1
Introduction and Motivation

Maximizing growth, maintaining full employment, minimizing inflation, and advancing the population’s living standards are primary economic policy goals. A healthy, market economy is complex, and is characterized by dynamic interactions between businesses and households. The ability to measure these dynamics is of critical importance for understanding the sources of productivity and job growth and, in turn via demand and supply factors, price inflation. Economic policy makers, including the Federal Reserve, rely heavily on timely and accurate statistics on the dynamics of U.S. businesses. Although the U.S. statistical system is a world leader in producing data on business activity, the dynamism of U.S. businesses and the implications of this dynamism for productivity and job growth are only recently becoming evident.

1.1
THE CURRENT SYSTEM

Data on business activity are useful for a broad spectrum of research, policy, and commercial purposes. Business data provide key building blocks for national and local statistics on income, output, employment, productivity, investment, prices, and other economic measures. Data on U.S. businesses are actively used by policy makers at the national, state, and local levels, and by the business community itself in tracking U.S. economic activity. Beyond the public-release statistics that often dominate news reports, micro-level business data are useful for the analysis of productivity growth, job creation and destruction, business entry and exit, the role of



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 13
Understanding Business Dynamics: An Integrated Data System for America’s Future 1 Introduction and Motivation Maximizing growth, maintaining full employment, minimizing inflation, and advancing the population’s living standards are primary economic policy goals. A healthy, market economy is complex, and is characterized by dynamic interactions between businesses and households. The ability to measure these dynamics is of critical importance for understanding the sources of productivity and job growth and, in turn via demand and supply factors, price inflation. Economic policy makers, including the Federal Reserve, rely heavily on timely and accurate statistics on the dynamics of U.S. businesses. Although the U.S. statistical system is a world leader in producing data on business activity, the dynamism of U.S. businesses and the implications of this dynamism for productivity and job growth are only recently becoming evident. 1.1 THE CURRENT SYSTEM Data on business activity are useful for a broad spectrum of research, policy, and commercial purposes. Business data provide key building blocks for national and local statistics on income, output, employment, productivity, investment, prices, and other economic measures. Data on U.S. businesses are actively used by policy makers at the national, state, and local levels, and by the business community itself in tracking U.S. economic activity. Beyond the public-release statistics that often dominate news reports, micro-level business data are useful for the analysis of productivity growth, job creation and destruction, business entry and exit, the role of

OCR for page 13
Understanding Business Dynamics: An Integrated Data System for America’s Future young and small businesses, the characteristics of business owners, outsourcing, interactions among firms, the impact of natural disasters on local economic activity, and other critical issues. A simple but useful generalization about the U.S. statistical system is that it has been designed to measure levels of business activity—in terms such as business outputs, and business inputs like labor and physical capital—on a timely and accurate basis. Statistical agencies have traditionally focused greater attention on larger, more mature businesses. This approach is capable of producing accurate and cost-effective estimates of aggregate economic activity because a relatively modest number of businesses produce a large share of total output and employ a large share of economic inputs. It is also easier to identify and promptly capture the activity of large, long-established businesses. But there are drawbacks to this approach; the focus on levels as opposed to growth has led to an underemphasis on young and small businesses. These businesses account for a relatively small share of the level of economic activity but are critically important in measuring and understanding the growth of economic activity. Also when business dynamics vary systematically with business size or age, a focus on larger and more mature units can yield less accurate, potentially misleading, measures of changes in economic activity. For example, given the current focus, the tracking of the response of U.S. businesses to the business cycle is likely mismeasured to the extent that young and small businesses are especially sensitive to the cycle. Equally importantly, a focus on larger and more mature units limits our ability to measure and analyze the early life-cycle dynamics of businesses and to evaluate the factors that impact business formation, selection, and growth. Thus, a full understanding of economic progress and business dynamics requires careful attention to data on younger and smaller businesses. Over the past decade or so, U.S. statistical agencies have greatly improved the measurement of business activity through the intense development of longitudinal databases constructed from administrative records. For example, the Bureau of Labor Statistics (BLS) has developed the Business Employment Dynamics database, and now regularly produces quarterly statistics on gross job gains and losses by industry, region, and business size class. The Census Bureau has developed the Quarterly Workforce Indicators, which provide measures of worker separations and accessions and related measures of job gains and losses at the local economy level. The Census Bureau has also developed the Longitudinal Business Database (LBD) from its business registers and used the LBD to produce new public-use statistics.1 The LBD also serves as a micro-level analytical database for 1 In a closely related program, the Census Bureau has been using its business registers to produce aggregate statistics on the dynamics of U.S. businesses in the Statistics on U.S. Businesses program.

OCR for page 13
Understanding Business Dynamics: An Integrated Data System for America’s Future use by researchers at secure sites for valid and approved statistical purposes. Both BLS and the Census Bureau have taken advantage of their new LBDs to help quantify dislocations caused by economic disruptions, such as those caused by Hurricane Katrina and other natural disasters.2 Related improvements in databases on young and small businesses have been supported by private research foundations. The Kauffman Foundation, for example, has supported the Panel Study of Entrepreneurial Dynamics, which provides information about nascent entrepreneurs who are considering a business start-up, and the new Kauffman Firm Survey, which will provide information about the early life cycle of business start-ups. While these developments on the statistical and research fronts represent real progress, the data gaps for young and small businesses are still very substantial. The administrative records data exploited by the U.S. statistical agencies are comprehensive in their coverage, but they provide little depth of information about individual businesses. Administrative records are usually limited to measures of payroll, employment, revenue, industry, business age, legal form of organization, and business location. To quantify and better understand the role of younger and smaller businesses in productivity growth, technology adoption, new product development, and opportunities for economic advance, we need richer types of data on business enterprises, business owners, and the legal, fiscal, and economic characteristics of the environments in which they operate. 1.2 STUDY SCOPE This study assesses the strengths and weaknesses of existing business databases—their accuracy, coverage, timeliness, richness, and accessibility—and the overall adequacy of the U.S. data infrastructure for measuring and analyzing business outcomes. We compare the current data infrastructure with a feasible ideal system, and advance several recommendations to improve the quality, timeliness, and coverage of U.S. business databases. We emphasize the need for better measurement of younger and smaller businesses, including their evolution over time, and richer analyses of their 2 Both BLS and the Census Bureau actively used their business registers to study the impact of Hurricane Katrina and to adjust statistics. The detailed location data in their registers permitted measurement and assessment of the businesses impacted by the disaster and to make adjustments to business survey estimates for nonresponse by businesses in the impacted area. The standard approach to nonresponse is often to impute data for the nonrespondent but in the case of Katrina-impacted businesses, BLS, for example, was able to treat nonresponse as nonactive businesses.

OCR for page 13
Understanding Business Dynamics: An Integrated Data System for America’s Future economic performance and role in the larger economy; timely and accessible data on entrepreneurial activities is also a major concern. In presenting our recommendations, we prioritize the steps needed to fill the data gaps identified in the report. Given the scarcity of resources for new statistical programs and the desire to keep respondent burden low, key elements of our recommendations stress more effective use of existing data collection instruments, continued development of longitudinal data sets, and greater interagency collaboration in the sharing and linking of data sources within the federal government. We also suggest forms of cooperation between the federal statistical agencies and nongovernmental producers of business data that can yield richer data sets on business characteristics and outcomes while respecting confidentiality requirements. In considering these issues, we primarily limit our attention to the measurement of business activity in the private, nonfarm sector of the U.S. economy. The agricultural sector involves a number of data sources and measurement issues that are outside the scope of this study. Likewise, the government and nonprofit sectors have distinct features that give rise to different data needs and measurement issues. To keep the scope of this study within reasonable bounds, an assessment of the data infrastructure needs for the agricultural, nonprofit, and government sectors is left for other occasions.3 The charge of the Panel on Measuring Business Formation, Dynamics, and Performance is to develop strategies for improving the accuracy, currency, coverage, and integration of data used in academic and agency research on business formation and dynamics, and in the production of key national, regional, and local statistics. The panel’s focus is on business formation, young and small businesses, and entrepreneurial activities. Of particular interest are data used to measure and track business entry and exit, job and worker flows, productivity, investment, wages, and prices. Given the keen interest in business formation and growth, the integration of real and financial data that permit the measurement and analysis of financing for young and small businesses is also a key area of interest. The specific goals of the study are to: 3 Among the recent work focusing on the nonprofit sector is that carried out by the UN/ Johns Hopkins project on nonprofits (http://www.jhu.edu/~gnisp/) and efforts at the Urban Institute’s Center on Nonprofits and Philanthropy (http://www.urban.org/center/cnp/index.cfm). Much of the data work on the agricultural sector takes place at the U.S. Department of Agriculture’s Economic Research Service. Summaries of leading work on measuring the government sector (for the United States and Great Britain, respectively) can be found in National Research Council (1998) and Atkinson (2005).

OCR for page 13
Understanding Business Dynamics: An Integrated Data System for America’s Future catalogue the currently available cross-sectional and longitudinal databases on business-level outcomes; identify gaps in current data sources that impede the production of accurate and timely statistics on business dynamics; identify gaps that hamper research on business entry and exit, business evolution over time, interactions among firms, factors that influence business adaptation and growth, the dynamics of the self-employed, useful definitions of business organizations and their scope of operations, job and worker flows, financial and other business-to-business linkages, and the transformation of business activities and organizations; and develop recommendations for better use of existing data sources, new and improved collection of business data, and more effective integration of existing data collection projects. The recommendations must respect legal obligations, confidentiality requirements, and access needs, and they must recognize issues related to survey response rates, respondent burdens, and the high cost of longitudinal surveys. 1.3 BUSINESS DATA USES AND CHALLENGES U.S. statistical agencies collect business-level data to construct national statistics on aggregate income, profits, output, productivity, employment, investment, prices, and other measures of economic activity. There is a high-priority need by the user community to measure both the level and the changes in U.S. business economic activity. While national aggregates have the top priority, statistics on the levels and changes in U.S. business activity are often classified by industry and by location of business activity. Detailed data by industry and location are essential for understanding the rapid pace of restructuring within many industries, the growth of the service sector, the diffusion of advanced technologies and new business practices, and many other aspects of economic development. Measurement of business activity at the regional, state, and county level is also important for a variety of policy questions. Recent natural disasters such as Hurricane Katrina highlight the need for timely information on local business activity. Challenges in the measurement of business activity at the national, industry, and regional level are many and well known. The ongoing shift in industrial structure imposes its own challenges. There is invariably a lag in the response of the statistical agencies in shifting the focus of measurement to new and growing sectors of the economy. Reflecting their historical importance, much more information is currently collected on the agricultural and manufacturing sectors than the service sector. However, in the last couple of decades the U.S. statistical agencies have responded to this

OCR for page 13
Understanding Business Dynamics: An Integrated Data System for America’s Future challenge by developing more comprehensive economic censuses, and annual and quarterly surveys of the nonfarm, service-producing sectors of the economy. Some large and rapidly growing industries are difficult to measure adequately because of the nature of their activities. It is well known, for example, that the financial services sector poses challenges in the measuring output. Researchers frequently cite measurement of the output of banks which, given the wide range of services offered and the difficulty of quantifying the revenue streams and prices associated with these services, pose serious problems. This challenge has yielded puzzling findings such as negative measured productivity growth in the banking sector despite the presence of apparent and significant technological innovations. A related challenge is the measurement of output and prices of advanced technology products such as computers, semiconductors, software, telecommunications, and new medical treatments. Rapid technological changes and quality improvements can make it difficult for the statistical agencies to adequately capture developments in the market place. Globalization and the increased role of large, multinational firms is another ongoing measurement challenge for the statistical agencies. Complex, vertically and horizontally integrated firms—with research and development labs and customer service call centers around the globe, and many components produced offshore—make the measurement of business activity increasingly difficult. Another challenge is reconciling and integrating the measurement of business activity with the economic activity of households. The center point of this reconciliation is employment statistics. Household and business surveys of total employment as well as employment growth rates differ substantially and systematically in booms and recessions. Possible sources of these discrepancies include inconsistent treatment of the self-employed, multiple job holders, and off-the-book workers that might show up in household surveys but not in establishment surveys and administrative records. Yet another challenge is to generate the core measures of business activity on a timely basis. There is high demand for information on U.S. business activity that is both current and accurate by both the policy and business communities. For example, for the purpose of setting monetary policy, the Board of Governors of the Federal Reserve System requires accurate and timely information up to the latest week and month prior to its Open Market Committee meetings (held every six weeks). Given all of these challenges, and the potentially conflicting goal of low respondent burden, it might be argued that other needs merit higher priority than better data on entrepreneurial activity, business start-ups, and young businesses. Two basic arguments suggest otherwise. First, it is a

OCR for page 13
Understanding Business Dynamics: An Integrated Data System for America’s Future fallacy to argue that since large and mature businesses account for the largest share of activity, they account for most of the changes in aggregate activity. For example, young and small businesses play a disproportionately large role in the creation and destruction of jobs and, in certain circumstances, changes in national and local economic activity. Second, the U.S. economy constantly reinvents itself with new business practices, new products, and new processes. Young and small businesses play a vital role in the ongoing restructuring of the U.S. economy. Failure to measure this role can mean missing much of the story. The panel put a high weight on the resource constraints for business data collection. Resources for U.S. statistical agencies are limited, and the challenges they face are considerable. Moreover, keeping respondent burden to a reasonable level (and perhaps reducing it) is an important consideration. Given these resource constraints and objectives, we explore ways that existing data collection can be made more efficient through data integration. This means the integration of survey and administrative records data and the combination of data across surveys. Challenges for this approach include legal restrictions on data sharing and access across U.S. statistical agencies. The panel’s findings and recommendations have these resource and legal constraints in the background of all of the discussion. 1.4 THE VALUE OF STUDYING BUSINESS DYNAMICS Longitudinal databases have yielded several insights into business dynamics and the operation of the larger economy. For example, we now know that gross job creation and destruction dwarf net employment changes at the national, regional, and industry levels.4 Indeed, about one in seven jobs in the U.S. private sector disappears in an average year, and an even larger number of new jobs are created. Much of this job creation and destruction reflects business start-ups and shutdowns. Exits and other deep employment cutbacks at the level of individual businesses translate into job losses for workers and, often, unemployment spells and reduced earnings. Thus, there is a close connection between individual business dynamics and the fortunes of workers and their families. Better measurement of business activity, especially at young and small businesses, is also essential for addressing other key economic issues. The 4 Gross job creation is calculated by summing employment gains over new and expanding employers. Likewise, gross job destruction is calculated by summing employment losses over exits and contracting employers.

OCR for page 13
Understanding Business Dynamics: An Integrated Data System for America’s Future large-scale turnover of firms, jobs, and workers in the U.S. economy reflects an ongoing process of business responses to idiosyncratic shocks and differential responses to common shocks. Longitudinal studies find that the continuous reallocation of jobs, workers, and capital from less to more productive businesses is an important source of aggregate productivity gains. Younger firms appear to play an especially important role in this process, as suggested by their relatively high and variable growth rates. Some of the dramatic outcome differences among young businesses reflect high levels of experimentation with different business methods, production processes, organizational structures, new products, and new locations. Preliminary research findings suggest that each cohort of entering businesses is quite heterogeneous, and that entrants often experiment with a variety of new methods, products, and processes.5 Some of these businesses discover or develop commercially successful innovations, become profitable and expand rapidly, thereby contributing to employment gains and productivity growth. Many other new businesses, however, do not survive the competitive selection process, and they eventually shrink or exit. The available evidence suggests that this market selection process contributes to productivity gains in the sense that less productive, less profitable businesses tend to exit, while the more productive, more profitable firms tend to endure and expand.6 These findings about the churning of jobs, workers, and firms and their contribution to productivity gains are preliminary—in part because the measures of productivity available for young and small businesses are so limited. As emphasized above, most of the data on outputs and inputs, including key inputs like capital expenditures on advanced technologies, are collected mainly for large, mature businesses. Hence, the productivity measures available for younger and smaller firms are often quite crude. As a consequence, many critical questions cannot be adequately addressed with existing data sources. If business start-ups and the postentry dynamics of young businesses play a vital role in economic growth and fluctuations, as suggested by the available evidence, then it is important to measure and understand the factors that influence these dynamics. Likewise, measuring and understanding the factors that influence the decision to become an entrepreneur is important. In addition, relatively little is known about the activities of young and small businesses in terms of busi- 5 See, e.g., Davis, Haltiwanger, and Schuh (1996); Foster, Haltiwanger, and Krizan (2001); Syverson (2004); and Becker et al. (2006). 6 See Bartelsman and Doms (2000) and Foster, Haltiwanger, and Krizan (2001, 2002).

OCR for page 13
Understanding Business Dynamics: An Integrated Data System for America’s Future ness methods, products, and processes. And little is known about the obstacles to the survival and growth of young businesses. Some researchers argue that access to external financing is critical for the success of business start-ups, but there is little comprehensive research on this issue because of data limitations. The share of privately held firms that obtain venture capital financing is very small, but many of the businesses that have gone public in the last decade or so have had such financing (Kaplan, Sensoy, and Strömberg, 2005). Moreover, young businesses that go public sometimes exhibit especially rapid growth. Because young businesses that offer a promising business model can more readily attract venture capital and go public, the causal connection between financing and growth is difficult to pin down, but the interaction between financial markets and young business dynamics is one key issue that calls for better data. Some existing data sources do focus on small business financing. For example, the Federal Reserve conducts a survey on small business financing that produces much useful information. The latter survey is a rich data resource for these issues but, with no intended criticism of this very valuable survey instrument, it offers far too little given the above characterization of business dynamics. As described above, business start-ups and young businesses must make decisions about the business model, the product, the process, and the location of activity. These decisions are yielding rich and heterogeneous outcomes on key measures like productivity, investment in physical capital and advanced technologies, and job growth. To understand these outcomes, measures of the latter need to become available (in many cases they are not) and then integrated with information from surveys about financing. 1.5 APPLICATIONS THAT WOULD BE ADVANCED BY FURTHER DEVELOPMENT OF DATA ON YOUNG AND SMALL BUSINESSES The Federal Reserve is among the most prominent users of business data. Consider the Federal Reserve Open Market Committee (FOMC) meeting at which the current status of the economy is evaluated and its behavior over short and long horizons forecasted. The FOMC uses high-frequency statistics on key indicators like employment, unemployment, and sales to conduct this evaluation and make these forecasts. Start-up and young businesses are volatile and among the most sensitive to business cycle fluctuations. The rapidity with which the economy emerges from recession may very well turn on how well business start-ups and young businesses cope with a changing economic environment. Currently, the employment statistics from businesses are based on the current establishment survey which adds start-ups and young businesses to its sample frame with a lag. More-

OCR for page 13
Understanding Business Dynamics: An Integrated Data System for America’s Future over, given that it is a high-frequency survey with substantial nonresponse, BLS primarily uses the employment changes reported by establishments that responded to the survey in both the current and prior month to generate its estimate of employment growth. BLS realizes this shortcoming and has developed sophisticated statistical methods for imputing or forecasting the contribution of business entry and exit. However, they have relatively limited information on which to build such imputations. If real-time data on business entry and exit were available (or with a short time lag) this would significantly improve the ability of the FOMC to detect business cycle turning points. Next, consider national and local policy planners attempting to evaluate the impact and plan for the future in response to economic dislocation from natural disasters like Hurricane Katrina or from military base closings and realignment. Details on the spatial variation of firms and workers down to the block level are essential to measure the impact of such events. The tracking of business start-ups and shutdowns as well as the growth dynamics of young businesses is vital for evaluating the planning and recovery from the economic dislocation. For example, in New Orleans, tracking business shutdowns that are temporary or permanent, and measuring and tracking the types of businesses that are returning, starting up, or expanding is of critical importance. In turn, consider the U.S. statistical agencies charged with measuring the activities and productivity of different industries. Anecdotal and their own data collection reveal that there are blurring of boundaries across firms in a variety of ways. Outsourcing of activity implies that some part of the production process is now conducted in a different physical location (either domestically or offshore) and in a different industry. Changes in the employer and employee contractual relationship take the form of increased use of temporary help, personnel service, or employee leasing firms. In both cases, some part of the business start-ups that are observed reflect these changing boundaries of firms. Thus, even for the measurement of the activity of large, mature firms tracking the business start-ups and their activities is of critical importance to measure the activity and productivity of the industry. Failure to capture outsourcing or employee leasing can yield spurious changes in the measured productivity of the firms and the industry as it might appear that the firms and in turn the industry are able to produce the same amount or even more with seemingly fewer inputs. Finally, consider an academic researcher who is exploring the idea that the financial market deregulations and innovations of the 1980s and 1990s played a fundamental role in the improved U.S. economic performance in the 1990s and in the new century. Testing the hypothesis depends critically on exploring whether financial market innovations permitted greater risk-taking by businesses as even young businesses were able to find financial

OCR for page 13
Understanding Business Dynamics: An Integrated Data System for America’s Future backing for risky but high-potential-payoff projects. Both the research community and the policy community have enormous interest in this issue since it impacts regulation and legislation of financial markets in the United States. This issue can only be explored if researchers have access to longitudinal data with information on business start-ups and on tracking measures of real activity like sales, employment, investment in innovative activity, investment in physical capital, the organizational structure of the firm, and the worker mix at the firm. In addition, researchers need information about the sources of financing of these start-ups and young businesses. To make matters even more challenging, the longitudinal business data must also accurately track exits since studies would be biased if the sample included only firms that survived and succeeded. Researchers could only conduct this type of pathbreaking work with substantially improved data on young and small businesses along the lines discussed in this study. Moreover, researchers may only conduct their analyses if they have access to such data. 1.6 THE PANEL’S WORK During its deliberations, the committee identified a set of principles to guide its work and, in turn, the development of its recommendations. The first principle relates to confidentiality and privacy: Principle 1: Statistical agencies have the responsibility to data providers and data subjects to protect the confidentiality of information that is provided. Data collected by the government must be maintained in such a way that identifiable information is not disclosed for administrative, regulatory, or enforcement purposes. In addition to administrative data, agencies collect information under a pledge of confidentiality for exclusively statistical purposes (National Research Council, 1993, pp. 56-57). Such information may not be disclosed in identifiable form for any nonstatistical purpose without the informed consent of respondents. Statistical purposes include “the description, estimation, or analysis of the characteristics of groups, without identifying the individuals or organizations that comprise such groups” (The Confidential Information Protection and Statistical Efficiency Act of 2002 (CIPSEA)). Nonstatistical purposes include using information for administrative, regulatory, law enforcement, judicial, or other purposes that may affect the rights, privileges, or benefits of a respondent. Avoiding disclosure of confidential data is essential from an ethical perspective, and for maintaining data systems that rely heavily on organizations and individuals to respond to voluntary surveys. Confidentiality protections are intended to minimize respondents’ concerns that data will be misused and,

OCR for page 13
Understanding Business Dynamics: An Integrated Data System for America’s Future in turn, encourage respondents to provide more accurate information to data collecting agencies and investigators.7 CIPSEA has gone far in establishing a consistent set of cross-agency guidelines, including penalties for unauthorized disclosure of confidential statistical information. The second principle relates to the public purpose of statistical agency products, and draws from Principles and Practices for a Federal Statistical Agency (National Research Council, 2005a): Principle 2: Subject to the confidentiality requirements identified above, data sharing among government statistical agencies and data access by others should be facilitated when it serves a substantial public purpose. Data uses that serve a substantial public purpose include those that (1) lead to improvements in the quality, breadth, and usefulness of government statistical systems; (2) provide evidence-based analyses of government policies and of social and economic issues; and/or (3) contribute to advances in scientific knowledge. The rationale for the public purpose principle is straightforward: Government administrative record systems and survey databases generate enormous public value in terms of informing decision makers (including those in the private sector) and are maintained at considerable cost to the public in the form of taxes and the time and monetary outlays associated with complying with reporting requirements.8 As such, the public is entitled to the full and effective use of these government assets, provided that such uses do not compromise the confidentiality and privacy assurances afforded to respondents. Data systems should be designed to fulfill, to the greatest extent possible, the needs of users—researchers, policy makers, businesses, and the statistical agencies themselves—conditional on budget and on adhering to pledges of respondent confidentiality. The statistical agencies (BLS, Census Bureau, and the Bureau of Economic Analysis) require data to produce key aggregate income, product, and employment statistics. Currently, economic statistics are generated from multiple sources without an agreed-upon, centrally maintained universe of businesses. As a result, our business statis- 7 Private Lives and Public Policies (National Research Council, 1993) and follow-up reports (National Research Council, 2000, 2005) comprehensively cover data access and confidentiality issues associated with social science data. Chapter 7 of Private Lives and Public Policies specifically deals with statistical data on organizations and includes a discussion on sharing business lists. 8 The arguments underlying the “public purpose principle” are articulated in detail in National Research Council (1993, 2000, 2005b).

OCR for page 13
Understanding Business Dynamics: An Integrated Data System for America’s Future tics undersample identifiable business groups in cross-sectional data and produce multiple independent measures of relatively straightforward economic statistics such as the monthly employment and payroll figures. While differences in estimates of economic activity across data sets can generate new insight into the underlying dynamics of our economy, it can also generate unnecessary confusion. Establishing a more harmonized data collection system would reduce this confusion. In addition to the need for information to monitor aggregate trends, researchers and policy makers also require data to perform microanalyses—on topics such as firm entry and exit; the role of young and small businesses in innovation, economic growth, and job creation; trends in employment and productivity; interactions and linkages among firms/ establishments, particularly between large and small ones (e.g., former employees consulting for old firm, small companies selling out to large ones); offshore activities, outsourcing, supply dynamics; and characteristics of businesses/business owners (finances, demographics, nonemployer vs. employer businesses). Throughout this report, we have described how these topics relate to policy. For example, analysts at the Federal Reserve—one of the most prominent sets of users of business statistics—are concerned about measuring output and, in turn, productivity by industry. Some of these kinds of data could be significantly improved simply by better coordination of the business lists residing at BLS and the Census Bureau. Developing analytic capacity within statistical agencies so that data collection and database management systems are also designed for use in “evidence-based policy” may require statistical agencies to reexamine their mission. Businesses themselves benefit from timely and reliable data as they make employment, production, and investment decisions. When data products help meet business information and planning needs, not only does it contribute to a well-functioning economy, it may enhance the data collection enterprise itself by encouraging higher response rates and participation by businesses. By promoting more effective use of government databases, application of the public purpose principle may produce a more favorable attitude among respondents to government reporting requirements and surveys. Participation in voluntary surveys, as well as compliance with mandatory reporting requirements, may improve when businesses perceive that the resulting databases are put to useful purposes. Principle 3 emphasizes strengthening the weakest data links in the system; that is, prioritizing areas where current federal statistics are least developed: Principle 3: Improvements to data collection should focus first on areas

OCR for page 13
Understanding Business Dynamics: An Integrated Data System for America’s Future where policy and research relevance is high but where statistics needed to inform those policies and research are weakest. In other words, resources should be devoted to data improvement in such a way that the greatest marginal returns can be exploited. For business data, this points to the need for building up the statistical infrastructure for measuring dynamics. In other words we need to collect information on rapidly growing sectors in the economy where activities of smaller and younger firms are disproportionately important, but for which data coverage is weak. Maintaining data relevance also requires keeping abreast of trends in the quickly changing economic landscape. This requires, for example, improving product codes to keep up with expanding areas of the economy (such as communications equipment and other high-tech components of manufacturing). The Census Bureau has been increasingly developing more extensive product codes for services in response to the dynamic economy in which services have become much more important over time. However, much of the old economy is still embedded in the system. Federal Reserve Governor Randall Kroszner (2006) recently illustrated this point using the example of the product category “broadcast, studio, and related equipment.” That product category includes 16 subcategories of product data with modest total shipments (e.g., AM and FM radio transmitters, $103 million; cable-TV subscriber equipment, $41 million; studio transmission links, $18 million). In contrast, the product code “data communications equipment” has no break-downs into subcategories, even though it is a $10.5 billion industry. Kroszner concluded that “the task of updating product lists is resource intensive and time consuming, but it is critical to gaining a more comprehensive understanding of developments in the most vibrant sectors of our economy.” The fourth principle has to do with achieving cost efficiency in data programs: Principle 4: The statistical agencies should prioritize actions that can be done expeditiously and at low cost—the low hanging fruit. In this report, we identify a number of cases whereby more creative use of existing data can be used for the purpose of producing useful statistics. The idea is to get as much information out of the system as possible for a given level of resource and data protection commitment. This first chapter has discussed the scope, the objectives, and provided an overview of the case for improvements in U.S. business data with a focus on improved measurement of data on young and small businesses. This chapter has only opened the door to the issues and much remains to be discussed. Chapter 2 takes on the fundamental question of what is meant

OCR for page 13
Understanding Business Dynamics: An Integrated Data System for America’s Future by a business. Understanding this question is critical for evaluating measurement of business dynamics. Chapter 3 describes the ideal business data system. The discussion is intended to outline the objectives for further development of the system while at least keeping in the background the many factors constraining the collection of and access to business data. Chapter 4 provides an overview of the current system and discusses the gaps between the (constrained) ideal and the actual system in the United States. This discussion is a natural springboard for Chapter 5 which includes the recommendations of the study.