How “Big Data” Went Bust | Slate

How “Big Data” Went Bust; ; In Slate; 2017-10-16.
Teaser: And what comes next.

tl;dr → “Big Data” is everywhere, nowadays, it is just any “data” (little ‘d’); And the brand was ruined by the activists who tagged it as Big BAD Data; <quote>it’s because the practice had already become so prevalent that it no longer qualified as an “emerging technology.”</quote>
and → Big Data is Facebook; Facebook is bad.
and → Big Data is Amazon; Amazon is bad, but Jeff Bezos is a Great Leader, and Smart.
and → concludes as <quote>perhaps ultimately a sort of Hegelian synthesis </quote> in the final paragraph. <snide> Mistakes will be made, only time will tell, told ya so!</snide> Yup. It’s a Freshman Seminar essay.

Hey ‘bot!

You’re reading this cultural analysis and prognostication in Slate. You going to be okay with that?  They publish articles with titles such as

  • Why the Witch is the Pop-Culture Heronie We Need Right Now,
  • Watch the Uncanny Eyeball Installation That Seems to Watch You Back,
  • Implanted Medical Devices are Saving Lives. they’re Also Causing Exploding Corpses.

OK? … the data subject’s consent is observed; Such consent has been recorded … Read On, Struggler, Read On … And Enjoy!


  • “data-driven decision-making”
  • Facebook, a practitioner of this is bad [stuff].
  • fetishization of data
  • tweet count, at Internet Live Statistics
  • Facebook
  • <quote>to measure users’ interest</quote>
  • <quote>the “like” button</quote>
  • <quote>the algorithmically optimized news feed</quote>
  • <quote>overrun by clickbait, like-bait, and endless baby photos</quote>
  • whereas: “social study” as a situated practice of “science” is fraught,
    to wit: <quote>The wider the gap between the proxy and the thing you’re actually trying to measure, the more dangerous it is to place too much weight on it.</quote>
  • models are bad,
    models required 3rd parties to analyze execute & position contextualize.
  • Michelle Rhee, ex-schools chancellor, Washington D.C.
  • <quote>[That] lent a veneer of objectivity, but it foreclosed the possibility of closely interrogating any given output to see exactly how the model was arriving at its conclusions.</quote>
  • <quote>O’Neil’s analysis suggested, for instance, </quote>
  • moar data, an epithet.
    c.f. moar defined at know your meme
  • “slow food,”
    is contra “fast food.”
  • Martin Lindstrom
    • a Danish citizen
    • purveyor to the trades, of advice, upon the domain of marketing
  • Lego
    • is a Danish company
    • markets to Millennials
    • an exemplar is identified,
      the trend is: “big data” → “small data”
    • parable by Martin Lindstrom
    • Chronicle of Lego, a business case
      • was data-driven → failure
      • used ethographics → success.
    • Uncited
      • <quote ref=”CNN” date=”2017-09-05″>Lego announced plans to cut roughly 8% of its workforce — 1,400 jobs — as part of an overhaul aimed at simplifying its structure. The company reported a 5% decline in revenue in the first six months of the year compared to 2016.</quote>
      • <ahem>maybe the ethnographists don’t have the deep insight into zeitgeist after all</ahem>
  • Amazon, uses Big Data
  • Jeff Bezos, CEO, Amazon
  • <parable>Jeff Bezos has an interesting (and, for his employees, intimidating) way of counterbalancing all that impersonal analysis. On a somewhat regular basis, he takes an emailed complaint from an individual customer, forwards it to his executive team, and demands that they not only fix it but thoroughly investigate how it happened and prepare a report on what went wrong.</quote> filed under: how the great ones do it.
  • <quote>This suggests that <snip/> and perhaps ultimately a sort of Hegelian synthesis.</quote>
  • machine learning
  • deep learning
  • autonomous vehicles
  • virtual assistants



In archaeological order, in Slate


Partnership on AI

Partnership on AI
Uses Responsive Web Design (RWD) so it only “works” on a handset form factor is “mobile first” [scrape-scroll down, which is non-obvious in the officework environment]


Line 1
  • Amazon
  • Apple
Line 2
  • DeepMind, of Google
  • Google, of Alphabet (GOOG)
Line 3
  • Facebook
  • IBM
  • Microsoft

Separately noted.

The Evolving Data Landscape: Veracity, Convergence And Anonymity | Ad Exchanger

Ramsey McGrory (Mediaocean); The Evolving Data Landscape: Veracity, Convergence And Anonymity; In AdExchanger; 2017-09-21.
Ramsey McGrory, chief revenue officer at Mediaocean

tl;dr → something about accuracy of imputations in consumer profiles, accuracy of “data.”

Original Sources

Ramsey McGrory (AddThis); The Data Providers One Quadrant Chart To Rule Them All; 2013-02.
tl;dr → it’s a metaphor with four (4) quadrants induced by a 2-axis “system”; later a 3rd access, a Z-axis

  1. online ↔ offline
  2. anonymous → personal
  3. singleton → conglomerate


  • <quote>data being neither intrinsically “good” nor “bad,” but rather having “qualities.”<quote>, attributed to Ted McConnell.
  • behaviors, drive actions.
  • Viewability
  • Verification
  • Something allegorical about Viewability and Trust & Safety vending as a separable service of attestation, 2012 → 2017.
  • <quote>Viewability speaks to a broader metadata theme of trust, as well as an underlying theme of data quality and users’ engagement with content delivered against this data.</quote>
  • Hey! That’s not a business, that’s a Business Unit;
    Hey! That’s not a BU, that’s a Product.
    Hey! That’s not a Product, that’s a Feature.
    <quote>Then, these vertical standalone organizations and solutions were horizontally integrated into the operating agencies as capabilities.</quote>


  • SafeGraph <quote>works with universities and health organizations to understand movement data and the spread of infectious diseases.</quote>
  • [all] device IDs are persistent
  • <quote>there are growing trends toward people taking control of their anonymization through the use of virtual private networks and Tor</quote>
    • As stated:
      • casual consumer use of VPNs is prevalent [enough to measure]
      • casual consumer use of Tor is prevalent [enough to measure]
    • Contrast with:
      <surely>IPv6 use is prevalent,
      IPv6 use is prevalent enough to warrant dual-stack interfaces on the great centralized ad exchanges.</surely>
  • <quote>mobile, where cookies can’t be used</quote>
  • <quote>that major brands may view agencies as differentiated commodity services, put their media in review with greater frequency and bid them down.</quote>
  • The adtech bubble is ongoing; adtech will be forward-funded on an ongoing basis:<quote><snip/> will continue to be funded with massive capital because the opportunities for innovation and disruption are huge.</quote>


Three Four V’s of Data
  1. volume
  2. velocity
  3. variety
  4. veracity


Big (conglomerates)
  • Adobe
  • Amazon
  • Google
  • IBM
  • Oracle
  • Salesforce
  • SAP
Cross-Device Fingerprinting
  • AdSafe
  • comScore
  • DoubleVerify
  • Moat
  • Amino
  • White Ops
Data Brokers
  • Experian
  • Acxiom
  • TransUnion
  • Equifax
Trading Desks
  • Xaxis of WPP
  • Nerve Center of VivaKi of Publicis
Data Breached
  • Yahoo
  • Equifax

Hearts & Science
<honorific>won major accounts on a transparent, data-centric and deeply integrated vision.</honorific>


  • Ted McConnell, practitioner.
    <quote>Ted McConnell, an independent consultant in the digital marketing space.</quote>



In Ad Exchanger


The Suitecase Words
  • ”data truth”
  • “moat for data”
  • “truth of the inference”
  • intenders, as “auto intenders”
  • attitudes
  • demographics
  • measurement
  • cross-device,
    cross-device mapping.
  • deterministic
  • Television
    • connected television
    • addressable television
    • advanced television
    • data-enabled television
    • targeted television
    • integrated television and video,
      integrated television and video initiatives
  • strategic elements
    strategic elements of advertising campaigns.
  • holistic planning
  • anonymous data
  • digital data
  • ad block
  • cookie block
  • mobile,
    growth of mobile.
  • device IDs
  • persistent
  • anonymity,
    desire for anonymity.
  • breaches,
    data breaches,
    massive data breaches

    • Yahoo
    • Equifax
  • sensitive information
    • Social Security numbers
    • birthdates
    • credit card numbers
  • collaboration
  • competition
  • companies,
    services companies,
    technology and services companies
  • space,
    media space.
  • to verb… with large agencies
    • partner
    • coexist
    • compete
  • Z-axis
  • execute,
    acquisitively execute,
    aggressively and acquisitively execute,
    continue to aggressively and acquisitively execute,
    continue to aggressively and acquisitively execute on their strategies,
    continue to aggressively and acquisitively execute on their strategies to deliver on

    • infrastructure
    • data
    • services
  • agencies,
    holding company agencies.
  • solutions,
    data-driven solutions,
    converged, data-driven solutions.
  • vision,
    • transparent vision
    • data-centric vision
    • integrated vision,
      deeply integrated vision.
  • The side,
    • The downside
    • The upside
  • brands,
    major brands.
  • services,
    commodity services,
    differentiated commodity services,
    agencies as differentiated commodity services.
  • themes
    • convergence
    • data activation
    • people
  • change,
    great change,
    in a time of such great change,
    wait for it … wait for it … the only constant is change …thank you, thank you very much, I’ll be here all week.
  • The Bottom Line
  • In a world of…
  • adjectivedata,
    • first-party data
    • third-party data
    • personal data
    • census data
    • anonymous data
    • panel data,
      <mmmmm>…panel data..…</mmmmm>
    • pixel data
  • understanding,
    deeper understanding,
    deeper understanding of consumers’ …
    deeper understanding of consumers’ awareness and interests,
    deeper understanding of consumers’ awareness and interests while enjoying <snip/> profitability,
    deeper understanding of consumers’ awareness and interests while enjoying short- and long-term profitability,
    deeper understanding of consumers’ awareness and interests while enjoying short- and long-term profitability of their brands.
  • vision,
    this vision,
    delivering on this vision.
  • infrastructure,
    data infrastructure,
    extensive data infrastructure.
  • understanding,
    deep understanding,
    deep understanding of

    • advertising
    • publishing,
      media publishing
    • ecommerce
  • ecosystems,
    technology ecosystems,

    • advertising technology ecosystems
    • marketing technology ecosystems
    • content technology ecosystems
  • ecosystems,
    the ecosystems,
    all the ecosystems,
    And across all the ecosystems
  • nounof data
    • consolidation of data
    • standardization of data
    • interpretation of data
    • activation
  • winners
    winners and losers
    winners and losers will be decided.
  • transformation,
    massive transformation,
    enable massive transformation,
    enable massive transformation at <snip/> lower costs.
    enable massive transformation at materially lower costs.

As Google Fights Fake News, Voices on the Margins Raise Alarm | NYT

As Google Fights Fake News, Voices on the Margins Raise Alarm; Daisuke Wakabayashi; In The New York Times (NYT); 2017-09-26.

tl;dr → Google Bad. They change their indexing; publishers beholden to search-generated traffic sourcing schemes are affected.
and → <quote>The New York Times could not find the same level of traffic declines at all of those publications, based on data from SimilarWeb</quote> <ahem>then why write the article about a non-event?</ahem>


The voices on the margins,
The marginal voices.
  • Socialists, specifically, David North
  • SourceFesters
  • Breitbartists
  • Frank Pasquale


Frank Pasquale; The Black Box Society: The Secret Algorithms That Control Money and Information; Harvard University Press; 2016-08-29; 320 pages; ASIN:0674970845: Kindle: $10, paper: $11+SHT; separately filled.


  • World Socialist Web Site (WSWS)
  • Project Owl, of Google
    • Announced 2017-04.
    • <google>algorithmic updates to surface more authoritative content</google>
      <ny-times>stamp out fake news stories from its search results</ny-times>
  • Google performs search results page rating
    • A panel method, of living humans.
    • The panel is paid-staff of Google.
    • N=10,000.
  • Search Quality Evaluator Guidelines; Google; 2013.
  • Alexa, of Amazon,
    not the robot, the web analytics shop.
    has independent traffic estimates.
  • David North (WSWS); open letter to Google, World Socialist Web Site (WSWS); 2017-08-25.
  • SimilarWeb, a web analytics firm.
  • some video, unattributed; hosted on SourceFed; 2016-06.
    tl;dr → accuses Google; asserts there is manipulation of the search results.
  • Four Times Google was Linked Directly to Hillary Clinton; Some Screeching Troll (SST); On Breitbart; 2017-08-14


  • is buried.
  • The New York Times (NYT) is not able to replicate or validate the claims of traffic falloff.
    <quote>The New York Times could not find the same level of traffic declines at all of those publications, based on data from SimilarWeb, a web analytics firm. </quote>


  • Michael Bertini, expert, iQuanti.
    iQuanti is a marketing agency.
  • Pandu Nayak, spox, fellow, Google.
  • David North, the editorial chairman, World Socialist Web Site
  • Frank Pasquale, professor, law, information law, University of Maryland.


In The New York Times (NYT)…

As IBM Ramps Up Its AI-Powered Advertising, Can Watson Crack the Code of Digital Marketing? | Ad Week

As IBM Ramps Up Its AI-Powered Advertising, Can Watson Crack the Code of Digital Marketing?; ; In Ad Week (Advertising Week); 2017-09-24.
Teaser: Acquisition of The Weather Company fuels a new division

tl;dr → Watson (a service bureau, AI-as-a-Service) is open for business.


The 4 pillars of Watson Advertising.
  1. Targeting, Audience construction & activation.
  2. Optimization, Bidding & buying.
  3. Advertising, Synthesis of copy and creative.
  4. Planning, Campaign planning for media buying.

Separately noted.


Grand ideas from 12 disruptive marketing thought leaders distilled into 6 marketing & advertising predictions for 2030 | Michael Haupt

1Michael Haupt; Grand ideas from 12 disruptive marketing thought leaders distilled into 6 marketing & advertising predictions for 2030; In His Blog, centrally hosted on Medium; 2016-11-11.
Teaser: Grand ideas from 12 disruptive marketing thought leaders distilled into 6 marketing & advertising predictions for 2030.

tl;dr → What’s hot circa 2015: Interactive Voice Response, The Data Licentiate, The Universal Dossier, (Ever-more) Precise Targeting, Propensity Prediction


  1. The End of Privacy Concerns
  2. The Transfer of Data Ownership
  3. The End of Broadcast Advertising
  4. The Rise of Personal ChatBots
  5. The Shift Toward Evolved Enterprises (more services, more persuasion)
  6. The Shift From Communicating to Predicting


  1. Jay Abraham
  2. Paul Adams
  3. Alex Bogusky
  4. Cindy Gallop
  5. Seth Godin
  6. Bob Hoffman
  7. Naomi Klein
  8. Kalle Lasn
  9. Mary Meeker
  10. Al Ries & Laura Ries
  11. Luke Sullivan
  12. Monte Wilson
  • Peter Diamandis
  • Ray Kurzweil
  • Gerd Leonhard
  • Mat Schlicht


  • A universal history approach.
    as technological megashifts
    • Funding: $23M, Series B funding.
  • Additive Manufacturing
    his neologism for 3-dimensional Printing (3D Printing)
  • The consumer (the users) are the product, of the social venues.
  • Something about predictive analytics (propensity scoring) in content marketing
    but the concept is not developed.
  • Precision>target audiencesto timely circumstances</quote>


Understood as transitions current state to next state.

Concern Current State Next State
Focus Science, Technology, Engineering and Math (STEM) Creativity, Originality, Reciprocity, Empathy and Intuition (COREI)
World Rational, Logical, Predictable Random, Empatetic, Emotional
World View Mechanistic Ecological
Interactions Competition and Manipulation Collaboration and Problem Solving
Organizations Hierarchical Command & Control Circles, Swarms, Swirls
Fetish Efficiency, Cost Reduction, Speed, Profit Connection, Nurturing, Love.


In order of apparance in the work…

Social Venues

  • Google
  • Twitter
  • Facebook
  • LinkedIn
  • Siri of Apple
  • Cortana of Microsoft
  • Now of Google
  • Echo of Amazon
    (sic) Alexa
  • Google
  • Microsoft
  • HTC
  • Samsung


  • Infinite Computing
  • Artificial Super-Intelligence
  • Sensors & Networks
  • Robotics
  • 3D Printing
  • Virtual and Augmented Reality:


Jay Abraham
Jimi Wales’ Wiki.
Paul Adams
ex-Google, ex-Facebook, “head” of product, Intercom.
Grouped; Publisher?; 2011; ASIN:0321804112: Kindle: $20?
Alex Bogusky
Jimi Wales’ Wiki.
The 9-Inch Diet; Publisher; 2009; ASIN:157687320X: Kindle: $20?
tl;dr → Burger King is bad.
Peter Diamandis
Cindy Gallop
Honorific: is brash.
Make Love Not Porn, a talk, at Theater, Entertainment, Distraction (TED), hosted on YouTube; WHEN? (these performances typically run ~20 min).
Seth Godin
Claimed: has popularized “permission marketing.”
Bob Hoffman
Scrivener, the Ad Contrarian, a blog <quote>who’s been “making marketers uncomfortable since 2013.</quote>
Naomi Klein
Jimi Wales’ Wiki
Honorific: an activist.
Claimed: branding is oppression.
No Logo; Publisher?; 2000; ASIN:000734077X: Kindle: $20?
Ray Kurzweil
Chief futurist, Google.
Kalle Lasn
Jimi Wales’ Wiki
Founder, Adbusters (magazine).
“Chief architect,” Occupy Wall Street Movement.
Claimed: consumerism is evil.
Culture Jam: America’s Suicidal Binge; Publisher?; WHEN? ASIN:B00DY4O5GE: Kindle: $20?
Gerd Leonhard
Seer, booster.
Mary Meeker
Staff, Partner title?, Kleiner Perkins, Caulfield & Byers (KPCB)
Claimed: publishes Internet Trends, serial slideware, annual.
Al Ries
Jimi Wales’ Wiki
Claimed: “the father of positioning”
Al Reis, Laura Reis, The Fall of Advertising; self-published (ebook); 209; ASIN:B000FC11PG: Kindle: $20?
Website, Al & Laura Ries, father and daughter marketing strategists
Laura Ries
Jimi Wales’ Wiki
Al Reis, Laura Reis; The Fall of Advertising; ibidem.
Website, ibidem.
Luke Sullivan
Hey Whipple, Squeeze This; Publisher?; WHEN? ASIN:B01AVKWLCS: Kindle: $20?; Website Twitter.
tl;dr → <quote>a diatribe</quote>.
Matt Schlicht
Monte Wilson
ex-Adobe, Oracle, EMC.
Some Talk; At Some Venue, hosted On YouTube; 2016.
tl;dr → on the scientism of “sided” brain thinking.



In His Blog


Also His Blog

Facebook Failed to Protect 30 Million Users From Having Their Data Harvested by Trump Campaign Affiliate | The Intercept

Facebook Failed to Protect 30 Million Users From Having Their Data Harvested by Trump Campaign Affiliate; Mattathias Schwartz; In The Intercept; 2017-03-30.

tl;dr → the tale

Escape The Matrix | Wired

Escape The Matrix: The Internet is the Uncanniest Valley, Don’t Get Trapped There; Virginia Heffernan; In Wired; 2017-09.
Teaser: The Great Tech Panic: The Internet is The Uncanniest Valley
Virginia Heffernan performs the tweeting at @page88.

tl;dr → The techno panic is discursive: internet life is not A Life Well Lived; as such, and wrapped in 2125 words.
and → Computers are a fetish (just like any other).


The Great Tech Publishing Panic of 2017.


Virginia Heffernan; Magic and Loss: The Internet as Art; Simon & Schuster; 2017-06-027; 272 pages; ASIN:1501132679: kindle: $13, paper: $2+SHT.


  • Amazon
    • Alexa, of Amazon
    • Echo, of Amazon
  • Facebook
  • Google
  • Instagram
  • Snapchat
  • Snopes
  • Spotify
  • Twitter
  • YouTube


  • Elaine Scarry, a philosoph.
    • Professor of English and American Literature and Language, the Walter M. Cabot Professor of Aesthetics and the General Theory of Value at Harvard University; via Jimi Wales’ Wiki
  • David Kessler
    • this guy
      David Kessler, expert, popularizer

      • grief counseling
      • adviser to the stars of Hollywood.
    • not this guy
      David Aaron Kessler. In Jimi Wales’ Wiki.

      • pediatrics
      • law
      • Commissioner of the Food &amp Drug Administration (FDA)
  • Masahiro Mori, professor, robtics.


<quote>The same anxiety turned contempt attends much of today’s social media, notably Twitter and Snapchat, where the sheen of fatuousness, cryptic UX, and clubhouse jargon appears designed to humiliate and enfeeble.</quote>

<quote>David Kessler has written about mental illness, thoughts, ideologies, and persistent images of past or future can “capture” a person and stall their mental freedom.</quote>

<quote>Paradoxically, framing the internet as a text to be read, not a life to be led, tends to break, without effort, its spell. Conscious reading, after all, is a demanding ocular and mental activity that satisfies specific intellectual reward centers. And it’s also a workout; at the right time, brain sated, a reader tends to become starved for the sensory, bodily, three-dimensional experience of mortality, nature, textures, and sounds—and flees the thin gruel of text.</quote>
Challenge to the reader: edit this down to ten words, but retain the metaphor of “breaks the spell” and the (physical) “workout” concept.


  • Elaine Scarry, Dreaming by the Book, 1999
    tl;dr → <quote>a manifesto on literature and the imagination.</quote>
  • Arrival of a Train at La Ciotat,, a movie, 1896.
  • The Polar Express, a movie, 2010+ (recent)


The Suitcase Words

Oh my, lots of them…
  • Acela
  • Artificial Intelligence (AI)
  • blockchain
  • Coke
    Diet Coke
  • James Comey
  • cyber, the cyber
    • cyberattack
    • cyberwarefare
  • Diet Coke
  • drones
  • digitization
  • GIF
  • GPS
  • McModern design
  • Mentos
  • OGG (sic)
    Ogg, definition
  • Barack Obama
  • PGP
  • Pokémon Go
  • Redit
    • reditor
    • subredditor
  • Russia
  • Super Mario Odyssey
  • web metropolis
  • UX
  • vapors
    suffering the vapors
  • YouTube

Break Up the Tech Giants? No, Just Level the Field | Bloomberg

Break Up the Tech Giants? No, Just Level the Field; Leonid Bershidsky; In Bloomberg View; 2017-09-11.
Teaser: Facebook, Google and Uber should be held to the same rules as their older rivals.

tl;dr → regulate them, just like every other industry
and → besides the real question is about tax obligations, the taxes are not being paid.

Question → <quote>How can these benign, universally loved innovators be stopped from turning into evil, soulless corporate behemoths? </quote>
Answer → regulate them (a.k.a. “mend it, don’t end it”).


  • United States
  • European Union (EU)
  • Facebook
  • Google
  • search market share
  • Amazon
  • presidential campaign
  • fake accounts
  • Russia
  • Whole Foods, of Amazon
  • Facebook
  • Google
  • Amazon
  • Barry Lynn
  • New America Foundation
  • Open Markets (program)
  • Eric Schmidt
  • “hipster antitrust,” attributed to Joshua Wright, from a tweet
    Joshua Wright, is professor of law, George Mason University.
  • Margrethe Vestager, antitrust commissioner, The European Union.
  • Amazon
  • Uber, a taxi company
  • Airbnb, is a hospitality company
  • “platforms”
  • eBay
  • an Indian law
    requires 100 percent foreign ownership of companies that operate mainly as a marketplace, with no more than 25 percent of their sales coming from one vendor, such as the company itself
  • the mantle of innovation


<quote>It shouldn’t be allowed to “tech” companies either; otherwise, the playing field is not level and older rivals have less resources to invest in new technology to compete more effectively.
This is not really about antitrust, though state aid laws in Europe are the purview of competition authorities; this is about closing obvious, well-known tax loopholes.</quote>


  • tech giants
  • tech leaders
  • these U.S. behemoths
  • a Caribbean shell company holding the rights to a distribution scheme or an ad-selling technique.



In Bloomberg View

In Bloomberg

The Fall of the Labor Share and the Rise of Superstar Firms | Autor, Dorn, Katz, Patterson, Van Reenen

David Autor (MIT) David Dorn (Zurich) Lawrence F. Katz (Harvard), Christina Patterson (MIT), John Van Reenen (MIT); The Fall of the Labor Share and the Rise of Superstar Firms; In Some Venue Surely, <sour>or maybe this is one of those half-decade duration “working papers” that the social scientists meditate upon before reporting out a “completed work” long after the effect has dematerialized <advice>give it a DOI number and be done with it, everyone else has already used or ignored the implications for policymakers concepts in the remediatory nostrums</advice></sour>; 2017-05-01; 74 pages.

tl;dr →The rise of superstar firms and decline in the labor share also appears to be related to changes in the boundaries of large dominant employers with such firms increasingly using domestic outsourcing to contracting firms, temporary help agencies, and independent contractors and freelancers for a wider range of activities previously done in-house, including janitorial work, food services, logistics, and clerical work.</quote>


Proven (shown with the evidence available at the time).

  1. industry sales will increasingly concentrate in a small number of firms.
  2. industries where [industry sales] concentration rises most will have the largest declines in the [unweighted mean] labor share.
  3. the fall decline in the [unweighted mean] labor share will be driven largely by caused by between-firm reallocation
    the fall decline in the labor share will be independent of (primarily) a fall decline in the unweighted mean labor share within firms.
  4. the between-firm reallocation component of the fall decrease in the [unweighted mean] labor share will be greatest in the sectors with the greatest increases in market [industry sales] concentration.
  5. the effect is pervasive, always and everywhere <quote>such patterns will be observed not only in U.S. firms, but also internationally<quote>.


The fall of labor’s share of GDP in the United States and many other countries in recent decades is well documented but its causes remain uncertain. Existing empirical assessments of trends in labor’s share typically have relied on industry or macro data, obscuring heterogeneity among firms. In this paper, we analyze micro panel data from the U.S. Economic Census since 1982 and international sources and document empirical patterns to assess a new interpretation of the fall in the labor share based on the rise of “superstar firms.” If globalization or technological changes advantage the most productive firms in each industry, product market concentration will rise as industries become increasingly dominated by superstar firms with high profits and a low share of labor in firm value-added and sales. As the importance of superstar firms increases, the aggregate labor share will tend to fall. Our hypothesis offers several testable predictions: industry sales will increasingly concentrate in a small number of firms; industries where concentration rises most will have the largest declines in the labor share; the fall in the labor share will be driven largely by between-firm reallocation rather than (primarily) a fall in the unweighted mean labor share within firms; the between-firm reallocation component of the fall in the labor share will be greatest in the sectors with the largest increases in market concentration; and finally, such patterns will be observed not only in U.S. firms, but also internationally. We find support for all of these predictions.


In this paper we have considered a new “superstar firm” explanation for the widely remarked fall in the labor share of GDP. We hypothesize that markets have changed such that firms with superior quality, lower costs, or greater innovation reap disproportionate rewards relative to prior eras. Since these superstar firms have higher profit levels, they also tend to have a lower share of labor in sales and value-added. As superstar firms gain market share across a wide range of sectors, the aggregate share of labor falls. Our model, combined with technological or institutional changes advantaging the most productive firms in many industries, yields predictions that are supported by Census micro-data across the bulk of the U.S. private sector. First, sales concentration levels rise across large swathes of industries. Second, those industries where concentration rises the most have the sharpest falls in the labor share. Third, the fall in the labor share has an important reallocation component between firms—the unweighted mean of labor share has not fallen much. Fourth, this between-firm reallocation of the labor share is greatest in the sectors that are concentrating the most. Fifth, these broad patterns are observed not only in U.S. data, but also internationally in European OECD countries. Notably, the growth of concentration is disproportionately apparent in industries experiencing faster technical change as measured by the growth of patent-intensity or total factor productivity, suggesting that technological dynamism, rather than simply anti-competitive forces, is an important driver of this trend.

The work in this paper is of course descriptive and suggestive rather than the final word in this area. Future work needs to understand more precisely the shocks that lead to the emergence of superstar firms. We have presented our model as one where productivity (or quality) differences between firms are magnified when the competitive environment changes, turning leading firms into dominating superstars. One source for the change in the environment could be technological: high tech sectors and parts of retail and transportation as well have an increasingly “winner takes all” aspect. But an alternative story is that leading firms are now able to lobby better and create barriers to entry, making it more difficult for smaller firms to grow or for new firms to enter. In its pure form, this “rigged economy” view seems unlikely as a complete explanation. The industries where concentration has grown are those that have been increasing their innovation most rapidly as indicated by patents (Figure 14). One might be concerned that these patents are designed to thwart innovation and enshrine monopolies (e.g., Boldrin and Levine, 2008). However, we also observe similar relationships when measuring innovation by citation-weighted patents or TFP growth.

A more subtle story, however, is that firms initially gain high market shares by legitimately competing on the merits of their innovations or superior efficiency. Once they have gained a commanding position, however, they use their market power to erect various barriers to entry to protect their position. Nothing in our analysis rules out this mechanism, and we regard it as an important area for subsequent research.

The rise of superstar firms and decline in the labor share also appears to be related to changes in the boundaries of large dominant employers with such firms increasingly using domestic outsourcing to contracting firms, temporary help agencies, and independent contractors and freelancers for a wider range of activities previously done in-house, including janitorial work, food services, logistics, and clerical work (Weil, 2014; Katz and Krueger 2016). This fissuring of the workplace can directly reduce the labor share by saving on the wage premia (firm effects) typically paid by large high-wage employers to ordinary workers and by reducing the bargaining power of both in-house and outsourced workers in occupations subject to outsourcing threats and increased labor market competition (Dube and Kaplan, 2010; Goldschmidt and Schmieder, 2017). The increased fissuring of the workplace has been associated with a rising correlation of firm wage effects and person effects (skills) that accounts for a significant portion of the increase in U.S. wage inequality since 1980 (Song et al., 2016). Linking the rise of superstar firms and the fall of the labor share with the trends in inequality between employees should also be an important avenue of future research.




The “superstar firms” are “winner take most”

  • AirBNB
  • Amazon
  • Apple
  • Facebook
  • Federal Express
  • Google
  • Uber
  • Walmart


  • But not for Sears (giggle)
  • But not for coal (mining)
  • But not for oil (end-to-end)
  • But not for autos (design, integration, assembly)
  • But not for airlines
  • But not for bulk steel
  • But not for shipbuilding
  • But not for furniture making
  • But not for semiconductors
  • But maybe for (mobile) CPU chips; e.g. Samsung of ARMdroid.
    But maybe for (desktop) CPU chips; e.g. Intel of Wintel.