Investments in Organizational Capital: Evidence, Challenges, and Future Research Directions

by

Lisa M. Lynch

Tufts University and European University Institute

June 2005Preliminary version, please do not cite or circulate

Prepared for the 5th ZEW Conference on The Economics of Information and Communication Technologies

Average Annual Compound Productivity Growth Non-Farm Business – U.S.

1.35 1.39

2.523.09

0

0.5

1

1.5

2

2.5

3

3.5

1973-1990 1973-1995 1995-2000 1995-2004

Role of Total Factor Productivity in Productivity Story:

Computers (ICT)

Organizational capital

A Working Definition of Organizational capital

Human Capital Theory Education Training

% of workers trained Number of hours of training % of payroll spent on training Types of training offered

Theories of Worker Voice -- Freeman and Lazear (1995) and Malcomson (1983)

Works councils TQM Employees meeting in teams Self-managed teams unions

Traditional System Team Work

A Working Definition of Organizational capital (cont.)

Work Design - Bundles/Synergies of Practices - Milgrom and Roberts (1995), Kandel and Lazear (1992), Athey and Stern (1998)

Reenginnering # of employees per supervisor Levels of organization Incentive based compensation Benchmarking Job rotation & multi-tasking

Additional issues related to Organizational capital

Computers and workplace practices - Brynjolfsson and Hitt (2003), Brynjolfsson, E, L. Hitt and S. Yang, (2002 Bresnahan et. al. (2002)

Organizational capital and labor demand - Kremer and Maskin (1996), Acemoglu (2000), Aghion, Caroli and Garcia-Peñolosa (1999)

Empirical Issues

Sources of data – case studies, intra-industry, more nationally representative surveys

Different definitions of workplace practices across surveys and over time

Data limitations -- cross section vs. longitudinal data, response rates, ability to link with other data sources

Endogeneity and measurement error

Impact on Productivity at Firm Level

Intra-industry studies – HR practices such as flexible job definitions, cross-training, team work, and extensive reliance on incentive pay

Are associated with

Substantially higher levels of productivity than more traditional human resource management practices.

Impact of practices in nationally representative data setsCross Section Results

Study Practice Outcome

Ichniowski (1990) index of incidence HR Tobin’s q(US) practices

Black and Lynch (1996) 10 indicators of HR plus labor productivity(US) interaction terms

Huselid (1997) intensity of HR market value

(US)

Bresnahan et. al. (2002) 3 measures of teamwork Value added(US)

Characteristics of Data used in Black and Lynch (various years)

Nationally representative telephone survey of establishments

1621 manufacturing plants in 1993 and 2479 in 1996, panel 1993-1996 almost 700.

Can match the manufacturing businesses with the Census LRD

High response rate (75% in 1993 and 66% in 1996)

Depth of questions on business characteristics – book value of the capital stock, age of equipment,

materials costs, employment by 5 occupational categories; worker characteristics – average education by occupation, % female, % minority,

% at firm for less than 1 year; workplace practices – teams, employee involvement in decision making, profit

sharing, training, union status, job rotation, job sharing, # of organizational levels, TQM, re-engineering;

technology usage - percent of managers using computers and percent of non-managers using computers

outcome measures including labor productivity, wages and labor demand

Production function estimation – Augmented Cobb Douglas(cross section)

(1) ln(Y/P)i = αln(K/P)i + βln(M/P)i + γ(ln(N/P)i + δ'Zi + εi

Where Y is values of real sales; P is the number of production workers; K is the real book value of the capital stock; M is real materials cost; N is the number of non-production workers; δ' is a vector of coefficients on Zi which are establishment specific

workplace practices and characteristics of employees; εi is the error term.

(note imposed constant returns to scale)

Production function estimation – 2 step estimation(longitudinal data on Y,K,M,L but only cross section on Z)

Consider:

(2) Yit = α’Xit + δ'Zi + vi + εit

Where Y is sales per workersX is a vector of capital, materialsZ is a vector of establishment and worker characteristics as well as

workplace practicesV is a time invariant establishment fixed effect

εit is the idiosyncratic component of the error term.

In the first step estimate using within estimator or GMM to obtain estimates of coefficients on X – i.e. do a better job on coefficients for capital and materials (1988-1993)

Generate predicted values of Yit - α'Xit = δ'Zi + vi + εit using the within estimator or the GMM estimator of α'.

Then average that value over the period 1988-1993 for eachbusiness to get an estimate of the firm specific-time invariant component of the residual.

In the second step, regress average residual on the various human resource management practices, human capital measures, a variable to capture diffusion of information technology, industry dummies, and other worker and employer characteristics in order to obtain estimates of δ'.

i.e. how well do workplace practices in place in 1993 explain this residual?

Panel results from Black and Lynch (2004)

Dependent Variable: ln value of sales/production workers

ln Capital/production workers 0.194**

ln material/production workers 0.274**

ln nonproduction/production workers 0.300**

Proportion of non-managers using computers 0.363**0.363**

% of ees less than 1 year -1.085**

Re-engineer 0.312*0.312*

Proportion of workers in self-managed teams -0.535**-0.535**

Union -0.148

Proportion of workers in meeting in groups -0.172

Union*meet 0.408*0.408*

Note: Adjusted R squared = 0.798. Other variables included: average education, proportion of workforce female, proportion of workforce male, benchmarking, no. of managerial levels, profit sharing, union interacted also with profit sharing, re-engineering, and self-managed teams

Compound Average Annual Rates of Output Growth and the Contribution of Factor Inputs and Multifactor Productivity

Manufacturing (% per year) 1993-1996

BLS Black and Lynch (2004)

Output 4.2 4.7

Combined Inputs 2.3 3.2(includes capital, labor andmaterials)

Multifactor Productivity 1.9 1.6

Contribution of Workplace - 1.4Practices

Remaining Residual - 0.2

Source: Bureau of Labor Statistics, Multifactor Productivity Trends, 1998, released September 21, 2000 and authors’ own calculations from the EQW-NES first and second round cross sectionswith a 1% trim as presented in Black and Lynch (2004).

Impact of practices in nationally representative data setsPanel Estimates

Study Practice Outcome

Black and Lynch (2001) 10 HR practices labor productivity(US) quasi panel plus interaction terms

Cappelli and Neumark (2001) 9 workplace practices labor productivity & labor costs(US) quasi panel plus interaction terms

Huselid and Becker (1996) intensity of HR (2 years) market valuepanel (US)

Caroli & Van Reenen (2001) organ change (1984-’90) skill share (UK panel) organ change (1992-’96) skill share (French panel)

organ change (1992-’96) productivity (French panel)

Bauer (2003) index of decentralization labor productivitypanel (Germany) (1993-’95)

instruments with US data

Black and Lynch (2004) 10 HR practices labor productivityPanel (US) plus interactions

Black, Lynch and Krivelyova (2004) “ wages by occupation Panel (US)

Impact on workers -- Labor demand & wages

Empirical evidence of impact on wages and wage inequality mixed

Some association between lower demand for unskilled workers and firms with more organizational capital

Supervisors in businesses with more “high performance workplace practices” have higher wages – insurance against sabotage?

Who adopts?

Those who are doing badly need to adopt and it is easier to get people to change in a crisis

These are expensive systems to put in place so only those who are doing well can afford it

Empirical Evidence mixed

Understanding this issue is key to addressing the endogeneity problem

Summary of empirical work

There is a large impact of workplace organization on productivity and wages and there is evidence of bundling of practices. These effects are large at the macro level as well

Synergies between investments in ICT and organizational practices

BUT

How these synergies are examined empirically though is problematic – ad hoc with some studies doing full interactions, others selecting arbitrary groupings and others using factor analysis to generate an index of practices

Adoption process less well understood and limited by length of available data

How these investments impact survival of firms - not studied

Future Research Directions

Adoption Decision – need more analysis

Interaction between physical capital investments (IT in particular) and organizational capital investments

Endogeneity – find instruments or get longer panels of workplace practices and outcomes

Follow the evolution over time of practices and long run productivity effects Integration of intra-industry studies with national representative studies of

firms

What we can learn from cross country comparisons

Survey challenges and requirements