New technologies, occupational tasks, and earnings inequality in the United States

A woman signs up for a resume writing workshop. A new working paper finds that the adoption of new technologies such as Microsoft Office and Java leads to increases in pay inequalities between more- and less-educated workers.

A new working paper published today by the Washington Center for Equitable Growth explores the relationship between the adoption of new technologies in occupations across the U.S. economy and how technology usage within occupations is contributing to growing income inequality. The four authors—economists Enghin Atalay and Phai Phongthiengtham at the University of Wisconsin-Madison, Sebastian Sotelo at the University of Michigan, and Daniel Tannenbaum at the University of Nebraska—examine how the adoption of these new technologies, among them the Microsoft Office suite of software products, the operating language Unix, and the programming language Java, affect the demand for routine and nonroutine occupational tasks, and thus earnings inequality, between 1960 and 2000.

The four authors build upon their earlier working paper published in 2017, which showed that since the 1960s, occupational tasks in the United States have shifted broadly away from routine tasks and toward nonroutine tasks—a shift that occurred within occupations rather than between them. I wrote about these changes in the nursing occupation, for example, throughout the 20th century here. For that earlier working paper, the authors constructed an open source database of occupational characteristics, including skills requirements, technology use, and work activities, from text analyses of job ads published between 1940 and 2000 in The Boston Globe, The New York Times, and The Wall Street Journal. Now, in the new working paper, they built upon this prior work with data on 48 new technologies to document the relationship between the tasks that workers perform and the technologies they use.

The authors use these data to construct a general equilibrium model that shows the introduction of new technologies shifted workers’ tasks within occupations toward nonroutine analytical tasks. This increase in demand for nonroutine analytical tasks such as problem solving, intuition, creativity, and persuasion translates into greater demand for highly educated workers who are relatively better at conducting these types of tasks.

They find that the adoption of new technologies in turn led to an increase in pay inequality between more- and less-educated workers. Their analysis shows that the introduction of a range of new technologies is responsible for 17 percent of the increase of the difference in earnings between college- and high school-educated workers from 1960 and 2000. The one exception is Microsoft Office, which instead increased demand for nonroutine interactive tasks and thus slightly decreased the overall skills premium and earnings inequality.

The four researchers leveraged their rich database on occupational characteristics such as task content, skill requirements, and technology usage during the post-industrial period to ask important questions about occupational sorting and the advent of new technologies on earnings inequality. This same database could be leveraged by others interested in how labor markets have changed over time. Open questions about shifts in educational requirements among and between occupations or the changes in characteristics of internships, apprenticeships, and other temporary training opportunities could still be explored. There are plenty of analyses that researchers could conduct with these data to examine changes in particular occupations over the course of the 20th century.

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Economic history becomes big data: Ran Abramitzky, Roy Mill, and Santiago Pérez: Linking individuals across historical sources: A fully automated approach

Economic history becomes BIG DATA: Ran Abramitzky, Roy Mill, and Santiago Pérez: Linking Individuals Across Historical Sources: a Fully Automated Approach: “Linking individuals across historical datasets relies on information such as name and age that is both non-unique and prone to enumeration and transcription errors…

…These errors make it impossible to find the correct match with certainty. We suggest a fully automated method for linking historical datasets that enables researchers to create samples that minimize type I (false positives) and type II (false negatives) errors. The first step of the method uses the Expectation-Maximization (EM) algorithm, a standard tool in statistics, to compute the probability that each two observations correspond to the same individual. The second step uses these estimated probabilities to determine which records to use in the analysis. We provide codes to implement this method…

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As chief acolyte of the “hysteresis view”, I must protest!: Benoît Cœuré: Scars that never were? Potential output and slack after the crisis

As Chief Acolyte of the “hysteresis view”, I must protest! The very sharp Benoît Cœuré thinks “hysteresis” means that the sharp downward shock to the level of production in 2008-2010 has caused a permanent fall in the long-run growth rate. But that is not what we ever said: We said that not all of the sharp downward shock to the level of production in 2008-2010 would be made up, not even after decades. We are now one decade after the shock, and so far “not all” means “20%”—only one-fifth of the gap between production after the downward shock and the previous trend has been recouped. I hope and expect that 20% to grow over the next two decades. But it is not going to reach 100%: the “hysteresis view” has proved correct: Benoît Cœuré: Scars that never were? Potential output and slack after the crisis: “To be clear… I do believe that deep recessions can have effects on the supply capacity of the economy that may take some time to unwind…

…The crisis has affected the “intensive margin” of the euro area labour market… people working involuntarily in part-time or temporary positions. But it is not plausible that those effects could be as dramatic and long-lasting as the “hysteresis view” would suggest. Since these workers remained attached to the labour market, they represented a broader definition of slack rather than a new category of structurally unemployed workers…. Such people… had been scratched by the crisis, but not necessarily scarred. This is not to say that these scratches are not deep…. Current estimates of structural unemployment do indeed confirm that the initial revisions were exaggerating the impact the recession would have on labour force participation. And they might currently exaggerate the impact the current expansion might have on lowering structural unemployment…. In other words, it may well be that potential growth fell by less than we estimated during the depths of the crisis, and it is rising by less than we believe as the economy strengthens…. Both the sudden drop in estimated potential growth in 2009, and the sharp rebound thereafter, are likely to be statistical artefacts, at least to some extent….

I would argue that there are two, largely complementary, reasons for cautious optimism. Both are related to the fourth industrial revolution, or the digitisation and automation of our economies. The first… [is] the time it… takes for new technologies to reach critical mass. History suggests that technology usually takes considerable time…. It could be less of a concern that we are not yet seeing the effects of digitisation…. It may simply be a matter of time…. Second… transformation of business models along the lines of digitisation is typically more difficult in periods of weak demand…. This is different from hysteresis effects, where some observers argue that we are permanently entering a “1% economy” of low growth, low inflation and low neutral rates of interest as firms invest less in new capacity and technology, causing productivity growth to stabilise at lower levels and weak potential growth to become self-fulfilling…. Another scratch so to speak, not a scar…

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Equal access to a good education is not just about sound school budgets

Children born into poor and wealthy families alike should have an equal chance of achieving their dreams. But this is not the reality in practice. The income of a child’s parents is strongly correlated with the child’s life outcomes—such as educational attainment, occupational choice, or earnings—in adulthood. Opportunity is far from equal.

Recent research shows that the strength of the association between the child’s parents’ income and her own adult income—a useful measure of inequality of opportunity—varies a great deal between different places across the United States. In some areas, such as Salt Lake City and Los Angeles, the intergenerational association is small, which means the chances for children from low-income families to get ahead are close to those of children from high-income families, so opportunity is relatively equal.


Updated Working Paper
Inequality of educational opportunity? Schools as mediators of the intergenerational transmission of income


In other areas, however, such as Cincinnati and Memphis, intergenerational associations are strong, meaning differences in life outcomes between children from low-income versus high-income families are large and that opportunity is far from equally distributed. Children from low-income families are less likely to do better than their parents, while children of high-income parents will stay in the upper income brackets.

What explains the geographic variation in this intergenerational transmission of economic status? What is going right in Los Angeles and Salt Lake City but going wrong in Cincinnati and Memphis? A natural hypothesis is that these differences have to do with the quality of the local schools. Schools should be an engine of economic mobility, allowing bright, hardworking children to advance regardless of their family backgrounds. But schools are themselves often unequally distributed, and many schools serving poor children are poorly funded and low-performing.

So perhaps some low-transmission, high-economic-equality cities such as Salt Lake City and Los Angeles simply have better, more equal school systems that produce better and more equal educational outcomes and thus more equal incomes as adults. Or perhaps the differences have nothing to do with schools and instead relate to differences in labor markets in these cities—perhaps when job opportunities are more plentiful and pay is more equal, it does not matter as much whether a child had access to good schools.

In a recent paper, I assess the contributions of education and labor markets to differences across regional labor markets—commuting zones—in the United States in the intergenerational transmission of economic status. To do this, I ask whether parental income matters more for children’s educational outcomes, such as test scores and college completion, in local areas where there is stronger transmission of parental income to children’s incomes, as would be expected if the school system were a key link in this transmission.

I find that there is a great deal of variation in educational transmission across the country. Some areas do much better than others at producing closer-to-equal test scores for children from poor and rich families. Yet areas with small test-score gaps do not have lower-than-average income transmission. In other words, differences in access to high-quality elementary and secondary schools are not a key channel driving the strength of the association between parents’ incomes and their children’s incomes when they reach adulthood.

There is a bit more evidence that higher education is an important part of the story. Gaps in college enrollment and graduation are associated with intergenerational income transmission, though even here, the association is too small to account for much of the variation between regions in income transmission.

The upshot: There is little evidence that differences in the quality of primary, secondary, or postsecondary schools, or in the distribution of access to good schools, are a key mechanism driving variation in intergenerational mobility. The evidence instead points toward other factors influencing income inequality. In particular, labor markets seem to be quite important. Even when children’s test scores and educational attainment are held constant, children from poor families have higher adult earnings when they grow up in low-transmission (greater-opportunity) areas than when they are from low-opportunity, high-transmission areas. This is in part because the high-transmission areas have unusually large returns to human capital, or stronger relationships between education and earnings. Children from wealthy families do better in school than children from poor families everywhere, so a labor market that puts inordinate weight on skill will be unusually favorable to the wealthy children.

Marriage patterns also seem to matter. In cities and regions where income transmission is weaker, the “marriage gap” between those in their mid-20s from low- and high-income families is much smaller, implying that children from low-income families are more likely to have spouses contributing toward the family budget.

Together, differences in labor markets and marriage patterns account for the great majority of variation in intergenerational mobility across cities and regions in the United States. Variation in relative skill accumulation—the only portion plausibly related to schools—accounts for only 11 percent of the differences between high- and low-opportunity areas, with the rest due to marriage patterns (about 40 percent) and differences in labor market outcomes operating through channels other than skill accumulation, such as the return to skill in the labor market, discrimination, or referral networks that offer advantaged children a leg up in the job market (nearly half). (See Figure 1.)

Figure 1

Understanding better how policymakers can promote equality of opportunity in education as well as in the economy overall should remain a top priority. Educational quality is certainly a key tool to improve opportunity, yet my research indicates that it is far from the whole story. The educational system plays only a small role in explaining differences between high- and low-opportunity areas. Labor market institutions—such as minimum wages, the ability to form and join unions, the career structures of local industries, and other determinants of earnings inequality—are likely to play much larger roles and are also likely to be more powerful levers with which to promote equality of opportunity.

The principle of equal access to the pursuit of happiness is deeply rooted in American history and society. We have never accomplished it, but it remains our country’s highest aspiration. The best measure of the progress we have made toward this goal is the extent to which the circumstances of a child’s birth do or do not predict his or her life outcomes. By this measure, as by others, we have very far to travel. To do better, we need to examine all of the different ways that our society and economy work to erect hurdles in front of children from disadvantaged families—whether those hurdles are limiting access to educational opportunity or ensuring that the children will do worse in the labor market than their more advantaged peers even if they do well in school.

(This post updates an earlier post that ran on August 15, 2017, using updated data and analysis from the author’s April 2018 revision of his working paper.)

—Jesse Rothstein is a professor of public policy and economics at the University of California, Berkeley, and director of the Institute for Research on Labor and Employment at the University of California, Berkeley.

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Kevin Bryan: The 2018 John Bates Clark: Parag Pathak

Kevin Bryan: The 2018 John Bates Clark: Parag Pathak: “Consider the old ‘Boston mechanism’…. Everyone would be allocated their first choice if possible…

…If a school is oversubscribed, some random percentage get their second choice, and if still oversubscribed, their third, and so on. This mechanism gives clear reason for strategic manipulation: you certainly don’t want to list a very popular school as your second choice…. Pathak and Abdulkadiroglu show… sophisticated parents may prefer the old Boston mechanism because it makes them better off at the expense of the less sophisticated! The latter concern is a tough one for traditional mechanism design to handle…. There remains some debate about what is means for a mechanism to be “better” when some agents are unsophisticated or when they do not have strict preferences over all options….

Pathak has also contributed… to the literature on large matching markets…. Where both sides have preferences… there are no stable matching mechanisms where both sides want to report truthfully…. When the market is large, it is (in a particular sense) an equilibrium for both sides to act truthfully; roughly, even if I screw one student I don’t want out of a slot, in a thick market it is unlikely I wind up with a more-preferred student to replace them. There has more recently been a growing literature on what really matters in matching markets: is it the stability of the matching mechanism, or the thickness of the market, or the timing, and so on…

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Mark Thoma on the 2009-2015 Dark Age of macroeconomics: Weekend Reading

Weekend Reading: I would have said that the Dark Age is over. But the behavior of professional Republican economists formerly of note and reputation—and I am looking at you, Robert Barro, Harvey Rosen, Douglas Holtz-Eakin, Larry Lindsey, John Taylor, John Cochrane Glenn Hubbard, Michael Boskin, Charlie Calomiris, Jim Miller, Jagdish Bhagwati, and George Shultz: you know better. And, Marty Feldstein, you really should not have written your defense of Trump’s China tariffs. The 2009-2014 Dark Age looks to me, mostly, like a deliberate decision to be stupid and not think issues through. This one looks like a last, vain attempt to gain some influence on Republican policy: Mark Thoma (2009): “A Dark Age of Macroeconomics”: “Quoting an email [from Paul Krugman], economists who…

…have spent their entire careers on equilibrium business cycle theory are now discovering that, in effect, they invested their savings with Bernie Madoff…

I think that’s right, and as they come to this realization, we can expect these economists to flail about defending the indefensible, they will be quite vicious at times, and in their panic to defend the work they have spent their lives on, they may not be very careful about the arguments they make. I don’t know if the defenders of the classical faith have come to this realization yet, at least beyond the subconscious level, and the profession will most likely move in the same old direction for awhile due to research inertia if nothing else. But I think what has happened will have a much bigger impact on the profession and the models it uses to describe the world than most economists currently realize:

A Dark Age of macroeconomics… Paul Krugman: “Brad DeLong is upset about the stuff coming out of Chicago these days—and understandably so….

…First Eugene Fama, now John Cochrane, have made the claim that debt-financed government spending necessarily crowds out an equal amount of private spending, even if the economy is depressed—and they claim this not as an empirical result, not as the prediction of some model, but as the ineluctable implication of an accounting identity. There has been a tendency, on the part of other economists, to try to provide cover to claim that Fama and Cochrane said something more sophisticated than they did. But if you read the original essays, there’s no ambiguity—it’s pure Say’s Law, pure “Treasury view”, in each case…. Both… are asserting that desired savings are automatically converted into investment spending, and that any government borrowing must come at the expense of investment—period. What’s so mind-boggling about this is that it commits one of the most basic fallacies in economics—interpreting an accounting identity as a behavioral relationship….

So how is it possible that distinguished professors believe otherwise? The answer, I think, is that we’re living in a Dark Age of macroeconomics… knowledge had been lost, that so much known to the Greeks and Romans had been forgotten by the barbarian kingdoms that followed…. The knowledge that S=I doesn’t imply the Treasury view—the general understanding that macroeconomics is more than supply and demand plus the quantity equation—somehow got lost in much of the profession….

Given their understanding of… the basics not the hard stuff, it’s becoming a lot easier to understand how financial economists missed the developing bubble and the effect it would have…. One thing I’ve learned from the current episode is not to automatically trust that the most well-known economists in the field have done due diligence before speaking out on an issue, even when that issue is of great public importance, or even to trust that they’ve thought very hard about the problems they are speaking to.

I used to think that, for the most part, the name brands in the field would live up to their reputations, that they would think hard about problems before speaking out in public, that they would provide clarity and insight, but they haven’t. In fact, in many cases they have undermined their reputations and confused the issues.

People have been deferential in the past, myself included, and these people have been given authority in the public discourse-even when they are demonstrably wrong their arguments show up in the press as a “he said, she said” presentation. But, unfortunately for the economics profession and for the public generally, the so called best and brightest among us have not lived up to the responsibilities that come with the prominent positions that they hold…


Should-Read: Paul Krugman (2009): Economists, ideology, and stimulus: “Mark Thoma and Brad DeLong are both, in slightly different ways, perturbed by the state of debate over fiscal stimulus. So am I…

…This has not been one of the profession’s finest hours. There are certainly legitimate arguments against spending-based fiscal stimulus. You can worry about the burden of debt; you can argue that the government will spend money so badly that the jobs created are not worth having; and I’m sure there are other arguments worth taking seriously.

What’s been disturbing, however, is the parade of first-rate economists making totally non-serious arguments against fiscal expansion. You’ve got John Taylor arguing for permanent tax cuts as a response to temporary shocks, apparently oblivious to the logical problems. You’ve got John Cochrane going all Andrew-Mellon-liquidationist on us. You’ve got Eugene Fama reinventing the long-discredited Treasury View. You’ve got Gary Becker apparently unaware that monetary policy has hit the zero lower bound. And you’ve got Greg Mankiw — well, I don’t know what Greg actually believes, he just seems to be approvingly linking to anyone opposed to stimulus, regardless of the quality of their argument.

Needless to say, everyone I’ve mentioned is politically conservative. That’s their right: economists are citizens too. But it’s hard to avoid the conclusion that all of them have decided on political grounds that they don’t want a spending-based fiscal stimulus — and that these political considerations have led them to drop their usual quality-control standards when it comes to economic analysis.

Has there been any comparable outbreak of mass bad economics from good liberal economists? I can’t think of one, although maybe that’s my own politics showing. In any case, what’s happening now is pretty disturbing…


Should-Read: Paul Krugman (2011): It’s Not About Welfare States: “Whenever a disaster happens, people rush to claim it as vindication for whatever they believed before. And so it is with the euro…

…As an aside, the interesting thing about the euro from a political point of view is the way it cut across the ideological spectrum. It was hailed by the Wall Street Journal crowd, who saw it as a sort of milestone on the way back to gold, and by many on the British left, who saw it as a way to create an alliance of social democracies. It was criticized by Thatcherites, who wanted to be free to move Britain in an American direction, and by American liberals, who believed in the importance of discretionary monetary and fiscal policy.

But now that the thing is in trouble, people on the right are spinning this as a demonstration that … strong welfare states can’t work.

So, just to say what should be obvious, the countries in trouble are not in any way marked out by having especially generous welfare states. You can use a number of indicators; here’s the OECD measure of “social expenditure”, measuring (separately and together) both public spending and private spending that is channeled and regulated by public policy, like US employer-based health insurance.

Sweden, with the largest social expenditure, is doing just fine. So is Denmark. And Germany, which is the up side of the pulling-apart euro, has a bigger welfare state than the GIPS.

Not that the facts will convince anyone on the right, but the blame-the-welfare-state meme is nonsense…


Should-Read: Mark Thoma (2009): “Can Economists Be Trusted?” “Are There Ever Any Wrong Answers in Economics?”: “Let’s ask another question. Does Greg Mankiw believe in the classical model he is using to defend Fama (in the classical model, the LM curve is vertical, and a vertical LM curve leads to a vertical supply curve, and to the result that demand side policies such as a change in government spending or taxes cannot change real output)?…

…I disagree … that the LM curve is vertical… Introspection is not a particularly reliable way to measure elasticities. There is a substantial empirical literature on money demand that demonstrates that it is interest-elastic…. According to Ball, the interest semi-elasticity of money demand is -0.05: This means that an increase in the interest rate of one percentage point, or 100 basis points, reduces the quantity of money demanded by 5 percent.

How far off is the vertical LM case as a practical matter? One way to answer this question is to look at the fiscal-policy multiplier. In chapter 11 of my intermediate macro text, I give the government-purchases multiplier from one mainstream econometric model. If the nominal interest rate is held constant, the multiplier is 1.93. If the money supply is held constant, the multiplier is 0.60. If the LM curve were completely vertical, the second number would be zero…

Greg has been pretty good at saying there is a lot of uncertainty about the fiscal policy multipliers, and about explaining why estimates differ across studies, and why he favors one set of estimates over another, so I don’t want to come down too hard on his disagreement with the 1.93 figure in his “favorite textbook”, but it does seem like he is defending Fama with a model that he does not believe in…


Should-Read: Paul Krugman (2010): The Instability of Moderation: “Brad DeLong writes of how our perception of history has changed in the wake of the Great Recession…

…We used to pity our grandfathers, who lacked both the knowledge and the compassion to fight the Great Depression effectively; now we see ourselves repeating all the old mistakes. I share his sentiments.

But watching the failure of policy over the past three years, I find myself believing, more and more, that this failure has deep roots–that we were in some sense doomed to go through this. Specifically, I now suspect that the kind of moderate economic policy regime Brad and I both support–a regime that by and large lets markets work, but in which the government is ready both to rein in excesses and fight slumps–is inherently unstable. It’s something that can last for a generation or so, but not much longer.

By “unstable” I don’t just mean Minsky-type financial instability, although that’s part of it. Equally crucial are the regime’s intellectual and political instability.

 

Intellectual instability: The brand of economics I use in my daily work–the brand that I still consider by far the most reasonable approach out there – was largely established by Paul Samuelson back in 1948, when he published the first edition of his classic textbook. It’s an approach that combines the grand tradition of microeconomics, with its emphasis on how the invisible hand leads to generally desirable outcomes, with Keynesian macroeconomics, which emphasizes the way the economy can develop magneto trouble, requiring policy intervention. In the Samuelsonian synthesis, one must count on the government to ensure more or less full employment; only once that can be taken as given do the usual virtues of free markets come to the fore.

It’s a deeply reasonable approach–but it’s also intellectually unstable. For it requires some strategic inconsistency in how you think about the economy. When you’re doing micro, you assume rational individuals and rapidly clearing markets; when you’re doing macro, frictions and ad hoc behavioral assumptions are essential.

So what? Inconsistency in the pursuit of useful guidance is no vice. The map is not the territory, and it’s OK to use different kinds of maps depending on what you’re trying to accomplish: if you’re driving, a road map suffices, if you’re going hiking, you really need a topo.

But economists were bound to push at the dividing line between micro and macro – which in practice has meant trying to make macro more like micro, basing more and more of it on optimization and market-clearing. And if the attempts to provide “microfoundations” fell short? Well, given human propensities, plus the law of diminishing disciples, it was probably inevitable that a substantial part of the economics profession would simply assume away the realities of the business cycle, because they didn’t fit the models.

The result was what I’ve called the Dark Age of macroeconomics, in which large numbers of economists literally knew nothing of the hard-won insights of the 30s and 40s–and, of course, went into spasms of rage when their ignorance was pointed out.

 

Political instability: It’s possible to be both a conservative and a Keynesian; after all, Keynes himself described his work as “moderately conservative in its implications.” But in practice, conservatives have always tended to view the assertion that government has any useful role in the economy as the thin edge of a socialist wedge. When William Buckley wrote God and Man at Yale, one of his key complaints was that the Yale faculty taught–horrors!–Keynesian economics.

I’ve always considered monetarism to be, in effect, an attempt to assuage conservative political prejudices without denying macroeconomic realities. What Friedman was saying was, in effect, yes, we need policy to stabilize the economy–but we can make that policy technical and largely mechanical, we can cordon it off from everything else. Just tell the central bank to stabilize M2, and aside from that, let freedom ring!

When monetarism failed–fighting words, but you know, it really did—it was replaced by the cult of the independent central bank. Put a bunch of bankerly men in charge of the monetary base, insulate them from political pressure, and let them deal with the business cycle; meanwhile, everything else can be conducted on free-market principles.

And this worked for a while–roughly speaking from 1985 to 2007, the era of the Great Moderation. It worked in part because the political insulation of central banks also gave them more than a bit of intellectual insulation, too. If we’re living in a Dark Age of macroeconomics, central banks have been its monasteries, hoarding and studying the ancient texts lost to the rest of the world. Even as the real business cycle people took over the professional journals, to the point where it became very hard to publish models in which monetary policy, let alone fiscal policy, matters, the research departments of the Fed system continued to study counter-cyclical policy in a relatively realistic way.

But this, too, was unstable. For one thing, there was bound to be a shock, sooner or later, too big for the central bankers to handle without help from broader fiscal policy. Also, sooner or later the barbarians were going to go after the monasteries too; and as the current furor over quantitative easing shows, the invading hordes have arrived.

 

Financial instability: Last but not least, the very success of central-bank-led stabilization, combined with financial deregulation – itself a by-product of the revival of free-market fundamentalism–set the stage for a crisis too big for the central bankers to handle. This is Minskyism: the long period of relative stability led to greater risk-taking, greater leverage, and, finally, a huge deleveraging shock. And Milton Friedman was wrong: in the face of a really big shock, which pushes the economy into a liquidity trap, the central bank can’t prevent a depression.

And by the time that big shock arrived, the descent into an intellectual Dark Age combined with the rejection of policy activism on political grounds had left us unable to agree on a wider response.

In the end, then, the era of the Samuelsonian synthesis was, I fear, doomed to come to a nasty end. And the result is the wreckage we see all around us…

#weekendreading #shouldread
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Paul Krugman (2012): Economics in the crisis: Weekend Reading

School of Athens

Weekend Reading: This still cuts me to the quick. I am not at all a depressive personality—but if I were, this would have me pulling an Oblomov every time I remember that I am part of a professional discipline that was, collectively, so useless in 2009-12, and that I used to praise the analytical acumen and tell people to listen to and take seriously so many of those who made us so useless: Paul Krugman (2012): Economics in the Crisis: “We’re now in the fourth year of a truly nightmarish economic crisis. I like to think that I was more prepared than most for the possibility that such a thing might happen; developments in Asia in the late 1990s badly shook my faith in the widely accepted proposition that events like those of the 1930s could never happen again. But even pessimists like me, even those who realized that the age of bank runs and liquidity traps was not yet over, failed to realize how bad a crisis was waiting to happen–and how grossly inadequate the policy response would be when it did happen…

…And the inadequacy of policy is something that should bother economists greatly—indeed, it should make them ashamed of their profession, which is certainly how I feel. For times of crisis are when economists are most needed. If they cannot get their advice accepted in the clinch—or, worse yet, if they have no useful advice to offer—the whole enterprise of economic scholarship has failed in its most essential duty.

And that is, of course, what has just happened.

In what follows I will talk first about the general role of economics in times of crisis. Then I’ll turn to the specifics of the role economics should have been playing these past few years, and the reasons why it has for the most part not played this role. At the end I’ll talk about what might make things better the next time around.

 

Crises and useful economics: Let me start with a paradox: times of economic disturbance and disorder, of crisis and chaos, are times when economic analysis is especially likely to be wrong. Yet such times are also when economics is most useful.

Why the paradox? Well, first of all, consider what economics can contribute in calm times.

The answer, I’d submit, is surprisingly little. OK, economists can explain why the system works the way it does, and offer useful advice about reforms that would make it better; there’s always use for good microeconomics.

But if you’re trying to make predictions, economists won’t have much to contribute. Take the case of exchange rates, one of my original home areas of research. In ordinary times, it’s very, very hard for structural models to beat a random walk—that is, models based on an attempt to track the forces moving the exchange rate, such as changes in prices and changes in monetary policy, are barely if at all better than the simple guess that tomorrow’s exchange rate will be the same as today’s. And it’s even harder to beat an experienced trader, who has been through many fluctuations and has developed both useful rules of thumb about price patterns and a strong intuitive sense of what comes next.

Economic modelers may be better placed to engage in policy analysis. But even here, experienced practical hands may have the better advice to offer; they know from experience what will soothe the markets, what will rile them, and as long as events remain within the range of their experience, this informal understanding may trump the inevitably simplified and stylized analysis of those who know the world through equations and diagrams.

But now let there be a severe disruption that pushes the economy into terrain experienced practical men have never seen—say, an environment in which credit markets collapse, or short-term interest rates on assets considered safe are pushed all the way to zero. Because there are large and normally unforeseen disruptions, the sheer unpredictability of events will mean many bad economic forecasts, so if you ask how nearly right economists are in their ability to predict events, they will seem to be doing very badly compared with calmer times.

But the question you should ask is how economists are doing compared with those who use other ways to understand the world, and in particular how they are doing compared with sober, serious, experienced men in suits. And it is precisely in disturbed times that economists can and sometimes do offer dramatically better predictions and policy judgments than what we normally consider wise men.

Take, for example, the relationship between deficits and interest rates. It’s not an example chosen at random, of course; I believe that it gets to the heart both of the nature of the crisis we’re in and the terrible failure of economists—plus, not incidentally, it happens to be something I personally got right. More about that shortly. But for now, let’s just focus on what we should have known.

Most practical men, confronted with the prospect of unprecedented deficits in the United States, the UK, and elsewhere, extrapolated from their usual experience, in which increased borrowing drives up interest rates. And so there were widespread predictions of sharp rate rises. Most famously, perhaps, Morgan Stanley predicted in late 2009 that interest rates on 10-year US bonds, then around 3.5 percent, would shoot up to 5.5 percent in 2010; in early 2011 Pimco’s legendary head, Bill Gross—who had correctly predicted low rates in 2010—predicted a rate spike by the summer. And in each case these views were very widely held.

But economists who knew basic macroeconomic theory—specifically, the IS-LM model, which was John Hicks’s interpretation of John Maynard Keynes, and at least used to be in the toolkit of every practicing macroeconomist—had a very different take. By late 2008 the United States and other advanced nations were up against the zero lower bound; that is, central banks had cut rates as far as they could, yet their economies remained deeply depressed. And under those conditions it was straightforward to see that deficit spending would not, in fact, raise rates, as long as the spending wasn’t enough to bring the economy back near full employment.

It wasn’t that economists had a lot of experience with such situations (although Japan had been in a similar position since the mid-1990s). It was, rather, that economists had special tools, in the form of models, that allowed them to make useful analyses and predictions even in conditions very far from normal experience.

And those who knew IS-LM and used it—those who understood what a liquidity trap means—got it right, while those with lots of real-world experience were wrong. Morgan Stanley eventually apologized to its investors, as rates not only stayed low but dropped; so, later, did Gross. As I speak, deficits remain near historic highs—and interest rates remain near historic lows.

Crises, then, are times when economics and economists can and should really prove their worth. And I’d like to say that some of my friends and colleagues did; maybe some of them will say that I did OK, too. But one can’t say that of the profession as a whole. On the contrary, all too many of us had rejected the very kinds of analysis that were to prove so useful. And more than that, all too many actively opposed the policy measures the crisis called for.

Actually, let me talk a bit more about the failures of the economics profession in this crisis.

 

What should economists have known?: The most common accusation against economists in this crisis is that they failed because they didn’t see it coming. Even the Queen of England has demanded that economists explain their failure to predict the crisis. But I would actually defend my colleagues against assertions that this predictive lapse was, in and of itself, all that much of a failure.

To take the most absurd case, nobody could realistically have demanded that the economics profession predict that Lehman Brothers would go down on September 15, 2008, and take much of the world economy with it. In fact, it’s not reasonable to criticize economists for failing to get the year of the crisis right, or any of the specifics of how it played out, all of which probably depended on detailed contingencies and just plain accident.

What you can criticize economists for—and indeed, what I sometimes berate myself for—is failing even to see that something like this crisis was a fairly likely event. In retrospect, it shouldn’t have been hard to notice the rise of shadow banking, banking that is carried out by non-depository institutions such as investment banks financing themselves through repo. And it shouldn’t have been hard to realize that an institution using overnight borrowing to invest in longer-term and somewhat illiquid assets was inherently vulnerable to something functionally equivalent to a classic bank run—and, furthermore, that the institutions doing this were neither backed by deposit insurance nor effectively regulated.

Economists, of all people, should have been on guard for the fallacy of misplaced concreteness, should have realized that not everything that functions like a bank and creates bank-type systemic risks looks like a traditional bank, a big marble building with rows of tellers.

And I plead guilty to falling into that fallacy. I was vaguely aware of the existence of a growing sector of financial institutions that didn’t look like conventional banks, and weren’t regulated like conventional banks, but engaged in bank-like activities. Yet I gave no thought to the systemic risks.

Even more broadly, economists should have been aware of the dangers of leverage. This was hardly a new concern. Back in 1933—yes, 1933—Irving Fisher published his classic paper on debt deflation, that is, on the way high levels of debt create the possibility of a self-reinforcing downward spiral. And the paper remains astonishingly relevant; aside from a few archaisms of style it could have been written from today’s headlines. So remembering Fisher all by itself should have been enough to rouse at least a few worries as household debt rose dramatically relative to income, not just in America, but in a number of European nations too.

Again, I plead guilty to negligence.

I had especially little excuse for being oblivious to these dangers given that I had actually laid great stress on balance-sheet factors in causing financial crises in emerging market. True, those crises had a lot to do with currency mismatch—basically, private debt in other countries’ currencies, so that a speculative attack on a currency could quickly translate into a crippling collapse of domestic demand. But I and others should have seen that this was only one possible channel for balance-sheet crises, that plunges in housing prices or for that matter income could have the same effect.

So economists fell down on the job by not seeing what were in retrospect clear warning signs that the kind of crisis that struck in 2008 was both possible and becoming increasingly likely.

Yet I would submit that these predictive failures were venial sins compared with the much more important failure to speak with anything like a unified voice on how to respond to the crisis when it came.

 

Depression economics and how it was lost: Suppose that something like the crisis of 2008 had struck, say, 40 years ago. At that point, I believe, there would have been widespread agreement on the part of economists about what to do. Everyone in the profession knew IS-LM analysis; everyone understood the case for expansionary monetary policy to fight recessions when it was available, and at least understood the argument that there are times when conventional monetary policy is not available and fiscal policy may be the best tool at hand.

By the time the crisis actually did strike, however, all too many of my colleagues had either rejected or forgotten the analysis they needed. And as a result there was a cacophony of voices when we needed a chorus, intellectual fog at the very moment when we desperately needed clarity of vision.

How did that happen?

There was, of course, a deep divide within macroeconomics about the right kind of model, and I believe that one side of that divide got it very wrong (and I am, of course, right in that view!). But that is the sort of thing that happens in any field, and the principle that I personally am always right isn’t a good basis for intellectual inquiry. What was wrong, instead, were three consequences of that intellectual divide that reflect very badly on the profession.

  • First, one side of the divide became intellectually insular in a way that proved disastrous in the crisis.
  • Second, much of the profession reacted to the dispute by running away from the whole issue of slumps and what to do about them, again crippling the response to crisis.
  • Finally, even the “right” side of the divide—that is, my side—let itself be bullied into a style of analysis that was inherently biased against any kind of readiness for crisis.

 

Macroeconomics: What went wrong?: I assume that most of those hearing or reading this speech at all closely are aware of the great divide that emerged in macroeconomics in the 1970s. For those who aren’t familiar with the story:

  • in the 1930s Keynesian economics emerged as a response to depression, and
  • by the 1950s it had come to dominate the field.
  • There was, however, an undercurrent of dissatisfaction with that style of modeling,
  • not so much because it fell short empirically as because
  • it seemed intellectually incomplete.
  • In “normal” economics we assume that prices rise or fall to match supply with demand.
  • In Keynesian macroeconomics, however, one simply assumes that wages and perhaps prices too don’t fall in the face of high unemployment, or at least fall only slowly.

Why make this assumption? Well, because it’s what we see in reality—as confirmed once again by the experience of peripheral European countries, Portugal included, where wage declines have so far been modest even in the face of very high unemployment. But that’s an unsatisfying answer, and it was only natural that economists would try to find some deeper explanation.

The trouble is that finding that deeper explanation is hard. Keynes offered some plausible speculations that were as much sociological and psychological as purely economic—which is not to say that there’s anything wrong with invoking such factors. Modern “New Keynesians” have come up with stories in terms of the cost of changing prices, the desire of many firms to attract quality workers by paying a premium, and more. But one has to admit that it’s all pretty ad hoc; it’s more a matter of offering excuses, or if you prefer, possible rationales, for an empirical observation that we probably wouldn’t have predicted if we didn’t know it was there.

This, understandably, wasn’t satisfying to many economists.

So there developed an alternative school of thought, which basically argued that the apparent “stickiness” of wages and prices in the face of unemployment was an optical illusion. Initially the story ran in terms of imperfect information; later it became a story about “real” shocks, in which unemployment was actually voluntary; that was the real business cycle approach.

And so we got the division of macroeconomics.

  • On one side there was “saltwater” economics—people, who in America tended to be in coastal universities, who continued to view Keynes as broadly right, even though they couldn’t offer a rigorous justification for some of their assumptions.
  • On the other side was “freshwater”—people who tended to be in inland US universities, and who went for logically complete models even if they seemed very much at odds with lived experience.

Obviously I don’t believe any of the freshwater stories, and indeed find them wildly implausible. But economists will have different ideas, and it’s OK if some of them are ones I or others dislike.

What’s not OK is what actually happened, which is that freshwater economics became a kind of cult, ignoring and ridiculing any ideas that didn’t fit its paradigm.

This started very early; by 1980 Robert Lucas, one of the founders of the school, wrote approvingly of how people would giggle and whisper when facing a Keynesian. What’s remarkable about that is that this was all based on the presumption that freshwater logic would provide a plausible, workable alternative to Keynes—a presumption that was not borne out by anything that had happened in the 1970s. And in fact it never happened: over time, freshwater economics kept failing the test of empirical validity, and responded by downgrading the importance of evidence.

This was, by the way, not a symmetric story: saltwater economists continued to read Lucas and his successors. So only one side of the divide shut itself off from opposing views.

And this inward turning had what can now be seen as a fateful consequence: freshwater macro, basically something like half or more the macroeconomics field, stopped teaching not only new Keynesian research but the past as well. And what that meant was that when crisis struck, we had half a generation of economists who not only had no model that could make sense of the crisis, but who blithely reproduced classic errors of the past. Keynes spent a good part of his magnum opus, The General Theory of Employment, Interest, and Money, refuting Say’s Law—the proposition that income must be spent, so that shortfalls of demand are impossible, and government spending in particular cannot add to demand.

Yet in 2008 and 2009 we had well-known professors from Chicago and elsewhere opposing stimulus because … income must be spent, so government spending cannot increase demand. Intellectually, much of the profession had unknowingly regressed 75 years.

Worse yet, the consequences were not limited to the acolytes of freshwater economics.

Quite a few economists responded to the bitter warfare between schools of thought by running away from business cycle issues in general. I know whereof I speak: when Robin Wells and I began writing our principles of economics textbook, the general view was that you should focus on long-run growth, and relegate things like recessions and recoveries to a brief section at the end. Why? Because focusing on the long run was safer, less likely to get the committees that choose textbooks riled up.

The problem, of course, is exactly the one Keynes himself diagnosed in his most famous quote:

But this long run is a misleading guide to current affairs. In the long run we are all dead. Economists set themselves too easy, too useless a task if in tempestuous seasons they can only tell us that when the storm is long past the ocean is flat again…

Finally, all was not well even in saltwater economics.

Even though saltwater economists had too much reality sense to accept the notion that unemployment is an illusion and recessions are voluntary, indeed optimal, they were not immune to the push for more rigor and more math. You might say that they suffered from rigor envy. And so New Keynesian models tried to have as few deviations from perfect markets as possible, and tried to embed their analysis in a framework where everyone knew what was going on and behaved optimally except for a few ad hoc constraints. The result was DSGE—dynamic stochastic general equilibrium—models, which looked a lot like real business cycle models, except for the assumed wage/price stickiness.

So what’s wrong with that?

Well, DSGE models have three aspects that make them unsuited to times like these:

  • First, they’re unwieldy; you can’t easily sketch out your argument on a piece of paper, and you can’t easily translate it into ordinary language to explain it to a politician.
  • Second, they normally assume that the data we see come from a regular process of random shocks, with strong incentives for the modeler to assume that the shocks are more or less normal, not involving large, low probability events—which leaves you unready for the Big One when it happens.
  • Finally, the desire to make the things tractable tends to favor linearity, or at least models that can be done in terms of linear approximations; again, that’s not a modeling style that leaves you ready to deal with sudden financial crisis, which may involve multiple equilibria and at the very least involves regime change in which the effects of a given policy or shock may suddenly become quite different.

What we really needed, I’d submit, was a large number of economists ready and willing to go for good first approximations—quick and dirty but intellectually sophisticated approaches that would let them respond to a radically changed economic environment. Good old-fashioned IS-LM fits the bill, and as I see it the economists who did best in this crisis began with IS-LM, then backed it up later with simplified versions of New Keynesian analysis. But knowledge of IS-LM has become surprisingly rare, and comfort with it—appreciation of its virtues as well as its vices, and understanding of just how sophisticated it really is in some ways—has become even rarer.

And this has had terrible consequences.

 

From analysis (or lack thereof) to policy: In the years after 1980, and even more so, the years after 2000, the foundations for crisis were laid. The banking system became, de facto, largely unregulated and unsecured. Leverage rose, both fueling and fueled by housing bubbles (and, in Europe, the false confidence fostered by the creation of the euro). The conditions for disaster became ever better; and the disaster came.

Now what? The answer should have been simple, and backed by an overwhelming consensus. The immediate problem was a huge shortfall of demand, as the private sector moved from large financial deficit to large financial surplus. To avoid terrible effects on output and employment—effects that would only magnify the problems of excess leverage—we needed not just a rescue of the financial system but also strong government action to support demand while the wreckage was cleared.

What kind of action? There was and is a case for large-scale unconventional monetary policy, which in a zero-bound economy has to work largely through inflation expectations. But the more proximate tool, with the greatest known effectiveness, was fiscal policy, especially increased government purchases of goods and services.

Anyone who knew the IS-LM model understood that. But too much of the economic profession had lost the hard-won understanding of earlier generations. So instead of a common call for action, we got acrimonious argument, with quite a few economists essentially acting as spoilers, undermining the credibility of those trying to get governments to do the right thing. And as I said, to a remarkable extent the “learned” arguments against government action were actually repeating fallacies like Say’s Law and the Treasury View that had been thoroughly refuted in the 1930s.

Should we be surprised, then, that economic policy makers, after responding fairly effectively to the banking crisis, proceeded to lose the thread?

What happened, in fact, was that to a large extent policy makers ended up going for economic doctrines that made them feel comfortable, that corresponded to the prejudices of men not versed in economics.

Thus, it’s normal to think of the economy as a whole as being like a family, which must tighten its belt in hard times; it’s also completely wrong. But lacking any clear message from the economists about how and why this is wrong, it became the common standard of discussion in America, where both Republicans and, alas, President Obama became very fond of the statement that the government should tighten its belt because families were tightening theirs.

It’s also normal to think of economics as a morality play, a tale of sin and redemption, in which countries must suffer for their past excesses. Again, this normal reaction is wrong, or at least mostly wrong—mass unemployment does nothing to help pay off debt. But absent clear guidance from the people who are supposed to explain that economics is not, in fact, a morality play, moralizing became the core of economic policy thinking in Germany, and hence played a huge role in European policy more generally.

Finally, government officials who hang out with businessmen—and almost all of them do—naturally tend to be attracted to views that put business confidence at the heart of the economic problem. Sure enough, belief that one should slash spending even in a depressed economy, and that this would actually promote growth because it would have positive effects on confidence, spread like wildfire in 2010. There were some economic studies used to justify the doctrine of expansionary austerity—studies that quickly collapsed under scrutiny. But really, the studies became popular because they suited the prejudices of politicians, prejudices that would have been totally familiar to Herbert Hoover or Heinrich Brüning.

And so our response to the crisis has been utterly inadequate.

 

The failure of economics: The best you can say about economic policy in this slump is that we have for the most part avoided a full repeat of the Great Depression. I say “for the most part” because we actually are seeing a Depression-level slump in Greece, and very bad slumps elsewhere in the European periphery. Still, the overall downturn hasn’t been a full 1930s replay. But all of that, I think, can be attributed to the financial rescue of 2008-2009 and automatic stabilizers. Deliberate policy to offset the crash in private spending has been largely absent.

And I blame economists, who were incoherent in our hour of need. Far from contributing useful guidance, many members of my profession threw up dust, fostered confusion, and actually degraded the quality of the discussion. And this mattered. The political scientist Henry Farrell has carefully studied policy responses in the crisis, and has found that the near-consensus of economists that the banks must be rescued, and the semi-consensus in favor of stimulus in the initial months (mainly because the freshwater economists were caught by surprise, and took time to mobilize) was crucial in driving initial policy. The profession’s descent into uninformed quarreling undid all that, and left us where we are today.

And this is a terrible thing for those who want to think of economics as useful. This kind of situation is what we’re here for. In normal times, when things are going pretty well, the world can function reasonably well without professional economic advice. It’s in times of crisis, when practical experience suddenly proves useless and events are beyond anyone’s normal experience, that we need professors with their models to light the path forward. And when the moment came, we failed.

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To a large extent the power of NIMBYism springs from the right-wing tax revolt of the 1980s: Issi Romem: America’s new metropolitan landscape: Pockets of dense construction in a dormant suburban interior

Not just center city but suburban regions in NIMBY regions need to increase their density: more townhouses, more ADUs.

To a large extent the power of NIMBYism in suburban—and many truly urban—jurisdictions springs from the right-wing tax revolt of the 1980s. With property taxes capped, development ceases to be an opportunity and becomes a problem—how are we going to fund their services?—for the technocrats and the bureaucrats who manage America’s local government and infrastructure.

Their shift from being pro- to anti-development is a big part of the problem:

Issi Romem: America’s New Metropolitan Landscape: Pockets of Dense Construction in a Dormant Suburban Interior: “City planners tend to favor concentrating residential development in dense hubs because they lend themselves to service by public transit, which helps reduce the impact of new residents on emissions and traffic congestion…

…Yet this rationale for limiting densification to transit hubs and corridors amounts to acquiescing the battle for development elsewhere…. Confining development to dense hubs is a sensible approach, but it has come at a great cost. Over recent decades, America’s expensive coastal cities have slowed down their outward expansion and increasingly come to rely on residential densification within the developed footprint to accommodate the people drawn to them. Yet rather than pick up its pace, densification has become less common. As a result, residential construction in the expensive coastal cities has failed to meet demand and prevent runaway housing price appreciation, resulting in an affordability crisis…. The track record of the current paradigm–minimize metropolitan expansion and concentrate new housing in dense hubs–suggests they will keep under-producing housing in the future as well…. I am suggesting that, while cities continue to fight the battle for development in dense hubs, they also question the de facto exemption granted to low-density suburban areas from the onus to produce more housing….

In order to nurture new residential development in the dormant suburban interior, local land use policy would need to undergo a revolution. The construction industry and the financial ecosystem would need to evolve as well, and infrastructure would need to be greatly upgraded. The very first step, however, involves grasping America’s new metropolitan landscape and realizing just how much of it has gone dormant. That is where the problem is, as well as the opportunity…

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We call them “AI”, but that confuses and distracts us: Michael Jordan: Artificial Intelligence—The revolution hasn’t happened yet

“Societal-scale, inference-and-decision-making systems that involve machines, humans and the environment”: We call them “AI”, but that confuses and distracts us: Michael Jordan: Artificial Intelligence—The Revolution Hasn’t Happened Yet: “Artificial Intelligence (AI) is the mantra… intoned by technologists, academicians, journalists and venture capitalists alike…

…The idea that our era is somehow seeing the emergence of an intelligence in silicon that rivals our own entertains all of us—enthralling us and frightening us in equal measure. And, unfortunately, it distracts us…. Whether or not we come to understand “intelligence” any time soon, we do have a major challenge on our hands in bringing together computers and humans in ways that enhance human life. While this challenge is viewed by some as subservient to the creation of “artificial intelligence,” it can also be viewed more prosaically—but with no less reverence—as the creation of a new branch of engineering. Much like civil engineering and chemical engineering in decades past, this new discipline aims to corral the power of a few key ideas, bringing new resources and capabilities to people, and doing so safely. Whereas civil engineering and chemical engineering were built on physics and chemistry, this new engineering discipline will be built on ideas that the preceding century gave substance to—ideas such as “information,” “algorithm,” “data,” “uncertainty,” “computing,” “inference,” and “optimization.” Moreover, since much of the focus of the new discipline will be on data from and about humans, its development will require perspectives from the social sciences and humanities.

While the building blocks have begun to emerge, the principles for putting these blocks together have not yet emerged, and so the blocks are currently being put together in ad-hoc ways. Thus, just as humans built buildings and bridges before there was civil engineering, humans are proceeding with the building of societal-scale, inference-and-decision-making systems that involve machines, humans and the environment. Just as early buildings and bridges sometimes fell to the ground — in unforeseen ways and with tragic consequences—many of our early societal-scale inference-and-decision-making systems are already exposing serious conceptual flaws. And, unfortunately, we are not very good at anticipating what the next emerging serious flaw will be. What we’re missing is an engineering discipline with its principles of analysis and design.

The current public dialog… uses “AI” as an intellectual wildcard… that makes it difficult to reason…. Most of what is being called “AI”… is… “Machine Learning” (ML)…. That ML would grow into massive industrial relevance was already clear in the early 1990s, and by the turn of the century forward-looking companies such as Amazon were already using ML throughout their business, solving mission-critical back-end problems in fraud detection and logistics-chain prediction, and building innovative consumer-facing services…. ML would soon power not only Amazon but essentially any company in which decisions could be tied to large-scale data. New business models would emerge. The phrase “Data Science” began to be used…. This confluence of ideas and technology trends has been rebranded as “AI”…. This rebranding is worthy of some scrutiny….

Since the 1960s much progress has been made, but… not… from the pursuit of human-imitative AI…. Optimization… statistics researcher… find themselves suddenly referred to as “AI researchers.”… We now come to a critical issue: Is working on classical human-imitative AI the best or only way to focus?… [But] success in human-imitative AI has in fact been limited… and success in these domains is neither sufficient nor necessary…. On the sufficiency side… self-driving cars… engineering problems… may have little relationship to human competencies…. As for… necessity argument… did civil engineering develop by envisaging the creation of an artificial carpenter or bricklayer?… Humans are in fact not very good at some kinds of reasoning…. We did not evolve to perform the kinds of large-scale decision-making that modern II systems must face, nor to cope with the kinds of uncertainty that arise in II contexts…. We need to realize that the current public dialog on AI—which focuses on a narrow subset of industry and a narrow subset of academia—risks blinding us to the challenges and opportunities that are presented by the full scope of AI, IA and II. This scope is less about the realization of science-fiction dreams or nightmares of super-human machines, and more about the need for humans to understand and shape technology as it becomes ever more present and influential in their daily lives…

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Post-1500 Ottoman retardation and chronic plague?: Ulysse Colonna: Infectious, elegant and maybe wrong: Sketch for an explanation of the long divergence

Ulysse Colonna: Infectious, elegant and maybe wrong: sketch for an explanation of the Long Divergence: “Three different but linked literatures… blissfully ignoring each other…

  • Long Divergence: books and articles dedicated to the vexing question of why did the Ottoman Empire not develop at the same pace as Western Europe after 1500….
  • Empire of Plague: this striking expression is from Nükhet Varlik who is one of the main names in the new wave of historians looking at the history of infectious diseases in the Ottoman Empire…. The plague came to be seen as a quasi normal state of affair in the sultan’s realm, bouts of the disease became features of the Ottoman lands along with turbans and camels.
  • Health-and-Growth: macroeconomists have been exploring ties between health and development….

(full disclosure: I’m just an amateur toying with the subject, I’m nothing close to a specialist and my knowledge is far from up to speed with the state-of-the-art literature, so there is a non-trivial probability for everything I’ve said so far and for everything that will follow to be complete and utter saçmalık) Again, as far as I can tell, these three strands of literatures have not directly addressed each other…

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