Investments in Organizational Capital: Evidence, Challenges, and Future Research Directions by Lisa M. Lynch Tufts University and European University Institute June 2005 Preliminary version, please do not cite or circulate Prepared for the 5 th ZEW Conference on The Economics of Information and Communication Technologies

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

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 SystemTeam 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 sets Cross Section Results Study PracticeOutcome Ichniowski (1990) index of incidence HR Tobin’s q (US) practices Black and Lynch (1996) 10 indicators of HR pluslabor productivity (US) interaction terms Huselid (1997) intensity of HRmarket value (US) Bresnahan et. al. (2002) 3 measures of teamworkValue 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 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'Z i + ε i 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) Y it = α’X it + δ'Z i + v i + ε it Where Y is sales per workers X is a vector of capital, materials Z is a vector of establishment and worker characteristics as well as workplace practices V is a time invariant establishment fixed effect ε it 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 ( )

Generate predicted values of Y it - α'X it = δ'Z i + v i + ε it using the within estimator or the GMM estimator of α'. Then average that value over the period for each business 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 workers0.194** ln material/production workers0.274** ln nonproduction/production workers0.300** 0.363** Proportion of non-managers using computers0.363** % of ees less than 1 year-1.085** 0.312* Re-engineer0.312* ** Proportion of workers in self-managed teams-0.535** Union Proportion of workers in meeting in groups * Union*meet0.408* Note: Adjusted R squared = 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) BLS Black and Lynch (2004) Output Combined Inputs (includes capital, labor and materials) Multifactor Productivity Contribution of Workplace Practices Remaining Residual 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 sections with a 1% trim as presented in Black and Lynch (2004).

Impact of practices in nationally representative data sets Panel Estimates Study PracticeOutcome Black and Lynch (2001) 10 HR practiceslabor productivity (US) quasi panel plus interaction terms Cappelli and Neumark (2001) 9 workplace practiceslabor productivity & labor costs (US) quasi panel plus interaction terms Huselid and Becker (1996) intensity of HR (2 years)market value panel (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 decentralizationlabor productivity panel (Germany) (1993-’95) instruments with US data Black and Lynch (2004) 10 HR practiceslabor productivity Panel (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