1. 1 Firm Size and Information Technology Investment: Beyond Simple Averages Tianyi Jiang Leonard N. Stern School of Business New York University December 16, 2003

2. 2 Motivation Effectiveness of information technology (IT) investments … Specifically: How does IT impact firm sizes and firm boundaries

3. 3 IT ’ s Theoretical Impact on Firm IT … decreases decision cost, agency cost, & coordination costs between & across firms - Gurbaxani et al. 1991 could make firms smaller - Malone et al. 1987 lead to outsourcing from fewer suppliers - Bakos et al. 1993

4. 4 Empirical Evidence on the impact of IT IT investment … is negatively correlated with firm size across all industries. – Brynjolfsson, et al. 1994 is negatively correlated with vertical integration is weakly positively correlated with diversification – Hitt, 1999

5. 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 goodmeasure?

6. 6 NAICS Industries IT investment ratio in 1992

7. 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 0.87 Durbin-Watson 1.92 F Statistic 2637.40 Observations 6577 Key: *= Significant at 90% level; **= Significant at 95% level; ***= Significant at 99% level; 13/16 industries significant at 99% level

8. 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. 9 Problems with median firm sizes 1992 Professional Services Employee Histogram Median.8% total Sales 99.2% total Sales 1% total Emp 99% total Emp 1992 Professional Services Sales Histogram

10. 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 * Kumar, K., Rajan, R., & Zingales, L. “What Determines Firm Size?” Working Paper, The University of Chicago Graduate School of Business, 2001.

11. 11 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

12. 12 Automated bin partition Recursive Minimum Entropy Partitioning – Fayyad et al. 1993 Entropy: A measure of homogeneity of values – Mitchell 1997 Example: 2 distinct values, i,j, i  j S be a bin of firms with i or j employees then P i = percentage of firms with i employees

13. 13 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. 14 Recursive Minimum Entropy Partitioning (cont.) Recursive Splits

15. 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

16. 16 Firm size measures across NAICS industries with low IT investment ratio

17. 17 Firm size measures across NAICS industries with low IT investment ratio (cont.)

18. 18 Firm size measures across NAICS industries with medium IT investment ratio

19. 19 Firm size measures across NAICS industries with high IT investment ratio (cont.)

20. 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 mean

21. 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 of Economic 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. 22 Regression Results – Across 6 NAICS Industries All Industry Regression Table VariableSIZE1 (Simple Average)SIZE2 (Employee Weighted Average)SIZE3 (Sales Weighted Average) Constant 1.159.20***5.25 IT Investment Ratio by year ITINVRATIO(0) -0.04*0.02-0.05 ITINVRATIO(-1) 0.05*0.01-0.03 ITINVRATIO(-2) -0.010.04-0.01 ITINVRATIO(-3) 0.01-0.04-0.06 ITINVRATIO(-4) -0.04***-0.04*-0.05 NetSales 0.11**-0.33***-0.13 Industry Dummies Professional Services -1.32***-0.58**0.60 Manufacturing -0.98***-1.80***-1.62*** Finance & Insurance -1.25*** -2.09*** Education -1.81***-5.85***-4.54*** WholeSale Trade -1.85***-2.25***-2.01*** R-Squared 0.99 0.96 Durbin-Watson 1.260.730.60 F Statistic 709.90 539.00136.76 Number of observations 72

23. 23 Regression Result: Professional Services Professional Services Regression Table VariableSIZE1 (Simple Average) SIZE2 (Employee Weighted Average) SIZE3 (Sales Weighted Average) Constant -2.45**1.182.06 IT Investment Ratio by year ITINVRATIO(0) 0.22*0.56*0.52* ITINVRATIO(-1) 0.04-0.06-0.10 ITINVRATIO(-2) -0.01-0.03460.1065 ITINVRATIO(-3) -0.20-0.82**-0.87** ITINVRATIO(-4) -0.020.160.17* NetSales 0.31***0.220.18 R-Squared 0.870.98 Durbin-Watson 2.592.302.72 F Statistic 5.66 52.2352.32 Number of Observations 12

24. 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. 25 Summary Technical 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. 26 Thank You! Special thanks to Ramesh Sankaranarayan & Shinkyu Yang