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3 IT ’ s Theoretical Impact on Firm IT … decreases decision cost, agency cost, & coordination costs between & across firms - Gurbaxani et al could make firms smaller - Malone et al lead to outsourcing from fewer suppliers - Bakos et al. 1993
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1
Firm Size and Information Technology Investment:
Beyond Simple Averages
Tianyi JiangLeonard N. Stern School of BusinessNew York UniversityDecember 16, 2003
2
MotivationEffectiveness of information technology (IT) investments…
Specifically: How does IT impact firm sizes and firm boundaries
3
IT’s Theoretical Impact on Firm
IT…
decreases decision cost, agency cost, & coordination costs between & across firms
- Gurbaxani et al. 1991could make firms smaller
- Malone et al. 1987
lead to outsourcing from fewer suppliers - Bakos et al. 1993
4
Empirical Evidence on the impact of ITIT investment…
is negatively correlated with firm size across all industries. – Brynjolfsson, et al. 1994
is negatively correlated with vertical integrationis weakly positively correlated with diversification
– Hitt, 1999
5
Research Questions:1. In the context of new NAICS classifications, are IT
investments negatively correlated with firm size across all industries?
2. In measuring impact of IT investments at the industry level, is average employees per firm a good measure?
6
NAICS Industries IT investment ratio in 1992
0%
5%
10%
15%
20%
25%
30%
Rat
io o
f IT/
Oth
er in
vest
men
ts
7
Regression on 1992 COMPUSTAT Data All Industry Regression for 1992 COMPUSTAT Data
Dependent Variable
Log(Employees)
Constant -1.16***IT Investment Ratio -0.10***
Log(Net Sales) 0.85***Industry Dummies
R-Squared 0.87Durbin-Watson 1.92F Statistic 2637.40Observations 6577
Key: *= Significant at 90% level; **= Significant at 95% level; ***= Significant at 99% level;
13/16 industries significant at 99% level
8
Problems with simple firm averages
Observations: • Large numbers of small firms can bring down average firm sizes even if the bigger firms got bigger
example: firms sizes = {1,1,1,1,100,100}
average size = 34
• Most entry & exit has relatively little effect on the largest firms in the industry - Sutton 1997
9
1992 Employee Sizes - Professional Services
-4
-3
-2
-1
0
1
2
3
Companies Sorted by Employees
Log(
Em
ploy
ees)
1992 Annual Sales - Professional Services
-3
-2
-1
0
1
2
3
4
5
6
Companies Sorted by EmployeesLo
g(An
nual
Sal
es)
Problems with median firm sizes
1992 Professional Services Employee Histogram
MedianMedian
.8%.8% total Salestotal Sales 99.2%99.2% total Salestotal Sales1%1% total Emptotal Emp 99%99% total Emptotal Emp
1992 Professional Services Sales Histogram
10
Employee weighted firm sizes
• Emphasize the size of larger firms to minimize the effects of entry & exit - Kumar et al. 2001 *
Weighted Average Number of Employees =
= total number of employees in a bin
= total number of employees in the sector
= total number of firms in a bin
n
Firmsbin
Empbin
EmpSector
Empbin
NN
NN
1
EmpbinNEmpSectorNFirmsbinN
* Kumar, K., Rajan, R., & Zingales, L. “What Determines Firm Size?” Working Paper, The University of Chicago Graduate School of Business, 2001.
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Employee size calculation example
Example: firms sizes = {1,1,1,1,100,100} average size = 34
Employee weighted average: 2 bins: {1,1,1,1} and {100, 100}
weighted average = (4/204)*(4/4) +(200/204)*(200/2)
=98.05882
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0
0.25
0.5
0.75
1
0 0.25 0.5 0.75 1
% of firms with i employees
Entro
py
Automated bin partitionRecursive Minimum Entropy Partitioning – Fayyad et al. 1993
Entropy: A measure of homogeneity of values – Mitchell 1997
Example: 2 distinct values, i,j, ij S be a bin of firms with i
or j employeesthen
jjii ppppSEntropy 22 loglog)(
Pi = percentage of firms with i employees
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Recursive Minimum Entropy Partitioning
Let S = original bin A = set of newly split bins Gain (S,A) = Entropy(S)-E[Entropy(A)]
Idea: Recursively split data into smaller bins with nearly homogenous values until gain < threshold
14
Recursive Minimum Entropy Partitioning (cont.)
RecursiveSplits
15
Sales weighted firm sizes• Alternatively, we could emphasize firms with higher proportion of sales to minimize the effects of entry & exit
Sales Weighted Employees Sizes =
= total number of employees in a bin
= total number of firms in a bin
= total amount of sales in a bin
= total amount of sales in a sector
n
Firmsbin
Empbin
SalesSector
Salesbin
NN
NN
1EmpbinNFirmsbinN
SalesbinNSalesSectorN
16
Firm size measures across NAICS industries with low IT investment ratio
Real Estate & Rental & Leasing (Employees per firm)
0
0.5
1
1.5
2
2.5
3
3.5
1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Year
Aver
age
Empl
oyee
s (th
ousa
nds)
0
10
20
30
40
50
60
70
80
Wei
ghte
d Av
erag
e Em
ploy
ees
(thou
sand
s)
AverageEmployees WeightedAverageEmpolyees SalesWeightedAvgEmployee
17
Firm size measures across NAICS industries with low IT investment ratio (cont.)
Utilies (employees per firm)
0
1
2
3
4
5
6
7
8
1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Year
Aver
age
Empl
oyee
s (th
ousa
nds)
0
10
20
30
40
50
60
70
80
90
100
Wei
ghte
d Av
erag
e Em
ploy
ees
(thou
sand
s)
AverageEmployees WeightedAverageEmpolyees SalesWeightedAvgEmployee
18
Firm size measures across NAICS industries with medium IT investment ratio
Information Industry (Employees per firm)
0
2
4
6
8
10
12
1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Year
Aver
age
Empl
oyee
s (th
ousa
nds)
0
50
100
150
200
250
Wei
ghte
d Av
erag
e Em
ploy
ees
(thou
sand
s)
AverageEmployees WeightedAverageEmpolyees SalesWeightedAvgEmployee
19
Firm size measures across NAICS industries with high IT investment ratio (cont.)
Professional Services (Employees per firm)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Year
Aver
age
Empl
oyee
s (th
ousa
nds)
0
50
100
150
200
250
300
Wei
ghte
d Av
erag
e Em
ploy
ees
(thou
sand
s)
AverageEmployees WeightedAverageEmpolyees SalesWeightedAvgEmployee
20
Regression Model
= natural log of 3 different employee measures in year t = natural log of IT investment ratio per industry
in year t = natural log of net sales per industry per year
= 17 industry dummy variables
= i.i.d. error term with zero meanitINDUSTRY
t
ttiti
tttttt
NetSalesINDUSTRY
ITITITITITSIZE
76
4534231210
tNetSalestIT
tSIZE
21
Data & Methodology• Computed industry level employee measure & net sales
via COMPUSTAT data from 1982 to 2001 (443,507 records)
• Extracted IT investment ratio from BEA (Bureau ofEconomic Analysis) Input-Output use tables for the benchmark years of 1982, 1987, 1992, & 1997(Required many to many mappings of NAICS to SIC
and SIC to IO codes)
• Interpolated IT investment ratios for other years
22
Regression Results – Across 6 NAICS Industries
All Industry Regression TableVariable SIZE1 (Simple Average) SIZE2 (Employee Weighted
Average)SIZE3 (Sales Weighted Average)Constant 1.15 9.20*** 5.25
IT Investment Ratio by year
ITINVRATIO(0) -0.04* 0.02 -0.05ITINVRATIO(-1) 0.05* 0.01 -0.03ITINVRATIO(-2) -0.01 0.04 -0.01ITINVRATIO(-3) 0.01 -0.04 -0.06ITINVRATIO(-4) -0.04*** -0.04* -0.05NetSales 0.11** -0.33*** -0.13
Industry Dummies Professional Services -1.32*** -0.58** 0.60Manufacturing -0.98*** -1.80*** -1.62***Finance & Insurance -1.25*** -1.25*** -2.09***Education -1.81*** -5.85*** -4.54***WholeSale Trade -1.85*** -2.25*** -2.01***R-Squared 0.99 0.99 0.96Durbin-Watson 1.26 0.73 0.60F Statistic 709.90 539.00 136.76Number of observations 72 72 72
23
Regression Result: Professional Services Professional Services Regression Table
Variable SIZE1 (Simple Average)
SIZE2 (Employee Weighted Average)
SIZE3 (Sales Weighted Average)
Constant -2.45** 1.18 2.06
IT Investment Ratio by year
ITINVRATIO(0) 0.22* 0.56* 0.52*ITINVRATIO(-1) 0.04 -0.06 -0.10ITINVRATIO(-2) -0.01 -0.0346 0.1065ITINVRATIO(-3) -0.20 -0.82** -0.87**ITINVRATIO(-4) -0.02 0.16 0.17*NetSales 0.31*** 0.22 0.18
R-Squared 0.87 0.98 0.98Durbin-Watson 2.59 2.30 2.72F Statistic 5.66 52.23 52.32Number of Observations 12 12 12
24
Research Limitations• Need yearly IT investment data across all industries
• Tried Brookings panel data, replicated previous results across industries, but lacked the data for Professional Services
25
SummaryTechnical Research Contributions:
• Apply recursive minimum entropy methods to the empirical economics domain
Economic Research Contributions:
• Utilize weighted average employee sizes to replicate previous studies on IT investments and firm sizes
• Found varying patterns of evolving firm sizes across industries with different IT investment ratios
26
Thank You!Special thanks to Ramesh Sankaranarayan & Shinkyu Yang