IT and Economic Performance: Evidence From Micro Studies

Micro data—that is, data on individual businesses that underlie key economic indicators—allow us to go behind published statistics and ask how IT affects businesses’ economic performance. Years ago, analyses indicated a positive relationship between IT and productivity, even when official aggregate statistics still pointed towards a “productivity paradox. Now, such analyses shed light on how varied that relationship is across businesses, and how IT makes its impacts. This chapter focuses on research about businesses based on micro data collected by the U. S. Census Bureau. We highlight the kinds of questions about the use and impact of IT that only micro data allow us to address. Micro data studies in the United States and in other OECD countries show that IT affects the productivity and growth of individual economic units. Specific estimates of the size of the effect vary among studies.

Researchers comparing manufacturing plants in the United States and Germany, for example, find that in each country investing heavily in IT yields a productivity premium, but that the premium is higher in the United States than it is in Germany. They also find that the productivity premium varies much more for U. S. manufacturers. This greater variability is consistent with the view that the U. S. policy and institutional environments may be more conducive to experimentation by U. S. businesses. What kind of IT investments do U. S. businesses make? Census Bureau data on U. S. manufacturing establishments show that they invest in both computer networks and the kind of complex software that coordinates multiple business processes within and among establishments. About 50 percent of these plants have networks, while fewer than 10 percent have invested in this complex software.

Careful micro data research shows that the relationship between IT and economic performance is complex. “IT” emerges as a suite of alternatives from which businesses make different choices. Estimates of the size of the effect, and how IT makes its impact, remain hard to pinpoint. Data gaps make it hard to conduct careful analyses on the effect of IT. Continuing efforts by researchers and statistical organizations are filling some of the data gaps, but the gaps remain largest for the sectors outside manufacturing—the sectors that are the most IT-intensive.

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More definitive research requires that statistical agencies make producing micro data a priority. What Are Micro Data? Micro data generally contain information about many characteristics of the economic unit, such as plant employment, years in business, share of IT in costs, ways it uses IT, and its economic performance. Micro data exist for both businesses and individuals, and can be developed by private and public organizations. This chapter focuses on research using micro data about businesses that are collected by the U. S.Bureau of the Census.

BENEFITS OF MICRO DATA RESEARCH

Standard analyses of productivity and similar economic phenomena frequently assume that businesses are identical, at least within an industry, and therefore also respond similarly to changes in economic circumstances. However, it is easy to challenge this assumption simply by observing the variety of businesses in any industry, no matter how narrowly the industry is defined, and how diverse their responses appear to be. Case studies in specific industries repeatedly bear out this observation.

Micro data allow us to assess the diversity of businesses and track behaviors such as their entry and exit into an industry. They also allow us to document changes in businesses’ performance, such as employment, sales, and productivity, and see whether those changes are uniform among industries, within industries, or among businesses of given ages, sizes, and so forth. Two decades of research using micro data reveal tremendous variety in the economic characteristics and performance of businesses at any time, and over time. 1 An excellent summary is E. Barltesman and M. Doms, “Understanding Productivity: Lessons from Longitudinal Microdata,” Journal of Economic Literature, Vol. 38 (September 2000).

They also allow researchers to apply econometric techniques that take account of the kinds of complex relationships that simply cannot be presented in tables or other aggregated formats. Comparing findings from research studies using different data sets allows us to see which estimates appear to be robust, and which ones seem to depend on the specific data we use, and on the specific equations we estimate.

RESEARCH REQUIRES GOOD MICRO DATA

Micro data research takes advantage of the high-quality information about individual businesses that underlies major economic indicators.

The micro data sets typically are large and nationally representative, making it more likely that they capture the tremendous diversity among businesses. 2 Researchers often are able to link data at the micro level across surveys and over time. For example, consider the new information on whether businesses have computer networks, and how they use those networks that was collected in the Computer Network Use Supplement (CNUS) to the 1999 Annual Survey of Manufactures (ASM). The plant-level micro data about computer networks collected in the CNUS can be linked to information about employment, shipments, use of other inputs, etc. , collected about the same plants in the 1999 ASM and to ASMs for other years, and to data that was collected about the same plants in the 1997 Economic Census. Such exact linkages yield much richer information bases than any single supplement, survey, or census alone. When micro data can be linked, researchers also can use econometric techniques to control for unobserved characteristics that are specific to an individual plant or business.

These techniques allow researchers to have more confidence that findings, such as the effect of IT actually are due to IT and not to related but unmeasured characteristics, such as good management or a skilled work force. The Role of Information Technologies in Business Performance Recent research using micro data generally concludes that IT and productivity are related. Indeed, micro data analyses indicated a positive relationship between IT and productivity when official aggregate statistics still pointed towards a “productivity paradox. Two recent reviews of plant- or firm-level empirical studies of information technology (including but not limited to computers) and economic performance conclude that the literature shows positive relationships between information technology and productivity. However, specific estimates of the size of the effect vary widely among studies. How IT makes its impact also remains hard to pinpoint. While micro data provide raw material for important analyses, they are not a panacea. Researchers must address significant challenges when using existing micro data to analyze questions about the economic performance of businesses.

THE ROLE OF IT IN PRODUCTIVITY—A BRIEF SURVEY OF THE LITERATURE

Many recent studies use micro data to document and describe the productivity of different kinds of businesses, and to examine its sources.

The simple model that suggests productivity growth occurs among all existing 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, relatively productive plants continue, and the new entrants that survive are more productive than either. Micro data research on the effect of IT explores how IT fits into this complex picture of business behavior.

Dozens of research papers over the last decade examine various facets of the relationship between IT and productivity. Two recent reviews summarizing the current literature on IT and productivity conclude that there is an impact, although there is much variation among studies in the estimated magnitudes of that effect.

Most studies do not adjust for the high obsolescence rate of information technology capital, which lowers net returns. Also, total investment in information technology may be understated because most studies measure only computer hardware, but not related labor or software, or costs of coinvention, such as re-engineering business processes to take advantage of the new 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.

However, the estimates differ across studies, and the studies differ in many dimensions, including time periods covered and specific estimation techniques used. Stiroh looks for predictable effects 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.

His 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. Finally, Stiroh notes a potential for publication bias. Because theory 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 “right” results on the impact of IT. However, as his research demonstrates, estimates are sensitive to both the data used and the particular equation that is estimated.

He concludes that information technology matters, but the wide variation in empirical estimates means that much “depends on the details of the estimation” and “one must be careful about putting too much weight on any given estimates. ” The conclusion that recent studies show a positive effect of information technology stands in contrast to earlier studies, many of which found no relationship. Both Dedrick (2003) and Stiroh (2002) note that the best data available to early researchers suffered from small sample sizes, few or no small firms or plants, and lack of data on information technology investment. These data gaps may be why early micro data studies failed to find a relationship between IT and performance.

CAUSE AND EFFECT: DOES USING IT MAKE BUSINESSES MORE PRODUCTIVE?

The literature so far yields mixed findings on cause and effect between IT and plant-level economic performance. Early research is limited to manufacturing. The first findings in this area were that more productive plants may be more likely to adopt best 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 boost their productivity. 6 Recent research expands the scope of analysis 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 number 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.

While the United States and a few other economies enjoyed the boom of the late 90s, many European economies experienced sluggish growth. Several explanations have 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 have 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 environment permits firms to experiment more. An important component of the U. S. bility in this regard is the efficient reallocation of resources away from firms whose experiments in the marketplace 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 market 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 higher productivity firms enter and grow. Is it the case that IT has had a greater impact on business performance in the United States because the U. S. policy and institutional environment is more conducive to market selection and learning?

Recent research using micro data from the United States and Germany attempts to address this question. The analysis first compares the differences between various groups (e. g. , young vs. old, or those that invest heavily in IT vs. those that do not) of manufacturing establishments within each country. These differences are then compared 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 after controlling for several plant specific factors such as industry, age, size, and so on.

The researchers examined investment in both general and IT-specific equipment. The core comparison group had no investment. The other two groups—with investment in any equipment, and investment in IT equipment—were split into “high” and “low” investment groups at the 75th percentile of the investment intensity distributions. Plants with “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 structures) and for IT equipment, giving a combined seven investment intensity categories.

Then 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. Panel 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) have more varied productivity outcomes as measured by the standard 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 caution as they relate to only two countries and there are many factors the researchers do not control for.

DOES IT MATTER HOW IT IS USED?

Businesses in the United States have used 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 longer be enough. The question for economic performance is no longer whether IT is used, but how it is used.

New 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 regular ASM survey forms, such as the value of shipments, employment, and product class shipments. Figure 5. 2 presents researchers’ estimates of the diffusion of computer networks. The research finds that computer networks are widely diffused within manufacturing, with networks at about half of all plants.

The share of employment at plants with networks is almost 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 integrated enterprise resource planning software (FIERP); that is, the kind of software that links different kinds of applications (such as inventory, tracking, and payroll) within and across businesses.

Plants in all manufacturing industries use this complex software. However, FEIRP software remains relatively rare compared to computer networks. While about half of all manufacturing plants have networks, fewer than 10 percent have this kind of software. Initial research finds that computer networks have a positive and significant effect on plant’s labor productivity. After accounting for multiple 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 affected productivity between 1990 and 2001.

In 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 training 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. 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, EVERYWHERE? —EVIDENCE FROM STUDIES OF MARKET STRUCTURE IT

K. Motohashi, “Firm level analysis of information network use and productivity in Japan,” presented at the conference on Comparative Analysis of Enterprise (micro) Data, London (September 2003). S. Black, and L. Lynch, “How to Compete: The Impact of Workplace Practices and Information Technology on Productivity,” Review of Economics and Statistics, Vol. 83 No. 3 (August 2001); and D. Neumark and P. Cappelli, “Do ‘High Performance’ Work Practices Improve Establishment-Level Outcomes? ” Industrial and Labor Relations Review (July 2001).

Similarly, use of the Internet might make it easier for consumers to compare prices, and so lead to a reduction in prices for products sold on-line or in “bricks and mortar” establishments. At the same time, a firm building 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 multifaceted nature and complex economic effects.

Trucking 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 affected the trucking industry. Each of these studies 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’ “black boxes,” recording how drivers operate the trucks. These enable dispatchers to verify how truck drivers drive.

Advanced 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 advanced capabilities help dispatchers make and implement better scheduling decisions, and help them avoid 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 increased individual trucks’ utilization rate, as measured by their loaded miles during the time they are in service.  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 billion 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 accrue years after trucking firms first began to adopt OBCs. In contrast, the study finds no evidence that standard OBCs have led to increased truck utilization.

Combined, 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 far from their base are on the road and loaded. These gains, however, appear to have lagged adoption by several years.

This implies that IT-enabled improvements 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 systematic 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, “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 “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 led trucking firms to subcontract fewer hauls out to such individuals.

Residential Real Estate The Internet vastly increases the amount of information on housing vacancies that is readily available to consumers. Previous research had shown that high costs of information and lack of access to information limited housing searches. The best 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 influenced the options that consumers considered. The increased information that the Internet makes available to consumers potentially reduces or eliminates those limits.

Consumers can readily learn about properties far from their current locations, and can do so relatively directly (there still may be some influence exerted in how web sites are set up, for example, and consumers may not immediately, or ever, get to the best web site for their needs). Two recent studies use micro data to assess the effect of using the Internet to search for housing. In these cases, micro data from the public-use Current Population Survey provide basic information on what kinds of consumers use the Internet to search for housing. However, the CPS does not have information about the homes that Internet users purchased.

To address questions about the kinds of homes purchased, the researchers surveyed a sample of recent home purchasers in a county in North 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, except that Internet users were younger. The researchers conclude that using the Internet to shop for housing does not seem to effect geographic search patterns, or to lead consumers to pay lower prices for comparable homes.

The 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 throughout many workplaces. Workers may use computerized diagnostic equipment and programmable logic controllers, for example, in production applications. Office and service workers may use word processors and spreadsheets, e-mail, computerized billing systems, and so forth.

Such 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 substitute 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.

However, 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 regardless 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.

This research suggests that IT is associated with substantial wage differentials, but does not cause them. Studies for France and Canada find similar wage differentials. 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 they make use of information about changes in both the worker and employer characteristics.

Estimates differ by country and study, but the final differentials are modest, 1 to 4 percent. 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 reasons for such differences remain unresolved.

It may be more costly 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.

For some occupations, the case study found that computers substitute for the routine work that individuals previously performed, reducing 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. 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.

Insights 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 participating in the OECD. 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.

Two 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 suited to their data. Studies such as these contribute important insights, particularly 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.

Studies 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. 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. Canadian research finds that adopting IT is associated with growth in both productivity and market share. 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-organization, and its innovativeness.

On 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. 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 period.

Early 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.

Because 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 draw 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.

The 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 reconcile movements in the aggregate statistics 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.

Collecting more of the data underlying aggregate statistics in ways that enrich their value as micro data, such as using common sampling 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 understand 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.

IT emerges as a multifaceted 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 risk 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 tandem with those factors.

RDCs 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 utility 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 dozens of decisions about definitions, classifications, coding rocedures, processing rules, editing rules, disclosure rules, and so on. Analyses test the validity of all these decisions and uncover 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 Annual 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.

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