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Campus Presentation at National Taiwan University

Campus Presentation at National Taiwan University. Wesley Shu Assistant Professor San Diego State University. Short Biography. BA in Economics, National Taiwan University MBA in Finance & Decision Sciences, Indiana University Ph.D. in MIS, University of Arizona.

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Campus Presentation at National Taiwan University

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  1. Campus Presentation at National Taiwan University Wesley Shu Assistant Professor San Diego State University

  2. Short Biography • BA in Economics, National Taiwan University • MBA in Finance & Decision Sciences, Indiana University • Ph.D. in MIS, University of Arizona

  3. IT Productivity and Productive Efficiency in Taiwan

  4. What is Productivity? • The amount of output produced given an input • Output/Input • What if multiple input?

  5. A Cobb-Douglas Function

  6. Other Functional Forms • CES • translog

  7. Productivity and Productive Efficiency • Productivity is to measure how much business value an input factor can contribute to. • Productive efficiency is to measure the gap between observed and optimal values of output and input.

  8. Three Types of Inefficiency • Technical inefficiency • Allocative inefficiency • Scale inefficiency

  9. Technical Inefficiency • The gap between the observed output and the production frontier under the current technology

  10. Technical Inefficiency, continued x2 Production frontier A B x1

  11. Allocative Inefficiency • A firm chooses the input ratio when the marginal ratio = input price ratio to minimize its total cost. When they are not equal,

  12. Scale Inefficiency • A firm chooses its production level when the marginal cost = output price. If not, then

  13. Characteristics of Previous Studies • Measuring single deterministic production function • Not incorporating some basic business assumptions

  14. Deterministic Approach • Deterministic approach assumes all deviations except the error terms are under management control • It in fact uses observed data to construct the production frontier (optimal output level.)

  15. Not Imbedding the Basic Business Assumption • Firms want to either maximize their profits or minimize their costs • So, they will decide the output and input quantities based on the price information • This price information and firms’ decision behavior are not captured in a single production function approach, but in the error terms. • So, there is bias because the explanatory variables are correlated with the error terms.

  16. Not Imbedding the Basic Business Assumptioncontinued Hal Varian, Microeconomic Analysis, 3rd Edition “If the managers observe these effects (of price changes,) then they will certainly take that information into account when they determine their optimal choice of inputs. Thus, the right-hand variables (of a production function) will not be statistically independent from the error term.”

  17. Our Model - formalize the business assumption Profit maximization model with inefficiency measurement

  18. Our Model, continued Endogenous variable: xi Exogenous variable: p, wi

  19. Our Model, continued Intrilligator, Bodkin, and Hsiao • Estimating the complete system is generally superior to estimating only the first equation (the production function) from both economic and econometric standpoints. • From an economic standpoint, estimating the complete system expresses the assumption that the data reflect both the behavior of the decision maker (the firm) and the technology, while the first equation (the production function) reflects only the technology. • From an econometric standpoint, the estimators of only the first equation involves simultaneous equations bias, so the estimators will be biased and inconsistent.

  20. Data Requirement • Assets, output price, Employment Compensation are publicly available. • IT Employment Compensation • IT Spending, including hardware, software, maintenance, and training • Prices (Price deflators)

  21. Our Production Function

  22. Data Source • Year 2000 - 2002 • Survey of more than 300 companies, • 187 with valid data (all three years) • A variety of industries

  23. Data Requirement – IT Capital • From survey • The survey is “IT Spending”. Need to convert “flow” into “stock”. • Companies may know ‘spending’ but not ‘stock’ or ‘asset’. • Since we only have 3-year data, we assume IT life cycle is 2 years.

  24. Data Requirement – IT Capital Rate of depreciation, ex., in a year, ½ of IT to be obsolete.

  25. Data Requirement – IT price • Rental price = very complicated formula • Our research: survey

  26. Finding, Productivity

  27. Findings - Inefficiency • Technical: -0.1350 • Allocative uNIT: -0.7989 decrease non-IT • uLIT: 0.6138 • uNLIT: -0.2314 • Scale: 0.2423 over produce

  28. Findings - Overall Percentage Loss

  29. Future Direction • After March when 2003 data available – complete the research • Add ‘panel data consideration’ into the model

  30. Analysis of Panel Data • Cross section and time series • With consideration of stochastic form or not Y Company C Company B Company A I

  31. Future Direction continued • Put into consideration the company size and industry difference • Relax constraints - CES • Measure input substitution effect – translog function

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