation: Evidence from the Freedman’s Savings Bank\” (Review of Financial Studies, 33:11, November 2020, pp. 5333–5377, subscription requiredhttps://academic.oup.com/rfs/article-abstract/33/11/5333/5732662). Also, Áine Doris offers a readable overview in the Chicago Booth Review (August 10, 2020).

Stein and Yanellis note:

The Freedman’s Savings Bank was an early government-sponsored private enterprise that was created by Congress to provide financial services to formerly enslaved African Americans. … The bank spread rapidly, and at one point had more interstate branches than any other U.S. financial institution, and approximately one in eight Blacks in the South lived in a family that held an account with the bank. … We obtain novel data on Freedman’s Savings Bank account holders from 27 branches with surviving bank records. These 107,197 account records include names of main account holders and their family members, totaling 483,082 non-unique individuals, roughly 12% of the 1870 Black population in the American South.

The main focus on the paper is that authors match the actual names of these depositors to data from the 1870 census, and then carry out a variety of calculations: for example, those who lived in the same county or within 50 miles of a branch of the bank, and those who did not. They can compare those who lived in areas where a branch was actually established, and those in similar areas where a branch was planned but never established. The result of these and other comparisons makes a persuasive case that access to a bank account had a clearly positive effect: \”We find that individuals in families that hold Freedman’s Savings Bank accounts are more likely to attend school, are more likely to be literate, are more likely to work, and have higher income and real estate wealth.\” For example, the freed slaves with access to banks and savings were more able to buy land, start businesses, and build and attend schools (at the time, many adult freed slaves immediately sought to become literate and numerate).
Stein and Yanellis also offer some suggestive evidence that the failure of the Freedman\’s Savings Bank had long-lasting intergenerational effects on black attitudes about banking. They write:

Historians, notably Osthaus (1976), have long noted that the collapse  of Freedman’s Savings Bank—which many African Americans thought was fully backed by the federal government—and loss of savings led to a lack of trust in financial institutions by African Americans, and at least in part explains persistent gaps in utilization of financial services. The FDIC National Survey of Unbanked and Underbanked Households concludes that African-American households are considerably more likely to be unbanked: 2015 survey results indicate that 18.2% of African-American households were unbanked, compared with 3.1% of white households. Almost one-third of households indicate a lack of trust in banks as the primary reason that they did not have bank accounts, with this explanation more common among African Americans. … [W]e show that African Americans in the present day who live in counties that once had a Freedman’s Savings Bank branch are more likely to list mistrust of financial institutions as a reason for being unbanked; this association is not present for Whites.

I dug back a little bit to get more information on what why the Freedman\’s Savings Bank collapsed. The US Office of the Comptroller of the Currency has a website with a smattering of details.  Although the OCC had been founded in 1863 to provide oversight to banks and limit risk-taking that would put deposits at risk, but the Freeman\’s Savings Bank was exempted from OCC oversight and instead was to be overseen by Congress. The result was a lesson in the potential for dysfunction of boards, with a takeover by the corrupt.

For those who would like heart-breaking and angry-making details of how the Freedman\’s Savings Bank was mismanaged, I found especially useful the account of historian Walter Fleming, \”The Freedmen\’s Savings Bank,\” published in the Yale Review, May 1906, pp. 40-76, and available via the magic of HathiTrust).
I should note that Fleming\’s essay has the curious trait of making occasional racist statements about black Americans, and then going on to provide evidence that the racist statements are not actually correct–without apparently noticing the contradiction. For example, Fleming states early in the paper: \”Before the close of the war several experiments in the way of savings banks had been made among the negro soldiers for the purpose of preventing them from squandering their pay and bounty money, as it is the nature of the race to do.\” But  a few pages later, Fleming is writing about how black Americans flocked to deposit money in the bank. He writes: \”The negroes, believing that their deposits would be secure in these banks, which they understood were supported by the government, eagerly availed themselves of the opportunity to lay up small sums for the future.\”
But even with his prejudices hanging out in the open, Fleming provides a useful step-by-step overview of what happened with the bank. The law did not specify that the bank was allowed to open branches, but several of those involved in passing the law clearly saw it as part of the mission. They travelled around the South together with officials of the Freedman\’s Bureau (which was not legally connected to the bank) talking to those who had run military savings banks, many of which became the basis for branches, as well as those in prominent black communities.  Those who deposited money in the bank had often been led to believe that the federal government stood behind the bank. Bank officials wore US uniforms. Depositors were given a passbook, which had pictures of Lincoln, Grant, the US flag on the cover. The version of the passbook used in New York City had printed on the cover: “The Government of the United States has made this bank perfectly safe.” In Fleming\’s words:

The negroes were given to understand that the bank was absolutely safe, being under the guarantee of Congress, and having the funds invested in United States securities, which were safe as long as the government should last, and that it was a benevolent scheme solely for the benefit of the blacks. The profits, they were told, would be returned to the depositors as interest, or would be expended for negro education.

Many of the early discussions of the banks at the time were quite positive. The bank branches offered a safe haven for funds, and education on savings and accumulation of interest. As Fleming writes:

In the branch banks and at Washington, after 1868, an efficient body of negro business men was being trained. There was a sentiment that, since the bank was for the benefit of the negroes, the latter should be its officers as much as possible, and about one-half the employees were colored. At nearly all of the branches, especially after 1870, when some of the branch banks were allowed to do a regular banking business, there was an advisory board, or board of directors, of responsible colored property holders. These men were very proud of the Freedmen\’s Bank and of their position in connection with it. They took a deep interest in all that pertained to the institution, advised in regard to loans and investments, and promoted in every way the habit of saving on the part of their people.

But the inner workings of the bank went badly. Some of it was incompetence, a lot of it was corruption, but the underlying issue was that far too many people viewed the money in the bank as a large pot of honey, just waiting for them to scoop up what they could. Fleming describes a range of problems. Expenses were high, including $260,000 for building a pricey new headquarters building in Washington, DC. State governments often disliked the bank because the deposits were flowing to US securities, and out of their influence. Many of the employees were deeply inexperienced,  and the financial accounts were a mess. There was one inspector who was supposed to cover all the branches.
Although the branches of the banks were not supposed to make loans before 1870, when a prominent community leader wanted to draw out more money than was in his account, the cashiers often found it hard to say \”no\”–and hard to get the money back later. Then the banks were allowed to make loans under supposedly strict conditions, but many of of them. After 1870, Fleming writes:
As soon as the authority was given to the cashiers to make loans, they were besieged by a dangerous class of borrowers, who would have received scant consideration at the ordinary bank. Often the law of 1870, requiring that loans be made only on property worth double the loan, was violated and the cashiers proceeded to make investments on their own responsibility. Some of them loaned funds on the worthless script issued by the carpet-bag State and local governments; others loaned on cotton; some even made loans on perishable crops. The Jacksonville branch put money on everything that offered, from saw-mills out in the woods to shadowy claims on property. … Most of the incompetent officials, it seems, were blacks; most of the corrupt ones were white. There was a belief, often expressed after the failure of the bank, that when a white cashier had stolen funds and involved the accounts of a branch, a negro official would be put in his place to serve as a scapegoat. The white clergymen who were cashiers proved to be quite unable to withstand the temptations offered by the presence of the cash in the vaults. One of the trustees (Purvis) afterwards said: “The cashiers at most of the branches were a set of scoundrels and thieves—and made no bones about it—but they were all pious men, and some of them were ministers.\”
Bt the real coup-de-grace for the bank was a result of what should have been criminal actions, even under the laws of the time, at the top levels of management.  As Fleming points out, the original bill named the 50 people who would be trustees of the institution. In his telling, the original 50 were (white) businessmen of good character. They were to meet at least once a month. However, it only needed nine members (one of whom had to be the president or vice president) to form a quorum, and a decision could be made with support of seven out of those nine. The trustees were to receive no compensation, and were not allowed to borrow funds from the bank. The original bill said that deposits would be invested in US securities only, except for an amount to be held as an \”available fund\” for withdrawals and current expenses. But then the headquarters of the bank moved from New York to Washington, DC, and the board turned over. Fleming tells the story in some detail of how the \”hoarded deposits of the Freedmen\’s Bank drew the attention of the speculators in Washington,\” and how the new trustees stripped the banks of assets, but here\’s a quick sense of the kind of thing that went on:
The places on the board [after the move to Washington DC] were somewhat difficult to fill, and it came about that most of those who were put in were incompetent persons elected simply to fill up the lists. They had little business capacity, no business connections, no property. The incapable ones were controlled by the few capables, who, after 1869-1870, were the District of Columbia members. These latter formed a kind of a “ring\” for their mutual benefit. They were involved in other schemes that made their connection with the bank of great use to them. They were at once officials of the bank, and officers of the Bureau or of the army or of the government of the District of Columbia. Howard, Balloch, Alvord, and Smith were bureau officials and were connected with Howard University, and extensive borrowers from the bank; Cooke and Huntington were officials in another bank that put its bad loans off on the Freedmen\’s Bank; Cooke, Eaton, Huntington, Balloch, and Richards were officials of the notorious District government; Howard, Alvord, Eaton, Stickney, Kilbourn, Latta, Clephane, Huntington, Cooke, and Richards were connected with firms that borrowed large sums from the bank, notwithstanding the fact that officials were prohibited by law from using the funds of the bank, directly or indirectly.
The trustees were under no penalties for the proper execution of their trust. They were not required to make any deposits in the bank. The law fixed as a quorum nine out of fifty trustees, and further required the affirmative vote of at least five on money matters. The trustees provided in the by-laws for a finance committee of five, of whom three should be a quorum. Thus three could and did habitually dispose of the financial business of the bank when the law required at least five. Often two trustees, or one, or even the actuary (cashier), negotiated important loans without reference to the trustees.
When this situation was followed by the Panic of 1873 and crash in real estate values, there wasn\’t much left.  Pretty much no one was prosecuted or held responsible. Fleming tells hard-to-read stories of working people who faithfully put money in the branches for years, only to find that it had all been taken. As Stein and Yannellis write: \”In June 1874, the Freedman’s Savings Bank was forced to suspend operations with only 50 cents to cover obligations per depositor. The failure of a bank catering to former slaves, and the loss of their savings, led to general public concern and sympathy for the fate of depositors. Following a congressional investigation, Congress created a program to reimburse up to 62% of savings, but many depositors were never compensated.\”
conversableeconomist

More on the Origins of "Pushing on a String"

Tie a string to an object. When you pull on a string, the object on the other end comes toward you. But when you push on a string, the object at the other end of the string is unaffected, because the string just crumples up. For economists, \”pushing on a string\” refers to the idea that monetary policy may (in certain circumstances) be more effective at reducing inflation even at the cost of a recession than it is at stimulating an economy. Back in 2015, I posted about an early use of the \”pushing on a string\” metaphor during Congressional hearings in March 1935. 

However, Samuel Demeulemeester has recently written to me with several example from the same time frame, but slightly earlier. Jeff Busby was at the time a Congressman from Mississippi. Willford King was a professor of economics at New York University, and a Fellow of the American Statistical Association. Irving Fisher is in the story, too. In short, the metaphor was not a one-off comment in 1935, but was demonstrably familiar to policy-makers and academics at this time.  What follows is from Demeulemeester, with his permission (for ease of reading, I have not inserted additional indenting or quotation marks to  his email):  
__________

I just came across your post of July 30, 2015, “Pushing on a String: An Origin Story”, in which you suggest that this metaphor might first have been used by Representative T. A. Goldsborough during hearings held on March 18, 1935.

Goldsborough actually seems to have gotten this metaphor from Willford King of New York University, who used it during hearings before a subcommittee (presided by Goldsborough) of the Committee on Banking and Currency, House of Representatives, on January 30, 1934. King had himself heard it from “somebody” else:

Mr. BUSBY. Therefore, I think it is more essential we go to the question of dealing with both the up and down amount of bank money that may be issued so as to control it.

Mr. KING. You can prevent overissuance of this bank-deposit currency, but it is very hard to prevent underissuance of the same thing. As somebody said, you can push on a string but it doesn’t produce very good results.

Mr. BUSBY. That is exactly what I have in mind, and I want to see some kind of scheme worked out where we are pushing on the string and you can see some effect on the other end of the string.

(See here, p. 71)

This Mr. Busby would himself use the metaphor in an exchange with Irving Fisher two days later, on February 1, 1934, before the same subcommittee:

Mr. BUSBY. As Dr. King said yesterday, you can pull on a string and feel the effect of it. Therefore, you can pull down the fixed media of exchange, but you can’t push it up, just as you can push on a string and feel no effect at the other end. So in adverse times when property prices are falling the only remedy I see to get efficiency out of the arrangement is for the Government to step in and supply where this by-product of the banks\’ activities is wiped out.

(Ibid., p. 85)

And Fisher himself would use a variation of the “pushing on a string” metaphor in the following passage of his book 100% Money (1935):

Such must often be our predicament so long as we have a system under which our circulating medium is a by-product of private debt. The time when nobody wants to go into debt is the very time when we most need money and so most hope that somebody will kindly accommodate us by going into debt. Few will do so, despite all the official urging and coaxing and despite the low rates of interest. It is a case of leading a horse to water without being able to make him drink. Or it is like “pushing on the lines” to make the horse go.

(1935, 1st ed., p. 94; [1935] 1936, 2nd ed., p. 105; this passage already appeared in a 1934 draft version of the book)

_______________________ 

As Demeulemeester points out in a follow-up email: `By the way, King\’s statement, as I re-read it, only says that he did not originate the phrase \”you can push on a string but it doesn\’t produce very good results\’. This leaves open the possibility that he was the first to apply this metaphor to monetary policy.\” 

conversableeconomist

An Interview with John Roemer on Inequality of Opportunity

The editors of the Erasmus Journal for Philosophy and Economics, Akshath Jitendranath and Marina Uzunova, have prepared \”What Egalitarianism Requires: An Interview with John Roemer\” (Winter 2020, 13: 2, pp. 127–176).  As they note in the introduction: \”Roemer’s work spans the domains of economics, philosophy, and political science, and, most often, applies the tools of general equilibrium and game theory to problems of political economy and distributive justice—problems often stemming from the discussions among political philosophers in the second half of the twentieth century.\”

A substantial chunk of the interview covers Roemer\’s background; how his professor parents left the US to work work in Switzerland and Canada for several years in the early 1950s after the State Department found that they were \”disloyal\”; how he took almost all math classes as a Harvard undergraduate and originally enrolled at Berkeley for a PhD in mathematics; how he was suspended from Berkeley for occupying an administration building in 1968; how took a job teaching math in a \”virtually all-black\” San Francisco junior high school for five years; how he returned to Berkeley in the economics PhD program; and how he did not read any Marx until 1976, after he took a job at UC-Davis. 
In about 1980, Roemer became part of the \”No-BullShit Marxist Group.\” \”Members included philosophers, economists, sociologists, historians, and political scientists. The common task was to re-state Marxian questions in a modern way, and to study them using the tools of analytical social science and philosophy. The school of ‘analytical Marxism’ was quite influential in the 1980s: it was attacked from the left by traditional Marxists, who believed that using these ‘bourgeois’ tools of analysis would surely infect our conclusions. In reply, we called these critics bible-thumpers. … I tend not to call myself a Marxist anymore because I do not credit many of the ideas that Marx believed were at the center of his view: the labor theory of value, the falling rate of profit, and the claim that dialectical materialism is a special kind of logic.\”
During this time, Roemer often wrote mathematical expositions of how to define \”exploitation\” and how exploitation could lead to a defined class structure. But over time, he came to believe that while exploitation was real, it wasn\’t the central problem of capitalism, and his beliefs shifted to a focus on equality of opportunity. He says: 

I believe that all young adults should begin their productive years with the same amount of wealth. This implies that the inheritance of wealth, and in vivos transfers to the young, must be sharply constrained. If the educational system has succeeded in eliminating inequality of opportunity, and people make different career choices, then differential wealth will emerge during adult lifetimes, and I believe those differences are consistent with justice, as long as there is sufficient income and wealth taxation to prevent income differences from becoming too extreme—so extreme as to threaten solidarity. As I said, Marx’s condemnation of the distribution of capital was based on the history of ‘primitive accumulation’ that he presented. If wealth accumulation is a result of freely chosen labor with equal-opportunity background conditions, I do not believe modest wealth differences are unjust. … 

Many leftists believe the key to understanding capitalism is to understand the extraction of labor from labor power at the point of production. And indeed, I think Marx sometimes erred in thinking this, as well. My view is that the essence of capitalism is the set of institutions which sanctify and enforce private and unequal ownership of capital—that is, vastly unequal wealth. Now, workers, surely, do face all kinds of oppression at the point of production—bosses who crack the whip, speed up the assembly line, fire workers who organize, etc. There is a constant struggle at work between workers and bosses about the conditions of work.  … In the last analysis, power comes in the police force that enforces property relations. This is the key locus of power; oppression of workers at the point of production, though perhaps very important in building class consciousness of workers, is relatively small potatoes. Coercion at the point of production was essential in feudalism and slavery, but capitalism has subtler techniques for accumulating wealth.

This approach led Roemer to studies of how much of the current level of inequality in incomes could be accounted for by factors over which a person had no control, including race and gender, but also a range of other factors. He says: 

Before 1993, almost all measures of unequal opportunity focused upon one circumstance: the rank of the individual’s father in the income/wealth distribution of his generation. What these studies call intergenerational immobility is a special case of opportunity inequality. Societies in which the individual’s rank in the income distribution of his generation was only weakly related to the father’s rank in the income distribution of his generation were ones with relatively equal opportunity. These studies, to be precise, looked at only one circumstance in explaining the child’s income: his father’s income rank. It turns out, using the algorithm that I propose to measure inequality of opportunity, that circumstance (father’s rank) accounts for less than 10% of income inequality in a society. Today, in the plethora of studies measuring inequality of opportunity (IOp), it is not uncommon to explain 30%, even 50% of income inequality, as due to circumstances. Of course, these studies look at many other circumstances in addition to father’s income rank! This shows how the IOp theory has greatly reduced the set of actions for which people are implicitly held responsible. … If I can show that, in my country, 50% of income inequality is due to factors that anyone would agree individuals should not be held responsible for, whereas the standard conservative view in my country is that everyone should be capable of pulling herself up by her bootstraps, I have a powerful argument to reform tax, educational, and healthcare policy.
These arguments led Roemer in several directions. He argues that if some share of the talents we have are \”morally arbitrary\”–not the result of our own efforts but rather passed along by family or limited by social constraints–then part of income should be \”owned\” by the community, too. He says: 

We should not have …  full ownership of our labor power. If the talents we have are in part morally arbitrary, they should in part be owned by the community. For a person not to be a full self-owner does not mean the community is free to harvest one of his kidneys to transplant into another, but it may well mean that he must pay taxes on his earnings to the state. …  The libertarian attack on common ownership of talents—that it would expose everyone to possible kidney harnessing—is a non sequitur.

Thinking about equality of opportunity in these broad terms has led Roemer to work outside of utilitarian frameworks: in his view, what matters is not just a welfare level for each individual, but a combination of thinking about the circumstances of people and their effort level.

I extended Sen’s critique of welfarism in the theory of equality of opportunity that I proposed. The language of that theory includes circumstances, effort, and type. These are fundamentals, along with utility. One cannot judge how just a situation is by knowing only the welfare levels of people in it: one must know how hard they tried and what their circumstances were. The equal-opportunity theory is non-welfarist. 

As an economist, Roemer is also well aware of the power of market forces and the perils of government ownership when it comes to achieving efficiency. Thus, he has for some years advocated versions of \”market socialism,\” which would seek to combine market forces–like workers who get paid wages–with a much higher level of equality of opportunity. He says: 

I do not believe there is an inherent injustice in wage labor. If I did believe there were, I could not advocate the use of markets under socialism. And I think that without markets, we would be—at this point, before we discover some other way of allocating resources—condemned to terrible inefficiency and poverty. In my recent work …  I argue that markets combined with solidaristic optimization by workers and investors, produces much better results than capitalism—in terms of both efficiency and equity. …

I am saying that the history of the last several centuries can be viewed as one of rectifying the terrible truncation of opportunities of certain peoples, due to certain circumstances—morally arbitrary characteristics of persons, that come to inhibit their chances of leading a fulfilling life. In the middle of the twentieth century, John Rawls provided a general argument that race and sex were only special cases of the morally arbitrary distribution of circumstances whose effects on income and welfare would be eliminated in a just society. Of course, as you say, it will be impossible ever to eliminate completely these effects. Highly talented people will probably always lead lives that are more successful and happier than they deserve. But we proceed incrementally: we do the best we can. The Enlightenment, beginning, let us say, with the French Revolution, is still far from complete. … My goal is to focus on building solidaristic societies, and I think that the most important barrier to solidarity is the individualistic ethos of capitalist society where the accumulation of private wealth is the guiding force. We are still very much in the era when inequality of income and wealth is the main problem. I speak not only of poverty, but of the way capitalist society distorts human behavior and politics.

Finally, I wanted to pass along one of Roemer\’s thoughts about academic research: \”I find it takes about ten years for my work on a problem to become mature, so be patient. In an intellectual life of forty years, count yourself a success if you can develop to fruition three or four good ideas.\”

For me, Roemer is one of the answers to the question I sometimes get: \”If I wanted to read the work of a serious and well-regarded modern economist coming from the Marxist tradition, where should I start?\” Those who want an additional dose of Roemer might begin with the four articles in this issue of the Erasmus Journal for Philosophy and Economics that are reviews of Roemer\’s recent book on \”Kantian optimization\”–that is, a society where people optimize based on cooperative values rooted in social solidarity rather than on individualistic ones. Roemer also offers a rejoinder. Other accessible starting points for more Roemer include: 

conversableeconomist

Facing the Long-Term Problem of Low Interest Rates

Interest rates have been declining for several decades, both in the US economy and around the world. Why has this happened, what problems is it causing, and what monetary and fiscal policy responses might be appropriate? Elga Bartsch, Agnès Bénassy-Quéré, Giancarlo Corsetti and Xavier Debrun tackle many of these issues in \”It\’s All in the Mix: How Monetary and Fiscal Policies Can Work or Fail Together\” (December 2020, Geneva Reports on the World Economy 23). 
As a starting point, here are a few examples of falling interest rates. The first figure shows the interest rate for borrowing money for a 30-year fixed-rate mortgage in the US.

This figure shows the \”federal funds\” interest rate, which is the interest rate targeted by the US Federal Reserve when it conducts monetary policy. 

As a third example, more theoretical but more conceptually accurate than the first two, consider the interest rate that economists call R*. As Bartsch, Bénassy-Quéré, Corsetti and Debrun explain:

R* is the real rate of interest that, averaged over the business cycle, balances the supply
and demand of loanable funds, while keeping aggregate demand in line with potential
output to prevent undue inflationary or deflationary pressure. Two key features of R* are
that it is (i) expressed in real terms (i.e., excluding inflation) and (ii) not subject to credit
risk. Hence R* is meant to capture the equilibrium (real) rate of return of a safe asset.

The R* interest rate is estimated, rather than observed directly. The authors report estimates showing a declining R* interest rate since 2000. 
Again, the R* interest rate is a real interest rate, stripping out any effects of inflation, and also a risk-free rate, stripping out risk of defaulting on loans. So what makes it decline? As usual for economists, the standard answer involves supply and demand: specifically, the global supply of savings has risen, and this higher supply of saving has not been matched by a concomitant rise  in investment demand for saved funds. The authors write: 

More generally, however, an abnormally low R* is first and foremost a sign of economic malaise and imbalances … Consensus explanations point to factors that simultaneously and inefficiently push global savings up and global investment down. Some of these factors are slow-moving and predictable. This is the case of accelerating population ageing in the West and in East Asia (China, Japan, South Korea). As individuals approach retirement age, higher wage income and the prospect of lower pension payments encourage them to save more to smooth their living standards over their remaining lifetime.

Yet, the issue is why these extra savings do not find their way into investment. After all, the effects of ageing on investment are not necessarily negative on balance. While the anticipation of shrinking markets in large economies is undoubtedly a drag on investment plans, a relative fall in the working-age population could be expected to raise the return on capital. In addition, a larger elderly population could entail reallocations of productive capacity, fostering innovation and, ultimately, investment as the aggregate consumption basket changes in favour of goods and services intended to alleviate the consequences of dependency. For instance, the return on investment in robotics, telemedicine and other innovative options to deal with dependency could be expected to rise. What prevents these arguably desirable developments from moving faster and on a greater scale?

Other slow-moving but less easily predictable factors include the significant rise in income inequality in many countries, the slowdown in trend productivity growth and an increase in market concentration. Greater income inequality is generally thought to raise aggregate saving as the income share of more affluent households – who tend to save relatively more – increases. Weaker productivity gains and greater market power could both contribute to lower private investment and boost corporate savings, resulting in sizable stock buybacks and purchases of low-risk financial assets, such as sovereign bonds from reserve currency issuers. And in fact, the corporate savings glut is an essential part of the story.

(For more on reasons behind lower interest rates, see here and here. For more on the corporate savings glut, where companies are holding very large cash reserves, see here and here.)

For borrowers, like those who are buying a house (or refinancing an existing mortgage) or for the government running large budget deficits, these ultra-low low interest rates seem like a real benefit. Why might one be concerned about them as a problem, or at the authors put it, \”a sign of economic malaise and imbalances\”? There are a number of concerns, some more fundamental than others. 
One practical problem is that investors who had been relying on interest rates at more normal levels, like insurance companies or pension funds, have experiences lower-than-expected returns, which in turn affects their ability to meet their commitments. 

A more fundamental problem is that as one looks around the world, it certainly seems as if there are plentiful opportunities for productive investment. The world is full of poverty and low consumption levels, together with low levels of health and education, environmental concerns, aging populations, and many other issues. There are also waves of new technologies, including not just digital technologies but scientific breakthroughs in biology, materials science, and many other areas. However, the high levels of global savings are not translating into a surge of global investment. This suggests something is wrong with the economic environment at a deeper level, perhaps with the financial system, or with maybe with issues like taxes, regulation, trade, or how scientific breakthroughs are being translated (or not translated) into consumer products. 

Instead, we seem to be in a global economy where low interest rates lead to situations of high borrowing and funds flowing into financial assets like real estate or stock markets. These high levels of borrowing create a risk that when a negative economic event occurs, it will propagate through the financial system in a way that leads to bigger problems or widespread recession. The report notes (italic type in original): 

After the Global Financial Crisis, advanced economies had to come to terms with their vulnerability to the kind of economic and financial instability usually confined to some chronically unstable emerging and developing economies. … As in emerging and developing countries, the events that bring about instability are not necessarily low-probability, high-impact shocks, such as devastating earthquakes or deadly pandemics. They can also be (and usually are) the result of a vicious circle of negative feedback loops, which allow even a seemingly benign disturbance to quickly escalate into ‘tail events’. Bernanke (2013, pp. 71-72) explains this when he recalls that ahead of the GFC,

“… if you took all the subprime mortgages in the United States and put them all together and assume they were all worthless, the total losses to the financial system will be about the size of one bad day at the stock market, they just weren’t that big.” 

Taken in isolation, a root disturbance of the size of a bad day in a given segment of financial markets would likely not be enough to instantly alarm policymakers and push them to act … Hence, when things unexpectedly get out of control, policymakers can be caught unprepared and unable to respond as quickly and effectively as possible with the right mix of instruments. Whatever their root causes, the endogenous vicious circles that define tail risk are mostly financial. 
Thus, we have been living for some years now in an economy where, by conventional measures, the government is making large efforts to stimulate the economy with low interest rates and high budget deficits. But interest rates remain low. Borrowing stays high. The risks of smaller economic shocks being magnified into bigger shocks remains. The unemployment rate is sometimes lower or higher, but productivity doesn\’t rise. With each recession, we rely on ever-larger budget deficits and ever-more-creative monetary policies like emergency lending funds and quantitative easing. The Federal Reserve has become more focused on acting as an arm of the US Treasury, and helping to finance government debt, rather than acting as an independent agency. We seem unable to return to a more \”normal\” economy. 
Bartsch, Bénassy-Quéré, Corsetti and Debrun emphasize that this issue is global, because supply and demand for capital is a global market. Thus, they suggest a coordinated global response: for example, one can imagine a situation  in which countries around the world increase their deficit spending with two main purposes in mind. One goal would be to provide improved social insurance so that people have less reason to save. Another goal would be to make a range of public investments in infrastructure, R&D, technology centers, and human capital, which would both raise investment directly and also (one hopes) provide an improved investment climate for firms.  Taken together perhaps supportive actiosn on tax, trade, and regulatory issued, one can imagine a shift to less global saving (especially by firms) and to more global investment. 
But of course, the political economy behind these policy choices are not straightforward. The US government, for example, has been shifting for years toward an emphasis on writing checks either to individuals or on behalf of individuals (say, via Medicare and Medicaid) and away from investment spending. The 2020 election campaign was not focused on building public support for a shift to more investment-focused federal spending. 
It\’s also true that even if the issue of low interest rates is global, the policy responses would still be taken on a national basis. For example, one can imagine that if the US was to enact a substantial long-run increase in deficit spending, given that the US domestic savings rate is already fairly low, the US economy will need to rely on very large inflows of international financial capital and will be running very large trade deficits. This policy mix has political and economic tradeoffs of its own. 
Finally, as the authors emphasize, the idea of running large government budget deficits for a time all over the world, and then turning off the spigot when interest rates return to more normal levels, poses some obvious issues of implementation. 
Ultra-low interest rates are central to the macroeconomic challenges of our time. They are a main way in which conventional macroeconomic wisdom about monetary and fiscal policy from as recently as 15-20 years ago doesn\’t apply nearly as well in the present. 
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Some Economics of the Middle Class

There are two things that \”everyone knows\” about the US middle class: it\’s shrinking in size and the government isn\’t helping. However, when one digs into the data, the evidence for these claims and some implications of that evidence are considerably more nuanced. 

Here, I draw upon a collection of essays called Securing Our Economic Future, edited by Melissa S. Kearney and Amy Ganz, and published by the Aspen Institute Economic Strategy Group late last year. In h first essay, Bruce Sacerdote asks, \”Is the Decline of the Middle Class Greatly Exaggerated?\” In the second essay, Adam Looney, Jeff Larrimore, David Splinter look at \”Middle-Class Redistribution: Tax and Transfer Policy for Most Americans.\”

For a flavor of Sacerdote\’s argument, define the middle class as those with between 75% and 200% of the median income. Then over time, the share of household incomes going to this group does decline. However, a closer look shows that the reason for the decline in the share of household incomes in the \”middle class\” category is not because the share in the below-75-percent-of-median group has rise, but rather because the share going to the above-200-percent-of-median group has risen.

In an arithmetic sense, this is a decline of the middle class. But it is not a shift to a bimodal or two-humped income distribution with both poor and rich rising. Instead, the middle class still has the largest share of income overall and is declining only because more households are moving up to the higher category.

To think about this shift, imagine a \”society\” in Scenario A with 100 people: 35 poor people, 51 middle-income people, and 14 rich people. Compare this with Scenario B, which has 35 poor people, 43 middle class people, and 22 rich people. (The numbers here are chosen to match Sacerdote\’s chart.) In other words, the change here is that eight of middle-income people move up to the \”rich\” category, and let\’s hypothesize that no one else is affected negatively.

Is society better off in Scenario A or B? For the purposes of this exercise, it\’s not fair to invent Scenarios C, D, or E: yes, we might make a case that more broad-based growth, or movement from the poor to the middle-class, would be preferable. But the question here is how to think about the actual shift that happened. In the shift from A to B, the size of the middle class has declined and inequality has risen. However, a standard argument for social welfare comparisons makes the plausible claim that if comparing two scenarios where at least some people are better off, and no one is worse off, then social welfare as a whole has improved

For those who hesitate before accepting this conclusion, consider running this argument in reverse: Say that you start in Scenario B, but then eight people moves from the \”rich\” to the \”middle-class\” category in Scenario A. In this situation, the share of those in the middle class has risen, and inequality has diminished. But it would seem perverse to argue that a society where some people have become worse-off (again assuming no effect on others) is a preferable outcome. Or to put it another way, one has to argue that income equality has such a high value that it is worth \”levelling down\” incomes so that some people are worse-off, even if there is no direct benefit to others from doing so. As Sacerdote writes: \”[T]he astounding growth at the top of the distribution need not be making the middle class worse off in absolute terms.\”

Sacerdote also refers back to the findings of an OECD study in 2019, which argued that \”middle class\” is associated in people\’s minds with certain kinds of consumption: in particular, it\’s associated with a certain level of housing, with relatively easy access to health insurance and health care, and with access to higher education. In the US and around the world, prices for housing, health care, and higher education have risen faster than average incomes. As he points out, one can \”ask whether homeownership or college attendance for children in the family has risen or fallen for people in the middle quintiles of the income distribution. I find that since the 1980s, homeownership, square footage of housing consumed, number of automobiles owned, and college attendance have all been rising. The one exception is the modest dip in homeownership that occurred immediately after the financial crisis of 2008.\”

My sense is that rising inequality has meant that our market-oriented economy will tend to focus more on what it can sell to the rising share of households with higher incomes than on the falling share of househoods with middle-class incomes. But again, the stress of the middle-class looking at this shift, or the stress of those have crossed over into the upper income class only to find that their housing, higher education, and health care expenses still look pretty high, is quite different from arguing that the middle-class are objectively worse off.

For a flavor of the argument from Looney, Larrimore, and Splinter, they look at the \”middle class\” as representing the middle three-fifths of the income distribution. They write: \”The `middle class\’ has benefitted from government redistribution in recent decades. For individuals in non-elderly households in the middle three income quintiles (the middle class), the share of federal taxes decreased, and the share of transfers increased. Between 1979 and 2016, market income per person increased 39 percent. But when accounting for taxes and transfers income increased 57 percent. Middle-class income support, however, is a recent phenomenon. Before 2000, market income and income after taxes and transfers grew together. Since 2000, middle-class income after taxes and transfers grew three times faster than market income.\”

Notice that their analysis is focused on the non-elderly, so the results have nothing to do with changes in Social Security or Medicare. Basically, they find that since about 2000, the US government has been able to use a pattern of gradually higher budget deficits along with the ongoing decline in defense spending (as a share of GDP) to finance lower taxes and higher spending for the middle class. Here are a couple of illustrative figures. 

Here\’s the fall in taxes paid by the middle class. Of course, part of the reason why the top 20% are paying more is because of rising inequality of incomes. But the shift is bigger than what can be accounted for by that factor alone. 

Here\’s a figure showing the rising share of transfer payments going to the middle three-fifths. To put this another way, many of the expansions of means-tested federal programs over recent decades have been less focused on raising the level of support for the poor, and more focused on expanding the program to the near-poor who would not previously have been covered. 

The authors summarize: 

Over the last several decades, more federal support flowed to the middle class, while the payments they made for federal programs through taxes have declined. Focusing just on amounts for non-elderly households, between 1979 and 2016, the share of means-tested transfers received by middle-class households increased from 27 percent to 49 percent. Their share of federal taxes paid fell from 45 to 31 percent. These changes are partially the result of economic trends, which reduced the share of market income earned by the middle class. However, changes in federal tax policy eliminated income tax liability for more middle-class households and reduced average tax rates on all but the highest-income households. Since 1979, the share of nonelderly adults facing no income tax nearly doubled, to about 40 percent. At the same time, average federal tax rates for non-elderly middle-class households fell about 4 percentage points. Since 1979, the cumulative effect of these policies was to boost the increase in non-elderly middle-class incomes by 18 percentage points. Federal support for middle-class households has clearly improved their economic stability and material well-being.

So if the federal government is doing less to tax and more to pay benefits to the middle class now than a few decades ago, why doesn\’t it feel that way to so many people? 

One main reason is that many of these benefits do not flow to households directly, but rather go to  health care providers.  For example, the value of excluding employer-provided health insurance from taxation has been rising. But that value doesn\’t show up in anyone\’s paycheck. Similarly, the cost of Medicaid has been rising, but this program involves payments from the federal government to health care providers, so the beneficiaries of this program do not see any additional income coming directly to their household. Another reason is that when inequality is rising, the middle-class may be more focused on the gap that is opening up with the rich, rather than on the calculations mentioned here. But the authors write: \”Since 2000, non-elderly, middle-class incomes grew three times faster when accounting for transfers and federal taxes.\”

Looney, Larrimore, and Splinter were writing their before the COVID-related financial rescue packages of 2020 and 2021. However, they were already pointing out that this shift toward rising federal support for the incomes of middle-class households faced some natural limits: for example, defense outlays as a share of GDP rose from about 3% of GDP in the pre-9/11 days of 2000 to above 4.5% of GDP in 2010, but since then has fallen back to the 3% level. Budget deficits were high during the Great Recession, and will be much higher in 2020. Looking ahead, it will be hard for the federal government to use lower defense spending and ever-higher deficits to increase incomes of the middle three-fifths. 

conversableeconomist

Will Population Fall for Many Countries–and the World?

During my adult life, the main arguments about global population typically revolved around the topic of whether growth in population would overwhelm natural resources and lead to mass starvation and environmental collapse, or whether growth in population would be accompanied by technological progress in a way that would lead to a generally rising standard of living. Although the dire predictions of overpopulation from the 1960s and 1970s have not materialized on schedule, those concerned about overpopulation could always argue that even if the doomsday predictions were premature or delayed, they were nonetheless on their way. 

However, both sides of this controversy started from an assumption that population levels would continue to rise. In the 21st century, this assumption may be proven false. 

US birthrates have been in decline for some years. William Frey recently reported some historical figures on US population growth from the Census Bureau. Here\’s population growth by decade. Notice that the rate of population growth in the 2010s is the lowest of any decade in US history.

Here\’s US population growth annually since 1900. It looks as if 2020 will be the lowest population growth in that time. 

The US pattern is reasonably representative of the world as a whole: that is, population growths is faster in some countries and slower in others. In Japan, Russia, and Spain, for example, total population has already peaked in the last few years and how has started to decline. For an look at projected global population growth, a group of 24 demographers published a \”Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: a forecasting analysis for the Global Burden of Disease Study (The Lancet, October 17, 2020, pp. 1285-1306).  Here\’s a flavor of their results (for readability, I\’ve deleted footnotes and parenthetical references to statistical confidence intervals):

Our reference scenario, based on robust statistical models of fertility, mortality, and migration, suggested that global population will peak in 2064 at 9·73 billion and then decline to 8·79 billion (6·in 2100 … 

Responding to sustained low fertility is likely to become an overriding policy concern in many nations given the economic, social, environmental, and geopolitical consequences of low birth rates. A decline in total world population in the latter half of the century is potentially good news for the global environment. Fewer people on the planet in every year between now and 2100 than the number forecasted by the UNPD would mean less carbon emission, less stress on global food systems, and less likelihood of transgressing planetary boundaries. …

Although good for the environment, population decline and associated shifts in age structure in many nations might have other profound and often negative consequences. In 23 countries, including Japan, Thailand, Spain, and Ukraine, populations are expected to decline by 50% or more. Another 34 countries will probably decline by 25–50%, including China, with a forecasted 48·0% decline.

For the United States, their baseline scenario suggests that population will rise from 324 million in 2017 to a peak of 363 million by 2064, before declining to 335 million in 2100. 

When confronted with predictions that are decades in the future, it\’s of course important to note that they rely on underlying estimates about fertility and longevity, which in turn rely on estimates about factors like education levels and use of contraception. Perhaps there will be a new global baby boom that will surprise the demographers. But it\’s also important to note that much of the future population has already been born. For example, those who will be 40 or older by the year 2061 are already born right now. A sizeable share of those born in the last few years will live to see 2100. Thus, it\’s worth some thought as to where we seem to be headed. 

One obvious shift is that countries around the world will be much more focused on the elderly, because the elderly will be a much large share of the population. The demographers writing in the Lancet note: 

In 2100, if labour force participation by age and sex does not change, the ratio of the non-working adult population to the working population might reach 1·16 globally, up from 0·80 in 2017. This ratio implies that, at the global level, each person working would have to support, through taxation and intra-family income transfers, 1·16 non-working individuals aged 15 years or older (the working age population is defined by the International Labour Organization as those aged 15 years or older).41 Moreover, the number of countries with a dependency ratio higher than 1 is expected to increase from 59 in 2017 to 145 in 2100. Taxation rates required to sustain national health insurance and social security programmes might be so large as to further reduce economic growth and investment. Insecurity from the risk that these programmes could fail might generate considerable political stress in societies with this demographic contraction …

When thinking of these challenges, one\’s mind immediately turns to financing of government programs that support the elderly, like Social Security and Medicare in the United States, but that\’s only the beginning. For example, the US and other countries are going to face an enormous challenge in financing and providing long-term care options for the elderly. There are also likely to be hard-to-predict effects on the rate of economic growth: 

Having fewer individuals between the ages of 15 and 64 years might, however, have larger effects on GDP growth than what we have captured here. For example, having fewer individuals in these age groups might reduce innovation in economies, and fewer workers in general might reduce domestic markets for consumer goods, because many retirees are less likely to purchase consumer durables than middle aged and young adults. Developments such as advancements in robotics could substantially change the trajectory of GDP per working-age adult, reducing the effect of the age structure on GDP growth. However, these effects are very difficult to model at this stage. Furthermore, the impact of robotics might have complex effects on countries for which the trajectory for economic growth might be through low-cost labour supply.

These population shifts will alter perspectives on the magnitude of of countries around the world, too. For example, China is the now the most populous country in the world with a population of 1,412 million in 2017. However, China took dramatic steps to reduce fertility back in the early 1970s, later culminating in the \”one-child\” policy. Thus, the forecast is for China\’s population to peak in 2024 at 1,431 billion, and then fall by nearly half to 731 million in 2100. 

The decline in fertility for India started later. India\’s population is 1,380 million in 2017, but it will overtake China in the next few years, before peaking in these projections at 1,605 million in 2048–and then falling back to 1,093 million by 2100. 

Meanwhile, the fertility decline has barely started in Nigeria. Thus, Nigeria\’s current population of 206 million is forecast to rise continually through the rest of this century, and by 2100 the 790 million Nigerians would outnumber the population of China. 

I do not know if the problems of flat and falling population will ultimately be bigger or smaller than the problems of continually rising population, but the problems will be different ones, and it\’s none too early to start thinking about them. 

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The Curse of Knowledge: Bad Writing, Bad Teaching, and Bad Communication

The \”curse of knowledge\” refers to a bias documented in various psychology and behavioral economics studies. Once you know something, it can be hard to  remember what it was like before you knew it, or to put yourself in the shoes of someone who doesn\’t know it. It\’s a barrier to communication.

Iwo Hwerka provides a short readable overview of some of the evidence behind \”the curse of knowledge at the \”Towards Data Science\” blog (November 26, 2019). For example, one study asked a group of experience salespeople how long it would take an novice to learn to do certain tasks with a cellphone: their estimate were about twice as long as it actually took. 
One aspect of the curse of knowledge is what psychologists sometimes call \”hindsight bias.\” Say that you make a prediction, and later events show that your prediction was incorrect. Do you remember making the incorrect prediction? Or do you find some reason to believe that your prediction was actually correct all along? One of the early studies of this phenomenon was \”\”1 Knew It Would Happen\”: Remembered Probabilities of Once-Future Things\” by Baruch Fischhoff and Ruth Beyth (Organizational Behavior and Human Performance, 1975 13, 1-16).
For example, one of their sets of questions revolved around President Nixon\’s trip to China in 1972. Before Nixon went, they distributed a questionnaire to students asking them to estimate the probabilities of specific events: for example,  \”(1) The USA will establish a permanent diplomatic mission in
Peking, but not grant diplomatic recognition;(2)  President Nixon will meet Mao at least once; (3) President Nixon will announce that his trip was successful.\” Several weeks after the trip was done, they then gave the same students the same questions. They asked the students whether these events had actually happened, and asked them to remember what they had predicted. As it turns out, when students believed that an event had happened, they were more likely to believe that they had previously predicted it. 

Fischhoff and Ruth Beyth refer to this pattern as \”creeping determinism,\” by which they mean that once something has happened, we can\’t readily imagine it not happening. Scholars of events like wars (say, the US Civil War or World War II) or election outcomes often tend to emphasize that the outcome was not preordained. It could have gone the other way. There was an element of chance involved in the outcome. But once the event has happened, for many of us the nuance quickly falls away, and it becomes easy to explain–with the operation of full 20:20 hindsight–why the  outcome that happened was really almost certain to happen all along.  

The label of this bias seems to have originated in a 1989 Journal of Political Economy article, \”The Curse of Knowledge in Economic Settings: An Experimental Analysis,\” by Colin Camerer, George Loewenstein, and Martin Weber. They write that the term was suggested to them by Robin Hogarth. Their article is focused on a point that will immediately have occurred to economists: in most models, a party with more knowledge can in some way benefit from that knowledge over the party with less knowledge. But the curse of knowledge seems to suggest that the party with more knowledge won\’t be able to imagine not having that knowledge, and thus will not benefit from it (or at least will not benefit as much as expected). 
They set up a series of classroom experiments in which one group of students were given financial information about companies from 1970-1979, and then asked to make a prediction about those companies for 1980. Another group of students were given the same information from 1970-79, and then also were told the actual outcome for the companies in 1980. The set-up of the experiment then rewarded the second group of students (the ones who knew the outcome in 1980) for being able to estimate the predictions of the first group of students (the one who did not know the outcome in 1980). Could the students ignore the outcome they knew had happened, and instead just replicate the thinking of the other students, if there was a cash reward on the line? The answer is \”partly:\” \”[W]e find that market forces reduce the curse by approximately 50 percent but do not eliminate it.\”
Indeed, a different study found that those selling cars tend to overestimate how much consumers know about cars, and thus they underestimate how much ignorant customers would have been willing to pay for cars. 
The \”curse of knowledge\” leads to a variety of bad communications outcomes. The psychologist Stephen Pinker wrote: 

I once attended a lecture on biology addressed to a large general audience at a conference on technology, entertainment and design. The lecture was also being filmed for distribution over the Internet to millions of other laypeople. The speaker was an eminent biologist who had been invited to explain his recent breakthrough in the structure of DNA. He launched into a jargon-packed technical presentation that was geared to his fellow molecular biologists, and it was immediately apparent to everyone in the room that none of them understood a word and he was wasting their time. Apparent to everyone, that is, except the eminent biologist. When the host interrupted and asked him to explain the work more clearly, he seemed genuinely surprised and not a little annoyed. This is the kind of stupidity I am talking about. Call it the Curse of Knowledge: a difficulty in imagining what it is like for someone else not to know something that you know. 

Haven\’t we all been an audience of that kind, at one time or another? Maybe it was an academic lecture. Maybe it was your car mechanic telling you what was wrong with the engine, or your neighbor explaining their gardening tips, or a distant relative explaining their job to you. Pinker also writes: 

The curse of knowledge is the single best explanation of why good people write bad prose. It simply doesn\’t occur to the writer that her readers don\’t know what she knows–that they haven\’t mastered the argot of her guild, can\’t divine the missing steps that seem too obvious to mention, have no way to visualize a scene that to her is as clear as day. And so the writer doesn\’t bother to explain the jargon, or spell out the logic, or supply the necessary detail.

My guess is that the curse of knowledge goes well beyond these settings, and causes problems in all kind of communications between specialists in one area and others. In many companies, the communications between engineers and marketing departments is fraught with misunderstandings. When doctors and patients interact, can doctors really remember what it was like not to know about symptoms and health conditions? 

How does one fight the cognitive bubble that is the curse of knowledge? One of my own methods is to get comfortable with saying: \”I was wrong about that\” or \”I really didn\’t expect that.\” Admitting that you had inaccurate expectations is not a confession of weakness or gullibility: no one has a crystal ball for the future. After all, even if you are 90% confident that something will happen, you should expect to be wrong 10% of the time; indeed, Damon Runyon\’s law (as exposited by some characters in his 1935 story \”A Nice Price\”) holds that nothing between human beings deserves odds of more than three to one. 
Perhaps the deeper challenge is to be aware of how others perceive a given topic, including the likelihood that they may just not know (or care) much about it, so if you want to communicate with them, you need to reach out and meet them where they are, not where you are. 
I can\’t complain about the curse of knowledge: after all, as the editor of an academic journal, revising papers by authors afflicted to greater or lesser extents by the curse of knowledge is how I make my living. In some ways, this blog is an effort to avoid the curse of knowledge, too. 
Still, saying that one\’s professional goal is to avoid \”the curse of knowledge\” doesn\’t exactly sound complimentary. In a way, the \”curse of knowledge\” is misnamed, because it\’s not the knowledge that the problem; instead, one might more properly call it the \”curse of socially oblivious knowledge.\” There. Now I feel much better. 
conversableeconomist

The Planning Fallacy or How I Ever Get Anything Done

The \”planning fallacy\” refers to a psychological theory that people systematically underestimate how long it will take them to complete a given task. My work life is one long example of the planning fallacy. I set deadlines for myself, scramble to meet them, miss the earlier deadlines, rinse, lather, and repeat until the work somehow gets done. 
The context of planning provides many examples in which the distribution of outcomes in past experience is ignored. Scientists and writers, for example, are notoriously prone to underestimate the time required to complete a project, even when they have considerable experience of past failures to live up to planned schedules. A similar bias has been documented in engineers\’ estimates of the completion time for repairs of power stations (Kidd, 1970). Although this \’planning fallacy\’ is sometimes attributable to motivational factors such as wishful thinking, it frequently occurs even when underestimation of duration or cost is actually penalized. 

Roger Buehler, a researcher in this area, put it this way in a short explainer piece in 2019 (Character and Context blog of the Society for Personality and Social Psychology, \”The Planning Fallacy: An Inside View,\” May 30, 2019). 

The planning fallacy refers to an optimistic prediction bias in which people underestimate the time it will take them to complete a task, despite knowing that similar tasks have typically taken them much longer in the past. An intriguing aspect of the planning fallacy is that people simultaneously hold optimistic expectations concerning a specific future task along with more realistic beliefs concerning how long it has taken them to get things done in the past. When it comes to plans and predictions, people can know the past well and yet be doomed to repeat it. …
 For example, university students typically acknowledge that they have typically finished past assignments very close to their deadlines, yet they insist that they will finish the next project well ahead of the new deadline. Then, predictably, they go on to finish the next project (as usual) right at the deadline.

The planning fallacy is remarkably robust. It appears for small tasks like daily household chores (such as cleaning), as well as for large scale infrastructure projects such as building subways. It generalizes across individual differences in personality and culture, and it applies both to group and individual projects. For example, conscientious people often get things done well before procrastinators, but both groups typically underestimate how long it will take them to get things done.

Why does the planning fallacy happen? Kahneman and Tversky explained in 1977:  

The planning fallacy is a consequence of the tendency to neglect distributional data, and to adopt what may be termed an \’internal approach\’ to prediction, where one focuses on the constituents of the specific problem rather than on the distribution of outcomes in similar cases. The internal approach to the evaluation of plans is likely to produce underestimation. A building can only be completed on time, for example, if there are no delays in the delivery of  materials, no strikes, no unusual weather conditions, etc. Although each of these disturbances is unlikely, the probability that at least one of them will occur may be substantial. This combinatorial consideration, however, is not adequately represented in people\’s intuitions (Bar-Hillel, 1973). Attempts to combat this error by adding a slippage factor are rarely adequate, since the adjusted value tends to remain too close to the initial value that acts as an anchor (Tversky and Kahneman, 1974). The adoption of an \’external approach\’, which treats the specific problem as one of many, could help overcome this bias. In this approach, one does not attempt to define the specific manner in which a plan might fail. Rather, one relates the problem at hand to the distribution of completion time for similar projects. We suggest that more reasonable estimates are likely to be obtained by asking the external question \”how long do such projects usually last?\”, and not merely the internal question \”what are the specific factors and difficulties that operate in the particular problem?\”

At some level, this explanation strikes me as exactly correct. I am overly optimistic when thinking about how long it will take me to do a task because I assume that everything will go smoothly and that I won\’t be interrupted or distracted by other immediately pressing tasks. When I edit a paper, I think about the actual editing going smoothly, not about what happens when I hit a snag that takes a day or two to resolve or what happens when the rest of my life sneaks up on me and demands attention. 

I find myself asking: If wasn\’t subject to the planning fallacy, would I get things done on time? For me, daily motivation seems to be some combination of optimism and self-imposed stress. Both of these are embodied in the planning fallacy: that is, the planning fallacy gives me an optimistic view of how much progress I should be making, but then also stresses me when that progress doesn\’t happen.  If I instead started each day with: \”Things are going OK, more-or-less on schedule and it\’s 50:50, at best, whether I will get that paper edited by tomorrow,\” I\’m not confident that I would be happier or more productive.  
The end result of this dynamic is that I\’ve been the Managing Editor of an economics journal for 34 years, putting out quarterly issues more-or-less on time, but also feeling perpetually behind schedule. This seems a potentially unhealthy combination.
So I try to strike a balance. At some level, I know I\’m fooling myself most mornings. In some strictly rational part of my brain, I know the day\’s work isn\’t likely to go as well as I hoped, and I also know that I\’m not as behind as I feel. For me, the tradeoff is that when a task is completed like editing a paper, or when an issue of the journal is published, I get a jolt of surprise and happiness. In that same strict strictly rational corner of my brain, it\’s really not a surprise. After all, I\’ve been editing papers and putting out issues for a long time. But rather than seeing my life as a straightforward process of linear movement, plodding step-by-step to an expected outcome. I apparently prefer to see it as a mini-drama: I could do it! But I\’m not doing it as quickly as hoped! I\’m behind! But it\’s getting done! It\’s a race against the schedule! I\’ve done it! Then I start all over again. 
For 2021, I hope that you too can use the emotional energies unleashed by the planning fallacy to your advantage, both as an encouragement and a goad for daily effort, and to give you a sense of accomplishment at the result.
conversableeconomist

Lessons from World War II Statisticians: Survivorship Bias and Sequential Analysis

During World War II, a Statistical Research Group was formed to assist the war effort. W. Allen Wallis, who was Director of Research, tells the story in in \”The Statistical Research Group, 1942-1945\” (Journal of the American Statistical Association, 75:370, June 1980, pp. 320-330, available vis JSTOR): \”The Statistical Research Group (SRG) was based at Columbia University during the Second World War and supported by the Applied Mathematics Panel (AMP) of the National Defense Research Committee (NDRC), which was part of the Office of Scientific Research and Development (OSRD).\” Wallis was Director of Research. Some prominent members of the group included Milton Friedman, Harold Hotelling, Leonard Savage, and Abraham Wald. Indeed, Wallis writes: \”SRG was composed of what surely must be the most extraordinary group of statisticians ever organized, taking into account both number and quality.\”
The backstory goes like this. On behalf of himself and some other other Stanford statistics professors, Wallis wrote to the US government government in 1942, offering to help in some way with the war effort. He got a letter back from W. Edwards Deming, the engineer who later became a guru of industrial quality control, but who at this time was working in the US  Census Bureau. Deming wrote back \”with four single- spaced pages on the letterhead of the Chief of Ordnance, War Department,\” and suggested that the statisticians prepare a short course for engineers and firms in how statistical methods could be used for quality control. As Wallis dryly noted in 1980: \”The program that resulted from Deming\’s suggestion eventually made a major contribution to the war effort. Its aftermath, in fact, continues to make major contributions not only to the American economy but also to the Japanese economy.\”
By mid-1942, Wallis soon ended up moving to Columbia to run the Statistical Research Group. One bit of back-story is that, in those pre-computer days, \”the computing … was done by about 30 young women, mostly mathematics graduates of Hunter or Vassar. Some of the basic statistical tables published in Techniques of Statistical Analysis (SRG 1948) were computed as backlog projects when there was slack in the computing load.\”
The SRG carried out literally hundreds of analyses: how the ammunition in aircraft machine guns should be mixed; quality examination methods for rocket fuel; \”the best settings on proximity fuzes for air bursts of artillery shells against ground troops\”; \”to evaluate the comparative effectiveness of four 20 millimeter guns on the one hand and eight 50 caliber guns on the other as the armament of a fighter aircraft\”; calculating \”pursuit curves\” for targeting missiles and torpedoes. \”Statistical studies were also made of stereoscopic range finders, food storage data, high temperature alloys, the diffusion of mustard gas, and clothing tests.\”
Several of the insights from the SRG have had a lasting effect in terms of statistical analysis. Here, I\’ll focus on two of them: survivorship bias and sequential sampling. 
\”Survivorship bias\” refers to a problem that emerges when you look at the results of data, not realizing that some data points have dropped out over time. For example, suppose you look at the average rate of return from stock market mutual funds. If you just look at the universe of current funds, you will be leaving out funds that did badly and were closed or merged for lack of interest. Or suppose you argue in favor of borrowing money to attend a four-year college by citing evidence about higher salaries earned by college graduates, but you leave out the experience of those who borrowed money and did not end up graduating.  In health care, the issue of survivorship bias can come up quite literally in studies of trauma care: before drawing conclusions, such studies must of course beware of the fact that the data of those who suffered and injury but did not end up in the trauma care unit, or those who died of the injury before arriving at the trauma care unit, will not be included in the study. 
In a follow-up comment on the main article, appearing in the same issue, Wallis describes the origins of the idea of survivorship bias: 

In the course of reviewing the history of SRG, I was reminded of some ingenious work by Wald that has never seen the light of day. Arrangements have now been made for its publication, although the form and place are yet undecided. Wald wrote a series of memoranda on estimating the vulnerability of various parts of an airplane from data showing the number of hits on the respective parts of planes returning from combat. The vulnerability of a part (engine, aileron, pilot, stabilizer, elevator, etc.) is defined as the probability that a hit on that part will result in destruction of the plane (fire, explosion, loss of power, loss of control, etc.). The military was inclined to provide protection for those parts that on returning planes showed the most hits. Wald assumed, on good evidence, that hits in combat were uniformly distributed over the planes. It follows that hits on the more vulnerable parts were less likely to be found on returning planes than hits on the less vulnerable parts, since planes receiving hits on the more vulnerable parts were less likely to return to provide data. From these premises, he devised methods for estimating the vulnerability of various parts.

In other words, just looking at damage on the planes that returns would not be useful, but when adjusting for the fact that the returning planes are the ones that survived, it can offer real insights. Wald\’s 1943 manuscript \”A Method of Estimating Plane Vulnerability Based on Damage of Survivors,\” was published in 1980 by the Defense Technical Information Center. 

But clearly the most prominent statistical insight from the SRG was the idea of sequential analysis, which Wallis calls \”one of the most powerful and seminal statistical ideas of the past third of a century.\” In his 1980 article, he reproduces a long letter that he wrote in 1950 on the subject. Doing quality control testing on potential new kinds of ordnance required firing thousands of rounds. Apparently, a general observed to Wallis that if someone \”wise and experienced\” was on hand, that person could tell within a few thousand or even a few hundred rounds if the new ordnance was either much worse or much better than hoped. The general asked if there was some mechanical rule that could be devised for when the testing could be ended earlier than the full sample. Wallis noodled around with this idea, and expressed it this way in his 1950 letter: 

The fact that a test designed for its optimum properties with a sample of predetermined size could be still better if that sample size were made variable naturally suggested that it might pay to design a test in order to capitalize on this sequential feature; that is, it might pay to use a test which would not be as efficient as the classical tests if a sample of exactly N were to be taken, but which would more than offset this disadvantage by providing a good chance of terminating early when used sequentially. 

Wallis remembers a series of conversations with Milton Friedman on the subject, after Friedman joined the SRG in 1943. They made some progress in thinking about tradeoffs between sample size and statistical power and what is learned along the way. But they also ended up feeling that the discovery was potentially important to the war effort and that they weren\’t well-equipped to solve it expeditiously. Wallis remembers a momentous walk:  

We finally decided to bring in someone more expert in mathematical statistics than we. This decision was made after rather careful consideration. I recall talking it over with Milton walking down Morningside Drive from the office to our apartment. He said that it was not unlikely, in his opinion, that the idea would prove a bigger one than either of us would hit on again in a lifetime. We also discussed our prospects for being able to work it out ourselves. Milton was pretty confident of our (his?) ability to squeeze the juice out of the idea, but I had doubts and felt that it might go beyond our (my!) depth mathematically. We also discussed the fact that if we gave the idea away, we could never expect much credit, and would have to take our chances on receiving any at all. We definitely decided that even if the credit situation turned out in a way that disappointed us, there would be nothing to do about it, 

They ended up getting permission to talk with Abraham Wald on the subject, which wasn\’t easy, because Wald\’s time was \”too valuable to be wasted.\” 

At this first meeting Wald was not enthusiastic and was completely noncommital. … The next day Wald phoned that he had thought some about our idea and was prepared to admit that there was sense in it. That is, he admitted that our idea was logical and worth investigating. He added, however, that he thought nothing would come of it; his hunch was that tests of a sequential nature might exist but would be found less powerful than existing tests. On the second day, however, he phoned that he had found that such tests do exist and are more powerful, and furthermore he could tell us how to make them.

It took a few more years for the underlying theory to be worked out, and Wald\’s book on Sequential Analysis is published in 1947. But the roots of the idea go back to an army general noting that someone with expert and informed judgment could sometimes make a faster decision than the existing quality control algorithms.
The SRG is an example of how ideas and statistical methods invented out of immediate practical necessity–like new methods of quality control–had longer-run powerful results. As this year draws to a close, I find myself wondering if some of the ideas and methods that have been used to create vaccines and to push back against COVID-19 will find broader applicability in the years ahead, perhaps in areas reaching well beyond health care.