The Determinants and Consequences of Admission to Highly Selective Colleges

Source: Forked Lightning blog, Jul 2023
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We received internal admissions and attendance records from hundreds of U.S. colleges, including several of the 12 Ivy-Plus colleges (the Ivy league, plus MIT, Stanford, Duke, and University of Chicago), and data on all SAT and ACT test takers between 2010 and 2015. We linked all of it to U.S. tax records, which allowed us to measure family income at the time of application (because parents claim their kids as dependents) and then – eventually – the adult incomes of the students who were admitted to and attended each college.

We first show you a simple fact – applicants from families in the top 1% of the income distribution are more than twice as likely to attend Ivy-Plus colleges, even among applicants with the same SAT or ACT scores.

Three factors explain almost all of it. The biggest chunk – about half – is preferential admission for legacies. Legacies are richer than the average applicant. Also, the legacy admissions boost is greater for high-income legacy applicants (see Figure 7b). The second factor is recruited athletes. Again, athletes are richer than average Ivy-Plus student, and they comprise 10% of the total class at such colleges, compared to 5% or less at large public universities.

A Visual Guide to Stagflation, Inflation, and Deflation

Source: Visual Capitalist, Sep 2022

TSMC: Taiwan’s Security Defense

Source: NY Times, Aug 2022

Taiwan is the biggest producer of the world’s most advanced chips. It is also rapidly becoming one of the world’s most dangerous geopolitical flash points. The fear is that in the event of a conflict, firms won’t get the microchips they need to make phones and drones, set up supercomputers and cellular networks, and even build new weapons.

Taiwan’s president, Tsai Ing-wen, told one group that she saw the island’s tech prowess as a means of shoring up support for its democracy. Calling economic security a “pillar” of national security, she said Taiwan was willing to work with partners to build sustainable supply chains for what she called “democracy chips.”

In the event of a military conflagration, Taiwan’s importance to global chip supplies also means the damage to all sides — and to the wider world’s digital infrastructure — is hugely amplified. Not for nothing do people in Taiwan call TSMC their “sacred mountain, protector of the nation.”

few deny that Taiwan’s centrality in the supply chain makes such considerations a factor, a concept generally referred to as the “silicon shield.” An invasion of Taiwan would mean a form of mutually assured destruction, not necessarily of the world, but for the many modern gadgets we use every day.

That does confer a dose of security, said Jason Hsu, a former Taiwan legislator and current fellow at the Harvard Kennedy School focused on technology.

The key to a nation’s economic power is less about which one is first to develop a major new technology than which is best at applying it broadly.

Source: Stanford, Jan 2022

Jeffrey Ding, a postdoctoral fellow at Stanford’s Center for International Security and Cooperation and at the Stanford Institute for Human-Centered Artificial Intelligence, argues that China and the United States are both taking the wrong lessons from previous industrial revolutions.

As revolutionary as artificial intelligence may be, he says, both governments are overly preoccupied with being dominant in breakthrough advances. Over the long term, it may be more important to figure out how a wide range of industries can make practical use of them all.

I find the rise and fall of great powers are affected by their ability to adapt new general-purpose technologies across many different industrial sectors. General purpose technologies affect growth differentials through a slow process of diffusion across the economy.

That has implications for policy.

If a country concentrates on being the first to innovate and to have explosive growth in a new industry, that country might overoptimize for scientific research, advanced R&D, and deepening the expert pool.

If instead you focus on spreading general-purpose technologies, then a country might invest more in broadening its engineering skill base, optimizing for widening the pool of general electrical engineers versus producing the top electrical experts. It would also invest more in building strong links between private industry and universities to help rapidly diffuse the knowledge associated with the new technology.

In the Second Industrial Revolution, from 1870 to 1914, the United States took advantage of those developments in machine tools to spread the practices of “interchangeable” manufacturing, where products have standardized, interchangeable parts.

That gave rise to the “American system” of manufacturing, which in turn drove American productivity growth and allowed the United States to become the world’s preeminent economic power.

What’s important to notice here is that the United States was not the uncontested leader in innovation around machine tools. Britain, France, and Germany were all more sophisticated scientific powers at that time, and they developed very sophisticated machine tools. What propelled the rise of U.S. manufacturing is that it was more successful in adopting machine tools across all these industries.

In the U.S., by contrast, you saw a huge flurry of initiatives to increase the talent base in mechanical engineering.

First and foremost, you had the Morrill Act, which created land-grant universities in every state.

Within a few decades, the number of universities had multiplied from fewer than 20 to more than a hundred, and many of them were setting up technical training programs for engineers. The mission of the Morrill Act was specifically to increase expertise in the mechanical arts.

important to standardize best practices in general-purpose technologies via systems that facilitate interoperability between sectors and information flows from one sector to another.  Germany’s efforts to standardize such practices in mechanical engineering was much slower than what was happening in the U.S.

The history also suggests that the focus shouldn’t be exclusively on being first to introduce new advances in AI. Both sides have an opportunity to invest more in diffusion-centered institutions, such as technology-transfer bodies that transfer innovations from the lab to startups and other companies.

This could involve things like “innovation vouchers,” where governments offer subsidies to small- and medium-sized firms to purchase services from universities, research institutes, or companies on the technological frontier. The idea is to stimulate the transfer of leading-edge R&D to a broad pool of adopters.

The United States’ approach to standardization is industry-led, which may be more effective than China’s, which is government-led.

When the government attempts to impose standards from the top down early in the process, it’s operating as what Paul David called a “blind giant.” It doesn’t know enough about the technology, so it often locks in dated technologies. China’s top-down approach might stifle the ability of these technologies to spread.

China Demolishes 15 skyscrapers that had sat vacant for nearly a decade

Source: NotTheBee, Sep 2021

Authorities in China recently demolished over a dozen massive buildings in one fell swoop, bringing down 15 skyscrapers in the city of Kunming in the country’s Yunnan province.

The buildings had reportedly sat vacant and unused for nearly a decade. The entire demolition took less than a minute.

Re-upping the stunning demolition videos showcasing housing oversupply in China: 15 skyscrapers in China that were part of the Liyang Star City Phase II Project were just demolished after sitting unfinished for eight years due to absent market demand. pic.twitter.com/UByqjk8QXX

— Jon Hartley (@Jon_Hartley_) September 15, 2021

Stay-At-Home Orders (lockdowns) Backfired

Source: FEE, Jun 2021

In a new paper, economists from the University of Southern California and the RAND Corporation examined the effectiveness of “shelter-in-place” (SIP) mandates, aka stay-at-home orders, using data from 43 countries and all 50 US states. The experts analyze not just deaths from COVID-19, but “excess deaths,” a measure that compares overall deaths from all causes to a historical baseline. 

The authors explain that lockdown orders may have had lethal unintended consequences in their own right, such as increased drug overdoses, worsened mental health problems, increased child abuse, deadly delays in non-COVID medical care, and more. So, to find out whether stay-at-home orders truly helped more than they hurt, examining excess deaths, not just pandemic outcomes, is key.

The results aren’t pretty.

“We fail to find that shelter-in-place policies saved lives,” the authors report. Indeed, they conclude that in the weeks following the implementation of these policies, excess mortality actually increases—even though it had typically been declining before the orders took effect. And across all countries, the study finds that a one-week increase in the length of stay-at-home policies corresponds with 2.7 more excess deaths per 100,000 people.

The lockdowns simply didn’t work.

“We failed to find that countries or U.S. states that implemented SIP policies earlier, and in which SIP policies had longer to operate, had lower excess deaths than countries/U.S. states that were slower to implement SIP policies,” the authors explain.

And their finding is no outlier. A number of other credible studies have similarly concluded that lockdowns were ineffective at slowing the spread of COVID-19. Plus, other research now shows that most COVID-19 spread occurred at home, not out in the world, making stay-at-home orders all the more absurd in hindsight.

The coming productivity boom

Source: MIT Technology Review, Jun 2021

The productivity J-curve describes the historical pattern of initially slow productivity growth after a breakthrough technology is introduced, followed years later by a sharp takeoff.

Our research and that of others has found that technology alone is rarely enough to create significant benefits.

Instead, technology investments must be combined with even larger investments in new business processes, skills, and other types of intangible capital before breakthroughs as diverse as the steam engine or computers ultimately boost productivity.

For instance, after electricity was introduced to American factories, productivity was stagnant for more than two decades. It was only after managers reinvented their production lines using distributed machinery, a technique made possible by electricity, that productivity surged.

 

Crowdfunding with Refund Bonuses

Source: ScienceDirect, May 2021

The assurance contract mechanism is often used to crowdfund public goods.

This mechanism has weak implementation properties that can lead to miscoordination and failure to produce socially valuable projects.

To encourage early contributions, we extend the assurance contract mechanism with refund bonuses rewarded only to early contributors in the event of fundraising failure.

The experimental results show that our proposed solution is very effective in inducing early cooperation and increasing fundraising success. Limiting refund bonuses to early contributors works as well as offering refund bonuses to all potential contributors, while also reducing the amount of bonuses paid. We find that refund bonuses can increase the rate of campaign success by 50% or more.

Moreover, we find that even taking into account campaign failures, refund bonuses can be financially self-sustainable suggesting the real world value of extending assurance contracts with refund bonuses.

Statistics for Strategic Scientists

Source: A Fine Theorem, Apr 2021

Though there is an empirical link between rising prices and a strong economy, we cannot generate a strong economy in the long run just by inflating the currency.

Econometrics, then, is largely concerned with the particular statistical problem of identifying certain parameters that explain what will happen if we change one part of the system through policy or when we place people with different known preferences, costs, and so on in the same strategic situation.

communications model vs decision model

As we are all aware from the disastrous Covid-related public science in the past year (see Zeynep Tufekci’s writing for countless examples), there is often a tension between reporting results truthfully and the decisions taken based on those results. Andrews and Shapiro model this as a Wald-style game where scientists collect data and provide an estimate of some parameter, then a decision is made following that report.

The estimate is of course imprecise: science involves uncertainty. The critical idea is that the “communications model” – where scientists report an estimate and different agents take actions based on that report – differs from the “decision model” where the scientist selects the actions (or, alternatively, the government chooses a common policy for all end-users on the basic of scientist recommendations).

Optimal communication depends heavily on which setting you are in. Imagine that a costly drug is weakly known to improve health, but the exact benefit is unknown. When they can choose, users take the drug if the benefit exceeds their personal cost of taking it. In an RCT, because of sampling error, sometimes you’ll get that the drug is harmful when you try to estimate how “beneficial” it is.

In a communications model, the readers adjust for sampling error, so you just report truthfully: there is still useful information in that “negative” estimate because it still tells you that the effect of the drug is likely to be close to zero the more negative the point estimate. No reason to hide that from readers!

In a “decision model”, you would essentially be forcing a tax on the drug just because of sampling error, even though you know this is harmful, so optimally you censor the reporting and just give “no effect” in your scientific communications. There is a really interesting link between decision theory and econometrics going back to Wald’s classic paper. The tension between open communication of results to users with different preferences, and recommended decisions to those same users is well worth further investigation.

Jerome Powell: The Fed Will Support the Economy

Source: SchiffGold, Apr 2021

Powell closed out the interview with a guarantee. The Fed is will support the economy for as long as it takes to complete the recovery.

But Peter said there’s a problem with this promise. The Fed doesn’t have the power to do anything but create inflation.

The idea that the Fed can create economic growth, can create prosperity, when it boils down to it, the only thing that it can do is print money.

However you want to describe it, that does not create prosperity.”