Income  Targe,ng  &  Surge  Pricing  

Fish  VP  Analy,cs,  Zenefits  

(thanks  to  Uber  data  science)  11/18/15  

Tradi,onal  Economics  

Behavioral  Economics  (Kahneman  &  Tversky,  1979)  

Camerer  et  al.  (1997)  –  Income  Targe,ng  

•  “Daily  targe,ng  makes  exactly  the  opposite  predic,on  of  the  intertemporal  subs,tu,on  hypothesis:  When  wages  are  high,  [the  worker]  will  reach  their  target  more  quickly  and  quit  early;  on  low-­‐wage  days  they  will  [work]  longer  hours  to  reach  the  target.”  

•  Taxi  cab  driving  is  a  natural  context  for  studying  this...  –  “Schedules”  are  flexible  –  “Wages”  fluctuate  daily  –  Wages  are  correlated  within  day,  but  weakly  across  days  –  Heterogeneity  of  drivers  –  Strong  wage  proxies  –  Good  data  (for  1997)  

 

Rejects  posi,ve  response  to  wages  

Becer  data,  same  context,    &  important  component  of  mission  

“push  a  bucon  and  get  a  ride  in  minutes”  

Lye  “Prime  Time”  and  Uber  “Surge”  

•  Wage  Flexibility  

–  Lye  “Prime  Time”  •  Prime  Time  adds  a  percentage  to  your  

ride  subtotal.    “When  ride  requests  greatly  outnumber  available  drivers,  our  system  will  automa,cally  turn  on  Prime  Time.”    

–  Uber  “Surge  Pricing”  •  “At  ,mes  of  high  demand,  the  number  

of  drivers  we  can  connect  you  with  becomes  limited.  As  a  result,  prices  increase  to  encourage  more  drivers  to  become  available.”  

Hall,  Kendrick,  Nosko  (2015)  •  Inves,gates  2  events  

–  Ariana  Grande  concert  at  MSG  –  Technical  glitch  on  NYE  

 •  Strong  response  to  surge  pricing  (counter  to  Diakopoulos,  2015)  

–  Drivers  go  to  the  surge  area  –  Riders  less  inclined  to  request  a  ride  

•  Focus  on  economic  efficiency  of  Surge  –  Increased  total  surplus  (riders  +  drivers)  

•  Surge  Pricing  transfers  some  surplus  from  rider  to  driver  •  Higher  value  riders  matched  (lower  value  riders  drop  out)  •  Increased  driver  supply  allows  more  matches  

–  Matching  completed  under  15  minutes  is  high  with  aid  of  Surge  Pricing  

Customer  response   Driver  response  

Natural  Experiment  (Technical  Glitch)  

Results  

Driver  (non)  response  to  (non)  surge  

Evidence  of  posi,ve  intertemporal  subs,tu,on    

Selec,on?  Hall  &  Krueger  (2015)  

•  Sor,ng  by  most  opportunis,c  

–  Valuing  flexibility  •  “A  variety  of  ques,ons  made  it  

clear  that  Uber's  driver-­‐partners  value  the  flexibility  that  the  Uber  plaoorm  permits,  and  many  are  drawn  to  Uber  in  large  part  because  of  this  flexibility.”  

–  Outside  op,ons  •  Most  of  Uber’s  driving  partners  

con,nued  full-­‐  or  part-­‐,me  jobs.    “Uber’s  driver-­‐partners  also  oeen  cited  the  desire  to  smooth  fluctua,ons  in  their  income  as  a  reason  for  partnering  with  Uber.”  

What  I  liked  

•  Novel,  extensive  data  

•  Simple,  Clear,  Robust  – The  result  is  in  the  visualiza,on,  not  the  model  specifica,on  

•  Making  the  most  of  a  “natural  experiment”  

What  I  didn’t  like  

•  The  measure  of  (driver)  responsiveness  and  efficiency  •  Diakopoulos  finds  heterogeneity  of  impact  in  Washington,  D.C.  neighborhoods  

•  Sharing  limited  results  publicly  –  Focused  on  economic  efficiency  of  Surge  Pricing  –  Not  es,ma,ng  a  coefficient  of  elas,city  –  Not  exploring  the  data  for  more  results  

•  What  happens  to  the  app  openers  who  did  not  request  

•  Responsiveness  to  an  unknown  shock  is  the  more  relevant/interes,ng  es,ma,on  –  New  Years  Eve  and  Ariana  Grande  are  predictable  events  

Next  steps  &  learnings  •  What  mo,vates  Lye/Uber  drivers?  

–  Higher  wages  –  Intertemporal  subs,tu,on  

•  Farber  (2005)  vs.  Camerer  et  al.  (1997)  –  Farber  argues  cumula,ve  hours  dominate  (increasing  disu,lity)  

•  Do  drivers  during  a  surprise  surge  drive  longer?  •  How  does  Surge  Pricing  impact  long-­‐term  driver  response?    

•  Do  we  observe  heterogeneity  in  response?    –  Camerer  et  al.  found  posi,ve  intertemporal  effects  in  high  experienced  drivers,  and  nega,ve  effects  in  low  experienced  

–  Can  we  get  increased  economic  efficiency  through  •  Experience?  •  Informa,on  /  Training