Reflections on Editing from the Podhoretz Clan, Father and Son

The Journal of Economic Perspectives, where I have worked as Managing Editor since 1986, is in many ways quite different from Commentary magazine. JEP is sponsored by the American Economic Association and aimed primarily at an audience of academic economists and their students, along with a wider circle of interested policy-makers and journalists. Commentary was sponsored by the American Jewish Committee for its first 60 years, although it has now been a free-standing journal for the last 15 years.  The current editor describes its purpose as "defense of the West and its institutions, defense of Israel, serving as a bulwark against anti-Semitism, and reflecting in its pages the cultural legacy of the West ..." 

Commentary is now commemorating its 75th anniversary. Norman Podhoretz was the editor from 1960 to 1994, and his son John has been the editor since 2009. The two of them sat down for a conversation which is published in the November 2020 issue.  If you want details of Commentary's history or memorable stories about what was like editing Daniel Patrick Moynihan or Jeane Kirkpatrick, or what it was like jousting with the American Jewish Committee for editorial independence, check out the interview as a whole. As a hands-on editor myself, I found myself interested in their comments on what it means to edit. Here's a selection:

JOHN: People don’t really understand what editors do, which is completely understandable. I don’t know what a stereo technician does when he opens up a stereo to fix it. Editing is in part a technical process. You commission an article, you get the article back, and it’s rare that an article doesn’t need some form of massaging, grammatical or thematic. It’s missing points, or it elides them, or it doesn’t have good transitions. So, like a mechanic, you try to repair it. ... Why do you think some people are good at all this and some people aren’t? Is it a skill that can be learned?

NORMAN: Good editors, really good editors, are very rare, in fact even rarer than good writers. It’s a special kind of talent because it takes two qualities that rarely go together in the same person. On the one hand, great arrogance, and on the other hand, great selflessness. The arrogance lies in the fact that you, the editor, thinks he knows better than the author, who is usually a specialist, on how to say what it is he wants to say. The humility or selflessness, which is very important, is that you are willing to lend your talents to someone else’s work without getting any credit for it. ...

JOHN: The most self-confident, intellectually self-confident writers in my experience have very little problem with being edited. When editing triggers a defensive or hostile response, it’s because the writer himself is insecure and believes that he is coming under attack or criticism. ...

JOHN: Line editing is the act of going through an article sentence by sentence or paragraph by paragraph and improving the article that way—even to the point of reorganizing the piece if necessary because the argument is flipped around or points are made that would be better made later, whatever.

NORMAN: I always described it as putting the manuscript under a microscope. And that’s where the defects would reveal themselves. But the other aspect of editing is ideas. A good editor can be a good editor even without technical skills, but good because he or she has some sense of what’s going on out there, what’s relevant, what’s interesting. Not so much to the audience, but to himself, and taking himself as the audience, what would I like to read about and what would I like to hear. And that’s different. ..

NORMAN: That’s why heavy editing was necessary. Without it, it would have been impossible to maintain that combination of, what shall I say, authority, sophistication, and accessibility. It won’t happen if you just sit back and publish what is sent to you. It really has to be created.

For the (perhaps vanishingly small number of) readers of this blog who would like some additional thoughts about editing, here you go:   

• "Stephen King: `The Editor Is Always Right ... to Edit is Divine'" (August 21, 2019)
• "Editor Hell" (November 30 2013) 
• "Albert J. Nock on Three Rules of Editorial Policy" (August 3, 2018)
• "Christopher Walton, William Law, and the 296-Page Footnote" (August  17, 2020) 
• "I Surpass Keynes: 33 Years Plus One" (August 4, 2020)
• Taylor, Timothy. 2012. "From the Desk of the Managing Editor." Journal of Economic Perspectives, 26 (2): 27-40.

Some 12-Grader Education Inequalities by Race

One of the most concerning US racial inequalities is the one embodied in K-12 academic preparation, which often sets the stage for additional education, jobs, income levels, the neighborhood where you live, and more. The National Assessment of Educational Progress is often referred to as the "Nation's Report Card." It's a nationally representative test. The results for 12th graders who graduated in 2019 are now available. 

On the math side, the median score (that is, half scored higher and half scored lower) was 150. The 25th percentile score was 125, and the 75th percentile score was 150. Here's the breakdown by race/ethnicity. 

As the chart shows, average math scores are up for some groups. But the gap between white and black scores is just a bit larger in 2019 than in 2005.  Remember, an entire cohort of K-12 student who were just entering school about 2005 have now graduated. The gaps that were apparent in 2005 persist. 

For reading scores, the median was a score of 288, with the 25th percentile at 258 and the 75th percentile at 315. The data for reading is not presented in the same way as for math (!), but it's possible to go through some tables and create a similar graph for 12th grade reading scores by race/ethnicity.

Again,  average reading scores are up for some groups. But the gap between white and black scores is just larger in 2019 than in 2005; indeed, a different figure going back to 1992 shows that the white/black gap in scores in 2019 is the largest during this time. 

There is of course a considerable body of research that seeks to explore and explain these gaps. A common finding in this research is that there are strong correlations between levels of parental income and education and student achievement.  Here are a couple of examples that crossed my desk.

Kenneth A. Shores, Ha Eun Kim, and Mela Still, "Categorical Inequality in Black and White: Linking Disproportionality across Multiple Educational Outcomes" (American Educational Research Journal, October 2020, 57:5, pp. 2089–2131). From the abstract: "We characterize the extent to which Black-White gaps for multiple educational outcomes are linked across school districts in the United States. Gaps in disciplinary action, grade-level retention, classification into special education and Gifted and Talented, and Advanced Placement course-taking are large in magnitude and correlated. Racial differences in family income and parent education are strikingly consistent predictors of these gaps, and districts with large gaps in one outcome are likely to have large gaps in another. Socioeconomic and segregation variables explain 1.7 to 3.5 times more variance for achievement relative to non-achievement outcomes." 

Sean F. Reardon,  Ericka Weathers,  Erin Fahle, Heewon Jang, and Demetra Kalogrides have written 
"Is Separate Still Unequal? New Evidence on School Segregation and Racial Academic Achievement Gaps" (Stanford Center for Education Policy Analysis, September 2019).  From the abstract: "In this paper we estimate the effects of current-day school segregation on racial achievement gaps. We use 8 years of data from all public school districts in the U.S. We find that racial school segregation is strongly associated with the magnitude of achievement gaps in 3rd grade, and with the rate at which gaps grow from third to eighth grade. The association of racial segregation with achievement gaps is completely accounted for by racial differences in school poverty: racial segregation appears to be harmful because it concentrates minority students in high-poverty schools, which are, on average, less effective than lower-poverty schools. Finally, we conduct exploratory analyses to examine potential mechanisms through which differential enrollment in high-poverty schools leads to inequality. We find that the effects of school poverty do not appear to be explained by differences in the set of measurable teacher or school characteristics available to us."

This post isn't the place to try to solve America's K-12 education issues, which are of long standing. But I'll add a few thoughts. 

1) It's not a good thing to have large and persistent gaps in educational performance across groups. It perpetuates previous inequalities. Moreover, a country that does a better job of building its human capital will also be a country with higher levels of growth and wealth.

2) There are surely places in the vast K-12 system where additional funding would help. But by international standards, the US K-12 education system is not performing especially well. However, the per-student spending is pretty much what would be expected by international standards, given the US level of per capita GDP.

3) There's a line of research which suggests that what students often need is to build up the personal skills and resources that they need to be successful at school. Christopher L. Quarles,  Ceren Budak and Paul Resnick make this argument about community college students in "The shape of educational inequality" (Science Advances,  July 15, 2020,)

4) Many of our social arguments about education these days seem to revolve around  issues like about admissions criteria for selective colleges: Harvard, Yale, Michigan, Texas, Berkeley and others. But the problem of racial inequality in the US education system is not that all the problems are pretty much fixed, except for fairness of admissions at some top universities.  Indeed, the prominence given to these arguments about admissions patterns at selective universities sometimes seems to me like a way of avoiding racial inequalities that have now persisted for multiple generations of families.  

Return of the Tontine?

I first learned about tontine contracts as a child reading from Agatha Christie mysteries, like 4.50 From Paddington and The Pale Horse. A "tontine" is a financial contract where a certain sum of money is set aside and invested for the benefit of a group of people--but if some of those people die, their share of the funds pass to the survivors. If the group is relatively small and the members of group are known to alll, it's an excellent set-up for a murder mystery. 

But a tontine-style contract might also has some genuine practical advantages in retirement planning. J. Mark Iwry, Claire Haldeman, William G. Gale, and David C. John tell the story in "Retirement Tontines: Using a Classical Finance Mechanism as an Alternative Source of Retirement Income" (Brookings Institution, October 2020). 

For those, like me, who tend to associate tontines with fictional plot-lines, it's perhaps useful to point out that for several centuries they were useful financial products--not for small groups who would then try to murder each other, but for larger groups who did not know each other. Here's a working definition of a tontine: "[T]ontine is an investment scheme in which so called shareholders create a common investment pool and derive some form of profit or benefit (usually financial) while they are alive. After the death of the shareholder his/her share gets split between the surviving shareholders in the pool and is not subject to inheritance rights. Tontine investment comes to an end when the number of surviving shareholders in the pool reaches a previously agreed on, small number."

The idea of a tontine contract goes back to a 1653 proposal from Lorenzo de Tonti, a 17th century Italian financier, who was advising the French government on how to borrow money. His notion was for the government to sell shares in a fund. The fund was to be divided by age groups, with each age group receiving a different rate of interest. As people in a certain age group died, their payments would be reallocated to the surviving members of that age group. However, the first government to enact something like Tonti's proposal was Holland in 1670, and France did not try a tontine contract until 1689, five years after Tonti's death. 

Versions of tontine-based contracts were widely used for centuries, and were a basis for US life insurance markets in the late 1800s. As Iwry, Haldeman, Gale, and John point out (footnotes omitted): 

Capital investment tontines were popular revenue-raising schemes in Europe from the 17th to the 19th century, helping governments and monarchies raise money for public works and wars. They even made inroads into the United States, as Alexander Hamilton proposed a capital investment tontine to pay off Revolutionary War debt. Although the federal government declined to pursue this option, many communities in the Colonial Era used tontines to finance local investments, and a capital investment tontine financed the construction of the original home of the New York Stock Exchange in the Tontine Coffeehouse. Tontines enabled governments to pay lower interest rates than they had to offer on other types of investment because surviving investors received not only the promised interest rate but also mortality credits, in exchange for giving up the right to pass their investment interest on to their heirs.

The life-insurance style tontine came to prominence in the U. S. in the late 1800s. Under these arrangements, policy holders paid premiums during a term (typically 20 years). If the policy holder died during the term, their beneficiaries would receive a payout. Policy holders who survived the term were entitled to a life annuity or equivalent lump-sum payout funded by the remaining pooled premiums from their deceased counterparts after insurance payouts to their beneficiaries as well as premiums from those whose policy had lapsed for failure to make a required premium payment at any point. This product drove the broad uptake of life insurance in the U.S. in the late 1800s and proved to be an effective vehicle for accumulating retirement savings before the advent of Social Security or private pensions. By 1900, two thirds of life insurance policies in the U.S. were tontine-style products, accounting for 7% of national wealth.

The popularity of life-insurance-style tontines, which essentially left large amounts of capital in the hands of insurers for decades, combined with a lack of regulation and oversight, made these policies ripe for corruption. The 1905 Armstrong Commission investigation in New York uncovered substantial embezzlement and misuse of funds, as well as unduly draconian triggers for lapse or forfeiture of the policy, leading New York lawmakers to effectively outlaw life insurance tontines as they then existed. This essentially ended the use of tontine-style products nationwide, since New York had regulatory authority over 95% of the national insurance market.

But the fact that tontine-style products were poorly regulated in 1905 doesn't mean that the idea itself is without merit. The Iwry, Haldeman, Gale, and John team point out that it's possible to design a "natural" or "constant-payout" tontine contract: instead of the most extreme murder-mystery version of a tontine, where all the payout goes to a lone survivor, the investment would be structured so that it pays out a certain amount every year (at least after a certain age). The total amount in the fund would decrease over time, as these payouts occurred, but as some members of the fund died, their payments would be redistributed to survivors. 

This kind of tontines contract is one possible answer to key difficulty of retirement planning is to address the danger of outliving your assets (at least for all of us who don't have a pension plan that will guarantee our payments for life). One of the best-known solution is to use some of your assets to purchase an annuity, which will then make payments from some predetermined age for the rest of your life.  

Perhaps the key difference between an annuity and a tontine is that when you buy an annuity contract, you are counting on the firm that sold you the contract to be able to pay off on its promises in the future. There is a thicket of rules and regulations applying to firms that sell annuities, all designed to make sure the firm will pay off in the future, but also all imposing costs on the transaction. In contrast, the amount of money put into a tontine is fixed. If the investments from the tontine went very poorly, the payments from the tontine would automatically drop. A tontine doesn't offer a guarantee of how much will be paid, like certain kinds of annuities (or a defined benefit pension). But precisely because it doesn't offer a guarantee, it can be regulated in much different and less costly ways, and for that reason it could offer both a lifetime stream of income and higher returns than many annuities. 

Iwry, Haldeman, Gale, and John write:

While commercial annuities guarantee a specified income for life, tontine pooling offers more expected income with less but still meaningful protection at what should be a lower cost. They would not require the charges insurers need to impose or the reserves they need to maintain to cover their annuity payment guarantees and insurance against systematic longevity risk. Tontines would also require less and less costly regulation. ...

[T]he U.S. is lagging the rest of the world in tontine-like arrangements. European Union member states permit tontines, usually as a supplement to government-paid or occupational benefits. The pension for former employees of SwissAir is structured as a tontine. In Sweden, the national pension system redistributes the accrued pension wealth of the deceased among all survivors of the same age cohort. In Japan, some workers pay into a tontine-like annuity from their 50s until retirement, when they begin receiving payouts to supplement their national pensions; when they die most of what they have contributed is reallocated among the other policy holders. Tontine-style funds are explicitly legal in the UK. Canada paved the way for tontine-style products when its 2019 budget proposed legislation to permit them under the name Variable Payment Life Annuities. In South Africa, a tontine style investment designed to improve retirement security for poor workers has begun enrolling participants.

In short, a lot of the regulatory details have already been worked out, and tontine-style contracts are working in other places. The US could steal a page from the Canadian public relations playbook and refer to these contracts as Variable Payment Life Annuities, and then proceed. 

The Need for Large Firms in Developing Countries

The US economy had about 6 million firms in 2017 (the most recent data). About 20,000 of those firms employed more than 500 people, and those 20,000 firms (about one-third of 1 percent of the total) accounted for 53% of all US employment by firms. Another 90,000 firms employed between 100 and 499 workers, and those 90,000 firms (about 1.5% of the total) accounted for another 14% of all US employment by firms. The job totals here don't take into account employment by the public sector and by nonprofits. But the point I'm making is that an important social function of firms is to coordinate production in a way that provides a bridge between workers and suppliers on one hand and the desires of customers on the other hand. In high-income economies, large firms coordinating the efforts of hundreds of workers play a major role in this activity. 

But many lower-income countries have only very small numbers of larger firms, which is one of the factor hindering their development. A group of World Bank researchers--Andrea Ciani, Marie Caitriona Hyland, Nona Karalashvili, Jennifer L. Keller, Alexandros Ragoussis, and Trang Thu Tran--address this topic in "Making It Big: Why Developing Countries Need More Large Firms" (September 2020). 

The available evidence on firm size, employment, and productivity in low- and middle-income countries is sometimes sketchy, so the report pulls together data, studies, and comparisons from a range of sources. The evidence strongly suggests benefits from large firms:

This report shows that large firms are different than other firms in low- and middle-income countries. They are significantly more likely to innovate, export, and offer training and are more likely to adopt international standards of quality. Their particularities are closely associated with productivity advantages—that is, their ability to lower the costs of production through economies of scale and scope but also to invest in quality and reach demand. Across low- and middle-income countries with available business census data, nearly 6 out of 10 large enterprises are also the most productive in their country and sector.

These distinct features of large firms translate into improved outcomes not only for their owners but also for their workers and for smaller enterprises in their value chains. Workers in large firms report, on average, 22 percent higher hourly wages in household and labor surveys from 32 low- and middle-income countries—a premium that rises considerably in lower-income contexts. That is partly because large firms attract better workers. But this is not the only reason: accounting for worker characteristics and nonpecuniary benefits, the large-firm wage premium remains close to 15 percent. Besides higher wages—which are strongly associated with higher productivity—large firms more frequently offer formal jobs, secure jobs, and nonpecuniary benefits such as health insurance that are fundamental for welfare in low- and middle-income countries. 

Using various measures, the authors argue that there is a pattern of a "truncated top" in the distribution of firm sizes in low- and middle-income countries. 

Smaller and lower-income markets tend to host smaller firms. But even in relative terms, there are too few larger firms in these countries relative to the size of the economy and the number of smaller firms—there is a “missing top.” In 2016, for example, for every 100 medium-size firms, more than 20 large firms were operating in the nonagricultural sector in the United States, as opposed to less than 9 in Indonesia—a lower-middle-income country with roughly the same population. A closer study of the firm-size distribution in country pairs suggests that what is missing are the larger of large firms—that is, those with 300+ employees—as well as the more productive and outward-oriented firms. ... The evidence suggests that larger firms employing more than 300 workers are systematically underrepresented in the lower-income countries under observation. In Ethiopia, for example, large firms have a 7-percentage-point lower share of employment than what is predicted by the optimal distribution, while in Indonesia, the gap is 4.6 percentage points, corresponding to a rough estimate of 230,000 missing jobs in manufacturing. 

Why are there fewer large firms than expected, and how might low- and middle-income countries generate more large firms? As the report points out, there are basically four ways in which a large firm forms: "foreign firms creating new affiliates, other large firms spinning off new ventures, governments, and entrepreneurs."

Given that the lack of large firms is the problem in the first place, spin-offs from existing large firms is not likely to address the problem. Having governments of low- and middle-income countries start large firms hasn't usually worked well. 

To fill the “missing top,” governments have often resorted to the creation of state-owned enterprises (SOEs). These firms rarely deliver the benefits one might expect from their scale. First, it has proven difficult to establish governance sufficiently independent of the state to operate in a commercial manner. SOEs often pursue a mix of social and commercial objectives, which are used to justify regulatory protection from competition. It is also difficult for governments to manage the conflict of interest that arises between exposing SOEs to competition, on the one hand, and the risk of job losses and changes in product offerings that come with this exposure, on the other. As a result, SOEs in lower-income economies rarely emulate the productivity and dynamism of privately owned firms: they are three times less likely to be the most productive firm in their country and sector.

The remaining options are to have foreign firms start a larger company, or to have domestic entrepreneurs build one. But many low-income countries have set up rules and regulations that make it hard for larger firms to operate. For example, there are often a set of taxes, regulations, and rules about employment and wages that only apply to firms larger than certain employment size--often set around 100 workers or in countries even less. Foreign firms are often blocked. There are often a variety of rules aimed at protecting small incumbent firms from competition, making it hard for larger firms to get a foothold. My own sense is that governments in low- and middle-income countries often tend to view large firms as an alternative power structure and (not without reason) as a threat to their own political power. For a detailed explanation of how this dynamic plays out in Mexico, a useful starting point is my post on "Mexico Misallocated" (January 24, 2019).

To put it another way, larger firms have some natural advantages in productivity, at least in certain contexts, but in many low- and middle-income countries, the sum total of government actions counterbalances and offsets that advantage. Thus, the World Bank researcher suggest that policies to encourage larger firms (and remember, we're only talking here about firms with 100 or few hundred employees, not giant global multinationals) mostly involve existing governments getting out of the way: 

In low-income countries, governments can achieve that objective with simple policy reorientations, such as breaking oligopolies, removing unnecessary restrictions to international trade and investment, and putting in place strong competition frameworks to prevent the abuse of market power. Opening markets to competition benefits entrants of all sizes. In practice, however, regulation is often designed for the benefit of large incumbents using statutory monopolies and oligopolies, preferential access to natural resources and government contracts, or barriers to foreign competitors that rarely enter at small scale in new markets. The entry of more large firms to compete with incumbents would aim to disperse power by any one firm. There is a long way to go in this regard: regulatory protection of incumbents in lower-middle-income countries is more than 60 percent greater, on average, than the level observed in high-income countries.

Beyond the entry point, operational costs associated with a range of government policies can greatly influence investors’ decisions to establish new, large firms. Large firms in low- and middle-income countries are significantly more likely than small firms to report customs operations, the court system, workforce skills, transportation, and telecommunications infrastructure as constraining their operations. Bread-and-butter reforms that aim to improve market regulation, trade processes, and tax regimes and to protect intellectual property rights stand to make a difference in that respect, even when these long-term reforms do not have large-firm creation as the objective.

The need for expanding employment into jobs with decent pay is a huge topic for many low- and middle-income countries of the world--from India and south Asia to sub-Saharan Africa, from China to the countries of the Middle East. That policy goal is not likely to be achievable without a surge in large firms in these countries. 

Thinking about Better Graphs and Use of Color

When I started working as the Managing Editor of the Journal of Economic Perspectives back in 1986, making figures for academic articles was still relatively expensive. The changeover to software-generated figures was getting underway, but with lots of hiccups--for example, we had to purchase a more expensive printer that could produce figures as well as text. At my home base at the time,  Princeton University still employed a skilled draftsman to create beautiful figures, using tools like plotting points and tracing along the edge of a French curve, which have now gone the the way of the slide rule.  

Generating figures has now become cheap: indeed, I see more and more first drafts at my journal which include at least a dozen figures and often more. I sometimes suspect that the figures were generated for slides that can be shown during a live presentation, and then the paper was written around the series of figures. Economists and other social scientists, like it or not, need to know something about what makes a good graph.  Susan Vanderplas, Dianne Cook, and Heike Hofmann give some background in "Testing Statistical Charts: What Makes a Good Graph?" (Annual Review of Statistics and Its Application, 2020, subscription required). 

With a good statistical graph or figure, readers should be able to read information or see patterns with reasonable accuracy (although people have a tendency to round up or down). As the authors write (citations omitted): 

A useful starting point is to apply gestalt principles of visual perception, such as proximity, similarity, common region, common fate, continuity, and closure, to data plots. These principles are useful because good graphics take advantage of the human visual system’s ability to process large amounts of visual information with relatively little effort.

The authors discuss research on the extent to which certain graphs meet this goal: for example, one can use "think-aloud" methods where subjects talk about what they are seeing and thinking about as they look at various figures, or eye-tracking studies to find what people are actually looking at. They also focus on statistical charts, not on the production of more artistic "infographics." Along with general tips, I've been interested in recent years about the use of color. 

The authors argue that when using a range of colors, best practice is to use a neutral color in between a range of two other colors. They also point out that the human eye does not discern gradations in all colors equally well: "It is also important to consider the human perceptual system, which does not perceive hues uniformly: We can distinguish more shades of green than any other hue, and fewer shades of yellow, so green univariate color schemes will provide finer discriminability than other colors because the human perceptual system evolved to work in the natural world, where shades of green are plentiful." In terms of human physiological perceptions, " a significant portion of the color space is dedicated to greens and blues, while much smaller regions are dedicated to violet, red, orange, and yellow colors. This unevenness in mapping color is one reason that the multi-hued rainbow color scheme is suboptimal—the distance between points in a given color space may not be the same as the distance between points in perceptual space. As a result of the uneven mapping between color space and perceptual space, multi-hued color schemes are not recommended." In addition, some people are color-blind: the most common kind is an inability to distinguish between red and green, but there are also people who have difficulties distinguishing between blues and greens, and between yellows and reds. 

Given these realities, what range of color is recommended? The bottom purple-orange gradient both circles through a neutral color and is also distinguishable by people with any sort of color-blindness. Of course, this doesn't mean it should always be used: people may have mental associations with colors (say, blue associated with cold) that make it useful to use other colors. But it's worth remembering. 

For an example of how a better graph can help with perception, consider this example. The graph is looking at notifications for tuberculosis in Australia in 2012, divided by age and gender. The top panel shows gender side-by-side for each age group, with two colors used to distinguish gender. The bottom panel shows age groups side-by-side for each gender, with five colors used to distinguish ages. The authors argue that "common region" arguments make it easier for most viewers get information from the top figure. 

Finally, here's an example of a graph that is "interactive," even though it is static.  The graph shows the average number of births on each day of the year. Notice that although there's a lot of shading, it's in green so the distinctions are easier to perceive. Key takeaways stand out easily: like more babies born in summer than in winter, and fewer births around holidays like July 4, Thanksgiving, Christmas, and New Year's. Also, the natural tendency for a reader is to check out their own birthday--which is what makes the figure interactive. It's easy to imagine other kinds of figures--by age, gender, location, income, education, and so on--that might cause readers to interact in a similar way by checking out the data for their own group.

For those who want to dig deeper, the article has lots more examples and citations. For more on graphic presentations of data, a useful starting point from the journal where I work as Managing Editor is the paper by Jonathan A. Schwabish in the Winter 2014 issue. "An Economist's Guide to Visualizing Data." Journal of Economic Perspectives, 28 (1): 209-34. From his abstract: "Once upon a time, a picture was worth a thousand words. But with online news, blogs, and social media, a good picture can now be worth so much more. Economists who want to disseminate their research, both inside and outside the seminar room, should invest some time in thinking about how to construct compelling and effective graphics."

For some history of graphs, I've blogged on "William Playfair: Inventor of the Bar Graph, Line Graph, and Pie Chart" (August 9, 2017), and on "Florence Nightingale: Innovator in Statistics and Data Presentation" (May 12, 2020). 

Will China Be Caught in the Middle-Income Trap?

The "middle-income trap" is the phenomenon that once an economy has made the big leap from being a lower-income country to being a middle-income country, then it may find it difficult (although not impossible) to make the next leap from being middle-income to high-income. Matthew Higgins considers the situation of China in "China’s Growth Outlook: Is High-Income Status in Reach?" (Federal Reserve Bank of New York, Economic Policy Review, October 2020, 26:4, pp. 68-97). 

Higgins provides the basic backdrop for China's remarkable economic growth in the last four decades. 

China’s growth performance has been remarkable following the introduction of economic reforms in the late 1970s. According to the official data, real GDP growth has averaged 9.0 percent since 1978. ... Rapid economic growth has led to a similar increase in living standards, lifting China out of poverty and into middle-income status. According to official figures, real per capita income has risen by a factor of 25 since 1978. Annual per capita income now stands at about $16,100 measured at purchasing power parity, in “2011 international dollars.” ... This places China at roughly the 60th percentile of the global income distribution, though still slightly below 30 percent of the U.S. level.

A first question, of course, is whether we really believe the official growth numbers, and the answer is "not quite." One difficulty with huge growth numbers over sustained periods of time is that you can project backwards to what the original level of income must have been at the start of the process. Thus, if current Chinese real per capita income is $16,100, and the growth rate has been 9% for (say) 40 years, then the real per capita income for China would have been about $500 before the reforms started. As Higgins spells out the implication: 

Indeed, real per capita income [in China] at the start of the decade [the 1980s] would have been below that of most countries in sub-Saharan Africa as well as neighbors such as Bangladesh, Laos, and Myanmar. Although China was clearly a poor country at the time, few would have rated it as one of the poorest. Such a ranking is also inconsistent with data on life expectancy, literacy, and other quality-of-life indicators. Growth rates from the Penn World Table, more plausibly, place China at roughly the 30th percentile of the global income distribution in the early 1980s, ahead of most countries in sub-Saharan Africa but still behind neighbors such as Indonesia, the Philippines, and Thailand.

For comparison, here are China's official growth rates and those from the Penn World Tables: 

As you might expect, there's been an ongoing controversy for a couple of decades now over what numbers are most accurate, which I will sidestep here (although other papers in this issue of the Economic Policy Review do address them). I'll just point out that if you start adjusting numbers for one country, you need to adjust them for all countries, and when all is said and done, it remains true that China has had decades of extraordinary growth and has become a middle-income economy. 

Here, I want to focus on the question of what it would take for China to become a high-income economy, and thus not to succumb to the middle-income trap. As the figure shows, China's growth rates were slowing down even before the trade wars and now the pandemic. Higgins looks at past patterns of countries moving from middle-income to high-income status and writes: 

Our middle-income category includes countries with per capita incomes at 10 to 50 percent of the U.S. level (at current purchasing power parities); our high-income category includes anything above that. ... Out of 124 countries, 52 qualified as middle-income in 1978 and 49 in 2018. Of the original cohort of 52 middle-income countries, just 8 had advanced to high-income status by 2018.

Of course, if China can maintain a 6% growth rate for the next few decades, it will keep catching up to high-income countries like the US, Japan, Canada, and nations of western Europe. But for most countries reaching middle-income status, sustaining such high growth rates for additional decades doesn't usually happen. For example, Higgins point out that after Japan had several decades of rapid growth and reached China's current level of per capita GDP back in 1976, Japan's growth rate steadily dropped over time, and has been at about 1% per year in recent decades. Or after South Korea had several decades of rapid growth and reached China's current level of per capita GDP back in 1994, its growth rate has steadily decline to less than 3% per year. 

How likely is continued rapid growth for China? Higgins digs down into the underlying sources of growth for some insights. Thus, one source of economic growth is known as the "demographic dividend," which happens when a country has a rising share of its population in the prime working years from age 20-64: "According to U.N. figures, China’s working-age population is expected to
decline by about 12 percent over the next twenty years even as the total population rises
slightly." As the figure shows, the share of China' population that is working-age started declining af ew years ago: for other rapid-growth cases like Japan or the east Asian "tiger" economies, the working-age share of the population was still rising when they hit China's current level of per capita GDP. 

Another issue is that other examples of rapid growth, like Japan, South Korea, and the other east Asian "tigers" kept their growth rates high in part with very high levels of physical capital investment. But China has already gone through a stage of extremely high levels of investment, and is now trying to shift to an economy in which growth is based more on human skills/education, technology, and services.  

On the other side, because China's real per capita GDP has only reached about 30% of the US level, there is certainly still room for growth. Higgins writes: "Prospects for rapid growth in China are buoyed by two key factors: the country’s distance behind current global income leaders and its relatively low rate of urbanization. These factors could provide scope for continued rapid growth through `catch-up' effects and structural transformation. ... China’s unfinished structural transformation leaves it with plenty of room to run. How fully China exploits this potential will depend largely on its own policies."

Higgins points out one set of "institutional" policies as measured by the World Bank. The rankings for these policies have been adjusted so that the average for the 121 countries included is set at zero, and the standard deviation is set at 1.0. On five of the six measures, China is below the global average. On all six measures it is well below the high-income countries of the world. One can of course quarrel with the details of how such measures are calculated, but the overall pattern is clear.  

Perhaps the fundamental challenge for China is to recognize that the past 40 years of economic growth were an excellent start to becoming a high-income country, but really only a start, and additional future growth will require even more sweeping and additional changes to the economy and society.  

As noted above, this issues of Economic Policy Review has a group of articles on "China in the Global Economy." The four articles are: 

• "How Stable Is China’s Growth? Shedding Light on Sparse Data," by Hunter L. Clark, Jeffrey B. Dawson, and Maxim L. Pinkovskiy
• "Alternative Indicators for Chinese Economic Activity Using Sparse PLS Regression," by Jan J. J. Groen and Michael Nattinger
• "China's Growth Outlook: Is High-Income Status in Reach?" by Matthew Higgins
• "The Impact of Foreign Slowdown on the U.S. Economy: An Open Economy DSGE Perspective," by Ozge Akinci, Gianluca Benigno, and Paolo Pesenti

interview with Sandra Black: Education Outcomes and A Stint in Politics

Douglas Clement has an interview with Sandra Black in the Fall 2020 issue of For All, a publication of the  Opportunity & Inclusive Growth Institute at the Minneapolis Federal Reserve. The title sums up the topics: "Seeing the margins: An interview with Columbia University economist Sandra Black

Sandra Black on education, family wealth, her time at the White House, COVID-19, and the cost of bad policy." Like a lot of the interviews done by Clement, the interviewee is encouraged to describe the basic insight behind some of their own prominent research, which in turn gives a look into how economists think about research. 

For example, Black wrote an article back in 1999 on the subject of how much value parents place on living in a school district with higher test scores (Sandra E. Black, "Do Better Schools Matter? Parental Valuation of Elementary Education,"  Quarterly Journal of Economics, 114: 2, May 1999, pp. 577–599). Here's how Black describes the issue and her approach: 

Let’s look at how parents value living in a house that is associated with a better school. That’s an indirect value of the school—what the parents are willing to pay to have the right to send their children to a particular school. The problem is that when you buy a house, it has a whole bunch of different attributes. You’re buying the school that you get to send your kids to, but you’re also buying the neighborhood and the house itself and all the public amenities and all kinds of other things. And those things tend to be positively correlated. Better school districts tend to be in better neighborhoods with nicer houses—so isolating the part due just to schools is somewhat complicated. ... 

What I did was look, in theory, at two houses sitting on opposite sides of the same street, where the attendance district boundary divides the street. The houses are clearly in the same neighborhood, they’re of similar quality, et cetera. The only difference between them is which elementary school the child from each home attends. And then you can ask, How different are the prices of those houses, and how does that difference relate to the differences in school quality?

What I found was that parents were willing to pay more for better schools, but much less than you would casually estimate if you didn’t take into account all these other factors. In Massachusetts, parents were willing to pay 2.5 percent more for a 5 percent increase in school test scores. ... 

[T]this was a long time ago, so pretty much all the information was hand-collected. The housing prices were in a database, but for the attendance district boundaries, I had to contact each school district to ask for their map. I would call them and say, “Can I get the map of your boundaries?” And they would ask, “What house are you thinking of buying?” I’d reply, “No, I actually just want the map.” They’d usually send me a list of streets that were in the attendance district, and a friend of mine and I would sit down and try to create these maps. She was a very good friend.

Here's another example. Back in 1997 the state of Texas passed the "Top Ten Percent Plan." The idea was that anyone in the top 10% of their high school class would be automatically admitted to any University of Texas campus they wished. One of the hopes was to improved diversity at flagship U-Texas campus in Austin. Both for those admitted to the traditionally more selective UT-Austin campus and for those who missed out on going to that campus as a result of the change, what happened? (The paper is Sandra E. Black, Jeffrey T. Denning, and Jesse Rothstein, "Winners and Losers: The Effect of Gaining and Losing Access to Selective Colleges on Education and Labor Market Outcomes," March 2020, NBER Working Paper 26821). Black tells the story: 

The idea is that the top 10 percent of every high school in Texas would be automatically admitted to any University of Texas institution—any one of their choice. All of a sudden, disadvantaged high schools that originally sent very few students to selective universities like the University of Texas, Austin—the state’s top public university— found that their top students were now automatically admitted to UT Austin. If they wanted to go, all the student had to do was apply. There was also outreach, to make students aware of the new admissions policy. The hope was that it would maintain racial diversity because the disadvantaged high schools were disproportionately minority.

It’s not obvious that the goal of maintaining diversity was realized, in part because even though a school may have a disproportionate number of minority students, its top 10 percent academically is often less racially diverse than the rest of the school. There is some debate about whether it maintained racial diversity.

What you do see, however, is that more students from these disadvantaged schools started to attend UT Austin. And students from the more advantaged high schools who were right below their school’s top 10 percent were now less likely to attend. So there’s substitution—for every student gaining admission, another loses. I think that is true in every admissions policy, but we don’t always consciously weigh these trade-offs. ...  Here, we’re trying to explicitly think about, and measure, these trade-offs. ... 

[W]e show that the students who attend UT Austin as a result of the TTP plan—who wouldn’t have attended UT Austin prior to the TTP plan—do better on a whole range of outcomes. They’re more likely to get a college degree. They earn higher salaries later on. It has a positive impact on them.

But what was really interesting is that the students who are pushed out—that’s how we referred to them—didn’t really suffer as a result of the policy. These students would probably have attended UT Austin before the TTP plan. But now, because they were not in the top 10 percent [of their traditional “feeder” school], they got pushed out of the top Texas schools like UT Austin. We see that those students attend a slightly less prestigious college, in the sense that they’re not going to UT Austin, the flagship university. But they’ll go to another four-year college, and they’re really not hurt. They’re still graduating, and they’re getting similar earnings after college.

So the students who weren’t attending college before [because they didn’t attend a traditional feeder school] now are, and they’re benefiting from that in terms of graduation rates and income, while the ones who lose out by not going to Texas’ top university aren’t really hurt that much. It seems like a win-win.

Back in 2015, Black spent some time at the White House Council of Economic Advisers. Here's one of her reflections on that time:  

[W]hich job do I prefer: adviser or academic? That’s easy to answer: being a professor. I like thinking about things for long periods of time, and it was quite the opposite when I was in D.C. There, I was scheduled every 15 minutes. Each meeting would cover a different topic, and I had to be ready to be an expert on A, then an expert on B, and then an expert on C.

It is the antithesis of being an academic, and it’s a skill that I think a lot of academics don’t naturally have, me included. It was a really hard transition from academia to the policy world. Coming back to academia was hard too. I noticed that my attention span had become so much shorter. It took six months, at least, before I could sit and read a whole paper and just think about that paper. Being at the CEA was a very different experience. I really enjoyed it, but I was happy to come back to academia.

The Google Antitrust Case and Echoes of Microsoft

The US Department  of Justice has filed an antitrust case against Google. The DoJ press release is here;  the actual complaint filed with the US District Court for the District of Columbia is here. Major antitrust cases often take years to litigate and resolve, so there will be plenty of time to dig into the details as they emerge. Here, I want to reflect back on the previous major antitrust case in the tech sector, the antitrust case against Microsoft that was resolved back in 2001. 

For both cases, the key starting point is to remember that in US antitrust law, being big and having a large market share is not a crime. Instead, the possibility of a crime emerges when a company with a large market share leverages that market share in a way that helps to entrench its own position and block potential competition. Thus, the antitrust case digs down into specific contractual details.

In the Microsoft antitrust case, for example, the specific legal question was not whether Microsoft was big (it was), or whether it dominated the market for computer operating systems (it did). The legal question was whether Microsoft was using its contracts with personal computer manufacturers in a way that excluded other potential competitors. In particular, Microsoft signed contracts requiring that computer makers license and install Microsoft's Internet Explorer browser system as a condition of having a license to install the Windows 95 operating system. Microsoft had expressed fears in internal memos that alternative browsers like Netscape Navigator might become the fundamental basis for how computers and software interacted in the future. From the perspective of antitrust regulators, Microsoft's efforts to used contracts as a way of linking together its operating system and its browser seemed like anticompetitive behavior. (For an overview of the issues in the Microsoft case, a useful starting point is a three-paper symposium back in the Spring 2001 issue of the Journal of Economic Perspectives.)

After several judicial decisions went against Microsoft, the case was resolved with a consent agreement in November 2001. Microsoft agreed to stop linking its operating system and its web browser. It agreed to share some of its coding so that it was easier for competitors to produce software that would connect to Microsoft products. Microsoft also agreed to an independent oversight board that would oversee its actions for potentially anticompetitive behavior for five years. 

As we look back on that Microsoft settlement today, it's worth noting that losing the antitrust case in the courts and being pressured into a consent agreement certainly did not destroy Microsoft. The firm was not broken up into separate firms. In 2020, Microsoft ranks either #1 or very near the top of all US companies as measured by the total value of its stock. 

Looking again at the antitrust case against Google, the claims are focused on specific contractual details. For example, here's how the Department of Justice listed the issues in its press release: 

As alleged in the Complaint, Google has entered into a series of exclusionary agreements that collectively lock up the primary avenues through which users access search engines, and thus the internet, by requiring that Google be set as the preset default general search engine on billions of mobile devices and computers worldwide and, in many cases, prohibiting preinstallation of a competitor. In particular, the Complaint alleges that Google has unlawfully maintained monopolies in search and search advertising by:

• Entering into exclusivity agreements that forbid preinstallation of any competing search service.
• Entering into tying and other arrangements that force preinstallation of its search applications in prime locations on mobile devices and make them undeletable, regardless of consumer preference.
• Entering into long-term agreements with Apple that require Google to be the default – and de facto exclusive – general search engine on Apple’s popular Safari browser and other Apple search tools.
• Generally using monopoly profits to buy preferential treatment for its search engine on devices, web browsers, and other search access points, creating a continuous and self-reinforcing cycle of monopolization.

As noted earlier, I expect these allegations will result in years of litigation. But I also strongly suspect that even if Google eventually loses in court and signs a consent agreement, it ultimately won't injure Google much or at all as a company, nor will it make a lot of difference in the short- or the medium-term to the market for online searches. If this is the ultimate outcome, I'm not sure it's a bad thing. After all, what are we really talking about in  this case? As Preston McAfee has pointed out, "First, let's be clear about what Facebook and Google monopolize: digital advertising. The accurate phrase is `exercise market power,' rather than monopolize, but life is short. Both companies give away their consumer product; the product they sell is advertising. While digital advertising is probably a market for antitrust purposes, it is not in the top 10 social issues we face and possibly not in the top thousand. Indeed, insofar as advertising is bad for consumers, monopolization, by increasing the price of advertising, does a social good." 

Ultimately, it seems to me as if the most important outcomes of these big-tech antitrust cases may not be about the details of contractual tying. Instead, the important outcome is that the company is put on notice that it is being closely watched for anticompetitive behavior, it has been judged legally guilty of such behavior, and it needs to back away from anything resembling such behavior moving forward.  

Looking back at the aftermath of the Microsoft case, for example, some commenters have suggested that it caused Microsoft to back away from buying other upstart tech companies--like buying Google and Facebook when they were young firms. A common complaint against the FAANG companies— Facebook, Apple, Amazon, Netflix, and Google--is that they are buying up companies that could have turned into their future competitors. A recent report from the House Judiciary Committee ("Investigation of Competition in Digital Markets") points out that "since 1998, Amazon, Apple, Facebook, and Google collectively have purchased more than 500 companies. The antitrust agencies did not block a single acquisition. In one instance—Google’s purchase of ITA—the Justice Department required Google to agree to certain terms in a consent decree before proceeding with the transaction."

It's plausible to me that the kinds of contracts Google has been signing with Apple or other firms are a kind of anticompetitive behavior that deserves attention from the antitrust authorities. But the big-picture question here is about the forces that govern overall competition in these digital markets, and one major concern seems to me that the big tech fish are protecting their dominant positions by buying up the little tech fish, before the little ones have a chance to grow up and become challengers for market share. 

Mark A. Lemley and Andrew McCreary offer a strong statement of this view in their paper "Exit Strategy (Stanford Law and Economics Olin Working Paper #542, last revised January 30, 2020).  They write (footnotes omitted): 

There are many reasons tech markets feature dominant firms, from lead-time advantages to branding to network effects that drive customers to the most popular sites. But traditionally those markets have been disciplined by so-called Schumpeterian competition — competition to displace the current incumbent and become the next dominant firm. Schumpeterian competition involves leapfrogging by successive generations of technology. Nintendo replaces Atari as the leading game console manufacturer, then Sega replaces Nintendo, then Sony replaces Sega, then Microsoft replaces Sony, then Sony returns to displace Microsoft. And so on. One of the biggest puzzles of the modern tech industry is why Schumpeterian competition seems to have disappeared in large swaths of the tech industry. Despite the vaunted speed of technological change, Apple, Amazon, Google, Microsoft, and Netflix are all more than 20 years old. Even the baby of the dominant firms, Facebook, is over 15 years old. Where is the next Google, the next Amazon, the next Facebook?

Their answer is the "exit strategy" for the hottest up-and-coming tech firms isn't to do a stock offering, remain an independent company, and keep building the firm until perhaps it will challenge one of the existing tech Goliaths. Instead, the "exit strategy," often driven by venture capital firms, is for the new firms to sell themselves to the existing firms. 

This particular antitrust case against Google's allegedly anticompetitive behavior in the search engine market is surely just one of the cases Google will face in the future, both in the US and around the world. The attentive reader will have noticed that nothing in the current complaint is about broader topics like how Google collects or makes use of  information on consumers. There's nothing about how Google might or might not be manipulating the search algorithms to provide an advantage to Google-related products: for example, there have been claims that if you try to search Google for websites that do their own searches and price comparisons, those websites may be hard to find. There are also questions about whether or how Google manipulates its search results based on partisan political purposes. 

As I look back at the Microsoft case, my suspicion is that the biggest part of the outcome was that when Microsoft was under the antitrust microscope, other companies that eventually became its big-tech competitors had a chance to grow and flourish on their own. With Google, the big issue isn't really about details of specific contractual agreements relating to its search engine, but whether Google and the other giants of the digital economy are leaving sufficient room for their future competitors. 

For more posts on antitrust and the big tech companies, some previous posts include:

• "Antitrust in the Digital Economy" (July 19, 2019)
• "Three Questions for the Antitrust Moment" (July 9, 2018)
• "A Skeptical View in Favor of an Antitrust Push" (March 15, 2018)
• "Limits for Corporate Bigness on Acquisitions, Patents, and Politics" (August 13, 2019)

Will Vote-by-Mail Affect the Election Outcome?

For the 2020 election, the United States will rely more heavily on vote-by-mail than ever before. Is it likely to affect the outcome? Andrew Hall discusses some of the evidence in "How does vote-by-mail change American elections?" (Policy Brief, October 2020, Stanford Institute for Economic  Policy Research).

There are several categories of vote-by-mail. The mild traditional approach was the absentee ballot, used by people who knew in advance that they wouldn't be able to make it to the polls in person on Election Day for some specific reason (like being an out-of-state college student or deployed out-of-state in the military). Over time, this has evolved in many states into "no excuses" absentee voting, where anyone can request an absentee ballot for pretty much any reason. 

Perhaps the most aggressive version is universal vote-by-mail, where the state mails a ballot to every registered voter. The vote can then vote by mail, bring the mailed ballot in person to vote, or ignore the mailed ballot and just vote in-person on Election Day. Hall notes: "Prior to 2020, only Colorado, Hawaii, Oregon, Utah, and Washington employed universal vote-by-mail, while California was in the process of phasing it in across counties. In response to COVID-19, three more states, Nevada, New Jersey, and Vermont, along with the District of Columbia, have implemented the policy, while California accelerated its ongoing implementation. Montana has also begun to phase in the practice."

In 2020, most states are experimenting with something in-between: not quite universal vote-by-mail (in most states), but often more encouragement for vote-by-mail than had been common in the previous situation of no-excuses absentee voting. Thus, thinking about what will happen in 2020 requires looking back at earlier experience. 

For example, the universal mail-in states often phase in the process a few randomly chosen counties at at time. Thus, social scientists can compare, in the same election, how voting behavior changed when mail-in voting first arrived. Hall writes: 

In our first study, published recently in the Proceedings of the National Academy of Sciences, we examined historical data from California, Utah, and Washington, where universal vote-by-mail was phased in over time, county by county ... We found that, in pre-COVID times, switching to universal vote-by-mail had only modest effects on turnout, increasing overall rates of turnout by approximately two percentage points. Because universal vote-by-mail has such modest effects on overall turnout, it’s not surprising that we also found that it conveyed no meaningful advantage for the Democratic Party. When counties switched to universal vote-by-mail, the Democratic share of turnout did not increase appreciably, and neither did the vote shares of Democratic candidates. Our largest estimate suggests that universal vote-by-mail could increase Democratic vote share by 0.7 percentage points---enough to swing a very close election, to be sure, but a very small advantage in most electoral contexts, and a much smaller effect than recent rhetoric might suggest.

Of course, this evidence is about a move to universal mail-in voting, and what is actually happening in most states is more like a dramatic expansion of no-excuse-needed absentee balloting. However, I confess that I am less sanguine than Hall about a swing of "only" 0.7 percentage points. If the presidency or control of the US Senate comes down to a few key, close-run states, that amount may represent the margin of victory. Also, this pre-COVID evidence may underestimate the partisan difference in 2020, given that there is some survey evidence from April and June suggesting that Democrats are more enthused about mail-in voting than Republicans. But what has seemed to happen in other states is that while Democrats are more likely to vote by mail, overall turnout and voting margins are not much affected. 

As another piece of evidence, Hall discussed the Texas run-off primary on June 14. For research purposes, it's useful that this vote happened when the pandemic was already underway. Also, it's useful that in this election, only those 65 and over could vote-by-mail with no reason needed. Thus, one can compare voting patterns of those just under 65 and just over 65, and see whether among voters who were close in age but had different rules for mail-in voting, did the pandemic change the patterns? For example, would the 64 year-olds who did not have easy access to a mail-in ballot vote less? The short answer is that gap between 64 and 65 year-old voters did not change. 

I'll admit here at the bottom that although I've had to vote absentee a couple of times in my life, I'm not a big fan of vote-by-mail. I like the idea of most people voting at the same time, with the same information, and early mail-in voting raises the problem that if new news arrives and you want to change your vote, you're out of luck.  In addition, I'm a big fan of the secret ballot. No matter what you say to other people, when you are alone in that voting booth, you can choose who you want. Vote-by-mail will inevitably be a less private experience, where those who might wish to defy their family members or friends or those in their apartment building or their assisted care facility may find it just a little harder to do so. 

There are also security concerns about mail-in ballots being delivered and practical concerns about difficulties of validating them and counting them expeditiously. I'm confident that in at least one state in the 2020 election, probably a state with little previous experience in mail-in voting, the process is going to go wincingly wrong.  As Hall writes: "That being said, there are important November-specific factors our research cannot address. The most important issue concerns the logistics of vote-by-mail. Historically, mail-in ballots are rejected at higher rates than in-person votes. Capacity issues in the face of an enormous surge in voting by mail could drive these rejection rates higher. And if Democrats cast more mail-in ballots than Republicans, as looks extremely likely, these higher rejection rates could mean that vote-by-mail paradoxically hurts Democrats."

Of course, vote-by-mail is only one of the multiple differences across states in how voting occurs, including differences in voter registration, voter ID, recounts, and others For an overview, see "Sketching State Laws on Administration of Elections" (September 26, 2016). 

The Ada Lovelace Controversies

 Ada Lovelace (1815-1852) is generally credited with being the first computer programmer: specifically, after Charles Babbage wrote down the plans for his Analytical Engine (which Britannica calls "a general-purpose, fully program-controlled, automatic mechanical digital computer"), Lovelace wrote down a set of instructions that would allow the machine to calculate the "numbers of Bernoulli" (for discussion, see here and here). Suw Charman-Anders gives an overview of the episode and some surrounding historical controversy in "Ada Lovelace: A Simple Solution to a Lengthy Controversy" (Patterns, October, 9, 2020, volume 1, issue 7). 

The historical controversy is whether Lovelace really truly deserves credit for the program, or whether her contemporaries who gave her credit for doing so were just being chivalrous to a fault (and perhaps being generous to the only daughter of Lord Byron and his wife). For example: 

In a letter to Michael Faraday in 1843, Babbage referred to her as “that Enchantress who has thrown her magical spell around the most abstract of Sciences and has grasped it with a force which few masculine intellects (in our own country at least) could have exerted over it”. Sophia De Morgan, who had tutored the young Lovelace, and Michael Faraday himself were both impressed with her understanding of Babbage’s Analytical Engine. Augustus De Morgan, Sophia’s husband and another of Lovelace’s tutors, described her as having the potential, had she been a man, to become “an original mathematical investigator, perhaps of first-rate eminence” ...

 Apparently, some modern writers have pored over what remains of the imprecisely dated correspondence between Lovelace and her tutor Augustus de Morgan, and decided that Lovelace didn't know enough math to have written the program. (Personally, I shudder to think of what judgments would be reached about my own capabilities if I was judged by the questions I sometimes felt the need to ask!) But Charman-Anders makes a persuasive case that the whole controversy is based in a mis-dating of Lovelace's mathematical education in general and her correspondence with de Morgan in particular; that is, critics of Lovelace were mistakenly treating early questions she asked her tutor as if they were questions asked several years later. 

For me, the more interesting point that Charman-Anders makes is to emphasize that writing a computer program was its own conceptual breakthough. There had long been mechanical computing machines, where you plugged in a problem and it spit out an answer. But the breakthrough from Lovelace was to see that the Babbage's Analytical Engine could be viewed a set of rules for working out new results; indeed, Lovelace  hypothesized that such a machine could write music based on a set of rules.   Charman-Anders writes (quotations in first paragraph from Lovelace's 1843 notes, footnotes omitted): 

Although Lovelace was the first person to publish a computer program, that wasn’t her most impressive accomplishment. Babbage had written snippets of programs before, and while Lovelace’s was more elaborate and more complete, her true breakthrough was recognizing that any machine capable of manipulating numbers could also manipulate symbols. Thus, she realized, the Analytical Engine had the capacity to calculate results that had not “been worked out by human head and hands first,” separating it from the “mere calculating machines” that came before, such as Babbage’s earlier Difference Engine. Such a machine could, for example, create music of “any degree of complexity or extent”, if only it were possible to reduce the “science of harmony and of musical composition” to a set of rules and variables that could be programmed into the machine. ...

While calculating devices have a long history, the idea that a machine might be able create music or graphics was contrary to all experience and expectation. Lovelace and her peers would have been familiar with the artifice of the automaton, clockwork machines which looked and acted like humans or animals but were driven by complex arrangements of cams and levers. And indeed, Babbage is said to have owned one called the Silver Lady, which could “bow and put up her eyeglass at intervals, as if to passing acquaintances”. But the Analytical Engine would have been in a category all its own.

One of the biggest leaps the human mind can make is extrapolating from current capabilities to future possibilities. The “art of the possible”, as it has been called, is an essential skill for innovators and entrepreneurs, but envisioning an entirely new class of machine is something for which few people have the capacity. Babbage’s design for the Analytical Engine was astounding, but none of his peers seemed to truly grasp its meaning. None except Lovelace.

Interview with Gary Hoover: Economics and Discrimination

The Southwest Economy publication of the Federal Reserve Bank of Dallas has published "A Conversation with Gary Hoover" (Third Quarter 2020, pp. 7-9). Here are some of Hoover's comments: 

On  his own career path: 

Although I have been successful in economics, it has not come without some amount of psychological trauma. When I arrived at the University of Alabama in 1998, the economics department had never hired a Black faculty member. Sadly, that is still the case at more economics departments than not. I would not call those initial years hostile, but they were not inviting either.

I stuck to my plan, which was to publish articles to the best of my ability and teach good classes. The pressures were there to mentor Black students, serve on countless committees to “diversify” things and be a role model. I took on the extra tasks but never lost track of my goal. I saw so many of my Black counterparts fall into the trap. They had outsized service burdens compared to their peers, which they took on with the encouragement of the administration. However, when promotion and tenure evaluation time arrived, they were dismissed for not “meeting the high standards of the unit.”

On labor market impediments for black workers:

The impediments begin for Blacks seeking employment from the very outset. Some research has shown that non-Black job applicants of equal ability receive 50 percent more callbacks than Blacks. To further amplify on the issue, some research has shown that Black males without criminal records receive the same rate of callbacks for interviews as white males just released from prison when applying for employment in the low-wage job market.

With such handicaps existing from the start, it is no surprise that a wage gap exists. Some estimates show that gap to be as large as 28 percent on average and as large as 34 percent for those earning in the highest end (95th percentile) of the wage distribution. ,,,

Employers want workers who are trainable and present. Black workers, who have been poorly trained or suffer inferior health outcomes, will suffer disproportionately. In addition, the impacts of the criminal justice system cannot be overlooked. Some recent research has shown that for the birth cohort born between 1980 and 1984, the likelihood of incarceration transition for Blacks was 2.4 times greater than for their white counterparts. Given this outsized risk of incarceration, the prospects of long-term unemployment are dramatically increased.

On whether "the economy will evolve quickly enough to ensure the success and prosperity of minority groups":

I think that I must be optimistic about the future. What employers are yet to realize, but will have to come to grips with, is that successful market outcomes for minority groups mean success for them also. By that I mean, this is not a zero-sum game where one group will only improve at the expense of the other. In fact, history has shown us the opposite. Once minorities are fully utilized and integrated in the labor force, the economy as a whole will enjoy a different type of prosperity than has ever been experienced in the U.S. Once again, we must remember the introductory idea we teach to our college freshmen about the circular flow of the economy in that those fully engaged minority employees become fully engaged consumers.

For more on Hoover's thoughts about racial and ethnic diversity in the economic profession, a useful starting point is his co-authored article in the Summer 2020 issue of JEP, written with Amanda Bayer and Ebonya Washington. "How You Can Work to Increase the Presence and Improve the Experience of Black, Latinx, and Native American People in the Economics Profession" (Journal of Economic Perspectives, 34: 3, pp. 193-219).

For an overview of how economists seek to understand discrimination in theoretical and empirical terms, and how the views of economists differ from sociologists, a useful starting point is the two-paper
symposium on "Perspcctives on Racial Discrimination" in the Spring 2020 issue of JEP: 

• "Sociological Perspectives on Racial Discrimination," by Mario L. Small and Devah Pager
• "Race Discrimination: An Economic Perspective," by Kevin Lang and Ariella Kahn-Lang Spitzer

COVID-19 Risks by Age

It seems well-known that the health risks of COVID-19 are larger for the elderly. But how much larger? And what is the trajectory of risk across age?  Andrew T. Levin, William P. Hanage, Nana Owusu-Boaitey, Kensington B. Cochran, Seamus P. Walsh, Gideon Meyerowitz-Katz provide a set of estimates "Assessing the Age Specificity of Infection Fatality Rates for COVID-19: Meta-Analysis & Public Policy Implications" (NBER Working paper 27597, as revised October 2020, also available via medRxiv, which is a "preprint server for the health sciences"). 

Also, Andrew Levin is the genial and informative talking head in a 15-minute video discussing the main approach and results. 

As the title implies, the paper is an effort to pull together evidence on the health effects of COVID-19 by age from a variety of sources. Two figures in particular caught my eye. This figure shows the "infection fatality rate"--that is, the the ratio of fatalities to total infections. The different kinds of dots on the figure show results from different kinds of studies. The red line is their central estimate, which is surrounded with estimates of the uncertainty involved. 

As the authors write: "Evidently, the SARS-CoV-2 virus poses a substantial mortality risk for middle-aged adults and even higher risks for elderly people: The IFR is very low for children and young adults but rises to 0·4% at age 55, 1·3% at age 65, 4·2% at age 75, 14% at age 85, and exceeds 25% for ages 90 and above." 

The COVID-19 risk for the elderly is clearly substantial. But how does one think about the risk for those, say, in the 45-65 age bracket. Their COVID-19 risk is clearly lower than for the 85 year-olds. But how does their COVID-19 risk compare with other everyday risks? In his talk, Levin offers a comparison with risks of death from an automobile crash by age. 

One wouldn't want to pretend that this comparison literally apples-to-apples. For example, the risks of driving are somewhat under the control of the drive, while the risk of dying after being infected by COVID-19 is not. In addition, this is comparing the risks of dying after being infected, which applies to only a subset of the population, with the overall risk of driving for the entire population. 

However, the comparison nonetheless seems quite useful to me, in the sense that many of us accept that driving a car has some risk, but it's a risk we take almost every day without excessive concern. Thus, seeing that for the average person under age 34, the COVID infection fatality rate is below the auto fatality rate gives a sense that for that age group taken as a whole (and of course with exceptions for a small number of people with certain pre-existing conditions), the personal risk of COVID-19 shouldn't bother them much. 

Interpreting the risks of those in the age brackets from, say, 35-64 is a little trickier. The COVID-19 risk number for these age brackets do not look especially high in absolute terms, certainly not as compared to the risks for the 85+ group. But from another perspective, for the 45-54 group, the COVID-19 risk is something like 16 times the auto fatality risk; for the 55-64 group, the COVID-19 rise is more than 54 times the auto fatality risk. 

Most people, myself included, are not good at thinking about these kinds of small risks. If I take a risk that I think of as negligible, and multiply it by 16, does "16 x negligible" equal something I should worry about? Maybe "16 x negligible" is like the risk of driving home in the dark on a snowy day, which is a risk I think about, but not one that stops me from driving home. 

What about about "54 x negligible" for the 55-64 age group, of which I have the honor to be a member? Is that enough to do more than raise my eyebrows? For my age group, the risk of dying if I got COVID-19 is 0.7%. which is like saying 1 chance out of 143. There are a lot of contexts where I wouldn't pay much attention to 1 chance in 142. But if it's life and death, I'm willing to take some steps to reduce the risk of that outcome. 

There are certain risks I don't take while driving, like driving with alcohol in my system. Granted, I don't take the risk of driving under the influence not so much because I fear I will kill myself, but because I fear accidents and, even worse, harming someone else. But if the COVID-19 danger to me is in some way comparable to driving while intoxicated, then consistency in thinking about risks suggests that I should make efforts to avoid being exposed to the disease--and also to avoid being a carrier to my wife or any other above-age-35 people with whom my life intersects. 

To put it another way, many of us adjust our behavior in a variety of ways to reduce moderate health risks, like wearing a helmet while bicycling, or not driving in an unsafe manner, or throwing away food that seems to have spoiled in the refrigerator. The reduction in risk from these behavior may not be large in absolute terms, but they feel worth taking. In a similar sense, the health risks of COVID-19 for those in the 35-64 age group are probably not exceptionally high in absolute terms, but for many of us who act to reduce other risks in our lives, the COVID-19 risks are also high enough to justify efforts that will reduce those risks. 

Of course, these sorts of comparisons are about averages, not at individuals who will be above- or below-average in various risks. But general public health guidance needs to be aimed at averages. 

Will Services Trade Lead the Future for US Exports?

At least for a time, one legacy of the pandemic is likely to be a decrease in physical connections around the world economy, from tourism and business travel to shipping objects. But international trade in services is delivered online. For the US, trade in services has been becoming a bigger part of the overall trade picture, and the pandemic may give it an additional boost. Alexander Monge-Naranjo and Qiuhan Sun provide some background in "Will Tech Improvements for Trading Services Switch the U.S. into a Net Exporter?" (Regional Economist, Federal Reserve Bank of St. Louis, Fourth Quarter 2020). 

The authors point out that shifts in transportation routes or shipping method like containerization have had large effects on international trade in the past. They write: 

The U.S. is a world leader in most high-skilled professional service sectors, such as health, finance and many sectors of research and development. Moreover, leading American producers have been ahead of others in the adoption of ICT in their production networks. The global diffusion of ICT—including possibly the expansion of 5G networks—is prone to make many of these services tradeable for servicing households and businesses....  Similarly, the day-to-day activities of many businesses all involve tasks that can be automated and/or performed remotely and, of course, across national boundaries. Thus, a natural prediction would be that the U.S. should become a net exporter of high-skilled, knowledge-intensive professional services because of its comparative advantage.

Here are some illustrations of the patterns already underway. This figure shows the US trade balance separating out goods and services. The US trade deficit in goods plummetted from the early 1990s up to about 2006--with an especially sharp drop after China entered the World Trade Organization in 2001 and China's global exports exploded in size. But notice that US trade in services has consistently been running a trade surplus over this time, and the services trade surplus has been rising in recent years. 

Indeed, the long-run pattern seems to be that for the US economy, services have stayed about the same proportion of total imports in recent decades, but have become a rising proportion of total exports. 

Some of the big areas of gains for US services exports have been information technology and telecommunications services, insurance and financial services, and other business services (which includes areas like "professional and management consulting, technical services, and research and development services"). 

Monge-Naranjo and Sun don't actually make a case that a rise in services exports could be enough to turn the overall US trade deficit into a surplus; in that sense, the title of their short article overstates their case. But they do show that trade in services is not only a large and rising part of US exports, but may be the part of US economic output with the biggest upside for expanding US exports in the future. 

Supporting this potential for rising US exports in services requires a different public-sector actions. It's not about better transportation systems for physical goods, but rather about faster and more reliable virtual connections across the US and to other places around the world. A substantial and ongoing improvement in this virtual infrastructure also seems potentially quite important for the US economy as it adapts to a new reality of online meetings, online healthcare, online education, online retail, online work-from-home, and more. The US economy isn't going to move back to its manufacturing-dominant days of several decades ago, and at least in the medium-term, it probably isn't going to move back to to the social-clustering times way back in January 2020, either.   

In addition, there is "A Fundamental Shift in the Nature of Trade Agreements," as I called it in a post a few years ago, where the emphasis is less about tariffs and import quotas, and more about negotiating the legal and regulatory frameworks to open up foreign markets for US exporters of services. The kinds of trade agreements needed to facilitate, say, US insurance companies operating overseas, are quite different from the trade agreements about tariffs on objects like tariffs or steel.