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

1. 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

2. 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.

3. Background Point 1: Firms pay a lot for star software workers

4. 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)

5. 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)

6. 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

7. 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 firm’s 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.

8. Model of Innovation: Projects have a Payoff Distribution (some projects have huge payoffs; some have losses)

9. 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)>(PB2-PB1)  firms in high variance product lines should hire more stars

10. Shifts in the Payoff Distribution Due to Reductions in False Positive or False Negative Errors

11. 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

12. 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 • N=83,497 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  N=51,859 • Those who also have complete firm information  N=26,726 • A subset of these who are workers in software occupations in 2000 Decennial Census of population  N=2,638

13. 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 firm’s 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.

14. 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.]

15. Empirical Hypotheses • Auxiliary hypotheses: • Wages should be more sensitive to the firm’s 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.

16. 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)

17. Tests • Do firms operating in software sectors that have high variance payoffs pay higher wages? Is α>0? • How does this affect earnings distribution? Is effect at 90th percentile > 10th percentile? • What is contribution of skill vs. effort (i.e. importance of screening)? Examine starting salaries • How do firms structure compensation? Examine • Experienced earnings including stock options (end of spell) • Experienced salaries excluding options and bonuses • Do firms reward loyalty? Examine within firm earnings growth vs. between firm growth.

18. 1 and 2: α significant at 90th percentile; not at mean

19. 3: Screening important: α significant at 90th percentile; not at mean; disappears with controls

20. 4: How do firms structure compensation? Pay talent more; both options and salary

21. 5: How do firms retain workers? Reward loyalty.

22. 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

23. 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 firm’s payoff dispersion and wages suggests that workers are being paid for innovation.

24. “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.

28. 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.