Showing posts sorted by date for query recession. Sort by relevance Show all posts
Showing posts sorted by date for query recession. Sort by relevance Show all posts

Sunday, March 1, 2026

What Ails U.S. Student Math and Reading Skills?

Some might claim educational technology is to blame for declining U.S. student capabilities in math and reading. That probably is not the most-common explanation, however. 


U.S. student performance in reading and mathematics has been tracked primarily through the National Assessment of Educational Progress (NAEP). Overall, scores showed steady improvements from the 1970s through the early 2010s, peaking around 2013. 


Since then, performance has stagnated or declined, with sharp drops during the COVID-19 pandemic (2019–2022) due to school disruptions. Test scores indicate high school scores for the 2024 graduating classes have dropped. 


Many reasons have been advanced, but some might point to a couple of decades of declining scores. 


Post-pandemic recovery has been uneven: math has shown slight rebounds in some grades, while reading continues to decline. 


These trends are more pronounced among lower-performing students, widening achievement gaps. By 2024–2025, 12th-grade scores reached historic lows, with reading 10 points below 1992 levels and math at its lowest since 2005.


Study/Author(s)

Year

Key Proposed Reasons

Scammacca et al 

2019

Initial proficiency levels create Matthew effects (higher initial skills lead to faster growth); demographic factors (socioeconomic status, ethnicity) influence starting scores and growth rates; grade-level differences affect trajectories, with lower performers growing slower over time.

Wyckoff (Annenberg Working Paper) 

2025

Pre-2013 gains slowed due to policy shifts (reduced accountability post-NCLB); Great Recession funding cuts; Common Core disruptions; rising smartphone/social media use; demographic shifts (more English learners); pandemic accelerated existing declines, especially for low performers.

Malkus (AEI Report) 

2025

Similar to Wyckoff: accountability rollback, funding reductions, smartphone proliferation, and out-of-school factors (student well-being) explain stagnation since 2013; pandemic worsened trends but isn't the sole cause; factors outside school (e.g., screens) play a major role.

DeBord (Dissertation) 

2026

Post-pandemic: classroom factors (rebuilding routines, teacher adaptability); school collaboration; family/district supports; persistent gaps in foundational knowledge, reduced struggle tolerance, technology habits, and socioemotional needs hinder recovery.

NWEA Brief 

2021

COVID-specific: remote learning disruptions led to below-pre-pandemic gains; greater impacts on math than reading; inequities in access (devices, support) affected lower performers more; recovery requires addressing missed foundations.


But there may be lots of other cumulative reasons, including social promotion (advancing students to the next grade despite inadequate performance to maintain age-appropriate grouping) and efforts to avoid stigmatizing learners (through reduced retention or labeling to prevent emotional harm), may contribute to declining academic achievement in U.S. students. 


These practices are often adopted to mitigate short-term social-emotional risks like low self-esteem or dropout likelihood from grade retention, but research suggests they can exacerbate long-term performance issues by allowing students to advance without mastering foundational skills, leading to compounded learning gaps, disengagement, and lower scores on assessments like NAEP.


This is particularly evident among lower-performing students, where declines have been steepest since the early 2010s. It’s complicated.

Wednesday, February 25, 2026

Disintermediation, Again

Disintermediation, the removal of elements in a value chain particularly related to distribution, was a primary effect of the internet. 


Artificial intelligence should cause even more disintermediation, as processes and value chain roles dependent on information asymmetry are removed. 


source: WallStreetMojo


Worse, some might fear, there might be no natural restorative mechanism similar to the boom-bust; recession-recovery; supply-demand cycles we commonly see in economies. 


Instead, in a worst-case scenario, AI continually depresses consumer spending (which represents about 70 percent of all economic activity) as AI leads to layoffs, which leads to less consumer spending, which increases the necessity of relying on AI to protect firm profit margins. 



Industry

How AI Removes Friction/Asymmetry

Expected Impact

Real Estate Brokerage

AI agents instantly access MLS data, decades of transaction records, valuations, and matching—eliminating agents' knowledge advantage and buyer/seller search friction.

Commissions drop sharply (e.g., from 5-6% to under 1%); many deals close agent-free or via AI "agent-on-agent"; widespread disintermediation and value loss for traditional firms.

Insurance Brokerage/Underwriting

AI enables instant policy comparison, re-shopping, risk prediction via vast data, and fraud detection—eroding inertia-based renewals and broker expertise.

15-20% premium loss from passive renewals; brokerage spreads compress; shift to direct/AI-driven models; broker selloffs (e.g., triggered by tools like Insurify).

Wealth Management / Financial Advisory

AI delivers personalized portfolio advice, tax strategies, and real-time market analysis—democratizing what once required expensive human experts and proprietary insights.

Erosion of 1% AUM or high advisory fees for basic services; "basic financial advisory" faces collapse; robo-advisors and AI tools dominate routine work.

Legal Services (Routine)

AI automates contract drafting, precedent research, due diligence, and basic navigation of laws—reducing asymmetry between lawyers and clients on standard matters.

Commoditization of routine work; reduced demand for junior/entry-level roles; faster/cheaper services pressure billable hours and firm margins.

Travel Booking Platforms

AI agents autonomously assemble custom itineraries (flights, hotels, etc.) faster/cheaper than platforms, bypassing search friction and default inertia.

Margin compression for intermediaries; habitual booking models disrupted; platforms lose value as direct AI routing prevails.

Logistics / Freight Brokerage

AI optimizes routes, matches shippers/carriers directly, and forecasts with superior data—eliminating broker coordination friction and information edges.

Broker fees collapse; rapid selloffs (e.g., truck brokers like RXO); shift to automated direct platforms.

Consulting / Professional Advisory Services

AI handles research, data analysis, and initial recommendations—eroding human expertise moats and proprietary knowledge asymmetry.

Reduced fees for routine/cognitive tasks; white-collar headcount cuts; productivity gains but revenue destruction for traditional models.

Ratings Agencies / Index Providers / Credit Checks

AI aggregates and analyzes public/proprietary data at scale—undermining exclusive research advantages and manual verification friction.

Proprietary edges erode; commoditization of ratings/indexing; lower barriers and pricing pressure.


Payments also are an obvious place to look for changes, though the changes might come from use of blockchain-based payment processors offering stablecoins.


source: Citrini Research


 


Friday, August 1, 2025

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.


Friday, May 9, 2025

Bye Bye Skype

As Microsoft retires Skype in favor of Teams, it might be useful to recall just how impactful “voice over IP” services such as Skype were in dismantling the telco profit engine.


For example, looking only at revenue, in 2000 global international call revenues were in the range of $80 billion to $100 billion, with very-high profit margins. By 2020, international calling revenues had dropped to about $15 billion to $20 billion, with profit margins compressed. 

 

Decline in International Calling Revenue with VoIP Adoption (2000–2020)

Year

Est. Int'l Calling Revenue (Billion USD)

VoIP Adoption & Skype Milestones

Notes

2000

~80–100

Minimal VoIP presence; traditional PSTN dominates

High tariffs for international calls; telecom monopolies prevalent.

2003

~75–90

Skype launched; 11M users by 2004

Skype introduces free VoIP calls and low-cost PSTN calls, challenging telecom pricing.

2005

~70–85

Skype acquired by eBay ($2.6B); 54M users

VoIP gains traction; telecoms begin lowering rates to compete.

2008

~60–75

Skype grows to 405M users

Economic recession impacts telecom revenue; VoIP alternatives expand (Viber, WhatsApp emerging).

2010

~50–65

Skype disables third-party integrations; 663M users

Telecoms lose market share to VoIP; mobile data plans begin reducing VoIP dependency.

2013

~40–55

Skype-to-Skype int’l traffic up 36% (214B minutes)

TeleGeography notes VoIP capturing significant call volume; traditional revenue continues to decline.

2015

~30–45

WhatsApp, Viber, and others compete with Skype

Mobile apps erode Skype’s dominance; telecoms shift to data-driven models.

2018

~20–35

Skype daily users at 40M (2020 peak)

VoIP services saturate the market; telecom firms  focus on broadband and mobile data revenue.

2020

~15–25

Skype usage spikes 70% during COVID-19

Despite Skype’s decline to 36M daily users by 2023, VoIP remains dominant; traditional int’l calling revenue nears obsolescence.


Profit margins were an important part of the early, pre-VoIP story. Net profit margins on international voice were as high as 25 percent back in 2000. Current net margins are in the range of three percent to possibly five percent. 


In a real sense, VoIP services including Skype disrupted the telecom industry profit driver. 


Year

Domestic Long-Distance Margin (%)

International Long-Distance Margin (%)

Discussion

2000

~12–18%

~15–25%

High margins due to limited competition and high per-minute rates. Domestic margins are slightly lower than international due to local competition. Estimated from telecom sector data and peak long-distance revenue.

2001

~12–17%

~15–24%

Stable margins but early pressure from mobile and VoIP adoption. International margins are higher due to termination fees. Estimated from sector trends.

2002

~11–16%

~14–23%

Decline in domestic voice revenue began as mobile plans offered "bucket" minutes. International margins remained higher but faced VoIP competition. Estimated from sector data.

2003

~10–15%

~13–22%

Continued erosion from mobile and VoIP (e.g., Skype). International margins supported by high termination rates. Estimated from sector trends.

2004

~9–14%

~12–20%

VoIP and internet-based calling reduced costs and rates, squeezing margins. Domestic margins lower due to flat-rate plans. Estimated from sector data.

2005

~8–13%

~11–18%

Long-distance business peaked in 2000; by 2005, revenues were declining rapidly. International margins are higher due to mobile international calling demand. Estimated from sector data.

2006

~7–12%

~10–17%

Domestic margins hit by unlimited calling plans and VoIP. International margins supported by slower price erosion in mobile long-distance. Estimated from sector trends.

2007

~6–11%

~9–16%

Domestic long-distance became commoditized; international margins pressured by OTT apps (e.g., WhatsApp). Estimated from sector data.

2008

~5–10%

~8–15%

Long-distance revenues halved from 2000 peak. Economic recession and VoIP adoption further reduced margins. Estimated from sector data.

2009

~5–9%

~8–14%

Smartphone adoption and VoIP apps (e.g., Skype) eroded margins. International mobile long-distance retained higher margins. Estimated from sector trends.

2010

~4–8%

~7–13%

Domestic long-distance margins near sector average (~4.82%). International margins are higher due to termination fees and mobile demand. Estimated from sector data.

2011

~4–7%

~7–12%

Domestic margins low as carriers bundled unlimited long-distance. International margins declined due to VoIP growth. Estimated from sector trends.

2012

~3–7%

~6–11%

Domestic long-distance fully commoditized; international margins affected by outsourcing and fraud. Estimated from sector data.

2013

~3–6%

~6–10%

Mobile data surpassed voice revenue; domestic long-distance margins were minimal. International margins supported by the wholesale voice market. Estimated from sector trends.

2014

~3–6%

~5–10%

Voice over LTE and VoIP reduced standalone voice profitability. International margins pressured by low-cost VoIP providers. Estimated from sector data.

2015

~3–5%

~5–9%

Domestic long-distance margins were negligible as unlimited plans dominated. International margins declined due to OTT apps. Estimated from sector trends.

2016

~3–5%

~4–8%

Voice services bundled with data; domestic margins near zero. International margins are low but supported by wholesale carriers. 

2017

~2–5%

~4–8%

Domestic long-distance margins minimal; international margins affected by grey routes and fraud. Estimated from sector trends.

2018

~2–4%

~4–7%

Domestic margins near zero as voice bundled with data plans. International margins low due to VoIP and 5G adoption. Estimated from sector data.

2019

~2–4%

~3–7%

Voice commoditization is complete; international margins slightly higher due to wholesale voice demand. Estimated from sector trends.

2020

~2–4%

~3–6%

COVID-19 increased communication demand, but voice margins remained low due to free VoIP apps. Estimated from sector data.

2021

~2–4%

~3–6%

Domestic long-distance margins negligible; international margins low but supported by enterprise demand. Estimated from sector trends.

2022

~2–4%

~3–6%

Telecom services margin ~4.82%; domestic voice margins near zero. International margins are low due to wholesale price wars. Estimated from sector data.

2023

~2–4%

~3–5%

North America wholesale voice market faced intense competition, eroding international margins. Domestic margins are negligible. Estimated from sector trends.

2024

~2–4%

~3–5%

Wholesale voice market valued at $40.26 billion in 2025, but margins low due to VoIP and 5G. Domestic margins near zero. Estimated from sector data.


Also, VoIP was not the only huge driver of a shift in consumer behavior. “Calling” became something most people did on their mobile phones. 


Fixed-network revenue dropped from $200 billion globally in 2000 to under $50 billion by 2020, while mobile revenue grew from $500 billion to $1.6 trillion, for example. U.S. telco revenues likewise shifted from fixed to mobile; legacy voice to VoIP. 


U.S. Telco Revenues 2000 to 2024

Year

Mobile Voice

PSTN Voice

VoIP

2000

10

100

1

2010

60

60

21

2020

110

20

41

2024

130

4

49


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