'IT and Economic Performance: Evidence From Micro Studies\r'

'CHAPTER V: IT AND ECONOMIC PERFORMANCE: EVIDENCE FROM sm solely info STUDIES By B. K. Atrostic and Ron Jarmin* little selective t separatelyingâ€that is, entropy on individual seames that under prevarication key stintingal indicators†resign us to go behind published statistics and bear how IT affects linees’ frugal mathematical operation. Years ago, analyses suggestd a haughty birth betwixt IT and join oniveness, stock-st unhinged when official conglomeration statistics unsounded pointed towards a â€Å"productiveness paradox. no., intimately-nigh(prenominal)(prenominal) analyses shed fair on how varied that blood is across worryes, and how IT happen upons its squeezes. This chapter foc enforces on interrogation around businesses based on sm any entropy unruffled by the U. S. count power. We highlighting the attractives of questions close the engross and clash of IT that still little instruction al hapless us to addr ess. little show studies in the decrease in States and in former(a)(a) OECD countries translate that IT affects the productiveness and harvest-home of individual frugal wholes. unique(predicate) estimates of the surface of the resolution interpolate among studies.\r\n questioners comparing manufacturing brings in the coupled States and Germ whatsoever, for example, generate that in each(prenominal) republic giveing firmly in IT give outs a productiveness bounty, exclusively that the premium is high in the unite States than it is in Ger legion(predicate). They to a fault meet that the productiveness premium varies much more than(prenominal) than than than for U. S. manufacturers. This massiveer variability is lucid with the view that the U. S. insurance policy and institutional surrounds whitethorn be more conducive to experimentation by U. S. businesses. What sort of IT enthronizations do U. S. businesses make? number Bureau entropy on U.\r\n S. manufacturing establishments show that they come in in twain(prenominal) electronic schooling processing system benefit lends and the mixture of thickening softwargon that coordinates cardinalfold business processes in spite of seemance and among establishments. About 50 portion of these coiffures boast ne twainrks, while fewer than 10 pct lease invested in this complex softw ar. much(prenominal) a wide resistence in the midst of the presence of net subject fields and * Ms Atrostic (barbara. kathryn. [email protected] gov) is Senior Economist, and Mr. Jarmin (ron. s. [email protected] gov) is Acting Director, come to for frugal Studies, U. S. nose count Bureau. 61\r\ndigital miserliness 2003 complex softw atomic consider 18 in manufacturing, and equ eithery varied differences in their presence among elaborate manufacturing industries, highlight the change of IT employment among businesses. Plants with networks acceptedise high productive ness, even by and by checkerling for more of the plant’s scotch characteristics in the legitimate and prior periods. Similar results atomic number 18 prime in antithetical(a) OECD countries. al close studies refer that businesses need to make parallel coronations in actor reading and revised workplace practices ahead IT investment fundss yield productiveness gains.\r\nC beful little instruction enquiry shows that the birth in the midst of IT and stintingalal execution is complex. â€Å"IT” emerges as a suite of alternatives from which businesses make variant choices. Estimates of the size of the arrange, and how IT makes its encounter, remain delicate to pinpoint. selective learning gaps make it hard to conduct protective(predicate) analyses on the ready of IT. proceed efforts by investigateers and statistical organizations be filling just virtually of the information gaps, nevertheless the gaps remain volumedst for the secto rs outside manufacturingâ€the sectors that be the most IT-intensive.\r\nMore definitive query take aims that statistical agencies make producing little data a priority. What ar little entropy? small data primarily convey study or so numerous characteristics of the economic unit, much(prenominal) as plant employment, years in business, sh be of IT in damage, trends it examples IT, and its economic death penalty. Micro data exist for both businesses and individuals, and sewer be developed by private and public organizations. This chapter foc applys on enquiry apply little data some(predicate) businesses that ar tucked by the U. S.\r\nBureau of the Census. BENEFITS OF MICRO DATA RESEARCH Standard analyses of productiveness and mistak fitting economic phenomena frequently assume that businesses be homogeneous, at least within an fabrication, and on that pointfore standardisedly resolve similarly to changes in economic circumstances. However, it is easy to altercate this assumption hardly by observing the form of businesses in whatever industry, no social function how narrowly the industry is delineate, and how diverse their responses turn up to be. Case studies in precise industries repeatedly bear out this observation.\r\nMicro data allow us to evaluate the diversity of businesses and track behaviors much(prenominal)(prenominal) as their entry and allow for into an industry. They as well allow us to document changes in businesses’ surgical procedure, much(prenominal) as employment, sales, and productivity, and see whether those changes are supply among industries, within industries, or among businesses of given ages, sizes, and so forth. two decades of explore victimisation little data find tremendous variety in the economic characteristics and completeance of businesses at any time, and everywhere time. 1 An fantabulous summary is E. Barltesman and M.\r\nDoms, â€Å"Understanding productivi ty: Lessons from longitudinal Microdata,” journal of scotch Literature, Vol. 38 (September 2000). It reviews query conducted at the U. S. Census Bureau and gives references for reviews of little data enquiry conducted elsewhere. A detailed report on initial small data look for on productivity is erectd in M. Baily, C. Hulten, and D. Campbell, â€Å"productiveness Dynamics in Manufacturing Plants,” Brookings musical themes on scotch Activity: Microeconomics 1992. 1 62 digital preservation 2003 Micro data grass keystone a clearer flick of how aggregate economic statistics change.\r\nThey similarly allow queryers to apply econometric techniques that take accounting of the kinds of complex affinitys that save when can non be demonstrateed in tables or former(a) aggregated formats. Comparing findings from investigate studies employ different data sets allows us to see which estimates fall out to be robust, and which ones seem to depend on the peculia r(prenominal) data we handling, and on the particular equations we estimate. RESEARCH REQUIRES redeeming(prenominal) MICRO DATA Micro data research takes advantage of the high- caliber randomness intimately individual businesses that underlies study economic indicators.\r\nThe little data sets typically are boastful and nationally representative, making it more in all likelihood that they capture the tremendous diversity among businesses. 2 Researchers lots are able to bear on data at the micro level across analyzes and everyplace time. For example, bet the spic-and-span instruction on whether businesses deplete calculator networks, and how they design those networks that was collected in the Computer earnings practise subjunction (CNUS) to the 1999 Annual cartoon of Manufactures (ASM). The plant-level micro data most estimator networks collected in the CNUS can be coupled to learning about employment, shipments, workout of separate inputs, etc. , coll ected about the same plants in the 1999 ASM and to ASMs for separate years, and to data that was collected about the same plants in the 1997 Economic Census. such(prenominal) exact linkages yield much richer loveledge bases than any undivided supplement, survey, or census alone. When micro data can be linked, researchers also can exercise econometric techniques to control for unobserved characteristics that are specialised to an individual plant or business.\r\nThese techniques allow researchers to rent more confidence that findings, such as the upshot of IT actually are receivable to IT and non to cerebrate still un natived characteristics, such as bully management or a skilled work force. The Role of learning Technologies in lineage surgical procedure impudent-fashioned research exploitation micro data generally concludes that IT and productivity are related. Indeed, micro data analyses indicated a positive family amidst IT and productivity when official agg regate statistics still pointed towards a â€Å"productivity paradox. Two refreshing-made reviews of plant- or firm-level empirical studies of breeding engineering science (including only when non limited to ready reckoners) and economic performance conclude that the lit shows positive relationships in the midst of schooling engineering and productivity. However, specific estimates of the size of the effect vary widely among studies. How IT makes its impact also mud hard to pinpoint. While micro data provide raw material for crucial analyses, they are non a panacea. Researchers must address meaning(a) challenges when exploitation subsisting micro data to analyze questions about the economic performance of businesses.\r\nSee Z. Griliches, â€Å"productiveness, R&D, and the Data Constraint,” American Economic suss out, Vol. 84 zero(prenominal) 1 ( contact 1994); and Z. Griliches, and J. Mairesse, â€Å"Production functions: The Search for Identification, ” NBER on the descent(p) motif 5067 (March 1995). 3 2 More breeding on these surveys is usable at http://www. census. gov/eos/www/ebusiness614. htm. 63 DIGITAL preservation 2003 THE ROLE OF IT IN PRODUCTIVITYâ€A draft SURVEY OF THE LITERATURE Many late studies map micro data to document and describe the productivity of different kinds of businesses, and to insure its seeds.\r\nThe simple model that suggests productivity growth occurs among all existent plants simply does not fit with what the micro data show. Instead, the micro data show that much of aggregate productivity growth comes about through a much more diversified and dynamic process. Less productive plants go out of business, comparatively productive plants continue, and the parvenu entrants that abide are more productive than either. Micro data research on the effect of IT explores how IT fits into this complex picture of business behavior.\r\nDozens of research papers everyplace the last decade exa mine various views of the relationship between IT and productivity. Two impertinently reviews summarizing the authoritative literature on IT and productivity conclude that at that place is an impact, although there is much vicissitude among studies in the estimated magnitudes of that effect (Dedrick, J. , Gurbaxani, V. , and K. Kraemer, 2003, â€Å" education engineering and Economic execution of instrument: A detailed Review of the Empirical consequence,” ACM Computing Surveys, Vol. 35, No. 1, March and Stiroh, K. J. 2002, â€Å"Reassessing the uphold of IT in the Production Function: A Meta-Analysis,” Federal Reserve Band of invigoratedborn York, November). 4 Dedrick et al. (2003) review over 50 articles published between 1985 and 2002, close to of which are firm-level studies with productivity as the performance measure. They conclude that firmlevel studies show positive relationships, and that gross returns to instruction engine room investments ex ceed returns to an some opposite(prenominal) investments. They warn against last-place that high gross returns mean that plants are under- invest in cultivation engine room.\r\n approximately studies do not countersink for the high obsolescence rate of information technology capital, which lower berths net returns. Also, total investment in information technology whitethorn be understated beca physical exercise most studies measure only estimator hardware, tho not related excavate or software, or be of coinvention, such as re-engineering business processes to take advantage of the rude(a) information technology. Stiroh (2002) reviews twenty recent empirical studies of the relationship between information technology and output and productivity. The studies generally find a positive effect of information technology on output.\r\nHowever, the estimates differ across studies, and the studies differ in umpteen dimensions, including time periods covered and specific estim ation techniques apply. Stiroh looks for predictable make of differences in characteristics of the studies, such as time periods, level of aggregation (e. g. , industry, sector, or entire economy), and estimation techniques. He finds that much of the variation across studies in the estimates of the effect of information technology probably reflects differences in characteristics of the studies. 4\r\nMany of those studies, including some(prenominal) studies discussed in this chapter, were conducted at the focus for Economic Studies (CES) at the U. S. Census Bureau. Appendix 5. A describes both CES, a research unit that conducts research and supports the inevitably of researchers and purpose makers end-to-end government, academia, and business, and some of the major data generators operable there for micro data research on the impact of IT. 64 DIGITAL prudence 2003 Stiroh also reports the findings of additional research he conducts employ a wholeness industry-level databa se to estimate many a(prenominal) of the different equations utilise in the studies he reviewed.\r\nHis research finds that information technology matters, but that even within a single database, estimates of the magnitude of that effect depend on the particular equation that is estimated. closingly, Stiroh notes a potential for publication bias. Beca mathematical function scheme predicts a positive relationship between IT and productivity, researchers may tend to report, and editors may tend to accept for publication, only those papers with the â€Å"right” results on the impact of IT. However, as his research demonstrates, estimates are sensitive to both the data use up and the particular equation that is estimated.\r\nHe concludes that information technology matters, but the wide variation in empirical estimates means that much â€Å"depends on the dilate of the estimation” and â€Å"one must be careful about putting too much weight unit on any given esti mates. ” The conclusion that recent studies show a positive effect of information technology stands in contrast to earlier studies, many of which establish no relationship. Both Dedrick (2003) and Stiroh (2002) note that the outflank data available to beforehand(predicate) researchers suffered from small arche fount sizes, few or no small firms or plants, and overleap of data on information technology investment.\r\nThese data gaps may be why primaeval micro data studies failed to find a relationship between IT and performance. CAUSE AND EFFECT: DOES USING IT keep BUSINESSES MORE PRODUCTIVE? The literature so furthest yields mixed findings on cause and effect between IT and plant-level economic performance. Early research is limited to manufacturing. The runner findings in this area were that more productive plants may be more in all likelihood to invite outdo practices, including new technologies, and that they are able to afford to do so. However, later research suggests that less productive plants may invest in those technologies, perhaps trying to advertize their productivity. 6 modern research expands the scope of outline of the effect of IT in the retail sector. It examines the relationship between investments in information technology and two performance measures for retail firms, productivity and growth in the turning of establishments. The research finds that, in retail, IT is closely related to productivity growth, but not to growth in the number of establishments that retail firms operate. 5 R. H. McGuckin, M. L. Streitwieser, and M. E. Doms, â€Å"The Effect of Technology practice session on Productivity ingathering,” Economic Innovation and bracing Technology Journal, 7 (October 1998). 6 Stolarick Kevin M. , â€Å"Are many steadfasts Better at IT? Differing transactionhips between Productivity and IT Spending,” nucleus for Economic Studies on the job(p) Paper CES-WP-99-13, U. S. Census Bureau, Washington , DC (1999); and B. K. Atrostic, and S. Nguyen, â€Å"IT and Productivity in U. S. Manufacturing: Do Computer Networks Matter,” C bring out for Economic Studies Working Paper CES-02-01, U. S.\r\nBureau of the Census, Washington, DC (2002). M. Doms, R. Jarmin, and S. Klimek, â€Å"IT investiture and Firm Performance in U. S. sell Trade,” Center for Economic Studies Working Paper CES-WP-02-14, U. S. Bureau of the Census, Washington, DC (2002). 7 65 DIGITAL parsimony 2003 Does the Business Environment Matter? â€International Comparisons Although researchers contrive arrange evidence of the effect of IT on productivity at the micro level across many countries, the effect on aggregate productivity and economic growth has varied across countries. This is true even though IT is universally available.\r\nWhile the United States and a few separate economies enjoyed the boom of the late 90s, many European economies experienced sluggish growth. Several explanations ware been put forward including differences in the policy and institutional settings across countries, measurement issues, and time lags (micro data research showed positive effects of IT in the United States before aggregate statistics). Some cede hypothesized that the U. S. economy was able to make more effective use of the new general-purpose technology of IT because its regulatory and institutional environs permits firms to experiment more. An important component of the U. S. bility in this count on is the efficient reallocation of resources away from firms whose experiments in the grocery place fail, to those whose experiments succeed. The OECD’s Growth Project (Box 5. 1) study found evidence that the Schumpeterian processes of churning and creative destruction (or grocery selection) yield greater economic effects in the United States than in other OECD countries. These processes affect aggregate productivity growth as lower productivity firms shrink and exit and high er(prenominal) productivity firms enter and grow. Is it the show window that IT has had a greater impact on business performance in the United States because the U.\r\nS. policy and institutional environment is more conducive to grocery selection and learning? Box 5. 1. OECD International Micro Data Initiative No single solid ground has the resources and technical expertise to independently resolve all the measurement issues and fill all the information gaps associated with quantity the impact of IT. The OECD Growth Project provided a encyclopedic outline of the impact of information and talk technology (ICT) on productivity and economic growth in several OECD countries, using aggregate, industry-level, and plant-level data. Based on that retch’s success, U. S.\r\nCommerce Secretary Evans pass additional micro data research, and provided the OECD with seed money. This new nominate seeks to build on efforts already under way in several OECD member countries. One face t of the OECD micro data project on ICT is a serial publication of multi-national coactions, with a small number of countries problematical in each collaboration. Each class is ontogenesis its testify way of reconciling the differences in each country’s existing micro data that are important to comparative studies, such as the sectors covered, the scope of businesses implyd in each sector, and the specific questions asked.\r\nThe OECD project also seeks explicitly to foster coordination and collaboration on e-business issues between data producers and data users in each country. Project members are from both the OECD’s Statistical Working Party of the Committee on Industry and Business Environment (largely data users rivet on productivity and growth statistics) and the new Working Party on Indicators on the instruction monastic cast (largely producers of statistical indicators). 66 DIGITAL thrift 2003\r\n new research using micro data from the United States and Germany attempts to address this question. 8 The analysis graduation exercise equivalences the differences between various groups (e. g. , young vs. old, or those that invest severely in IT vs. those that do not) of manufacturing establishments within each country. These differences are then examined across the two countries. This allows the researchers to contrast the impact of IT on economic performance between the two countries. The results suggest that U. S. anufacturing establishments benefit more from investing in IT and are more likely to experiment with different ways of conducting business than their German counterparts even by and by controlling for several plant specific factors such as industry, age, size, and so on. Figure 5. 1 summarizes results from an analysis of the impact of changing technologies on productivity outcomes. For the analysis, businesses undergoing an installing of high investment are assumed to be actively changing their technology. Manufact urers in both countries were separate according to investment intensity as defined by investment per character reference player.\r\nThe researchers examined investment in both general and IT-specific equipment. The core comparison group had no investment. The other two groupsâ€with investment in any equipment, and investment in IT equipmentâ€were split into â€Å"high” and â€Å"low” investment groups at the 75th percentile of the investment intensity distributions. Plants with â€Å"high” investment intensities were those with intensities exceeding at least 75 percent of all other investing plants. These computations were done for both overall investment in equipment (excluding expressions) and for IT equipment, giving a combined seven investment intensity categories.\r\nBusinesses undergoing an episode of high investment intensity can be thought of as actively changing their technologies. The market will reinforcing stimulus some of these and punish others. The crux of the analysis summarized in Figure 5. 1 is to first compare the performance of plants across the various investment intensity groups to a baseline of firms with no investment within each country (i. e. , the bars for the listed investment intensity categories in the figure represent the percent difference from the omitted cypher investment category for each country).\r\nThen the researchers compared the within country differences across the United States and Germany to see in which country the reward for experimentation (as measured by high investment episodes) is highest. display board A shows that U. S. businesses that invest heavily, both overall and in IT, are much more productive than those that invest little or none at all. The same holds true for Germany, but the productivity premium is much higher in the United States. Panel B shows that U. S. businesses that invest heavily (i. e. are experimenting with new technologies) commence more varied productivi ty outcomes as measured by the stock deviation than do firms that invest little or not at all. This is not the case in Germany. In fact, the German data show that firms that invest intensively have less varied productivity outcomes. This is consistent with the notion that the U. S. policy and institutional environment is more conducive to market experimentation. These results should be viewed with caveat as they relate to only two countries and there are many factors the researchers do not control for. 8 J.\r\nHaltiwanger, R. Jarmin, and T. Schank, â€Å"Productivity, enthronement in ICT and Market experiment: Micro Evidence from Germany and the U. S. ,” Center for Economic Studies Working Paper CES-03-06, U. S. Bureau of the Census, Washington, DC (2003). 67 DIGITAL ECONOMY 2003 Figure 5. 1. Differences in Productivity Outcomes between Germany and the United States Panel A: U. S. Firms Investing hard in IT and separate smashing Have higher(prenominal) Productivity Prem iums 100% % Difference in dream up Productivity Relative to Group with No Investment U. S. 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% base / 0\r\nGermany High / 0 commencement / suffering embarrassed / High High / Low High / High Investment Intensity (Equipment / IT) Panel B: U. S. Firms Investing Heavily in IT and Other Capital Experience More Varied Productivity Outcomes 50% U. S. % Difference in Standard going away of Productivity Relative to Group with No Investment Germany 40% 30% 20% 10% 0% -10% -20% -30% -40% Low / 0 High / 0 Low / Low Low / High High / Low High / High Investment Intensity (Equipment / IT) preeminence: Differences are in logs and are shown relative to a reference group of firm with zero total investment.\r\nSource: Haltiwanger, Jarmin and Schank 2003. DOES IT MATTER HOW IT IS USED? Businesses in the United States have utilise IT for fifty years. Originally, firms that used IT may have had advantage over competitors who did not. But today, simply investing in IT may no monthlong be copious. The question for economic performance is no longer whether IT is used, but how it is used. 68 DIGITAL ECONOMY 2003 Figure 5. 2. Computer Networks Were Common in U. S. Manufacturing Industries in 1999, But Sophisticated Network Software Was not 100 90 80 70 60\r\nPlants with Networks Employment at Plants with Networks Plants with Fully Integrated opening move Resource Planning Software Percent 50 40 30 20 10 0 G ts ts s s s s s o t es ry ts s al em ic d ru bb er pr od l ts ill re ts uc rie ou en al ill ts ts rie uc te pe uc cc ts N ne uc uc RI iti pm od ne ba tm od st pa uc uc et TU od od Pa la st od tiv hi od od du du pr re lla ac til uc pr to Ap pr ui †ac pr pr od m e FA C od in ce an Te x ar eq al od M W oo 32 al d an d im is al c d s 5 Ch pr in pr d †d d 2 pr y uc m uc ts d s r pr lie ni Fo er te od 31 U et te †n od M co an d †t, tio tro e Pr AN in la m al la 3 e go en go 33 til m re ra rta 31 †ec ed M m Te x le 31 v e nd 1 9 1 1 d lic el 33 e d re 32 Pl as tic s ††3 †32 po ra 33 ab an an bl AL nd qu ns ra ur he et br re g du ra le Tr a itu 31 m tin at D on Le ca te on rn 31 †in pu tri N 6 Fu 2 Pr N †Pe †tro Fa 2 le †4 um Be al ic †ec 33 m 33 6 †††31 †Co El 7 3 4 32 4 33 33 5 33 †NAICS 3-Digit Industry Source: Atrostic, B. K. and J. Gates, 2001, â€Å"U. S. Productivity and Electronic Business Processes in Manufacturing,” CES-WP-01-11, Center for Economic Studies, U. S. Bureau of the Census, Washington, DC.\r\n clean data from the 1999 Computer Network Use Supplement (CNUS) to the 1999 Annual Survey of Manufactures (ASM) are beginning to be used to model how manufacturing plants use computer networks in the United States. Respondents’ answers to questions about processes can be linked to the information the same respondents reported on unfaltering ASM survey forms, such as the value of shipments, employment, and prod uct class shipments. Figure 5. 2 presents researchers’ estimates of the diffusion of computer networks. The research finds that computer networks are widely piano within manufacturing, with networks at about half of all plants.\r\nThe share of employment at plants with networks is roughly identical in durable and non-durable manufacturing. Use of networks varies a great deal within those sub-sectors; the share of plants with networks ranges from lows of about 30 percent to highs of about 70 percent. The CNUS also provides new information about some aspects of how plants use computer networks. Figure 5. 2 reports estimates of the diffusion of fully corporate enterprise resource planning software (FIERP); that is, the kind of software that colligate different kinds of applications (such as inventory, tracking, and payroll) within and across businesses.\r\nPlants in all manufacturing industries use this complex software. However, FEIRP software remains relatively rare compar ed to computer networks. While about half of all manufacturing plants have networks, fewer than 10 percent have this kind of software. 69 32 32 †32 7 6 †an at ip L 5 †s DIGITAL ECONOMY 2003 Initial research finds that computer networks have a positive and of import effect on plant’s labor productivity. After accounting for quadruplicate factors of production and plant characteristics, productivity is about five percent higher in plants with networks. When economic characteristics in prior periods and investment in computers are also accounted for, there continues to be a positive and statistically significant relationship between computer networks and U. S. manufacturing plant productivity. 10 These initial findings for the United States are consistent with findings for other countries. Recent research for Canada, the Netherlands, and the United Kingdom, for example, all find positive relationships between using computer networks and productivity. 11 Research for Japan finds that computer expenditures and computer networks both alter productivity between 1990 and 2001.\r\nIn more recent years, the effects are larger, but they also vary much more among industries. 12 Some micro data research for the United States during the 1990s suggests that IT needs to be used together with worker schooling and revised workplace practices to yield productivity gains. These findings are based on data containing detailed information about the use of computers in the workplace. They also contain information rarely available in other sources on the employers’ management and worker training policies. 3 Research for Australia and Canada, previously cited, also finds that returns to IT are intertwined with the use of R&D, innovation, and changes in workplace practices and organization. This line of research suggests that IT is important, but that it makes its impact when accompanied by changes in other factors and practices. IS THE IMPACT OF IT THE SAME FOR ALL KINDS OF IT, all over? â€EVIDENCE FROM STUDIES OF MARKET STRUCTURE IT was widely pass judgment to alter the structure of markets. The direction, however, was unclear.\r\nLower information costs energy make it easier for smaller businesses to collect, analyze, and use information and so allow them to enter distant markets or compete more effectively with larger firms. At the same time, the lower information costs might make it easier for larger businesses 9 Atrostic and Nguyen (2002). 10 Atrostic and Nguyen, â€Å"The match Of Computer Investment And Computer Network Use On Productivity,” paper presented NBER-CRIW Conference on â€Å"Hard-to-Measure Goods and work: Essays in Memory of Zvi Griliches,” Washington, DC (September 2003). J. Baldwin, and D.\r\nSabourin, â€Å"Impact of the borrowing of Advanced Information and Communication Technologies on Firm Performance in the Canadian Manufacturing Sector,” Research Paper Series, 174, Ana lytical Studies Branch, Statistics Canada (October 2001) present findings for Canada. E. Bartlesman, G. van Leeuwen, and H. R. Nieuwenhuijsen, â€Å"Advanced Manufacturing Technology and Firm Performance in the Netherlands,” Netherlands Official Statistics, Vol. 11 (Autumn 1996) present findings for the Netherlands. C. Criscuolo and K. Waldron, â€Å"e-Commerce use and firm productivity,” Economic Trends (November 2003) present findings for the United Kingdom.\r\nK. Motohashi, â€Å"Firm level analysis of information network use and productivity in Japan,” presented at the conference on Comparative Analysis of enterprisingness (micro) Data, London (September 2003). S. Black, and L. Lynch, â€Å"How to Compete: The Impact of Workplace Practices and Information Technology on Productivity,” Review of Economics and Statistics, Vol. 83 No. 3 ( rattling(a) 2001); and D. Neumark and P. Cappelli, â€Å"Do ‘High Performance’ Work Practices Improve E stablishment-Level Outcomes? ” Industrial and exertion Relations Review (July 2001). 13 12 11 70 DIGITAL ECONOMY 2003 to retain a combative advantage.\r\nSimilarly, use of the network might make it easier for consumers to compare prices, and so lead to a reduction in prices for products sold on-line or in â€Å"bricks and mortar” establishments. At the same time, a firm grammatical construction an on-line sales-based business may incur costs that brick and mortar businesses might not, such as cost associated with having inventories available for immediate delivery anywhere in the United States (or the world). The issues are scarcely settled. In this section, selected examples from micro data research illustrate IT’s varied nature and complex economic effects.\r\nhauling A series of studies make use of public-use truck-level data from the Census’ Vehicle Inventory and Use Surveys to examine how IT has unnatural the trucking industry. Each of these stu dies indicates the importance of knowing not just that IT is used, but also the details of the IT and how it is used. These studies examine the impact of two classes of on-board computers (OBCs). Standard OBCs function as trucks’ â€Å"black boxes,” recording how set outrs operate the trucks. These enable dispatchers to ascertain how truck drivers drive.\r\nAdvanced OBCs also contain capabilities that, among other things, allow dispatchers to determine where trucks are in real time and communicate schedule changes to drivers while drivers are out on the road. These move capabilities financial aid dispatchers make and implement break down scheduling decisions, and help them block situations where trucks and drivers are idle, awaiting their next haul. One of these studies assesses OBCs’ impact on productivity by estimating how much they have append individual trucks’ example rate, as measured by their loaded miles during the time they are in service . 4 It finds that advanced OBCs have increased truck utilization by 13 percent among trucks that adopt them; overall, this effect implies a three percent increase in capacity utilization industry-wide, which translates to about $16 zillion in annual benefits. The vast majority of this increase comes from trucks in the for-hire, long-haul segment of the industry, and most of these returns only began to hang years after trucking firms first began to adopt OBCs. In contrast, the study finds no evidence that measuring rod OBCs have led to increased truck utilization.\r\nCombined, these results indicate not just the magnitude of IT’s impact on productivity in the industry but also its nature and timing. IT adoption has led to large productivity gains due to advanced OBCs’ real-time communication capabilities, which enable trucking firms to ensure that trucks operating outlying(prenominal) from their base are on the road and loaded. These gains, however, appear to have l agged adoption by several years. The other two studies examine how OBCs have affected how the industry is organized. One study investigates how OBCs affect whether shippers use intragroup fleets or for-hire carriers to ship goods. 5 This study finds that the different classes of OBCs have different effects on this T. Hubbard, 2003, â€Å"Information, Decisions, and Productivity: On-Board Computers and Capacity Utilization in Trucking,” American Economic Review, September. G. Baker and T. Hubbard, â€Å"Make Versus Buy in Trucking: Asset possession, Job Design, and Information,” American Economic Review, Vol. 93 No. 3 (June 2003). 15 14 71 DIGITAL ECONOMY 2003 decision. The diffusion of standard OBCs has tended to increase shippers’ use of internal fleets, but the diffusion of advanced OBCs has tended to increase their use of for-hire fleets.\r\nThis implies that IT-enabled reformments in monitoring drivers have led shippers to integrate more into trucking, but IT-enabled improvements in scheduling capabilities have led to more contracting-out of trucking. This authoritative difference indicates that whether IT tends to lead to larger, more integrated firms or to smaller, more focused firms depends critically on the new capabilities the IT provides. The second of the two organizational studies is similar: it investigates how OBCs have affected whether drivers own the trucks they operate. 6 Traditionally, â€Å"owner-operators” have been an important part of the industry. An advantage associated with owner-operators is that they have strong incentives to drive in ways that preserve their trucks’ value; these incentives have traditionally been far weaker for â€Å"company drivers,” who do not own their trucks. This study shows that OBC diffusion has diminished the use of owner-operators. By allowing firms to monitor how drivers drive, OBCs have eliminated an important incentive advantage of owneroperators, and have le d trucking firms to rent out fewer hauls out to such individuals.\r\nResidential solid Estate The mesh vastly increases the amount of information on housing vacancies that is readily available to consumers. former research had shown that high costs of information and lack of vex to information limited housing searches. The trump information available to consumers tended to be for properties near their current location. In addition, research found that information intermediaries such as real estate agents functiond the options that consumers considered. The increased information that the lucre makes available to consumers potentially reduces or eliminates those limits.\r\nConsumers can readily learn about properties far from their current locations, and can do so relatively straightway (there still may be some influence exerted in how nett sites are set up, for example, and consumers may not immediately, or ever, get to the go around web site for their needs). Two recent s tudies use micro data to assess the effect of using the internet to search for housing. In these cases, micro data from the public-use la strain Population Survey provide basic information on what kinds of consumers use the Internet to search for housing. However, the hertz does not have information about the homes that Internet users purchased.\r\nTo address questions about the kinds of homes purchased, the researchers surveyed a sample of recent home purchasers in a county in jointure Carolina. Characteristics of buyers who used the Internet as a source of information about housing vacancies were generally similar to those of buyers who only used conventional information sources, but that Internet users were younger. The researchers conclude that using the Internet to betray for housing does not seem to effect geographic search patterns, or to lead consumers to pay lower prices for comparable homes.\r\nAlthough using the Internet might be expected to decrease the number of h omes buyers visited, because they would have more information about the houses and neighborhoods, the studies G. Baker and T. Hubbard, â€Å"Contractibility and Asset Ownership: On-Board Computers and governance in U. S. Trucking,” http://gsbwww. uchicago. edu/fac/thomas. hubbard/research/papers/paper_424. pdf (April 2003). 16 72 DIGITAL ECONOMY 2003 instead find that homebuyers who use the Internet as an information source make personal visits to more houses. 7 The Impact of IT on salarys Do â€Å"knowledge workers” induce prosecute premiums because they use computers? Does the use of IT increase the get hold of for more-educated workers? Does the growing use of computers by workers in some sectors of the economy explain shifts in the distribution of earnings? Initial micro data research answered the first question with a resounding â€Å"yes. ” One early study, for example, found that the pay of workers who used computers was 10 to 15 percent higher than the pay of similar workers who did not. 8 However, more recent studies that make use of more detailed information about workers and jobs over fivefold periods find that the answer is more nuanced. IT potentially affects many aspects of the performance of businesses. It also may affect the takings, and other characteristics of jobs. Asking how IT affects charters is actually asking two questions. The first question is whether jobs where workers use computers pay higher wages. If the answer is yes, the second question is why. As with IT use in businesses, determining cause and effect of IT use on wages is hard.\r\nThe jobs might pay higher wages because they require high skill levels. Some IT-using jobs, such as computer programmers and systems analysts, clearly require high skill levels, as do jobs such as architects who use computer-assisted design programs. However, computers appear end-to-end many workplaces. Workers may use computerized diagnostic equipment and programmable l ogical system controllers, for example, in production applications. Office and service workers may use word processors and spreadsheets, e-mail, computerized billing systems, and so forth.\r\nSuch jobs might pay higher wages if using a computer makes a worker with a given skill level more productive, but they generally do not require the workers to know much about principles of programming, or system or network design. Finally, the use of IT may allow computers to rest period for low-skilled workers performing repetitive tasks. Micro data studies in the United States, Europe, and Canada all find that workers using computers at work have much higher wages than workers who do not. The difference typically is on the order of 10 to 20 percent.\r\nHowever, these studies all used data from a single period, and many of them lack information about other aspects of the job, the worker, and the employer. This makes it hard to determine whether the workers have higher wages because they use a computer, or because important unobserved characteristics of the employer (is it highly productive heedless of the use of computers? ) or the worker (is the worker already highly skilled before using a computer? ) may affect managers’ decisions on investing in computers and R. Palm and M.\r\nDanis, â€Å"Residential Mobility: The Impacts of Web-Based Information on the Search Process and Spatial Housing filling Patterns,” Urban Geography, Vol. 22, No. 7 (2001); and R. Palm and M. Danis, â€Å"The Internet and Home Purchase,” Journal of Economic and Social Geography, Vol. 93, No. 5 (2002). A. Krueger, â€Å"How Computers Have Changed the Wage Structure: Evidence from Microdata, 1984â€1989,” Quarterly Journal of Economics, Vol. 108 No. 1 (February 1993). 18 17 73 DIGITAL ECONOMY 2003 assigning them to which employees. A new study reviewing recent research on the impact of IT on employment, skills, and wages concludes that the taradiddle is complex. 9 Studies find that having information on plant characteristics and work practices matters. For example, a study finding that workers using computers in Germany had higher wages than workers who did not also found that a similar wage derived function accrued to workers using telephones or pencils, or who worked posing down. 20 The implication is that the wage differential rightfully reflected the fact that workers using computers, telephones, or pencils, or who work sitting down, apprehend higher wages because they have higher skills.\r\nThis research suggests that IT is associated with substantial wage differentials, but does not cause them. Studies for France and Canada find similar wage differentials. 21 Researchers using French and Canadian micro data also have matched sets of data on employers and workers in those countries, and have two or more years of data. Studies using these matched data all find that substantial cross-section returns to computer use fall sharply when t hey make use of information about changes in both the worker and employer characteristics.\r\nEstimates differ by country and study, but the final differentials are modest, 1 to 4 percent. 22 These studies also find that the relatively modest wage differential associated with computer use varies markedly across occupations and among workers with different levels of education. For example, a study for Canada finds that more highly educated workers, white-collar workers, and those adopting the computer for scientific applications receive higher than average wage premiums, while other workers do not receive wage premiums when they start using computers on the job. The springs for such differences remain unresolved.\r\nIt may be more dearly-won to teach some groups of workers to use computers, or groups may differ in the proportion of computer training costs that they share with the employer (with lower employer shares resulting in higher wages). The researchers find that controlling for training increases the small or zero wage premiums they otherwise find for many low-skilled groups. They speculate that, if appropriate data were available to test for long-run effects, controlling for training and other worker characteristics might show positive wage differentials for most workers using computers. 3 Some detailed case studies (studies of specific businesses, usually anonymous) suggest another reason for differences in the wage differential associated with using computers at work. One M. Handel, â€Å"Implications of Information Technology for Employment, Skills, and Wages: A Review of Recent Research,” SRI International, SRI Project Number P10168, Final Report (July 2003). J. DiNardo and J. Pischke, â€Å"The Returns to Computer Use Revisited: Have Pencils Changed the Wage Structure Too? ” The Quarterly Journal of Economics, Vol. 112 No. 1 (February 1997). H. Entorf, M. Gollac, and F.\r\nKramarz, â€Å"New Technologies, Wages, and Worker Selectio n. ” Journal of agitate Economics (1999), and H. Entorf, and F. Kramarz, â€Å"Does Unmeasured Ability Explain the higher(prenominal) Wages of New Technology Workers? ” European Economic Review, Vol. 41 (1997); and C. Zoghi and S. Pabilonia, â€Å"Which Workers Gain from Computer Use? ” Paper presented at NBER Summer Meetings (July 2003). 22 23 21 20 19 E. g. , Entorf and Kramarz 1997. C. Zoghi and S. Pabilonia 2003. 74 DIGITAL ECONOMY 2003 case study examined the effect of introducing computers into the operations of a financial organization.\r\nFor some occupations, the case study found that computers substitute for the routine work that individuals previously performed, trim the need for such workers. In other occupations, however, computers appear to take on routine tasks and free workers to perform more complex, higher skilled, problem-solving activities. 24 If IT also allows the business to alter the way it works and organize itself more productively, it may raise the skill requirements for all workers in the business, even if they do not directly use computers.\r\nInsights from the International Micro Data Initiative A wave of new literature in plant- or firm-level research on the effects of IT has been conducted in countries take part in the OECD. 25 (See box 5. 1. ) As with research using U. S. micro data, the micro data research conducted in other countries also find links between IT and productivity. Where information on computer networks is available, or other measures of how computers are used, the research again suggests that it is not just having IT, but how IT is used that effects economic performance measures such as productivity.\r\nTwo kinds of studies are being undertaken. Some studies base their research on new data on IT for a single country. They make use of as much information as they can, and choose empirical techniques best conform to to their data. Studies such as these contribute important insights, speciall y when one country has information that other countries do not, or researchers are able to use techniques that help ensure that the measured effects indeed are due to IT. However, this strength also makes it hard to compare such estimates across countries.\r\nStudies from individual OECD countries find that IT has an impact on productivity and economic performance. Significant effects of IT on productivity are found in the service sector in Germany. 26 Recent research for France finds that one specific kind of network, the Internet, is associated with productivity gains, but other kinds of networks, which have been in use much longer, are not. 27 Canadian research finds that adopting IT is associated with growth in both productivity and market share. 8 Use of computers in Australia also is associated with productivity growth, with effects that vary across industries and are intertwined with other factors, such as the skill of a business’ work force, its organization and re-or ganization, and its innovativeness. 29 24 D. Autor, F. bill and R. Murnane, â€Å"Upstairs, Downstairs: Computer-Skill Complementarity and Computer-Labor Substitution on Two Floors of a Large Bank,” Industrial & Labor Relations Review 55(3) (2002). Research to date is summarized in D.\r\nPilat, ICT and Economic Growth: Evidence from OECD Countries, Industries, and Firms (Paris: OECD, 2003). T. Hempell, â€Å"What’s Spurious, What’s Real? metre the Productivity Impacts of ICT at the Firm-Level,” Discussion Paper 02-42, centerfield for European Economic Research (Zentrum fur Europaische Wirtschaftsforschung GmbH; ZEW, 2002), FTP://ftp. zew. de/pub/zew-docs/dp/dp0242. pdf. B. Crepon, T. Heckel, and N. Riedinger, http://www. nber. org/CRIW/papers/crepon. pdf, Paper presented at â€Å"R&D, Education, and Productivity,” NBER CRIW conference in honor of Zvi Griliches (Paris: August 2003). 8 29 27 26 25 J. Baldwin and D. Sabourin 2001. G. Gre tton, J. Gali, and D. Parham, â€Å"Uptake and impacts of ICTs in the Australian economy,” paper presented at OECD, Paris, December 2002. 75 DIGITAL ECONOMY 2003 other group of studies tries to use as many variables and analytical techniques as possible that are similar to those used by researchers in a few other countries. 30 This arise may exclude some variables and some analytical techniques, if researchers in several countries cannot use them.\r\nOn the other hand, this kind of coordination makes it more likely that similar empirical findings are actually due to IT, and that differences in empirical findings are due to differences in economic conditions and other factors among countries. An example is a group of researchers conducting parallel analyses for the United States, Denmark, and Japan. 31 Preliminary findings are that IT is positively related to productivity in all three countries, but that the relationship depends on the type of IT used, the sector, and time p eriod.\r\nEarly results for Denmark show a significant correlation between several measures of the firm’s performance and use of the Internet, but not for other uses of IT. For Japan, productivity levels are consistently higher for firms using IT networks. However, growth in labor productivity varies by type of network and how the network is used, and the effect of Internet use is higher for retail trade firms than for manufacturing firms. For U. S. manufacturing plants, there is a strong relationship between use of computer networks and labor productivity. Better Micro Data Research Requires Better Micro Data\r\nBecause the micro data are typically collected for other purposes, such as constructing key economic indicators, we almost always find that they lack some (often, much) of the information needed to address questions such those about the pervasiveness of IT and its effect. These gaps simply do not allow us to piss firm conclusions about the effect of IT. For example, research exploring the micro-level link between IT and economic performance may not always be able to separate the role of IT from other related but unobserved characteristics of the plant.\r\nWell-managed plants may use IT as one of many tools to fulfill performance goals. If we have information about IT, but not about management practices, the research may attribute performance effects to IT that really are due to good management. Estimating plant-level relationships among computers, computer networks, and productivity also is hard to do with existing data because many of the most important conceptionsâ€what a business produces (output), and all the factors it uses to make its product (such as labor, capital, energy, etc. cognise as â€Å"inputs”), as well as IT itselfâ€are difficult to define, and data based on these concepts are hard to collect. 32 Continuing research on these concepts leads to improve- For example, researchers in several countries are using the approach taken by U. S. researchers (Atrostic and Nguyen 2002), and using its findings as the benchmark against which they are comparing research findings using their own countries’ data. B. K. Atrostic, P. Boegh-Nielsen, K. Motohashi, and S. Nguyen, â€Å"Information Technology, Productivity, and Growth in Enterprises: Evidence from New International Micro Data,” L’acutalite economique (forthcoming 2004).\r\nA large literature lays out major data gaps in estimating the impact of information technology on economic performance. For example, conferences conducted by the NBER Conference on Research in Income and Wealth (CRIW) addressing capital and labor measurement over the last 20 years include D. Usher, The meter of Capital (NBER CRIW Volume 45 (Chicago University cut, 1980)); J. Triplett, The Measurement of Labor Cost (NBER CRIW Volume 48 (Chicago University Press, 1983)); and C. Corrado, J. Haltiwanger, and D. Sichel, Measuring Capital in the New 32 31 0 7 6 DIGITAL ECONOMY 2003 ments in what statistical agencies collect, but a dynamic and evolving economy continually presents new challenges. Even when concepts are well defined, it is pricy for statistical agencies to collect data and for respondents to provide the requested information. As a result, some key information needed for analysis may not be collected often or at all. Examples include information such as the number of computers and computer networks that businesses have, how they use them, and how much businesses invest in computers and other IT.\r\nThe divergent findings in the resulting empirical literature on the effects of IT are likely related to these data gaps, and to differences in the techniques researchers use to try to deal with them. 33 One way to improve the micro data available for research would be by better integrating aggregate economic indicators and their underlying micro data. It currently is not always easy to go down movements in the aggregate statisti cs with changes observed in the micro data. Aggregate indicators often are constructed from multiple micro data sources, and different sources of data for any concept (such as employment or payroll) may disagree.\r\n amass more of the data underlying aggregate statistics in ways that enrich their value as micro data, such as using common take frames and keeping information that allows linkage of same economic unit over time and across surveys, would improve both the micro data and our ability to meet changes in the aggregate economic indicators. Conclusion Micro data research conducted in the United States and in OECD countries shows that IT is related to economic performance and productivity. Careful research also shows that the relationships are complex.\r\nIT emerges as a sundry(a) factor. The kind of IT that is used and how it is used appear to matter in many (but not all) settings, including the ownership structure of trucking markets, the relative dynamism of retailing, and the relative jeopardy taking and innovativeness of manufacturing sectors across countries. At the same time, the use of IT alone does not appear to be enough to affect economic performance. When researchers have information about the characteristics of businesses, workers, jobs, and markets, they find that IT appears to work instead in bicycle-built-for-two with those factors.\r\nEconomy (NBER CRIW Volume 65 (Chicago University Press, forthcoming)). A series of meetings of international experts, known as the â€Å"Canberra Group,” intercommunicate capital measurement issues during the late 1990s (http:// unstats. un. org/unsd/methods/citygroup/capitalstock. htm). An refined manual describing how to calculate productivity devoted considerable text to issues in measuring capital can be found in P. Schreyer, Measuring Productivity: Measurement of Aggregate and Industry-Level Productivity Growthâ€OECD manual(a) (Paris: OECD 2001).\r\nMeasuring intangible capital, potent ially important in both IT and non-IT capital, received much attention tardily (see for example B. Lev, Intangibles: Management, Measurement, and Reporting (Brookings Institution Press: 2001)). 33 See, for example, Dedrick et al. (2003); D. Pilat, 2003; B. K. Atrostic, J. Gates, and R. Jarmin, 2000, â€Å"Measuring the Electronic Economy: Current office and Next Steps,” Working Paper CES-WP-00-10, Center for Economic Studies, U. S. Bureau of the Census, Washington DC; and J. Haltiwanger, and R.\r\nJarmin (2000), â€Å"Measuring the digital Economy,” in E. Byrnjolfsson and B. Kahin (eds. ), Understanding the Digital Economy (MIT Press 2000). 77 DIGITAL ECONOMY 2003 Separating out the effect of IT remains difficult because the analysis requires detailed information, and requires it for multiple periods. However, such detailed and repeated information is rare. Most business micro data contain only the information needed to calculate important economic indicators. The mi cro data are most slight for the sectors outside manufacturingâ€the most IT-intensive sectors.\r\nMore definitive research on the impact of IT requires that producing micro data sets becomes a statistical agency priority. 78 DIGITAL ECONOMY 2003 Appendix 5. A. Conducting Micro Data Research on the Impact of IT THE CENTER FOR ECONOMIC STUDIES, U. S. CENSUS office staff The Center for Economic Studies (CES) is a research unit of the Office of the Chief Economist, U. S. Bureau of the Census, established to abet and support the analytic needs of researchers and decision makers throughout government, academia, and business. CES currently operates eight Research Data Centers (RDCs) throughout the United States.\r\nRDCs offer qualified researchers restricted access to confidential economic data collected by the Census Bureau in its surveys and censuses. CES and the RDCs conduct, facilitate, and support research using micro data to increase the advantage and quality of Census Bureau data products. The best way for the Census Bureau to assess the quality of the data it collects, edits, and tabulates is for knowledgeable researchers to use micro records in rigorous analyses. Each micro record results from slews of decisions about definitions, classifications, coding rocedures, processing rules, editing rules, apocalypse rules, and so on. Analyses test the validity of all these decisions and reveal the data’s strengths and weaknesses. Research projects at CES and its RDCs are examining how facets of the electronic economy affect productivity, growth, business organization, and other aspects of business performance using both new data collected specifically to provide new information about IT, and existing data. Projects using existing Census Bureau micro data on businesses include McGuckin et al. 998; Dunne, Foster, Haltiwanger and Troske, 2000; Stolarick 1999; and Doms, Jarmin, and Klimek, 2002). Research making use of the new 1999 supplement to the Ann ual Survey of Manufactures linked to existing Census Bureau micro data include Atrostic and Gates 2001; Atrostic and Nguyen 2002; Haltiwanger, Jarmin, and Schank 2002; and Bartelsman et al. 2002. Research findings from many of these projects are discussed in this chapter. The research also helps the Census Bureau assess what current data collections can say about the electronic economy so that we can more efficiently allocate resources to any new measurement activities.\r\nMore information about CES, RDCs, requirements for access to data, and examples of research produced at the RDCs is at http://www. ces. census. gov/ces. php/home. DATA SOURCES AT CES Researchers at CES and the RDCs built, and use, a longitudinal data set linking manufacturing plants over time. The data are based on surveys and economic censuses, and contain detailed data on shipments and factors used to produce them, such as materials and labor, as well as characteristics of the plant, such as whether it exports. Recent CES research broadens the range of available micro data beyond manufacturing.\r\nA new micro data set, the Longitudinal Business Database, currently contains the universe of all U. S. business establishments with paid employees from 1976 to present. It allows researchers to examine entry and exit, gross job flows, and changes in the structure of the U. S. economy. The LBD can be used alone or in conjunction with other Census Bureau surveys at the establishment 79 DIGITAL ECONOMY 2003 and firm level. In addition, micro data from surveys and censuses of the retail, wholesale, and some service sectors is now fit available.\r\nThe subject area Employer Survey, conducted by the Census Bureau for the National Center on the Educational Quality of the Workforce, collects detailed information about work practices, worker training, and the use of computers. Restricted access to confidential data from the survey is available to qualified researchers through the RDCs. Information about the National Employer Survey can be found at http://www. census. gov/econ/overview/mu2400. html. PUBLIC-USE DATA This chapter also refers to research conducted using two other sets of micro data collected by the Census Bureau.\r\nThe Current Population Survey (CPS) is a survey of households that is collected by the Census Bureau for the Bureau of Labor Statistics. The CPS periodically collects information about people’s use of computers at work and at home. More information can be found at http://www. census. gov/ community/www/socdemo/computer. html. The Truck Inventory and Use Surveys collect information about on-board trip computers and electronic fomite management systems as part of the Census of Transportation. Information about the Census of Transportation can be found at http://www. census. gov/econ/www/tasmenu. html. 80\r\n'