technology and skill: an analysis of within and between firm differences john abowd, john...
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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.
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