FREDRIK ANDERSSON, CORNELL UNIVERSITY, MATTHEW FREEDMAN, UNIVERSITY OF MARYLAND, JOHN HALTIWANGER,...

FREDRIK ANDERSSON, CORNELL UNIVERSITY,MATTHEW FREEDMAN, UNIVERSITY OF MARYLAND,JOHN HALTIWANGER, UNIVERSITY OF MARYLAND AND NBER,JULIA LANE, NSFKATHRYN SHAW, STANFORD UNIVERSITY AND NBER

Reaching for the Stars: Who Pays for Talent in Innovative

Industries?

Focus of paper2

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.

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

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Table 1 Summary Earnings Statistics, Workers 21-44

Mean Median* 90th* SD

(a) 2000 Decennial Census (PUMS) Data – Total Earnings 35+ Hours/Week & 35+ Weeks/Year

All Industries 40,918 31,891 70,160 183,134

Software Industry (SIC 7372) 80,787 63,782 127,563 334,906

Computer Software Engineers (Census Occupation Code 102) in the Software Industry 90,668 70,691 138,193 369,374

(b) LEHD Data for Ten States - Earning $50,000+ Annualized

All workers in Software Industry

Starting Earnings (Excludes Left-Censored) 69,353 59,665 108,692 82,432

Worker Earnings at end of job spell (Censored and Uncensored) 344,268 95,508 310,644 2,051,985

Top Decile of Workers in Software Industry

Starting Earnings (Excludes Left-Censored) 107,660 80,899 184,951 142,526

Worker Earnings at end of job spell (Censored and Uncensored) 2,532,500 670,993 6,688,470 6,064,204 * Average within a 10% band around the true percentile. ** Annualized earnings three quarters prior to last observed full quarter. *** Includes only individuals for whom we observe a prior spell in the data.

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)4

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)5

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

Paper Objectives: Linking the Background Points

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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

Paper Objectives7

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.

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? 9

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

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

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(a) More Risky Payoff Distribution

(b) Less Risky Payoff Distribution

Advantages of Focusing on Software11

High variance product payoffsKnowledge workers are key inputsProduction function: output is a function of

personal innovationVariance of product payoffs is very different

in different segments of the industryIndustry contributes to economic growth

The Data Set12

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

The Measures13

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.

Empirical Hypotheses14

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.]

Empirical Hypotheses15

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.

Empirical Specification16

Pj

ijPjjiij ZXW '')ln(

i indexes workers and j indexes firms – dependent variable is log quarterly earnings fora 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 (dependingon 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 variablesmeasured 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)

Tests17

1. Do firms operating in software sectors that have high variance payoffs pay higher wages? Is α>0?

2. How does this affect earnings distribution? Is effect at 90th percentile > 10th percentile?

3. What is contribution of skill vs. effort (i.e. importance of screening)? Examine starting salaries

4. How do firms structure compensation? Examine1. Experienced earnings including stock options (end of spell)2. Experienced salaries excluding options and bonuses

5. Do firms reward loyalty? Examine within firm earnings growth vs. between firm growth.

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The Relationship between Product Payoff Dispersion and Different Earnings Measures OLS Quantile Regressions

Dependent Variable 10th Percentile 90th Percentile

Starting Earnings 0.0526 -0.1848 0.2129

(0.0331) (0.0460)*** (0.0557)*** Earnings with Previous Employer

0.1840 0.1145 0.1406

(0.0563)*** (0.1149) (0.0722)* Starting Earnings (with control for previous employer)

-0.0460 -0.1082 -0.0647

(0.0289) (0.0390)*** (0.0580)

Experienced Earnings

0.3868 0.0537 0.8279

(0.0629)*** (0.0340) (0.0990)*** Lagged Experienced Earnings

0.1312 -0.1674 0.4983

(0.0518)** (0.0445)*** (0.1001)***

Experienced Salary

0.0551 -0.1251 0.3731

(0.0340) (0.0343)*** (0.0697)*** Experienced Earnings (with control for previous employer)

0.1519 -0.0287 0.5709

(0.0552)*** (0.0346) (0.1172)*** Within-Job Earnings Growth

0.0706 -0.0060 0.1837

(0.0120)*** (0.0073) (0.0191)***

Within Job Salary Growth -0.0025 -0.0136 0.0499

(0.0131) (0.0092) (0.0146)*** Between-Job Earnings Growth -0.2169 -0.2352 -0.2597

(0.0476)*** (0.0653)*** (0.0921)***

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

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0.1840 0.1145 0.1406


-0.0460 -0.1082 -0.0647

(0.0289) (0.0390)*** (0.0580)


0.3868 0.0537 0.8279


0.1312 -0.1674 0.4983

(0.0518)** (0.0445)*** (0.1001)***

Experienced Salary

0.0551 -0.1251 0.3731


0.1519 -0.0287 0.5709


0.0706 -0.0060 0.1837

(0.0120)*** (0.0073) (0.0191)***



(0.0476)*** (0.0653)*** (0.0921)***

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

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0.1840 0.1145 0.1406


-0.0460 -0.1082 -0.0647

(0.0289) (0.0390)*** (0.0580)


0.3868 0.0537 0.8279


0.1312 -0.1674 0.4983

(0.0518)** (0.0445)*** (0.1001)***

Experienced Salary

0.0551 -0.1251 0.3731


0.1519 -0.0287 0.5709


0.0706 -0.0060 0.1837

(0.0120)*** (0.0073) (0.0191)***



(0.0476)*** (0.0653)*** (0.0921)***

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

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0.1840 0.1145 0.1406


-0.0460 -0.1082 -0.0647

(0.0289) (0.0390)*** (0.0580)


0.3868 0.0537 0.8279


0.1312 -0.1674 0.4983

(0.0518)** (0.0445)*** (0.1001)***

Experienced Salary

0.0551 -0.1251 0.3731


0.1519 -0.0287 0.5709


0.0706 -0.0060 0.1837

(0.0120)*** (0.0073) (0.0191)***



(0.0476)*** (0.0653)*** (0.0921)***

5: How do firms retain workers? Reward loyalty.

Summarizing Results22

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

Interpreting the Wage Regression Results

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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.

“Loyalty” Pays24

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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Figure 3A Predicted Starting Earnings at Minimum and Maximum

Product Market Payoff Dispersion

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Figure 3B Predicted Experienced Earnings at Minimum and Maximum

Product Market Payoff Dispersion

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Figure 3C Predicted Experienced Salary at Minimum and Maximum Product Market Payoff

Dispersion

Summary28

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.

FREDRIK ANDERSSON, CORNELL UNIVERSITY, MATTHEW FREEDMAN, UNIVERSITY OF MARYLAND, JOHN HALTIWANGER,...

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