Famous quotes

"Happiness can be defined, in part at least, as the fruit of the desire and ability to sacrifice what we want now for what we want eventually" - Stephen Covey

Sunday, August 02, 2026

China's Emerging AI Landscape and the Digital Marketplace

 Anxious Chinese students are trusting AI to help pick colleges and majors

Alibaba, ByteDance, Baidu, and Tencent guide hundreds of millions of high-school grads to optimize the high-stakes university match system.

By VIOLA ZHOU, 29 JULY 2026

  • Chinese high school graduates are turning to free AI chatbots to navigate the college admission process.
  • The automated tools are disrupting a costly private counseling market by offering highly utilitarian, job-focused major recommendations.
  • Despite the convenience, students must cross-check AI suggestions to avoid database errors that could ruin their matching chances.

After taking part in China’s grueling national college entrance exam, Guo Xinyan has three weeks to make what could be the most important decision of her life: which universities and programs she should apply to. The 18-year-old turned to an AI chatbot for help.

On Alibaba’s chatbot Qwen, Guo entered her score, her rank in her home province of Shandong, her desired majors (law, accounting, or finance), her budget for tuition (moderate), and where she wants to live (coastal cities). The bot generated a report with dozens of choices.

“My family and I have no experience with college applications, so we had to rely on social media and AI,” Guo told Rest of World, adding that her parents had also asked ByteDance’s Doubao to suggest college programs for her. “There’s nothing in particular I want to study. We are just looking for good career prospects.”

Every year, around 10 million Chinese students take the college entrance exam known as the gaokao. Students wait for the results and then have just a few weeks to factor those scores into their application decisions. These grades determine whether a student can attend prestigious universities or pursue popular majors like business and engineering. Filling out these preferences is a high-stakes decision because, unlike in the U.S., Chinese universities have stringent requirements for transferring to a different field of study. Most students have only this one opportunity to decide on their future profession and survival in an increasingly competitive job market.

China’s Biggest AI Players

Many families pay private coaches to guide them through this decision, but AI companies are now offering the service for free to attract consumers to their chatbots. Since 2025, tech giants including Alibaba, ByteDance, Tencent, and Baidu have launched AI tools specializing in researching and recommending college programs.

These are typically agentic AI systems that analyze universities' historical admission cutoff scores. They ask for gaokao scores, career interests, Myers-Briggs Type Indicator (MBTI) personality assessments, tuition fees, and local climate. Eventually, the bots produce a list of suggested programs divided into “reach,” “safety,” and “backup” categories based on the student's admission probability.

Alibaba’s Qwen recruited 300 college search specialists to improve the system's reasoning, while Baidu promised human expert reviews for its recommendations. By July 8, Qwen had produced 23 million recommendation reports for gaokao takers. Tencent’s Yuanbao bot answered 200 million inquiries related to college admission by July 10, and Baidu reported 15 million users for its AI college advisory feature in late June.

While American families also use AI virtual consultants, Chinese high schools rarely provide career counseling. Before AI, families relied on guidance books, social media influencers, and independent tutors whose coaching sessions could cost thousands of dollars. Consultancy iiMedia estimated this advisory service industry at $160 million. Choosing the right major has become especially critical during a time of high youth unemployment and fears of AI automation. Paradoxically, AI itself has become a popular major choice as universities add programs like “embodied intelligence.”

“Don’t talk about dreams”

The best-known college admission adviser was Zhang Xuefeng, who had over 27 million followers on Douyin. He was known for bluntly telling working-class families which majors (like math and engineering) led to a bright future and which (like journalism and philosophy) did not. His company charged up to $2,700 for assistance.

After Zhang passed away in March, engineers built AI versions of him. One developer created a “Zhang Xuefeng skill” on GitHub, using his books and remarks to improve how AI agents perform tasks. Anyone can use this skill to make an AI agent speak in Zhang’s signature utilitarian manner. For example, the bot might tell an arts-interested student: “If your family has no money, don’t talk about dreams... Pick a major that puts food on the table, like computer science, education, or getting a government job.”

Xindy Lin, a designer in Fujian, built her own chatbot on OpenAI’s Codex to help her sister, combining the “Zhang Xuefeng skill” with current admission data and historical results. The AI suggested majors like supply-chain management and e-commerce marketing. Lin noted that individuals cannot digest the massive amount of information required in such a short time without help.

The Life Consequences of an AI Error

The college coaching industry has long been criticized for profiting from anxiety. Some human coaches, unfamiliar with the vast number of programs, now secretly use AI to generate recommendations. However, economist Ye Xiaoyang pointed out that rural students often lack the computer skills to effectively prompt a chatbot.

Ye launched a free AI coaching platform to help students explore career directions, noting that “a good AI system should be able to guide students to think for themselves.” He warned that AI tools could make mistakes, such as using outdated data or omitting strong options. Furthermore, if AI gives similar recommendations to students with similar scores, it could steer them toward the same programs, leading to match failures that force students into much lower-ranked schools.

Guo, the Shandong graduate, opted to cross-check her chatbot’s suggestions with official books and social networks like RedNote to investigate school dormitory conditions. “I still find real people’s experiences more trustworthy,” she said.

Saturday, August 01, 2026

The Financialization Power of Public REITs

 Real Estate Investment Trusts (REITs) are massive financial actors that have fundamentally reshaped the U.S. economy by separating real estate ownership from the operation of productive enterprises. While often categorized as "passive investors" due to tax regulations, the sources argue they are aggressive actors driving the financialization of industries like healthcare and hospitality.

Overview and Legal Framework

REITs were established by the 1960 Real Estate Investment Trust Act to allow individual retail investors to access commercial real estate markets, similar to a mutual fund. To maintain their tax-exempt "pass-through" status, they must meet several core requirements:

  • Asset Allocation: Invest at least 75% of their assets in real estate.
  • Income Source: Derive at least 75% of their gross income from real property.
  • Dividend Payout: Distribute at least 90% of their taxable income as shareholder dividends annually.

Under these rules, REITs pay no corporate taxes, and only the investors pay taxes on dividends.

Scale and Growth of Public REITs

The scale of the REIT industry has expanded exponentially since the 1990s:

  • Asset Value: REITs control over $3.5 trillion in gross assets and more than 500,000 properties in the U.S..
  • Market Capitalization: Publicly-traded REITs alone had an equity market capitalization of over $1.35 trillion in 2022, up from just $138 billion in 2000 and an "insignificant" amount before 1990.
  • Market Reach: Roughly 145 million Americans (44% of households) held REIT investments in 2020, either directly or through retirement funds like 401(k)s.
  • Commercial Real Estate (CRE) Share: REITs represented 9.4% of the total U.S. CRE market in 2021, though their share of "institutional-quality" properties is estimated to be much higher at 18.7%.

Industry Restructuring and the OpCo/PropCo Model

REITs have driven a significant restructuring of U.S. industries by promoting the OpCo/PropCo model. This model legally separates the ownership of real property (the "Property Company" or PropCo) from the commercial enterprise producing goods or services on that property (the "Operating Company" or OpCo).

The sources highlight several consequences of this restructuring:

  • Separation of Logic: This separation is driven by financial logic (maximizing investor returns) rather than business logic (providing high-quality integrated services).
  • Wealth Extraction: In healthcare, REITs often partner with private equity (PE) firms in sale-leaseback agreements. The PE firm buys a provider (like a nursing home), sells the real estate to a REIT to pay out dividends to itself, and leaves the healthcare provider burdened with high rents and "triple net" leases where the tenant must still pay for maintenance, taxes, and insurance.
  • Industry Consolidation: Because REITs are tax-exempt, they can pay higher premiums for properties than non-REIT owners, allowing them to dominate Mergers and Acquisitions (M&A). As REITs consolidate local properties into national or global corporations, they facilitate the consolidation of their tenants into "mega-chains".
  • Financialization: REITs turn real property into "financial widgets"—tradable assets that are often disconnected from the actual purpose of the productive enterprises (e.g., patient care in hospitals) that occupy the buildings.

Sector-Specific Restructuring

  • Healthcare: REITs have grown hand-in-hand with for-profit nursing home and hospital chains. By 2021, 18 publicly traded healthcare REITs owned roughly 8% of all healthcare properties in the U.S., including 12% of skilled nursing facilities.
  • Hotels: The industry has shifted toward an "asset-light" model where hotel brands spin off their real estate to REITs to conserve cash and increase share prices. To manage the legal requirement for "arms-length" relations, hotel REITs use Taxable REIT Subsidiaries (TRS) as lessees, which then contract with operating companies.

Public REITs have had a profound impact on the U.S. economy, primarily by driving financialization—a process that expands the reach of finance capital into productive sectors by turning real property into "financial widgets" or tradable assets. These impacts are best understood through the lens of industry restructuring and the resulting shifts in wealth distribution.

1. Industry Restructuring: The OpCo/PropCo Model

REITs have fundamentally restructured industries by promoting the legal separation of a business into two entities: the Property Company (PropCo), which owns the real estate, and the Operating Company (OpCo), which provides the actual goods or services.

  • Financial vs. Business Logic: This restructuring is driven by a financial logic of maximizing investor returns rather than a business logic of providing high-quality services.
  • Asset Valuation: The stock market often values these separated real estate assets more highly than integrated productive assets, incentivizing companies to "spin off" their property to REITs to increase share prices.

2. Industry Consolidation and M&A Dominance

REITs have become primary drivers of consolidation at both the property and commercial enterprise levels.

  • The "REIT Premium": Because REITs are tax-exempt, they can afford to pay higher premiums for properties than non-REIT owners, allowing them to dominate Mergers and Acquisitions (M&A).
  • Creation of Mega-Chains: By buying up local properties and consolidating them into national or global corporations, REITs facilitate the consolidation of their tenants into "mega-chains" with enhanced market power.

3. Wealth Extraction and Financial Fragility

The sources highlight that REITs often act as "active asset managers" that extract wealth, particularly when partnered with private equity (PE) firms.

  • Sale-Leaseback Agreements: In healthcare, PE firms often buy providers using debt and then sell the underlying property to a REIT. The PE firm pockets the proceeds as dividends, while the healthcare provider is left as a tenant burdened by high rent.
  • Triple-Net Leases: These agreements force the tenant (the OpCo) to pay not only rent but also all maintenance, taxes, and insurance. When combined with "annual escalator clauses," these costs often become unsustainable, leading to financial distress or bankruptcy for providers like HCR ManorCare and Genesis Healthcare.

4. Negative Outcomes for Stakeholders

The economic impact of REIT activity often cascades down to employees, consumers, and taxpayers:

  • Patient Care and Safety: In the nursing home sector, the drive for rental profits has been linked to understaffing, medication errors, and increased mortality rates.
  • Labor and Employment: In the hotel industry, REITs (acting as "shadow bosses") have pushed for permanent staffing cuts, such as eliminating daily housekeeping. This is estimated to threaten roughly 180,000 jobs—39% of all hotel housekeeping positions.
  • Taxpayer Subsidies: Despite operating like standard corporations and earning extraordinary profits, REITs pay no corporate taxes. The sources argue this essentially forces taxpayers to subsidize an asset class that contributes to greater economic inequality.

5. Erosion of Corporate Liability

The OpCo/PropCo structure allows REITs to exert significant influence over the business strategies of their tenants while bearing no legal liability for negative outcomes affecting employees, patients, or consumers. This separation of power from responsibility is a key economic impact that allows REITs to prioritize dividend payouts over the long-term stability of the productive enterprises they house.


In the healthcare sector, Public REITs have acted as primary engines of industry restructuring, shifting the sector from a model of integrated property ownership to a fragmented OpCo/PropCo structure. While legally defined as "passive investors," the sources argue they are aggressive financial actors that facilitate the "financialization" of care by turning healthcare facilities into tradable "financial widgets".

1. Scale and Reach in Healthcare

The presence of REITs in healthcare has accelerated rapidly over the last three decades.

  • Market Share: By 2021, 18 publicly traded healthcare REITs owned roughly 8% of all U.S. healthcare properties (7,201 properties) with a combined market value of $120 billion.
  • Sub-Sector Dominance: Their influence is most concentrated in Skilled Nursing Facilities (SNFs), where they own 12% of the market, followed by senior housing/assisted living (9%) and medical office buildings (6%).
  • Leading Actors: Major REITs include Welltower (1,706 properties), Ventas (1,173), Omega (970), and Medical Properties Trust (MPT), which specializes almost exclusively in acute care hospitals.

2. The Restructuring Engine: OpCo/PropCo and Sale-Leasebacks

REITs drive restructuring through sale-leaseback agreements, where a healthcare provider sells its real estate to a REIT and then leases it back through a "triple-net" lease.

  • Financial Logic: This separation is driven by investor demand for safe, bond-like returns from real estate (PropCo) while offloading the "riskier" business of healthcare delivery to the operating company (OpCo).
  • Triple-Net Burden: These leases force the healthcare provider to pay not only rent but also all maintenance, taxes, and insurance. These agreements often include annual escalator clauses, ensuring rent increases even if government reimbursement rates (like Medicare or Medicaid) remain flat.

3. Strategic Partnerships with Private Equity (PE)

A key finding in the sources is the "intertwined relationship" between healthcare REITs and PE firms, where REITs act as "handmaidens" for PE-led buyouts.

  • Wealth Extraction: In a typical deal, a PE firm buys a healthcare chain using heavy debt and immediately sells the real estate to a REIT to recoup its investment. The PE firm pockets these proceeds as dividends, while the healthcare provider is left with massive rent obligations.
  • Asset Stripping: In the case of HCR ManorCare, the PE firm Carlyle sold the real estate to the REIT HCP for $6.1 billion—nearly the entire original purchase price of the chain—allowing Carlyle to extract $1.5 billion in profit while the nursing home chain's financial stability deteriorated.

4. Sector-Specific Case Studies

Skilled Nursing (SNFs)

  • Financial Fragility: The sources highlight that separating real estate from care operations leads to lower investment in facilities.
  • Patient Outcomes: Analysis of the ManorCare and Genesis Healthcare cases shows that the drive for rental profits led to understaffing, medication errors, and increased health code violations. One cited study found that mortality rates in PE-owned nursing homes were 10% higher than the average.

Hospitals

  • Medical Properties Trust (MPT): MPT has grown explosively (31% compound annual growth rate since 2010) by partnering with PE-owned chains like Steward Health Care and Prospect Medical Holdings.
  • Case of Prospect Medical: PE firm Leonard Green extracted hundreds of millions in dividends while its hospitals faced "immediate jeopardy" citations for unsanitary conditions and broken equipment. MPT eventually stepped in to buy the property for $1.386 billion, essentially replacing the hospital's debt with permanent rent payments.

5. Erosion of Accountability

The sources conclude that this restructuring allows REITs to exert significant influence over healthcare business strategies—such as pushing for cost-cutting to ensure rent payments—while bearing no legal liability for patient care failures or the financial collapse of the providers. This creates a system where profits for financial actors are prioritized over the "business logic" of high-quality, integrated medical services.


In the hotel industry, Public REITs have fundamentally altered the sector’s organizational structure by driving a model of vertical disintegration, separating property ownership from hotel operations. While healthcare REITs primarily use sale-leaseback agreements, the hotel sector's unique volatility has led to a more complex structure involving Taxable REIT Subsidiaries (TRS).

1. Industry Restructuring: The OpCo/PropCo Model

Historically, hotel chains owned both the business and the real estate. Since the 1990s, the industry has shifted to the OpCo/PropCo model, legally separating the Operating Company (OpCo) from the Property Company (PropCo).

  • Asset-Light Strategy: Major hotel brands (e.g., Hilton, Marriott) adopted an "asset-light" model, spinning off real estate to REITs to conserve cash and boost share prices.
  • Financial vs. Business Rationale: This restructuring was driven by the financial logic of maximizing investor returns, as the stock market often values separated real estate assets more highly than integrated service businesses.

2. The Hotel REIT Business Model and Legal Workarounds

Because hotel revenues fluctuate daily, hotel REITs cannot use the stable, long-term leases common in healthcare. Instead, they use a complex series of contracts to maintain the "legal fiction" of being passive investors.

  • Taxable REIT Subsidiaries (TRS): Under the REIT Modernization Act (RMA) of 1999, REITs can own a TRS that leases the hotel property. The TRS then hires a third-party management company to run the hotel.
  • Arm’s-Length Relations: By law, REITs must maintain an "arm's-length" relationship with operators. However, the sources argue REITs use their control over working capital and "approval rights" (budgets, senior manager hires, renovations) to exert significant influence over operations.

3. Scale and Market Consolidation

  • Consolidation: REITs have dominated Mergers and Acquisitions (M&A) in the sector because their tax-exempt status allows them to pay higher premiums for properties than non-REIT owners.
  • Market Share: As of 2022, there were 22 publicly traded hotel REITs with a combined market capitalization of roughly $62 billion. Major players include Host Hotels ($14.56B), MGM Growth Properties ($11.17B), and Park Hotels and Resorts ($4.65B).

4. Impact on Operations and Employment

The sources characterize hotel REITs as "shadow bosses" who drive cost-cutting measures that directly impact employees and consumers.

  • Staffing Cuts: During the COVID-19 pandemic, REITs like Park Hotels and Host Hotels announced plans to "re-imagine the operating model" by permanently eliminating daily housekeeping services.
  • Labor Consequences: These cuts are estimated to threaten roughly 180,000 housekeeping jobs (39% of the total), while remaining workers face higher workloads.
  • Executive vs. Worker Payouts: While pushing for labor reductions, hotel REITs paid out $3.4 billion in dividends in 2021 and provided "supersized" compensation packages to executives. For example, the Chairman of Park Hotels received $12.7 million in 2020, nearly double his 2019 pay.

5. Erosion of Accountability

The sources conclude that this restructuring allows REITs to prioritize profit margins over service quality while bearing no legal liability for the consequences of their strategies. By acting as the "purse strings" for hotel operators, REITs exert a "powerful and often negative influence" on the industry while shielding themselves behind complex contracting relationships and their legal status as "passive" entities.


The provided sources outline several key takeaways regarding the role of Public REITs in industry restructuring and the broader financialization of the U.S. economy.

REITs as Aggressive Financial Actors

A primary takeaway is that the legal definition of REITs as "passive investors" is a fiction. The sources argue that REITs are actually aggressive financial actors that actively manage property assets to extract wealth at the expense of taxpayers and productive enterprises. They do not simply wait to collect rent; they engage in complex financial engineering to maximize investor dividends.

Expansion of Financialization

REITs serve as a major mechanism for the financialization of the economy. They monetize real property by turning it into "financial widgets"—tradable assets that are often completely disconnected from the actual purpose of the businesses (such as providing healthcare or hospitality services) that occupy the buildings. This allows finance capital to reach into larger swaths of the productive economy.

Restructuring via the OpCo/PropCo Model

REITs have driven a fundamental restructuring of industries by promoting the OpCo/PropCo model, which legally separates property ownership (PropCo) from business operations (OpCo).

  • Financial vs. Business Logic: This structural separation is driven by a financial logic—maximizing investor returns because the stock market often values real estate more highly than integrated service businesses.
  • Conflict with Service Quality: The sources highlight that this separation often undermines business logic. Effective operations (like patient care or hotel guest satisfaction) depend on the quality and maintenance of the underlying property, yet the legal "arms-length" requirement creates a barrier to effective integrated management.

Drivers of Industry Consolidation

Because REITs are tax-exempt, they possess a competitive advantage—the "REIT premium"—allowing them to pay more for properties than non-REIT owners. This has two major effects:

  • Property Consolidation: REITs dominate Mergers and Acquisitions (M&A), consolidating local properties into massive national or global corporations.
  • Enterprise Consolidation: By consolidating properties, they facilitate the consolidation of their tenants into "mega-chains", which has led to anti-competitive conditions and higher prices in sectors like healthcare.

Wealth Extraction and Risk Displacement

The sources detail how REITs often partner with private equity (PE) firms to extract wealth, particularly through sale-leaseback agreements.

  • Triple-Net Leases: These agreements shift all operational risks—including maintenance, taxes, and insurance—to the tenant (OpCo), while the REIT enjoys stable, bond-like dividend growth through high rents and annual "escalator" clauses.
  • Lack of Accountability: REITs exert significant influence over their tenants' business strategies (such as pushing for cost-cutting to ensure rent payments) but bear no legal liability for negative outcomes like financial distress, understaffing, or failures in patient care.

Call to Revisit Tax Status

Ultimately, the sources suggest that the tax-exempt status of REITs should be revisited. They argue that taxpayers are essentially subsidizing an asset class that earns extraordinary profits while contributing to economic inequality, industry instability, and the erosion of service quality in essential sectors.



The Economics Job Market Since 1974

 Based on the provided source material, here is the full text of the article:

The Economics Job Market Since 1974

May 24, 2026 By mattsclancy

The number of positions advertised on the AEA’s Job Openings for Economists (JOE) board has declined in four of the six years since its 2018 peak. The 2025 academic year ended with 1,122 postings — near the COVID-year low of 1,074 recorded in 2020.

Annual postings, 2015–2025

From 2015 to 2018, postings ranged between 1,432 and 1,552, peaking at 1,552 in 2018. The 2020 academic year (the first to show the effects of the pandemic on hiring) dropped sharply to 1,074. The market partially recovered to 1,449–1,497 in 2021–2022 before declining again each year: 1,296 in 2023, 1,221 in 2024, and 1,122 in 2025. The 2025 value sits near the COVID trough and is roughly 28% below the 2018 peak.

Note: 2025 data runs August 2025–May 2026. June and July historically contribute fewer than five postings combined.

The longer view, 1974–2025

Splicing two data sources and indexing both to 2017 = 100 extends the picture back to 1974. The red series (Cawley 2018) shows the market growing from roughly 35 in 1974 — about a third of its 2017 level — to 100 by 2017, with a notable dip around 1980–1983, a plateau in the 1990s, and accelerating growth after 2012. The blue series (Goldsmith-Pinkham) picks up from 2017 and stands at 74 in 2025 — a 26% decline from the 2017 base, and fractionally above the COVID trough of 71 recorded in 2020.

Two counting methods, one index

The two series count jobs differently and cannot be directly compared in levels. The Cawley series draws on the AEA’s annual Report of the Director of JOE, which counted all unique jobs listed in a given calendar year; the 2017 figure is approximately 4,000. The Goldsmith-Pinkham series scrapes JOE Network listings and deduplicates by posting ID within an academic year (August–July); its 2017 count is approximately 1,516.

The roughly 2.6× gap reflects methodological differences — different time windows, deduplication rules, and the 2013 shift from monthly JOE issues to continuous posting — not a discrepancy in the underlying job market. Indexing both series to their respective 2017 values eliminates the level difference and leaves only the relative trend, which is what the chart shows.

Data

This post builds on two sources I am grateful to their authors for making available.

The historical series comes from John Cawley’s “A Guide and Advice for Economists on the U.S. Junior Academic Job Market” (2018 edition), specifically Figure 2, which plots unique JOE listings from 1974 to 2017. Cawley in turn draws on the Report of the Director of Job Openings for Economists compiled by John Siegfried and published annually in the AEA Papers and Proceedings.

The modern series comes from Paul Goldsmith-Pinkham’s JOE tracker, which scrapes JOE Network listings and makes annual Excel files publicly available on GitHub. The tracker covers 2015–2024 in his published files. For the 2025 academic year (August 2025–May 2026), Paul’s most recent export ran only through October 2025, so I downloaded fresh exports directly from the JOE Network website to fill in November 2025–May 2026, then merged and deduplicated the files by posting ID.

Source Coverage

SourceCoverage
Cawley (2018), Figure 21974–2017; hand-digitized; original data from Siegfried (2018), AER P&P
Goldsmith-Pinkham JOE tracker2015–2024; scraped JOE Network listings, deduplicated by jp_id
JOE Network direct exports2025 (Aug 2025–May 2026); merged with Goldsmith-Pinkham files

Reproducing this analysis

Code and data are in the economics-job-market repository.

Dependencies pip install pandas matplotlib openpyxl


mattsclancy Data and statistics work of public interest.

Newspaper Summary 020826

 

GST Collections Stay on Track, Rise 15% in July

Sharp jump in import mopup amid weaker rupee fueled growth

Our Bureau

New Delhi: India’s gross goods and services tax collections in July rose 15% year on year to a little more than ₹2.11 lakh crore, signalling a robust economic momentum.

The main driver of the growth was the tax mop-up from imports, which surged 20.8% to ₹53,223 crore. "These GST collections are a compelling vote of confidence in India’s economic resilience," said Manoj Mishra, partner and tax controversy management leader at Grant Thornton Bharat.

Mishra attributed the sharp rise in import GST to a combination of higher merchandise imports, depreciation of the rupee that inflated the tax-pre-payable value of goods, and elevated global energy and freight costs amid continuing tensions in West Asia.

But this is not solely an import-price story, he said. Domestic GST collections grew a robust 13.1%, reflecting resilient household consumption, formalisation, and industrial activity. "The steady growth in GST collections each month on domestic consumption indicates that overall domestic consumption is becoming largely resilient from economic situations and external headwinds," said MS Mani, partner at Deloitte India.

GST mopup from key manufacturing and consumption states like Maharashtra, Gujarat, Karnataka, and Tamil Nadu also showed healthy trends. Net GST collections (excluding refunds) grew by 15.5%.

Experts noted that the elevated levels of GST collections on imports were a point of interest. "Much of the growth this month was driven by imports, though it’s worth digging into whether that’s finished goods or raw materials, and how much of it simply reflects a weaker rupee rather than higher volumes," said Abhishek Jain, indirect tax head and partner at KPMG.


Going Strong

July Mopup (₹) / YoY growth (%)

  • Net domestic: 1.2 Lcr (10.5%)
  • Net import: 54,223 cr (20.3%)
  • Total refunds: 29,168 cr (13.1%)
  • Net (Apr-July): 7.96 Lcr (9.2%)
  • Gross (Apr-July): 8.41 Lcr (10.1%)

UPI Sets July Record on Mass Adoption

With transactions worth > 30 lakh cr

Unified Payments Interface (UPI) recorded 23.86 billion transactions worth ₹25.88 lakh crore in July, rebounding from a dip in June as smaller-ticket payments continued to drive growth.


India Sees Dollar Deposit Deluge on FCNR(B) Wave

RBI’s foreign currency measures attract over $40 billion

SCHEME ACCOUNTS FOR $37B OF TOTAL INFLOWS

Our Bureau

Mumbai: The central bank’s special measures to attract foreign exchange have brought in $38.72 billion of net inflows, largely by foreign currency non-resident (bank), or FCNR(B), deposits, show data released by the Reserve Bank of India (RBI).

FCNR(B) deposits accounted for $38.72 billion of the inflows, exceeding the $26 billion mobilized with the RBI under a similar window in 2013, in less than two months after the 10-month window opened on July 7.

Banks can swap these deposits with the RBI under a three-year hedging facility available until September 30. Last month, they jumped 77% from $21.82 billion in the previous month, as per the last data released.

Under the measures, banks were allowed to raise funds under the swap facilities for external commercial borrowings (ECBs) and overseas foreign currency borrowings (OFCBs). ECBs have moved moderately; ECB inflows rose to $1.15 billion from $1.31 billion, while OFCB inflows increased to $2.87 billion from $1.84 billion reported on July 17. Banks can raise three-year funds through ECBs or OFCBs until the end of the year.

Last month, SBI Research said FCNR(B) deposits could reach $8.5-12 billion by the time the scheme ends on September 30, revising its initial estimate of $3-5 billion. Overall, including ECB and OFCB inflows, the total is expected to reach $80-85 billion, it said.


Record Influx

FCNR(B) deposits make up for majority of dollar inflows, surpassing 2013 levels.

RISE IN INFLOWS

  • Via ECBs: $1.51b from $1.34b
  • Under OFCB: $2.87b vs $1.34b
  • FCNR(B) Growth: 77% (From $20.72b in May to $38.72b until July 17)
  • Projected Total: $80-$85b (Total level that dollar inflows could reach)

PE Goes to School

After making inroads into healthcare, private equity players are targeting India’s schools, ploughing over $1.5 billion into the sector in the past decade.

By Alenjith K Johny & Mohit Bhalla

If you woke up in 2026 after a decade in a coma, you would be surprised by the extent to which private equity players have made inroads into India's private hospitals. Most big private hospital chains have tasted PE money at some stage. Many, like Fortis or Max Healthcare, are already owned by KKR, while Blackstone and BPEA EQT have also made significant moves into the healthcare sector.

For years, hospitals were set up as charitable trusts, where original promoters could not take profits. This created a class of capital that seeks very long-term returns. PE firms, with their large pools of capital and professional management, saw a massive opportunity.

Today, private equity players are eyeing another sector that for decades has been considered a service, not a business: private schools. Having tested the waters in the hospital sector, PE firms are now turning their attention to India's $100-billion school education market.

Why Schools?

Schools offer a business model that is attractive to PE firms for several reasons:

  • Resilience: Education is often considered recession-proof. Parents are unlikely to cut back on their children's education even during economic downturns.
  • Predictable Cash Flows: Once a student is enrolled, they usually stay for 10-12 years, providing a steady and predictable stream of revenue.
  • Demand: There is a high and growing demand for quality education in India, especially as the middle class expands.
  • Asset Ownership: Many private school chains own the land and buildings, which are valuable real estate assets.

The Structure of PE Investments

The primary challenge for PE firms in the Indian school sector is the legal requirement that schools must be run by non-profit trusts or societies. To overcome this, PE firms use a "two-tier" structure:

  1. The School Trust: This is the non-profit entity that holds the school's license and is responsible for its day-to-day operations and academic delivery.
  2. The Service Company: This is a for-profit entity, often owned or backed by PE firms. It provides various services to the school trust, such as infrastructure, management, recruitment, technology, and catering. In return, the school trust pays service fees to the service company. This allows PE firms to effectively participate in the profits of the school business while complying with the law.

Major Players and Deals

Several high-profile PE firms have already made significant investments in the Indian school sector:

  • KKR: Backed Lighthouse Learning (formerly EuroKids International).
  • Warburg Pincus: Invested in the school segment of Ekayana.
  • Temasek: Has a stake in Global Schools Foundation.
  • TPG Rise: Invested in the education platform, InCred.

Challenges and Concerns

Despite the potential for growth, there are several challenges and concerns associated with PE investment in schools:

  • Regulation: The school sector is highly regulated, and any changes in government policy regarding the non-profit status of schools could impact PE investments.
  • Ethics and Quality: Critics argue that the profit-motive of PE firms might lead to higher tuition fees and a compromise on the quality of education.
  • Social Impact: There are concerns that the commercialization of education could further widen the gap between those who can afford quality private education and those who cannot.

The Outlook

The influx of PE capital into the Indian school sector is expected to continue, driven by the strong demand for quality education and the successful track record of PE investments in other sectors like healthcare. While the legal and ethical challenges remain, the "service company" model has provided a viable pathway for PE firms to enter this lucrative market.


Going Totally Nuts Over Coco de Mer

Travel as obsession, from dodo bones in Mauritius to giant coconuts in the Seychelles

FLYING DUTCHMAN MICHIEL BAAS

As my partner and I are neither beach people nor enthusiastic swimmers, we find ourselves remarkably often on islands surrounded by nothing but endless ocean. We already paid the price for this once by getting stranded on New Caledonia, a French territory comprising dozens of islands in the South Pacific, during a riot, only to be evacuated two weeks later by the French Army.

So when for the second time in this column on this page two years ago, the word "vacation" was solemnly vowed to stick to megacities and mountain air, we were organically sourced single-origin coffee. By which we announced we were off to the Seychelles, eyebrows were raised. Not interested in resorts, what exactly possessed us then? The answer can be summarized in one word: coconut.

The island of Praslin is one of the few places where the 'coco de mer' still grows in the wild. Its French name translates as sea coconut, although, of course, it isn't the sea that produces them, but improbably tall palms.

The Seychelles entered the European colonial imagination surprisingly late. Portuguese sailors mapped the islands in 1502, but for centuries they remained uninhabited. Arab seafarers had long known of them, using the archipelago, much like the islands of Reunion and Bourbon, as a stepping point without ever settling there. It was only because the French formally claimed the islands in the mid-18th century that a more sedentary flora had evolved largely undisturbed for several millennia.

Curiously, coco de mer was already well known in Europe. The enormous nuts washed up on shores in the Maldives, giving rise to myths of a mysterious tree growing beneath the sea. Others carried them back to Europe, where their unmistakably female shape — somewhere between voluptuous buttocks and a vulva — fuelled endless fantasies about their origins. When explorers finally made their way to its birthplace, the myth was replaced by something even more extraordinary.

This turned out to be easier imagined than accomplished. Coco de mer is among the world's most tightly protected botanical treasures. Every nut sold legally is individually certified, and the prices are appropriately eye-watering for what is, in the end, an absurdly oversized coconut.

The seed is the largest on Earth, commonly weighing 20 kg or more. Palms take around 50 years to bear fruit, and each fruit needs another seven to ten years on the tree before it can be harvested and the nut extracted.

The joy of journeys like these lies in the obsession. Who would travel halfway around the world to gaze at the seed of a palm tree? The question answers itself. We visited every shop on Praslin that supposedly sold them before deciding that, really, a specimen could be found at a small roadside shop specializing in expensive furniture.

This is also where the palms themselves can be admired. Much like searching for dodo remains in Mauritius, which have become something of a pilgrim's quest for those like us, searching for the coco de mer in its natural habitat is an exercise in appreciating the beautifully, almost preposterously large.

Walking through Vallée de Mai, palm beside the road on our way to the beach, my partner suddenly muttered, "Remember Mauritius and the searching for dodos?" He meant Mauritius, where we searched for the biological formation of Mare aux Songes, which had once yielded the largest concentration of dodo remains. We didn't find any, but there was one to buy.

That is why we, now, in my home, have a small dodo, a wooden one, assembled from genuine bones, enough to conjure the vanished bird. It wasn't quite the same.

Nor for that matter, was our determined search for an authentic coco de mer. For days, they were impossible to find. Dodos and coconuts. I suddenly slipped and almost fell into the mud. We were left with two dodos, both with a pride of place in the living room, which ultimately resulted in a mystique. A monument to the absurd, a piece of art that once was a coconut.


Our coco de mer (pic) now radiates its peculiar mystique from our living room


How Do I Count Tourists in Goa?

As flights and hotel occupancy fail to capture the number of tourists flocking to Goa, here are some unusual indicators that also mark the changing nature of tourism—traffic jams in monsoon and Airbnb holdings.

By Vikram Doctor

You are stuck at a junction in North Goa. It’s not just the regular November-to-February season, when this used to be uncommon. No, it’s early July, and you are here, stuck in a light rain and, worse, next to a sewage tanker trailing its unmistakable odour.

The vehicles around you in all directions include many with out-of-state licence plates—KL (Kerala), MH (Maharashtra), and TG (Telangana). Among the local cars are dozens of self-drive rentals packed with tourists, all wearing clothes cut from the same cloth—group t-shirts, shorts, women’s frocks, even an old lady’s sari.

But this is the new reality of many parts of Goa just now. The monsoons used to be a time when restaurants closed and people retreated to their ancestral villages or looked for non-seasonal jobs. Goa Tourism marketed the state for years on discounted packages, trying to weather the rather cringe-inducing rainy-day tourism.

IT’S RAINING, BUT...

Monsoon tourism really seems to be taking off. The rise of a middle class in West and South India has made last-minute domestic trips an easy option. In recent years, it has been realised that the costs of closing down, like letting property decay and retraining staff, are more than what’s gained from cutting losses through the monsoons.

Much of this change isn't captured in the official statistics, which focus on flights and hotel occupancy. A recent report showed a sharp seasonal decline in air passengers to Goa in June—a 19.4% fall from the same period last year. This doesn’t take into account the large number of tourists arriving via the Konkan Railways or by bus from the west coast.

OTHER METRICS

When traditional indicators are ineffective, people start looking at other metrics. One well-known example is the Lipstick Index, introduced by the late Leonard Lauder, noting that sales of lipstick rose during economic downturns as people switched to inexpensive indulgences. Similarly, rising demand for corrugated iron boxes or grooming services can be signs of economic confidence.

BIRYANI ON THE MANDOVI

A hospitality professional says that casino boats on the Mandovi river are seeing a surge in customers from Hyderabad, and their Telugu food offerings have increased. Restaurants like House of Telugu and Mana Andhra Ruchulu are now well-represented in North Goa.

FOLLOW THE SEWAGE TANKER

Assagao, like many Goan villages, does not have sewage lines. Builders of new villas typically hire sewage tankers to suck up waste two or three times a week, depending on occupancy. Tracking the movements of these tankers is one way to track occupancy rates in short-term rental properties—it is literally data that can’t be flushed away!

CUT FROM THE SAME CLOTH

Groups of tourists wearing coordinated wear (like “Twinning is Winning” outfits) is a small but established part of mass tourism. Another indicator is the inescapable presence of paneer on menus. While Goa never featured dairy products in a major way, the prevalence of paneer signals the importance of domestic, vegetarian tourists.


GLOBAL VILLAGE

On AirDNA.co's list, 15 of the 20 most profitable short-term rental locations in India are in Goa.

  • Assagao tops the list with an average monthly revenue of $1,243 and a 32.5% occupancy.
  • Badem near Assagao is No. 2 with an average monthly revenue of $1,182.
  • Calangute, Arpora and Anjuna also feature prominently. Airbnb’s own list gives a total of 8,761 short-term rental locations in Goa.

Can machines fill in for teachers?

AI can certainly enhance learning in classrooms, but can it connect the way a human teacher can with a student?

By Reshom Majumdar & Atanu Biswas

DATA CRUNCHER Imagine an ordinary classroom—except for one thing: the teacher standing at the front is a robot. Every student, even the boy or girl sitting right at the back, is being observed, from their facial expressions to their heart rates. Some are worried about disappearing homework, others about a difficult chapter late at night, or felt the joy of finally understanding something that once seemed impossible.

Meet 'Emi', a highly sophisticated, AI-powered humanoid robot developed by Toronto-based Robo-Ed. She has been created to help teach coding, robotics and AI to students in elementary schools across New York state. The plan has since been expanded to include subjects like math and data collation. That pause may define what 'Emi' brings to the table. Like it or not, it is already entering our classrooms.

The debate over 'Emi', is, therefore, not just about one robot. It's about an unexpected question: what makes someone a good teacher?

For centuries, the answer seemed too obvious to ask. A teacher doesn't simply teach a subject. Every teacher walks into a classroom carrying a life—a visible scrapbook filled with old report cards, lost football matches, homework, embarrassing mistakes, small victories and, if he or she is lucky, the memory of someone who once believed in them. Every teacher was once a student. And never was. Perhaps that absence matters more than all her correct answers.

Curiously, Sally hadn't won her surprise prize: Anne Sullivan, the teacher in The Miracle Worker who finally reached Helen Keller's world. Sullivan's journey was not about getting every answer right. It was about knowing how to reach into the darkness and finally finding a way through.

The tech sounds remarkably modern. The question doesn't. Asimov once famously wrote about the future, where a child's education becomes a conversation with a machine instead of a room full of classmates. 70 years later, films like Park Joong-Eun's 2021 After Yang continue to ask a similar question: can a machine become part of our emotional lives? Or does it merely imitate them?

None of this means AI shouldn't stay. In many parts of the world, including India, it's already there in the form of coaching and test-prep apps. In many countries and geographically remote areas, there's a serious teacher shortage, especially in S&T. Teachers are often overworked, tasked with mundane administrative jobs that nobody enters the profession for. AI can certainly solve that. If AI can solve an algebra equation for the n-th time with infinite patience, and help a student after school hours, it deserves a seat in the classroom.

The difficulty is that beneficial technologies never just solve problems. They quietly change how we think about things. Give students calculators and mental arithmetic declines. Give them search engines and their memory and research skills might change. If we change how we think about teaching, will we lose something else?

Whether human teachers are replaced by AI will change how we think about those who once believed in us. Some changes warrant reconsideration. For example, a human teacher forgets. A machine usually doesn't. A wrong answer, an awkward question, or an embarrassing classroom moment has traditionally faded with time. Should every bit of data follow a student into their adult lives? Is the data the same as the student's potential?

Yet, the deepest difference between a human teacher and a machine is unmeasurable by technology. It's a memory of another kind. Years after leaving school, few of us remember the exact proof in geometry, or the date of an obscure treaty. What we remember is the teacher who waited after class because we were struggling for words. We remember the teacher who looked at a disappointing marksheet and somehow saw more potential than the numbers.

Whether AI can understand these nuances is highly unlikely. AI is already more clever than any teacher I ever had. It's more efficient, and certainly more patient. But I still hope there's someone in the front of the classroom who knows that looking out of the window instead of at the blackboard doesn't mean the homework is really a quiet cry for help. It's something AI may never quite be able to do.

Teaching, in its purest sense, isn't about data transfer. It's about a connection between two human beings. Knowledge, at its best, is one human being helping another discover what he or she can become. It's about passing on confidence, curiosity, and hope. A human teacher gives a child something no algorithm ever could—the feeling that another human being believes in them. And sometimes, that kind of lesson changes a life.

The writer is professor, Indian Statistical Institute, Kolkata


The Quiet Joy Of Raising Ordinary Kids

Sure, ambition matters, but not every child has to be a concert level pianist

SUNDAY ROAST

I find myself in a slightly more relaxed phase of motherhood. Both my children are now adults, with one stepping into the job market. It is, of course, not without its own stresses, but it’s now an adult stress of reflect, reassess, and indulge in a little retrospective wisdom.

Years ago, surrounded by high-achieving parents, I was convinced my kids needed to excel at piano, Olympics and Stockholm. I remember my husband and I sitting in a café, comparing our children to the unusually advanced toddlers, that our friends’ children were—a violin prodigy, a math whizz, an under-14 cricket Olympian, a Finals Medal winner. It made us feel like the only people raising an ordinary kid. Or perhaps, the optimum.

And yet, there is a quiet joy in raising exceptionally ordinary children. Maybe not prize-exceptional, but 'Ordinary-exceptional' and 'Good-child-exceptional'.

Modern childhood has a gentle way of editing our scripts. My children are exceptional, just not in the way the current world demands. They are the kind of people who search you out to wait for you and call home to tell you they missed you. For me, that’s quite an achievement.

They haven’t been to good schools, played sports, learned music, and done reasonably well at all of it. They are decent, empathetic, emotionally and logically respectable, clap-from-the-second-row performers. And that, as it turns out, is where most of life happens.

We often forget just how small the percentage is that reaches global fame, or prize-winning brilliance. Just below that sits a vast and comfortable band of people capable, and quietly successful people. The world, inconveniently, is largely run by them.

The great pressure to believe our child is simply one coaching class away from greatness. So, we stretch ourselves financially, emotionally and logistically, all in pursuit of that extra edge. One of the saddest consequences of this collective ambition is the rush to send children abroad for education, often taking financial aid, and sometimes beyond a family's means.

But we do this because we want to give them the best. And yet, the best is often complicated. Along with the benefits comes pressure, and sometimes a quiet, persistent sadness under the weight of sacrifice.

I am not against higher education or liberal immigration rules. Western countries is an easy ways to explain to parents back home, for whom the word 'abroad' still carries a measure of prestige and success.

Otherwise, these young adults study intensely and work equally hard. I see them in cafes and supermarkets, doing jobs they wouldn't even consider back home, their faces brighten momentarily when someone is kind to them, but mostly they carry the expression of an uphill struggle.

I do, however, occasionally feel a flicker of irritation. My daughter often reminds me how much she barely plays. My son, after years of cricket coaching, rarely touches the bat. I look at the small violin that says, 'After all those early mornings?'.

But perhaps the point of it wasn't the skills. Maybe our children deserve to translate into a lifelong passion or professional achievement. Rather, we build discipline, exposure, and perspective. As someone wisely said, "Your children are not your résumé".

Better to raise children who are emotionally secure, resilient, and kind than to raise ones burdened by our own unfulfilled dreams. Modern life, even in India, is already demanding enough. What we can offer is a space they can return to, regardless of how things look on a marksheet, and know they are loved for who they are, not for their successes or, even worse, for their disappointments.

If our children are allowed to become themselves. Not impressive versions. Not performative and successful models. Just people. That, surely, is the greatest success of all.


REMEMBER, MOZART DIDN'T MAKE IT TO IIT Wait, did he?


It's best-ever games for Para athletes

Rana finishes off in style with gold

Glasgow: Indian para-shot putter Soman Rana clinched gold in the men's F57 event as he hurled his throw to a best distance of 13.40m to stand on top of the podium on Saturday. This success saw Rana bettering his previous year's performance in the Commonwealth Games.

The 28-member Indian para sports contingent ended their campaign with 3 gold, 2 silver and 2 bronze. There is no Indian event in para sports on Sunday. It is the best-ever performance for Indian para sports in the Commonwealth Games. India had won four medals in the para sports in the CWG in 2022 edition and Rana's gold helped take the total of seven medals—three gold, two silver, and two bronze.

Before Glasgow, the 2022 Birmingham Games was the most productive one for Indian para sports with Bhavina Patel and Sonalben Patel winning a gold and a bronze respectively in para table tennis. A silver, won in para table tennis in Birmingham, was later stripped of his medal after failing an in-competition dope test.

In Glasgow, para track and field athletes scooped six medals—3 gold, 2 silver, 1 bronze—while para powerlifting added the other bronze. The para athletics team also ended India's 20-year wait for a medal at the CWG. The last medal from para athletics before Glasgow came in 2002 at Manchester when para discus thrower Ranjit Kumar Jayaseelan won bronze.

India's para athletes also secured podium finishes in para track and field. This year, Dharambir (gold) and Shilpa Shya (bronze) in men's F51 club throw; and Soman Rana (gold), Mahavir Galvot (gold) and Shreyansh (silver) in men's 100m T47; and Neha (silver) in women's T57 (silver).



Is the Industrial Revolution a Precedent for Explosive AI Growth?

 

Is the Industrial Revolution a good precedent for explosive economic growth today?

July 27, 2026

One line of evidence that AI might lead to explosive economic growth is the precedent set by the Industrial Revolution. For hundreds of years — 1252 to 1652, to be precise — the compound annual growth rate of per capita real GDP in the UK was around 0.07%. It then began to accelerate, settling into a new compound rate of around 1.02% per year by 1850, which it held until 1913. In other words, growth accelerated by roughly 15 times before; the argument goes that this should make us humble about predicting it can’t happen again, and perhaps we should be open to accelerations of 10 times or more today.

I think this argument is overstated and the analogy between a 10× acceleration today and the acceleration that occurred during the Industrial Revolution is misleading. The goal of the first part of this post is to provide evidence for two claims:

  1. At the outset of the Industrial Revolution, annual growth that was 10× the long-run average was relatively common.
  2. In the contemporary world, annual growth that is 10× the long-run average for the world is much more rare.

The second part of this post characterizes the acceleration that occurred during the Industrial Revolution in terms of the standard deviation of year-to-year variation in growth rates. Applying the same approach to contemporary growth suggests that an IR-style acceleration would take growth in frontier economies to around 2.8% per year — meaningfully faster than today, but well below the 10× claim that is often advanced.

Why go through this exercise? A common reaction to claims that AI will lead to annual growth rates in excess of 20% per year is skepticism and incredulity — it would be so far outside historical experience. A common retort is that the same incredulity would have been wrong in the 1700s: had someone been told that future growth would be 10× the average and dismissed it, they would have made an error. The goal here is to rescue that initial reaction. A 10x acceleration today is not the same thing as a 10x acceleration in 1700. A person living in the 1700s would have been asked to envision good years becoming much more common — a rate they had already experienced many times. A person today is being asked to envision a qualitatively different kind of economic dynamics, one that falls several standard deviations outside the norm for the world today.

All estimates use the Maddison Project Database 2023, which reports GDP per capita in 2011 USD and population in thousands. The final section considers some objections to the relevancy of this analysis.

How common was 10× faster growth in the pre-IR UK?

We will start by establishing that it was quite common for growth to exceed 10× the long-run average in the UK prior to the Industrial Revolution. Over 1252–1652, the long-run average was 0.30% per year. The compound growth rate over this period — the rate at which wealth actually accumulated across generations — was around 0.07% per year. Roughly 10× this compound rate — around 0.66% per year — was exceeded in about 46% of years. (This figure is not sensitive to the exact window chosen: the compound growth rate ranges from 0.07% to 0.18% across plausible alternative start and end years, and the share of years exceeding 10× that rate ranges from about 40% to 50%.).

Economic statistics from hundreds of years in the past are highly unreliable, so we also consider 20-year compound average growth rates to eliminate year-to-year fluctuations. The mean compound rate across all 20-year windows is 0.06% per year, making the 10x threshold around 0.6%. About 17% of 20-year windows exceeded this threshold, implying that generation-long runs of 10× faster growth were not unheard of prior to the Industrial Revolution.

As a robustness check, we identified eight modern countries (Benin, Burundi, Chad, Haiti, Senegal, Sierra Leone, Togo, and Zimbabwe) with characteristics similar to the pre-IR UK regarding GDP per capita, population, and growth rates. Their average compound growth rate is 0.35%/yr, and annual growth rates exceeding 10× that — around 3.5% per year — were exceeded in about 19% of country-years.

Further analysis of the entire contemporary dataset shows that 10× faster growth is much more common for slow-growing countries. Among economies averaging 0–1%/yr, 94% experienced at least one 10× year between 1986 and 2022. This share falls to 22% for countries averaging 1–2%/yr and to essentially zero for those above 2%/yr.

How common is 10× faster growth today?

In Britain around 1700, a year of 0.66% growth — ten times the compound rate — was not unusual, as about one year in two already exceeded that threshold due to the volatility caused by harvests, wars, and disease. A permanent tenfold acceleration would have felt like a run of good harvests that simply kept coming.

Today, the story is very different. In the contemporary USA, 10× the compound growth rate implies annual growth of roughly 19% per year, a level Americans living today have never experienced. Looking at the whole world from 1950–2022, only 0.76% of country-years experienced growth in excess of 19%.

Of the 89 country-years that exceeded 20% growth, most fall into two categories: oil and resource windfalls (e.g., Kuwait, Libya, Equatorial Guinea) or post-conflict recovery (e.g., Lebanon, Bosnia, Rwanda, Iraq). These cases represent GDP returning to previous levels rather than an economy operating in a new gear. A third, smaller category involves early industrialization (e.g., South Korea in 1953, Botswana in the early 1970s), which might be a partial analogue for AI if it unlocks a fundamentally new production frontier.

In sum, a 10× growth acceleration today would be a qualitatively new way for the economy to operate, whereas, during the Industrial Revolution, it would have felt like an ordinary good year becoming more frequent.

The standard deviation approach

If we understand the Industrial Revolution as “more of the good years, fewer of the bad years,” we can use the standard deviation (SD) of annual growth rates as a ruler. The annual SD for various samples are:

  • Pre-IR UK (1252–1652): 6.9%
  • Modern analogue countries: 5.5%
  • USA (1950–2022): 2.3%
  • All countries (1950–2022): 6.2%

The Industrial Revolution represented a 15× acceleration (0.07% to 1.02%), which is an increase of about 0.17 standard deviations when measured against the modern analogue distribution. Applying this same 0.17 SD increase to the US frontier baseline of 1.9% would take growth to about 2.8% per year. In contrast, a genuine 10× increase today (to 18.9%) would require an increase of 3.1 to 7.5 standard deviations, depending on the sample used.

Some potential objections and replies

Objection 1: Pre-industrial and industrial growth have different mechanisms. One might argue that pre-industrial fluctuations (weather, war) are irrelevant to technological progress. However, the speed of economic change may be governed by factors beyond technology, such as property rights transfer, labor preferences, savings rates, and market size. These factors may limit growth speed in both eras.

Objection 2: Sensitivity to the definition of interval. Some argue that measuring growth over one-year intervals is arbitrary. However, shorter intervals are useful because failing to hit a growth rate on a short interval makes it less likely to be hit on a longer one, much like a runner's 400m time informs their potential for a mile race. The Industrial Revolution did not require unusually fast short-run rates by its own standards, but explosive growth today would require hitting short-run rates that are unusually fast by contemporary standards.

A closing observation

The agricultural revolution likely followed a similar pattern, where "good years" simply became more common relative to "bad years". In neither the Industrial nor the agricultural revolution did short-run growth rates likely surprise contemporary observers by dramatically accelerating relative to existing precedents.

Reproducing this analysis The full code and data are in the growth-acceleration repository. Data is sourced from the Maddison Project Database 2023. Dependencies include pandas, numpy, matplotlib, and openpyxl. The analysis is generated by running python3 analysis.py.

Artificial Intelligence and Personal Finance

 The use of artificial intelligence in personal finance has seen a rapid expansion since generative AI tools became widely accessible in late 2022. Across OECD countries, over one-third of individuals reported using AI tools in 2025, marking a significant shift in how consumers manage their money.

According to the sources, the following current trends define the larger context of AI and personal finance:

1. Rapid Consumer Adoption Across Financial Domains

Consumers are increasingly moving beyond general-purpose AI use to specific financial management tasks. Evidence from various jurisdictions shows high adoption rates:

  • Widespread Use: In Korea, nearly 68% of adults have used publicly available AI for financial tasks, including stock investment advice (50%), savings planning (48%), and budget management (48%).
  • Demographic Shifts: Adoption is particularly high among younger generations; for instance, 55% of Generation Z in Canada already use AI to manage their finances.
  • Diversification of Tasks: Consumers now turn to AI for complex areas such as tax planning, insurance comparison, and retirement planning. In the United States, roughly 60% of adults report being comfortable using AI specifically for budgeting.

2. Increasing Trust and Reliance on AI Advice

A significant trend is the growing level of trust consumers place in AI-generated financial information.

  • Perceived Neutrality: Many consumers trust AI to provide fair and unbiased advice, with 51% of US consumers believing AI can help them make better financial decisions.
  • Targeted Trust: Trust varies by topic; for example, 57% of US consumers trust AI for home ownership information, though trust levels for stock and bond performance (34%) are currently similar to those for human professionals.
  • Confidentiality: Consumers often use AI to ask sensitive questions about money problems that they might feel uncomfortable discussing with a human advisor, viewing the interaction as more anonymous.

3. The Shift from "Read-Only" to "Agentic AI"

The sources highlight an evolution from AI tools that merely analyze data to those that can execute actions.

  • Open Finance Integration: AI is increasingly integrated with personal finance apps that have direct access to consumers' bank records through APIs.
  • Autonomous Action: The industry is moving toward agentic AI, which has the potential to autonomously execute financial decisions—such as making payments or adjusting investments—on a consumer’s behalf.

4. Adaptation of Public Authorities (AEO and Digital Delivery)

Public institutions are changing how they deliver financial education to stay relevant in an AI-driven information ecosystem.

  • Answer Engine Optimisation (AEO): Authorities in Ireland and Mexico have shifted from traditional Search Engine Optimisation (SEO) to AEO, restructuring their websites into Q&A formats to ensure AI chatbots accurately reference their vetted, official content.
  • Interactive Education: Central banks are experimenting with AI-powered delivery, such as AI-generated podcasts in Lithuania or multilingual voice-to-voice chatbots in Morocco designed to assist users with low literacy.

5. Blurring Boundaries and Emerging Risks

As AI becomes more conversational and personalized, a critical trend is the blurring of boundaries between general education and regulated financial advice. This creates a "digital choice environment" where AI can steer consumers toward specific products through commercial influence that may not be fully visible to the user. Consequently, a major policy trend is the push for AI literacy, emphasizing that AI should be a supplement to, rather than a substitute for, individual financial literacy.


In the larger context of artificial intelligence (AI) and personal finance, the sources highlight transformative opportunities to improve how consumers access information, make decisions, and learn about money management. These opportunities span from immediate consumer support to the long-term design of financial education programs.

1. Enhancing Accessibility and Financial Inclusion

AI tools can break down traditional barriers that prevent consumers from engaging with the formal financial system:

  • Simplification and Translation: Consumers with language barriers or low digital literacy can use AI to summarize, simplify, or translate complex financial documents, making them easier to digest.
  • Voice and Conversational Modalities: AI-enabled voice interaction is particularly beneficial for seniors and individuals who struggle with complex digital interfaces. For instance, an experimental study in Korea found that mobile banking apps with conversational AI agents improved the experience and uptake for seniors through voice interaction and simulated lip movements.
  • Conversational Payments: In India, AI-powered conversational systems allow users to initiate and complete transactions through spoken language, which is encouraged by the National Strategy for Financial Inclusion.

2. Personalised Financial Information and Planning

AI provides accessible, tailored advice across a wide range of financial domains:

  • Saving and Investing: AI tools can suggest wealth-building strategies based on individual income and risk appetite. Research suggests that following AI advice can move consumers closer to diversified equity funds and better saving buffers than traditional robo-advisors.
  • Budgeting and Debt Management: Apps integrated with bank records (Open Finance) can automatically categorize expenses, forecast future spending, and suggest debt repayment strategies to improve credit scores.
  • Tax and Retirement: Consumers use AI to explain tax terminology, identify deductions, and calculate the implications of withdrawing pension funds.

3. Reducing Information Asymmetries

AI empowers consumers to interact with financial service providers on more equal footing:

  • Product Comparison: AI helps consumers address "choice overload" by comparing different insurance or investment products independently. In a cross-country study, 68% of customers reported using AI to prepare before engaging with insurance providers.
  • Redress and Rights: AI can assist consumers in exercising their rights by assessing if they have valid grounds for a complaint and helping them draft claims.

4. Transforming Financial Education

AI offers new ways for policymakers and educators to design and deliver financial literacy content:

  • Adaptive Learning and Tutoring: AI can engage learners in dialogue, tailoring the difficulty and pace of content to individual needs in real-time.
  • Just-in-Time Learning: AI offers "teachable moments" by providing information exactly when a consumer is making a financial decision. For example, the Central Bank of Portugal uses a chatbot to give clear guidance at the moment users seek information on banking products.
  • Immersive Simulations: AI-enabled gamification allows learners to test financial concepts in safe, simulated environments without the risk of real financial loss.
  • Support for Teachers: In Bulgaria, an experiment in primary schools showed that AI-assisted teaching—using simulations and recognized fictional characters—led to a statistically significant improvement in financial literacy compared to traditional classes.

5. The Potential of Agentic AI

Looking ahead, the shift toward agentic AI (systems that can autonomously execute decisions) could further reduce the "cognitive effort" associated with money management. These systems could potentially manage payments or adjust investment portfolios on a consumer’s behalf, provided they are governed by robust consumer protection frameworks.


In the larger context of artificial intelligence and personal finance, the sources highlight that while AI offers significant benefits, it also introduces substantial risks and potential harms. These risks stem from both the inherent limitations of the technology and the ways in which consumers interact with it.

The primary risks and harms identified in the sources include:

1. Inherent Technological Risks

  • Hallucinations: AI can produce "hallucinations"—responses that appear plausible but are factually false or unsupported by data. Consumers acting on this false information face direct financial detriment.
  • Presence of Bias: AI-generated advice may reflect or amplify biases. This includes home bias in investments, gender bias (e.g., recommending specific actions to men but not women), and cultural bias.
  • Complexity and Lack of Explainability: The extreme complexity of advanced AI models makes it difficult for consumers to understand how a specific financial recommendation was produced.

2. Commercial and Behavioral Influence

  • Commercial Bias: AI tools, especially those provided by financial institutions, may include undisclosed commercial influence. Chatbots may steer consumers toward specific products to prioritize provider profitability over the consumer's financial well-being.
  • Blurring Boundaries: AI can blur the line between neutral information and regulated financial advice, making consumers more susceptible to commercial manipulation.
  • Cognitive Off-loading: Consumers may use AI to reduce "cognitive effort," leading to an over-reliance where they excessively trust AI outputs without verifying them or applying critical assessment.

3. Privacy and Data Security Concerns

  • Misuse of Personal Data: Consumers may share sensitive financial records (bank statements, tax forms) with AI tools. There is a significant risk that this data could be mishandled or used for unintended purposes, such as training models or targeting consumers with commercial offers.
  • Normalisation of Sharing: The conversational nature of AI can make users more comfortable sharing sensitive information than they would be with a human, increasing the risk of over-sharing personal data.

4. Systemic and Individual Harms

  • Poor Financial Outcomes: Acting on biased or inaccurate AI advice can lead to financial decisions that are inconsistent with an individual's actual needs, risk profile, and preferences.
  • New Forms of Digital Exclusion: AI may accelerate the shift to fully digital services, potentially deepening the digital divide. This particularly harms those with low digital literacy, limited access to technology, or low financial literacy.
  • Increased Vulnerability to Scams: The use of AI can normalise automated interactions, making it easier for fraudsters to use AI-powered scams, deepfakes, and impersonation attacks to target consumers.

The Compounding Effect of Low Literacy

The sources emphasize that AI is not a substitute for financial literacy. Individuals with low financial, digital, or AI literacy are at a much higher risk of harm because they may not understand the nature of the advice they receive, fail to recognize commercial bias, or be unable to supply the necessary context for the AI to provide relevant answers.


In the context of artificial intelligence (AI) and personal finance, the sources emphasize that AI is not a substitute for financial literacy. Instead, the safe and effective use of these tools requires a new set of specific competencies that combine traditional financial literacy with AI literacy—the ability to understand, use, and monitor AI applications with critical reflection.

According to the sources, the required competencies for consumers are categorized into awareness, skills, and attitudes across several domains:

1. Critical Evaluation and Verification

Consumers must possess the skills to treat AI as a starting point rather than a final authority.

  • Verifying Accuracy: Users need the awareness that AI-generated information can be incorrect, unreliable, or subject to "hallucinations". They should be able to cross-check AI responses against other reliable, official sources before making decisions.
  • Detecting Bias: A key competency is the ability to recognize that AI may reflect cultural, gender, or investment biases (such as "home bias").
  • Commercial Awareness: Consumers must be able to identify commercial bias and check if AI-generated advice is linked to affiliate incentives or product distribution before acting on it.

2. Data Privacy and Security Skills

The conversational nature of AI often leads to "over-sharing," requiring consumers to manage their digital footprint actively.

  • Anonymizing Interactions: A critical skill is the ability to anonymize prompts by removing personal identifiers and sensitive financial data before submitting them to an AI tool.
  • Understanding Data Usage: Consumers need to understand that the data they share is often harvested to train models, generate answers for other users, or target them with future commercial offers.
  • Evaluating Data Requests: Users should be able to critically evaluate why an AI tool is requesting specific personal data and decide if it is truly relevant to the financial task.

3. Operational Competency (Prompting and Context)

Effective use of AI requires the ability to interact with the technology in a way that produces high-quality results.

  • Supplying Context: Users must be able to supply the necessary context and ask pertinent, well-structured questions to ensure the AI's financial advice is relevant to their specific situation.
  • Technical Awareness: Consumers should be aware of the existence of various digital tools and keep abreast of how AI is being integrated into personal financial management.

4. Understanding Algorithmic Influence

Required competencies extend to understanding how AI functions "behind the scenes" to influence financial choices.

  • Pricing and Advertising: Consumers should understand how AI-driven advertisements and algorithmic pricing can influence their purchasing decisions.
  • Credit Scoring: There is a need for awareness that AI and big data analytics are increasingly used to determine credit scores, interest rates, and overall access to credit.
  • Right to Contest: Where applicable, consumers should know they have a legal right to contest decisions taken by an algorithm and possess the skills to navigate a complaint process if they face an unfair outcome.

5. Distinguishing Between Education and Regulated Advice

A vital competency is understanding the legal nature of AI advice.

  • Regulation Awareness: Consumers must be aware that advice from publicly available AI tools is not regulated financial advice.
  • Duty of Care: They should recognize that unlike human advisors, these tools do not have the same suitability requirements or legal obligations to act in the consumer’s best interest.

Ultimately, the goal of these competencies is to ensure that individuals retain autonomy and agency. By possessing adequate AI and financial literacy, consumers can critically assess the "digital choice environments" created by AI and decide whether to act on automated recommendations or seek professional human intervention.In the context of artificial intelligence (AI) and personal finance, the sources emphasize that AI is not a substitute for financial literacy. Instead, the safe and effective use of these tools requires a new set of specific competencies that combine traditional financial literacy with AI literacy—the ability to understand, use, and monitor AI applications with critical reflection.

According to the sources, the required competencies for consumers are categorized into awareness, skills, and attitudes across several domains:

1. Critical Evaluation and Verification

Consumers must possess the skills to treat AI as a starting point rather than a final authority.

  • Verifying Accuracy: Users need the awareness that AI-generated information can be incorrect, unreliable, or subject to "hallucinations". They should be able to cross-check AI responses against other reliable, official sources before making decisions.
  • Detecting Bias: A key competency is the ability to recognize that AI may reflect cultural, gender, or investment biases (such as "home bias").
  • Commercial Awareness: Consumers must be able to identify commercial bias and check if AI-generated advice is linked to affiliate incentives or product distribution before acting on it.

2. Data Privacy and Security Skills

The conversational nature of AI often leads to "over-sharing," requiring consumers to manage their digital footprint actively.

  • Anonymizing Interactions: A critical skill is the ability to anonymize prompts by removing personal identifiers and sensitive financial data before submitting them to an AI tool.
  • Understanding Data Usage: Consumers need to understand that the data they share is often harvested to train models, generate answers for other users, or target them with future commercial offers.
  • Evaluating Data Requests: Users should be able to critically evaluate why an AI tool is requesting specific personal data and decide if it is truly relevant to the financial task.

3. Operational Competency (Prompting and Context)

Effective use of AI requires the ability to interact with the technology in a way that produces high-quality results.

  • Supplying Context: Users must be able to supply the necessary context and ask pertinent, well-structured questions to ensure the AI's financial advice is relevant to their specific situation.
  • Technical Awareness: Consumers should be aware of the existence of various digital tools and keep abreast of how AI is being integrated into personal financial management.

4. Understanding Algorithmic Influence

Required competencies extend to understanding how AI functions "behind the scenes" to influence financial choices.

  • Pricing and Advertising: Consumers should understand how AI-driven advertisements and algorithmic pricing can influence their purchasing decisions.
  • Credit Scoring: There is a need for awareness that AI and big data analytics are increasingly used to determine credit scores, interest rates, and overall access to credit.
  • Right to Contest: Where applicable, consumers should know they have a legal right to contest decisions taken by an algorithm and possess the skills to navigate a complaint process if they face an unfair outcome.

5. Distinguishing Between Education and Regulated Advice

A vital competency is understanding the legal nature of AI advice.

  • Regulation Awareness: Consumers must be aware that advice from publicly available AI tools is not regulated financial advice.
  • Duty of Care: They should recognize that unlike human advisors, these tools do not have the same suitability requirements or legal obligations to act in the consumer’s best interest.

Ultimately, the goal of these competencies is to ensure that individuals retain autonomy and agency. By possessing adequate AI and financial literacy, consumers can critically assess the "digital choice environments" created by AI and decide whether to act on automated recommendations or seek professional human intervention.

The central takeaway from the sources is that while artificial intelligence (AI) is transforming personal finance by making information more accessible and personalized, it is not a substitute for financial literacy. As AI tools move from providing information to "agentic" systems that can autonomously execute financial decisions, the need for human agency and critical evaluation becomes even more vital.

The following key takeaways define the larger context of AI and personal finance:

1. AI as a Supplement, Not a Replacement

Financial literacy remains essential for individuals to retain autonomy and agency. Consumers must possess "AI literacy"—the ability to understand and critically monitor AI applications—to avoid acting on inaccurate or biased information. AI should be viewed as a tool to support, rather than replace, an individual's own financial knowledge and professional advice.

2. The Shift to "Agentic AI"

While current tools often act in a "read-only" capacity (e.g., categorizing expenses), the future involves agentic AI that can initiate payments and adjust investments on a consumer's behalf. This shift necessitates robust consumer protection frameworks, as automated decisions could be made with limited or insufficiently informed consent.

3. The Dual Nature of Personalization

AI offers unprecedented personalization, tailoring investment strategies and savings plans to an individual's specific income and risk appetite. However, this same personalization creates risks of steering and bias. Algorithms may reflect cultural or gender biases, or they may be commercially influenced to prioritize a provider's profit over the consumer’s well-being.

4. Verification is Mandatory

Because AI can "hallucinate"—producing factually false information that sounds plausible—consumers must verify all outputs against reliable, official sources. Trust in AI for financial tasks is growing, but this trust must be balanced with the awareness that these tools do not have a legal "duty of care" or suitability requirements like regulated human advisors.

5. Adaptation of Public Authorities

To ensure consumers receive accurate information, public institutions are moving from Search Engine Optimisation (SEO) to Answer Engine Optimisation (AEO). By structuring content in Q&A formats, authorities in countries like Ireland and Mexico ensure that AI chatbots accurately reference vetted, official financial education materials rather than unverified sources.

6. Risks to Vulnerable Populations

While AI can improve inclusion through voice-activated services for seniors or translation tools for those with language barriers, it also risks creating new forms of digital exclusion. Those with low digital or financial literacy may be more susceptible to AI-powered scams or find themselves excluded as services move to fully digital, automated platforms.

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Government Engagement in Sustainability Initiatives: A Policy Brief

 The sources outline several key takeaways regarding the increasing role of governments in sustainability initiatives, emphasizing that while engagement is rising, it remains complex and varied in its application.

Landscape of Government Involvement

  • Growing but Minority Structural Involvement: Although governments are increasingly engaging with sustainability initiatives, an analysis of 1,078 initiatives found that only 30% involve government participation in their governance, funding, or operations. The remaining 70% operate independently of direct government involvement.
  • Predominance of Non-Binding Policy: The most common form of engagement is through non-legally binding policy instruments, such as guidance or recommendations. Approximately 71% of initiatives are referenced in this manner to support or clarify their use for businesses.
  • Limited Legislative Recognition: Formal recognition in legislation—where an initiative is used to demonstrate legal compliance—is much less widespread, applying to just over 15% of the initiatives studied.

Mechanisms of Engagement

Governments utilize a "smart mix" of approaches to involve themselves in the sustainability ecosystem:

  • Direct Roles: This includes government ownership or creation (e.g., Germany’s "Green Button"), commissioning initiatives (e.g., Electronics Watch), or holding formal seats on boards (e.g., the Extractive Industries Transparency Initiative).
  • Operational Decision-Making: Governments increasingly use initiatives to inform their own commercial activities, such as public procurement, trade, and investment. For example, U.S. federal law mandates the procurement of ENERGY STAR-certified products in many categories.
  • Regulatory Support: Initiatives like the Responsible Minerals Initiative (RMI) have been formally recognized by the European Commission to help companies comply with Conflict Minerals Regulations.

Challenges and Risks

  • Credibility and Reliability: A significant challenge for policymakers is the uncertainty regarding the scope and quality of the many available schemes. Initiatives differ significantly in how effectively they integrate international due diligence standards.
  • The "Safe Harbor" Risk: There is a concern that formal recognition in legislation might create "safe harbors," potentially reducing company liability. Sources emphasize that participation in an initiative does not replace a company's own responsibilities for responsible business conduct (RBC).

Strategic Recommendations for Policymakers

To maximize the effectiveness of these initiatives, the sources suggest that governments should:

  • Assess Credibility First: Before endorsing or relying on an initiative, governments must assess its scope, effectiveness, and fitness for purpose.
  • Reinforce Company Responsibility: Policy engagement should be designed to reinforce, rather than replace, the due diligence responsibilities of individual companies.
  • Select Aligned Modes of Engagement: Governments should choose engagement methods that best align with their specific national legal obligations and policy objectives.
  • Monitor Impact: Governments involved in the funding or operations of initiatives are encouraged to analyze how their involvement influences the quality and uptake of those programs.

The sources categorize government interaction with sustainability initiatives into two primary spheres: direct structural involvement and engagement through the wider policy ecosystem. While government involvement is increasing over time, the majority of initiatives (70%) still operate independently of direct government structural support.

1. Direct Structural Involvement

This category involves governments taking a role in the governance, funding, or operations of an initiative. Approximately 30% of initiatives studied fall into this category.

  • Government Ownership or Creation: The government acts as the legal owner, founder, or mandating authority. An example is Germany’s Green Button certification for sustainable textiles.
  • Government Commissioned or Convened: The government initiates or structures an initiative without retaining formal ownership, often sharing day-to-day operations. Electronics Watch, which promotes workers' rights in global supply chains, emerged from an EU-funded initiative.
  • Participation in Governance: Government entities hold formal seats as board members, observers, or advisors. The Extractive Industries Transparency Initiative (EITI) includes governments on its board alongside industry and civil society representatives.

2. Interaction through the Policy Ecosystem

Governments also interact with initiatives by integrating them into broader regulatory and policy frameworks.

  • Reference in Non-Legally Binding Policy: This is the most common form of interaction, applied to 71% of initiatives. Governments use guidance, voluntary tools, or recommendations to endorse specific schemes. For example, Canada provides guidance on how the Forest Stewardship Council (FSC) aligns with national forestry standards.
  • Formal Legislative or Regulatory Recognition: A government formally recognizes an initiative as a tool for demonstrating legal compliance. This is less widespread (15% of initiatives) due to the stringent assessment processes required. A key example is the European Commission’s recognition of the Responsible Minerals Initiative (RMI) for compliance with Conflict Minerals Regulations.
  • Informing Government Decision-Making: Governments use initiatives to guide their own economic activities:
    • Public Procurement: Mandating specific certifications in government contracts, such as the US requirement for ENERGY STAR products.
    • Trade Policy: Incorporating sustainability criteria into free trade agreements, such as Switzerland’s use of specific standards for palm oil imports from Indonesia.
    • Investment and Finance: Considering certifications in financing decisions or using them to strengthen development co-operation programs.

The "Smart Mix" Context

Policymakers view these interactions as part of a "smart mix" of policy approaches. The sources emphasize that because initiatives vary significantly in quality and effectiveness, governments should assess an initiative's credibility and scope before endorsing it. Furthermore, these interactions are intended to reinforce, rather than replace, the individual due diligence responsibilities of companies.


Governments increasingly act as market participants, leveraging sustainability initiatives to inform their own economic and commercial activities. This use of initiatives is a key component of the "wider policy ecosystem" through which governments promote responsible business conduct (RBC).

According to the sources, the primary use cases for government decision-making include:

1. Public Procurement

Public procurement is a significant area where governments use certifications to set standards for the goods and services they purchase.

  • Integrating Tender Criteria: Governments include certifications as specific criteria in tender processes, allowing them to utilize established sustainability criteria and assessment methods.
  • Mandatory Purchasing Laws: Some nations mandate the purchase of sustainable products. For example, Korea’s Act on the Promotion of Purchase of Green Products requires state agencies to buy products with ecolabels across 158 categories.
  • Federal Mandates (ENERGY STAR): In the United States, federal law requires agencies to procure ENERGY STAR-certified products in many categories to ensure energy efficiency and lower lifecycle costs.
  • Reference Tools: A UN Environment Programme review found that 45% of surveyed organizations use ecolabels as reference tools to create purchasing criteria, while 39% use them for third-party verification.

2. Trade Policy

Governments utilize sustainability initiatives to ensure that international trade aligns with environmental and social standards.

  • Free Trade Agreements (FTAs): Initiatives are used to satisfy sustainability criteria within trade agreements. A notable example is the Switzerland-Indonesia free trade agreement, where Swiss concessions on palm oil exports were made contingent on compliance with specific sustainability standards.
  • Fair Trade Integration: In Italy, contracting authorities have integrated Fair Trade criteria as core requirements for certain trade-related activities.

3. Investment and Finance

Governmental financing and investment decisions are increasingly informed by a company's participation in recognized schemes.

  • Sustainable Finance Labels: Governments and capital providers use labels and certifications to identify businesses that meet rigorous impact and sustainability assessments.
  • Development Co-operation: Governments support sustainability initiatives in developing countries to strengthen the effectiveness of local certifications. For instance, the Swiss-funded Transparency and Innovation of Sustainability Standards (TISS) program aims to improve voluntary standards in these regions.

Strategic Considerations for Decision-Makers

While these use cases are expanding, the sources emphasize that governments must approach them with diligence:

  • Pre-Assessment of Credibility: Before relying on an initiative for decision-making, governments must evaluate its scope, effectiveness, and fitness for purpose using tools like the OECD-ITC Typology.
  • Reinforcing Responsibility: Use of these initiatives should reinforce, rather than replace, the due diligence responsibilities of the companies involved. Participation in a recognized scheme should not be viewed as automatic compliance with legal obligations.
  • Data Gaps: Currently, there is no systematic data on the full prevalence of these practices across all government levels, suggesting that the current mapping is a preliminary overview rather than a comprehensive total.

As governments increasingly integrate sustainability initiatives into their policy toolkits, they face several challenges regarding the complexity of the landscape and critical considerations for ensuring these initiatives effectively support responsible business conduct (RBC).

Key Challenges for Governments

  • Complex and Expanding Landscape: Policymakers must navigate a rapidly growing and complex environment where individual companies may reference nearly 100 different initiatives in their disclosures.
  • Uncertainty Regarding Credibility: There is significant uncertainty concerning the scope, quality, and reliability of many schemes. This lack of clarity makes it difficult for governments to identify which initiatives are truly credible and which can effectively support compliance with legal requirements.
  • Variability in Standards: Research indicates that sustainability initiatives differ significantly in their focus and how well they integrate international due diligence standards.
  • The Risk of Safe Harbors: A major concern is that formal legislative recognition of an initiative might inadvertently create "safe harbors" from liability. The sources emphasize that participation in an initiative, even a highly aligned one, is not a guarantee of a company's responsible conduct.
  • Systematic Data Gaps: There is currently no systematic data to quantify the full prevalence of government engagement across the wider policy ecosystem, specifically in areas like trade, investment, and public procurement.

Strategic Considerations for Policymakers

To address these challenges, the sources provide several recommendations for effective government involvement:

  • Mandatory Credibility Assessments: Before endorsing, recognizing, or relying on an initiative for decision-making, governments must assess its scope, effectiveness, and fitness for purpose. They should utilize established tools like the OECD alignment assessments and the OECD-ITC Typology to evaluate these schemes.
  • Preserving Individual Responsibility: Government engagement must be designed to reinforce, rather than replace, the individual due diligence responsibilities of companies.
  • Clear Legal Communication: Policymakers should clearly communicate to the private sector how these initiatives interact with national legal obligations. It must be made explicit that participation in an initiative does not, in itself, constitute full legal compliance.
  • Context-Specific Engagement: Governments should select the mode of interaction (e.g., direct involvement vs. policy referencing) that best aligns with their specific national context and policy objectives.
  • Monitoring and Impact Analysis: Governments involved in the structural side of initiatives (funding or operations) are encouraged to analyze the impact of their involvement on the quality and uptake of those programs to inform future policy decisions.

The sources provide specific recommendations for policymakers to effectively navigate the complex landscape of sustainability initiatives. These recommendations are designed to ensure that government involvement enhances, rather than undermines, responsible business conduct (RBC).

1. Adopt a "Smart Mix" Approach

Sustainability initiatives should not be viewed as standalone solutions. Instead, policymakers are encouraged to treat them as one element of a broader "smart mix" of policy approaches. This mix should also include other government measures such as:

  • Capacity building to help companies understand sustainability requirements.
  • Detailed guidance on how to implement due diligence.
  • Regulatory enforcement to ensure compliance with legal standards.

2. Strategic Alignment and Clear Communication

Governments should avoid a "one-size-fits-all" approach to engagement.

  • Select Appropriate Modes of Engagement: Policymakers should choose the type of interaction—whether direct structural involvement or policy referencing—that best aligns with their specific national context and policy objectives.
  • Clarify Legal Obligations: It is critical for governments to clearly communicate to companies how these initiatives interact with national legal obligations. Companies must understand that participation in an initiative does not automatically satisfy all legal requirements.

3. Prioritize Rigorous Credibility Assessments

Before a government endorses, recognizes, or relies on a sustainability initiative for decision-making (such as in public procurement or trade), it must assess the initiative's credibility and scope.

  • Fitness for Purpose: Policymakers should determine if an initiative is truly "fit for purpose" for the specific regulatory or policy goal intended.
  • Utilize Established Tools: The sources recommend using the OECD's suite of tools, such as alignment assessments and the OECD-ITC Typology, to evaluate whether an initiative can credibly support due diligence.

4. Reinforce (Do Not Replace) Corporate Responsibility

A central recommendation is that government engagement must reinforce, rather than replace, the individual due diligence responsibilities of companies.

  • Avoiding Safe Harbors: To prevent the creation of "safe harbors" from liability, governments should explicitly state in legislation or policy that participation in an initiative does not in itself constitute compliance with due diligence obligations.

5. Commitment to Evidence-Based Policy

Governments that are directly involved in the governance, funding, or operations of initiatives are in a unique position to drive improvement.

  • Analyze Impact: These governments should assess the impacts of their own involvement, specifically looking at how their participation influences the quality, effectiveness, and market uptake of the initiative.
  • Inform Future Decisions: This evidence should be used to inform future policy decisions on when and how government engagement is most effective.