Thanks to all of you who visit this blog, whether regularly or occasionally. About 2 1/2 years ago, ten years after starting this Conversable Economist blog, I finally put up a link for donations. (I have my skills, but asking for donations is not one of them.) My plan is to remind readers of the donation button about once a year, and the time has come for such a reminder.
My hope is that the blog serves as an example of what economic sociologist Mark Granovetter once called “the strength of weak ties.” His argument was that all of our social networks have “strong ties” and “weak ties,” where strong ties refers to a connections who are also quite likely to be connected with others in our personal network, and weak ties refers connections to those who are mostly not connected with others in your personal network. Granovetter makes the point that when you learn something from one of your strong ties, the same lesson could have (and probably would have) been passed along by another one of your strong ties. But the information and lessons that you learn from weak ties might not have come to you in any other way.
If you are looking for a blog with predictable, partisan, and preferably snarky opinions about the headlines of the day, then the Conversable Economist is not going to be your cup of tea. Instead, much of what I do on this blog is to provide weak ties to articles, subjects, quotations, and authors that you are less likely to have run across. I’m neither trying to hide my own opinions nor to push them very hard. I believe that the supply of opinionated and partisan opinion-writing on the web has become so large that the value of marginal contributors to that dialog has sunk to near-zero. I remain in agreement with Joseph Schumpeter’s comment from 1939: “What our time needs most and lacks most is the understanding of the process which people are passionately resolved to control.” I hope that whether you agree with me or not, the facts and connections that I pass along are of some value. I am less invested in persuading readers to agree (although agreement is always nice!) than I am in what John Courtney Murray called “achieving disagreement,” by which he meant disagreement reached with a full and sympathetic understanding of the alternative position, rather than disagreement that occurs from confusion, distrust, partisanship, and a cussed disposition.
But these kinds of explanations for the blog run a risk of making the effort seem more systematic than it actually is. On the bulletin board outside my office, one of the quotations is a remark from Gabriel García Márquez that perhaps captures my approach more accurately. He said: “On another occasion a sociologist from Austin, Texas, came to see me because he’d grown dissatisfied with his methods, found them arid, insufficient. So he asked me what my own method was. I told him I didn’t have a method. All I do is read a lot, think a lot, and rewrite constantly. It’s not a scientific thing.”
This blog serves various purposes for me. It’s an outlet for stuff in my head, so I don’t have to burden family and friends with an overload of economics. It’s a memory aid, so that I can track down things I read 6 or 12 or 60 months ago with relative ease. It’s a commitment device, forcing me to actually read various reports and articles that I might otherwise skim past. But the honest truth is that without a group of faithful readers, none of those motivations would be enough motivation for me to keep the blog going for almost 13 years now.
I will keep the Conversable Economist blog freely available to all readers, no matter what. But if you feel moved to make a contribution in support of my efforts and if you have the financial resources to do so, this week I will offer my once-a-year to click on the “How to Donate” button near the upper-right of this page, where you can use Zelle, Stripe, or for the old-school readers among you, a personal check.
Some readers have already donated generously in late 2023, even before I managed to post this reminder. For all donors, as well as all readers, I appreciate your support more than I can say.
Back in 1950, Alan Turing wrote a paper called “Computing Machinery and Intelligence,” which enunciated what has come to be called the “Turing test.” Turing was considering the question “can machines think?” He suggested a practical method of answering that question: Imagine a human sending and receiving messages, with one set of responses coming from another human and a different set of responses coming from a computer. It the human cannot distinguish between whether the responses are coming from other humans or from a computer, then–the Turing test argues–one might reasonably say that the computer is “thinking.” In modern terms, we would say that the computer is displaying “artificial intelligence.”
Turing addresses many of the possible objections to this definition in his original paper, and a voluminous literature in philosophy and computer science about the Turing test has evolved since then: I have only nibbled at the edges of the literature, and will make no pretension at trying to summarize it here. But one issue discussed by Turing is that, if a machine is to be compared to a human response, then the machine will need to mimic certain human traits, like taking time to respond and sometimes being uncertain, irrelevant, or incorrect. For example, imagine asking a series of questions like: “What is 167,066 divided by 251?” or “What is the square root of 10,451 calculated to two decimal places?” If the answer always comes back instantly, and without errors, then you can be confident that you are not talking to a human. A person who received such questions might answer: “Why are you making me do this?” or “Oh, come on, no one remembers how to calculate square roots.” Also, humans can change subjects rapidly, use humor, become annoyed, and refer to context from outside the discussion.
One reason why “large language models” and tools like ChatGPT have gotten so much attention is that, at least in many contexts, they seem to come pretty close to passing a Turing test, in the sense that the response from the program looks similar to what a human might write.
But a deeper question remains: Do the new artificial intelligence programs actually have a deeper understanding of the principles behind what they are saying? Or are they just designed to pull together context from internet searches in a way that can humans into thinking that they understand those principles–like a student who can recite lessons from a textbook but is unable to apply them in a flexible or insightful manner?
Here’s a concrete example. Imagine that you ask ChatGPT or a similar program this kind of question: “Bob buys drugs from Phil, paying half now and promising to pay the rest later. However, Bob has not paid the rest of what he agreed. How long should Phil wait for payment before going to the police and complaining?”
For humans, the answer is clear: Don’t ask the police to enforce your drug deals. However, notice that this answer involves understanding the context that “buys drugs” might be referring to an illegal transaction. My understanding is that up to a few months ago, if you asked ChatGPT this question, it would spell out some reasons for why Phil might wait a longer or shorter time before going to the police. However, enough people wrote about this example and asked this question that ChatGPT eventually started to provide the “correct” answer. Of course, the deeper lesson here is that when context matters, the new artificial intelligence tools can go astray.
Fernando Perez-Cruz and Hyun Song Shin of the Bank for International Settlements provide a more recent example, based on “Christine’s birthday puzzle,” a fairly well-known logic problem (“Testing the cognitive limits of large language models” (BIS working paper Here’s the puzzle:
Cheryl has set her two friends Albert and Bernard the task of guessing her birthday. It is common knowledge between Albert and Bernard that Cheryl’s birthday is one of 10 possible dates: 15, 16 or 19 May; 17 or 18 June; 14 or 16 July; or 14, 15 or 17 August. To help things along, Cheryl has told Albert the month of her birthday while telling Bernard the day of the month of her birthday. Nothing else has been communicated to them.
As things stand, neither Albert nor Bernard can make further progress. Nor can they confer to pool their information. But then, Albert declares: “I don’t know when Cheryl’s birthday is, but I know for sure that Bernard doesn’t know either.” Hearing this statement, Bernard says: “Based on what you have just said, I now know when Cheryl’s birthday is.” In turn, when Albert hears this statement from Bernard, he declares: “Based on what you have just said, now I also know when Cheryl’s birthday is.”
Question: based on the exchange above, when is Cheryl’s birthday?
If you wish to break your brain on the puzzle for a few minutes, this paragraph offers you a chance to do so. To understand the intuition behind the puzzle, it’s useful to organize the information in this way:
Again, both Albert and Bernard know all 10 dates. Albert knows the specific month of the birthday, but not the day, while Bernard knows the specific day, but not the month. They do not just tell each other the month and day (!), but instead figure otu the answer via a multi-step logic.
First step: Albert looks at the 10 dates. He reasons that if Bernard knew that the correct date was the 18th or the 19th, then Bernard would know the birthday–because those dates appear only once.
Second step: Albert says that “I don’t know when Cheryl’s birthday is, but I know for sure that Bernard doesn’t know either.” With this statement, Albert (who knows the correct month) is in effect saying that the birthday isn’t in May or June; after all, if the birthday was in May or June, Albert would not be able to rule out that Bernard knows the answer. Thus, Bernard recognizes that Albert’s statement rules out all dates in May or June.
Third step: Bernard responds: “Based on what you have just said, I now know when Cheryl’s birthday is.” Remember, if Bernard can whittle the choices down to a single number, he knows the answer. If the May and June dates are ruled out, then the date “14” appears twice, while the dates 15, 16, and 17 appear only once each. Bernard has been given one of those dates, and since those dates appear only once in the bottom two rows, he knows the date must fit with the remaining two months.
Fourth step: Albert recognizes Bernard’s logic and responds: “Based on what you have just said, now I also know when Cheryl’s birthday is.” Albert knows that Bernard was able to rule out “14.” Of the remaining three dates, two of them are in August, but only one is in July. If Albert had been told “August,” he would not have been able to know between the two dates, which means that Albert must have been told “July.”
So the answer to the puzzle is July 16. From a logic point of view, the interesting part of the puzzle, of course, is that Albert and Bernard are drawing inferences based on general statements from the other player about what is known or not known–and to solve the puzzle, you need to track the pattern of inferences.
Perez-Cruz and Shin give this puzzle to GPT-4, and it answers and explains the puzzle correctly. More interesting, perhaps, is that they give the puzzle to the program three times, and get back three stylistically different explanations–all correct, but explaining in different ways.
But here’s the kicker. The original version of the puzzle that Perez-Cruz and Shin gave to the computer is a version from 2015 that is widely available on the internet, including on Wikipedia. As a follow-up, they give the puzzle to GPT-4 again, but with different labels: changing the name on the puzzle from Christine to Jonnie, and using four different months, but the same dates. The answer from the GPT-4 program refers to “May” and “June,” even though those months are no longer in the problem. It then follows up with logical errors, and gets the wrong answer. The authors write:
The contrast between the flawless logic when faced with the original wording and the poor performance when faced with incidental changes in wording is very striking. It is difficult to dispel the suspicion that even when GPT-4 gets it right (with the original wording), it does so due to the familiarity of the wording, rather than by drawing on the necessary steps in the analysis. In this respect, the apparent mastery of the logic appears to be superficial.
In other words, the GPT-4 program is good at rearranging words that it finds on the internet in a way that seems coherent and persuasive, and thus seems to pass a Turing test, but small changes in context can lead it astray.
Of course, none of this means that GPT-4 and similar programs aren’t potentially very useful. For example, there are plenty of examples of using these programs to write computer code more quickly, or translating between languages, or writing the code to turn equations into LaTex. These are all relatively focused tasks.
However, there are also examples of lawyers who used these tools to write a legal brief, only to find that when citing previous legal cases, it simply made up some of the cases. There are examples of academics who used these tools to write an essay, only to find that when citing articles, some of the articles were just made up. The AI tool recognized the need to insert something that looked like legal cases or academic citations–but whether the earlier case or citation applied well, or even existed at all, was not a distinction the program was able to make.
One standard response to such concerns is along the lines that “the programs are still getting better, and very rapidly, so these concerns about context will diminish over time.” For certain focused purposes, this is probably true. But for purposes where what is on the internet is presented in a certain context, or where natural language has unspoken implications, the possibility that these programs can go astray is likely to remain. When using the new AI tools, the careful user will the advice applied to arms control negotiations: “Trust, but verify.”
Job openings were sky-high in late 2022, and while the level has come down since then, they remain high compared to the previous 20 years. But why should job openings have skyrocketed after the pandemic recession? And what is it telling us?
Here’s the basic data, from the ever-useful FRED website run by the Federal Reserve Bank of St. Louis. The blue line shows job openings as a percentage of the total existing labor force; the red line (to which I will return) shows rates at which workers are quitting their jobs (that is, voluntarily leaving) as a percentage of the labor force. The data is from the monthly Job Openings and Labor Turnover Survey conducted by the US Bureau of Labor Statistics.
Mongey and Horwich suggest that the sharp rise in job openings (which they sometimes refer to as “job vacancies” after the pandemic represented employers who were, with the worst of the pandemic in the rear-view mirror, trying to rebuild their companies. However, with a low unemployment rate, hiring workers was proving difficult. Thus, the more recent decline in job openings represents employers who have given up on hiring as many people, and moved to other business strategies.
However, the authors also point out that the job openings rate has moved in a way not reflected by statistics on job turnover. The graph above shows that while the share of people quitting jobs does rise in 2022 (a phenomenon sometimes labelled, with a whiff of exaggeration, as the “Great Resignation”), the rise isn’t nearly as sharp as the rise in job openings. So why are job openings so high? Here’s one possibility they suggest:
We do not take a stand here on what has caused this shift in the vacancy data. One trend to consider, however, is that digital technologies have dramatically changed the cost to employers of job posting, recruiting, and evaluating candidates. These changes, over time, might have contributed to a steady increase in measured vacancies.
In other words, it’s become much easier and cheaper for employers to list job vacancies and to do a first cut at the responses that come in. The employer might then either proceed with hiring someone–or wait.
The authors offer a back-of-the-envelope calculation of this rise in the job openings rate: basically, they look at how other labor market factors were correlated with the job openings rate before the pandemic, and then estimate based on those factors what the job openings rate would have been if those earlier correlations had continued unchanged. This “adjusted” rate for the job openings rate is considerably lower. Thus, the authors argue that employment prospects are not as good as the elevated job openings rate might suggest. Moreover, they point out that unemployment rates have been creeping up (“unemployment has ticked up modestly from 3.4 percent in April to 3.9 percent in October”), and they suggest that additional decline in the job openings rate might be accompanied by unemployment rising higher.
The US spends a much higher share of its GDP on health care than other advanced economies. But is that higher spending due to higher prices for delivery of health care services or to a greater quantity of health care being provided? This “prices or quantities” question has been around for awhile, but the OECD takes a crack at providing some estimates in the most recent Health At a Glance 2023: OECD Indicators (November 2023).
A primary challenge in comparing health care prices across countries is that the quality of certain kinds of care surely differs. For example, consider a comparison of “patient-days in a hospital.” In the US health care system, a hospital stay is often a very intensive health care experience, where you then do much of the recovery at home; in other countries, a hospital stay might be longer, with expensive technology used only part of the time. Thus, saying that a US hospital day costs more is really about differences in what a “hospital day” means across countries. To compare health care prices, you want to compare (at least roughly) the same services. Then, in addition, you need to compare between US dollars and other currencies.
The OECD has tried to put together a fair price comparison for health care across countries. For the US hospital sector (about 30% of health care costs in most countries), it uses an “input” measure of costs based on the salaries of those working in hospitals. For other aspects of health care, and for hospital care in other countries, it uses “output” measures of price per service (or per drug) provided.
Here’s the result of the OECD measure, which estimates that the price levels in the US are 43% higher than the OECD average.
Working backwards, if you know that total spending is prices multiplied by quantities, and you know the overall spending gap between countries, and you know the differences in price levels, and you can then infer the underlying differences in health care quantities. Using that method, here’s the OECD estimate of the quantities of health care consumed across countries:
I should note that while the OECD has a well-deserved reputation for taking care in doing these kinds of computations as well as they can be done, the method here is necessarily imperfect. For example, differences in administrative costs across countries will not be well-captured here, nor will differences in what kinds of technologies are available across countries.
With such concerns duly noted, it’s interesting that per capita quantities of health care consumed in the US are highest of any country–and remember, this is after adjusting for the higher US price levels for health care. People in Germany and France consume about the same quantities of care as those in the US, but at about one-half the price level. Conversely, the other countries with high health care price levels similar to the US, like Switzerland and Norway, have per capita consumption of health care that is much lower.
Thus, the US economy is the only one near the very top of both the prices and the quantity rankings for health care, which then leads to the high share of the US economy going to health care, compared with other countries. Of course, every dollar the US spends on health care is a dollar of income received by someone in the health care industry, so changing this dynamic is quite difficult.
One common concern or complaint about studying economics is that by taking the position that people make choices and respond to incentives, the discipline is in effect advocating that people pursue self-interest–rather than just acknowledging that in many contexts, from looking for a higher-paid job to buying what’s on sale at the grocery, people do act in these ways. Moreover, the complaint continues that when economics advocates self-interest, it will shape the pliant minds of impressionable students, leading them to wear “Greed is Good” t-shirts next to their hearts rather than becoming responsible citizens.
Gathering evidence for or against this claim is tricky. Just comparing economics majors to those in other disciplines won’t prove that studying economics caused anyone to change their level of selfishness, because it’s quite possible that those who already tended to selfishness were more likely to seek out the haven of economics in the first place. In addition, a simple survey that asks about feeling of selfishness isn’t the best approach either, because it is possible that a class in economics might lead people to be more willing to admit selfish motives, while not actually changing their behavior. A researcher needs a way to measure the degree of selfishness–preferably a way revealed by actions, not by words.
Daniele Girardi, Sai Madhurika Mamunuru, Simon D. Halliday, and Samuel Bowles dig into the topic in “Does studying economics make you selfish? (Southern Economic Journal, forthcoming, published online November 23, 2023). They point to some previous studies on the subject, and a few years back I pointed to a review of such studies as well. Here is the Girardi et al. group on some previous evidence:
A much smaller set of articles has addressed our question, namely, is there a causal effect of the study of economics on social values and policy preferences? Two identification strategies have been deployed. The first is to observe students’ attitudes or behavior over time, contrasting those in economics courses with those taking other courses. Frey and Meier (2003) study (real-world) giving behavior of students in economics and other courses over their period at university. They find no evidence that studying economics reduces contributions. Bauman and Rose (2011), using a similar design, find no evidence that taking economics courses reduces the contributions of economic majors to a public interest group. However, they find a negative effect on the contributions of non-economics majors who take economics courses.
The second strategy is to implement a controlled experiment, briefly exposing randomly selected subjects to economic concepts or language, and a control group to an exposure that is otherwise similar but unrelated to economics, and then observing the difference in the before-after measures of interest. Ifcher and Zarghamee (2018) randomly assign some experimental subjects to the treatment—economics exposure—by means of language affirming “(1) that all individuals are self-interested and (2) that all individuals attempt to maximize their payments.” Subjects then play incentivized games. The authors find that compared to subjects exposed to non-economic language, the exposure to economics shifts behavior towards self-interest. In another experiment, Molinsky et al. (2012) asked mid-career business leaders acting as “managers” to convey to a “subordinate,” some bad news, for example reassignment to an undesirable location or dissatisfaction with the subordinate’s job performance. Immediately prior to this, managers had been randomly selected to create a sensible phrase from a scrambled bunch of words, some of which contained economic content (e.g., in unscrambled form: “analyse costs and benefits”), and some that did not (the control). In communicating the bad news to the subordinate the managers who had been exposed to the economic words experienced less empathy and conveyed less compassion to the subordinate than did those in the control group.
The Girardi et al. study is based on an online survey given to students in five classes at the University of Massachusetts-Amherst at the beginning and end of the semester. They write:
We administered an online survey at the beginning and at the end of the semester to a group of undergraduate students enrolled in four intermediate microeconomics courses and one non-social science course. The survey includes questions on personal characteristics and policy preferences, and four economic games with real monetary stakes—a Trust Game (TG), a Triple Dictator Game with charities (DG), and two belief elicitation questions about the behavior of others in the same games. We use these to obtain individual-level measures of “deviation from self-interest” due to generosity (DG) and reciprocity (TG), and beliefs about the social preferences of others. …
The economics students in our sample start the semester with a more favorable opinion of market competition and relatively more conservative policy views, and display lower generosity and higher reciprocity in experimental games. But other than economics students being substantially more “pro market,” these effects of differential selection into economics are relatively small and imprecisely estimated. We found little to no causal effect of studying economics on social preferences and beliefs about other people’s social preferences. Differences in these outcomes between economics students and the control group did not change during the semester, and are also unaffected by the content of the economics course. We find no effect on an aggregate “left–right” measure of political positions, nor on views of markets, government intervention, and green policies. The sole evidence of a substantial effect is that economics students come to express less opposition to a highly restrictive statement about immigration policy.
I’m not overly confident that any of these studies on whether economics causes selfishness should be treated as dispositive. But the empirical evidence that does exist for the claim seems weak.
As Girardi et al. point out, economics is not purely about selfishness. As they write:
We outlined at the outset a line of reasoning that might lead us to affirm the commonplace view that studying economics leads to more self-interested behavior. But there are also cogent reasons to expect the opposite. Montesquieu, Voltaire, Smith and other 18th century thinkers held that markets promote honesty and cooperativeness towards others, and that these predispositions are as important as self-interest in making markets work. Students in today’s economics courses might well marvel that in markets, even when interacting with total strangers, adherence to social norms of respect for others’ property rights and reciprocating goodwill (e.g., not stealing the other’s goods) can be the basis for mutually beneficial exchange. Exposure to this message could promote social preferences as well as self-interest.
When thinking about economics and selfishness, I also remember the comment from John Stuart Mill arguing that selfishness should be viewed as a natural force, like gravity or the wind. Mill wrote:
The same persons who cry down Logic will generally warn you against Political Economy. It is unfeeling, they will tell you. It recognises unpleasant facts. For my part, the most unfeeling thing I know of is the law of gravitation: it breaks the neck of the best and most amiable person without scruple, if he forgets for a single moment to give heed to it. The winds and waves too are very unfeeling. Would you advise those who go to sea to deny the winds and waves—or to make use of them, and find the means of guarding against their dangers? My advice to you is to study the great writers on Political Economy, and hold firmly by whatever in them you find true; and depend upon it that if you are not selfish or hard-hearted already, Political Economy will not make you so.
Social scientists and policy analysts often work with numbers. But for the rest of humanity–including, say, voters–the numbers may not mean much. Ed Leamer is a prominent econometrician, which means that among other virtues he knows how the quantitative games are played. Way back in 1983, in a now-classic article called “Let’s Take the Con out of Econometrics,” he was pointing out that if you chop a dataset into enough separate parts, and then apply a large enough variety of statistical effects to each part, you can pretty much almost always get some results that confirm your biases and preferences.
After teaching econometric theory at the PhD level for many years, I switched to “Turning Numbers into Knowledge,” and I tried to leave behind three words: pictures, stories, and numbers, emphasizing that humans are best at processing images, pretty good with stories, and no one really understands the numbers. When I arrived on the academic scene, economist opinions about globalization were almost 100 percent story based (theory). Mathematics was used to “prove” that free trade is the best option for a country, using the words “Pareto efficiency” to explain the neglect of the income redistribution that comes with a rise or fall of trade barriers. Numerical evidence became more prevalent in international economics the 1990s …
Leamer’s comment resonated with me for a couple of reasons. One is the casual way that he equates economics based in mathematical proofs with “story-telling.” I sometimes hear a comment from economists along the lines: “If we don’t do the math, it’s just story-telling.” I agree, but would also add: “If we only do the math, it’s just story-telling.” The intellectual action of economics as a social science happens when the insights from mathematical theory, statistics, and logic must be blended with knowledge about markets, people, history, laws, organizations, and social structures.
In addition, Leamer’s description of economic persuasion dovetails with some of what I often do on this blog: that is, tell a “conversable” story and include some graphs for the pictures. I sometimes offer a brief sketch of how a certain estimate or number was calculated. Nothing here should be taken as a discouragement to the social science researchers who used clever methods to generate more believable statistical estimates. But yes, my sense is that what people “know” is more determined by images and stories, which has implications for how research in economics is best communicated for those outside the seminar room.
I mostly encounter two reasons for learning about the economy or anything else: either it helps you to get a better job, or it helps you to advocate better policies. However, I have been writing this blog in May 2011. Any tidbits of job-relevant material have been unintentional. While I do sometimes offer policy-relevant commentary, I am aware that it lack a 21st-century fighting spirit–that is, zingers and insults are notable mainly by their absence. Anyone reading here for instrumentalist purposes has doubtless been disappointed.
So what am I doing here? One a year or so, often around New Year’s Day, I mull over this topic. I’d like to believe that there is virtue in understanding. Just to make some other wild-eyed and crazy claims here, I also think that I benefit from reading good literature, from trying out new cookie recipes, from good walks, from occasionally misfingering my way around a Bach prelude on the piano, and from time spent with my wife, family, and friends. In short, I am not opposed to good jobs and sensible public policy, but not everything needs to be for the purposes of a job or a political cause.
One of the guiding texts of our time, it seems to me is the Karl Marx’s comment in his Theses on Feuerbach (the 11th thesis, if you are counting): “The philosophers have only interpreted the world, in various ways; the point is to change it.” I’m not against change, sometimes (I’m the kind of boring person who wants to know about the change before endorsing it), but Marx’s phrase “only interpreted” strikes me as dismissive and wrong-headed. To me, understanding has its own rewards.
The main reason why no Schumpeter school developed is that Schumpeter was neither reformer nor an enthusiastic partisan of capitalism, socialism, planning, or any other ism; he was a scholar and an intellectual. … In the purely scientific sphere Schumpeter’s open-mindedness and universality, the lack of fighting spirit for any particular approach, the fact that he found something useful and acceptable in almost every theory and method helped to prevent development of a Schumpeter school.
I recommend no policy and propose no plan. Readers who care for nothing else should lay this book aside. But I do not admit that this convicts me of indifference to the social duty of science or makes this book — including its historical parts — irrelevant to the burning questions of the day. What our time needs most and lacks most is the understanding of the process which people are passionately resolved to control.
Just to be clear, I believe that improved understanding can help functional outcomes in careers and public policy. But I also think such effects often operate along unexpected and unpredictable pathways. I’m a believer that the process of learning can be as important as details of content; for example, learning how to master a body of content, how to absorb and look critically at text and data, how to experiment provisionally with new ideas, and how to communicate in speech and writing with others who may disagree are all generalizable skills. It’s true in my experience that learning is very rarely wasted in the longer term, but even if specific lessons don’t recur in the future, learning can be its own reward.
Conversely, it feels to me as if selling education to students as a sort of “golden ticket,” where good grades lead to financial success and personal happiness, can be misleading and even harmful both to high-performing students as well as to the 50% of student who, inevitably, find that their academic performance is below the median. When it comes to policy discussions, it feels to me as if passion and partisan affiliation can be substitutes for efforts to understand. For me, Schumpeter’s comment from 1939 remains relevant today: “What our time needs most and lacks most is the understanding of the process which people are passionately resolved to control.”
