Author: conversableeconomist

There is a widespread sense, not just in the United States but in European countries like Denmark, that government assistance for low-income households should be linked to participation in the labor force. So what do employment patterns look like for low-income American–that is, those who are also eligible for various means-tested government assistance programs? Lisa Barrow, Diane Whitmore Schanzenbach, and Bea Rivera lay out some basic data patterns in “Work, Poverty, and Social Benefits Over the Past Three Decades” (Federal Reserve Bank of Cleveland, Working Paper No. 24-22, October 2024).

The next two figures show employment rate over time, first for women and then for men. The employment rates are then divided up by whether the household is low-income, and whether the household includes children.

For women, the solid lines show the the employment rate for women with no children (green line) and the employment rate for women with children (orange line). You can see that back in the early 1990s, the employment rate for women without children was substantially higher, but gap shrinks, and since about 2010 the employment rates are about equal.

The dashed lines focus on those with low incomes (defined here as less than 200% of the federal poverty line). Employment rates for those with low incomes are below the rates for the entire population. Back in the early 199s, low-income women without children had higher employment rates. But after the passage of the welfare reform act back in 1996 under the Clinton administration–a law that emphasized work requirements for welfare recipients–low-income children with women consistently have higher employment rates than low-income women without children.

The next figure shows the patterns for men. The solid lines show overall employment rates for men without children (green line) and for men with children (orange line). Unlike the situation for women, where these two lines were much the same, men without children have much lower employment rates, and the gap is growing. The dashed lines focus on low-income men. Low-income men with children have much higher employment rate than low-income men without children–and the gap for men is much larger than the gap for women shown above.

One other pattern is worth noting here. Many of these lines show a relatively large decline in employment rates from the late 1990s up to about 2010–but since about 2010 (the tail end of the Great Recession), the employment rates for both the entire population and the low-income population are either flat or even up a little bit.

So what explains these patterns? In particular, what might explain the In the working paper, Whitmore, Schanzenbach, and Rivera consider a bunch of factors, including demographic factors like family composition, education, and race/ethnicity, and public policy factors like a shift away from cash welfare payments for the poor and toward payments that are delivered through tax credits and thus linked to work, like the Earned Income Tax Credit and the Child Tax Credit. They write:

We find that the characteristics of low-income adults have changed over time. They have become more highly educated, less likely to be married, and the share that is Hispanic has increased. We investigate to what extent these shifts in characteristics can help explain changes in employment and find that little employment change can be explained by these factors. …

Our results contribute to a growing literature documenting the shift in the structure of social benefits for non-elderly adults, especially those with children, to reward and encourage work. Low-income families with children and substantial earnings have received more income—both in levels and as a share of their total incomes—from social benefits in the last decade than they did 30 years ago. On the other hand, social benefits programs are little changed for low-income families without children.

Of course, any working paper is far from the final word on a big subject. But the patterns are consistent with a belief that the shift in social safety net programs toward adults that work, in households with children, is encouraging work effort for that group.

In total size, Japan’s economy is fourth-largest in the world, just behind Germany for third-largest. In per capita GDP, Japan is ahead of Spain and South Korea, although well behind Italy and France. With a life expectancy at birth of 84 years, ,Japan has one of the highest levels in the world. Clearly, Japan has some considerable economic strengths.

But there is a puzzle here. In the late 1980s and early 1990s, Japan’s economy experienced a dramatic boom-and-bust in stock market and real estate prices. For example, the Nikkei stock market index rose from about 10,000 in 1984 to almost 38,000 in 1989, and then fell back to 17,000 by 1992. In the early 2000s, the Nikkei had fallen to under 10,000, and it was under 10,000 in 2012, too. Since then, the Nikkei has climbed again, and in early 2024–35 years later–it exceeded the level it had reached in 1989. In short, Japan’s economy crashed about three decades ago and growth has been slow and halting ever since.

Japan faces ongoing demographic challenges, too. The “working-age” population in Japan, from ages 15-64, peaked back in the mid-1990s at about 87 million, but now has fallen to 73 million. Japan’s population is aging. Back in the mid-1990s, Japan had about five working-age people for every person over the age of 65; now, Japan has one working-age person for every person over the age of 65.

Japan has very high levels of government debt, too. According to IMF calculations, the US ratio of government debt to GDP ratio is about 120%; Japan’s ratio of government debt to GDP is about 250%.

So how is Japan’s economy adjusting to these underlying factors? Dany Bahar, Guillermo Arcay, Jesus Daboin Pacheco, and Ricardo Hausmann explore some of the underlying patterns in “Japan’s Economic Puzzle” (The Growth Lab at Harvard Kennedy School, CID Faculty Working Paper No. 442, revised July 2024). They focus in particular on the evolution of Japan’s interaction with the global economy. They write:

Our main findings put together suggests that in response to the enormous domestic challenges in the economy, Japanese firms have sought to offset these constraints by expanding their operations internationally through foreign investments. By investing in foreign markets, Japanese firms can access larger and more diverse labor pools, enabling them to continue growing despite domestic labor shortages. These Japanese investments abroad, accompanied by the unique accumulated knowledge of the Japanese economy (i.e., technology, best practices, and more), has resulted in very high returns to these investments. The subsequent increase in wealth to the economy has inevitably resulted in a domestic expansion into non tradable, less productive, sectors of the economy which lowers aggregate productivity growth. Overall, we argue, the sluggish productivity growth in Japan is a result of these dynamics.

In the last few decades, Japan’s share of global exports of goods has plummeted, in substantial part because of China’s rising share of global exports of goods. From a US perspective, a lot of the imported goods that were made in Japan back in the 1970s and 1980s are now being made in China.

But when it comes to exports of services, Japan has continued to do well. In particular, the “service” that Japan exports is often intellectual property, which refers to licenses to use Japanese patents elsewhere.

In addition, Japanese firms are building up their investments abroad. One way to think about this is that Japan’s firms are dealing with the declining labor force in Japan by finding workers in other countries. The authors write:

Japan has significantly increased its net foreign asset positions, particularly after the turn of the century. In fact, between 1996 and 2022, Japan nearly quadrupled the value of its assets abroad from USD 2.7 trillion to USD 10.3 trillion. An important driver of this growth is reflected in Japan’s stock of outward direct investment, which increased by a factor of almost 8 from USD 263 billion to USD 2.1 trillion during the same period. Moreover, the returns to those direct investments have grown significantly, too, with abnormal returns consistently much larger than for any other investment positions. The data shows that dividends stemming from direct investment abroad … [have] grown by a factor of almost 15 from USD 14 billion in 1996 to USD 206 billion in 2022.

Putting this together, the authors argue that Japan’s companies are involved in high productivity growth–it’s just that a lot of that productivity growth is happening outside the borders of Japan, in industries where Japanese firms end up exporting to the rest of the world. Japanese workers who work for a company that is involved in international trade and has rapid productivity growth have wages rising more quickly than those who work for companies in non-tradeable goods like retail, hotels, and services, which have lower or even negative productivity growth.

Is this Japanese economic model sustainable? The authors speak gently of “challenges.” With a declining domestic labor force and high-productivity firms operating abroad, Japan’s economic future seems intimately tied to a combination of technological and managerial know-how, along with global supply chains. Success for this economic formula requires that Japan’s export-oriented firm remain on the technology frontier, which can be easier said than done in the 21st century global economy. Also, as the size of Japan’s domestic workforce declines, it would help if Japan’s low-productivity domestic production sectors could find ways to make better use of technology to improve productivity.

From a conceptual point of view, the economics of research and development is the opposite of pollution. When a private party carries out an economic activity that leads to pollution, the private party gets the economic benefit, but the broader society bears the costs (in the jargon, a “negative externality”). However, when a private party carries out research and development, the private part benefits to some extent, but the additional benefits of the new knowledge spill over to the rest of the economy (a “positive externality”). Thus, it makes sense to have public policies that discourage pollution, but that encourage research and development.

I’ve long and frequently argued for a substantial increase in US R&D spending (for example, here, here, here, and here), so it seems appropriate to note that there has been a substantial increase in the last decade, driven by US business spending on R&D. Here are some graphs showing overall patterns of US R&D spending from “Trends in U.S. R&D Performance” (National Science Foundation, May 2024).

For a long time, I told the story of US R&D funding in this way: There was a big run-up in US R&D spending in the 1950s and into the 1960s, driven largely by US government R&D spending typically aimed at military and space programs. However, federal R&D spending as a share of GDP began to sag after the 1960s, while business R&D spending as a share of GDP increased.

These two forces more-or-less counterbalanced each other for several decades, so that total R&D spending as a share of GDP (blue line) hovered between about 2.3%-2.7% of GDP from the early 1980s up through about 2013. But then you see a substantial change. Although federal support for R&D as a share of GDP continues to lag, business spending on R&D takes off, and pushes US R&D spending up to about 3.5% of GDP. With a US GDP in 2024 at around $29 trillion, a 1% rise in GDP means that about $290 billion more is being spent on R&D this year than would have been spent if R&D had stayed in that lower range.

The same pattern emerges if you look at dollar amounts of R&D spending. The lines here are not adjusted for inflation, or for economic growth, so the picture is in some ways a little misleading. But you can see that government and industry R&D spending were similar in size as recently as the second half of the 1980s. Since then, the rise in US R&D spending has been driven by business R&D spending–especially in the last decade or so.

This figure shows the share of R&D funding coming from different sources. As you can see, the share of R&D coming from business spending has risen sharply, and is now approaching 80%.

This figure offers an international comparison. The blue bars show national R&D spending in absolute levels (measured on the left-hand axis): thus, the big economies like the United States, the EU-27 taken as a whole, and China have the biggest bars. The red diamonds show national R&D spending as a share of GDP (measured on the right-hand axis). As you can see, a couple of smaller economies, South Korea and Taiwan, spend more as a share of GDP than the United States. But in general, the US has both the highest level of absolute R&D spending and also–thanks the recent run-up in R&D spending by business–one of the highest levels of R&D spending as a share of GDP. To me, the gap between the US and the EU-27 economies in R&D spending is especially striking.

Finally, one concern sometimes expresses is that when it comes to R&D, business-funded spending can be more focused on the “D” of developing products for near-term sales in the market, and less on the “basic” research that can be so important for longer-term progress. On this point, here’s a figure from the “Analysis of Federal Funding for Research and Development in 2022: Basic Research” (National Science Foundation, August 15, 2024).

As the figure shows, government used to dominate funding for basic research, accounting for 70% of the total in the 1960s and 1970s, and for 60% of the total as recently as the early 2000s. But the rise in overall US business R&D spending has “basic” research spending by business as well. Now, it looks as if basic R&D spending by business is about to exceed that from the federal government.

One of the recent puzzles of the global economy is that the US economy seems to just keep growing, albeit at a moderate rate, while many other high-income economies like those in Europe, as well as Japan and Canada, seem stuck in slower growth patterns. My guess is that the surge in US R&D spending is part of the explanation for that pattern. Moreover, a higher level of R&D spending by business suggests that US firms are seeing opportunities to capitalize on their R&D efforts in the ever-changing and evolving US economy, while many European firms may not be seeing the same willingness and opportunity for change within their national markets.

The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2024 has been awarded to Daron Acemoglu, Simon Johnson and James Robinson “for studies of how institutions are formed and affect prosperity.” Each year, the Nobel Committee helpfully publishes both a “Popular information” overview of of the award and a “Scientific Background” essay that goes into greater depth. The Popular Information starts with the kind of basic fact about the world we live in that demands attention.

The richest 20 per cent of the world’s countries are now around 30 times richer than the poorest 20 per cent. Moreover, the income gap between the richest and poorest countries is persistent; although the poorest countries have become richer, they are not catching up with the most prosperous.

Pause for a moment to contemplate that 30-fold difference. When discussing differences in average incomes between, say, the US and France or Sweden or Japan or other high-income countries, one can suggest reasons why the differences in income levels may not reflect actual differences in the underlying standard of living for the average person. But when the difference is 30-fold, it means that the lower-income locations have less health, less education, less living space, less leisure, and dramatically less access to the banquet of goods and services available in high-income countries. It also means that many of the people in the lower-income countries will not be fans of a “de-growth” agenda: instead, would like to have–either in their own country or by migrating–a standard of living 30 times higher than they have at present.

What factors can explain these extraordinarily large differences? You can point to geographic factors like arable land, natural ports, navigable rivers, natural resources, and a temperate climate. But as you enumerate possible reasons, you find yourself pointing to ways in which some countries have been able to build economies based on innovation and technology, which in turn are based on widespread education, infrastructure, a sound financial system, and a rule of law. In a word, you find yourself talking about “institutions.”

Some of the most vivid effects of institutions are visible in satellite photos, like the pictures of the Korean peninsula at night, with lights shining from South Korea and North Korea nearly in darkness, or daytime pictures of the border between Haiti and the Dominican Republic, where the Haitian side of the border is denuded and deforested by poor people desperate for firewood, while the Dominican remains verdant.

But “institutions” is so broad a term that it’s not immediately clear what it includes or what it leaves out, so it’s not clear how to measure it. It’s also not clear how growth-promoting institutions are formed, and whether the institutions precede economic growth, or co-evolve with growth, or result from growth. It’s not clear whether institutions that accompany economic success in one place can be transplanted to other locations.

These questions are hard to tackle such that the argument is based on quantitative evidence, not just storytelling. Economist have been trying for a long time: indeed, the Nobel Prize in economics back in 1993 was given to Robert W. Fogel and Douglass C. North “for having renewed research in economic history by applying economic theory and quantitative methods in order to explain economic and institutional change.”

So what’s new about the Acemoglu, Johnson, and Robinson analysis? The Nobel committee writes: “Broadly, their contributions are twofold. First, Acemoglu, Johnson, and Robinson have made significant progress in the methodologically complex and empirically difficult task of quantitatively assessing the importance of institutions for prosperity. Second, their theoretical work has also significantly advanced the study of why and when political institutions change. Their contributions thus entail substantive answers as well as novel methods of analysis.”

Here’s a glimpse these two types of contributions. On “assessing the quantitative importance of institutions for prosperity,” some of their best-known work is based on the historical experience of the colonialism. Acemoglu,  Johnson and Robinson argue in a broad sense that there are two types of colonial institutions: those that encourage property rights and those those that are “extractive.” They further argue that colonial powers will choose whichever approach provides the greatest wealth for themselves.

Consider two factors. One is whether the population of the area being colonized is more or less dense. If the population in dense, then the colonial power is more likely to use “extractive” institutions to take from the people; if less dense, than the colonizers were more likely to send people from their own country to live in the country being colonized, and those settlers would demand property rights and more inclusive institutions before they were willing to go. A second factor is the disease environment of the country being colonized. If the country was prone to diseases like malaria, then the colonizing country would be less willing to send settlers, and more likely to choose “extractive” institutions; if the country was less prone to diseases, then the colonizing country would be more likely to send settlers, who again would demand more inclusive institutions before they were willing to go.

The most striking economic research is able to make unexpected predictions. This work suggests that areas which were already fairly prosperous and heavily populated before colonialization were more likely to end up with “extractive” institutions, while areas with less previous success and lower population densities should end up with more inclusive institutions. Thus, over a sustained period of time like a century and more–if the institutions of colonialism matter–one should see a “reversal of fortune”: that is, the places that were more economically successful at the time of colonization should later be overtaken by the places that had been less economically successful.

This very brief sketch suggests the challenges of this research agenda. You need to collect 19th-century data on population densities and disease mortality. You need to collect data on many kinds of “institutions” and classify them as extractive or inclusive. You need to draw connections. Follow-up work also looks for historical other than colonialization in which this general approach might be applied.

If one looks at the period of time after colonialization, an obvious question is how institutions might be chosen and changed. Say that there is a government in power which uses extractive institutions to amass wealth for elite insiders at the expense of ordinary citizens. What might cause this to change? Acemoglu, Robinson, and Johnson argue that the heart of the difficulty here is a “commitment problem”–that is, it can be hard for political leaders to keep their promises. The Nobel committee writes:

A promise by the elite or an autocrat to implement welfare-improving reforms today that will benefit the populace tomorrow is typically not credible because the elite have an incentive to renege on their promise later and act in their short-term interest. Similarly, promises by those advocating for political reform, who are willing to compensate the current elite for agreeing to it peacefully, are not credible because the incentives to compensate the former elite once they are no longer in power are also not credible. Social conflict combined with the credibility problem can even cause the elite to block technological innovation and change, if such changes are perceived as threatening their hold on power.

This approach has been a baseline for future research, in part because it created a common framework for the earlier main explanations of how modernization occurred. Again, the Nobel committee explains:

It is instructive to put the contribution of Acemoglu and Robinson in perspective and relate it to the literature that already existed in the late 1990s. … Recall that the standard answer to why elites gave up the control of economic and political institutions was embodied in modernization theory and related explanations (Lipset, 1959, 1960). According to these theories, the process of socioeconomic development would eventually bring about democratization, essentially as a by product of economic progress. As societies become richer, this wealth brings about rising education, a more plentiful middle class, and gradually milder conflict over income inequality, factors which all favor democratization. A second approach, which challenged modernization (and other structural) theories, argued that democratization is instead the by-product of patterns of strategic interaction among political elites. Personal skills, luck, or strategic mistakes are, according to this approach, part and parcel of what democratization is about. … While the second view thus holds that democracy is usually granted or undermined from above, a third approach to explaining democratization, by contrast, points to the importance of social forces in society, most importantly different class actors (Moore, 1966). The key assertion in this tradition is that democracy is imposed from below by the people through popular mobilization (Rueschemeyer et al., 1992). According to this view, incumbent authoritarian elites would not care to enact reforms or bargain with the democratic opposition if they did not fear the masses or an imminent threat of revolution.

Acemoglu and Robinson integrated these three traditions by providing structural conditions (such as economic crises), relating these to preferences over institutions and social forces (such as the threat of revolution), and by providing the conditions under which strategic elites chose to reform (such as extending the electoral franchise). This is one of the reasons why their approach has become so influential.

Ultimately, they argued for a “window of opportunity” approach to the evolution toward democracy and more inclusive institutions. Much of the time, the commitment problems described above would block reform. But certain kinds of economic and political stresses could fracture the forces that blocked reform, at least for a time, and at least open a window for reform.

Again, one value of a theory is that it can make sense of fact patterns that might not otherwise be obvious. For example, later work argued that countries which enter democratization often experience fall in GDP beforehand. This pattern suggests that it isn’t economic growth which leads to democratization, but instead economic stresses breaking up existing coalitions.

Nobel prizes in economics are often given not because they provide a final answer, but because they launched volumes of future research. By that standard, the work by Acemoglu, Robinson, and Johnson surely qualifies for the award.

Cardiff Garcia of the Economic Innovation Group interviews Paul Krugman at The New Bazaar website (October 9, 2024). The interview has a number of points of interest. Here are two themes that caught my eye.

The study of “economic geography” focuses on why economics activity may tend to cluster, or to spread out, or to happen in certain locations. The general theory is that there are “agglomeration economies,” which refers to the idea that clusters of certain kinds of firms, workers, and suppliers benefit from being close together, perhaps with clusters of consumers too. But on the other side, clusters of economic activity can also lead to congestion, crime, even disease. Thus, there is a push and pull of the clustering of economic activity over time. In the last few decades, should improved information and communications technology lead economic activity to spread out, or to become more centralized? Krugman notes:

I mean the idea that agglomeration economies of one form or another exist is obviously not new … There’s forces pulling things together and forces pulling them apart. And the balance can tip one way or another. In fact, over the past 200 years, it has tipped first one way, then the other, then back again.

And the modern forms of agglomeration are different from the ones that prevailed in the 19th century. I like to say that the models that I was writing down 30 years ago had this kind of steampunk feel. They all kind of were very much focused on manufacturing and on industrial clusters, and we all lavished attention on these great stories — like the detachable collar and cuff industry of Troy, New York, and that sort of thing. And which mostly have gone away in the United States, although not totally — they do exist to some extent even in manufacturing, but these days if you really want to find old style industrial clusters, you go to China.

So if you actually look at — there’s a variety of measures — but basically, in the United States, there was a lot of regional convergence, convergence in incomes, convergence in basically regions becoming more similar, from the 1920s up until about 1980. Then in 1980, they started pulling apart again, and you started to see metropolitan areas with highly educated workforces pulling in even more educated people, pulling in even more of the information economy — and stranding regions that didn’t have those preconditions. … [F]for prep for a future conference, I’ve been looking at just kind of within New York State, and looking at greater Buffalo versus greater New York City. Buffalo was not all that poor or backward compared to New York City in 1980, and now it’s vastly poorer. …

So we’re back in a world where we have these extremely localized clusters. And modern communication technologies, Zoom, work from home, they make it possible to do some things without being in the place, but they’re not a full substitute. And in a rich society other things tend to matter. Particularly for high skill, high pay workers, you need amenities. High tech workers are not going to move to someplace in the middle of the country, even if they have excellent internet access, because where are the good restaurants? Where are the live concerts? So in some ways the fact that we’re rich enough that people can make decisions on that basis matter. So I think we’re in some ways back to the kind of unequalizing development that we had in the late 19th century

While it’s agreed (among economists) that productivity growth is essential to growth of the standard of living, the exact recipe for sustained productivity growth remains elusive. Indeed, standard calculations of productivity are carried out by trying to measure what part of overall output growth can be accounted for by changes in hours worked, skill level of workers, and capital investment–and then “productivity” is measured as the leftover or residual amount of output growth that could not be explained by the other factors. Because the economist Robert Solow did some of the classic work in this area, the productivity residual is sometime called the “Solow residual.” Here’s Krugman on the mystery that is productivity growth:

But if you look at a chart of U.S. potential GDP growth over the past 50 years — we had some pretty big political swings in there, big changes in tax policy. If you didn’t know that there have been changes of administration, changes in tax policy, you would never guess. It’s pretty much just a flat line. The reason is that economic growth is largely driven by the Solow [productivity] Residual. And who knows how to make that change very much.

If someone says “I have a policy that could raise potential GDP growth by a quarter of a percentage point,” I’d say, “okay, I could possibly believe that, show me the details.” If somebody says they could raise it by one percentage point, I think that’s crazy. Nobody knows how to do that.

My favorite Bob Solow quote, although there are so many of his … he was actually talking about Britain lagging behind post World War II, but it applies to lots of things, he said, “every attempt to explain this ends up in a blaze of amateur sociology.” That, in the end, we really just don’t know very much about why countries have different rates of economic growth. We still don’t know why productivity slowed down in the early ’70s. We kind of know why it had a bump for a while around IT [information technology], mid ’90s to mid 2000s. But ultimately, talking about innovations, there’s a big mystery now. It feels like we’ve had a lot of technological change these past 16, 17 years, and yet, if we believe our numbers, which maybe we shouldn’t, but if we believe our numbers, total factor productivity growth has been really pretty lousy for that whole period.

There are of course a variety of reasons unrelated to economic policy to choose between Kamala Harris, Donald Trump, and other candidates running for President of the United States. But as an economist …

It would be nice to vote for someone who acknowledges that the US budgets and the accumulating US debt are a problem, and has a serious proposal to address it. Proposals for additional tax cuts and spending are not an arithmetically likely solutions.

It would be nice to vote for someone who recognizes that Social Security and Medicare are facing real and severe solvency problems in the not-too-distant future, and offers some proposals to address them. Reducing taxes on Social Security benefits or increasing benefits for those with low incomes, whatever the justifications for such policies, will not help the solvency problem.

It would be nice to vote for someone who sees “inflation” as what happens when too much demand is chasing too few goods, not as an upsurge of greedy sellers nor as something where interfering with the Federal Reserve is a useful approach.

It would be nice to vote for someone who doesn’t think that government controls over prices–whether for rent or credit card interest rates or prescription drugs or groceries–are more than a temporary and dysfunctional band-aid. Also, it would be nice to vote for someone who has a plan for slowing the rise in US health care costs, without pretending that price controls are the answer.

It would be nice to vote for someone who has a specific plan to dramatically increase the quantity of housing being built across the United States while working within the constraints of local control over zoning and building codes, rather than focusing on handing out subsidies to potential buyers for the existing housing and potential builders of new housing, and hoping for the best.

it would be nice to vote for someone who emphasizes that the role of government in encouraging economic growth should be to focus on an educational system that provides a stream of skilled workers, support for research and development, and ensuring that competing firms have a chance to grow, and not on handing out big subsidies to favored industries (say, semiconductors or green energy).

It would be nice to vote for someone who makes a point of emphasizing that the US needs a much more “active labor market policy” for the unemployed: that is, a policy of government support for active job search, job training, and mobility between jobs, not just paying unemployment benefits.

It would be nice to vote for someone who emphasizes the potential gains to the US economy from a rise in skilled and legal immigration–and focuses on this issue separately from questions of border enforcement.

It would be nice to vote for someone who doesn’t believe that the problem of carbon emissions can be solved with ever-expanding subsidies, and is willing to support putting a price on carbon. It would also be nice to vote for someone who has an actual plan for dealing with the fact that more than half of all global carbon emissions come from countries in the Asia/Pacific region, with China alone accounting for nearly one-third of global carbon emissions.

It would be nice to vote for someone who cares a lot about professional day-to-day administration and oversight of government programs–spending, taxes, and regulations–and not just giving speeches about the goals of such programs.

Of course, I am aware that when Harris and Trump occasionally bump into these issues as they carom along the campaign trail, they do not offer identical verbiage. I am also aware that these kinds of issues are mentioned in policy papers hidden away on campaign websites, which I assume are unread by anyone, including the candidates. I am personally familiar with the practical need to vote for a “less bad” candidate, rather than a one I can wholeheartedly support. But still, it is disconcerting to me that the campaign discussion of so many major economic issues seems to me evasive at best, and misguided at worst.

Orley Ashenfelter interviews Samuel Bowles “on his deep interest in the causes of inequality & his work to transform economics” (“The Work Goes On” podcast, posted October 7, 2024). The entire interview is worthwhile: for example, I did not know that Bowles attended school in a tent in India when he was 11 years old (his father was Ambassador to India at the time; or that he worked for the government of Nigeria as a teacher in a remote area after graduating from college; or that he offered economic advice to Martin Luther King, Jr. Bowles recounts the episode t his way:

I had the good fortune of being asked by Dr. Martin Luther King if I wouldn’t give him some advice about economics and of course, I was thrilled. I knew Dr. King through anti-Vietnam War activity that he and I had engaged in at the time. And so, he said he would send me some questions and I said, “well, I’ll definitely get back to you.” I opened the envelope and here’s a set of questions. And they’re all about economics … I looked at these questions and I said, “these are damn good questions. I don’t have a clue how to answer these.” I didn’t know where to look. They were empirical questions, but also conceptual ones. Imagine, a new successful PhD, and this could have been the high point in my life. This is why I studied economics so I could actually get into the fray and help out and make the world a better place. But that was kind of a shocker for me, and I decided that there was something wrong that I was actually teaching the grad students in micro. And I decided then and there that I was either going to leave economics, I considered that very seriously, or I would try to change it. And that’s what I’ve been trying to do since.

Here’s a different insight, from research about inequality from pre-history and today. Bowles says:

I was curious about how inequality wealth moved over time, and as I studied more and more, I came to be interested in pre-history. That is a period of time in the past in which I think there was a really big change in how humans lived. And so, let’s go back five to ten thousand years, go back to the time around when agriculture started maybe 11-thousand years ago. And before then we were hunting and gathering populations, quite egalitarian. We’ve of course since measured wealth inequality, both human wealth and material wealth among hunters and gatherers. We’ve measured the intergenerational transmission of wealth in these societies. But the first farmers were very egalitarian. They had a very equal distribution of wealth. They were, in fact not much different from the hunters and gatherers.

So, the first thing we learned is that most people think that inequality really came on when farming happened. It’s not true. We had something like four or five millennia, millennia of farming before we had this big uptick in wealth inequality. Now of course, we measure wealth inequality in fairly elementary ways like the size of houses or the size of storage areas or how much the goods that are buried with people in their burials. But there’s a very dramatic change that takes place sometime around five-thousand years ago, and that was associated with a change in technology, the introduction of plows and oxen, which basically was a labor-saving device. The ox-drawn plow was the robot of the late Neolithic early bronze age because it displaced labor and it made land scarce and it made labor abundant. And then there was also the development of political concentration of political power in the form of governments. And those two things, the concentration of power in the hands of the well to do and the technology which made labor redundant and material goods scarce, that seems to be where the thing
started.

Now, obviously running forward, what’s extraordinary is that we achieved five-thousand years ago levels of inequality which are fairly much like what we have today and not much happened between five-thousand years ago and the present. There are some ups and downs but try to figure out anything that matters for wealth inequality, whether it’s modern-day Sweden, feudal Europe, the city states of Italy and so on. Wealth inequality is at a very high-level Gini coefficient of point-six or point-seven right across the board. So, we found two things really. We found a dramatic increase doubling of the Gini coefficients taking place about five-thousand years ago, and a remarkable constancy since then.

I really like the idea that “ox-drawn plow was the robot of the late Neolithic early bronze age.” Technological change is not a recent event.

Back in high school, the first book I read making the arguments against global corporations and globalization was Global Reach, which had been published a few years earlier back in 1974. Since then, anti-globalization arguments have been a consistent drumbeat in the background. I remember controversies over the “Tokyo round” of world trade talks in the 1970s, and “Uruguay round” of talks in the 1980s. I remember the extreme fears of how trade with Japan was going to overwhelm the US economy from the 1970s into the late 1980s, and then the fear about how trade with Mexico would injure the US economy (if the North American Free Trade Agreement was signed) in the early 1990s. I remember highly vigorous protests against globalization and the World Trade Organization in 1999 in Seattle, and then in other cities. And of course, I’m aware of anti-globalization protests in the last quarter-century, as well.

Even as the US and the world economy have evolved in the last half-century, it feels to me as if the arguments against and for globalization have not changed very much. This seems odd. Surely, the accumulation of experience with globalization should influence the arguments for and against?

Jason Furman is a few years younger than I am, but he expresses sentiments that I share in “Globalization With Minimal Apologies,” delivered as a keynote address at a World Trade Organization Public Forum (September 11, 2024). Furman argues:

I first started learning economics at university in the late 1980s. At that point in time, a certain amount of economic theory said that there should be convergence among countries, where poorer countries grow faster than richer countries. But that theory wasn’t working in practice. Global inequality was growing, the rich countries were growing faster than the poor countries and pulling further apart from them. You might be tempted by that observation to subscribe to theories like Dependency Theory in Latin America, that rich countries were getting rich at the expense of poor countries, that trade was zero sum, and that to reverse this global inequality somehow needed to separate yourself from the rest of the world.

If you were sitting in the United States when I first started learning economics, you were 20 years into a dramatic productivity slowdown, a dramatic reduction in the growth of living standards, an increase in inequality, and you were also looking at other countries—in our case, at the time, Japan—worried that somehow they were getting rich at your expense and taking advantage of the United States. …

Now, after a quarter century of hyper globalization, the poor countries are, on average, growing faster than the rich countries. That’s happened on a sustained basis for about 30 years now. That’s even true if you take China out of the equation and look at the rest of the developing and emerging economies. This has happened because growth has increased in developing and emerging economies not because it has slowed in the rich countries. In fact, if anything, relative to when I first started studying economics, productivity growth has picked up, especially in the frontier economy of the United States.

This isn’t just an abstract set of economic statistics. … [O]ver the last quarter century, a billion people have been lifted out of extreme poverty. Even at the same time that the global population has increased by 2 billion, so if you look at the share of people in deep poverty around the world, it’s fallen by 70 percent. It’s not just poverty. It is life expectancy, maternal mortality, literacy, all of the things that matter to a good life, all of the things that are most important to us as humans, have gotten dramatically better over these 25 years.

Part of the improvement has been a function of the increase in incomes. Life expectancy, maternal mortality, all of those are very much a function of income. But also, amazingly, for any given income, you see less maternal mortality, less child mortality, higher life expectancies than you did before. So, we’ve also gotten more efficient at translating GDP into the things that matter for people.

Yes, globalization isn’t the only factor in these changes. But as Furman points out: “[N]o country has been very successful anywhere in the world without a very big component of that success being that their country is a major part of the phenomenon of globalization. And conversely, countries that have tried to separate themselves from it have done the worst job participating in this miracle that I’ve talked about.”

Even if one just focuses on the US economy, it seems clear that US economy did not crumble under competitive pressure from Japan, and has not crumbled under competitive pressure from China. Yes,. globalization has disrupted industries and jobs, but such disruptions are standard for as economies evolve and grow. For example, the disruptions in the US economy earlier in the 20th century as the workforce shifted from agriculture to manufacturing and then from manufacturing to services, or the shifts as the US population and economy shifted toward “sun belt” states of the south and west, were also considerable. The revolution in widespread use of information technology and its applications, especially since the internet entered into widespread use in the 1990s, would have disrupted the US economy for reasons having nothing to do with international trade. Indeed, the US economy, with its mammoth internal market, is much less disrupted by trade that most other countries in the world: for example, US imports of goods and services are about 15% of GDP, but imports for the average country in the world is 30% of GDP–and it’s much higher for many smaller economies.

Furman digs into some of reasons why positive effects of globalization are not more widely appreciated, and I’ll let you read that part of his essay on your own. But I did want to emphasize one of his other themes: the resilience of globalization. Furman writes:

I’ve been hearing about the imminent end of globalization for pretty much my entire career. Yet globalization has been much more like a dandelion than it is like an orchid. Dandelions can thrive no matter what you throw at them. Orchids are very sensitive and need to be nurtured with exactly the right conditions. Trade and other types of globalization are like a dandelion because the benefits are so large. All the things that I was talking about, all the gains from trade, are precisely why it is so strong and so resistant.

The nature of globalization does seem to be shifting: in particular, services and information flows delivered across national borders are becoming more important, compared with physical goods. But although government policy decisions will shape the course of globalization, the the fundamental drivers of globalization are about how gains from trade across international borders benefit people’s lives.

Each fall and spring, the Brookings Institutions holds a conference with a set of papers from prominent economists on leading policy topics, with comments and discussion. The Fall 2024 conference happened last Thursday and Friday. You can go to the website and spend hours watching the whole thing, or you can pick and choose through the topics, and read over some of the conference discussion papers and comments. If you want to know why in Washington policy circles it seems as if “everyone knows” some economic fact or insight, the source of that common knowledge can, reasonably often, be tracked back to papers presented at BPEA. Here’s the agenda, with some links:

Symposium on Federal Reserve’s Monetary Policy Framework Review

“Challenges Around the Fed’s Monetary Policy Framework and Its Implementation,” by William English and Brian Sack

“Considerations for a Post-Pandemic Monetary Policy Framework,” by Charles Evans

“Did the Federal Reserve’s 2020 Policy Framework Limit Its Response to Inflation? Evidence and Implications for the Framework Review,” by Christina Romer and David Romer

• English & Sack conference draft

• Evans conference draft

• Romer & Romer conference draft

“Dynamic Scoring: A Progress Report on Why, When, and How,” by Douglas Elmendorf, Glenn Hubbard, and Heidi Williams

• Elmendorf et al conference draft

“The Economic Impacts of Clean Power,” by Costas Arkolakis and Conor Walsh

• Arkolakis & Walsh conference draft

“Robust Fiscal Stabilization,” by Alan Auerbach and Danny Yagan

• Auerbach & Yagan conference draft

“u* = √uv: The Full-Employment Rate of Unemployment in the United States,” by Pascal Michaillat and Emmanuel Saez

• Michaillat & Saez conference draft

“The Economics of Sanctions: From Theory Into Practice,” by Oleg Itskhoki and Elina Ribakova

• Itskhoki & Ribakova conference draft

Economists commonly think about technology as an idea, but in one way or another, the technology interacts with physical forms–and these physical forms affect how the technology is applied and its social effects. In his da Vinci Medal Address, Donald MacKenzie considers some implications of this idea in “Material Political Economy” (Technology and Culture, July 2024).

One classic example he mentions is the conflict that arose some centuries ago, in feudal times, over how the technology of how grain should be milled. Feudal lords typically preferred a centralized system, in which commoners brought the grain to a watermill or windmill owned by the lord, and paid the lord for milling the grain. However, many commoners would have preferred to avoid the payment to the lord, and instead to mill the grain by hand. In turn, feudal lords sometimes sought to destroy handmills, where they could do so. In this setting, the technology choice is obviously not just about efficiency in an abstract sense, but about the interaction of efficiency with preexisting social structure.

As a modern example, I was intrigued by MacKenzie’s discussion of ultrafast high-frequency trading (HFT) for financial firms. He points out that when these firms were being established in the US in the 1990s, “HFT firms were sometimes excluded from trading or faced material barriers that protected incumbents’ slower systems.” But his focus is on more recent developments.

Just how fast is “ultrafast”? Each year, the European futures exchange Eurex publishes data from which we can infer the response times of the fastest HFT algorithms. Eurex’s 2023 measurements suggest a state-of-the-art response time (to a packet of market data that triggers a trading system’s action) of 8 nanoseconds, or billionths of a second. In a nanosecond, the fastest physically possible signal, light in a vacuum, travels only around 30 cm, or roughly a foot. That is not simply a helpful yardstick of HFT’s speed: getting messages to travel as close as possible to the speed of light in a vacuum is an important practical concern in HFT. Fiber-optic cable, for example, is not fast enough, because the refractive index of the glass at the core of such a cable slows laser-light signals to around two-thirds of light’s speed in a vacuum. Where possible, therefore, HFT firms send trading data and orders by microwave, millimeter-wave, or laser-light signals transmitted through the atmosphere, where they travel almost as fast as in a vacuum …

Thus, high-frequency trading is not an abstract technological innovation, but something embodied in the world of material, distance, light, and microwaves. Mackenzie writes:

HFT programmers cannot afford to consider the computer an abstract machine, as possibly presented during their college education. It must be seen as an ensemble of metal, semiconductors, and plastic through which signals pass, and ensuring that they do so as quickly as possible is an all-pervasive concern. For example, the preferred programming language in HFT is C++, which allows “close-to-the-metal” programming, not having to operate through layers of abstraction as with other languages. Since around 2010, furthermore, a conventional computer system, even if programmed in C++, is in many markets not fast enough for HFT. Trading algorithms are directly programmed into the hardware of the silicon chips known as field-programmable gate arrays (FPGAs) … There have been repeated rumors of firms moving beyond FPGAs to fully bespoke integrated circuits …

One result is what MacKenzie calls “speed races.” In the HPT universe, algorithms are programmed to react very quickly to new information. But when the fastest algorithms place orders and react first, and prices change, then the slightly slower algorithms realize that they are reacting to “stale” information. They frantically try to cancel orders, while faster algorithms try to take advantage of their “stale” bigs.

Either the bids for or offers of the underlying shares placed by market-making algorithms immediately become “stale,” as market participants describe them: if, for example, the price of the future has fallen, buying the shares at the preexisting bid price is likely to incur a loss. So market-making algorithms rush to cancel those stale bids as quickly as possible, while liquidity-taking algorithms race to execute against the stale bids before they are canceled. The difference between winning and losing those speed races, the Eurex data suggest, is now measured in billionths of a second.

These kinds of “speed races” are happening every minute. Another physical manifestation of this technology involves the towers and locations for transmitting signal from Chicago-based markets to New Jersey-based high frequency traders.

The need for ultrafast speed makes very specific physical locations exceptionally valuable, and those who own or control them can therefore exact rent. The fiber-optic cables or wireless links that transmit data from one financial trading computer data center to another have to follow as closely as possible the geodesic, the shortest path on the surface of the earth between the two data centers. In 2010, computer scientist Alex Pilosov led the building of the first microwave link for HFT between Chicago, where futures are traded, and northern New Jersey, the site of the data centers where U.S. shares are traded. Pilosov kept a low profile in this work, to avoid alerting potential competitors, but around a year later the owners of the attractively located microwave towers where he had leased space told him that others were also trying to place antennae on those towers. He says, “I was like, ‘Well, I’ll tell you what’s going on but you have to promise me that you have to charge them three times what you’re charging me. And I promise you that they will pay.’ And that’s what happened. That happened.” Similarly, Mike Persico, who built both millimeter-wave and atmospheric-laser links at the New Jersey share-trading data centers, reports that the owners of a tall building close to the relevant geodesics where this equipment could be placed suddenly possessed a very valuable resource. “Sometimes,” says Persico, “these landlords end up with the equivalent of a Willy Wonka golden ticket, because when they purchase[d] these properties, this was the furthest thing from their mind, and all of a sudden . . . it becomes very lucrative.”

Even for an economist, it’s possible to doubt whether the resources invested in ultra-fast trading are improving the economy for the average worker or consumer. But I’ll also add that development of new technologies often takes circuitous routes through different applications, and I wouldn’t be surprised to find that ultrafast communication, over time and as the price falls, turns out to have uses as-yet undreamed of.