Finance: History and Policy: 2023

Sunday, November 12, 2023

The College Curious Need New “ESG” Ratings

The publication of this two-parter by the Martin Center reminded me that I had drafted something on the topic of college ratings but dropped it. Here it is for your edification and "enjoyment":

Those interested in attending, or sending their children, to university must decide if the time and monetary investment is worth it and, if it is, where to spend their precious dollars. Strident claims by presumably knowledgeable government officials that student loans should be wholly or partially forgiven suggest that many students made the wrong decision and shouldn’t have gone to school at all, or at least not majored in Oppression Studies at Woke U. Some of the “college curious” may have decided on emotional or other irrational grounds based on family history or an affinity for certain sports, while others were undoubtedly led astray by college rankings.

The notion that colleges and universities can be confidently ranked from top to bottom smacks of deep intellectual hubris. Even the bond rating agencies attempt only to lump securities into classes based on risk of default and often get even that wrong, as anyone who lived through 2008, 1997, 1982, and so forth may recall. To ascertain that institution X is a smidge “better” than Y, the rankers rely upon small changes in various quantitative metrics. Because administrators’ careers and tuition rates often depend upon rankings, those quantitative metrics have been manipulated or even concocted, most recently by Columbia University, the shenanigans of which were exposed by a whistleblower who believes that college rankings are essentially worthless. Before making any decisions, the college-bound at least need to realize that a more highly ranked school may simply be better at gaming the ranking system, at being dishonest in other words.

In addition, properly interpreting many metrics requires context that is not easily quantified. A school with a high 8-year graduation rate (as measured by the Washington Monthly), for example, may have an abysmal unreported 4-year rate, suggesting that it is adept at bilking students for more tuition than expected by making it difficult for them to graduate on time but easy for them to eventually get a degree, perhaps by making courses challenging but pressuring faculty to relax the requirements for students making up incompletes or retaking classes who appear ready to bail. A relatively low graduation rate, by contrast, might indicate that a school is trying to maintain standards and willing to fail out students to do it.

Most importantly, major rankings never include arguably the two most important metrics, learning (what students know/can do upon graduation minus what they knew/could do upon admission) and lifetime earnings. Some measure purported job placement rates and even initial salaries but those skew toward schools with sticky reputations, usually hoary institutions that continue to attract the attention of recruiters from high paying firms because they presumably produced quality graduates in the past. Most of the college curious, however, care more about lifetime earnings than initial salary. Moreover, the trajectory of earnings provides more information about the quality of a school’s ability to educate, rather than to merely train or signal the employability of, their students because it proxies the original stated goals of higher education, which is to cultivate lifelong learning and independent thought, both of which remain essential to a robust private economy and a vibrant civil society.

I first called for such metrics over a decade ago, in a book (Higher Education and the Common Weal: Protecting Economic Growth and Political Stability with Professional Partnerships, 2010) so controversial it could only be published in India and is already out of print. Universities do not want to track systematically the careers of graduates, at least those unlikely to make big donations, or to measure learning because such information might expose their individual and collective weaknesses. Once informed of the industry’s overall ineffectiveness, fewer people would opt for “higher” education in the first place and many others would attend less expensive, but pedagogically equivalent, institutions. That, of course, would tend to dampen tuition, or at least its rate of increase, forcing universities to invest more in pedagogy (and its crucial cognate, research) and less in sports complexes and complex administrative systems. Rest assured, then, that the college curious will never know with certainty which schools are most likely to increase both their ability to earn a living and their ability to positively impact the social sphere.

Rankings, however, do not have to be so rank. To better aid those interested in attending college, a disinterested third party could create a grading system focused on three major cognates of lifetime learning and social and economic achievement. I call it “ESG,” not for the thoroughly debunked environmental, social justice, and corporate governance investment grading system recently popular in Woke circles but for intellectual energy, social engagement, and university governance.

Intellectual energy refers to the atmosphere on campus, including the number of outside speakers and respectful attendees of their talks (not anti-intellectual protestors). Contrast Hillsdale College, where I recently spoke to over two score faculty and economics students on a balmy weeknight during Homecoming, with another midwestern college of similar size where during an otherwise uneventful week only a few students turned out on the same subject (the economics of slavery) and had to be bribed with “extra credit” to sit physically in the room while investigating their social options later that evening on their phones.

By social engagement, I mean old-fashioned civic engagement and well-informed, dare I say research-based, attempts to ameliorate social problems. In other words, schools should be judged not on the extent that they encourage mere virtue signaling, which signals only iniquity and an anti-intellectualism unbecoming any institution devoted to “higher” education. Universities should be judged on the extent that they encourage students to engage in rational action. Society needs the energy, verve, and long-term outlook of its youth but is not aided by inducing young people to slavishly follow fads ginned up by the Left, or the Right for that matter. Universities should inculcate responsible free speech by directing students to research, write, and orally defend their positions before protesting or engaging in other direct action.

The quality of a university’s governance should be assessed by the checks and balances that it incorporates to ensure that it keeps its promises and does not distort its record. As the Martin Center has shown, some schools have forced out tenured non-Woke professors by threatening the budgets of noncompliant departments and members of promotion and tenure committees and by employing non-disclosure agreements in unethical, if not illegal, ways. If accreditors will not discipline, an outside rater should expose such schools because they cannot be trusted to administer donations in line with donor intent, let alone to put the interest of students first during public health or other emergencies.

The college curious need quality university quality ratings like “ESG” because often they do not (yet) have the intellectual tools needed to properly assess the claims that college admissions officers and marketing materials make. Few, for example, understand the implications of public choice theory or its application to public and private university administrators. They do not realize that the beautiful school with the great reputation and super sports teams may be run to serve the interests of administrators, coaches, and, to a lesser extent, faculty, not students. Such institutions of course claim to be student-centered but do not credibly commit to putting students first in any but the most cursory fashion. They may be highly ranked but in the “ESG” system sketched above would be graded low.

In fact, most of America’s colleges and universities would receive a failing “ESG” grade, at least initially, because most have repressive intellectual atmospheres where mindless Woke virtue signaling prevails, implicitly supported by faculty cowed into submission by the ouster of outspoken opponents of the status quo enabled by poor governance practices. FIRE and College Pulse join forces to rank universities on 13 free speech metrics. The rankings are relative, though, not absolute. The fact that the University of Virginia ranks sixth best suggests that the rankings only gauge speech prohibitions and do not measure positive campus intellectual energy (the E in my “ESG” rating) because a recent Heritage report reveals that Virginia’s universities are “drowning in” DIE (diversity, inclusion, and equity) administrators and policies, and that UVA is the second worst offender.

Presumably, though, to attract more students from a shrinking pool some universities will reform to achieve a higher “ESG” grade. Indeed, some new institutions with stronger “ESG” bona fides have formed and a few incumbents have reformed their cultures rather than joining the race to the bottom taking place in standards. American higher education remains sick, perhaps chronically ill, but by exposing its rotten parts while highlighting those institutions that remain true to the industry’s original mission of helping students to become independent thinkers capable of adding value to both the economy and society, it could improve outcomes without further ballooning the national debt.

Friday, November 10, 2023

Let's Ban Professional Sports 2nd Amendment SATIRE

NB: Tried this at several satirical websites but some of the humor was too high brow. I mean who jokes about the Ninth Amendment?

The government should ban sporting events forthwith because they encourage the consumption of alcohol and other inebriates, gambling, harming animals, idleness, and violence. It doesn’t matter that millions of Americans love to watch sporting events live or on television because sports are not explicitly protected by the Constitution, they divert resources away from BIPOCs, and they emit literally tons of carbon into the atmosphere.

Anti-sporters like myself have never played or watched any professional sport in our lives, but we know everything there is to know not to like them and that is sufficient to call for a ban. Millions of Americans just like me wonder how long policymakers are going to allow this, this, this genocide to continue. It has got to stop and here is why.

First, while like-minded allies long ago managed to curtail alcohol sales late in games, all that did was to induce people to start drinking earlier. Now, we’ve discovered via a thorough investigation conducted on Tik Tok, fans show up in the parking lots of sporting events hours early so they can get drunk, gorge themselves on animal products, and likely fornicate too.

Some might say that impaired driving, not alcohol or drug use per se, kills people and that responsible drug and alcohol use isn’t hurting anyone. Those people are idiots. We don’t have any reliable statistics, but we know that literally millions of babies have been killed by drunk or high sports fans. (Yes, some of those babies may have been squirrels but squirrels are people too!)

Namby-pamby types will also claim that gambling doesn’t hurt anyone, except the losers, but they knew what they were getting into. But gambling is an addiction, just like drinking and drugs. Again, we don’t have statistics, but we heard an anecdote about a baby run over by a guy checking his phone to see if “da Iggles” covered the spread. We don’t know what that means exactly but we know it is about sports gambling.

As for harming animals, footballs, we learned on Wikipedia, are made from pig skin. The thought of all those skinless hogs running around somewhere just makes our blood boil. There must be some pretty cold cows out there, too, because baseball gloves and balls are made from cowhide. We’re told that every time a baseball touches the ground, it gets replaced. That’s a lot of baseballs and although cows are pretty big that must be a lot of harmed cows.

The idleness and violence go hand-in-hand with drinking and gambling. How many trillions of dollars are wasted each year as people watch some guys pat each other’s butts and smash poor balls, or each other? Again, nobody is tracking these things but it is obvious that it is a giant waste.

It is equally obvious that professional sports are bad for the environment. We can’t find it right now, but we once saw a study that claimed that up to half of global warming is caused by lacrosse alone.

If people worked instead of wasting their precious time on professional sports, America could easily afford to pay reparations to BIPOCs and other oppressed groups du jour. The athletes themselves would have to get real jobs too, thus providing even more support for the economy. And taxpayers could stop subsidizing sports stadiums and increase subsidies for worthy things, like NPR and carbon pipelines.

Banning professional sports might sound unconstitutional. Didn’t the government have to pass an amendment before it banned alcohol? Yes! I’m an expert because I have read the Constitution all the way through, except for the boring and confusing parts, almost three times. One approach would be to convinces states to ban sports. Start easy, like the Dakotas, which don’t have any sports, except maybe for rodeos.

Then California because while policymakers there like drug use, they don’t like carbon emissions and need money to pay reparations. The leagues will then lose a lot of their teams and won’t be able to afford to defend themselves in other state legislatures. Then the federal government could step in under the interstate commerce clause. I couldn’t actually find an amendment saying this, but I have it on good authority that the greatest president of all time, Franklin D. Roosevelt, packed the Supreme Court full of the greatest justices of all time and they all agreed that the Constitution doesn’t mean what it says, it means what they say it means, and they said it means the federal government can do whatever the heck it wants if it affects the economy in any way. If you don’t believe me, ask Farmer Filburn.

There is an amendment that bothers us, though, the Ninth. It seems to say that people have a bunch of other rights not explicitly mentioned in the Constitution and, taken with the Preamble, suggests that the government ought to leave people to do what they want for the most part. Hardly anyone ever mentions that amendment, though, so we’re guessing it was really about slavery.

In sum, we don’t like sports though we know nothing about them but we feel that they are bad and are willing to concoct evidence and twist reality to convince a slim majority of our fellow Americans to join us in banning them.

Robert E. Wright is a Senior Research Fellow at the American Institute for Economic Research and a part time satirist who loves sports and also firearms, which are explicitly protected by the U.S. Constitution yet under assault in Massachusetts.

Tuesday, August 15, 2023

Worker Productivity Through the Ages

NB: 100% sure I wrote this but I don't recall when or why! Found it on my Google Drive. Pretty sure it isn't published anywhere.

Worker Productivity Through the Ages

Productivity is generally defined as total output divided by total input, which can be stated in terms of time or some unit of currency. If output increases (decreases) while input stays constant, or if output increases faster (slower) than inputs, productivity rises (falls). Productivity is related to, but should not be confused with, efficiency, which is the expected input divided by actual input needed to achieve some level of output, often stated in percentage terms by multiplying by 100.

Despite its simple definition, productivity remains so difficult to meaningfully measure that economists generally treat it as a residual by lumping the productivity of different types of workers into total factor productivity (TFP), the portion of increases in output not explained by increases in inputs (more formally, Y = A*K*L, where Y is total output, A is TFP, K is capital’s share of input, and L is labor’s share of input) (Comin 2008). Increases in TFP can usually be linked to specific technological advances, as Shackleton does for the USA after 1870 (Shackleton 2013). Economists generally dislike measures of worker productivity, however, because most merely measure the efficiency of individual workers, while others compute averages instead of the productivity of the marginal worker, i.e., the last worker toiling to complete some task. For over a century, economists have argued that marginal analysis trumps the analysis of averages or other central tendencies. (Harry Jerome, “The Measurement of Productivity Changes and the Displacement of Labor,” American Economic Review 22, 1 (March 1932): 32-40; George Stigler, “Economic Problems in Measuring Changes in Productivity” in NBER, Output, Input, and Productivity Measurement [Princeton: Princeton University Press, 1961], 47-78.)

In short, productivity measurement remains inherently contextual, varying with the question the measurer seeks to address as well as the physical realities of different workplaces and spaces (Sena 2020). Measurements appropriate for one time and place may prove entirely inappropriate, or downright impossible, for another. Even simple measures of labor productivity, like output per unit of time, can depend crucially on raw material input availability, incentives to work, market demand, and seasonality. Output quality must also be considered, especially when concepts like minimal acceptability are unavailable or inappropriate.

This section, “Worker Productivity Through the Ages,” provides important examples of the changing contextuality of productivity from humanity’s origins through the Neolithic Revolution to ancient historical civilizations and the modern productivity revolutions in agriculture, communication, manufacturing, and transportation, to the recent domination of labor share by construction, government, and knowledge workers.

Prehistoric Productivity

Early humans (hominins) coevolved with their technologies to the point that modern humans themselves could be considered the first general purpose technology (GPT-HS). It appears likely that evolution by means of natural selection drove early humans to use productivity gains – perhaps from technologies like fire or other ways of denaturing/predigesting food (Sanfelice and Temussi 2016), stone tools (Semaw et al. 2009), and trade – to biologically purchase bigger, more complex brains capable of developing yet more sophisticated technologies or of producing existing technologies more efficiently (Ofek 2001)(Wilson 2020). The medium of purchase was calories and the other nutritional inputs needed to grow and fuel brains, which are biologically expensive (Kotrschal et al. 2013). While the precise timing and mechanisms remain unknown, hominin encephalization certainly occurred over several million years as average brain volume grew faster than body weight, from 440 cc to over 1,300 cc. From impressions left on fossil craniums, scientists know that hominin brains also grew more complex as human-technology coevolution occurred (Rightmire 2009; Gunz et al. 2020; Tarlach 2020; Price 2021).

Except for stone and bone tools and hearths (fire pits), most early human technologies left little or no direct evidence in the archeological record. Although measuring stone or bone tool production productivity in the modern sense will remain impossible, scientists have estimated manufacturing efficiency by modeling the ratio of waste rock to useful blades, or length of cutting edge to original stone mass, after reconstructing the knapping or fracturing process employed. Some conduct experiments by knapping rocks themselves using the same tools and techniques that early humans did, and comparing the results to archeological data taken from ancient lithic quarries and tool manufacturing sites (Schlanger 1996).

Scientists have generally found gradual increases in raw material efficiency over lithic technological evolution (Castañeda 2016; Muller and Clarkson 2016). Other specialists have performed similar experiments on bone tools (Karr 2015; Karr and Outram 2015). The complexity of stone and bone tools has also been quantified and shown to have increased over time (Perreault et al. 2013), as exemplified by lithic miniaturization, or the production of microliths – very small stone tools with high cutting efficiency by weight (Pargeter and Shea 2019).

Similarly, while scientists will never know with certainty how long it took early humans to start or maintain fires (Alperson-Afil 2017), they can discern which fuels were used. Iron Age farmers in northwestern continental Europe, for example, used all available fuel sources: dung, peat, and wood (Braadbaart et al. 2017). The efficiency of hearth construction (Black and Thorns 2014; Graesch et al. 2014) and hearth placement in caves and manmade structures (Kedar et al. 2020; Kedar et al. 2022) has also been measured by comparing experimental to archaeological data (Brodard et al. 2016).

Measuring the productivity of hunting and gathering remains fraught because the amount of time it took to acquire sufficient food, clothing, and tool materials was undoubtedly partly a complex function of the ratio of the human population to target species and the intensity of trade networks (Deino et al. 2018). Scientists presume, however, that more complex technologies increased productivity by making it easier/faster to harvest animals as well as sundry vegetable materials like fruits, nuts, seeds, and tubers. The ability to haft stone points, for example, allowed early human hunters to more effectively kill large game animals starting half a million years ago (Wilkins et al. 2012), while the ability to create cordage from animal sinews or vegetable matter allowed them to make better clothing, carry packs, mats, huts/homes, and even boats (Hardy et al. 2020). Indeed, new evidence suggests that over 40,000 years ago some human groups regularly caught pelagic fish (e.g., tuna), implying both deep sea boating and fishing technologies (O’Connor et al. 2011).

Such improved technologies rendered early humans more productive, freeing their time to develop yet more complex technologies, plus cultural goods that both displayed and aided their conceptual prowess in ways too complex and distant to be disentangled (Wadley 2013).

Productivity Gains During the Neolithic Transition to Agriculture

Early humans were productive enough to survive and spread across most of the Old World (Deino et al. 2018; Gunz et al. 2009). Although population estimates vary, genetic and ecological studies indicate that early humans clearly were less populous than humans today (Huff et al. 2010). Adoption of agriculture, the domestication and deliberate production of numerous plant and animal species, drove additional technological changes, like those associated with small-scale metallurgy and manufacturing (Moorey 1999), that eventually made higher human population levels possible.

Scientists still do not fully understand, however, why the Neolithic Revolution, the transition from hunting and gathering to agriculture, occurred when and why it did (Weisdorf 2005) because farming initially meant more work, higher incidences of disease, and increased mortality (de Becdelièvre et al. 2021). It increasingly appears that small groups grew into farming over time instead of transitioning in large numbers in a single generation as sometimes supposed. Herding and hunting were complimentary activities, as were fishing and farming, suggesting that mixed subsistence strategies could sustain growing populations until agricultural productivity in the richest agricultural areas improved due to learning-by-doing, increased climatic stability (Matranga and Pascali 2021), and perhaps improved property rights (Bowles and Choi 2019; Bowles and Choi 2013).

Measuring Productivity in the Ancient Historical Era

The ancient Chinese, Greeks, Indians, Mayans, Mesopotomians, Persians, and Romans invented several crucial new general purpose technologies, including writing (Bywater 2013) and mathematics (Boyer and Merzbach 1993; Cuomo 2005), that increased productivity directly and also led to new or greatly improved specific technologies (Krebs 2004). The Romans, for example, developed or improved boats, wheeled vehicles, water-lifting technologies, and watermills, among many other technologies (Greene 1990), while the Greeks invented coins, a mechanical astronomical computer, and napalm, among other things (Freeth et al. 2021).

Although all flourished during golden ages, typically periods characterized by high levels of economic freedom (Bergh and Lyttkens 2014), none of those civilizations experienced the sustained, across-the-board increases in TFP associated with modern economies. Indeed, many collapsed politically and economically for reasons not fully understood (Tainter 1988). Some may have succumbed to the sunk cost fallacy, clinging to old habits and habitations even after they became environmentally untenable (Janssen and Scheffer 2004). Others appear to have suffered from the increased power of rent seeking institutions that constrained property rights and thus limited incentives to innovate (Westermann 1915; Bó et al. 2015).

Productivity in the Age of Economic Revolution

After the demise of the ancient civilizations, the productivity of agricultural workers stagnated, though subject to intermittent reversals and shocks like the Black Death (Jonathan Jarrett, “Outgrowing the Dark Ages: Agrarian Productivity in Carolingian Europe Re-evaluated,” Agricultural History Review 67 (2019): 1-28.) Introduction of the heavy plow around 1000 AD, for example, allowed for more extensive cultivation in Northern Europe that aided nascent urbanization and hence economic specialization, long considered a driver of non-agricultural productivity increases (Andersen et al. 2016).

Starting in Holland in the seventeenth century, rapid increases in agricultural productivity freed up farmers, and especially their children, to work in emerging or rapidly growing industries, including those in the trade, transportation, industrial, and communication sectors. Eventually, those sectors also shed workers as technology-induced productivity increases rendered their labor unnecessary (Ville 1986).

Agricultural productivity increases stemmed only in part from mechanization (Collins and Thirsk 2000), productivity increases in which were often driven by competition between small farm implement manufacturers (Binswanger 1986). At first, productivity increases derived mainly from improved techniques and seeds (Olmstead and Rhode 2008), as well as productivity improvements in fencing, ditching, and draining (Baugher 2001). Although it proved difficult to compare agricultural productivity internationally, slower agricultural productivity growth clearly constrained economic, especially industrial, development in twentieth-century Europe (O'Brien et al. 1992; Cosgel 2006) and elsewhere (Baumol 1987). Countries with robust increases in agricultural productivity, like the USA, by contrast, also experienced rapid increases in industrial productivity (Broadberry 1994; Broadberry and Irwin 2004).

Following Marx and others (Shantz et al. 2014), many scholars have assumed that industrialization, especially under the so-called “scientific management” principles of Frederick Taylor and his disciples (Gilbreth 1914), alienated and de-skilled workers, turning them from GPTs into the appendages of machines. Evidence of large scale de-skilling over the nineteenth and twentieth centuries remains scant (Form 1987), though deskilling may cycle (Sabel and Zeitlin 1985), increasing when disruptive new technologies proliferate rapidly but declining over time as workers learn to troubleshoot and fix the machines they tend and feed with raw materials or data (Form and Hirschhorn 1985).

Just as productivity increases freed agricultural workers to move into industrial jobs, productivity increases freed industrial workers to move into government and service jobs and to morph into “knowledge workers” who rely on the power of their brain rather than their brawn.

Difficulties Measuring the Productivity of Knowledge and Government Workers

In the second half of the twentieth century, knowledge workers came to dominate labor share in leading economies like that of the USA (Drucker 2018; Cortada 2009). Government workers, including direct employees and contractors, also became an increasingly large percentage of the workforce in many countries after World War II (Light 2019).

To this day, it remains difficult to measure the productivity of knowledge workers (Ramírez and Nembhard 2004), in part because worker inputs cannot be easily discerned. Engineers, for example, may be physically present at work but mentally absent (Jones and Chung 2006). Ditto financial services providers (Zieschang 2018). Construction industry productivity also fluctuates due to mental inattentiveness to measurements and plan details (Motwani et al. 1995). Measuring the productivity of nurses also remains difficult because of the mixed physical-mental nature of their jobs and the necessity of maintaining quality of care standards above all (Nania 2006). Measuring the productivity of knowledge workers who work in, or for, government remains notoriously difficult, but almost everyone concedes it is relatively low (Bouckaert 1990) due to the nature of bureaucracies and compulsory monopolies (Haenisch 2012).

In some specific contexts, knowledge worker productivity can be estimated (Iazzolino and Laise 2018) or deduced from efficiency, utilization, or quality measures (Al‐Darrab 2000). Trends in management productivity can also be deduced from changes in the productivity of factory workers or other laborers whose productivity can be more directly assessed (Goldman 1959). Moreover, knowledge worker productivity usually varies strongly and positively with compensation and other incentives (Kaufman 1992). Their productivity is also positively associated with educational level (Rangazas 2002), age (Burtless 2013), and healthy sleep patterns (Nena et al. 2010).