Productivity Growth, Technical Progress, and Efficiency Change in Information Technology Industries

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1 Productivity Growth, Technical Progress, and Efficiency Change in Information Technology Industries Benjamin B. M. Shao & Wesley S. Shu School of Accountancy and Information Management College of Business Arizona State University Main Campus Tempe, AZ Information and Decision Systems Department College of Business Administration San Diego State University San Diego, CA Abstract This paper examines the problem of measuring the productivity growth of the information technology (IT) industries across 14 OECD countries over 13 years. Because the IT industries are the providers of IT capital goods, this macro-level analysis intends to find out how productively IT capital are accumulated. The basic unit of analysis employed is the Malmquist total factor productivity (TFP) index. The Malmquist TFP index is then further decomposed into several components that explain different sources of productivity change: technical change, technical efficiency change, and the effects of economies of scale. The estimation approach is based the concept of distance function and employs the non-parametric frontier method of data envelopment analysis (DEA). Our results show that, among the 14 countries examined, ten had witnesses a productivity growth in their IT industries. All but one country, Korea, had experienced technical progress in their IT industries. Ten countries' IT industries were becoming more technically efficient over time. Finally, only two countries, Finland and Italy, received a gain in productivity that was attributed to scale factor. 1. Introduction The impact of IT has been a research topic receiving increased attention from IS researchers for the past two decades. Most of the research has focused on the contributions of IT adoption and deployment to individual's, organization's, and country's performance that is often measured in terms of productivity, efficiency, profitability, quality, etc. At the firm level, Hitt and Brynjolfsson (1996) employ a comprehensive data set to analyze the values generated by IT. They conclude that IT increases a firm's marginal output as well as consumer's surplus, but business profitability tends to remain unchanged due to fierce competition. Dewan and Kramer's study (2000) looks at panel data from 36 countries over the period and finds significant differences between developed and developing countries in the returns from their IT and non-it capital investments.

2 In comparison to the abundance of research activities on the values of IT adoption, there has been relatively efforts devoted to examining the accumulation of IT capital that forms the foundation on which IT's contributions are based. It is, therefore, our intention to investigate the economics of IT from a different perspective. Instead of focusing on the benefits of IT use, we are interested in the production of IT goods provided by the IT industries across different countries. The significance of IT production from the IT industries cannot be overlooked when IT spending over the last three decades has increased more than 40 times (Bureau of Economic Analysis 1999) and when IT has been regarded as one of the major drivers behind the productivity boost that has been observed since the mid 1990s. The critical issues to be examined in this study deal with whether the IT industries are productive, efficient, and advancing in their production processes. The answers to these questions are of particular importance to a country's economy as well as to the aggregate global economy for two reasons. First, since the IT industries are the providers of IT capital to the other industries in an economy, the effectiveness and efficiency of IT industries in producing IT goods, to some degree, determine the adoption rate of various IT products by the IT-using industries. Such an adoption rate, in turn, decides the productivity impact caused by IT. Second, the IT-producing industries continue to expand their shares in the economy for most countries, so their own productivity is playing an increasingly important role in deciding a country's total output productivity. In this paper, we apply the non-parametric frontier method of data envelopment analysis (DEA) to compute the Malmquist total factor productivity (TFP) indexes for a sample of 14 OECD country's information technology (IT) industries. The technique allows us to decompose the Malmquist TFP index into three components: shifts in production technology, changes in technical efficiency, and the effects of economies of scale. This paper aims to study whether the IT capital goods are accumulated productively and, if so, what the sources for the productivity growth are. The decomposition of the Malmquist productivity index is informative: the component of technical change represents the degree of innovation potential, and the component of technical efficiency change reflects the potential of an IT industry to catch up (Arcelus and Arozena 1999). The rest of the paper proceeds as follows. Section 2 reviews the relevant literature on IT's values and explains the connection between the current study and the prior research. Section 3 sets the theoretical stage with the specification of our DEA models for calculating the Malmquist TFP index and its three components. In Section 4, the country-level panel data set used for our study is discussed. Section 5 presents the empirical evidence and its implications for the growth of the IT industries of the 14 countries. Finally, Section 6 concludes the paper and suggests some topics for future research. 2. IT's Contributions to Productivity and Economic Growth As noted by Federal Reserve Chairman Alan Greenspan, the U.S. economy from 1995 to 2000 had benefited from structural gains in productivity that were enabled by a remarkable wave of technological innovation, especially in IT. For instance, businesses installed enterprise resource planning (ERP) systems to coordinate the intra- and inter-organizational information flow so as to streamline their business processes throughout the industry value chains. The phenomenal growth of the Internet at the same time spurred ample opportunities for individuals, organizations, and countries to improve the efficiencies of their various activities. To facilitate business-to-business (B2B) and business-to-customer (B2C) transactions, firms invested extensively in e-commerce infrastructure, equipments and logistics, including hardware, software, networks, and services. According to the U.S. Bureau of Economic Analysis, during

3 the period between 1996 and 2000, American businesses invested $1.7 trillion in IT, almost doubling the amount spent in the previous five years. Even in the presence of the recent economic downturn, such a gain will be more likely to persist, in a new form of efficiency, instead of evaporating or reversing (Burrows 2001). The productivity contributions of IT can be analyzed from three perspectives. The first and the most frequently studied one is the measurement of productivity using the framework of production functions. Most of the research on this topic employs a number of production factors and certain specifications of production functions, so as to measure the marginal product, output elasticity, and output/input ratio for IT. This approach mainly intends to corroborate whether the use and deployment of IT exerts a favorable effect on the outputs of the production process. A well-known controversy in this area is the so-called productivity paradox, which argues that IT seems to fail to deliver its promised returns in increasing productivity (e.g., Loveman 1994). The productivity paradox appeared settled after Brynjolfsson and Hitt (1996) presented their firm-level empirical evidence to claim the paradox disappeared by the mid 1990s. Shao and Lin (2001a, 2001b) later applied a parametric frontier method of stochastic frontiers and a non-parametric frontier method of DEA to confirm the positive effects of IT on technical efficiency, a component in deciding the productivity index, thus providing an explanation for the disappearance of the productivity paradox. Dewan and Kramer (1998) conducted a similar study on a country-level and found such a paradox did not exist for developed countries. The second perspective focuses on the relationship between IT capital accumulation and economic growth. In neoclassical growth theory, capital formation is an important factor in deciding the growth of an economy and is often represented by capital deepening, i.e., the change of capital growth rate. One may argue that although IT usage is productive in producing the output, its contributions may not be very significant since IT only accounts for a relatively small portion of total capital. Applying the neoclassical growth models, Jorgenson and Stiroh (2000), however, contend IT capital formation has been an important source of economic growth in the past four decades. Similarly, Oliner and Sichel (2000) reported that the capital stocks of computer hardware, software, and network infrastructure have boosted their contributions to growth and been responsible for two-thirds of the non-farm labor productivity growth between the first and second halves of the 1990s. The important implication of the second perspective is that an economy can still experience growth with a sustained increase of IT capital accumulation, even if IT productivity may not be decisive. The third perspective from which IT contributions can be examined is the production of IT itself. This approach is concerned with how IT industries contribute to the economic growth. The accumulation of IT capital comes from the production of IT industries or IT-producing industries. Both studies by Jorgenson and Stiroh (????) and Oliner and Sichel (2000) showed that a combination of accelerating technical progress in IT industries and the resultant investments in IT are driving recent productivity gains in the U.S. Stiroh (2001) even argued that most of the aggregate productivity growth is attributed to either IT industries or the industries that employ IT most intensively. It is essential to emphasize that the use of IT and the production of IT are two related but different issues. They are related because the production of IT produces what is being used. There would be no use of IT without the production of IT. However, the two issues are also different because the IT industries should have their own patterns in terms of productivity, efficiency, and technical progress that contribute to the economic growth through their IT production and capital accumulation.

4 The current study intends to examine the economics of IT from both the second and third perspectives by inspecting how IT capital is formed in the IT-producing industries. If IT capital accumulation indeed plays a critical role in boosting the productivity gains witnessed across other industries, as claimed by previous research, then such a capital deepening should come from the better performance of IT-producing industries, in terms of productivity, efficiency, and technical progress. 3. Measuring productivity and efficiency changes To measure the productivity of the IT industries across countries, we employed the Malmquist TFP index (MPI hereafter). The reasons for choosing the MPI over the other two popular Törnqvist and Fisher indexes are as follows (Arcelus and Arozena 1999). First and foremost, the MPI can be decomposed into two components for explaining the productivity sources: technical change and technical efficiency change. Second, the MPI does not require price data. Third, the MPI is capable of accommodating multiple inputs and outputs without worrying about how to aggregate them. Fourth, the MPI does not make any restrictive assumptions inherent in the Törnqvist and Fisher indexes, such as cost minimization or profit maximization. The MPI was originally devised by Malmquist (1953) for developing a distance function defined on a consumption space. The distance function is analogous to the Shepard input function of producers and is used in a ratio format to define the quantity index (Färe, Grosskopf, and Russell 1998). Later, this distance function was incorporated into the production theory and given a different economic meaning. This extension, in turn, lends its use to the measurement of productivity. In addition, the distance function has a value that is the inverse of technical efficiency defined by Farrell (1957). Therefore, we can easily employ the methodologies for measuring technical efficiency (like DEA or stochastic production frontiers) to calculate the MPI. The original MPI assumes constant returns to scale for the production process. As a consequence, the MPI typically overestimates productivity change if the production process displays decreasing returns to scale (or underestimates it for increasing returns to scale). To cope with variable returns to scale, Fare et al. (1994) recommend the MPI to include an additional component called scale index to represent the effect of economies of scale on productivity. We will include such a scale factor in our analysis. The MPI is composed of two parts: technical efficiency change (TEC) and technical change (TCH). The former is the change of technical efficiency between time t and t + 1. In other words, it represents the change in how far observed production is from maximum potential production. The second component is the shift in production technologies between time t and t + 1. Technical efficiency change (TEC) can be further decomposed as pure technical efficiency change (PEC) and scale change (SCH), as stated in Färe, et al The scale change indicates the effects of economies of scale on productivity. It is obtained simply by dividing TEC by PEC. To determine the MPI, we have to compute TCH, TEC, and PEC first and then derive SCH by dividing TEC by PEC. Each output distance corresponds to one particular DEA (Data Envelop Analysis) linear program.

5 4. Data and Results 4.1. Data We collected the country-level data from two sources. The first one is the OECD Stan Database for Industrial Analysis (OECD 1998a), and the second one is the OECD International Sectoral Database (OECD 1998b). Both were published in 1998, the latest version available so far. The IT industry is under the category of "Office and Computing Machinery" with an SIC code of There are two inputs, Capital (K) and Labor (L), and one output (Y) defined as the production of the IT industry. All of the three items were retrieved directly from the OECD publications. The unit of K and L is US dollar. Based on the data availability, only 14 countries are included in our data set: Australia, Canada, Denmark, Finland, France, Germany, Italy, Japan, Korea, Netherlands, Norway, Spain, the United Kingdom, and the United States. Among the 14 countries included, some have longer-period data while the others only possess the data from 1978 to Due to the requirements of a balanced panel for DEA modeling, we have to limit ourselves to the 13-year period of 1978 to 1990 although some countries have data up to 1994.

6 Country Australia Canada Denmark Finland France Germany Italy Japan Korea Netherlands Norway Spain U.K U.S All Countries Country Avg. Australia Canada Denmark Finland France Germany Italy Japan Korea Nethe rlands Norway Spain U.K U.S All Countries Table 1. Frontier Countries and Pure Technical Efficiency 4.2. Frontier Countries Since the MPI and its components are based on the computations of the output distance functions (which are the inverses of the Farrell s output-oriented technical efficiencies), we first present in Table 1 the pure technical efficiency (subject to variable returns to scales) for every country across years. In a given year, the countries that exhibit a pure technical efficiency score of 1 are the frontier countries used to construct the piece-wise, linear, convex, nonparametric production frontier in our DEA modeling. For instance, in 1978, Australia, Finland, Italy, Japan, Korean, and the U.S. have perfect technical efficiency scores of one. They are, comparatively speaking, the most efficient countries among the 13 examined and are utilized to establish the frontier for measuring the technical efficiency scores of the other remaining countries. The averages shown in the last column are the geometric means for a particular country across 13 years. As can be seen, Japan and the U.S. are the frontier countries for each year, indicating both have consistently been the best practice countries in producing the most IT capital output with a fixed set of inputs under a given production technology. Therefore, Japan and the U.S. have had the most technically efficient IT industries Productivity and Its Component Indexes The section measures the magnitude of the MPI productivity growth and identifies its sources for the 14 countries across the years. Table 2 presents the geometric means of the MPI for each

7 country and the breakdown of the MPI into its three source components: technical change (TCH), pure efficiency change (PEC), and scale change (SCH). Country MPI TCH PEC SCH Australia Canada Denmark Finland France Germany Italy Japan Korea Netherlands Norway Spain U.K U.S All Countries Table 2. Geometric Means of MPI and Its Components: For the TFP productivity growth, ten countries have experienced an increase in the MPI, with Norway being the best performer exhibiting a highest annual TFP growth rate of 11.9%. Four countries (Australia, Denmark, Japan, and Korea) did not record a boost in their IT industries productivity (though Korea with was very close to the no-growth rate of 1). It is noted, however, that even the largest decline rate of 2.3% associated with Australia is still relatively small in comparison with most of the TFP growth rates found. The overall TFP productivity in the 14 countries under study grew at an impressive rate of 3.2% on average from , compared with a paltry increase of 0.6% for the manufacturing sector of 14 similar OECD countries over (Arcelus and Arozena 1999) and a mere growth of 0.7% for the overall productivity of another 17 similar OECD countries during (Färe et al. 1994). These comparisons between our findings and those of prior studies suggest that IT industries, on average, are more productive in producing their IT capital goods than the other industries in a country s economy. Table 2 also presents the components of the MPI, which identify the sources of the TFP productivity growth, independent of industry size. The three sources are technical change (TCH), pure efficiency change (PEC), and scale change (SCH), each representing a different source for explaining the TFP productivity growth. TCH determines the extent to which the MPI growth is due to shifts in the production technologies (or the production frontiers) over time. TCH, thus, portrays the innovative capability of an IT industry. The second component, PEC, characterizes the ability of a particular IT industry to catch up to its more productive benchmarks, defined by a given production technology, over time. It should be pointed out that PEC in Table 2 and pure technical efficiency in Table 1 are two different concepts. Pure technical efficiency in Table 1 is measured as the closeness of an IT industry s actual output to its ideal output and, therefore, has a maximal value of 1. PEC in Table 2, on the other hand, measures the degree of change in pure technical efficiency over time and may have an index greater than, equal to, or less than 1, indicating an improvement, stagnation, or deterioration of pure technical efficiency, respectively.

8 The last component, SCH, indicates the contribution of scale economies to the MPI growth. A change in the scale of production contributes positively to productivity growth if it involves expansion (contraction) in the region of increasing (decreasing) returns to scale (Arcelus and Arozena 1999). In Table 2, overall speaking, TCH accounts for 3.9% of the increase of 3.2% in the MPI, while PEC accounts for merely 0.2%. Therefore, most of the TFP productivity increases witnessed in these IT industries are ascribable to the progress of the production technologies while the change in pure technical efficiency contributes only a little. Moreover, the effects of scale economies (SCH) actually lower the MPI by nearly 0.9%. Across countries, interesting patterns can be observed. First, TCH again plays a vital role in boosting the TFP productivity growth for almost every country s IT industry, except for Korea (0.998) with nearly no advance in its production technology. In other words, it can be said that the IT industries are very good innovators in advancing their production technologies. Such a technical progress is the major thrust for enhancing the productivity of IT industries. This finding is not surprising since IT industries are the providers of high-tech products that are oftentimes used to advance the production technologies for other industries. IT industries, thus, also can improve their own production technologies by incorporating the IT capital goods they produce, so in this regard they are their own beneficiaries. In terms of the average PEC over time, four countries (France, Norway, Spain, and the U.K.) showed an improvement in their pure technical efficiency, which in turn contributed positively to their respective TFP productivity growth. On the contrary, four different countries (Australia, Canada, Denmark, and Netherlands) displayed deterioration in their technical efficiency that unfavorably affected their corresponding MPIs. Six countries (Finland, Germany, Italy, Japan, Korea, and the U.S.) exhibited a PEC equal to one, indicating that on average these countries demonstrate no change in their technical efficiency, which has no, if not little, impact on their MPI scores. However, among the six countries, a cross-examination of Table 1 and Table 2 reveals that Japan and the U.S. have a PEC of 1 simply because they have been the best practice frontier countries over the years, while Germany has fluctuated up and down its geometric mean of 0.626, and eventually the gains and losses in its technical efficiency cancelled out to result in a PEC equal to 1. Finland, Italy and Korea, on the other hand, started and ended as the frontier countries, but in the mid 1980s they had experienced a down-and-up change in their technical efficiency. Again, the down and up changes cancelled out, led to a PEC of 1, and had no effect on their average annual TFP growth. Finally, the SCH column in Table 2 manifests that 11 of the 14 countries under evaluation hurt from the scale of production in their TFP growth. The only exceptions are Finland and Italy, both of which received a positive contribution from their scales of economies to the MPI scores. Korea had an SCH equal to 1 and thus did not gain or lose productivity from the scale effects. The fact that most of the countries did not benefit from their production scales may be due to the fierce competition and elusive demands typically associated with IT industries. Intensified competition among rival firms in IT industries in different countries prevents them from deciding the optimal production scales based on their own strengths and weaknesses of the production processes. Rather, they tend to be forced to produce at a level dictated more by the global market, competitors, and shorter product life cycles, among other external factors. What adds more to the situation is the relatively elusive demand for IT products. Compared with other traditional capital goods, IT industries face a more evasive demand, which makes its forecasting a difficult job and the corresponding decision of actual output level quite a challenge.

9 5. CONCLUSIONS Rank Country MPI Country TCH Country PEC Country SCH 1 Norway Netherlands Norway Italy France Finland France Finland Spain Norway Spain Korea Finland France U.K Australia U.K Spain Finland Canada Italy U.K Germany U.K U.S U.S Italy Netherlands Netherlands Germany Japan Spain Germany Denmark Korea U.S Canada Canada U.S Denmark Korea Australia Netherlands France Japan Italy Canada Japan Denmark Japan Denmark Norway Australia Korea Australia Germany Table 3. Country Rankings of MPI and Its Components This paper analyzed the productivity growth and efficiency in information technology industries for fourteen OECD countries using Malmquist Total factor productivity index (MPI) during the 1978 and Data Envelop Analysis was employed to obtain the MPI. MPI allows us to measure not only technological progress of each country, but also technical efficiency change and scale change. Compared to econometric methodologies to measure productive efficiency, MPI does not require cost minimization, profit maximization assumptions or price information. MPI reveals the total factor productivity (TFP) growth for each country. In our analysis, Norway has the highest TFP growth rate in the IT industry. The dissection of MPI shows the contributions to TFP growth. Most of the TFP increases witnessed in these IT industries are ascribable to the progress of the production technologies while the change in pure technical efficiency contributes only a little. Moreover, the effects of scale economies (SCH) actually lower the MPI by nearly 0.9%. Table 3 presents another view of the results by ranking the countries in the order of the MPI and its components. References Arcelus, F. J. and P. Arozena (1999): Measuring sectoral productivity across time and across countries. European Journal of Operational Research 119, Bureau of Economic Analysis (1999): National Income and Product Account of the United States, U.S. Department of Commerce. Brynjolfsson E. and L. Hitt (1996). Paradox lost? Firm-level Evidence on the Returns to Information Systems Spending. Management Science, 42(4), Coelli T., D.S.P. Rao, and G.E. Battese (1998): An Introduction to Efficiency and Productivity Analysis, Kluwer Academic, Norwell, MA. DEWAN S. and K. KRAEMER. International Dimensions of the Productivity Paradox. Communications of the ACM, August Färe, R., S. Grossskopf, M. Norris, and Z. Zhang (1994). Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries. American Economic Review, 84,

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