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<reference>
NBER WORKING PAPER SERIES

POLANYI’S PARADOX AND THE SHAPE OF EMPLOYMENT GROWTH
David Autor
Working Paper 20485
http://www.nber.org/papers/w20485

NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
September 2014

This paper is a draft prepared for the Federal Reserve Bank of Kansas City’s economic policy symposium
on “Re-Evaluating Labor Market Dynamics,” August 21-23 2014, in Jackson Hole, Wyoming. I thank
Erik Brynjolfsson, Chris Foote, Frank Levy, Lisa Lynch, Andrew McAfee, Brendan Price, Seth Teller,
Dave Wessel, and participants in the MIT CSAIL/Economists Lunch Seminar for insights that helped
to shape the paper. I thank Sookyo Jeong and Brendan Price for superb research assistance. The views
expressed herein are those of the author and do not necessarily reflect the views of the National Bureau
of Economic Research.
NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official
NBER publications.
© 2014 by David Autor. All rights reserved. Short sections of text, not to exceed two paragraphs, may
be quoted without explicit permission provided that full credit, including © notice, is given to the source.

Polanyi’s Paradox and the Shape of Employment Growth
David Autor
NBER Working Paper No. 20485
September 2014
JEL No. J23,J24,J31,O3
ABSTRACT
In 1966, the philosopher Michael Polanyi observed, “We can know more than we can tell... The skill
of a driver cannot be replaced by a thorough schooling in the theory of the motorcar; the knowledge
I have of my own body differs altogether from the knowledge of its physiology.” Polanyi’s observation
largely predates the computer era, but the paradox he identified—that our tacit knowledge of how
the world works often exceeds our explicit understanding—foretells much of the history of computerization
over the past five decades. This paper offers a conceptual and empirical overview of this evolution.
I begin by sketching the historical thinking about machine displacement of human labor, and then
consider the contemporary incarnation of this displacement—labor market polarization, meaning the
simultaneous growth of high-education, high-wage and low-education, low-wages jobs—a manifestation
of Polanyi’s paradox. I discuss both the explanatory power of the polarization phenomenon and some
key puzzles that confront it. I then reflect on how recent advances in artificial intelligence and robotics
should shape our thinking about the likely trajectory of occupational change and employment growth.
A key observation of the paper is that journalists and expert commentators overstate the extent of machine
substitution for human labor and ignore the strong complementarities. The challenges to substituting
machines for workers in tasks requiring adaptability, common sense, and creativity remain immense.
Contemporary computer science seeks to overcome Polanyi’s paradox by building machines that learn
from human examples, thus inferring the rules that we tacitly apply but do not explicitly understand.

David Autor
Department of Economics, E17-216
MIT
77 Massachusetts Avenue
Cambridge, MA 02139
and NBER
dautor@mit.edu

I. Introduction  
That  computers  are  ubiquitous  in  contemporary  life  is  self-­‐‑evident.  The  share  of  information  
processing  equipment  and  software  in  private,  non-­‐‑residential  investment  rose  from  
approximately  8  percent  to  more  than  30  percent  between  1950  and  2012,  with  the  largest  leap  
occurring  between  1990  and  2000.1  It  is  hard  to  think  of  a  prior  historical  episode  where  a  single  
category  of  capital  investment  came  so  rapidly  to  dominate  all  others,  now  accounting  for  close  
to  one  in  three  business  investment  dollars.2    
Given  their  ubiquity,  it  is  tempting  to  infer  that  there  is  no  task  to  which  computers  are  not  
suited.  But  that  leap  of  logic  is  unfounded.  Human  tasks  that  have  proved  most  amenable  to  
computerization  are  those  that  follow  explicit,  codifiable  procedures—such  as  multiplication—
where  computers  now  vastly  exceed  human  labor  in  speed,  quality,  accuracy,  and  cost  
efficiency.3  Tasks  that  have  proved  most  vexing  to  automate  are  those  that  demand  flexibility,  
judgment,  and  common  sense—skills  that  we  understand  only  tacitly—for  example,  developing  
a  hypothesis  or  organizing  a  closet.  In  these  tasks,  computers  are  often  less  sophisticated  than  
preschool  age  children.  The  interplay  between  machine  and  human  comparative  advantage  
allows  computers  to  substitute  for  workers  in  performing  routine,  codifiable  tasks  while  
amplifying  the  comparative  advantage  of  workers  in  supplying  problem  solving  skills,  
adaptability,  and  creativity.  Understanding  this  interplay  is  central  to  interpreting  and  
forecasting  the  changing  structure  of  employment  in  the  U.S.  and  other  industrialized  countries.  
This  understanding  is  also  is  at  the  heart  of  the  increasingly  prominent  debate  about  whether  
the  rapid  pace  of  automation  threatens  to  render  the  demand  for  human  labor  obsolete  over  the  
next  several  decades.    
                                                                                                            
  Author’s  calculations  based  on  Bureau  of  Economic  Analysis  National  Income  and  Product  Account  
data.  
1

2  Fences  used  to  demarcate  property  lines  and  secure  livestock  may  be  another  example.  In  1872,  the  
value  of  fencing  capital  stock  in  the  United  States  was  roughly  equal  to  the  value  of  all  livestock,  the  
national  debt,  or  the  railroads,  and  annual  fencing  repair  costs  were  greater  than  combined  annual  tax  
receipts  at  all  levels  of  government  (Hornbeck,  2010).    

  At  the  extreme,  there  are  tasks  that  computers  regularly  perform  that  could  not  be  accomplished  by  
human  labor  at  any  price,  for  example,  guiding  a  missile  to  intercept  another  missile  in  mid-­‐‑flight.    
3

  

1	
  

  This  paper  offers  a  conceptual  and  empirical  overview  of  the  evolving  relationship  between  
computer  capability  and  human  skill  demands.  I  begin  by  sketching  the  historical  thinking  
about  machine  displacement  of  human  labor,  and  then  consider  the  contemporary  incarnation  
of  this  displacement—labor  market  polarization,  meaning  the  simultaneous  growth  of  high-­‐‑
education,  high-­‐‑wage  and  low-­‐‑education,  low-­‐‑wages  jobs—a  manifestation  of  Polanyi’s  
paradox.  I  discuss  both  the  explanatory  power  of  the  polarization  phenomenon  and  some  key  
puzzles  that  confront  it.  I  finally  reflect  on  how  recent  advances  in  artificial  intelligence  and  
robotics  should  shape  our  thinking  about  the  likely  trajectory  of  occupational  change  and  
employment  growth.    
A  key  observation  of  the  paper  is  that  journalists  and  expert  commentators  overstate  the  extent  
of  machine  substitution  for  human  labor  and  ignore  the  strong  complementarities  that  increase  
productivity,  raise  earnings,  and  augment  demand  for  skilled  labor.  The  challenges  to  
substituting  machines  for  workers  in  tasks  requiring  flexibility,  judgment,  and  common  sense  
remain  immense.  Contemporary  computer  science  seeks  to  overcome  Polanyi’s  paradox  by  
building  machines  that  learn  from  human  examples,  thus  inferring  the  rules  that  we  tacitly  
apply  but  do  not  explicitly  understand.    

II. A  Brief  History  of  Automation  Anxiety  
Anxiety  about  the  adverse  effects  of  technological  change  on  employment  has  a  venerable  
history.4  In  the  early  19th  century,  a  group  of  English  textile  artisans  calling  themselves  the  
Luddites  staged  a  machine-­‐‑trashing  rebellion  in  protest  of  the  rapid  automation  of  textile  
production,  which  they  feared  jeopardized  their  livelihoods.  Their  actions  earned  the  term  
Luddite  an  (unflattering)  entry  in  the  popular  lexicon.  Economists  have  historically  rejected  the  
concerns  of  the  Luddites  as  an  example  of  the  “lump  of  labor”  fallacy,  the  supposition  that  an  
increase  in  labor  productivity  inevitably  reduces  employment  because  there  is  only  a  finite  
amount  of  work  to  do.  While  intuitively  appealing,  the  notion  that  productivity  gains  reduce  
employment  has  received  little  historical  support.  The  employment-­‐‑to-­‐‑population  ratio,  for  
                                                                                                            
  This  section  of  the  paper  draws  heavily  on  Autor  (2014a),  with  some  paragraphs  quoted  directly.    

4

  

2	
  

example,  rose  over  the  course  of  the  20th  century  as  women  moved  from  home  to  market,  and  
the  unemployment  rate  fluctuated  cyclically,  with  no  long-­‐‑term  increase.  
Yet,  despite  sustained  increases  in  material  standards  of  living,  fear  of  the  adverse  employment  
consequences  of  technological  advancement  has  recurred  repeatedly  in  the  20th  century.  In  his  
widely  discussed  Depression-­‐‑era  essay  “Economic  Possibilities  for  our  Grandchildren,”  John  
Maynard  Keynes  (1930)  foresaw  that  in  a  century’s  time,  “we  may  be  able  to  perform  all  the  
operations  of  agriculture,  mining,  and  manufacture  with  a  quarter  of  the  human  effort  to  which  
we  have  been  accustomed.”  Keynes  viewed  these  developments  as  posing  short-­‐‑term  
challenges,  “For  the  moment  the  very  rapidity  of  these  changes  is  hurting  us  and  bringing  
difficult  problems  to  solve.  .  .  .  We  are  being  afflicted  with  a  new  disease  of  which  some  readers  
may  not  yet  have  heard  the  name,  but  of  which  they  will  hear  a  great  deal  in  the  years  to  
come—namely,  technological  unemployment.”  But  Keynes  was  sanguine  about  the  long  run,  
opining  that  “this  is  only  a  temporary  phase  of  maladjustment,”  and  predicting  that  the  fifteen-­‐‑
hour  workweek  (supporting  a  high  standard  of  living)  would  be  commonplace  in  a  century’s  
time.    
Keynes’  projection  that  the  maladjustment  was  “temporary”  was  a  bold  one  given  that  he  was  
writing  during  the  Great  Depression.  But  the  end  of  the  Second  World  War  seemed  to  affirm  
the  rising  prosperity  that  Keynes  had  foreseen.  Perhaps  more  surprising  is  that  “automation  
anxiety”  recurred  two  decades  after  the  Second  World  War,  during  what  was  arguably  the  
height  of  American  economic  preeminence.  In  1964,  President  Johnson  empaneled  a  “Blue-­‐‑
Ribbon  National  Commission  on  Technology,  Automation,  and  Economic  Progress”  whose  
charge  was  “to  identify  and  assess  the  past  effects  and  the  current  and  prospective  role  and  
pace  of  technological  change;  to  identify  and  describe  the  impact  of  technological  and  economic  
change  on  production  and  employment,  including  new  job  requirements  and  the  major  types  of  
worker  displacement,  both  technologically  and  economic,  which  are  likely  to  occur  during  the  
next  10  years.”    
While  the  commission  ultimately  concluded  that  automation  did  not  threaten  employment  at  
that  time,  it  recommended  as  insurance  against  this  possibility,  “a  guaranteed  minimum  income  

  

3	
  

for  each  family;  using  the  government  as  the  employer  of  last  resort  for  the  hard  core  jobless;  
two  years  of  free  education  in  either  community  or  vocational  colleges;  a  fully  administered  
federal  employment  service,  and  individual  Federal  Reserve  Bank  sponsorship  in  area  economic  
development  free  from  the  Fed'ʹs  national  headquarters”  (The  Herald  Post,  1966).    
The  blue-­‐‑ribbon  commission’s  sanguine  conclusions  did  not  entirely  allay  the  concerns  of  
contemporary  social  critics.  In  an  open  letter  to  President  Johnson  in  1966,  the  self-­‐‑titled  Ad  Hoc  
Committee  on  the  Triple  Threat,  which  included  Nobel  laureates  Linus  Pauling  (chemistry)  and  
Gunnar  Myrdal  (economics),  as  well  as  economic  historian  Robert  Heilbroner,  opined  that  “The  
traditional  link  between  jobs  and  incomes  is  being  broken.  .  .  .  The  economy  of  abundance  can  
sustain  all  citizens  in  comfort  and  economic  security  whether  or  not  they  engage  in  what  is  
5

commonly  reckoned  as  work”  (quoted  in  Akst,  2013).   Writing  separately  in  the  Public  Interest  in  
1965,  Heilbroner  argued  that,  “the  new  technology  is  threatening  a  whole  new  group  of  skills—
the  sorting,  filing,  checking,  calculating,  remembering,  comparing,  okaying  skills—that  are  the  
special  preserve  of  the  office  worker.  .  .  .  In  the  end,  as  machines  continue  to  invade  society,  
duplicating  greater  and  greater  numbers  of  social  tasks,  it  is  human  labor  itself—at  least,  as  we  
now  think  of  “labor”—that  is  gradually  rendered  redundant”  (pp.  34–36).  
Figure	
  1.	
  Chicago	
  Booth	
  IGM	
  Expert	
  Poll:	
  Impact	
  of	
  Automation	
  on	
  Employment	
  and	
  Wages	
  
A. Advancing Automation has Not Historically Reduced Employment in the
United States...
70%
63%
60%

50%

40%

30%

25%

20%
8%

10%
4%
0%

0%
Strongly Disagree

Disagree

Uncertain

Agree

Strongly Agree

                                                                                                            
5

  The  three  threats  perceived  by  the  ad  hoc  committee  were:  the  cybernation  revolution;  the  weaponry  
revolution;  and  the  human  rights  revolution.    

  

4	
  

B. Information Technology and Automation Are a Central Reason Why
Median Wages Have Been Stagnant in the US Over the Past Decade,
Despite Rising Productivity.
50%
43%

45%
40%
35%
30%

30%
24%

25%
20%
15%
10%
5%

4%
0%

0%
Disagree

Strongly Disagree

Uncertain

Agree

Strongly Agree

Figure	
  1	
  notes.	
  Survey	
  date	
  February	
  25,	
  2014,	
  available	
  at	
  http://www.igmchicago.org/igm-­‐
economic-­‐experts-­‐panel/poll-­‐results?SurveyID=SV_eKbRnXZWx3jSRBb	
  (accessed	
  3/26/2014)	
  
In  the  five  decades  since  the  Ad  Hoc  Committee  wrote  its  open  letter,  human  labor  has  not  been  
rendered  redundant,  as  these  scholars  had  feared.  But  automation  anxiety  has  clearly  returned.  
Perhaps  most  telling  is  the  finding  of  a  recent  poll  of  leading  academic  economists  conducted  by  
the  Chicago  Initiative  on  Global  Markets  regarding  the  impact  of  technology  on  employment  
6

and  earnings.   Consistent  with  the  canonical  economic  view  that  technology  is,  in  the  
memorable  phrase  of  Joel  Mokyr,  the  “lever  of  riches,”  a  full  88  percent  of  economists  in  the  poll  
either  agreed  or  strongly  agreed  with  the  statement  that  “advancing  automation  has  not  
historically  reduced  employment  in  the  United  States”  (Figure  1).  Yet,  surprisingly,  43  percent  
of  those  polled  endorsed  (i.e.,  agreed  with)  the  statement  that  “information  technology  and  
                                                                                                            
6

  The  IGM  webpage  describes  the  panel  members  as  follows:  “Our  panel  was  chosen  to  include  
distinguished  experts  with  a  keen  interest  in  public  policy  from  the  major  areas  of  economics,  to  be  
geographically  diverse,  and  to  include  Democrats,  Republicans,  and  Independents  as  well  as  older  and  
younger  scholars.  The  panel  members  are  all  senior  faculty  at  the  most  elite  research  universities  in  the  
United  States.  The  panel  includes  Nobel  Laureates,  John  Bates  Clark  Medalists,  fellows  of  the  
Econometric  Society,  past  Presidents  of  both  the  American  Economics  Association  and  American  Finance  
Association,  past  Democratic  and  Republican  members  of  the  President'ʹs  Council  of  Economics,  and  past  
and  current  editors  of  the  leading  journals  in  the  profession.”  Caveat  emptor:  The  author  is  also  a  
member  of  the  panel.    

  

5	
  

automation  are  a  central  reason  why  median  wages  have  been  stagnant  in  the  US  over  the  past  
decade,  despite  rising  productivity.”  In  contrast,  only  28  percent  disagreed  or  strongly  
disagreed.  While  I  know  of  no  comparable  survey  data  from  a  decade  earlier,  I  find  these  poll  
results  stunning  because  they  suggest  that  a  plurality  of  mainstream  economists  has  accepted—
at  least  tentatively—the  proposition  that  a  decade  of  technological  advancement  has  made  the  
median  worker  no  better  off,  and  possibly  worse  off.  

III.

Employment  Polarization:  A  Manifestation  of  Polanyi’s  Paradox  

To  understand  the  wellspring  of  these  concerns,  it  is  useful  to  start  from  first  principles.  What  
do  computer  do?  And  how  does  their  widespread  adoption  change  what  workers  do?7  Anyone  
with  children  knows  that  computers  appear  “magical”  to  end  users.  But  anyone  who  has  
written  computer  software  knows  that  programming  a  computer  to  accomplish  even  the  most  
rudimentary  tasks  is  a  tedious  chore.  Computers  do  not  think  for  themselves,  do  not  have  
common  sense,  do  not  compensate  for  programmer  oversights  and  errors,  and  do  not  improvise  
solutions  for  unexpected  cases.  Fundamentally,  computers  follow  procedures  meticulously  laid  
out  by  programmers.  For  a  computer  to  accomplish  a  task,  a  programmer  must  first  fully  
understand  the  sequence  of  steps  required  to  perform  that  task,  and  then  must  write  a  program  
that,  in  effect,  causes  the  machine  to  precisely  simulate  these  steps.    
                                                                                                            
  This  essay’s  singular  focus  on  the  impact  of  computerization  on  the  labor  market  should  not  be  taken  to  
imply  that  computerization  is  the  only  important  factor  behind  the  employment  and  wage  trends  
considered.  Other  important  contributors  include  changes  in  the  relative  supply  of  college  and  non-­‐‑
college  labor,  rising  trade  penetration,  offshoring  and  globalization  of  production  chains,  declines  in  labor  
union  penetration,  the  falling  ‘bite’  of  the  minimum  wage,  and  shifts  in  tax  policy.  In  addition,  many  of  
these  forces  work  in  tandem.  Advances  in  information  and  communications  technologies  have  directly  
changed  job  demands  in  US  workplaces  while  simultaneously  facilitating  the  globalization  of  production  
by  making  it  increasingly  feasible  and  cost-­‐‑effective  for  firms  to  source,  monitor,  and  coordinate  complex  
production  processes  at  disparate  locations  worldwide.  The  globalization  of  production  has  in  turn  
increased  competitive  conditions  for  US  manufacturers  and  US  workers,  eroding  employment  at  
unionized  establishments  and  decreasing  the  capability  of  unions  to  negotiate  favorable  contracts,  attract  
new  members,  and  penetrate  new  establishments.  This  multi-­‐‑dimensional  complementarity  among  
causal  factors  makes  it  both  conceptually  and  empirically  difficult  to  isolate  the  “pure”  effect  of  any  one  
factor.  See  Blinder  (2009)  and  Blinder  and  Krueger  (2013)  for  related  theory  and  evidence  on  how  the  
composition  of  job  tasks—specifically,  the  demand  for  in-­‐‑person  interactions  and  physical  proximity  to  
customers—affect  the  potential  for  occupations  to  be  offshored.  
7

  

6	
  

One  early  example  of  computer  simulation  was  the  use  of  punch  card-­‐‑driven  computers  at  the  
Los  Alamos  National  Laboratory  to  calculate  the  physical  properties  of  explosions  and  
8

implosions  during  the  development  of  the  first  nuclear  warheads.   But  the  scope  of  computer  
simulation  is  not  limited  to  simulating  physical  processes.  When  a  computer  processes  a  
company’s  payroll,  alphabetizes  a  list  of  names,  or  tabulates  the  age  distribution  of  residents  in  
each  Census  enumeration  district,  it  is  “simulating”  a  work  process  that  would,  in  a  previous  
era,  have  been  done  by  humans  using  nearly  identical  procedures.    
The  principle  of  computer  simulation  of  workplace  tasks  has  not  fundamentally  changed  since  
the  dawn  of  the  computer  era.  But  its  cost  has.  An  ingenious  2007  paper  by  William  Nordhaus  
estimates  that  the  real  cost  of  performing  a  standardized  set  of  computations  has  fallen  by  at  
least  1.7  trillion-­‐‑fold  since  the  manual  computing  era,  with  most  of  that  decline  occurring  since  
1980.  This  remarkable  cost  decline  creates  strong  economic  incentives  for  firms  to  substitute  
ever-­‐‑cheaper  computing  power  for  relatively  expensive  human  labor,  with  attendant  effects  on  
employers’  demand  for  employees.  What  are  these  effects?    
The  first-­‐‑order  effect  is,  of  course,  substitution.  As  the  price  of  computing  power  has  fallen,  
computers  have  increasingly  displaced  workers  in  accomplishing  explicit,  codifiable  tasks—
multiplication,  for  example.  Autor,  Levy  and  Murnane,  2003  (ALM  hereafter)  term  these  
activities  as  “routine  tasks,”  meaning  tasks  that  follow  an  exhaustive  set  of  rules  and  hence  are  
readily  amenable  to  computerization.  9  Routine  tasks  are  characteristic  of  many  middle-­‐‑skilled  
cognitive  and  manual  activities,  such  as  bookkeeping,  clerical  work,  and  repetitive  production  
tasks.  Because  the  core  tasks  of  these  occupations  follow  precise,  well-­‐‑understood  procedures,  
they  are  increasingly  codified  in  computer  software  and  performed  by  machines.  This  force  has  

                                                                                                            
8

  In  many  cases,  the  workers  who  performed  these  tasks  were  given  the  job  title  of  “computer”  (Grier,  
2005).  Prior  to  the  Manhattan  Project,  an  even  earlier  example  of  industrial-­‐‑scale  simulation  was  the  use  
of  mechanical  “tabulators”  to  enumerate  the  1890  Census  of  Population,  which  was  stored  on  millions  of  
punched  cards.  
  Tasks  such  as  performing  a  set  of  mathematical  calculations,  retrieving,  sorting,  and  storing  structured  
information,  and  precisely  executing  a  repetitive  physical  operation  in  an  unchanging  environment,  are  
routine  in  the  sense  of  ALM  (2003)  not  because  they  are  mundane  but  because  they  can  be  fully  codified  
and  hence  automated.  
9

  

7	
  

led  to  a  substantial  decline  in  employment  in  clerical,  administrative  support  and,  to  a  lesser  
degree,  production  and  operative  employment,  as  I  document  below.    
But  the  scope  for  substitution  is  bounded:  engineers  cannot  program  a  computer  to  simulate  a  
process  that  they  (or  the  scientific  community  at  large)  do  not  explicitly  understand.  This  
constraint  is  more  binding  than  one  might  initially  surmise  because  there  are  many  tasks  that  
we  understand  tacitly  and  accomplish  effortlessly  for  which  we  do  not  know  the  explicit  “rules”  
or  procedures.  I  refer  to  this  constraint  as  Polanyi’s  paradox,  following  Michael  Polanyi’s  (1966)  
observation  that,  “We  know  more  than  we  can  tell.”  When  we  break  an  egg  over  the  edge  of  a  
mixing  bowl,  identify  a  distinct  species  of  birds  based  only  on  a  fleeting  glimpse,  write  a  
persuasive  paragraph,  or  develop  a  hypothesis  to  explain  a  poorly  understood  phenomenon,  
we  are  engaging  in  tasks  that  we  only  tacitly  understand  how  to  perform.10  Following  Polanyi’s  
observation,  the  tasks  that  have  proved  most  vexing  to  automate  are  those  demanding  
flexibility,  judgment,  and  common  sense—skills  that  we  understand  only  tacitly.    
At  a  practical  level,  Polanyi’s  paradox  means  that  many  familiar  tasks,  ranging  from  the  
quotidian  to  the  sublime,  cannot  currently  be  computerized  because  we  don’t  know  “the  rules.”  
At  an  economic  level,  Polanyi’s  paradox  means  something  more.  The  fact  that  a  task  cannot  be  
computerized  does  not  imply  that  computerization  has  no  effect  on  that  task.  On  the  contrary:  
tasks  that  cannot  be  substituted  by  computerization  are  generally  complemented  by  it.  This  
                                                                                                            
  Computer  scientists  often  refers  to  this  same  phenomenon  as  Moravec’s  paradox,  which  is  defined  by  
Wikipedia  as:  “the  discovery  by  artificial  intelligence  and  robotics  researchers  that,  contrary  to  traditional  
assumptions,  high-­‐‑level  reasoning  requires  very  little  computation,  but  low-­‐‑level  sensorimotor  skills  
require  enormous  computational  resources.  The  principle  was  articulated  by  Hans  Moravec,  Rodney  
Brooks,  Marvin  Minsky  and  others  in  the  1980s.  As  Moravec  writes,  ‘it  is  comparatively  easy  to  make  
computers  exhibit  adult  level  performance  on  intelligence  tests  or  playing  checkers,  and  difficult  or  
impossible  to  give  them  the  skills  of  a  one-­‐‑year-­‐‑old  when  it  comes  to  perception  and  mobility.’”  
(http://en.wikipedia.org/wiki/Moravec'ʹs_paradox,  accessed  8/10/2014).  I  prefer  the  term  Polanyi’s  
paradox  to  Moravec’s  paradox  because  Polanyi’s  observation  also  explains  why  high-­‐‑level  reasoning  is  
straightforward  to  computerize  and  sensorimotor  skills  are  not.  High-­‐‑level  reasoning  uses  a  set  of  formal  
logical  tools  that  were  developed  specifically  to  address  formal  problems  (e.g.,  counting,  mathematics,  
logical  deduction,  encoding  quantitative  relationships).  Sensorimotor  skills,  physical  flexibility,  common  
sense,  judgment,  intuition,  creativity,  spoken  language,  etc.,  are  ‘built-­‐‑in’  capabilities  that  the  species  
evolved  rather  than  developed.  Formalizing  these  skills  requires  reverse  engineering  a  set  of  activities  
that  we  normally  accomplish  using  only  tacit  understanding.  See  Hoffman  and  Furcht  (2014)  for  a  
discussion  of  the  challenge  that  Polanyi’s  paradox  poses  for  scientific  innovation.      
10

  

8	
  

point  is  as  fundamental  as  it  is  overlooked.  Most  work  processes  draw  upon  a  multifaceted  set  
of  inputs:  labor  and  capital;  brains  and  brawn;  creativity  and  rote  repetition;  technical  mastery  
and  intuitive  judgment;  perspiration  and  inspiration;  adherence  to  rules  and  judicious  
application  of  discretion.  Typically,  these  inputs  each  play  essential  roles;  that  is,  improvements  
in  one  do  not  obviate  the  need  for  the  other.  If  so,  productivity  improvements  in  one  set  of  tasks  
almost  necessarily  increase  the  economic  value  of  the  remaining  tasks.11    
Concretely,  consider  the  role  played  by  mechanization  in  construction.  By  historical  standards,  
contemporary  construction  workers  are  akin  to  cyborgs.  Augmented  by  cranes,  excavators,  arc  
welders,  and  pneumatic  nail  guns,  the  quantity  of  physical  work  that  a  skilled  construction  
worker  can  accomplish  in  an  eight-­‐‑hour  workday  is  staggering.  Naturally,  automation  has  
heavily  substituted  for  human  labor  in  performing  construction  tasks  and,  consequently,  many  
fewer  construction  workers  are  required  today  to  accomplish  a  given  construction  task  than  
fifty  years  ago.    
But  construction  workers  have  not  been  devalued  by  this  substitution.  Despite  the  array  of  
capital  equipment  available,  a  construction  site  without  construction  workers  produces  nothing.  
Construction  workers  supply  tasks  such  as  control,  guidance  and  judgment  that  have  no  
current  machine  substitutes  and  which  therefore  become  more  valuable  as  machinery  augments  
their  reach.  A  worker  wielding  a  single  shovel  can  do  a  fairly  limited  amount  of  good  or  harm  
in  an  eight-­‐‑hour  day.  A  worker  operating  a  front-­‐‑end  loader  can  accomplish  far  more.  To  a  first  
approximation,  automation  has  therefore  complemented  construction  workers—and  it  has  done  
so  in  part  by  substituting  for  a  subset  of  their  job  tasks.    
This  example  should  not  be  taken  to  imply,  however,  that  technological  change  is  necessarily  
Pareto  improving,  even  for  construction  workers.  There  are  three  factors  that  mitigate  or  
augment  its  impacts:  
                                                                                                            
  The  extreme  manifestation  of  this  idea  is  the  O-­‐‑ring  production  function,  discussed  by  Kremer  (1993).  
In  the  O-­‐‑ring  production  function,  failure  of  any  one  step  in  the  chain  of  production  leads  the  entire  
production  process  to  fail.  Thus,  improvements  in  the  reliability  of  any  given  link  increase  the  value  of  
improvements  in  all  of  the  others.  Intuitively,  if  n-­‐‑1  links  in  the  chain  are  reasonably  likely  to  fail,  the  fact  
that  link  n  is  somewhat  unreliable  is  of  little  consequence.  If  the  other  n-­‐‑1  links  are  made  reliable,  
however,  then  the  value  of  making  link  n  more  reliable  as  well  rises.    
11

  

9	
  

1. Workers  benefit  from  automation  if  they  supply  tasks  that  are  complemented  by  automation  
but  not  if  they  primarily  (or  exclusively)  supply  tasks  that  are  substituted.  A  construction  
worker  who  knows  how  to  operate  a  shovel  but  not  an  excavator  will  generally  experience  
falling  wages  as  automation  advances.    
2. The  elasticity  of  final  demand  can  either  dampen  or  amplify  the  gains  from  automation.  
Conceivably,  productivity  growth  in  construction  could  outstrip  demand  so  that  the  value  
of  further  construction  would  fall  even  faster  than  output  rose.12  But  this  hypothetical  
response  cannot  capture  the  general  case.  Because  household  consumption  has  at  least  kept  
pace  with  household  incomes  over  the  very  long  run,  we  know  that  most  technological  
improvements  have  ultimately  translated  into  increased  consumption  rather  than  greater  
savings.    
3. Labor  supply  changes  can  also  mitigate  wage  gains.  If  the  complementary  tasks  that  
construction  workers  supply  are  abundantly  available  elsewhere  in  the  economy,  it  is  
plausible  that  a  flood  of  new  construction  workers  will  temper  wage  gains  emanating  from  
complementarities  between  automation  and  human  labor  input.13    
The  construction  example,  writ  large,  explains  a  critical  consequence  of  computerization  that  is  
typically  overlooked  in  discussions  of  machine-­‐‑worker  substitution.  Because  machines  both  
substitute  for  and  complement  human  labor,  focusing  only  on  what  is  lost  misses  the  central  
economic  mechanism  through  which  productivity  growth  raises  the  value  of  the  tasks  that  
workers  uniquely  supply.    
I  now  return  to  Polanyi’s  paradox  as  it  applies  to  computerization,  focusing  separately  on  two  
different  margins  of  adjustment:  changes  in  the  occupational  distribution  (AKA  job  
polarization)  and  changes  in  the  wage  distribution.  I  will  argue  that  these  occupational  and  

                                                                                                            
  Arguably,  this  has  occurred  with  agricultural  products  over  the  long  run:  spectacular  productivity  
improvements  have  met  with  declines  in  the  share  of  household  income  spent  on  food.    
12

  While  it  is  unlikely  that  supply  effects  would  fully  offset  productivity-­‐‑driven  wages  gains,  there  are  
perverse  examples.  Hsieh  and  Moretti  (2003)  find  that  new  entry  into  the  real  estate  broker  occupation  in  
response  to  rising  house  prices  fully  offsets  average  wage  gains  that  would  otherwise  occur.    
13

  

10	
  

wage  effects  are  likely  to  be  distinct  from  one  another  for  reasons  hinted  at  in  the  discussion  of  
construction  labor  above.    
If  computers  largely  substitute  for  routine  tasks,  how  do  we  characterize  the  non-­‐‑routine  tasks  
for  which  they  do  not  substitute?  Autor,  Levy  and  Murnane  (2003)  draw  a  distinction  between  
two  broad  sets  of  tasks  that  have  proven  stubbornly  challenging  to  computerize.  One  set  
includes  tasks  that  require  problem-­‐‑solving  capabilities,  intuition,  creativity  and  persuasion.  
These  tasks,  which  ALM  term  ‘abstract,’  are  characteristic  of  professional,  technical  and  
managerial  occupations.  They  employ  workers  with  high  levels  of  education  and  analytical  
capability,  and  they  place  a  premium  on  inductive  reasoning,  communications  ability,  and  
expert  mastery.    
The  second  broad  category  of  non-­‐‑routine  tasks  that  ALM  identify  are  those  requiring  
situational  adaptability,  visual  and  language  recognition,  and  in-­‐‑person  interactions—which  
ALM  term  manual  tasks.  Manual  tasks  are  characteristic  of  food  preparation  and  serving  jobs,  
cleaning  and  janitorial  work,  grounds  cleaning  and  maintenance,  in-­‐‑person  health  assistance  by  
home  health  aides,  and  numerous  jobs  in  security  and  protective  services.  These  jobs  tend  to  
employ  workers  who  are  physically  adept  and,  in  some  cases,  able  to  communicate  fluently  in  
spoken  language.  While  these  are  not  highly  skilled  activities  by  human  labor  standards,  they  
currently  present  daunting  challenges  for  automation.  Equally  noteworthy  is  that  many  of  the  
outputs  of  these  jobs  (haircuts,  fresh  meals,  housecleaning)  must  be  produced  and  performed  
on-­‐‑site  or  in  person  (at  least  for  now),  and  hence  these  tasks  are  not  currently  subject  to  
outsourcing.  Yet,  because  these  jobs  generally  do  not  require  formal  education  or  extensive  
training  beyond  a  high  school  degree,  the  potential  supply  of  workers  who  can  perform  these  
jobs  is  very  large.    
Since  jobs  that  are  intensive  in  either  abstract  or  manual  tasks  are  generally  found  at  opposite  
ends  of  the  occupational  skill  spectrum—in  professional,  managerial,  and  technical  occupations  
on  the  one  hand,  and  in  service  and  laborer  occupations  on  the  other—a  straightforward  
implication  of  this  reasoning  is  that  computerization  of  routine  job  tasks  may  lead  to  the  
simultaneous  growth  of  high-­‐‑education,  high-­‐‑wage  and  low-­‐‑education,  low-­‐‑wages  jobs  at  the  

  

11	
  

expense  of  middle-­‐‑wage,  middle  education  jobs—a  phenomenon  that  Maarten  Goos  and  Alan  
Manning  termed  as  ‘job  polarization’  in  a  2003  working  paper.  A  large  body  of  US  and  
international  evidence  confirms  the  pervasive  presence  of  employment  polarization:  
computerization  is  strongly  associated  with  employment  polarization  at  the  level  of  industries,  
localities,  and  national  labor  markets  (Autor,  Katz,  and  Kearney,  2006  and  2008;  Goos  and  
Manning,  2007;  Autor  and  Dorn,  2013a;  Michaels,  Natraj,  and  Van  Reenen,  2014;  Goos,  
Manning,  and  Salomons,  2014).14  
Figure  2  illustrates  this  pattern  for  the  United  States.  The  Figure  plots  percentage  point  changes  
in  employment  by  decade  for  the  years  1979  -­‐‑  2012  for  10  major  occupational  groups  
encompassing  all  of  U.S.  non-­‐‑agricultural  employment.15  These  ten  occupations  divide  roughly  
into  three  groups.  On  the  right-­‐‑hand  side  of  the  figure  are  managerial,  professional  and  
technical  occupations,  which  are  highly  educated,  and  highly  paid.  Between  one-­‐‑quarter  and  
two-­‐‑thirds  of  workers  in  these  occupations  had  at  least  a  four-­‐‑year  college  degree  in  1979,  with  
                                                                                                            
  There  are  also  two  papers  that  do  not  find  occupational  polarization  in  the  U.S.  One  is  Katz  and  Margo  
(2013),  who  employ  the  extremely  coarse  occupation  1950  scheme  provided  by  the  IPUMS  (Ruggles  et  al.,  
2010).  The  1950  scheme  enables  long-­‐‑term  historical  comparisons  (Katz  and  Margo’s  focus)  at  the  expense  
of  precision  and  is  otherwise  not  normally  applied  to  data  from  1980  forward.  The  second  paper  is  
Mishel,  Shierholz  and  Schmitt  (2013),  who  offer  an  extended,  and  for  the  most  part  extremely  careful,  
critique  of  the  literature  on  technological  change,  employment  and  wage  inequality.  Their  paper  argues  at  
length  that  the  growth  of  low-­‐‑wage  service  employment  U.S.  does  not  commence  until  the  2000s,  a  
finding  that  is  at  odds  with  all  other  work  using  contemporary  occupation  codes  of  which  I  am  aware  
(including  the  Bureau  of  Labor  Statistic’s  own  tabulations  of  Occupational  Employment  Statistics  data  for  
this  time  period  provided  in  Alpert  and  Auyer,  2003,  Table  1).  While  I  concur  with  Mishel  et  al.  that  
decadal  revisions  to  the  Census  and  OES  occupational  coding  schemes  (affecting  the  U.S.  Census,  Current  
Population  Survey  and  Occupational  Employment  Statistics)  make  it  essentially  impossible  to  obtain  fully  
consistent  occupational  employment  counts  over  multiple  decades,  the  adjustments  that  Mishel  et  al.  
apply  to  the  data  generate  occupational  patterns  that  appear  anomalous.  As  later  below  in  panel  Figure  8  
below  (panel  B),  the  data  admit  no  ambiguity  about  the  steep  reallocation  of  non-­‐‑college  (high  school  or  
lower)  workers  from  routine-­‐‑intensive  occupations  (primarily  production,  operative,  and  clerical  work)  
to  manual  task-­‐‑intensive  service  occupations;  indeed,  the  left-­‐‑hand  tail  of  the  employment  polarization  
phenomenon  is  driven  by  the  changing  occupational  allocation  of  non-­‐‑college  workers.  Mishel  et  al.  
acknowledge  this  reallocation  (see  Tables  6  and  8  of  their  paper)  but  view  it  as  unimportant  and  argue  
that  it  is  primarily  explained  by  the  secular  decline  in  the  fraction  of  non-­‐‑college  workers  in  the  labor  
force  (though  it  is  unclear  why  a  decline  in  the  non-­‐‑college  share  of  the  labor  force  would  increase  the  share  
of  non-­‐‑college  workers  employed  in  service  occupations).    
14

  More  precisely,  the  figure  plots  100  times  log  changes  in  employment,  which  are  close  to  equivalent  to  
percentage  points  for  small  changes.  
15

  

12	
  

the  lowest  college  share  in  technical  occupations  and  the  highest  in  professional  occupations  
(Acemoglu  and  Autor,  2011).  Employment  growth  in  these  occupations  was  robust  throughout  
the  three  decades  plotted.  Even  in  the  deep  recession  and  incomplete  recovery  between  2007  
and  2012,  these  occupations  experienced  almost  no  absolute  decline  in  employment.  
Figure	
  2.	
  Percentage	
  Changes	
  in	
  Employment	
  by	
  Major	
  Occupational	
  Category,	
  1979	
  –	
  2012	
  

100 x Log Change in Employment
-20 -10 0 10 20 30 40

Changes in Employment by Occupation, 1979-2012

s
al
on

s
er
ag
an

M

s
an

si
es

of
Pr

ci
ni

ch
Te

ce
vi

r
Se
e

ic

rv
Se

s
le
Sa
in
dm
/A
ce
ffi
O
n
tio
uc
rs
re
od
bo
Pr
La
s/
or
at

r
pe

O
e

e

ar

g
in
an
le

tiv
ec

ot
Pr

/C
od

Fo

C
al
on

rs
Pe

1979-1989

1989-1999

1999-2007

2007-2012

  
Notes.	
  1980,	
  1990	
  and	
  2000	
  Census	
  IPUMS	
  files,	
  American	
  Community	
  Survey  combined	
  file	
  
2006	
  –	
  2008,	
  and	
  American	
  Community	
  Survey	
  2012.	
  Sample	
  includes	
  the  working-­‐age	
  (16-­‐64)	
  
civilian	
  non-­‐institutionalized	
  population.	
  Employment	
  is	
  measured	
  as  full-­‐time	
  equivalent	
  
workers.	
  
Moving  leftward,  the  next  four  columns  display  employment  growth  in  middle-­‐‑skill  
occupations,  comprising  sales;  office  and  administrative  support;  production,  craft  and  repair;  
and  operator,  fabricator  and  laborer.  The  first  two  of  this  group  of  four  are  middle-­‐‑skilled,  
white-­‐‑collar  occupations  that  are  disproportionately  held  by  women  with  a  high  school  degree  
or  some  college.  The  latter  two  categories  are  a  mixture  of  middle  and  low-­‐‑skilled  blue-­‐‑collar  
occupations  that  are  disproportionately  held  by  males  with  a  high  school  degree  or  lower  
education.  While  the  headcount  in  these  occupations  rose  in  almost  every  decadal  interval  
between  1979  and  2007,  their  growth  rate  lagged  the  economy-­‐‑wide  average  and,  moreover,  

  

13	
  

generally  slowed  across  decades.  These  occupations  were  hit  particularly  hard  after  2007,  with  
absolute  declines  in  employment  between  2007  and  2012  ranging  from  5  to  15  percent.  
The  leftmost  three  columns  of  Figure  2  depict  employment  trends  in  service  occupations,  which  
are  defined  by  the  Census  Bureau  as  jobs  that  involve  helping,  caring  for  or  assisting  others.  The  
majority  of  workers  in  service  occupations  have  no  post-­‐‑secondary  education,  and  average  
hourly  wages  in  service  occupations  are  in  most  cases  below  the  other  seven  occupations  
categories.  Despite  their  low  educational  requirements  and  low  pay,  employment  has  grown  
relatively  rapidly  in  service  occupations  over  the  past  three  decades.  All  three  broad  categories  
of  service  occupations,  protective  service,  food  preparation  and  cleaning  services,  and  personal  
care,  expanded  by  double  digits  in  the  both  the  1990s  and  the  pre-­‐‑recession  years  of  the  past  
decade  (1999  -­‐‑  2007).  Notably,  even  during  the  recessionary  years  of  2007  through  2012,  
employment  growth  in  service  occupations  was  modestly  positive—more  so,  in  fact,  than  the  
three  high-­‐‑skilled  occupations  that  have  also  fared  comparatively  well  (professional,  managerial  
and  technical  occupations).  As  noted  by  Autor  and  Dorn  (2013a),  the  employment  share  of  
service  occupations  was  essentially  flat  between  1959  and  1979.  Thus,  their  rapid  growth  since  
1980  marks  a  sharp  trend  reversal.  
Cumulatively,  these  two  trends  of  rapid  employment  growth  in  both  high  and  low-­‐‑education  
jobs  have  substantially  reduced  the  share  of  employment  accounted  for  by  ‘middle  skill’  jobs.  In  
1979,  the  four  middle  skill  occupations  (sales,  office  and  administrative  workers,  production  
workers,  and  operatives)  accounted  for  60  percent  of  employment.  In  2007,  this  number  was  49  
percent,  and  in  2012,  it  was  46  percent.  One  can  quantify  the  consistency  of  this  trend  by  
correlating  the  changes  in  occupational  employment  shares  across  these  ten  occupational  
categories  across  multiple  decades.  The  correlation  between  changes  in  occupational  shares  
between  1979-­‐‑1989  and  1989-­‐‑1999  was  0.64,  and  for  the  decades  of  1989-­‐‑1999  and  1999-­‐‑2007,  was  
0.67.  Remarkably,  the  correlation  between  occupational  share  changes  during  1999-­‐‑2007  and  
2007-­‐‑2012—that  is,  prior  to  and  during  the  Great  Recession—was  0.80.  
The  polarization  of  employment  across  occupations  is  not  unique  to  the  United  States.  Evidence  
of  this  fact  is  presented  in  Figure  3,  which  plots  changes  in  the  share  of  employment  between  

  

14	
  

1993  and  2010  within  three  broad  sets  of  occupations—low-­‐‑,  middle-­‐‑,  and  high-­‐‑wage—covering  
all  non-­‐‑agricultural  employment  in  16  European  Union  economies.  In  all  countries,  middle-­‐‑
wage  occupations  declined  as  a  share  of  employment,  high  wage  occupations  increased  as  a  
share  of  employment,  and  low-­‐‑wage  occupations  gained  in  size  relative  to  middle-­‐‑wage  
occupations  over  this  17-­‐‑year  period.16  The  comparability  of  these  occupational  shifts  across  a  
large  set  of  developed  countries—the  United  States  among  them—makes  it  likely  that  a  
common  set  of  forces  contributes  to  these  shared  labor-­‐‑market  developments.17  Simultaneously,  
the  substantial  differences  among  countries  apparent  in  the  data  underscores  that  no  single  
factor  or  common  cause  explains  the  diversity  of  experiences  across  the  United  States  and  the  
European  Union.  
Figure	
  3.	
  Change	
  in	
  Occupational	
  Employment	
  Shares	
  in	
  Low,	
  Middle	
  and	
  High	
  Wage	
  
Occupations	
  in	
  16	
  EU	
  Countries,	
  1993	
  -­‐	
  2010	
  	
  
15%
12%
9%
6%
3%

Au
st
ria
(
De
nm
ar
k(
Sw
ed
en
(
Fr
an
ce
(
No
rw
ay
Ne
(
th
er
la
nd
s(
Ge
rm
an
y(
Po
rtu
ga
l(

Ita
ly
(

bo
ur
g(
Gr
ee
ce
(
Fin
la
nd
(

m

UK
(

Lu
xe

(

Sp
ai
n(

Be
lg
iu
m

Ire

-3%

la
nd
(

0%

-4.9%

-6%
-9%
-10.9% -10.8% -10.7% -10.6% -10.6%

-12%
-15%

-10.4% -10.3%

-9.6%

-8.6% -8.5%

-7.6%

-6.7%

-12.1% -12.0%
-14.9%

-18%
Low$Paying$

Middle$Paying$

High$Paying$

  

                                                                                                            
  In  14  of  16  countries,  low-­‐‑wage  occupations  increased  as  a  share  of  employment.  

16

  Not  only  is  the  U.S.  not  unique  in  this  regard,  it  is  not  even  an  outlier—falling  roughly  in  the  middle  of  
the  pack  of  this  set  of  countries  in  the  extent  of  employment  polarization.    
17

  

15	
  

Figure	
  3	
  notes.	
  Source:	
  Goos,	
  Manning	
  and	
  Salomons	
  (2014,	
  Table	
  2).	
  High-­‐paying	
  occupations	
  
are	
  corporate  managers;	
  physical,	
  mathematical	
  and	
  engineering	
  professionals;	
  life	
  science	
  and	
  
health  professionals;	
  other	
  professionals;	
  managers	
  of	
  small	
  enterprises;	
  physical,	
  mathematical	
  
and  engineering	
  associate	
  professionals;	
  other	
  associate	
  professionals;	
  life	
  science	
  and	
  health  
associate	
  professionals.	
  Middle-­‐paying	
  occupations	
  are	
  stationary	
  plant	
  and	
  related	
  operators;  
metal,	
  machinery	
  and	
  related	
  trade	
  work;	
  drivers	
  and	
  mobile	
  plant	
  operators;	
  office	
  clerks;  
precision,	
  handicraft,	
  craft	
  printing	
  and	
  related	
  trade	
  workers;	
  extraction	
  and	
  building	
  trades  
workers;	
  customer	
  service	
  clerks;	
  machine	
  operators	
  and	
  assemblers;	
  and	
  other	
  craft	
  and	
  
related  trade	
  workers.	
  Low	
  paying	
  occupations	
  are	
  laborers	
  in	
  mining,	
  construction,	
  
manufacturing	
  and	
  transport;	
  personal	
  and	
  protective	
  service	
  workers;	
  models,	
  salespersons	
  
and	
  demonstrators;	
  and	
  sales	
  and	
  service	
  elementary	
  occupations.	
  	
  

IV.

Does  Employment  Polarization  Lead  to  Wage  Polarization?  

From  the  barbell  shape  of  occupational  employment  growth  depicted  in  Figures  2  and  3,  one  
might  surmise  that  occupational  polarization  would  also  catalyze  wage  polarization—that  is,  
rising  relative  wages  in  both  high-­‐‑education,  abstract  task-­‐‑intensive  jobs  and  in  low-­‐‑education  
manual  task-­‐‑intensive  jobs.  This  reasoning  is  appealing  but  incomplete  because  it  ignores  the  
role  played  by  the  three  mitigating  forces  discussed  above:  complementarity;  demand  elasticity,  
and  labor  supply.18    
Let’s  first  consider  the  impact  of  computerization  on  wages  in  abstract  task-­‐‑intensive  
occupations  such  as  managerial,  professional  and  technical  occupations.  A  key  attribute  of  these  
occupations  is  that  all  draw  upon  large  bodies  of  constantly  evolving  expertise,  e.g.,  medical  
reports,  legal  cases,  sales  data,  financial  analysis,  and  economic  statistics—so  much  so  that  
many  abstract  task-­‐‑intensive  occupations  employ  skilled  assistants  and  paraprofessionals  to  
support  their  information  processing  tasks  (e.g.,  medical  secretaries,  paralegals,  and  research  
assistants).  Given  this  production  structure,  a  clear  prediction  is  that  computerization  should  
strongly  complement  workers  performing  abstract  task-­‐‑intensive  jobs.  By  dramatically  lowering  
the  cost  and  increasing  the  scope  of  information  and  analysis  available  to  them,  
computerization  enables  workers  performing  abstract  tasks  to  further  specialize  in  their  area  of  

                                                                                                            
  Firpo,  Fortin  and  Lemieux  (2013)  provide  an  analysis  of  the  link  between  job  tasks  and  wage  
polarization  in  the  United  States.    
18

  

16	
  

comparative  advantage,  with  less  time  spent  on  acquiring  and  crunching  information,  and  more  
time  spent  on  interpreting  and  applying  it.19    
If  demand  for  the  output  of  abstract  task-­‐‑intensive  activities  were  inelastic,  however,  these  
productivity  gains  might  work  to  lower  expenditure  on  these  outputs,  which  could  in  turn  
mitigate  wage  gains.  While  it  is  hard  to  develop  a  strong  theoretical  prior  on  this  possibility,  all  
outward  evidence  suggests  the  opposite.  As  the  output  of  the  professions  has  risen,  demand  for  
their  services  has  seemingly  more  than  kept  pace.  A  leading  example  is  medicine,  where  
expenditures  for  medical  services  have  risen  substantially  as  a  share  of  GPD  as  the  efficacy  of  
medicine  to  address  a  larger  set  of  ailments  has  expanded.  But  one  can  readily  make  similar  
arguments  about  finance,  law,  engineering,  research,  and  design.20    
What  about  the  labor  supply?  If  workers  could  quickly  move  into  the  highly  educated  
professions  to  capitalize  on  rising  productivity,  this  would  mute  earnings  gains.  But  of  course,  
many  professions  require  both  college  and  graduate  degrees  (MBAs,  JDs,  MDs,  Ph.D.s),  
meaning  that  the  production  pipeline  for  new  entrants  is  five  to  ten  years  in  length  and,  hence,  
supply  almost  necessarily  responds  slowly.  Indeed,  as  discussed  in  Autor  (2014b),  young  U.S.  
adults  have  responded  remarkably  sluggishly  to  the  rising  educational  premium  over  the  last  
thirty  years  —and  this  is  particularly  true  for  males,  as  shown  in  Figure  4.  Thus,  while  the  stock  
of  workers  with  college  and  graduate  degrees  has  certainly  grown  in  response  to  rising  
productivity  in  these  occupations,  the  supply  response  has  not  been  nearly  large  enough  to  
swamp  the  contemporaneous  movements  in  demand.    
  

                                                                                                            
  By  the  same  token,  computerization  substitutes  for  many  of  the  support  occupations  that  these  
professions  employ.    
19

  There  are  counterexamples  as  well.  For  example,  computerization  appears  to  allow  “delayering”  of  
management  structures  (Caroli  and  Van  Reenen,  2001).  Arguably,  many  of  the  middle  managers  
displaced  by  delayering  performed  routine  information  processing  tasks.    
20

  

17	
  

15

20

College share of hours worked (%)
25 30 35 40 45 50 55

60

65

Figure	
  4.	
  College	
  Share	
  of	
  Hours	
  Worked	
  in	
  the	
  U.S.	
  1963-­‐2012:	
  	
  
Workers	
  with	
  Less	
  than	
  10	
  Years	
  of	
  Potential	
  Experience	
  

1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012

Males: 0-9 Yrs Experience

Females: 0-9 Yrs Experience

  
Notes.	
  Autor	
  (2014b,	
  Figure	
  S2)	
  based	
  upon	
  March	
  Current	
  Population	
  Survey	
  data	
  for	
  earnings	
  
years	
  1963	
  –	
  2012.	
  	
  
Workers  in  abstract  task-­‐‑intensive  occupations  have  therefore  benefit  from  computerization  via  
a  virtuous  combination  of  three  forces:  strong  complementarities  between  routine  and  abstract  
tasks;  elastic  demand  for  services  provided  by  abstract  task-­‐‑intensive  occupations;  and  inelastic  
labor  supply  to  these  occupations  over  the  short  and  medium  term.  In  combination,  these  forces  
mean  that  computerization  should  raise  earnings  in  occupations  that  make  intensive  use  of  
abstract  tasks  and  among  workers  who  intensively  supply  them.    
Do  these  same  synergies  apply  to  jobs  that  are  intensive  in  manual  tasks,  such  as  janitors  and  
cleaners,  vehicle  drivers,  flight  attendants,  food  service  workers,  and  personal  care  assistants?  
In  large  part,  the  answer  appears  to  be  no.  In  contrast  to  workers  in  abstract  task-­‐‑intensive  
occupations,  computerization  has  not  greatly  increased  the  reach  or  productivity  of  
housekeepers,  security  guards,  waiters,  cooks,  or  home  health  aids.  Because  most  manual  task-­‐‑
intensive  occupations  are  minimally  reliant  on  information  or  data  processing  for  their  core  
tasks,  there  are  very  limited  opportunities  for  either  direct  complementarity  or  substitution.  
  

18	
  

There  are  of  course  exceptions  to  this  generalization:  GPS  and  scheduling  software  allows  
truckers  to  minimize  wasted  mileage;  calendar  and  contact  software  assists  home  health  
workers  to  more  effectively  manage  time  and  bill  hours;  computerized  ordering  systems  permit  
food  service  workers  to  rapidly  tally  customer  tabs.  But  these  information-­‐‑intensive  tasks  are  
largely  peripheral  to  these  occupations’  core  job  tasks.21  Ironically,  manual  task-­‐‑intensive  
occupations  enjoy  relatively  minimal  direct  benefits  from  computerization  because  they  are  too  
well  insulated,  offering  limited  opportunities  for  substituting  or  complementing  human  labor  
with  information  technology.    
Say  for  the  sake  of  argument,  however,  that  demand  for  manual  task-­‐‑intensive  occupations  was  
rising  due  to  rising  societal  income  or  changes  in  preferences.  Would  these  demand  increases  
likely  translate  into  higher  occupational  earnings?  The  answer  turns  on  both  the  elasticity  of  
final  demand  and  the  elasticity  of  labor  supply,  as  noted  above.  Much  aggregate  evidence  
suggests  that  final  demand  for  manual  task-­‐‑intensive  work—services  in  particular—is  relatively  
price  inelastic  (Baumol,  1967;  Autor  and  Dorn,  2013a).  If  so,  productivity  gains  in  manual  task-­‐‑
intensive  occupations  will  not  necessarily  raise  expenditure  on  their  outputs.  On  the  other  hand,  
demand  for  manual  task-­‐‑intensive  work  appears  to  be  relatively  income  elastic  (Clark,  1951;  
Mazzorali  and  Ragusa,  2013),  meaning  that  rising  aggregate  incomes  will  tend  to  increase  
demand  for  these  activities.  Computerization  may  therefore  indirectly  raise  demand  for  manual  
task-­‐‑intensive  occupations  by  increasing  societal  income.22    

                                                                                                            
  While  surely  there  are  important  exceptions—for  example,  checkout  cashiers  using  scanner  driven  
point  of  sale  registers  that  interface  directly  to  centralized  inventory  systems—I  see  relatively  few  
examples  where  information  technology  has  fundamentally  transformed  the  work  activities  or  
productivity  of  workers  in  manual  task-­‐‑intensive  occupations.  (I  thank  Dave  Wessel  for  the  cashier  
example.)  
21

  This  can  result  from  either  of  two  economic  forces.  Clark  (1951)  argues  that  demand  for  services  is  non-­‐‑
homothetic:  the  expenditure  share  of  services  rising  with  income.  Baumol  (1967)  argues  that  growing  
expenditure  on  services  reflects  unbalanced  growth:  because  the  relative  prices  of  technologically  lagging  
activities  (e.g.,  haircuts  and  symphony  orchestra  performances)  necessarily  rise  over  time,  an  increasing  
share  of  societal  income  must  be  expended  on  these  activities  to  maintain  balanced  consumption.  Of  
course,  Baumol’s  argument  presupposes  that  demand  for  these  activities  is  relatively  inelastic—otherwise  
expenditure  would  fall  as  relative  prices  rose.  Mazzorali  and  Ragusa  (2013)  present  evidence  consistent  
with  Clark’s  view  while  Autor  and  Dorn  (2013a)  present  evidence  consistent  with  Baumol’s  thesis.    
22

  

19	
  

This  is  where  the  elasticity  of  labor  supply  becomes  most  critical,  however.  Due  to  their  
generally  low  education  and  training  requirements,  labor  supply  to  manual  task-­‐‑intensive  
occupations  is  intrinsically  elastic.23  Consequently,  wage  increases  in  manual  task-­‐‑intensive  
occupations  generally  spur  a  robust  supply  response.  Moreover,  workers  displaced  from  other  
sectors  of  the  economy  may  readily  obtain  employment  in  manual  task-­‐‑intensive  occupations  
due  to  their  low  entry  requirements.    
In  short,  while  abstract  task-­‐‑intensive  activities  benefit  from  strong  complementarities  with  
computerization,  relatively  elastic  final  demand,  and  a  low  elasticity  of  labor  supply,  manual  
task-­‐‑intensive  activities  are  at  best  weakly  complemented  by  computerization,  do  not  benefit  
from  elastic  final  demand,  and  face  elastic  labor  supply  that  tempers  demand-­‐‑induced  wage  
increases.  Thus,  while  computerization  has  strongly  contributed  to  employment  polarization,  we  
would  not  generally  expect  these  employment  changes  to  culminate  in  wage  polarization  except  
24

in  tight  labor  markets  (Autor  and  Dorn,  2013a).     
Figure  5  presents  evidence  consistent  with  this  logic.  Following  the  format  of  Figure  2,  this  
figure  depicts  percentage  point  changes  in  mean  weekly  wages  by  occupation  among  full-­‐‑time,  
full-­‐‑year  workers  for  1979  through  2012,  subdividing  the  time  interval  into  the  1980s,  1990s,  and  
the  pre-­‐‑  and  post-­‐‑recession  2000s.  To  provide  additional  detail,  Figure  6  plots  wage  changes  
across  all  occupational  categories,  weighted  by  initial  size  and  smoothed  for  clarity.  Specifically,  
the  figure  ranks  all  318  detailed  occupations  from  lowest  to  highest  by  their  initial  skill  level  (as  
measured  by  its  1979  mean  hourly  occupational  wage),  groups  these  detailed  occupations  into  
100  bins  of  equal  sizes,  and  plots  smoothed  changes  in  log  earnings  at  each  occupational  
percentile  over  each  sub-­‐‑period.    

                                                                                                            
  Baumol  (1967)  observes  that  even  absent  productivity  growth  in  technologically  lagging  occupations,  
wages  in  these  occupations  must  rise  over  time  with  societal  income  to  compensate  workers  for  not  
entering  other  sectors  (again,  assuming  that  demand  for  these  activities  is  relatively  inelastic).    
23

24

  Autor  and  Dorn  (2013a)  present  evidence  that  the  consumption  complementarity  effect  (due  to  rising  
incomes)  dominated  the  displacement  effect  on  net  between  1980  and  2005.  But  this  effect  was  primarily  
driven  by  wage  developments  in  the  1990s  when  labor  markets  were  extremely  tight.  After  2000,  the  
expansion  of  manual  task-­‐‑intensive  service  occupations  accelerated  but  wages  in  these  occupations  fell.    

  

20	
  

Changes in Mean Weekly Wages by Occupation, 1979−2012
Full−Time Full−Year Workers

s

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s

al

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s

an

on

si

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of
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ce

ce

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ch

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100 x Log Change in Mean Weekly Wages
−10 0
10 20 30 40

Figure	
  5.	
  Changes	
  in	
  Mean	
  Wages	
  by	
  Major	
  Occupational	
  Category	
  	
  
among	
  Full-­‐Time,	
  Full-­‐Year	
  Workers,	
  1979	
  –	
  2012	
  

1979−1989

1989−1999

1999−2007

2007−2012

  
Notes.	
  Calculated	
  using	
  1980,	
  1990	
  and	
  2000	
  Census	
  IPUMS	
  files;	
  American	
  Community	
  Survey  
combined	
  file	
  2006	
  –	
  2008,	
  American	
  Community	
  Survey	
  2012.	
  Sample	
  includes	
  the	
  working-­‐age  
(16-­‐64)	
  civilian	
  non-­‐institutionalized	
  population	
  with	
  48+	
  annual	
  weeks	
  worked	
  and	
  35+	
  usual  
weekly	
  hours.	
  Weekly	
  wages	
  are	
  calculated	
  as	
  annual	
  earnings	
  divided	
  by	
  weeks	
  worked.	
  	
  
The  right-­‐‑hand  two  thirds  of  these  wage  figures  look  much  like  the  plots  of  employment  
polarization.  From  1979  through  2007,  wages  rose  consistently  across  all  three  abstract  task-­‐‑
intensive  categories  of  professional,  technical  and  managerial  occupations.25  By  contrast,  wage  
growth  in  the  four  middle-­‐‑skill,  routine  task-­‐‑intensive  occupations  was  less  rapid  than  in  
abstract  task-­‐‑intensive  occupations  and  generally  decelerated  over  time—with  particularly  
anemic  (and  in  two  of  four  categories,  negative)  growth  after  2000.    

                                                                                                            
  Noting  that  these  occupations  are  also  the  highest  paid  initially,  their  greater  proportional  earnings  
growth  translates  into  even  larger  dollar  growth.  
25

  

21	
  

Figure	
  6.	
  Changes	
  in	
  Mean	
  Wages	
  by	
  Occupational	
  Skill	
  Percentile	
  	
  
among	
  Full-­‐Time,	
  Full-­‐Year	
  Workers,	
  1979	
  –	
  2012	
  

100 x Log Change in Mean FTFY Wage
−5
0
5
10
15
20

Smoothed Wage Changes by Skill Percentile
Among All Workers

0

20
40
60
80
Skill Percentile (Ranked by Occupation’s 1979 Mean Log Wage)
1979−1989

1989−1999

1999−2007

100

2007−2012

  
Notes.	
  Calculated	
  using	
  1980,	
  1990	
  and	
  2000	
  Census	
  IPUMS	
  files;	
  American	
  Community	
  Survey	
  
combined	
  file	
  2006  –	
  2008,	
  American	
  Community	
  Survey	
  2012.	
  The	
  figure	
  plots	
  changes	
  in	
  mean	
  
log	
  wages	
  by	
  1980  occupational	
  skill	
  percentile	
  rank	
  using	
  a	
  locally	
  weighted	
  smoothing	
  
regression	
  (bandwidth	
  0.8  with	
  100	
  observations),	
  where	
  skill	
  percentiles	
  are	
  measured	
  as	
  the	
  
employment-­‐weighted	
  percentile	
  rank	
  of	
  an	
  occupation’s	
  mean	
  log	
  wage	
  in	
  the	
  Census	
  IPUMS	
  
1980	
  5	
  percent	
  extract.	
  Sample	
  includes	
  the	
  working-­‐age	
  (16-­‐64)	
  civilian	
  non-­‐institutionalized	
  
population	
  with	
  48+	
  annual	
  weeks	
  worked	
  and	
  35+	
  usual	
  weekly	
  hours.	
  Weekly	
  wages	
  are	
  
calculated	
  as	
  annual	
  earnings	
  divided	
  by	
  weeks	
  worked.	
  	
  
The  low-­‐‑education,  manual  task-­‐‑intensive  occupations  on  the  left-­‐‑hand  side  of  Figures  5  and  6  
present  a  particularly  intriguing  pattern.  Wage  growth  in  these  occupations  was  somewhat  more  
rapid  than  in  the  routine  task-­‐‑intensive  occupations  in  the  1980s—and  even  more  so  in  the  
1990s—which  was  roughly  concordant  with  the  pattern  of  employment  polarization  taking  
shape  simultaneously.  However,  in  the  2000s,  employment  and  wage  trends  in  manual  task-­‐‑
intensive  occupations  diverged.  While  employment  growth  in  these  occupations  exceeded  that  
in  all  other  categories  between  2000  and  2007  (Figure  2),  wage  growth  was  generally  negative—
more  so  than  almost  all  other  categories  (Mishel,  Shierholz  and  Schmitt,  2013).    

  

22	
  

Why  did  wage  growth  in  manual  task-­‐‑intensive  occupations  go  from  positive  to  negative  after  
1999?  My  strong  hunch  is  that  the  explanation  is  shifting  labor  supply.  Recent  papers  by  
Christopher  Smith  (2013),  Cortes  et  al.  (2014),  and  Foote  and  Ryan  (2014)  find  that  declining  
employment  in  routine  task-­‐‑intensive  jobs  has  led  middle-­‐‑skill  workers—both  new  entrants  and  
those  displaced  from  routine  task-­‐‑intensive  jobs—to  enter  manual  task-­‐‑intensive  occupations  
instead.  This  likely  occurred  particularly  rapidly  in  the  2000s  as  flagging  employment  in  
middle-­‐‑skill  occupations  combined  with  slack  macroeconomic  conditions  spurred  middle-­‐‑skill  
workers  to  compete  with  less-­‐‑educated  workers  for  manual  task-­‐‑intensive  jobs,  thus  checking  
the  tendency  for  wages  to  rise  in  these  occupations.    
A  final  set  of  facts  starkly  illustrated  by  Figure  6  is  that  overall  wage  growth  was  extraordinarily  
anemic  throughout  the  2000s,  even  prior  to  the  Great  Recession.  Between  1999  and  2007,  real  
wage  changes  were  negative  below  approximately  the  15th  percentile,  and  were  below  5  
percentage  points  up  to  the  70th  percentile  of  the  distribution.  Indeed,  wage  growth  was  greater  
at  all  percentiles  during  both  the  1980s  and  1990s  than  in  the  pre-­‐‑recession  2000s.26  Of  course,  
wage  growth  was  essentially  zero  at  all  percentiles  during  the  recessionary  years  of  2007-­‐‑2012.27    

V. Polarization:  What  Have  We  Learned  from  Another  Decade  of  Data?  
Although  the  polarization  hypothesis  can  explain  some  key  features  of  the  U.S.  and  cross-­‐‑
national  data,  reality  invariably  proves  more  complicated  than  the  theory  anticipates.  The  
clearest  evidence  for  this  general  dictum  is  the  unexplained  deceleration  of  employment  growth  
in  abstract  task-­‐‑intensive  occupations  after  2000,  which  is  discussed  by  Beaudry,  Green  and  

                                                                                                            
  Since  the  2000-­‐‑2007  interval  is  two  years  shorter  than  the  1979-­‐‑1989,  one  should  multiple  the  2000-­‐‑2007  
changes  by  1.25  to  put  them  on  the  same  temporal  footing.  Net  of  this  adjustment,  wage  growth  is  still  
considerably  weaker  at  all  percentiles  than  in  the  earlier  two  decades.    
26

  Why  are  the  rapidly  rising  earnings  of  the  top  1  percent  (e.g.,  Atkinson  et.  al.  2011)  not  strongly  evident  
in  Figure  6?  There  are  two  reasons,  one  reflecting  substance,  and  the  other  data.  Substantively,  the  plot  
depicts  changes  in  earnings  by  occupational  percentile  rather  wage  percentile.  Wage  growth  by  
occupational  percentile  is  less  concentrated  than  wage  growth  across  wage  percentiles  since  the  highest  
earners  are  found  across  a  variety  of  occupations.  In  addition,  the  very  highest  percentiles  of  earnings  are  
censored  in  public  use  Census  and  American  Community  Survey  data  files,  which  further  masks  
earnings  gains  at  extreme  quantiles.    
27

  

23	
  

Sand  (2013,  2014)  and  Mishel,  Shierholz  and  Schmitt  (2013).28  This  can  be  seen  especially  clearly  
in  Figure  7,  which,  following  the  format  of  Figure  6,  plots  smoothed  changes  in  the  share  of  U.S.  
employment  (rather  than  wages,  as  in  Figure  6)  at  each  occupational  percentile.  Since  the  sum  
of  shares  must  equal  one  in  each  decade,  the  change  in  these  shares  across  decades  must  total  
zero  and,  thus,  the  height  at  each  skill  percentile  measures  the  growth  in  each  occupation’s  
employment  relative  to  the  whole.29  
Figure  7  contributes  three  nuances  to  the  occupational  polarization  story  above.  A  first,  visible  
on  the  left-­‐‑hand  side  of  the  figure,  is  that  the  pace  of  employment  gains  in  low-­‐‑wage,  manual  
task-­‐‑intensive  jobs  has  risen  successively  across  periods.  Gains  in  these  occupations  were  barely  
discernible  in  the  1980s,  intensified  in  the  1990s,  and  accelerated  again  in  the  2000s.  A  second  
nuance  is  that  the  occupations  that  are  losing  share  appear  to  be  increasingly  drawn  from  
higher  ranks  of  the  occupational  distribution.  For  example,  the  highest  ranked  occupation  to  
lose  employment  share  during  the  1980s  lay  at  approximately  the  45th  percentile  of  the  skill  
distribution.  In  the  1990s,  the  crossover  point  lay  at  approximately  the  55th  percentile.  In  the  
final  two  sub-­‐‑periods,  this  rank  rose  still  further  to  above  the  75th  percentile—suggesting  that  
the  locus  of  displacement  of  middle-­‐‑skill  employment  is  moving  into  higher  skilled  territories.  
The  final  empirical  regularity  highlighted  by  Figure  7  is  that  growth  of  high-­‐‑skill,  high-­‐‑wage  
                                                                                                            
  As  discussed  in  footnote  14  above,  Mishel,  Shierholz  and  Schmitt  (2013)  dispute  the  factual  basis  and  
economic  relevance  of  almost  all  empirical  and  theoretical  conclusions  of  the  polarization  literature.  
Substantively,  however,  I  believe  their  main  contention  is  not  that  employment  polarization  has  not  
occurred  but  rather  that  it  has  not  contributed  to  wage  polarization—or,  more  broadly,  that  occupational  
employment  patterns  are  uninformative  or  irrelevant  to  the  evolution  of  wage  inequality.  As  discussed  in  
section  IV  and  noted  in  Autor  and  Dorn  (2013),  occupational  polarization  does  not  necessarily  generate  
wage  polarization  since  there  are  two  countervailing  forces  operative:  labor  demand  shifts  stemming  
from  consumption  complementarities  between  goods  and  services  (which  tend  to  raise  wages  in  service  
occupations  over  the  long  run);  and  labor  supply  shifts,  stemming  in  part  from  movement  of  low-­‐‑
education  workers  out  of  middle-­‐‑skill,  routine-­‐‑intensive  occupations  and  into  traditionally  low-­‐‑skill,  
manual-­‐‑intensive  occupations  (which  place  downward  pressure  on  wages  in  service  occupations).  Autor  
and  Dorn  (2013)  provide  evidence  from  local  labor  markets  that  occupational  polarization  contributed  to  
wage  polarization  during  the  period  1980  through  2005.  But  this  relationship  is  clearly  not  immutable  
since  wage  polarization  reversed  course  after  2000  whereas  employment  growth  in  low-­‐‑wage  service  
occupation  employment  accelerated.  While  the  evidence  on  employment  polarization  appears  to  me  
unambiguous,  I  leave  it  to  the  reader  to  assess  whether  these  occupational  employment  shifts  are  helpful  
for  understanding  wage  polarization  or  wage  inequality  more  broadly.    
28

  Due  to  the  smoothing  of  the  plotted  series,  this  adding  up  property  holds  only  as  an  approximation.    

29

  

24	
  

occupations  (those  associated  with  abstract  work)  decelerated  markedly  in  the  2000s,  with  no  
relative  growth  in  the  top  two  deciles  of  the  occupational  skill  distribution  during  1999  through  
2007,  and  only  a  modest  recovery  between  2007  and  2012.  Stated  plainly,  the  U-­‐‑shaped  growth  
of  occupational  employment  came  increasingly  to  resemble  a  downward  ramp  in  the  2000s.    
Figure  8  takes  a  closer  look  at  this  phenomenon  by  plotting  the  distribution  of  occupational  
employment  changes  among  college-­‐‑educated  (panel  A)  and  non-­‐‑college  (panel  B)  workers  
across  the  three  broad  occupational  categories  above:  manual-­‐‑intensive,  routine-­‐‑intensive,  and  
abstract-­‐‑intensive.30  
Figure	
  7.	
  Smoothed	
  Employment	
  Changes	
  by	
  Occupational	
  Skill	
  Percentile,	
  1979	
  –	
  2012	
  

−.1

100 x Change in Employment Share
0
.1
.2

Smoothed Employment Changes by Skill Percentile
Among All Workers

0

20
40
60
80
Skill Percentile (Ranked by Occupation’s 1979 Mean Log Wage)
1979−1989

1989−1999

1999−2007

100

2007−2012

  

                                                                                                            
  Specifically,  I  collapse  the  ten  categories  in  Figure  2  into  three  broader  groupings.  Manual  occupations  
are  personal  care,  food/cleaning  service,  and  protective  services.  Routine  occupations  are  
operators/laborers,  production,  office/administrative,  and  sales.  Abstract  occupations  are  technicians,  
professionals,  and  managers.  
30

  

25	
  

Notes	
  for	
  Figure	
  7.	
  Calculated	
  using	
  1980,	
  1990	
  and	
  2000	
  Census	
  IPUMS	
  files;	
  American	
  
Community	
  Survey	
  combined	
  file	
  2006	
  –	
  2008,	
  American	
  Community	
  Survey	
  2012.	
  The	
  figure	
  
plots	
  changes	
  in	
  employment	
  shares	
  by	
  1980	
  occupational	
  skill	
  percentile	
  rank	
  using	
  a	
  locally	
  
weighted	
  smoothing	
  regression	
  (bandwidth	
  0.8	
  with	
  100	
  observations),	
  where	
  skill	
  percentiles	
  
are	
  measured	
  as	
  the	
  employment-­‐weighted	
  percentile	
  rank	
  of	
  an	
  occupation’s	
  mean	
  log	
  wage	
  in	
  
the	
  Census	
  IPUMS	
  1980	
  5	
  percent	
  extract.	
  Employment	
  in	
  each	
  occupation	
  is	
  calculated	
  using	
  
workers’	
  hours	
  of	
  annual	
  labor	
  supply	
  times	
  the	
  Census	
  sampling	
  weights.	
  Consistent	
  occupation	
  
codes	
  for	
  Census	
  years	
  1980,	
  1990,	
  and	
  2000,	
  and	
  2008	
  are	
  from	
  Autor	
  and	
  Dorn	
  (2013).	
  
One  would  anticipate  that  a  long-­‐‑term  rise  in  the  college-­‐‑educated  workforce  would  eventually  
lead  to  a  growth  in  college-­‐‑educated  workers  in  non-­‐‑traditional  occupations.  This  pattern  is  
seen  in  the  1980s:  the  fraction  of  college-­‐‑educated  workers  in  both  manual  and  routine  task-­‐‑
intensive  occupations  rose  modestly  in  this  decade  while  the  share  in  abstract-­‐‑intensive  
occupations  declines.  In  the  subsequent  decade  of  the  1990s,  employment  of  college-­‐‑educated  
workers  polarized,  with  a  sharp  reduction  in  routine  task-­‐‑intensive  employment,  a  steep  rise  in  
abstract  task-­‐‑intensive  employment,  and  a  modest  rise  in  manual  task-­‐‑intensive  employment.  
After  2000,  however,  occupational  employment  patterns  of  college-­‐‑educated  workers  turned  
sharply  downward,  as  discussed  by  Beaudry,  Green  and  Sand  (2013,  2014).  Between  1999  and  
2012,  the  fraction  of  college-­‐‑educated  workers  employed  in  abstract  occupations  fell  by  more  
than  a  percentage  point,  the  share  employed  in  routine  occupations  fell  by  0.4  percentage  
points,  and  the  share  employed  in  manual  occupations  rose  by  1.5  percentage  points.  Among  
non-­‐‑college  workers,  however,  we  see  a  much  more  consistent  pattern  of  sharp  reductions  in  
routine  employment  and  equally  large  gains  in  manual  employment  with  essentially  no  gains  in  
abstract  employment  except  during  the  first  decade  of  the  sample  (panel  B).  In  net,  these  
patterns  suggest  that  the  set  of  abstract  task-­‐‑intensive  jobs  is  not  growing  as  rapidly  as  the  
potential  supply  of  highly  educated  workers.  As  Beaudry,  Green  and  Sand  (2013,  2014)  
highlight,  the  coalescence  of  these  forces  has  likely  led  highly  educated  workers  to  seek  less  
educated  jobs,  which  in  turn  creates  still  greater  challenges  for  the  lower  educated  workers  
competing  for  routine  and  manual  task-­‐‑intensive  work.      

  

26	
  

Figure	
  8.	
  Changes	
  in	
  Employment	
  Shares	
  in	
  Broad	
  Occupational	
  Categories,	
  1979	
  –	
  2012:
Workers with	
  and	
  Without	
  a	
  Four-­‐Year	
  College	
  Degree	
  

-4

100 x Change in Employment Share
-3
-2
-1
0
1
2

3

A. Workers with At Least 4 Years of College

Manual

Routine

Abstract

-4

100 x Change in Employment Share
-3 -2 -1
0
1
2
3

B. Workers with Less Than 4 Years of College

Manual
1979-1989

Routine
1989-1999

Abstract
1999-2007

2007-2012

  
Notes.	
  Calculated	
  using	
  1980,	
  1990	
  and	
  2000	
  Census	
  IPUMS	
  files;	
  American	
  Community	
  Survey	
  
combined  file  2006  –	
  2008,	
  American	
  Community	
  Survey	
  2012.	
  Manual	
  occupations	
  are	
  
personal	
  care,  food/cleaning  service,	
  and	
  protective	
  services.	
  Routine	
  occupations	
  are	
  
operators/laborers,	
  production,  office/administrative,	
  and	
  sales.	
  Abstract	
  occupations	
  are	
  
technicians,	
  professionals,	
  and	
  managers.	
  	
  
  

27	
  

What  explains  the  slowing  growth  of  abstract  task-­‐‑intensive  employment?  One  possible  
interpretation  is  that  technological  progress  has  encroached  strongly  upward  in  the  task  
domain,  such  that  it  now  strongly  substitutes  for  the  work  done  by  professional,  technical  and  
managerial  occupations.  While  one  should  not  dismiss  this  possibility  out  of  hand,  
contemporaneous  data  on  computer  and  software  investment  militates  against  this  
interpretation.  One  would  expect  that  a  surge  of  new  automation  opportunities  in  highly  paid  
work  would  catalyze  a  surge  of  corporate  investment  in  computer  hardware  and  software.  
Instead,  the  opposite  occurred,  as  shown  in  Figure  9.  After  rising  near-­‐‑monotonically  from  
approximately  one-­‐‑half  of  one-­‐‑percent  to  almost  five  percent  of  gross  domestic  product  
between  1950  and  2000,  private  fixed  investment  in  information  processing  equipment  and  
software  dropped  by  a  full  percentage  point  (more  than  20  percent)  between  2000  and  2002,  and  
remained  depressed  thereafter.  As  of  the  first  quarter  of  2014,  information  processing  
equipment  and  software  investment  as  a  share  of  GDP  was  (only)  at  3.5  percent,  a  level  last  seen  
in  1995  at  the  outset  of  the  “dot-­‐‑com”  era.  
Given  that  both  employment  growth  in  abstract  task-­‐‑intensive  occupations  and  computer  
investment  tailed  off  simultaneously,  is  it  possible  that  one  caused  the  other?  That  is  roughly  
the  view  espoused  by  Beaudry,  Green  and  Sand  (2013).  Building  on  Welch  (1970)  and  Schultz  
(1975),  they  posit  a  model  in  which  the  introduction  of  a  new  technology  generates  demand  for  
managerial  and  problem-­‐‑solving  skills  (abstract  tasks)  during  an  adoption  period,  while  the  
technology  is  installed,  adapted,  mastered  and  routinized,  after  which  skill  demands  slacken  
since  the  challenge  of  adaptation  gives  way  to  the  more  quotidian  tasks  of  operation  and  
maintenance.  The  Beaudry,  Green  and  Sand  conceptual  model  can  therefore  rationalize  both  
decelerating  employment  in  abstract  task-­‐‑intensive  work  and  slowing  investment  in  
information  technology,  though  unfortunately  the  model  generates  few  testable  predictions  
beyond  these  aggregate  facts.    
An  alternative  interpretation  of  these  facts,  offered  by  Gordon  (2012),  is  not  that  the  computer  
revolution  has  been  fully  realized  but  rather  that  it  has  petered  out.  Gordon  argues  that  the  
gains  from  information  technology  have  been  relatively  superficial  and  short-­‐‑lived,  with  few  of  

  

28	
  

the  monumental  consequences  for  productivity  or  human  welfare  afforded  by  prior  eighteenth  
and  nineteenth  century  technological  revolutions—transportation,  power  generation,  
communications,  and  sanitation.  Thus,  the  slowdown  in  computer  investment  reflects  the  onset  
of  rapidly  diminishing  marginal  returns  to  information  technology  and  an  accompanying  
deceleration  of  productivity  growth.31    
While  I  do  not  have  a  strongly  evidenced  counter-­‐‑explanation  for  these  same  facts,  I  am  
skeptical  of  both  interpretations.  Each  would  seem  to  imply  that  IT  investment  would  plateau  
as  the  technology  either  attained  maturity  or  reached  a  point  of  diminishing  returns.  But  this  
does  not  accord  with  the  evidence  in  Figure  9.  Information  technology  investment  abruptly  
reversed  course  after  2000,  suggesting  a  rapid  pullback  in  demand.  Moreover,  in  the  five  years  
prior  to  this  pullback,  IT  investment  surged  at  an  unprecedented  rate,  rising  by  approximately  
two-­‐‑tenths  of  a  percent  of  GDP  in  each  year.  What  this  pattern  suggests  to  me  is  a  temporary  
dislocation  of  demand  for  IT  capital  during  the  latter  half  of  the  1990s  followed  by  a  sharp  
correction  after  2000—in  other  words,  the  bursting  of  a  bubble.  The  end  of  the  “tech  bubble”  in  
the  year  2000  is  of  course  widely  recognized,  as  the  NASDAQ  stock  index  erased  three-­‐‑quarters  
of  its  value  between  2000  and  2003.  Less  appreciated,  I  believe,  are  the  economic  consequences  
beyond  the  technology  sector:  a  huge  falloff  in  IT  investment,  which  may  plausibly  have  
dampened  innovative  activity  and  demand  for  high  skilled  workers  more  broadly.    
It  is  also  possible  to  read  a  more  optimistic  message  from  the  trends  in  Figures  7  and  8.  
Employment  in  highly  skilled  occupations  appears  to  have  shown  renewed  growth  after  2007,  
and  this  may  augur  renewed  investment  and  innovative  activity.  Simultaneously,  the  ongoing  
contraction  of  middle-­‐‑wage  occupations  and  rapid  expansion  of  lower-­‐‑wage  occupations  
provides  less  cause  for  comfort.    
  

                                                                                                            
  Gordon’s  thesis  does  not  address  the  labor  market  implications  of  the  anticlimactic  conclusion  of  the  
information  technology  revolution.  Thus,  there  is  no  implied  relationship  between  the  slowdown  of  IT  
investment  and  the  deceleration  of  employment  growth  in  abstract  task-­‐‑intensive  jobs.    
31

  

29	
  

Figure	
  9.	
  Private	
  Fixed	
  Investment	
  in	
  Information	
  Processing	
  Equipment	
  and	
  Software	
  as	
  a
Percentage	
  of	
  Gross	
  Domestic	
  Product,	
  1949-­‐2014	
  	
  
5.0%
4.5%
4.0%
3.5%
3.0%
2.5%
2.0%
1.5%
1.0%
0.5%
0.0%
1949

1954

1959

1964

1969

1974

1979

1984

1989

1994

1999

2004

2009

2014   

Source:	
  FRED,	
  Federal	
  Bank	
  of	
  St.	
  Louis.	
  http://research.stlouisfed.org/fred2/graph/?g=GXc	
  
(accessed	
  8/3/2014)	
  
	
  

VI. The  Future  of  Polanyi’s  Paradox  
Even  as  investment  in  information  technology  has  slowed,  popular  and  academic  discussion  of  
the  potentially  dire  consequences  of  automation  for  employment  has  accelerated.32  MIT  scholars  
Erik  Brynjolfsson  and  Andrew  McAfee  argue  in  a  2011  book  that  workers  are  in  danger  of  
losing  the  “race  against  the  machine.”  In  a  2012  working  paper,  Sachs  and  Kotlikoff  present  a  
model  in  which  “smart  machines”  yield  an  economy  of  “long-­‐‑term  misery”  because  workers  
whose  labor  is  devalued  by  automation  are  unable  to  make  the  human  capital  investments  that  
would  enable  their  children  to  profit  from  advancing  technology.  In  a  popular  vein,  journalist  

                                                                                                            
  This  paragraph  draws  on  Autor  and  Dorn  (2013b).    

32

  

30	
  

Kevin  Drum  (2013)  warns  in  Mother  Jones  that  our  “robot  overlords”  will  soon  take  our  jobs.33  
But  where  are  these  robot  overlords?  And  if  they  are  not  here  already—and  all  outward  
appearances  suggest  that  they  are  not—should  we  expect  their  imminent  arrival?  In  this  final  
section,  I  discuss  the  progress  of  computing  towards  overcoming  Polanyi’s  paradox.    
In  the  past  decade,  computerization  has  progressed  into  spheres  of  human  activity  that  were  
considered  off  limits  only  a  few  years  earlier—driving  vehicles,  parsing  legal  documents,  even  
performing  agricultural  field  labor.  Yet,  Polanyi’s  paradox  remains  relevant.  Indeed,  it  helps  to  
explain  what  has  not  yet  been  accomplished  and  to  illuminate  the  current  technological  
approaches  used  to  enlarge  the  set  of  machine-­‐‑feasible  tasks.  In  my  reading  of  the  technology  
landscape,  there  are  two  overarching  approaches  that  engineers  employ  to  computerize  tasks  
for  which  we  “do  not  know  the  rules.”  One  approach,  which  I  call  environmental  control,  bows  
to  Polanyi’s  paradox.  The  second,  machine  learning,  attempts  to  make  an  end-­‐‑run  around  it.    
A.  Environmental  Control    
Most  automated  systems  lack  flexibility—they  are  brittle.  Modern  automobile  plants,  for  
example,  employ  industrial  robots  to  install  windshields  on  new  vehicles  as  they  move  through  
the  assembly  line.  But  aftermarket  windshield  replacement  companies  employ  technicians,  not  
robots,  to  install  replacement  windshields.  Why  not  robots?  Because  removing  a  broken  
windshield,  preparing  the  windshield  frame  to  accept  a  replacement,  and  fitting  a  replacement  
into  that  frame  demand  far  more  real-­‐‑time  adaptability  than  any  contemporary  robot  can  
approach.    
The  distinction  between  assembly  line  production  and  the  in-­‐‑situ  repair  highlights  the  role  of  
environmental  control  in  enabling  automation.  While  machines  cannot  generally  operate  
autonomously  in  unpredictable  environments,  engineers  can  in  some  cases  radically  simplify  
the  environment  in  which  machines  work  to  enable  autonomous  operation.  The  factory  
assembly  line  provides  one  such  example  of  environmental  control.  But  there  are  numerous  
examples  that  are  so  ingrained  in  daily  technology  that  they  escape  notice.  Present  day  
                                                                                                            
  In  a  more  reflective  vein,  Noah  Smith  (2013)  considers  the  challenges  for  income  distribution  were  if  
advancing  in  robotics  were  to  substantially  devalue  the  stock  of  human  capital.    
33

  

31	
  

automobiles,  for  example,  are  highly  evolved  machines—efficient,  powerful,  safe  and  reliable.  
But  in  another  sense,  they  are  remarkably  helpless:  they  can  only  operate  on  smooth  paved  
surfaces,  something  that  is  almost  never  found  in  nature.  To  enable  their  operation,  humanity  
has  adapted  the  naturally  occurring  environment  by  leveling,  re-­‐‑grading  and  covering  with  
asphalt  a  non-­‐‑trivial  percentage  of  the  earth’s  land  surface.34  Environmental  control  for  
automobile  travel  has  meant  remaking  the  natural  landscape  to  machine  age  specifications.    
Executing  non-­‐‑routine  tasks  is  a  central  obstacle  in  computer-­‐‑based  automation.  Thus,  
environmental  control  in  computer  applications  often  means  eliminating  non-­‐‑routine  work  
tasks.  One  can  see  this  process  clearly  in  industrial  robotics.  Large  online  retailers,  such  as  
Amazon.com,  Zappos.com  and  Staples.com,  operate  systems  of  warehouses  that  stock,  pack  
and  ship  thousands  of  varieties  of  non-­‐‑homogenous  goods  directly  to  consumers  and  
businesses.  These  warehouses  employ  legions  of  dexterous,  athletic  “pickers,”  who  run  and  
climb  through  shelves  of  typically  non-­‐‑air  conditioned  warehouses  to  locate,  collect,  box,  label  
and  ship  goods  to  purchasers.  There  is  at  present  no  technologically  viable  or  cost-­‐‑effective  
robotic  facsimile  for  these  human  pickers.  The  job’s  steep  requirements  for  flexibility,  object  
recognition,  physical  dexterity,  and  fine  motor  coordination  are  too  formidable.    
But  large  components  of  warehousing  can  be  automated,  as  demonstrated  by  Kiva  Systems,  a  
robotic  warehousing  startup  that  was  purchased  by  Amazon  in  2012.  The  core  of  the  Kiva  
system  is  a  dispatch  program  that  oversees  the  flow  of  all  goods  through  the  warehouse  from  
stocking,  to  storage,  to  picking  and  shipping.  The  dispatch  software  directs  both  a  fleet  of  Kiva  
robots—essentially  motorized,  remotely  controlled  go-­‐‑karts—and  a  set  of  human  stockers  and  
pickers  who  work  in  tandem  with  the  robots.  The  Kiva  robots  circulate  through  a  warehouse  
that  is  filled  with  uniform  racks  of  freestanding  stocked  shelves.  The  robots’  sole  task  is  to  
transport  shelves  from  one  location  to  another,  which  they  accomplish  by  maneuvering  under  a  
rack  of  shelves,  raising  slightly  to  elevate  the  rack  from  the  floor,  motoring  to  a  new  location,  
and  then  lowering  the  rack.    
                                                                                                            
  According  to  Wikipedia,  so-­‐‑called  impervious  surfaces  (mostly  roads  and  parking  lots)  cover  43,000  
square  miles  of  land  in  the  lower  48  United  States—roughly  equal  to  the  land  area  of  Ohio  
(http://en.wikipedia.org/wiki/Impervious_surface,  accessed  8/4/2014).    
34

  

32	
  

As  objects  arrive  at  the  warehouse  for  stocking,  the  dispatch  software  directs  robots  to  transport  
empty  shelves  to  the  loading  area  where  they  line  up  for  loading.  The  software  simultaneously  
directs  human  stockers  to  place  merchandise  on  awaiting  shelves  at  precise  locations.  Once  
stocked,  shelves  are  sent  back  into  the  warehouse  on  their  robotic  carriers,  where  the  dispatch  
software  directs  their  dynamic  placement  to  optimize  product  availability  for  expected  product  
demand.  As  new  orders  arrive,  the  dispatch  software  sends  robots  to  retrieve  shelves  containing  
needed  items.  The  shelves  line  up  in  the  packing  area  where  they  await  a  human  picker  who,  
directed  by  a  laser  pointer  controlled  by  the  dispatch  software,  picks  objects  from  the  assembled  
shelves,  packs  them  in  shipping  boxes,  applies  a  shipping  label,  and  drops  the  package  in  a  
chute  for  delivery.    As  items  are  picked,  the  shelves  scurry  back  to  the  warehouse  floor  (perhaps  
dynamically  relocated)  until  needed  again  for  packing  or  restocking.  
Human  flexibility  is  still  required  in  the  Kiva-­‐‑operated  warehouse:  only  workers  handle  
merchandise;  robots  only  move  shelves.  But  the  demand  for  human  dexterity  is  dramatically  
reduced  by  automation:  all  non-­‐‑routine  motor  tasks  are  performed  during  stocking  and  
packing;  all  other  goods  movement,  organization,  storage  and  retrieval  is  delegated  to  robots,  
whose  sole  task  is  to  shuttle  shelves  across  a  level  surface  (a  routine  task).  Thus,  Kiva  applies  
environmental  control  to  minimize  the  need  for  human  flexibility.      
While  Kiva  Systems  provides  a  particularly  clear  example,  the  same  principle  of  environmental  
control  is  often  operative  in  unexpected  places.  Perhaps  the  least  recognized—and  most  
mythologized—is  the  Google  Car.  It  is  sometimes  said  by  computer  scientists  that  the  Google  
car  does  not  drive  on  roads  but  rather  on  maps.  This  observation  conveys  the  fact  that  the  
Google  car,  unlike  a  human  vehicle  operator,  cannot  pilot  on  an  “unfamiliar”  road;  it  lacks  the  
capability  to  process,  interpret  and  respond  to  an  environment  that  has  not  been  pre-­‐‑processed  
by  its  human  engineers.  Instead,  the  Google  car  navigates  through  the  road  network  primarily  
by  comparing  its  real-­‐‑time  audio-­‐‑visual  sensor  data  (collected  using  LIDAR)  against  
painstakingly  hand-­‐‑curated  maps  that  specify  the  exact  locations  of  all  roads,  signals,  signage,  
obstacles,  etc.  The  Google  car  adapts  in  real  time  to  obstacles  (cars,  pedestrians,  road  hazards)  
by  braking,  turning  and  stopping.  But  if  the  car’s  software  determines  that  the  environment  in  

  

33	
  

which  it  is  operating  differs  from  the  key  static  features  of  its  pre-­‐‑specified  map  (e.g.,  an  
unexpected  detour,  a  police  officer  directing  traffic  where  a  traffic  signal  is  supposed  to  be),  
then  the  car  signals  for  its  human  operator  to  take  command.  Thus,  while  the  Google  car  
appears  outwardly  to  be  as  adaptive  and  flexible  as  a  human  driver,  it  is  in  reality  more  akin  to  
a  train  running  on  invisible  tracks.    
These  examples  highlight  some  of  the  limitations  of  current  technology  to  accomplish  non-­‐‑
routine  tasks.  They  also  illustrate  the  genius  of  human  ingenuity  in  surmounting  these  
obstacles.  Humans  naturally  tackle  tasks  in  a  manner  that  draws  on  their  inherent  flexibility,  
problem  solving  capability,  and  judgment.  Machines  currently  lack  many  of  these  capabilities,  
but  they  possess  other  facilities  in  abundance:  strength,  speed,  accuracy,  low  cost,  and  
unwavering  fealty  to  directions.  Engineering  machines  to  accomplish  human  tasks  does  not  
necessarily  entail  equipping  machines  with  human  capabilities;  instead,  work  tasks  can,  in  some  
cases,  be  reengineered  so  that  the  need  for  specifically  human  capabilities  is  minimized  or  
eliminated.  
B.  Machine  Learning  
There  is  an  alternative  route,  however.  Polanyi’s  paradox—“we  know  more  than  we  can  tell”—
presents  a  challenge  for  computerization  because  conventional  programming  amounts  to  
“telling”  a  computer  precisely  how  to  accomplish  a  task.  If  people  tacitly  understand  how  to  
perform  a  task  but  cannot  “tell”  a  computer  how  to  perform  the  task,  then  seemingly  
programmers  cannot  automate  the  task—or  so  the  thinking  has  gone  historically.  But  this  
understanding  is  shifting  due  to  advances  in  machine  learning.  The  simple  idea  of  machine  
learning  is  to  applying  statistics  and  inductive  reasoning  to  supply  best  guess  answers  in  cases  
where  formal  procedural  rules  are  unknown.  Where  engineers  are  unable  to  program  a  machine  
to  “simulate”  a  non-­‐‑routine  task  by  following  a  scripted  procedure,  they  may  nevertheless  be  
able  to  program  a  machine  to  master  the  task  autonomously  by  studying  successful  examples  of  
the  task  being  carried  out  by  others.  Thus,  through  a  process  of  exposure,  training,  and  
reinforcement,  machine  learning  algorithms  may  potentially  infer  how  to  accomplish  tasks  that  
have  proved  dauntingly  challenging  to  codify  with  explicit  procedures.    

  

34	
  

As  one  concrete  example  of  machine  learning,  consider  the  challenge  of  task  of  visually  
identifying  a  chair.35  Applying  the  conventional  rules-­‐‑based  programming  paradigm,  an  
engineer  might  attempt  to  specify  ex  ante  what  features  of  an  object  qualify  it  as  a  chair—it  
possesses  legs,  arms,  a  seat,  and  a  back,  for  example.  One  could  then  program  a  machine  to  
identify  objects  possessing  these  features  as  chairs.  But  having  specified  such  a  feature  set,  one  
would  immediately  discover  that  many  chairs  that  do  not  possess  all  features  (e.g.,  no  back,  no  
legs).  If  one  then  relaxed  the  required  feature  set  accordingly  (e.g.,  chair  back  optional),  the  
included  set  would  clearly  encompass  many  objects  that  are  not  chairs  (e.g.,  tables).  Thus,  the  
canonical  routine  task  approach  to  object  recognition—and  many  more  sophisticated  variants—
would  likely  have  very  high  misclassification  rates.  Yet,  any  grade-­‐‑school  child  could  perform  
this  task  with  very  high  accuracy.  What  does  the  child  know  that  the  rules-­‐‑based  procedure  
does  not?  Unfortunately,  we  do  not  know—this  is  precisely  Polanyi’s  paradox.    
Machine  learning  potentially  circumvents  this  problem.  Relying  on  large  databases  of  so-­‐‑called  
ground  truth—concretely,  a  vast  set  of  curated  examples  of  labeled  objects—a  machine  learning  
algorithm  can  attempt  to  statistically  infer  what  attributes  of  an  object  make  it  more  or  less  
likely  to  be  designated  a  chair.  This  process  is  called  training.  Once  training  is  complete,  the  
machine  can  then  apply  this  statistical  model  out  of  sample  to  potentially  identify  chairs  that  are  
distinct  from  those  in  the  original  dataset.  If  the  statistical  model  is  sufficiently  good,  it  may  be  
able  to  recognize  chairs  that  are  somewhat  distinct  from  those  in  the  original  training  data  (e.g.,  
different  shapes,  materials,  or  dimensions).  What  makes  the  idea  of  machine  learning  powerful  
is  that  it  does  not  require  an  explicit  physical  model  of  “chairness.”  At  its  core,  machine  
learning  is  an  atheoretical  brute  force  technique—what  psychologists  call  “dustbowl  
empiricism”—requiring  only  large  training  databases,  substantial  processing  power,  and,  of  
course,  sophisticated  software.36    
How  well  does  machine  learning  work  in  practice?  If  you  use  Google  Translate,  operate  a  
smartphone  with  voice  commands,  or  follow  Netflix’  movie  suggestions,  you  can  assess  for  
                                                                                                            
  This  example  draws  on  the  discussion  in  Autor  (2014a).    

35

  Levy  and  Murnane  (2004)  provide  numerous  illustrative  examples  of  the  automation  of  job  tasks.  For  
introductory  material  on  machine  learning  written  by  and  for  economists,  see  Varian  (2014).  
36

  

35	
  

yourself  how  successfully  these  technologies  function.37  My  general  observation  is  that  the  tools  
are  inconsistent:  uncannily  accurate  at  times;  typically,  only  so-­‐‑so;  and  occasionally,  
unfathomable.38  IBM’s  Watson  computer  famously  triumphed  in  the  trivia  game  of  Jeopardy  
against  champion  human  opponents.  Yet  Watson  also  produced  a  spectacularly  incorrect  
answer  during  the  course  of  its  winning  match.  Under  the  category  of  U.S.  Cities,  the  question  
was,  “Its  largest  airport  was  named  for  a  World  War  II  hero;  its  second  largest,  for  a  World  War  
II  battle.”  Watson’s  proposed  answer  was  Toronto,  a  city  in  Canada.  Even  exemplary  
accomplishments  in  this  domain  can  appear  somewhat  underwhelming.  A  2012  New  York  Times  
article  (Markoff,  2012)  described  Google’s  X  Lab’s  recent  project  (Le  et  al.,  2012)  to  apply  a  
neural  network  of  16,000  processors  to  identify  images  of  cats  on  YouTube  (see  Figure  10  for  
examples).  The  article’s  headline  ruefully  poses  the  question,  “How  Many  Computers  to  
Identify  a  Cat?  16,000.”39    
Since  the  underlying  technologies—the  software,  hardware,  and  training  data—are  all  
improving  rapidly  (Andreopouos  and  Tsotsos,  2013),  one  should  view  these  examples  as  
prototypes  rather  than  as  mature  products.  Still,  the  long-­‐‑term  potential  of  machine  learning  for  
circumventing  Polanyi’s  paradox  is  a  subject  of  active  debate  among  computer  scientists.  Some  
researchers  expect  that  as  computing  power  rises  and  training  databases  grow,  the  brute  force  
machine  learning  approach  will  approach  or  exceed  human  capabilities.  Others  suspect  that  
machine  learning  will  only  ever  “get  it  right”  on  average  while  missing  many  of  the  most  
important  and  informative  exceptions.  
  
                                                                                                            
  By  logging  and  analyzing  the  clicks  of  users  in  response  to  earlier  queries,  search  engines  also  use  
machine  learning  to  dynamically  refine  search  results  offered  for  subsequent  queries.  For  example,  if  the  
majority  of  users  who  recently  searched  for  the  terms  “degrees  bacon”  clicked  on  links  for  Kevin  Bacon  
rather  than  links  for  best  bacon  cooking  temperatures,  the  search  engine  would  tend  to  place  the  Kevin  
Bacon  links  higher  in  the  list  of  results.    
37

38  A  lovely  irony  of  machine  learning  algorithms  is  that  they  also  cannot  “tell”  programmers  why  they  do  
what  they  do.  The  “decisions”  that  a  machine  learning  program  makes  following  training  are  something  
of  a  black  box.    

  As  further  evidence  of  the  inchoate  stage  of  machine  learning  (at  least  as  of  2012),  notice  in  Figure  10  
that  the  image  third  down  from  the  top  and  third  over  from  the  left  is  definitely  not  a  cat  and  appears  
more  likely  to  be  a  pair  of  coffee  cups.    
39

  

36	
  

Figure	
  10.	
  Images	
  Identified	
  as	
  Cats	
  by	
  Google	
  X	
  Labs	
  Team	
  
Using	
  a	
  Neural	
  Network	
  of	
  16,000	
  Processors	
  

  
Source:	
  British	
  Broadcasting	
  Company	
  (June	
  26,	
  2012,	
  http://www.bbc.com/news/technology-­‐
18595351,	
  accessed	
  8/4/2014).	
  
To  give  this  skepticism  heft,  return  to  the  challenge  of  training  a  machine  to  recognize  a  chair.  
Ultimately,  what  makes  an  object  a  chair  is  that  it  is  a  device  purpose-­‐‑built  for  a  human  being  to  
sit  upon.  This  “purposiveness”  may  be  difficult  for  a  machine  learning  algorithm  to  infer,  even  
given  an  arbitrarily  large  training  database  of  images.  As  Grabner  et  al.  (2011)  argue,  it  is  likely  
that  humans  recognize  chairs  not  simply  by  comparing  candidate  objects  to  statistically  
probable  feature  sets  but  also  by  reasoning  about  the  attributes  of  the  object  to  assess  whether  it  
is  likely  intended  to  serve  as  a  chair.  For  example,  both  a  toilet  and  a  traffic  cone  look  somewhat  
like  a  chair,  but  a  bit  of  reasoning  about  their  shapes  vis-­‐‑à-­‐‑vis  the  human  anatomy  suggests  that  
a  traffic  cone  is  unlikely  to  make  a  comfortable  seat.  Drawing  this  inference,  however,  requires  
reasoning  about  what  an  object  is  “for”  not  simply  what  it  looks  like.  Contemporary  object  
recognition  programs  do  not,  for  the  most  part,  take  this  reasoning-­‐‑based  approach  to  
identifying  objects,  likely  because  the  task  of  developing  and  generalizing  the  approach  to  a  

  

37	
  

large  set  of  objects  would  be  extremely  challenging.40    One  is  reminded  of  Carl  Sagan’s  remark  
that,  “If  you  wish  to  make  an  apple  pie  from  scratch,  you  must  first  invent  the  universe.”  

VII.

Conclusions  

A  principle  conclusion  from  the  discussion  above  is  that  the  challenges  to  computerizing  
numerous  everyday  tasks—from  the  sublime  to  the  mundane—remain  substantial.  Let  us  
assume,  however,  that  a  set  of  near-­‐‑term  breakthroughs  enables  rapid  technological  progress  in  
non-­‐‑routine  manual  and  abstract  domains.  What  does  this  augur  for  labor  demand?    
As  chronicled  in  section  II,  there  is  a  long  history  of  leading  thinkers  overestimating  the  
potential  of  new  technologies  to  substitute  for  human  labor  and  underestimating  their  potential  
to  complement  it.  The  green  revolution  displaced  labor  from  farming.  The  industrial  revolution  
replaced  skilled  artisanal  labor  with  unskilled  factory  labor.  The  mass-­‐‑produced  automobile  
drastically  reduced  demand  for  blacksmiths,  stable  hands,  and  many  other  equestrian  
occupations.  Successive  waves  of  earth  moving  equipment  and  powered  tools  displaced  
manual  labor  from  construction.  In  each  case,  groups  of  workers  lost  employment  and  earnings  
as  specific  jobs  and  accompanying  skill  sets  were  rendered  obsolete.    
Yet,  short-­‐‑term  employment  losses  sparked  by  rising  productivity  were  eventually  more  than  
offset  by  subsequent  employment  gains—in  some  cases  in  the  innovating  sectors,  in  many  cases  
elsewhere.  In  1900,  for  example,  41  percent  of  the  United  States  work  force  was  employed  in  
agriculture.  By  2000,  that  share  had  fallen  to  2  percent,  in  large  part  due  to  productivity  gains  
emanating  from  the  Green  Revolution    (Autor,  2014b).  It  is  unlikely,  however,  that  farmers  at  
the  turn  of  the  twentieth  century  could  foresee  that  one  hundred  years  later,  health  care,  
finance,  information  technology,  consumer  electronics,  hospitality,  leisure  and  entertainment  
would  employ  far  more  workers  than  agriculture.    

                                                                                                            
  Could,  for  example,  a  machine  that  recognizes  chairs  by  reasoning  about  their  potential  compatibility  
with  human  anatomy  also  be  readily  reprogrammed  to  recognize  bicycles—or  would  it  require  another  
set  of  reasoning  capabilities  to  determine  whether  the  object  could  support  a  human  being  in  the  act  of  
balancing  while  in  motion?  
40

  

38	
  

Arguably,  we  stand  at  a  similar  moment  today.  One  can  find  fresh  examples  daily  in  which  
technology  substitutes  for  human  labor  in  an  expanding—though  still  circumscribed—set  of  
tasks.  The  complementarities  are  always  harder  to  identify.  Despite  these  uncertainties,  there  
are  three  inferences  in  which  we  can  be  fairly  confident:  
A  first  is  that  the  technological  advances  that  have  secularly  pushed  outward  the  demand  for  
skilled  labor  over  many  decades  will  continue  to  do  so.  As  physical  labor  has  given  way  to  
cognitive  labor,  the  labor  market’s  demand  for  formal  analytical  skills,  written  communications,  
and  specific  technical  knowledge  has  risen  spectacularly.  If  the  nineteenth  U.S.  labor  force  were  
suddenly  restored  in  the  twentieth  century,  a  large  fraction  of  workers  would  be  surely  
unemployable  due  to  their  exceedingly  low  levels  of  education—averaging  approximately  nine  
years  of  completed  schooling  (Katz  and  Goldin,  2008).  While  some  have  speculated  that  the  
advent  of  labor  market  polarization—particularly  the  growth  of  low-­‐‑education,  manual  task-­‐‑
intensive  jobs—indicates  that  the  complementarity  between  higher  education  and  technological  
change  has  come  to  an  end,  this  reasoning  is  incorrect.  Though  computerization  may  increase  
the  fraction  of  jobs  found  in  manual  task-­‐‑intensive  work,  it  is  generally  unlikely  to  rapidly  boost  
earnings  in  these  occupations  for  the  reasons  discussed  above:  an  absence  of  strong  
complementarities  and  an  abundance  of  potential  labor  supply.  Thus,  human  capital  investment  
must  be  at  the  heart  of  any  long-­‐‑term  strategy  for  producing  skills  that  are  complemented  
rather  than  substituted  by  technology.    
A  second  observation  is  that  employment  polarization  will  not  continue  indefinitely.41  While  
many  middle  skill  tasks  are  susceptible  to  automation,  many  middle  skill  jobs  demand  a  mixture  
of  tasks  from  across  the  skill  spectrum.  To  take  one  prominent  example,  medical  support  
occupations—radiology  technicians,  phlebotomists,  nurse  technicians,  etc.—are  a  numerically  
significant  and  rapidly  growing  category  of  relatively  well-­‐‑remunerated,  middle  skill  
employment.  While  not  all  of  these  occupations  require  a  college  degree,  they  do  at  least  
demand  two  years  of  post-­‐‑secondary  vocational  training.  Significantly,  mastery  of  “middle  

                                                                                                            
  This  discussion  draws  on  Autor  (2013),  with  some  passages  quoted  directly.    

41

  

39	
  

skill”  mathematics,  life  sciences,  and  analytical  reasoning  is  indispensable  for  success  in  this  
training.    
Why  are  these  middle  skill  jobs  likely  to  persist  and,  potentially,  to  grow?  My  conjecture  is  that  
many  of  the  tasks  currently  bundled  into  these  jobs  cannot  readily  be  unbundled-­‐‑-­‐‑-­‐‑with  
machines  performing  the  middle  skill  tasks  and  workers  performing  the  residual-­‐‑-­‐‑-­‐‑without  a  
substantial  drop  in  quality.  Consider,  for  example,  the  commonplace  frustration  of  calling  a  
software  firm  for  technical  support  only  to  discover  that  the  support  technician  knows  nothing  
more  than  what  is  on  his  or  her  computer  screen—that  is,  the  technician  is  a  mouthpiece,  not  a  
problem  solver.  This  example  captures  one  feasible  division  of  labor:  machines  performing  
routine  technical  tasks,  such  as  looking  up  known  issues  in  a  support  database,  and  workers  
performing  the  manual  task  of  making  polite  conversation  while  reading  aloud  from  a  script.  
But  this  is  not  generally  a  productive  form  of  work  organization  because  it  fails  to  harness  the  
complementarities  between  technical  and  interpersonal  skills.  Stated  in  positive  terms,  routine  
and  non-­‐‑routine  tasks  will  generally  coexist  within  an  occupation  to  the  degree  that  they  are  
complements-­‐‑-­‐‑-­‐‑that  is,  the  quality  of  the  service  improves  when  the  worker  combines  technical  
expertise  and  human  flexibility.42    
This  reasoning  suggests  that  many  of  the  middle  skill  jobs  that  persist  in  the  future  will  combine  
routine  technical  tasks  with  the  set  of  non-­‐‑routine  tasks  in  which  workers  hold  comparative  
advantage-­‐‑-­‐‑-­‐‑interpersonal  interaction,  flexibility,  adaptability  and  problem-­‐‑solving.43  Medical  
support  occupations  are  one  leading  example  of  this  virtuous  combination,  but  this  example  is  
not  a  singularity.  This  broad  description  also  fits  numerous  skilled  trade  and  repair  
occupations—plumbers,  builders,  electricians,  HVAC  installers,  automotive  technicians—
marketing  occupations,  and  even  modern  clerical  occupations  that  provide  coordination  and  
decision-­‐‑making  functions  rather  than  simply  typing  and  filing.  Indeed,  even  as  some  formerly  
middle  skill  occupations  are  stripped  of  their  routine  technical  tasks  and  arguably  deskilled—
                                                                                                            
  Lawrence  Katz  memorably  titles  workers  who  virtuously  combine  technical  and  interpersonal  tasks  as  
“the  new  artisans”  (see  Friedman,  2010).  
42

  In  general,  these  same  demands  for  interaction  frequently  privilege  face-­‐‑to-­‐‑face  interactions  over  
remote  performance,  meaning  that  these  same  middle  skill  occupations  may  have  relatively  low  
susceptibility  to  offshoring.  
43

  

40	
  

for  example  the  stockbroking  occupation—other  formerly  high-­‐‑end  technical  occupations  are  
made  accessible  to  workers  with  less  esoteric  technical  mastery,  for  example,  the  nurse  
practitioner  occupation  that  increasingly  performs  diagnosing  and  prescribing  tasks  in  lieu  of  
physicians.  I  expect  that  a  significant  stratum  of  middle  skill,  non-­‐‑college  jobs  combining  
specific  vocational  skills  with  foundational  middle  skills—literacy,  numeracy,  adaptability,  
problem-­‐‑solving  and  common  sense—will  persist  in  coming  decades.  
A  final  observation  is  that  while  much  contemporary  economic  pessimism  attributes  the  labor  
market  woes  of  the  past  decade  to  the  adverse  impacts  of  computerization,  I  remain  skeptical  of  
this  inference.  Clearly,  computerization  has  shaped  the  structure  of  occupational  change  and  
the  evolution  of  skill  demands.  But  it  is  harder  to  see  the  channel  through  which  
computerization  could  have  dramatically  reduced  labor  demand  after  1999.  As  documented  in  
Figure  9,  the  onset  of  the  weak  U.S.  labor  market  of  the  2000s  coincided  with  a  sharp  deceleration  
in  computer  investment—a  fact  that  appears  first-­‐‑order  inconsistent  with  the  onset  of  a  new  era  
of  capital-­‐‑labor  substitution.  Moreover,  the  U.S.  labor  market  woes  of  the  last  decade  occurred  
alongside  extremely  rapid  economic  growth  in  much  of  the  developing  world.  Indeed,  
frequently  overlooked  in  U.S.-­‐‑centric  discussions  of  world  economic  trends  is  that  the  2000s  was  
a  decade  of  rising  world  prosperity  and  falling  world  inequality.  It  seems  implausible  to  me  
that  technological  change  could  be  enriching  most  of  the  world  while  simultaneously  
immiserating  the  world’s  technologically  leading  nation.    
My  suspicion  is  that  the  deceleration  of  the  U.S.  labor  market  after  2000,  and  further  after  2007,  
is  more  closely  associated  with  two  other  macroeconomic  events.  A  first  is  the  bursting  of  the  
“dot-­‐‑com”  bubble,  followed  by  the  collapse  of  the  housing  market  and  the  ensuing  financial  
crisis,  both  of  which  curtailed  investment  and  innovative  activity.  A  second  is  the  employment  
dislocations  in  the  U.S.  labor  market  brought  about  by  rapid  globalization,  particularly  the  
sharp  rise  of  import  penetration  from  China  following  its  accession  to  the  World  Trade  
Organization  in  2001.  As  documented  by  Autor,  Dorn  and  Hanson  (2013),  Pierce  and  Schott  
(2013)  and  Acemoglu  et  al.  (2014),  China’s  rapid  rise  to  a  premier  manufacturing  exporter  had  
far-­‐‑reaching  impacts  on  U.S.  workers,  reducing  employment  in  directly  import-­‐‑competing  U.S.  

  

41	
  

manufacturing  industries  and  depressing  labor  demand  in  both  manufacturing  and  non-­‐‑
manufacturing  sectors  that  that  served  as  upstream  suppliers  to  these  industries.44  
Globalization,  like  technological  change,  is  not  typically  Pareto  improving,  particularly  in  the  
short  run.  While  the  long-­‐‑run  effects  of  these  developments  should  in  theory  be  positive,  the  
adjustment  process,  as  with  technological  adaptation,  is  frequently  slow,  costly,  and  disruptive.  
  

  

                                                                                                            
  Borjas  and  Ramey  (1995)  present  evidence  from  the  1950s  through  1980s  that  rising  foreign  competition  
in  durable  good  industries  increased  U.S.  wage  inequality  by  eroding  rents  accruing  to  non-­‐‑college  
workers.    
44

  

42	
  

References  
Acemoglu,  Daron,  and  David  Autor.  2011.  “Skills,  Tasks  and  Technologies:  Implications  for  
Employment  and  Earnings.”  In  Handbook  of  Labor  Economics,  Vol.  4,  Part  B,  edited  by  Orley  
Ashenfelter  and  David  Card:  1043–1171.  Amsterdam:  Elsev

[The evaluation harness truncated this reference: showing the first 120000 of 129837 characters.]
</reference>

<statements>
1. Autor grounded the persistence of human employment in Polanyi's paradox—the observation that humans "know more than we can tell."
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.