Fredrik Andersson, Cornell University,

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Reaching for the Stars Who Pays for Talent in
Innovative Industries?

• Fredrik Andersson, Cornell University,
• Matthew Freedman, University of Maryland,
• John Haltiwanger, University of Maryland and
  NBER,
• Julia Lane, NSF
• Kathryn Shaw, Stanford University and NBER

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Focus of paper

• What is the link between product market and labor
  market?
• Theory of innovation-based theory of production
  creates implications for the structure of
  earnings.
• This paper examines how firms recruit, motivate
  and retain talented workers in a particularly
  innovative industry software.
• We examine the relationship between the variation
  in the returns to innovation and the variation in
  compensation.
• Product innovation in the software industry is
  very closely tied to the talents of the
  workforce.
• Software industry is characterized by skewed
  returns successful innovations can produce an
  enormous payoff to the firm, while failed
  products can lead to large losses. Also skewed
  compensation structure.
• Variance of product payoffs is very different in
  different segments of the industry.

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Background Point 1 Firms pay a lot for star
software workers
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Background Point 2 Stars earn more with
experience(or the variance of pay rises,
comparing the distribution of Beginning-of-Spell
End-of-Spell Earnings)
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Background Point 3 There is a high variance to
the gains to innovation in the software industry
(Table 2 Top Video Games, Ranked by 2007 Sales
Revenues)
Game Producer Units Sold (millions) Price/unit Sales (million)
Halo 3 Xbox 360 Microsoft 4.82 59.99  289.15
Wii Play w/remote Wii, Nintendo 4.12    49.99  205.96
Call of Duty 4 Xbox 360, Activision 3.04 59.99  182.37
Guitar Hero III Legends Of Rock w/guitar PlayStation Neversoft/Budcat/Activision) 2.72 89.99 244.77
Super Mario Galaxy Wii, Nintendo 2.52   49.99  125.97
Pokemon Diamond DS, Nintendo 2.48     34.99  86.78
Madden NFL 08 PS2 Electronic Arts 1.9 29.99  56.98
Guitar Hero II w/guitar PS2 Activision 1.89    89.99  170.08
Assassin's Creed Xbox 360 Ubisoft 1.87 59.99  112.18
Mario Party 8 Wii Nintendo 1.82   49.99  90.98
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Paper Objectives Linking the Background Points

• Hypothesis firms operating in product markets
  with high payoff dispersion will hire and reward
  stars more.
• We test this hypothesis using employer-employee
  matched data from the software industry

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Paper Objectives

• Bigger picture
• Labor economics The hypothesis is that the
  demand for innovation has pushed up the demand
  for and the wages of the most skilled knowledge
  workers, where skill is defined as the ability to
  innovate and solve problems.
• Personnel economics We are connecting the firms
  product market strategy to its human resource
  management practices explaining why some firms
  choose practices of careful selection and high
  incentive pay and some firms do not. Few
  empirical studies have identified which firms
  gain from specific HR practices.

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Model of Innovation Projects have a Payoff
Distribution (some projects have huge payoffs
some have losses)
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What do star workers do in firms?

• Stars are the innovative spark or creative talent
  that results in the success of innovative
  projects stars create or pick projects better
  than non-stars.
• Stars reduce the false positives and false
  negatives in project outcomes they shift out the
  payoff distribution they accept fewer bad
  projects (false positives) and reject fewer good
  projects (false negatives).
• The payoff gains for stars is lower in lower-risk
  payoff markets (PA2-PA1)gt(PB2-PB1)
• ? firms in high variance product lines should
  hire more stars

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Shifts in the Payoff Distribution Due to
Reductions in False Positive or False Negative
Errors
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Advantages of Focusing on Software

• High variance product payoffs
• Knowledge workers are key inputs
• Production function output is a function of
  personal innovation
• Variance of product payoffs is very different in
  different segments of the industry
• Industry contributes to economic growth

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The Data Set

• Employer-employee matched data, and
  product-matched data for software
• Employee Data individual income from
  Unemployment Insurance quarterly data for all
  employees, for all employers, for the full
  universe of employers and employees for 10 states
• Employer Data Firm-specific product revenue
  information for every software firm, from the
  Services Industry Economic Census of Software
  Publishing conducted every five years
• N83,497 employees with 143,485 job spells
• Advantages of the data income is all income from
  salary, bonuses, and exercised stock options, for
  all workers in all software companies
• Disadvantages of the data no information on
  occupation or hours of work
• Three data sets created
• Employees who earn more than 50,000 a year and
  ages 21-44 ? N51,859
• Those who also have complete firm information ?
  N26,726
• A subset of these who are workers in software
  occupations in 2000 Decennial Census of
  population ? N2,638

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The Measures

• Focus on the software industry
• Calculate product line payoff dispersion for each
  firm using the Census of Software Publishing for
  1997
• Steps to calculate the product-specific
  dispersion of sales per worker for all firms
• For each of the 30 product classes in software,
  using firms product line data, calculate the
  90/50 ratio of log of sales per worker
• Examples of Product classes game and
  entertainment business graphics design layout
  software etc.
• Given 90/50 for the 30 product classes, we create
  90/50 for each firm using the firms actual
  product sales mix weights
• This product payoff dispersion measure reflects
  the firms actual product mix, but not its actual
  revenue. A firm with a high Product Payoff
  Dispersion measure is not necessarily a high or
  low performing firm, but rather has a product mix
  with a right skewed distribution of payoffs
• Match all workers wages within all software
  firms to the Census data using the UI wage data
  for all individuals in the software industry (for
  ten U.S. states).
• Wages are measured for all full quarters of
  earnings, including wages, bonuses, and exercised
  stock options.

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Empirical Hypotheses

• Primary hypothesis star talent is sorted into
  firms with a high payoff dispersion because these
  firms value the star skills of project innovation
  the most.
• Testable hypothesis given that talent is
  unobserved, but wages are observed, we should
  find that the firms with the highest payoff
  dispersion should pay the highest wages, as
  talent sorts to those firms. These firms may
  also be offering incentive pay that raises effort
  the most.

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Empirical Hypotheses

• Auxiliary hypotheses
• Wages should be more sensitive to the firms
  payoff dispersion for more highly skilled
  workers. In software companies, it is the top
  talent (or the brilliant programmers) who should
  be paid the most for their skills in the firms
  operating in product markets with high payoff
  dispersion.
• The wage regression hypotheses should apply for
  workers at different experience levels, and for
  wage growth rates.

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Empirical Specification
i indexes workers and j indexes firms
dependent variable is log quarterly earnings
for a worker observed at some point in employment
spell that is ongoing in 1997 (beginning, end,
one year prior to end, etc.) X is a vector of
worker controls including quadratics of tenure at
job (depending on when in spell earnings are
measured), tenure in industry, and age, fully
interacted with each other and with left and
right censoring dummies Z is a vector of firm
controls include a quadratic (log) firm
employment, dummies for firm age, firm growth,
and a dummy for whether firm is in a high
density, high education and industrially
diversified county. Z also includes log revenue
per worker and firm worker turnover. All firm
variables measured in 1997. Main variable of
interest Product line dispersion measure
reflecting actual product mix but not actual
revenue Remarks (i) Focus only on high skilled
software workers (50K) from age 21-44 (ii)
Revenue per worker control to abstract from rent
sharing explanations of findings (iii) Worker
churning to abstract from risk (of worker
turnover)
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Tests

1. Do firms operating in software sectors that have
   high variance payoffs pay higher wages? Is agt0?
2. How does this affect earnings distribution? Is
   effect at 90th percentile gt 10th percentile?
3. What is contribution of skill vs. effort (i.e.
   importance of screening)? Examine starting
   salaries
4. How do firms structure compensation? Examine
5. Experienced earnings including stock options (end
   of spell)
6. Experienced salaries excluding options and
   bonuses
7. Do firms reward loyalty? Examine within firm
   earnings growth vs. between firm growth.

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1 and 2 a significant at 90th percentile not at
mean
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3 Screening important a significant at 90th
percentile not at mean disappears with controls
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4 How do firms structure compensation? Pay
talent more both options and salary
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5 How do firms retain workers? Reward loyalty.
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Summarizing Results

• When firms operate in product markets that have
  high payoff dispersion rates, these firms pay
  higher wages than do other software firms
• Starting salaries are higher in high payoff
  dispersion
• Experienced-worker compensation is much higher in
  high payoff dispersion firms
• Wage growth is higher
• These conclusions are not sensitive to different
  measures of income, or to different subsamples of
  the data we also test occupation subsamples
• More highly skilled workers, in higher wage
  quantiles, earn the most at high payoff firms)
• Workers are also paid more when the firm
  succeeds firms with high sales per worker pay
  more

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Interpreting the Wage Regression Results

• Workers are paid for performance
• highly skilled (or high effort) sort to high
  payoff dispersion firms highly skilled are paid
  more ex ante at these firms
• when software firms perform well, workers are
  paid more ex post
• The link between the firms payoff dispersion and
  wages suggests that workers are being paid for
  innovation.

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Loyalty Pays

• Wages rise with tenure, but more important
• Workers in high payoff-dispersion firms have the
  highest returns to tenure within the firm. High
  payoff-dispersion firms do not pay job hoppers
  higher wages.
• The most skilled workers have the highest returns
  to tenure in high payoff-dispersion firms.
• The raw data shows that the vast majority of wage
  growth arises from within the firm, not hopping
  across firms (Figure 4)
• ?Since most wage growth for workers comes from
  within job wage growth, we conclude that
  loyalty pays, and it pays the most in high
  payoff firms.

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Summary

• Our general hypothesis is that product market
  strategy determines the use of a star strategy
  in pay and selection
• Firms in higher-risk payoff product lines are
  more likely to pay higher wages hiring or
  building stars
• Workers are paid for loyalty and performance
• Workers achieve much higher wages (and wage
  increases) by staying with a firm rather than
  hopping between firms ? loyalty pays
• Workers are paid for performance over time within
  firms that might succeed and within firms that do
  succeed
• Our results suggest that labor demand has risen
  for workers who are skilled at innovating wages
  are high for workers in firms that value
  innovations the most.