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 1990-2003)– 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.

ittiitiit xw ),J(ln

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).

ˆ ofpart experience forcelabor ˆˆitiit xh

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 hijt

• Calculate the proportion of employment in any interval using Gjt(h)

jtLjtjt jhhhg ,,KDE)( 1

Establishment Human Capital Measures

• Using gjt(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):

),...,,( 1 Hjtjtjtjt LLZFy

,...)/,...,/,,( 1 HjtsjtHjtjtjtjtsjt wwwwyZSS

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

jtjtHjtjtk

kjtkbjt ywwZS lnln 3210

bjtm

mbjtbjt

jtb

Bjtbjtbk

kjtkbjt

S

ywwZS

1 1

1

32101

1

11

lnln1

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

• 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

Predicted Share of Bottom Quartile Workers in Manufacturing 1992

0.150

0.200

0.250

0.300

0.350

0.400

Age 0 to 1 Age 2 to 5 Age 6 to 10 Age 11 to 24

Business Age

Pre

dic

ted

Sk

ill D

em

an

d S

ha

re

Human Capital Person Effect Experience Component

Predicted Share of Top Quartile Workers in Manufacturing 1992

0.150

0.200

0.250

0.300

0.350

0.400

Age 0 to 1 Age 2 to 5 Age 6 to 10 Age 11 to 24

Business Age

Pre

dic

ted

Sk

ill D

em

an

d S

ha

re

Human Capital Person Effect Experience Component Predicted Share of Bottom Quartile Workers in

Services 1992

0.150

0.200

0.250

0.300

0.350

0.400

Age 0 to 1 Age 2 to 5 Age 6 to 10 Age 11 to 24

Pre

dic

ted

Sk

ill D

em

an

d S

ha

re

Human Capital Person Effect Experience Component

Business Age

Predicted Share of Top Quartile Workers in Services 1992

0.150

0.200

0.250

0.300

0.350

0.400

Age 0 to 1 Age 2 to 5 Age 6 to 10 Age 11 to 24

Pre

dic

ted

Sk

ill D

em

an

d S

ha

re

Human Capital Person Effect Experience Component

Manufacturing (ASM) Computer Investment

-0.0500

-0.0400

-0.0300

-0.0200

-0.0100

0.0000

0.0100

0.0200

0.0300

19931st

Qtile

1994 1995 1996 19932ndQtile

1994 1995 1996 19933rdqtile

1994 1995 1996 19934th

Qtile

1994 1995 1996

Year by Quartile

Co

eff

icie

nt

Human Capital

Person Effect Component

Experience Component

Services (BES) Computer Investment

-0.0500

-0.0400

-0.0300

-0.0200

-0.0100

0.0000

0.0100

0.0200

0.0300

19931st

Qtile

1994 1995 1996 19932ndQtile

1994 1995 1996 19933rdqtile

1994 1995 1996 19934th

Qtile

1994 1995 1996

Year by Quartile

Co

eff

icie

nt

Human Capital

Person Effect Component

Experience Component

Manuf. Services Manuf. Services Manuf. Services Manuf. Services

Capital Intensity -0.0473* -0.0064 -0.0181* -0.0012 0.0246* 0.0014 0.0391* 0.00650.0066 0.0066 0.0046 0.0037 0.0052 0.0041 0.007 0.0062

Computer Investment Share -0.0253 -0.0678* -0.0250* -0.0176* 0.0012 0.0066* 0.0542* 0.0789*0.0138 0.0018 0.0096 0.0010 0.0108 0.0011 0.0146 0.0017

Inventory/Sales -0.0395* -0.1133* 0.0004 0.0061 -0.0063 0.0765* 0.0322* 0.0292*0.0044 0.0059 0.0031 0.0033 0.0034 0.0037 0.0047 0.0056

Software Share -0.1649* -0.4987* -1.0995* 0.3797* -1.1726* 0.006 2.8011* 0.0863*0.0063 0.0112 0.0044 0.0063 0.0049 0.0069 0.0067 0.0105

Y, wage equation firm effect -0.0069 -0.0071 0.0264* 0.0070* -0.0087 0.0031 -0.0147 -0.00280.0102 0.005 0.0071 0.0028 0.008 0.0031 0.0107 0.0047

Inverse Mills Ratio -0.0216* -0.0063* -0.0027 0.0009* 0.0087* 0.0016 0.0181* 0.0041*0.0022 0.0005 0.0015 0.0003 0.0017 0.0003 0.0024 0.0005

Total Human Capital

Table 2b: Regression of Skill Mix on Technology --1992 Cross-section, With Selection ControlsFirst Quartile Second Quartile Third Quartile Fourth Quartile

Manuf. Services Manuf. Services Manuf. Services Manuf. Services

Capital Intensity -0.0552* -0.0026 -0.0071 -0.0014 0.0256* -0.0004 0.0350* 0.00470.0072 0.0064 0.0045 0.0039 0.0047 0.004 0.0067 0.0064

Computer Investment Share -0.0392* -0.0598* -0.0082 -0.0265* -0.0057 0.0041* 0.0573* 0.0835*0.015 0.0017 0.0094 0.0010 0.0098 0.0011 0.0141 0.0017

Inventory/Sales -0.0112* -0.1234* 0.0029 0.0263* -0.0195* 0.0860* 0.0175* 0.00940.0048 0.0057 0.003 0.0035 0.0031 0.0036 0.0045 0.0057

Software Share -0.0888* -0.5913* -1.7254* -0.0048 -0.6855* 0.0405* 2.7832* 0.5430*0.0069 0.0109 0.0043 0.0066 0.0045 0.0068 0.0064 0.0109

Y, wage equation firm effect -0.0329* -0.0003 0.0288* -0.0012 -0.0011 0.0002 0.0026 0.00210.0111 0.0048 0.0069 0.0029 0.0073 0.003 0.0104 0.0048

Inverse Mills Ratio -0.0227* -0.0080* -0.001 0.0000 0.0088* 0.0038* 0.0167* 0.0040*0.0024 0.0005 0.0015 0.0003 0.0016 0.0003 0.0023 0.0005

Person Effect

Table 2b: Regression of Skill Mix on Technology --1992 Cross-section, With Selection ControlsFirst Quartile Second Quartile Third Quartile Fourth Quartile

Manuf. Services Manuf. Services Manuf. Services Manuf. Services

Capital Intensity -0.0051 -0.0099 -0.0066 0.0028 -0.0011 0.0036 0.0137* 0.00340.0049 0.0083 0.0031 0.0037 0.0028 0.0035 0.0043 0.0046

Computer Investment Share 0.0007 -0.016 0.0238* 0.0190* -0.0022 0.0082* -0.0226* -0.0115*0.0103 0.0022 0.0066 0.001 0.0059 0.0009 0.0091 0.0012

Inventory/Sales -0.0749* -0.0185* 0.0169* -0.0547* 0.0308* -0.0076* 0.0251* 0.0808*0.0033 0.0074 0.0021 0.0033 0.0019 0.0031 0.0029 0.0041

Software Share -0.3031* 0.4958* -0.4473* -0.2224* 1.0298* -0.0675 -0.2833* -0.20560.0047 0.014 0.003 0.0062 0.0027 0.0058 0.0042 0.0078

Y, wage equation firm effect 0.0554* 0.0163* 0.0001 -0.0115* -0.0334* -0.01 -0.0224* 0.00520.0076 0.0062 0.0048 0.0028 0.0043 0.0026 0.0067 0.0035

Inverse Mills Ratio -0.0013 0.0016* -0.0036* 0.0007* -0.0005 -0.0004 0.0058* -0.0015*0.0017 0.0007 0.0011 0.0003 0.0009 0.0003 0.0015 0.0004

Experience

Table 2b: Regression of Skill Mix on Technology --1992 Cross-section, With Selection ControlsFirst Quartile Second Quartile Third Quartile Fourth Quartile

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.