A Stunning "Controlled Experiment" Shows How Hard "Good Public Policy" Can Be

Students of the effectiveness of public policy might agree, if they thought about it, that we rarely have any way of assessing the effectiveness of policies we devise to solve stated problems, such as protecting students and teachers from Covid infections, for example. 

But sometimes we accidentally do find the closest proxy to a controlled experiment that we are ever likely to find in real life. 

During the COVID-19 pandemic, particularly in the 2020-2021 school year when many public schools in the United States were closed or operating remotely due to lockdowns and health concerns, the majority of Catholic schools remained open for in-person or hybrid instruction. 

According to data from the National Catholic Educational Association, 92 percent of Catholic schools were offering full-time in-person or hybrid learning by early 2021, with most reopening in the fall of 2020 where local regulations permitted. 

This contrasted sharply with large public school districts in cities like New York, Los Angeles, and Chicago, which often stayed closed for the entire year. 

Regarding COVID-19 transmission, there were no reports of major widespread outbreaks or significant spikes in cases attributable to Catholic schools during this period. 

Multiple sources, including diocesan reports and analyses, indicate that infection rates in these open Catholic schools were generally low and comparable to or below community averages, thanks to rigorous health protocols. 

For instance, in the Archdiocese of Washington, officials reported in late 2020 that safety measures were effective even as regional cases rose, with no evidence of school-driven surges. 

Similarly, a 2023 analysis noted that Catholic schools maintained in-person learning without significant increases in infection rates, despite 17 percent of teachers being high-risk. 

Broader studies on school reopenings, such as those tracking U.S. districts in 2020-2021, found that in-person instruction in elementary and secondary settings (including private ones) did not lead to substantial community transmission when mitigation strategies were in place. While isolated cases occurred. Catholic schools were frequently highlighted as models for safe reopening, with no documented "major problems" like mass closures due to outbreaks. 

Title/Publication	URL	Publication Date
Why Catholic schools didn't fail while public ones did during COVID (New York Post)	New York Post	November 15, 2022
While public schools closed on fear and politics, Catholic schools opened on love and science (Washington Examiner)	Washington Examiner	June 28, 2023
Catholic Schools Have Stayed (Mostly) Open During The Pandemic (WGBH)	WGBH	February 25, 2021
COVID heroes: Catholic schools safely reopened (USA Today)	USA Today	February 12, 2021
COVID-19 cluster size and transmission rates in schools from ... (PMC)	NIH	N/A (academic study, circa 2022)
NCEA Releases 2020 - 2021 Data on State of Catholic Schools ... (NCEA)	NCEA	February 2021
ED624233 - Catholic School Enrollment Boomed during COVID ... (ERIC)	ERIC	2022
Analysis of Catholic School Reopening Plans in Fall 2020 (Digital Commons @ LMU)	Digital Commons	2021

It seems fairly obvious now, with our better understanding of Covid transmission, Covid vaccine effectiveness and lethality for people of different ages and health states, that the policies of closing public schools was a mistake. A rather big mistake.

Manufacturing Might be Growing Where We Do Not Expect

Manufacturing employment in the United States has surpassed its pre-Covid pandemic levels, the first time since the 1970s that the sector has regained all the jobs it lost in a recession. But the places where growth is happening have changed.

The manufacturing recovery has not reached the “Rust Belt” states of Pennsylvania, Ohio, Indiana, Illinois, Michigan, and Wisconsin. But states in the Sun Belt and Mountain West, such as Florida, Texas, and Utah, are well above pre-pandemic manufacturing employment.

The post-pandemic period also shifted manufacturing growth away from rural areas and towards small urban counties, which have become the sector’s primary drivers of job creation.

Before COVID 19, large urban and suburban counties enjoyed the fastest manufacturing jobs growth.  Since 2019, small urban counties have become dominant in manufacturing job creation. 

These areas, which previously accounted for less than 20 percent of new manufacturing jobs in the four years before the pandemic, have accounted for 61 percent of all manufacturing jobs added from 2019 to 2023, according to Bureau of Labor Statistics data. 

source: Economic Innovation Group

And what seems clear is that although most manufacturing industries have recovered from their pandemic job losses, computer and electronics manufacturing and chemical manufacturing are growing faster than before the pandemic.

The new jobs increasingly feature higher-skill roles, have grown most in small urban counties and seem to feature more contingent labor (contractors rather than employees). 

Change	Pre-Covid	Post-Covid
Automation/Digitalization	Gradual, uneven	Rapid, industry-wide
Workforce skill requirements	More low-skill jobs	Shift to high-skill roles
Supply chain strategy	Cost-driven, global	Resiliency, domestic focus
Growth Patterns	Rural & urban	Mostly small urban counties
Job Structure	More permanent	More temp/contract work
Government/Private Investment	Limited	Massive new investment

For economic development advocates, perhaps a takeaway is the growing importance of electronics and computer manufacturing, which seems to be growing faster and perhaps in locations one might not expect, especially smaller urban areas.

Why Apple Might Not Need to "Lead" AI

As Apple gears up for the typically-important Worldwide Developers Conference, many seem uneasy about Apple’s ability to provide evidence that its artificial intelligence strategy (“Apple Intelligence” and Siri) is working. Some worry Apple started too late or is in some ways hobbled because of its emphasis on data privacy, which limits the degree to which Apple software can take advantage of cloud-based processing. 

Those concerns might ultimately be a bit harsh.

Apple, despite the growth of its services revenue, remains a hardware company driven by sales of general-purpose devices such as smartphones. And there is an argument to be made that AI’s value is highest for special-purpose embodiments, less for general-purpose devices. 

In other words, AI’s value is crucial for automated vehicles. We might argue that AI-based health monitors likewise are instances where the AI has moderately-high value. 

For general-purpose smartphones, AI obviously aids photography operations, voice interactions or personalization. But we might expect too much of AI as a value driver beyond that. The specific value of AI for personal computers is arguably even lower.

Sure, voice interfaces are helpful, but most of the location-based personalization smartphones enable is not so pronounced with PCs. One might even argue AI provides lowish value for PCs. 

The point is that Apple’s AI strategy and imperatives are different from those of Alphabet, Microsoft or Meta. There is an argument to be made that Apple primarily has to employ AI to improve the value of phone apps (camera, for example) or user interface. AI is useful, but not existential.

For Microsoft, AI enables many of its core businesses, from gaming to enterprise productivity apps. AI might not emperil its core revenue drivers, so long as it can keep up. For Meta, whose revenue is built on content and advertising, AI might be a net plus.

Amazon might benefit directly mostly through AI-powered logistics efficiencies and recommendations and personalization for its customers.

Alphabet, on the other hand, faces the possible cannibalization of its search business model by AI alternatives. So for Alphabet, AI leadership might be an existential challenge.

So there is an argument to be made that Apple does not actually have to "lead" in broader AI. In fact, that has tended to be Apple's approach to innovation in the past, in any case. It rarely is "first" to introduce a new type of product or category. It tends to "do it better." 

And some of us recall that Apple has faced many periods when it seemed threatened. Eventually, Apple has been able to surmount such challenges. 

Period	Challenges	Rebound Strategy	Outcome
Mid-1980s to 1997	- Declining Mac sales - Fierce competition from Windows PCs - Poor leadership after Steve Jobs was ousted in 1985 - Mounting losses and product confusion	- Steve Jobs returned in 1997 through the NeXT acquisition - Simplified product line - Microsoft invested $150M in Apple (1997) - Launched iMac in 1998, designed by Jony Ive	- Apple returned to profitability - iMac became a major hit - Re-established design and innovation culture
Early 2000s (2001–2003)	- Skepticism over Apple's entry into consumer electronics - Slow Mac sales - Tech bubble aftermath weakened investor confidence	- Launched iPod in 2001 - iTunes Store in 2003 revolutionized digital music - Strengthened brand loyalty	- iPod became a cultural phenomenon - Boosted revenue and brand visibility - Set stage for future devices
2007–2009 (Post-iPhone Launch)	- iPhone faced strong criticism for lacking features (e.g., no 3G, no physical keyboard) - Doubts about Apple entering the mobile phone market - 2008 financial crisis hurt tech stocks	- Rapid iteration: iPhone 3G (2008), App Store launch - Aggressive global carrier partnerships - Focus on software ecosystem	- iPhone became Apple’s flagship product - App Store created a new app economy - Massive revenue growth
2011–2013 (Post-Steve Jobs Era)	- Concerns over Apple’s innovation capacity after Jobs’ death in 2011 - Critics claimed Apple was no longer a “visionary” company - Increasing Android competition	- Strong product roadmap under Tim Cook - Continued success with iPhone, iPad, and Mac - Services revenue growth began (iCloud, App Store, etc.)	- Stock rebounded and reached new highs - Apple maintained leadership in premium devices - Cemented Cook’s leadership credibility
2015–2016 (iPhone Saturation Fears)	- Slowing iPhone growth - China market concerns - Critics questioned reliance on a single product line	- Diversification: Apple Watch, AirPods - Expansion of services (Apple Music, iCloud, App Store) - Focus on ecosystem lock-in	- Apple became world's most valuable company again - Services and wearables became major revenue contributors
2020 (COVID-19 Pandemic)	- Factory closures and supply chain disruptions - Retail stores shut down - Global economic uncertainty	- Rapid pivot to remote work culture - Launched Apple Silicon (M1 chip) in 2020 - Robust online sales strategy	- Record-breaking quarters post-pandemic - M1 chip received critical acclaim - Apple solidified vertical integration strategy

"Lies, Damned Lies and Statistics"

What is a “fact,” and how do we know? 

Consider any number of statistical correlations we might care to investigate: whether crime, mental health (changing diagnostic criteria alter prevalence rates); poverty (different poverty line calculations yield dramatically different numbers); education (standardized test focus narrows what's measured as "learning," but some relatively objective means has to be used); public health (disease surveillance systems prioritize certain conditions over others). 

The statistics we collect about crime and human behavior are powerfully shaped by the decisions about what to count, how to count it, and what to prioritize. Whether one believes that is a reflection of societal power or something more simple, our choices about what to count influences both the “numbers” and the sense of significance. 

To use an obvious example, to the extent we decriminalize or legalize use of marijuana, the amount of crime related to “illegal” use goes away. Then there are issues related to which crimes we choose to prioritize over others which also are legally crimes. Law enforcement agencies, for example, have finite resources. They might choose to ignore some infractions to focus on others. That directly shapes crime statistics (enforcement increases volume; ignoring decreases volume of reported instances). 

There also is a difference between unreported and reported; prosecuted and not prosecuted; acquittal and conviction rates. 

Also, changes in recording practices can create statistical variances. Redefining deviance upwards or downwards (what is a crime; what is not) will affect the statistics. 

During the Covid-19 pandemic, there were complexities in how deaths were classified when COVID-19 was detected alongside other health conditions. 

In most jurisdictions, including the United States, the standard practice followed CDC guidance: deaths were counted as COVID-19 deaths if COVID-19 was listed as a cause of death on the death certificate, either as the underlying cause or as a contributing factor. 

This approach meant that someone who died with multiple conditions could be counted in COVID-19 mortality statistics if COVID-19 played a role in the death. But there were at least three distinct categories:

• Deaths directly caused by COVID-19 (e.g., respiratory failure due to COVID-19 pneumonia)

• Deaths where COVID-19 was a contributing factor that exacerbated existing conditions

• Deaths where someone tested positive for COVID-19 but died primarily from unrelated causes

The controversy centered on the inclusion of category 2 and sometimes category 3 cases. 

The CDC eventually distinguished between deaths "from" COVID-19 and deaths "with" COVID-19, though public reporting didn't always clearly separate these categories.

These classification decisions had significant implications for our understanding of the pandemic's impact and highlighted how methodological choices in mortality statistics can shape our perception of public health crises. 

The point is that there are “statistics” and there are “lies, damned lies, and statistics.” In other words, seemingly objective statistics are only partly thus.