Technology and Skill: An Analysis of Within and Between Firm Differences John Abowd, John Haltiwanger, Julia Lane, Kevin McKinney, and Kristin Sandusky

Outline of Talk Skill-biased technical change Our research and objectives Measuring human capital The demand for human capital –Cross sectional results –Partial adjustment results Conclusions

Skill-biased Technical Change

Capital-labor substitution Labor is differentiated by skill class –High skill –Low skill Capital is differentiated by investment type –Information technology –Other capital Information technology and high-skill workers are demand complements Information technology and low-skill workers are demand substitutes

Factor price equalization US comparative advantage in producing IT and high-skill intensive goods ROW comparative advantage in producing IT-using and low-skill intensive goods Factor price equalization via trade reducing the demand for low skill workers and increasing the demand for high skill workers

Macroeconomic evidence Hypothesis originally due to Zvi Griliches, who almost certainly would have attributed it to one of the fathers of microeconomics Berman, Bound, Griliches (1994) –increased use of non-production workers within manufacturing industries directly related to the increased IT investment and R&D. –Very little of the increase was associated with increased demand for goods produced by non- production worker intensive manufacturing industries (evidence against factor price equalization)

Microeconomic evidence Ichniowski, Shaw and Prennushi (1997) –combination of “high-performance” HRM practices, which included selection and training of skilled workers, complementary with the successful adoption of IT Bresnahan, Brynjolfsson and Hitt (2002) –increased use of IT directly related to increased demand for skilled employees Hellerstein, Neumark, and Troske (1999) –capital and skilled labor complements in main analysis (Table 3), but substitutes in other specifications (Table 4)

Our Research Objectives

Objectives Measure human capital employed by the business –Exploit the linked employer-employee data Gather facts: characterize changing distribution of human capital –Within-firm changes –Firm displacement (entry and exit) Explore why patterns exist –Theory: derived demand for human capital is a function of technology –Measure technology changes and relate to changes in demand for human capital

Measuring Human Capital

Motivation Distinguish among similar businesses using the human capital of the employees Normal measures: employment and wages, sometimes hours Our measures: a variety of skill indices based on the portable part of the individual's wage rate Use the differences in the human capital input to help explain differences in the outcomes

Theoretical Framework The general human capital of an employee is represented by h, which is estimated from the portable part of the individual’s wage rate. The firm-specific part of the wage rate is used to model compensation design issues. The un-normalized distribution f(h) measures the firm’s human capital choices. We estimate the normalized distribution of human capital, g(h). For details see Abowd, Lengermann and McKinney (2003).

Measuring Human Capital: Data State UI wage records and ES-202 –Universal for 3 states (among the seven listed in ALM) –Longitudinal (cover ) –Permits linkage of employees and firms Links to economic data –Annual Survey of Manufacturers (Manufacturing) –Business Expenditure Survey (Non-manufacturing) –Economic Census (1992 and 1997) –Business Register (1992 and 1997)

Measuring of Human Capital: Estimation We use a decomposition of the log real annualized full- time, full-year wage rate (ln w) into person and firm effects. The person effect is θ. The firm effect is ψ, where J(i,t) is the employer of i at t. Continuous, time-varying effects are in xβ, where some of the x variables are human capital measures (labor force experience) and some correct for differential quality in our measure of full-time, full-year wage rate.

Human Capital: Individual Measure Individual human capital, h, is the part associated with the person effect and the measurable time-varying personal characteristics (labor force experience). Our human capital measure is not a simple ranking by wage rate because of the removal of the firm effect and residual. Firm human capital measures, H, are based on statistics computed from the distribution of g(h).

Human Capital: Distribution Use the entire workforce present at the establishment at date t in firm j Take the kernel density estimator of the distribution of h ijt Calculate the proportion of employment in any interval using G jt (h)

Establishment Human Capital Measures Using g jt (h) measure –Proportion of employment in each quartile of the h distribution (1992 basis) –Separate measure for person effect –Separate measure for experience effect

The Demand for Human Capital and Technology

Basic Approach to Demand for Human Capital Production relationship at firm level as function of skill composition for firm j with technology Z: Treating Z as quasi-fixed, cost minimization (Shepherd’s lemma) yields for workers of type s (where S is share of type s workers):

Demand for Human Capital: Basic Features The demand for workers of type s by a particular firm depends on: – the type of technology adopted (Z) managerial/entrepreneurial ability Vintage Location Physical and intangible capital –the nature of the firm-worker type complementarities, –the scale of operations –the relative shadow wages

Empirical Specification Model 1: Levels Model 2: Partial Adjustment

Construction of Linked Data Human capital file containing worker and firm identifiers, detailed worker characteristics Business file containing firm identifiers and detailed business characteristics. These two files linked by employer identifiers to form a business-level file. Unit of business observation is the most detailed disaggregation available of EIN, State, 2-digit SIC, and county (pseudo- establishment)

Weights, Selection, and Other Issues The sampling frames of the ASM and BES make dynamic analysis difficult –We correct for differential sampling of large and small establishments using special weights –We correct for differential exit using a selection equation Not all measures are available every in both Censuses –There is no good correction for this

Construction of Technology Measures Data for the manufacturing sector for the 1992 and 1997 Annual Survey of Manufacturers (ASM). For services, wholesale trade and retail trade we use data from the Business Expenditure Survey (BES). In the majority of ASM cases, we are able to link the two files by EIN, State, 2-digit SIC (SIC2), and county. In the BES, there is no state county level detail and the survey is conducted using more aggregated business units (EIN, 2-digit SIC or Enterprise, 2-digit SIC)

Technology Measures –Computer Investment/Total Investment (ASM, BES, 1992 only) –Spending on Computer Software and Data Processing Services/Sales (ASM, BES, 1992 and 1997) –Inventory/Sales (higher inventories indirect indicator of lack of technology; ASM, BES, 1992 and 1997) Traditional Technology Measures –Average Beginning and Ending Assets/Employment (ASM 1992 and 1997, BES 1992) Firm Effect from Wage Equation –Potential proxy for “unmeasured” technology and other things

Summary of Findings There is a strong positive empirical relationship between technology and skill in a cross-sectional analysis of firms. Technology interacts with different components of skill quite differently: firms that use technology are more likely to use high ability workers, but less likely to use high experience workers. The partial adjustment analysis supports these conclusions.