Happiness  and  productivity:  Understanding  the  happy-­productive  worker  Global  Perspectives  Series:  Paper  4  DANIEL  SGROI  October  2015    SUMMARY  POINTS  o   “Happiness”  is  now  an  accepted  and  important  policy  objective  for  governments  alongside  big  aggregate  targets  such  as  economic  growth  or  unemployment.  However,  there  is  surprisingly  little   work   on   the   importance   of   happiness   as   an   input   to   economic   processes   or   measures  such   as   productivity;;   usually   it   is   considered   only   as   an   output   or   consequence   of   higher  growth  or  income  and  not  just  an  output.  
o   This  paper  draws  on  experimental  work  linking  happiness  to  productivity,  by  Andrew  Oswald,  Eugenio   Proto   and   Daniel   Sgroi   undertaken   at   the   University   of   Warwick   involving   over   700  participants.  
o   In  three  different  styles  of  experiment,  randomly  selected  individuals  are  made  happier  either  through   the   use   of   a   short   (10   minute)   comedy   clip   or   through   the   provision   of   drinks   and  snacks.  We  check  that  these  methods  make  the  subjects  happier  (they  do)  and  then  go  on  to  show  that  these  individuals  have  approximately  12%  greater  productivity  than  a  control  group.      o   A  fourth  experiment  studies  major  real-­world  shocks  (bereavement  and  family  illness)  and  the  impact   this   has   on   current   productivity.   Lower   happiness   is   systematically   associated   with  lower   productivity.   This   effect   lasts   for   approximately   2   years.   These   different   forms   of  evidence,  with  complementary  strengths  and  weaknesses,  are  consistent  with  the  existence  of  a  causal  link  between  human  well-­being  and  human  performance.  
o   The   productivity   measure   we   employ   allows   us   to   differentiate   between   productivity   that  comes  through  effort  and  ability.  We  find  that  the  main  route  from  happiness  to  productivity  is  through  increased  effort  by  workers.  
o   Private  sector  firms  cannot  ignore  potential  productivity  gains  in  the  current  economic  climate,  and  this  might  help  public  sector  departments  to  offset  the  negative  impact  of  spending  cuts.  
Our  findings  help  to  illuminate  what  sort  of  policies  might  work  and  which  sort  of  workers  will  benefit  most.  Having  scientific  support  for  generating  happiness-­productivity  cycles  within  the  workforce   should   also   help   managers   to   justify   work-­practices   aimed   at   boosting   happiness  on  productivity  grounds.  

  HAPPINESS  AND  PRODUCTIVITY    ABOUT  THE  SERIES  This  is  the  fourth  briefing  paper  in  the  Global  Perspectives  series  -­  a  new  collaboration  between  the  Social   Market   Foundation   (SMF)   and   Warwick’s   Centre   on   Competitive   Advantage   in   the   Global  Economy  (CAGE).    

ABOUT  THE  AUTHOR  Daniel  Sgroi  is  an  Associate  Professor  in  the  Department  of  Economics  at  Warwick  University.  He  is  a  theme  leader  of  Warwick’s  ESRC  Centre  on  Competitive  Advantage  in  the  Global  Economy  (CAGE).  
He   obtained   his   DPhil   at   Nuffield   College,   University   of   Oxford,   and   worked   at   the   University   of  Cambridge   prior   to   joining   Warwick.   Most   recently,   he   has   published   on   a   variety   of   applied  economics  topics  including  the  economics  of  wellbeing,  the  optimal  use  of  information  and  herding  in  financial   markets   in   numerous   journals   including   the   Economic   Journal,   the   Journal   of   Labor  Economics,   the   European   Economic   Review,   Games   and   Economic   Behavior,   Experimental  Economics,  the  Journal  of  Economic  Behavior  and  Organzation,  and  the  Journal  of  Economic  Theory.  

ABOUT  CAGE  Established   in   January   2010,   CAGE   is   a   research   centre   in   the   Department   of   Economics   at   the  University   of   Warwick.   CAGE   is   carrying   out   a   10   year   programme   of   innovative   research.   The  centre’s   research   programme   is   focused   on   how   countries   succeed   in   achieving   key   economic  objectives   such   as   improving   living   standards,   raising   productivity,   and   maintaining   international  competitiveness,  which  are  central  to  the  economic  wellbeing  of  their  citizens.  Our  research  analyses  the   reasons   for   economic   outcomes   both   in   developed   economies   like   the   UK   and   emerging  economies   such   as   China   and   India.   We   aim   to   develop   a   better   understanding   of   how   to   promote  institutions  and  policies  which  are  conducive  to  successful  economic  performance  and  endeavour  to  draw  lessons  for  policy  makers  from  economic  history  as  well  as  the  contemporary  world.  Research  at  CAGE   examines   how   and   why   different   countries   achieve   economic   success.   CAGE   defines  ‘success’   in   terms   of   well-­being   as   well   as   productivity.   The   research   uses   economic   analysis   to  address   real-­world   policy   issues.   The   centre   is   distinctive   in   providing   a   perspective   that   draws   on  economic  history  as  well  as  economic  theory  and  is  applied  to  countries  at  various  different  stages  of  economic  development.  www.warwick.ac.uk/cage  ABOUT  THE  SOCIAL  MARKET  FOUNDATION  The  Social  Market  Foundation  is  a  leading  cross-­party  think  tank,  developing  innovative  ideas  across  a   range   of   economic   and   social   policy.   The   SMF’s   current   research   themes   are:   Productivity   and  Growth;;  Cost  of  Living;;  and,  Public  Service  Reform.  We  champion  policy  ideas  which  marry  markets  with  social  justice  and  take  a  pro-­market  rather  than  free-­market  approach.  Our  work  is  characterised  by  the  belief  that  governments  have  an  important  role  to  play  in  correcting  market  failures  and  that  a  sustainable  market  economy  rests  on  social  and  political  foundations  that  are  widely  regarded  as  fair.  
www.smf.co.uk  
  
  
  

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SOCIAL  MARKET  FOUNDATION      Happiness  and  productivity:  Understanding  the  happy-­productive  worker    INTRODUCTION    There   is   now   no   doubt   that   “happiness”   as   a   policy   objective   is   here   to   stay.   This   should   not   be  surprising.  Happiness  is  after  all  something  that  everyone  wants  –  almost  by  definition.  Consider  how  central  the  concept  of  “happiness”  is  to  the  United  States  Declaration  of  Independence  and  countless  tracts  by  moral  philosophers  and  political  scientists  from  Plato,  Aristotle,  and  Confucius  onwards.  
  To   date   the   focus   has   been   largely   at   the   aggregate   level.   Objectives   such   as   building   subjective  wellbeing  measures  to  run  in  parallel  with  GDP,  or  adding  happiness  or  life  satisfaction  measures  to  national   surveys   have   all   been   about   positioning   happiness   alongside   the   likes   of   GDP,  unemployment,   inflation   or   economic   growth   as   key   government   objectives   (most   notably   in   2011  when   the   UN   released   a   World   Happiness   Report   and   the   OECD   launched   a   “Better   Life   Index”).  
However,  just  as  we  have  worried  in  the  past  about  the  links  between  national  income  or  economic  growth  and  national  level  happiness  measures,  so  too  should  we  be  interested  in  the  links  between  individual-­level  income  or  productivity  and  happiness.  The  reason  why  is  simple:  at  the  individual  level  it   may   be   possible   to   establish   the   direction   of   causation:   while   much   of   the   debate   so   far   has  focussed  on  whether  income  or  economic  growth  at  the  aggregate  level  generate  happiness,  we  turn  instead   to   look   at   the   converse   question:   can   happiness   at   the   individual-­level   generate   productivity  improvements?  This  briefing  paper  reports  on  an  attempt  to  address  this  question  (Oswald,  Proto  and  Sgroi,  2015)  by  running  a  series  of  laboratory  experiments  using  subjects  in  controlled  environments.  
  To  put  all  of  this  into  context,  in  the  post-­2008  climate  of  spending  cuts  in  the  public  sector,  intense  competition  in  the  private  sector  and  concern  about  future  sources  of  economic  growth  and  profit,  an  opportunity  to  boost  worker-­productivity  at  relatively  low  cost  would  seem  hard  to  dismiss.  The  hope  is  that   this   report   will   provide   an   impetus   for   governments   and   private   sector   alike   to   think   about   what  policies  might  work  and  which  types  of  worker  to  target.  We  will  of  course  have  things  to  say  on  both  of  these  issues  and  do  so  in  the  final  sections  of  the  report.  
  WHY  THE  LAB?  
A  key  advantage  of  examining  behaviour  in  a  laboratory  is  control.  In  the  real  world  a  great  deal  can  happen  and  does  happen  which  makes  it  hard  to  establish  causation  or  even  a  clear  relationship.  So  many   factors   may   come   into   play   that   are   hard   to   perceive   or   measure.   For   example   is   happiness  boosting   productivity,   or   is   productivity   boosting   happiness?   What   if   a   third   factor   (say   salary   or   the  work  environment)  is  boosting  both?  
In   the   laboratory   these   confounding   factors   disappear   since   we   can   take   two   randomly   selected  groups  of  subjects  and  make  one  change  to  their  situation:  any  resulting  change  in  behaviour  is  then  highly  likely  to  be  due  to  that  one  change.  In  our  experiments  we  “treated”  one  group  to  a  “happiness  shock”  (commonly  called  a  “mood-­induction  procedure”  in  Psychology)  but  left  a  second  control  group  “untreated”,  so  when  we  look  at  differences  in  productivity  across  the  two  groups  it  makes  sense  that  the  “happiness  shock”  was  causal.  Moreover,  we  can  check  that  it  had  the  effect  we  wanted,  and  also  2  HAPPINESS  AND  PRODUCTIVITY  examine  other  groups  with  placebos  or  alternative  shocks.  All  of  this  control  is  a  unique  feature  of  a  laboratory  experiment  which  makes  it  an  ideal  way  to  establish  relationships  and  causation.  
With  this  in  mind  we  ran  four  different  types  of  experiment:  o   Experiment   I:   the   focus   was   on   delivering   short-­run   “happiness   shocks”,   asking   a   happiness  question  to  check  the  “shock”  worked,  and  then  posing  a  productivity  task;;  o   Experiment  II:  which  was  similar  to  Experiment  I  but  also  asked  happiness  questions  throughout  the  lab  experiment  to  try  to  understand  how  happiness  changes  over  time;;  o   Experiment  III:  identical  to  Experiment  I  but  using  a  different  form  of  short-­run  happiness  shock;;  and  o   Experiment   IV:   the   focus   was   on   identifying   severe   (un)happiness   shocks   from   the   real-­world  and  measuring  what  impact  these  have  on  carrying  out  a  productivity  task.  
Following  convention,  we  randomly  assigned  our  subjects  to  different  groups;;  no  subject  took  part  in  more  than  a  single  experiment;;  individuals  were  told  that  the  tasks  would  be  completed  anonymously;;  they   were   asked   to   refrain   from   communication   with   each   other;;   and   they   were   told   not   to   use  electronic   devices   for   assistance.   Subjects   (Warwick   University   students)   were   told   in   advance   that  there  would  be  a  show-­up  fee  (of  £5)  and  the  likely  range  of  bonus  (performance-­related)  payments  (typically  up  to  a  further  £20  for  the  hour’s  work).    Following  the  economist’s  tradition,  a  reason  to  pay  subjects  more  for  correct  answers  was  to  emphasize  they  would  benefit  from  higher  performance.  We  wished  to  avoid  the  idea  that  they  might  be  paying  back  effort  -­  as  in  a  kind  of  ‘reciprocity’  effect  -­  to  investigators.      One  possible  criticism  of  our  approach  is  a  lack  of  realism.  This  is  why  it  often  makes  sense  to  follow  a  laboratory  experiment  with  a  similar  experiment  “in  the  field”  –  for  example,  in  a  real-­world  company.  
While  this  will  result  in  a  loss  of  the  sort  of  control  we  have  in  the  lab,  combining  the  results  from  the  lab  and  the  real  world  will  help  to  give  a  complete  picture.  We  discuss  this  more  in  the  section  entitled  “Next  Steps”.  
Before  getting  to  that  though,  or  describing  our  results,  the  prior  issue  we  need  to  address  is  what  sort  of   happiness   shocks   can   we   use   and   how   we   can   measure   productivity.   We   will   describe   our  approach  to  these  issues  next.    

    MAKING  PEOPLE  HAPPY  It   is   easy   enough   to   ask   how   happy   people   may   be   and   this   is   now   common   across   a   number   of  national-­level  surveys.  A  standard  question  (which  we  use  in  our  experiments)  is  as  follows:  How  would  you  rate  your  happiness  at  the  moment?  Please  use  a  7-­point  scale  where  1  is  completely  sad,   2   is   very   sad,   3   is   sad,   4   is   neither   happy   nor   sad,   5   is   fairly   happy,   6   is   very   happy   and   7   is  completely  happy.  
However,  what  if  everyone  answers  with  a  “6”?  That  gives  us  no  variation  and  so  no  ability  to  explain  anything  using  reported  happiness.  A  second  issue  may  be  that  we  might  worry  that  subjects  are  not  giving  us  a  true  answer.  We  can  address  both  of  these  potential  problems  by  using  a  “mood  induction  procedure”   to   generate   the   variation   we   need   in   happiness.   By   making   some   people   happier   and  leaving  some  “untreated”  by  our  procedure  we  can  create  a  happy  group  and  a  neutral  group.  We  can  3  SOCIAL  MARKET  FOUNDATION    check   this   has   worked   (using   the   reported   happiness   question)   and   see   whether   the   happier   group  are  more  productive.  
A   mood-­induction   procedure   is   a   “treatment”   or   undertaking   specifically   designed   to   change  someone’s  mood  or  temperament.  A  typical  approach  would  be  to  subject  one  group  to  some  text,  a  movie,  a  verbal  speech,  music  or  some  such  stimulus  designed  to  elicit  a  particular  mood,  and  subject  a  control  group  to  an  alternative  (which  could  be  nothing  or  a  placebo).  Research  within  Psychology  (such  as  the  meta-­study  by  Westermann  et  al,  1996)  indicates  that,  of  the  many  ways  that  have  been  used  to  induce  different  moods  in  experimental  subjects,  the  most  effective  is  the  use  of  a  movie  clip  particularly  for  the  induction  of  a  positive  mood  state  (such  as  making  someone  “happier”).    
Movie   clips   were   used   in   Experiments   I   and   II.   The   treatment   was   a   10-­minute   clip   of   comedy  sketches   by   a   well-­known   comedian.   The   questionnaire   results   indicate   that   the   clip   was   generally  found   to   be   entertaining   and   had   a   direct   impact   on   reported   happiness   levels.   We   also   have   direct  evidence   that   the   clip   raised   happiness   through   a   comparison   of   questionnaire   happiness   reports  directly   before   and   after   the   clip.   Experiment   II   in   particular   asks   subjects   to   report   their   happiness  level   throughout   the   session   and   provides   even   stronger   evidence   that   the   movie   clip   is   having   the  effect   we   want.   As   a   control,   we   used   either   a   calm   “placebo”   clip   or   no   clip   at   all.   In   fact   those  subjects  who  saw  the  movie  reported  a  happiness  level  of  about  1  point  higher  on  a  7-­point  scale  as  compared  to  those  who  did  not  see  the  clip  as  shown  in  Figure  1.  Finally,  it  is  useful  to  notice  that  the  reported  level  of  happiness  before  the  clip  for  the  treated  group  is  not  statistically  significantly  different  from  the  happiness  of  the  untreated  group.  
Figure  1:  Those  exposed  to  the  randomised  happiness  treatment  in  the  laboratory  have  higher  happiness  in  Experiment  II  

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5.5
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Untreated  Treated4
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3Happiness  after  the   Happiness  Female  clip  
after  the  clip

Happiness  Male  after  the  clip

Happiness  at  the  end

  Notes:  Here  the  happiness  treatment  is  a  comedy  movie  clip  in  the  laboratory  (the  lines  indicate  95%  confidence  intervals).  

    MEASURING  PRODUCTIVITY  The  flip-­side  to  being  able  to  induce  happiness  is  being  able  to  design  a  task  that  allows  productivity  to   be   measured.   The   task   we   chose   is   based   on   a   similar   task   in   Niederle   and   Vesterlund   (2007).  

4  HAPPINESS  AND  PRODUCTIVITY  Subjects   were   asked   to   answer   correctly   as   many   different   additions   of   five   2-­digit   numbers   as  possible  in  10  minutes,  with  a  typical  example  as  follows:    Figure  2:  Adding  five  two-­digit  numbers  under  timed  pressure  (example)  
  

31  

56  

14  

44  

87  

Total  =      This  task  is  simple  but  taxing  under  pressure.  We  think  of  it  as  representing,  admittedly  in  a  stylized  way,   a   white-­collar   job:   both   intellectual   ability   and   effort   are   rewarded.   One   nice   feature   of   our  chosen  task  is  that  it  allows  us  to  measure  productivity  in  three  ways:  1.   The  number  of  correct  additions  2.   The  number  of  addition  attempts  3.   The  percentage  of  correct  additions  The  first  of  these  can  be  considered  the  overall  measure  of  productivity  and  will  be  determined  by  the  other  two.  The  second  (attempts)  is  a  simple  proxy  for  effort:  if  this  increases,  holding  the  percentage  that  are  correct  constant,  the  overall  number  of  correct  additions  will  still  rise,  but  we  can  argue  that  it  is  effort  that  is  driving  any  productivity  increase.  The  third  measure  (percentage  of  correct  additions)  is  a   simple   proxy   for   ability   (or   precision).   If   the   percentage   correct   rises,   but   the   number   of   attempts  stays   constant,   the   overall   number   of   correct   additions   will   rise,   but   we   can   argue   that   it   comes  through  a  rise  in  the  ability  to  get  the  additions  correct  and  not  a  rise  in  effort.  
To  supplement  our  core  measure  of  productivity,  we  also  used  a  short  mathematical  reasoning  test  as  a  supplementary  control  on  ability.  This  involved  asking  subjects  5  questions  along  similar  lines  to  the  experiments  of  Gneezy  and  Rustichini  (2000)  where  a  mathematical  GMAT-­style  reasoning  test  was  administered.  Subjects  had  5  minutes  to  complete  the  5  questions  and  were  paid  at  a  rate  of  £0.50  per   correct   answer.   We   also   asked   our   subjects   to   tell   us   how   well   they   performed   in   school-­level  mathematical  ability  tests  and  for  their  overall  exam  performance.  Finally,  the  fact  that  subjects  were  randomly  allocated  to  groups,  and  we  used  several  hundred  subjects,  means  that  ability-­types  should  be  evenly  distributed  between  the  group  that  was  exposed  to  the  mood-­induction  procedure  and  the  group  that  was  not.  
  SHORT-­RUN  SHOCKS  (EXPERIMENTS  I-­III)  We   can   group   the   first   three   sets   of   experiments   together   since   they   all   deal   with   short-­run   shocks  induced  within  the  lab  and  share  similar  features.  
In   Experiment   I,   in   which   276   subjects   participated   in   total,   we   used   a   comedy   clip   as   our   “mood  induction   procedure”.      The   control-­group   individuals   were   not   present   in   the   same   room   with   the  treated   subjects;;   they   never   overheard   laughter   or   had   any   other   interaction.   The   experiment   was  carried  out  with  deliberate  alternation  of  the  early  and  late  afternoon  slots  to  avoid  time-­of-­day  effects.  
Table  1  provides  a  timeline  of  events  during  the  experiment.  
  5  SOCIAL  MARKET  FOUNDATION        Table  1:  Timeline  for  Experiment  I  Stage  

Activity  1:  Entry  Subjects  are  welcomed  and  given  basic  instructions  on  the  procedure.  

2:  Happiness  Shock  Subjects  in  treatment  1  view  a  comedy  clip  for  10  minutes,  otherwise  not.  

3:  Instructions  Subjects   are   given   additional   instructions,   including   a   statement   that   their   final  payment  relates  to  the  number  of  correct  answers.  

4:  Productivity  Task  Subjects  undertake  numerical  additions  for  10  minutes.  

5:  Ability  Control  Subjects  undertake  a  mathematical  reasoning  test  for  5  minutes.  

6:  Questionnaire  Subjects  complete  the  final  questionnaire  and  are  paid.  

7:  Experiment  Ends  The  experiment  ends  and  subjects  are  paid.  
    The  final  questionnaire  inquired  into  both  the  happiness  level  of  subjects  (before  and  after  the  clip  for  treatment  1),  and  their  level  of  mathematical  expertise.  The  core  sessions  took  place  over  4  days.  We  then  added  4  more  sessions  in  two  additional  days  designed  to  check  for  the  robustness  of  the  central  result  to  the  introduction  of  an  explicit  payment  and  a  placebo  film  (shown  to  the  otherwise  untreated  control  group).  Subjects  received  £0.25  per  correct  answer  on  the  arithmetic  task  and  £0.50  for  each  correct  mathematical  reasoning  answer.  
We  used  two  different  forms  of  wording:  o   For  days  1-­4  we  did  not  specify  exact  details  of  payments,  although  we  communicated  clearly  to  the  subjects  that  the  payment  did  depend  heavily  on  performance.  
o   For   days   5-­6   the   subjects   were   told   the   explicit   rate   of   pay   both   for   the   numerical   additions  (£0.25   per   correct   answer)   and   the   mathematical   reasoning   questions   (£0.50   per   correct  answer).  
This  achieved  several  things.  First,  for  days  5-­6  we  have  a  revealed-­payment  setup,  which  is  a  proxy  for  many  real-­world  piece-­rate  contracts,  and  for  days  1-­4  we  mimic  those  situations  in  real  life  where  workers  do  not  have  a  contract  where  they  know  the  precise  return  from  each  productive  action  they  take.   Second,   this   difference   provides   the   opportunity   to   check   that   the   wording   of   the   payment  method  does  not  have  a  significant  effect,  thereby  making  one  set  of  days  a  robustness  check  on  the  other.  
In   Experiment   I   it   is   not   possible   to   observe   how   subjects’   happiness   evolves   in   real   time,   although  they  are  asked  some  retrospective  questions,  the  happiness  levels  of  individuals  before  and  after  the  comedy  movie  clip.  To  deal  with  this,  we  designed  Experiment  II,  in  which  104  subjects  participated.  
We   ask   happiness   questions   before   playing   the   movie   clip,   and   then   longitudinally   throughout   the  experiment,  which  represents  a  departure  from  the  Experiment  I  timeline  in  Table  1  since  we  asked  subjects   about   their   happiness   level   on   three   occasions.      The   initial   measurement   was   at   the   very  start  of  the  experiment.  The  second  was  immediately  after  the  comedy  or  placebo  film.  The  third  time  6  HAPPINESS  AND  PRODUCTIVITY  was  at  the  end  of  the  experiment.  Experiment  II  used  explicit  payment  instructions  and  a  placebo  clip  (which  provided  a  gap  between  two  of  the  happiness  questions  for  the  control  subjects).  
In  Experiment  III,  a  different  treatment  was  adopted.  In  an  attempt  to  mirror  somewhat  more  closely,  admittedly   still   in   a   stylized   way,   the   sort   of   policies   that   might   potentially   be   provided   by   actual  employers,   our   treatment   subjects   were   provided   with   chocolate,   fruit,   and   drinks   as   the   mood  induction   procedure,   otherwise   everything   from   Experiment   I   remained   the   same.   The   experiment  involved   an   additional   148.   We   offered   the   selection   of   snacks   and   drinks   to   the   treatment   group  (comprising   74   subjects   in   4   sessions)   who   had   10   minutes   to   consume   whatever   they   chose.   We  provided  nothing  for  the  control  group  (who  were  a  different  set  of  74  individuals,  also  in  4  sessions).  
The  control  group  were  still  asked  to  sit  for  10  minutes  prior  to  the  experiment  beginning;;  this  was  to  ensure   that   any   effect   was   not   due   to   the   additional   minutes   of   experimental   time   for   the   treated  group.  

  RESULTS  FROM  EXPERIMENTS  I-­III  From  Figure  1  in  the  last  section  we  know  that  the  mood  induction  procedure  was  effective  at  raising  happiness   levels   by   about   1   point   on   a   7-­point   scale,   but   does   the   increase   in   happiness   carry  through  to  a  boost  to  productivity?  
Glancing   at   Figure   3   we   see   the   answer   is   yes.   In   fact,   over   10%   more   additions   were   successfully  completed  by  our  treated  group.  
Figure  3:  Those  exposed  to  the  randomised  happiness  treatment  in  the  laboratory  have  higher  productivity  in  Experiment  I  

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Untreated

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Treated141210Additions

Additions  Male

Additions  Female

Attempts

  Notes:  The  happiness  treatment  is  a  comedy  movie  clip  in  the  laboratory  (the  lines  indicate  95%  confidence  intervals).  

We  next  analysed  the  data  in  more  detail  in  an  attempt  to  understand  better  the  underlying  causes,  confirming  that  witnessing  the  clip  was  indeed  highly  significant  even  controlling  for  underlying  ability  (to   account   for   which   we   used   school-­level   results,   prior   maths   ability,   and   performance   in   the  mathematical  reasoning  test),  gender,  and  session.  The  impact  remained  at  around  a  10%  boost  to  productivity.  The  effect  was  equally  strong  across  male  and  female  subjects,  and  it  was  strong  across  7  SOCIAL  MARKET  FOUNDATION    the  full  ability  range  indicating  that  the  boost  from  happiness  applies  equally  well  to  high-­productivity  and  low-­productivity  workers.  
Wanting   to   delve   even   deeper   we   exploited   our   ability   to   analyse   the   two   different   parts   of  productivity:  effort  (attempts)  and  ability  (precision).  What  we  found  was  that  there  was  no  significant  increase  in  precision,  but  a  highly  significant  increase  in  effort  due  to  the  happiness  boost.  
We  also  compared  explicit  payment  (revealing  the  per  addition  fee)  to  masked  payment  (revealing  it  would  be  performance-­related  but  nothing  else)  and  then  we  compared  using  the  10  minute  “placebo”  clip  or  no  placebo.  These  variations  made  no  difference  to  the  main  result.  
The   results   from   Experiment   III   mirrored   those   from   Experiment   I.   As   before,   a   positive   productivity  effect  was  produced,  and  again  the  size  of  that  effect  was  substantial:  having  spent  approximately  two  dollars  per  person  on  drinks  and  snacks,  productivity  was  boosted  by  almost  20%  for  a  short  period  of  concentrated   work.   This   helped   to   confirm   that   the   result   was   not   reliant   on   any   particular   type   of  happiness  shock.  
  In   Experiment   II   we   tracked   happiness   throughout   the   session,   making   it   much   easier   to   look   at  causation  (rather  than  just  correlation).  As  people  are  randomly  assigned  to  the  treatment  group,  we  know   that   the   baseline   levels   of   productivity   of   the   treatment   and   control   group   are   identical.   It   is  therefore  possible  to  find  out  whether  there  is  a  link  between  their  measured  rise  in  happiness  and  the  measured  implied  effect  on  productivity.  Glancing  at  Figure  4  we  can  see  a  positive  relationship  (an  upward   slope)   between   the   difference   in   happiness   before   and   after   the   “shock”   and   the   resulting  boost  to  productivity.  This  provides  a  final  observation:  the  more  powerful  the  effect  of  the  happiness  shock,  the  greater  the  final  boost  to  productivity.    

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Figure  4:  The  greatest  rise  in  happiness  during  Experiment  II  is  associated  with  the  greatest  productivity  gain  

-4-20Happiness at the end - Happiness before clip
95% CI
Correct Additions

2

Fitted values

  Notes:  Here  those  not  exposed  to  the  happiness  treatment  have  the  same  baseline  productivity;;  hence  the  y  axis  can  be  viewed  as  a  change  in  productivity  from  the  common  baseline.  

  
  

8  

HAPPINESS  AND  PRODUCTIVITY    EXPERIMENT  IV:  FAMILY  TRAGEDIES  AS  REAL-­LIFE  SHOCKS  The   preceding   experiments   studied   small   happiness   interventions.   In   Experiment   IV   we   study   real-­
life  unhappiness  events  assigned  by  Nature  rather  than  by  us  for  179  subjects  (8  sessions  in  2  days.  
These  shocks,  for  which  we  use  the  generic  term  “Bad  Life  Events”  (or  BLE)  are  family  tragedies  such  as  recent  bereavement  or  serious  health  issues  in  the  immediate  family.  
In   Experiment   IV,   subjects’   productivities   are   measured   at   the   very   outset   with   no   “mood   induction  procedure”.  At  the  end  of  the  experiment,  these  subjects  are  quizzed,  by  questionnaire,  about  recent  tragedies  in  their  families’  lives.  One  other  key  aspect  is  that  we  asked  subjects  to  report  their  level  of  happiness   right   at   the   start   of   the   experimental   session.   This   was   to   avoid   ‘priming’   problems.   The  underlying  logic  is  that  we  wanted  to  see  if  people’s  initial  happiness  answers  could  be  shown  to  be  correlated   with   the   individuals’   later   answers   and   behavior.   A   final   questionnaire   included  supplementary   questions   designed   to   find   out   whether   they   had   experienced   at   least   one   of   the  following   BLEs:   close   family   bereavement,   extended   family   bereavement,   serious   life-­threatening  illness   in   the   close   family,   and/or   parental   divorce.   The   data   suggested   that   it   was   appropriate   to  aggregate   all   of   these   happiness-­shock   events   by   using   a   single   BLE   variable.   Bennedsen   et   al.  
(2010)   also   considered   something   similar   for   CEOs   and   suggested   that   company   performance   may  be  impeded  by  traumatic  family  events.  Table  1  presents  the  timeline  for  Experiment  IV.  
  Table  2:  Timeline  for  Experiment  IV  Stage  

Activity  1:  Entry  &  Instructions  Subjects  are  welcomed,  given  basic  instructions  on  the  procedure  and  a   statement   that   their   final   payment   relates   to   the   number   of   correct  answers.  

2:  Well-­being  Report  1  Subjects  are  asked  to  report  their  happiness  on  a  7-­point  scale.  

3:  Productivity  Task  Subjects  undertake  numerical  additions  for  10  minutes.  

4:  Ability  Control  Subjects  undertake  a  mathematical  reasoning  test  for  5  minutes.  

5:  Questionnaire  (including  life   Subjects   complete   the   final   questionnaire   including   questions   on  shock  reports)  recent  positive  and  negative  life  shocks  and  are  paid.  
6:  Well-­being  Report  2  In   some   treatments   we   ask   subjects   to   report   their   well-­being   for   a  second  time  before  payment.  

7:  Experiment  Ends  The  experiment  ends  and  subjects  are  paid.  
    RESULTS  FROM  EXPERIMENT  IV  Those  subjects  who  had  been  through  a  bad  life  event  within  3  years  were  noticeably  less  happy  and  less  productive  as  shown  in  Figures  8  and  9  respectively.  The  subjects  for  Experiment  IV  who  did  not  suffer   a   BLE   reported   similar   initial   happiness   to   the   untreated   subjects   from   Experiment   I,   a   useful  check  on  the  consistency  of  our  subject  pool.  Compared  to  the  control  group,  those  who  did  suffer  a  BLE  mark  themselves  nearly  half  a  point  lower  on  the  happiness  scale,  and  they  achieve  on  average  9  SOCIAL  MARKET  FOUNDATION    2.31   fewer   correct   additions.   They   also   make   fewer   attempts   which   seems   to   drive   the   reduction   in  productivity  and  the  effect  is  similar  for  males  and  females.  
Figure  5:  Individuals  with  a  recent  Bad  Life  Event  have  lower  productivity  in  Experiment  IV  

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10
Additions  Additions  Female

Additions  Male

Attempts

  Notes:  A  BLE  is  bereavement  or  family  illness  (lines  indicate  95%  confidence  intervals).  
Figure  6:  Individuals  with  a  recent  Bad  Life  Event  report  lower  happiness  in  Experiment  IV  

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Happiness

Happiness  Female

Happiness  Male  Notes:  A  BLE  is  bereavement  or  family  illness  (lines  indicate  95%  confidence  intervals).  

We  also  analysed  the  importance  of  when  the  BLE  took  place.  The  immediate-­run  (<1  year)  loss  of  happiness  is  approximately  one  full  point  on  our  7-­point  scale.  Therefore,  although  our  subjects  may  not  be  aware  of  it,  their  happiness  answers  at  the  start  of  Experiment  IV  are  correlated  with  whether  later   on   they   report   that   a   BLE   recently   occurred   in   their   family.   The   pattern   in   the   happiness  coefficients  is  itself  broadly  consistent  with  hedonic  adaptation  -­  the  well-­being  effect  declines  through  time.  Overall,  the  consequence  of  a  bad  life  event  is  empirically  strong  if  it  happened  less  than  a  year  ago,   and   becomes   insignificantly   different   from   zero   after   approximately   3   years.   Our   results   are  10  HAPPINESS  AND  PRODUCTIVITY  consistent   with   a   range   of   adaptation   findings   in   the   survey-­based   research   literature   on   the  economics  of  human  well-­being  (e.g.  Clark  et  al.  2008).  
We  are  especially  interested  in  the  effects  of  a  bad  life  event  upon  human  performance.    Having  had  a  bad  event  in  the  previous  year  is  associated  with  particularly  low  performance  on  the  additions  task,  our   results   indicate   that   the   size   of   the   productivity   effect   is   large;;   it   is   typically   more   than   two  additions   and   thus   greater   than   10%.   The   extent   of   the   deleterious   effect   of   a   Bad   Life   Event   upon  subjects’  productivity  is  a  declining  function  of  the  elapsed  time  since  the  event.  
In  the  questionnaire,  we  also  asked  about  parental  divorce  thinking  this  would  also  be  a  reasonable  BLE   to   consider.   However,   this   turned   out   to   have   a   tiny   (occasionally   positive)   and   statistically  insignificant  effect  on  the  individual;;  the  divorce  of  parents,  at  least  in  our  data  set,  does  not  appear  to  qualify   as   a   “bad   life   event”.   We   discuss   this   finding   in   greater   detail   in   Proto,   Sgroi   and   Oswald  (2012)   where   we   also   checked   for   consistency   with   the   British   Household   Panel   Survey   and   found  that   there   is   indeed   no   clear   causal   link   from   the   incidence   of   parental   divorce   through   to   reported  happiness.   Though   we   do   not   know   why   this   was   the   case,   it   might   have   been   perceived   by   our  subjects  as  a  release  from  a  more  difficult  situation  and/or  may  also  have  been  a  longer-­term  issue  granting  additional  time  for  the  subjects  to  get  used  to  the  situation,  but  these  are  only  conjectures.  

  POLICY  RAMIFICATIONS  Much   of   what   we   have   done   is   not   just   about   establishing   a   link   from   happiness   to   productivity   but  thinking   more   deeply   about   how   such   a   link   might   work   and   trying   to   characterise   features   of   the  process.  Let’s  consider  the  key  findings  distributed  across  the  four  experiments.  
o   The   effect   is   big:   In   terms   of   national   GDP   or   economic   growth,   rises   of   3%   or   so   are  considered   very   large.   The   impact   of   the   shocks   we   used   were   of   the   order   10%-­20%.   The  induced  shocks  were  relatively  cheap  (e.g.  £2  per  subject)  but  the  scale  of  the  effect  makes  it  likely   that   even   close   to   zero-­cost   “nudges”   would   have   impact.   This   is   an   important   idea   to  test  in  practice.  
What  does  this  mean  for  the  private  and  public  sector?  The  scale  of  the  effect  cannot  be  dismissed.  In  the  post-­2008  environment  of  public  sector  cuts  a  key  question  is  the  impact  spending  cuts  will  have  on  the  long-­run  sustainability  of  important  welfare  and  social  policies.  By  boosting  worker-­productivity  it  might  be  possible  to  partly  offset  the  impact  of  spending  cuts  and  also  to  help  rebound  back  as  and  when   more   funding   is   available.   In   the   private   sector   competitive   pressure   means   that   productivity  may  be  decisive  for  survival.  
o   The   long-­run:   Happiness   shocks   (our   “Bad   Life   Events”)   from   the   real   world,   if   they   are  significant  enough,  can  generate  an  effect  on  productivity  of  up  to  3  years  in  duration.  
A  more  or  less  permanent  “shock”  will  generate  a  long-­run  impact.  This  would  suggest  that  significant  environmental  changes  might  work  into  the  long-­run  while  smaller  shocks  like  the  ones  we  trialled  in  the  lab  will  produce  a  strong  but  possibly  temporary  effect.  Given  the  scale  of  the  short-­run  effects,  it  is   possible   that   this   might   be   driven   into   the   long-­run   through   a   positive   cycle   of   happiness   and  productivity.  
We   would   warn   against   misrepresenting   the   “bad   life   event”   study   as   suggesting   that   firms   should  avoid  hiring  workers  with  a  recent  tragic  past.  It  could  just  as  easily  be  argued  that  it  is  just  these  sorts  of  workers  who  would  benefit  the  most  from  a  positive  happiness  shock  if  their  happiness  levels  are  11  SOCIAL  MARKET  FOUNDATION    low   to   begin   with:   so   we   would   not   advise   using   any   of   our   findings   to   help   choose   who   to   employ  (which  would  not  be  acceptable  morally  nor  most  likely  legally  in  any  case).  
o   Effort   not   ability:  Making  subjects  happy  seems  to  encourage  them  to  put  in  more  effort,  it  does   not   make   them   better   at   the   task.   This   is   an   important   distinction   as   some   tasks   will  benefit  more  from  increased  effort  than  others.  
Our  findings  to  date  focus  on  the  type  of  work  for  which  effort  might  be  especially  important  such  as  simple  piece-­rate  easily  divisible  tasks  which  require  some  mental  capacity.  We  can  combine  this  with  existing   psychological   research,   such   as   Amabile   et   al   (2005),   which   suggests   that   creativity   is   also  boosted   by   happiness.   This   yields   a   wide   range   of   possible   jobs   to   consider,   but   at   least   from   our  research  we  would  prioritize  tasks  where  effort  is  key.  
o   The   effect   works   across   the   entire   distribution:   Irrespective   of   how   highly   productive  workers  may  be  initially,  making  them  happier  should  help  make  them  even  more  productive.  
Moreover,  the  greater  the  boost  to  happiness,  the  greater  the  resulting  boost  to  productivity.    
The  universal  impact  of  happiness-­boosting  policies  suggests  that  it  would  make  sense  to  target  the  entirety   of   workers   in   the   firm   rather   than   have   selective   policies   or   focus   on   especially   high   or   low  productivity   workers.   To   some   extent   this   might   complement   policies   such   as   “worker   of   the   month”  awards  or  bonus  schemes  but  care  needs  to  be  taken  to  avoid  clashes  of  impact  such  as  the  negative  happiness   implications   (“I   just   missed   a   bonus   this   month!”)   vs   the   powerful   incentive-­effects   of  performance-­related   pay.   It   may   well   be   easier   to   boost   happiness   towards   the   bottom   end   of   the  happiness   distribution,   and   so   a   policy   of   trying   to   bring   everyone   up   to   a   certain   happiness   level  would  seem  reasonable,  and  good  human  resources  divisions  should  be  thinking  in  terms  of  how  to  spot  and  help  those  who  are  suffering  especially  low  levels  of  happiness.  
  PRACTICAL  CONSIDERATIONS  Any   laboratory   experiment   is   a   simplification   of   a   real   firm.   Moving   to   real-­world   settings   introduces  complexity,   which   is   one   of   the   reasons   why   the   lab   is   a   good   place   to   start   any   such   research.  
However,   attempting   to   study   these   ideas   in   the   workplace   means   thinking   about   several   important  practical  considerations:  o   How   can   we   measure   productivity?   The   firm   needs   to   have   a   simple   measure   of  productivity.  This  can  be  formed  either  directly  from  output  or  through  a  measure  of  the  time  taken  to  achieve  a  task.  
o   How  can  we  boost  happiness  in  a  workplace?  Many  possibilities  exist  from  improvements  in  the  working  environment,  better  interaction  among  workers,  a  better  relationship  between  management   and   workers,   and   positive   feedback   or   encouragement.   The   issue   may   be   not  whether  a  sensible  procedure  for  boosting  happiness  exists,  but  which  one  to  select.  
o   The   procedure   needs   to   be   cheap.   Any   costly   boost   to   happiness   may   crowd   out   the  benefits  to  productivity.  This  brings  us  into  the  realm  of  “nudge  economics”:  in  which  the  aim  is   to   influence   behaviour   through   policies   which   cost   almost   nothing.   One   option   might   be  short  verbal  or  written  positive  feedback  designed  to  offer  encouragement.  
o   Time.   Many   issues   relate   to   time,   such   the   duration   of   the   mood-­induction   and   how   often   it  needs  to  be  repeated  as  well  as  how  quickly  workers  become  habituated  to  any  procedure  or  12  

HAPPINESS  AND  PRODUCTIVITY  change.  On  the  output  side  we  would  need  to  consider  the  duration  of  any  productivity  impact  and  how  this  relates  to  the  nature  of  the  procedure  used.  
o   Control   vs   treatment.  In  the  real  world  we  need  to  make  sure  that  the  control  group  are  as  similar  as  possible  to  the  “treated”  group.  Ideally  we  need  two  groups  of  workers  who  do  the  same   thing   in   more   or   less   the   same   way   and   in   similar   environments   or   we   might   confuse  the   direct   impact   of   happiness   with   complex   interactions   between   mood-­induction   and   the  environmental  differences  between  groups.  
Another  important  issue  for  any  firm  or  manager  considering  how  to  apply  happiness  boosting  policies  is  to  understand  the  pathways  from  happiness  through  to  productivity.  The  results  of  Killingsworth  and  Gilbert   (2010)   suggest   the   possibility   that   unhappiness   may   lead   to   a   lack   of   mental   concentration.  
The  appendix  to  Oswald,  Proto  and  Sgroi  (2015)  provides  a  model  that  explores  this  idea  further.  Its  main  result  stems  from  internal  resource-­allocation  by  the  worker.  In  the  model,  a  positive  happiness  shock   allows   the   employee   to   devote   more   attention   and   effort   to   solving   problems   at   work  (essentially  because  the  worker  can  switch  from  worrying)  which  can  explain  our  key  findings.  

  CONCLUSIONS  If   happiness   in   the   workplace   brings   increased   returns   to   productivity,   then   human   resource  departments,  business  managers  and  the  architects  of  progression  policies  will  want  to  consider  the  implications.  These  are  not  idle  questions.  With  small  increases  compounding  like  interest  in  a  bank  account,   productivity   is   a   crucial   component   in   the   quest   to   raise   standards   of   living.   To   borrow   a  phrase   from   Nobel   laureate   Paul   Krugman,   “Productivity   isn’t   everything,   but   in   the   long   run   it   is  almost  everything.”  In  this  briefing  paper  we  described  four  kinds  of  trial.  The  last  of  these  is  an  attempt  to  estimate  the  repercussions  of  life-­events  assigned  by  Nature.  The  design,  in  that  case,  has  the  disadvantage  that  we   cannot   directly   control   the   happiness   shock,   but   it   has   the   advantage   that   it   allows   us   to   study  large  shocks  of  a  fundamental  kind  in  real  human  beings’  lives.  The  other  three  experiments  examine  the  consequences  of  randomly-­assigned  “happiness  shocks”.  These  experiments  have  the  advantage  that  we  can  directly  control  the  happiness  shock  but  the  disadvantage  that  shocks  are  inevitably  small  and   of   a   special   kind   in   the   laboratory.   It   is   conceivable   in   the   last   experiment   that   there   is   some  unobservable  feature  of  people  that  makes  them  both  less  productive  and  more  likely  to  report  a  bad  life  event.  Yet  such  a  mechanism  cannot  explain  the  results  in  the  other  three  experiments.  By  design,  the  four  experiments  have  complementary  strengths  and  weaknesses.      Various   practical   implications   emerge   from   the   experimental   results.   If   happiness   in   a   workplace  carries   with   it   a   return   in   productivity,   then   managers   and   human   resources   specialists   may   wish   to  implement   their   own   “happiness   shocks”   to   help   maintain   productivity   in   the   context   of   a   highly  competitive  economic  climate  in  the  private  sector,  and  spending  cuts  in  the  public  sector.  We  have  suggested   ways   to   think   about   this   and   practical   considerations   that   need   to   be   overcome.   If   well-­being   boosts   people’s   performance   at   work,   this   also   raises   the   possibility   of   self-­sustaining   spirals  between   human   productivity   and   human   well-­being,   which   could   help   even   quite   small   short-­run  shocks  generate  longer-­run  gains.  
Finally,  we  need  to  acknowledge  the  work  done  by  our  predecessors.  To  the  best  of  our  knowledge  Oswald,   Proto   and   Sgroi   (2015)   is   the   first   examination   of   the   relationship   between   productivity   and  happiness  to  implement  a  monetary  piece-­rate  setup,  examine  large  real-­world  shocks  to  happiness  (and   not   solely   small   laboratory   ones),   use   a   range   of   different   experimental   designs,   and   offer  various  types  of  evidence  and  collect  longitudinal  data  in  a  way  that  provides  us  with  an  opportunity  to  13  

SOCIAL  MARKET  FOUNDATION    scrutinize  the  changes  in  happiness  within  our  subjects.  However,  there  exist  many  important  papers  spanning  several  decades  that  have  had  an  important  influence  on  our  work  and  more  generally  on  our  understanding  of  how  happiness  affects  behaviour.  Within  Psychology,  research  by  the  late  Alice  Isen  of  Cornell  University  has  been  especially  important  in  this  area,  for  instance  Erez  and  Isen,  2002,  and  there  is  also  a  large  literature  on  productivity  at  the  personal  and  plant  level.  For  a  review  of  the  relevant  literature  please  see  Oswald,  Proto  and  Sgroi  (2015).  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  

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HAPPINESS  AND  PRODUCTIVITY    REFERENCES  Amabile,   T.,   Barsade,   S.,   Mueller,   J.   and   Staw,   B.   (2005),   ‘Affect   and   Creativity   at   Work’,  Administrative  Science  Quarterly,  50:  367-­403  Bennedsen,   M.,   Perez-­Gonzalez,   F.   and   Wolfenzon,   D.   (2010),   ‘Do   CEOs   Matter?’,   Working   paper,  Stanford  GSB.  
Clark,  A.,  Diener,  E.,  Georgellis,  Y.  and  Lucas,  R.  (2008),  ‘Lags  and  Leads  in  Life  Satisfaction:  A  Test  of  the  Baseline  Hypothesis’,  Economic  Journal,  118:  222-­243.  
Erez,   A.   and   Isen,   A.   (2002),   ‘The   Influence   of   Positive   Affect   on   the   Components   of   Expectancy  Motivation’,  Journal  of  Applied  Psychology  89:  1055–1067.  
Gneezy,   U.   and   Rustichini,   A.   (2000),   ‘Pay   Enough   or   Don't   Pay   at   All’,   Quarterly   Journal   of  Economics  115:  791-­810.  
Killingsworth,  M.  and  Gilbert,  D.  (2010),  ‘A  Wandering  Mind  is  an  Unhappy  Mind’,  Science  330:  932-­
932.  
Niederle,  M.  and  Vesterlund,  L.  (2007),  ‘Do  Women  Shy  away  from  Competition?  Do  Men  Compete  too  Much?’,  Quarterly  Journal  of  Economics  122:  1067-­1101.  
Oswald,  A.,  Proto,  E.  and  Sgroi,  D.  (2015),  ‘Happiness  and  Productivity’,  Journal  of  Labor  Economics  33(4).  
Proto,  E.,  Sgroi,  D.  and  Oswald,  A.  (2012),  ‘Are  Happiness  and  Productivity  Lower  among  University  Students   with   Newly-­divorced   Parents?   An   Experimental   Approach’,   Experimental   Economics   15(1):  1-­23.  
Westermann,   R.,   Spies,   K.,   Stahl,   G.   and   Hesse,   F.   (1996),   ‘Relative   Effectiveness   and   Validity   of  Mood  Induction  Procedures:  A  Meta-­analysis’,  European  Journal  of  Social  Psychology  26:  557–580.  
  

  

  

  

  

	
  
	
  

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