Professor Kathryn Shaw Arbuckle Professor of Economics

Workshop on Confidential Data Collection for Innovation Analysis in Organization

Outline •  HR Technology Shocks and IT Technology Shocks •  Goal : Estimate models of the adoption of these new

technologies and the productivity gains from them. •  Research examples •  Implications

This talk is based in part on : Shaw, “Insider Econometrics: A Roadmap with Stops Along the Way” Labour Economics November 2009 and A dam Smith Speech, European Association of Labour Economics, September 2008

Ichniowski and Shaw, “Insider Econometrics: A Roadmap to Estimating Models of Organizational Performance” Handbook of Organizational Economic, ed. Gibbons and Roberts, forthcoming.

Ichniowski and Shaw, “Beyond Incentive Pay: Insiders’ Estimates of the Value of Complementary Human Resource Management Practices,” Journal of Economic Perspective (2003)

© Kathryn Shaw 2008

“Technology Shocks” Falling IT Prices: Rising IT Investment

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100

200

300

400

500

600

Total non-residential/5.91

Information processing equipment & software

“HR Shocks”: Increased Team Use (U.S.)

(Source: Shaw (2005, 2009, forthcoming)

“HR Shocks”: Increased Incentive Pay (U.S.)

Proportion of Plants with Computer-Aided Production Technologies (UK v. US) [within the valve-making industry]

Source: Bartel, Ichniowski, Shaw (QJE, 2007) Bartel, Ichniowski, Shaw and Correa (NBER book, 2008)

Proportion of Plants with New HRM Practices (UK v. US) [within the valve-making industry]

Source: Bartel, Ichniowski, Shaw (QJE, 2007) Bartel, Ichniowski, Shaw and Correa (NBER book, 2008)

HR Technology Shocks; Research Goals; Research Methods

•  Over time, managers have learned that new forms of teamwork and incentive pay are the best practices for HR management.

•  These are “HR technology shocks:” new inventions in technology. Example: the Japanese spread TQM around the world (i.e., the Toyota management model). There are massive new HR technology shocks occurring today.

•  Our research goals: estimate why some firms adopt and some don’t; estimate the productivity gains from adoption.

•  Our research methods: use the econometrics of ‘treatment effects,’ where the treatment is analogous to treating a patient with a new drug. Here the treatment is the new HR practice within the firm.

Consider first the software industry…

What do we know? •  Data Source: the Longitudinal Employee-Employer Matched

Data (LEHD) •  Results:

–  The level of pay and the variance of pay (across workers) rises with workers’ experience.

–  The level of pay also rises with the variance of the product market payoffs to the firm: software firms in innovative product spaces pay much more than firms in traditional software products.

Andersson, Freedman, Haltiwanger, Lane, Shaw, “Reaching for the Stars: Who Pays for Talent in Innovative Industries?” (Economic Journal, 2009).

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The Distribution of Earnings for Software Workers

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Background Point: There is a high variance to the gains to innovation in the software industry (Table 2: Top Video Games, Ranked by 2002 Sales) Revenues)

Grand Theft Auto Vice City Take 2 $218

Grand Theft Auto 3 Take 2 $120

Madden NFL 2003 Electronic Arts $119

Medal of Honor Electronic Arts $73

Kingdom Hearts Square Enix $59

Spider Man Activision $54

Halo Microsoft $51

SOCOM Seals Sony $50

Super Mario Sunshine Nintendo $49

Game Firm 2002 Revenues

(Millions)

Tony Hawks Activision $46

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The level of software earnings rises with the variance of product market payoffs.

Data from within one firm

•  Examples of data on the productivity of individual workers.

The LEHD data is essential for within industry and cross industry results. But now we need to go inside companies.

I want to showcase types of data and methods of analysis to do Data Driven Human Resource Management.

Example 1: Safelite auto glass Lazear (AER, 2000)

•  The data: confidential data from the firm on the productivity of workers before and after the adoption of new piece rate pay.

•  The treatment is moving to piece rate pay for auto windshield installers for one company

•  The treatment effect is: –  Productivity rises by 36% –  Why did productivity rise? Of the 36% gain: 20% was due to the

performance incentives; 16% was due to better worker selection workers’ individual responses to the treatment are

heterogeneous •  The adoption equation: only one firm adopts—it

is not random

Safelite Output-Tenure Profiles

Shaw and Lazear, “Tenure and Output,” Labour Economics (2008)

Example 1: Safelite (Lazear, 2000)

C C’

A’

A

B’

B

OLS gains

Those hired before piece rates who quit or are fired

Time

Those hired after piece rates

Fixed effects gains

Productivity

t* Safelite switches from hourly pay to piece rate pay at time t* and achieves a productivity gain of AA’. Those low-performers hired before piece rate pay (line BB’) quit when piece rates are imposed—so average productivity rises.

Identifying treatment effects

•  Do an experiment within a company – look before and after the treatment

•  Look at cross-sectional differences, across companies that have identical production functions.

Data from within many firms but one industry

Steel industry: •  HR practices raise productivity due

to teamwork and incentive pay and communications

Example 2: U.S. Integrated Steel Mills – Steel Finishing Lines

• 36 lines at 19 companies (Ichniowski, Shaw, Prennushi (AER, 1999))

Example 2: Integrated Steel Finishing Lines Ichiowski, Shaw, Prennushi (AER, 1997)

•  The data: confidential data on 36 production lines owned by 19 firms, for five years of data.

•  The treatment is moving to more teamwork and incentive based HR practices

•  The treatment effect is: –  Productivity rises by about 10%

•  The adoption equation: the firms with low transition costs adopt the innovative practices

•  Why did productivity rise? The joint adoption of many HR practices gave workers the incentive, the opportunity, and the ability to raise productivity. And they built connective capital.

Example 2: “Treatment” Variables are Systems of HR Practices at Steel Finishing Lines

1 High

Performance

2 Teams

3 Communication

4 Traditional

Incentive Pay √ √ √ Information Sharing

√ √ √

Teams √ √ some Training √ √ Employment Security

√ some

Flexible job design

√ some

Careful hiring √

Example 2: Treatment Effects are Predicted Productivity (Uptime) for HRM Systems for Steel Finishing Lines

Adoption Equation: Those steel mills that did not adopt the High Performance practices were mills owned by older steel companies with high transition costs. The adoption of HR is heterogeneous. Ichniowski and Shaw, Brookings Papers on Economic Activity: Microeconomics (1995)

Social network analysis

•  In addition: It is always nice if you can get more data to prove your point. –  Social networks in steel – we surveyed who talks to

who about operating problems on the steel line –  Social networks in fruit picking – they surveyed who

their friends were

Social Networks in Steel: Communications The High Performance Line

Crew A Crew B

Crew C Crew D

Each ‘node’ is a person/job -- like entry operator.

all crew members talk to each other

Social Networks in Steel: Communications on a Traditional HR Line

Crew A

Crew D Crew C

Crew B

Gant, Ichniowski, Shaw, 2002 Ichniowski Shaw, 2005

no crew members talk to each other

Social Networks: Communication Across Groups

Level of Inter-group Communication

The Crews run the mill The foremen run the mill

Each ‘node’ is a group: foreman, Crew A, etc.

High Performance steel mill Traditional Steel Mill

Data from within many firms but one industry

Valves industry: •  Information technology imbedded

within new capital raises productivity and results in the move to a strategy of greater product customization.

Example 3: Valve manufacturing Bartel, Ichniowski, Shaw (Quarterly Journal of Economics, 2007)

•  The data: confidential data on the productivity one firm making many different similar valve products; survey data of 250 firms on the productivity of making one product over time.

•  The treatment is moving to new capital equipment that is IT (computer) driven CNC machines, as well as changes in HR practices.

•  The treatment effect is: –  Productivity rises significantly –  Plants with new IT change their strategy – they produce more customized

products –  firms’ individual responses to the treatment are heterogeneous

Implications: why more support for new data sets and new analysis?

•  So we can address the overall questions: how are firms optimally managed; why do some firms adopt new practices and others don’t; how much does productivity rise?

Implications: why are firms reluctant to cooperate with researchers?

•  Firms are concerned about confidentiality. However: –  It is easy to mask the identify of individual employees –  It is harder to make the identity of the firm, but doable.

•  Firms are concerned about: –  Legal liability –  Revealing their sources of competitive advantage –  Time wasted by their employees –  Bosses don’t want the productivity of their group revealed

Why should firms work with researchers?

•  We will uncover new best practices for managing people.

•  When manufacturing “best practices” were developed in the 1980s – TQM and 6-sigma – these managerial advances were made through joint work with business and academia.

•  In the future, firms are going to use databases on their people, performance, and pay to manage people– new data bases on people will mean new management methods.

What kinds of data do we want?

•  Existing data that firms are developing.

•  New experiments within firms: social networks, tournaments, monitoring devices.

•  Surveys linked to the data above – Surveys on HR practices or workers’ traits – Links to existing Census data or LEHD data

In sum….

•  Better data sets and new managerial “best practices” are developing within firms

•  It we need to invest in developing the science of managing people

Example 4: Fruit-pickers (Bandiera, Barankay, and Rasul (QJE, 2005)

•  The data: confidential data from the firm on the productivity of workers before and after the adoption of new piece rate pay.

•  The treatment is moving to relative pay to piece rate pay for fruit-pickers on one farm

•  The treatment effect is: –  Productivity rises by 50% –  The workers who was working among friends becomes

more productive because before piece rates he was holding back effort when his friends were watching

workers’ individual responses to the treatment are heterogeneous

•  The adoption equation: only one firm adopts—it is not random

Example 5: Grocery store clerks Mas and Moretti (AER, forthcoming) •  The data: confidential data from the firm on the

productivity of workers before and after workers change their work shifts and work with different team members.

•  The treatment is moving workers across day and night shifts randomly

•  The treatment effect is: –  Productivity rises significantly –  A clerk becomes more productive when moved to a shift in which

a highly productive clerk is watching her work (i.e., peer effects)

workers’ individual responses to the treatment are heterogeneous

•  The adoption equation: only one firm adopts—it is not random

Example 6: Plumbing Stores Griffith (JOLE, forthcoming)

•  The data: confidential data from the firm on the productivity of workers before and after the adoption of new performance based pay.

•  The treatment is moving to “Balanced Scorecard” among store managers for xx outlets of one company

•  The treatment effect is: –  Productivity rises only among the outlets in which the store

manager is experienced at managing

workers’ individual responses to the treatment are heterogeneous

•  The adoption equation: only one firm adopts—it is not random