STUDYING INNOVATION IN BUSINESSES: NEW RESEARCHPOSSIBILITIESNicholas Greenia1

Internal Revenue Service
Kaye Husbands Fealing2

University of Minnesota
Julia Lane3

National Science Foundation

This paper was initially presented at the NBER/CRIW summer institute conference. We are grateful for thecomments received from participants in that conference, as well as the discussant, Chad Syverson. The views
expressed in this paper are the authors’ and not necessarily those of either the U.S. Internal Revenue Service or
the National Science Foundation.

1

Statistics of Income Division, (RAS:S) PO Box 2608, Washington DC 20013-2608
Hubert H. Humphrey Institute of Public Affairs, Center for Science, Technology and Public Policy, 164 Hubert H.
Humphrey Center, 301 19th Avenue South, Minneapolis, MN 55455
3
Suite 900, National Science Foundation, 4201 Wilson Blvd, Arlington, VA 22230
2

2

ABSTRACTThe rapid pace of globalization and technological change has created demand for more and better analysis toanswer key policy questions about the role of businesses in innovation. This demand was codified into law in theAmerica COMPETES Act. However, existing business datasets are not adequate to create an empirically based
foundation for policy decisions. This paper argues that the existing IRS data infrastructure could be used in a
number of ways to respond to the national imperative. It describes the legal framework within which such a
response could take place, and outlines the organizational features that would be required to establish an
IRS/researcher partnership. It concludes with a discussion of the role for the research policy community.
Keywords: Business microdata; innovation; confidentiality; researcher access; tax policy

3

1. INTRODUCTION
The rapid pace of globalization and technological change has created demand for more and better analysis toanswer key policy questions about the role of businesses in innovation. These include: Are American firms
competing, growing and surviving? What will be the response of businesses to different types of incentives? What
are the sources of productivity growth? What is technology-based innovation and how can it be sustained? How
can firms create high wage jobs? And, most importantly, where is the empirical evidence that can inform policy?
These calls took on the force of law in 2007. The America COMPETES Act requires studies and long-term reportingon various elements of our national system of innovation, making it clear that it has become a national imperative
to provide current and comprehensive statistical analyses of business evolution and business incentives. For
example, Section 1102 requests a study by the National Academy of Sciences on government regulations andincentive structures related to innovation, including:(1) incentive and compensation structures that could effectively encourage long-term value creation andinnovation; (2) methods of voluntary and supplemental disclosure by industry of intellectual capital,
innovation performance, and indicators of future valuation; …(5) costs faced by United States businesses
engaging in innovation compared to foreign competitors, including the burden placed on businesses byhigh and rising health care costs; …(10) all provisions of the Internal Revenue Code of 1986, including tax
provisions compliance costs, and reporting requirements, that discourage innovation.
The need for research and data is made even more clear in Section 1201, which requests that the President’s
Council on Innovation and Competitiveness take on several duties such as “monitoring implementation of publiclaws and initiatives for promoting innovation, including policies related to research funding, taxation, immigration,
4
trade, and education that are proposed in this Act….”
In this paper we will argue that the Internal Revenue Service has an important role in responding to policy-makers’
needs. The tax system is the only available data system that regularly captures the outcomes of innovative andcompetitive activity through detailed financial (complete income and asset statements) data for the population ofbusinesses, whether employer or not, whether publicly owned or not. Only the tax system captures informationon the effect of tax policy intended to stimulate innovation and competitiveness. That information can be used to
calculate effective tax rates at the firm or tax-reporting level through audits and other post-return events such as
amended filings and carry-backs. In addition, only the tax system can capture the complexity of organizational
inter-relationships through the existence of hierarchical ownership crosswalks, information about pass-throughentities as well as the relationship between individuals and organizations. In all cases, tax data are quite likely to
be more accurate and less subject to non-response than survey data given the enforcement penalties for noncompliance and the monetary advantages of participating in the tax system, such as tax credits, and, of course,
refunds.
In practical terms, the existing IRS data infrastructure could be used in a number of ways to respond to thenational imperative. Understanding the effects of incentives related to innovation at the firm level could be

4

th

st

110 Congress, 1 Session, S. 761, The America Creating Opportunities to Meaningfully Promote Excellence inTechnology, Education, and Science Act (or the America COMPETES Act), 2007.

4
advanced by analyzing microdata collected by the IRS in conjunction with other related survey or administrative
data. With appropriate protections, these data could yield invaluable insights into the prospects for economic
growth resulting from product, process and managerial innovation, while pinpointing the costs and missed
opportunities that arise from misdirected or misused incentives. Microdata analysis could be enhanced byincluding information from compliance reporting. Furthermore, the enormous sample size would permit study of
specific industries of interest, such as service sector data and inform new initiatives for developing service
5
science—an emerging discipline that is targeted in Section 1106 of the America COMPETES Act. In addition, tax
data could be used as a frame to launch and complement a survey on innovation. This survey could generate asmuch knowledge about innovation and competitiveness as the Survey of Consumer Finances has generated about
the sources of American individual and family wealth.
Failure to use the existing system would result in wasting an existing large-scale investment in the IRS data
infrastructure. Initiating new data collection would result in a substantial additional burden to the taxpayer at atime when resources are substantially constrained. In addition, new data collections would impose an onerous
burden on the business community by requiring that they devote resources to replicating information that has
already been provided to the Federal government.
In this paper we sketch an approach that describes how Federal tax data can be used to respond to the national
imperative outlined in the America COMPETES Act. We spell out three steps. First, data that can answer key policyquestions must be assembled in a form that can be analyzed. Second, access must be structured not only so thatgovernment or academic researchers can address the questions being asked but also so that the legal
requirements for access are met. Finally, an organizational infrastructure must be put in place to ensure that theanalytical work can be built on and replicated. We conclude by identifying a set of possible next steps.

2. BACKGROUND
2.1 EXISTING DATA ON BUSINESSESThe call for better information on businesses has been made clear in both America COMPETES Act and in recent
reports such as the report of the Advisory Committee on Measuring Innovation in the 21st Century(http://www.innovationmetrics.gov) and the National Academies’ report on Understanding Business Dynamics.
The reason is that businesses are the basic engines of innovation and economic growth, creating jobs andgenerating income. Changes in factors that affect firm behavior—such as taxes and regulation—can fundamentallychange their growth and job creation capacity. Yet, for a number of reasons, no database exists that is widely
accessible to academic researchers so that there is a broad-based examination and discussion of the impact of, forexample, tax policy, on firm behavior. The engagement of a scientific community with better access to data could
empirically ground the policy debate, and hence lead to wiser scientific and technological policy decisions.

5

Service Science comprises “the curricula, training and research programs that are designed to teach individuals toapply scientific, engineering and management disciplines that integrate elements of computer science, operations
research, industrial engineering, business strategy, management science, and social and legal sciences, in order toencourage innovation in how organizations create value for customers and shareholders that could not be
achieved through such disciplines working in isolation.” Source: America COMPETES Act, 2007.

5
Several approaches have been taken to create business datasets that researchers can use to increase academic
understanding about organizational change. One approach was a partnership between academics and businesses
that developed a business database called the PIMS project (Profit Impact of Market Strategy). This project
created a panel dataset on some 3,000 firms and provided new insights into business decisions such as market
entry, pricing and product quality. However, this project lacked sufficient financial sustainability and was
discontinued: there has been little academic research using the data in recent years.
Another approach, partially supported by the National Science Foundation, is to provide access to the Census
Bureau’s Business Register and related files by permitting researchers to work with the data at eight Research Data
Centers. The resulting research has generated new insights into firm behavior, job creation and job destruction. A
related infrastructure project was the Longitudinal Employer-Household Dynamics (LEHD) program which
provided, for the first time, an infrastructure that could analyze the impact of economic turbulence on worker job
ladders, career paths and firm performance. These data are not widely used, however, not least because accesscosts several thousand dollars a month and researchers must travel to one of the eight Data Center sites.
An important step has also been taken by the Ewing Marion Kauffman Foundation in supporting the Kauffman Firm
Survey, a panel study of 4,928 businesses founded in 2004. However, although it provides important information ina number of dimensions, such as the nature of new business formation activity, and a strong user community isdeveloping around its public use files, the focus is on new startups, rather than the universe of firms.
Other approaches that have been used include analyzing commercial datasets, such as Compustat and Center for
Research in Security Prices (CRSP). The availability of these files, which provide financial and accounting
information on publicly traded companies, has had a major influence on financial and accounting research.
Similarly, datasets such as Dunn and Bradstreet and ABI/Inform are often used as sample frames for academic
surveys. However, getting representative research data from such commercial sources is difficult since, in addition
to omitting small and non-publicly traded businesses, both Compustat and CRSP are aimed at serving institutional
investors, and the Dunn and Bradstreet and ABI/Inform datasets are primarily for marketing purposes. As a result,
there can be substantial quality issues with these data that make their use in the context of academic research lessthan optimal.

2.2 CONFIDENTIALITY RESTRICTIONS
The major reason for the lack of researcher access to the high quality business data collected by federal statisticalagencies is the protection of confidentiality. Every statistical agency is faced with the same tension. It is charged
with collecting high-quality statistical data to inform national policy. It is also charged with protecting theconfidentiality of respondents—not only because of the legal mandates but also because public trust andperceptions of that trust are important contributors to data quality and response rates. The legal framework forthe protection and dissemination of the administrative, clinical and survey data that underpin much empiricalresearch is complex. One recent, important piece of legislation is the Confidential Information Protection andStatistical Efficiency Act of 2002 (CIPSEA), which established minimum standards for protection of information
gathered for a statistical research purpose under a promise of confidentiality by a federal agency. Breaches ofconfidentiality—especially for tax data—can carry not only criminal penalties, including jail time and fines, but also
civil lawsuits for the data custodian responsible for the data release. The overriding requirement for data
custodians is that they take “reasonable means” to safeguard the confidentiality of respondent information.
However, since this requirement is not typically defined, but is left to the discretion of the agencies, disclosure
limitation methodologies vary substantially across agencies, often erring on the side of extreme caution (see Doyleet al., 2001).

6
The focus on confidentiality protection is not matched by guidance on researcher access. While the authorizing
legislation for government agencies typically requires them to produce information for decision makers,
researcher access to microdata is not an explicit part of their mandate. The ethical framework is similarly complex.
Statistical agencies, as most data collectors and custodians, provide respondents with a guarantee that theiridentities and the confidentiality of the information they provide will be protected from unauthorized access anduse. Safeguarding this guarantee is essential to maintaining the ethics of the researcher-respondent relationship,
in which respondents may make themselves vulnerable by disclosing information needed for research purposes.
Protection of respondent confidentiality is also critical to maintaining the agencies’ reputations and, notcoincidentally, their future response rates. Of particular importance in this context, confidentiality protection is
also necessary for administrative systems to fulfill their critical mandates in the functioning of government
programs, such as the Social Security system and the tax system—which is predicated on voluntary compliance.
Unfortunately, stringent confidentiality protections mean that the data, which cost so much to collect andproduce, are likely to become less valuable both systemically and from the standpoint of decision-making in both
the government and even the marketplace (Lane, 2003).
In sum, the complex legal and ethical frameworks and the severe adverse consequences associated with breachesof confidentiality lead to what Madsen (2003) refers to as the “privacy paradox.” As he points out, data custodians
who interpret the right to privacy as a nearly absolute ethical standard might view the responsibility of maintaining
confidentiality for individuals in a way that is less than socially optimal. Data custodians who operate within thisframework, and establish new and more restrictive controls on data access, act to reduce the scientific value ofdata, and hence substantially reduce the social benefits of the data collection—benefits that should redound to
the individuals who provided the data as well as the decision-making process itself.

3. ASSEMBLING TAX DATA FOR ANALYSIS OF INNOVATION AND COMPETITIVENESS
6

Tax data provided to the IRS on a small set of key forms might, if combined, be used to describe the lifecycle of abusiness, as well as its employees. Although Treasury and the Joint Committee on Taxation have long studied manyof these areas, this has necessarily been through the prism of tax analysis.
The beginning of a business employer entity—but not necessarily every new business—starts with the filing of an
SS4 form for assignment of an Employer Identification Number (EIN) by the IRS in order to establish its account in
the tax system’s Business Master File. In a sense, the BMF can be viewed as the business register of the tax
system, and, in fact, population extracts from the BMF provide the core of the Census Bureau’s own businessregister, with its annual infusion of selected data variables for the tax system’s business employer population. Of
great analytical interest in this context, the SS-4 requires the business to tell the IRS whether it is beginning as asole proprietorship, partnership, corporation or personal service corporation; the state or foreign country in which
it is incorporated, and whether it is applying because it is a new entity, has hired employees, has purchased a going
business or has changed type of organization (specifying the type). For sole proprietorships that require EIN’s(generally, employers) the form also asks for the name and Social Security Number (SSN) of the owner. In addition,
this information is requested for the principal officer, general partner; the form also begins classifying a firm interms of industrial activity by requesting a verbal description of its principal activity and principal line of business –information that is later used by SSA to assign its first (at least for this EIN) NAICS code.

6

All of the forms are provided in the appendix and clickable links are provided in the text.

7
The ongoing financial life of most entities is then described for corporations by a variant of the 1120 (U.S.
Corporation Tax Return); for pass-through entities by the 1120S (for a schedule S corporation) or 1065 (return on
partnership income) and their K-1 (shareholder’s/partner’s share of income and deductions); and for sole
proprietorships by the Schedule C or Schedule F filed with the proprietor’s 1040. These reports include muchdetail on both the firm’s financial stocks (balance sheet) and flows (income statement). The balance sheet
contains detail on assets and liabilities; the income statement contains detail on income and expenses, including
total sales, cost of goods sold, gross profits, inventory at the beginning of the year, purchases, cost of labor,
dividends, compensation of top officers, as well as foreign ownership. In addition, the Form 851 (affiliations
schedule) filed for consolidated corporations, associates subsidiaries (80% ownership rule) with their parent, whichfiles the related 1120, thus, delineating a corporate family of firms at the EIN level. Ultimate owner identification
requested on the Form 1120’s Schedule K, helps construct corporate family identifications for corporations notfiling on a consolidated basis, as well as the ownership for even parent corporations that do file consolidated.
Although not perfect, this interlocking ownership data can be helpful in trying to follow the ownership hierarchy ofthe corporate world.
The financial life of all employees can be traced using Form 1040, well known to every American, and theassociated W-2, which links employer/employee information by employer and employee for each employee “job”
in every tax year, including for partial years.
The coverage of tax data is unsurpassed. The information is universal and as such could provide a time series of7
population data. The data are annually replenished by individual return filings for the universe of businesses.
Such recordation and coverage are reasonably ensured, given not only the annual filing requirement for taxpayers
but also the incentive for businesses to be captured by the system in order to accrue the various tax benefits
8
available; e.g., credits, deductions, adjustments, and of course, refunds. The result is that data are posted
annually to each business’s account by EIN. In addition, the data receive at least initial data quality enhancements,
both for IRS compliance reasons and in order to correctly post to the taxpayer’s account and satisfy its filing
requirement. The demographic patterns of businesses, namely firm entrances to, transitions within, and exits
from the business universe can thus be accounted for with applications for EIN, entity transactions recording
changes within and across EIN accounts owing to business evolution, as well as mergers and acquisitions, and thefiling of final returns.

7

Although currently, tax year modules on the Business Master File (BMF) are only retained for three years, aprospective study could obviously capture more years. Also, the IRS is presently constructing a Compliance Data
Warehouse off-line from master file data, which would be used to capture more years for research purposes. In
addition, panel designs are being either considered or implemented for IRS’s Statistics of Income (SOI) samples ofboth corporate (1120 series) and individual (1040 series) data.
8
Obviously, the tax system is not perfect on either coverage or accurate reporting, as attested by the latest tax gap
estimate of $345 billion for 2001.

8

3.1 HOW ARE AMERICAN FIRMS COMPETING?
New light can be shed on the question of how American firms are competing by examining, for example, thedegree to which they are foreign owned from questions on Form
1120

FIGURE 1: SOURCE FORM 1120

FIGURE 2: SOURCE FORM 1120
And Form 1065

FIGURE 3: SOURCE FORM 1065

WHAT WILL BE THE RESPONSE OF BUSINESSES TO DIFFERENT TYPES OF INCENTIVES ?
The data also clearly provide a unique opportunity to understand the response of businesses to different types of
incentives. Precisely because the tax system’s incentive system of rewards for particular business behaviors isreflected in the form of credits, deductions, adjustments, and refunds, tax data can be critical for understanding
related economic performance in the marketplace, especially over time.
Of course, tax data are also the only real way of comprehensively understanding business responsiveness to taxes,
because effective tax rates can only be calculated using post-return filing information or tax adjustments, available
9from the filing of return amendments; carry-backs of an un-used credit, net capital loss (NCL), or net operating

9

A carry-back filed within one year of the loss year return’s end of accounting period requires usage of Form 1139
(Corporation Application for Tentative Refund). Form 1120-X (amended return) is required for carrybacks taken up

9loss (NOL); and IRS-initiated examination efforts of a taxpayer-initiated transaction, including not only the original
tax return but also the delinquent omission of or under-reporting on a return filing. Because the Business Master
File (BMF) is designed to retain a tax module (tax return and associated transactions for a given tax year) within an
EIN account for three years after the latest tax transaction, post-filing transactions, especially carry-backs, cankeep some modules active on the BMF for much more than three years. For example, losses owing to productliability can be carried back ten years (bad loans have also qualified for such treatment). For such a case, two
phenomena are worth mentioning. First, not only is the destination tax module retrieved – if it had expired andbeen purged from the BMF – but its retention clock is re-set for 3 more years. Second, for such cases, the IRS
retrieves previously removed tax modules between the loss year module or tax year originating the carry-back andthe destination tax year, providing a time continuum – and for at least another 3 years (the BMF retention rule). A
product liability carried back the full ten years effectively restores all seven years of of data scheduled for purging
10
under the routine BMF retention schedule. In combination with the ricochet effect these adjustment
transactions can, in some cases, vastly extend the “shelf life” of data retained on the BMF: in some cases, fordecades. In general the NOL and NCL come first, followed by credits, in the order they appear on the tax return.
For example, an NOL carry-back could free up a previously taken credit for further carry-back, and so on, evenresulting in the ultimately released credit being carried forward for use on a future tax return, possibly resulting insome of that year’s credits being carried back, triggering a similar fall of dominoes. This constant churning – therelease of previous carry-backs for further carry-back – means that some firms have a continuum of tax modules
for much more than three years. If any of these carry-back transactions draw examination interest, the three year
retention period can be re-triggered several times, further extending a module’s duration on the BMF.
Sophisticated tax avoidance strategies maximizing carry-back tax laws – over time -- can be employed by savvy
firms, but the transactions need to be large in order to reap the investments in legal and accounting capitalnecessary to optimize this usage. Thus, for many of the most interesting and complex industries and size classes—
often the predominant companies in corporate America—this continuous churning creates a dynamic and longterm record on the BMF that may provide a story of electron-level economic activity for the core of Americanbusiness.
Unfortunately, although the BMF captures the net tax liability effect of a tax adjustment, say, a carry-back, it does
not capture the detail; e.g., whether it was a credit and which type – essentially, the detail on Form 1139. The
reason is that almost all post-filing transactions are limited to 80 characters, including the net tax liability amounteffect, necessarily limiting the amount of information conveyable. However, the information is captured in theseparate processing systems in IRS responsible for processing the various tax adjustment transactions – generally
differentiated by whether or not they are IRS Examination-initiated. In conjunction with this knowledge and theproliferation of electronically filed returns, especially by corporations, it does not seem unreasonable to think thatthe merging of data from the BMF and the adjustments area could be done, given a compelling analyticalmotivation.

to 3 years after the loss year return’s end of accounting period. Generally, a credit or NOL must be carried back(the period has statutorily varied at 2 or 3 years) , before it can be carried forward (this period has also varied at 57 years) for use on future tax returns. An exception is the NOL, which can be carried directly forward if an
irrevocable waiver is established.
10

Carry-backs must be taken in order of priority so that, say, an NOL CBK could free up a previously taken credit for
further three year carry-back, etc.

10
Substantial detail on the adoption and implementation of different types of activities is evident from Form 1120.

Figure 4: Source Form 1120

3.2 WHAT ARE THE DYNAMICS OF PRODUCTIVITY GROWTH : THE ANALYSIS OF FINANCIALPERFORMANCE
What are the dynamics of productivity growth? The financial stocks and flows, frequently necessary to support
some of the tax rewards claimed, are reported in substantial detail with complete balance sheets and income
statements.

FIGURE 5: SOURCE FORM 1120
It may also be possible to examine the life course of leading entrepreneurs by following an initial filing of, say, aSchedule C to a Form 1120 series at corporate stature, and even later to the non-profit charitable foundation
created with Microsoft wealth. All of this activity should be regarded as economic, even with both paid andvolunteer workers engaged for the non-profit stage.

3.3 HOW CAN AMERICAN FIRMS CREATE HIGH WAGE JOBS?
The possible linkages include not only those enabled by EIN, such as employment and compensation from theForm 94X series, but individual level data enabled by the SSN/EIN cross-walk of the W-2 series. Work could beinitiated to replicate the very successful LEHD program developed at the US Census Bureau, which has clearly
demonstrated how much knowledge can be gained about high wage job creation using linked employer-employeedata.
A major related issue is the evolution on jobs with pension coverage. With care, it should also be possible to linkeven Form 5500 pension data to the business sponsor’s tax return data. Of course, the linking challenge should
not be minimized: the 5500 data are on yet another IRS master file, the Employee Plans Master File (EPMF).
Although these accounts of employee benefit plans (defined benefit/contribution pension plans, welfare benefit
plans) are also established by EIN, this EIN need not be the same as that of their business sponsor, making facile
linkage no guarantee of success. However, given that many of the sponsoring businesses take deductions under
section 401(a) for employee plan information (5500 and related; e.g., determinations), it seems reasonable to11
assume that the IRS could move from employee plan filing to a sponsor’s tax filing. Further research would benecessary to “unlock” this relationship, but the potential reward would seem to more than justify this endeavor.

3.4 CREATING A FRAME FOR THE STUDY OF INNOVATIVE ORGANIZATIONS
Of course, tax data alone cannot capture the complexities of product, process or organizational innovation.
However, they could be used in a number of creative ways to create a frame upon which innovative organizations
behavior can be studied. One obvious approach is to create a survey frame that oversamples firms likely to beinnovative—or of particular interest to policy makers. These could include: small firms or multi-nationals; firms inbiotechnology or information technology; or recent start-ups or long lived, successful businesses. Oversamples
could run the gamut of organizational structures, such as complex organizations or sole proprietorships, frompartnerships to non-profits.
Particular types of questions could be asked that match other innovation studies, such as the data being collectedon the newly designed Business R&D and Innovation survey being fielded by NSF’s Science Resources Statistics
division. The survey has five sections: four on R&D finance, R&D strategy, R&D funding and R&D human resources.
The fifth section, on innovation, collects a number of important measures, notably information on: innovative
activities in goods, services and related activities; patent activity and returns; Intellectual property transfer
activities and Intellectual property protection.

4. ACCESS TO TAX DATAThe next step in meeting the national imperative would be to provide researcher access to tax data within therequirements set out by law. There are multiple dimensions along which the case for such access can be made.
First, the value added of tax data collection can be increased through access, because data can be repurposed toaddress the national imperatives outlined above. Second, administrative data quality can be increased because, as
the IRS/Census criteria agreement has documented, the use of the data for different purposes can improve data
11
quality in a wide variety of ways. Third, the administrative functions of enforcement require statistical methods
themselves to be optimally effective and efficient. The very processing goals for administrative data--the ability toadminister the tax system effectively and efficiently--are precisely what make them useful for statistical purposes,
especially with the advent of e-filing.
Fulfilling the legal requirements for access is obviously critical, and it is important to note that access must be
statutorily authorized. There are some existing options that would seem to support the IRS responding to anational economic imperative. For example, researchers could access tax data at the IRS as a contractor(authorized by Section 6103(n) of Title 26).
However, there exists historical precedent for a more innovative approach for studying innovation. This precedentis the Survey of Consumer Finances, which has been conducted for decades by the Federal Reserve Board as acontractor (authorized by section 6103(n) of Title 26) for Treasury to support tax statistics mandated by section
6108(a).
If the nation’s policy-makers, particularly those in Congress and/or Treasury, were convinced that the study ofbusiness innovation is another national imperative requiring the use of tax data, a similar arrangement might becrafted, in which an institution with standing and gravitas similar to the Federal Reserve Board (FRB) might be11

http://www.ces.census.gov/index.php/ces/researchguidelines

12
engaged as a contractor. An obvious choice would be the National Science Foundation (NSF), which has a long
history of funding social science datasets, including the General Social Survey, the Panel Study of Income
Dynamics, and the American National Election Survey. The NSF, particularly the Science of Science and Innovation
Policy program, with which two of the authors have strong connections, has the additional advantage of being agovernment agency with many of the same characteristics as the Federal Reserve Board, as well as a mission topromote basic research in areas that are national priorities. It is worth noting that while each of the social sciencedatasets funded by NSF have been transformational in nature both within and across disciplines, none of themaddresses the complexities of organizations, and all were established at least thirty years ago.
The Survey of Consumer Finances (SCF) model provides an excellent example of how minimal tax data might beused for sampling frame purposes. Thus, there might be a two-stage proposal on using tax data to study
innovation: first, to provide the frame for the innovation survey, and second, to provide data for validating or
supplementing the survey data. The SCF model also presents several advantages over an approach focused onaccess, say, based upon Research Data Center consortium, controlled by a non-tax agency. First, confidentiality
perceptions might be helped from knowing that population tax data would be accessed directly under the IRS’
auspices, not by providing a population file to another agency for this purpose. Second, IRS analysts, particularly
those in the research and statistical functions, have a wealth of institutional knowledge that might be leveraged formore efficiently understanding not only the data in question but IRS processing needs related to the data. By
tapping this resource, not only could outside researchers benefit in their analysis of innovation, but the IRS
analysts could also benefit from working with the outside researchers in terms of new techniques learned,
whether analytical or processing oriented. This synergistic benefit might well exceed the required benefits of any
specific contractual agreement between outside researchers and IRS analysts. Third, amending either the statuteor regulations in order to provide an outside agency access to the tax items needed might be avoided entirely,saving precious resources, not the least of which is time to survey and analysis. Fourth, researchers would notneed to pay for access to the data, as such access would be integrated with and contingent upon benefiting the tax
agency’s statistical and research needs.
An alternative to the 6108a mechanism delineated above might be provided by section 6108(b); namely, thestatute that permits special statistical studies or tabulations to be conducted by the IRS as the result of an outside
request. In such cases, the IRS, usually its Statistics of Income (SOI) office, can accept reimbursement for the
additional cost of meeting the request. For a very large or complex study in which resources might be an issue
either owing to skill sets needed or competing priorities, it might be possible to use 6103n as authority forengaging such a contractor, which could include outside researchers. Under such an arrangement, funding mightcome from the outside requester and be used to compensate any contractors needed, in addition to reimbursing
SOI for its resources. Outside researchers might be used as consultants for designing the study, in conjunction
with inside guidance and expertise provided by the IRS, perhaps the SOI office.
Such a proposed usage of statute is admittedly exceptional, as it was for the SCF. However, if it encountered legal
resistance from either main Treasury or the IRS, this might be a finding required by America COMPETES Act’s under
its mandate to report on (10) all provisions of the Internal Revenue Code of 1986, including tax provisions
compliance costs, and reporting requirements that discourage innovation. Surely, the inability to study innovationoccasioned by current statute, especially when current statute and historical precedent appear to provide themeans, could be viewed by Congress and the President as something that discourages innovation, and worthy offast-track remedial action.

13

5. ORGANIZATIONAL INFRASTRUCTURE: DEVELOPING A PARTNERSHIP BETWEEN
THE IRS AND RESEARCHERS
One of the most complex challenges of this project would be the establishment of a collaborative partnership
between a federal agency (main Treasury and/or IRS) and academic researchers. Traditional organizational modelsof partnerships and strategic alliances are based on business-to-business relationships in the manufacturing or
information technology sectors, where firms endeavor to create new products or processes. Research in this area
mainly focuses on the motivation for and outcomes of partnerships or strategic alliances [see Roberts (1980);
Roberts and Berry (1985); Alster (1986); Contractor and Lorange (1988); Kogut (1988); Olleros and MacDonald(1988); Borys and Jemison (1989); Hamel, Doz and Prahalad (1989); Bertodo (1990); Hamel (1991); Ring and Van deVen (1992); Bleeke and Ernst (1993); Nichols (1993); Hagedoorn (2002); and Brinkerhoff (2002a)]. Taken together,
these studies create a paradigm which: distinguishes strategic alliances from other forms of market structure;
codifies a set of conditions which encourage the formation of inter-firm partnerships; establishes metrics by which
to measure the success of alliances; and suggests optimal structures of control and corporate governance forcooperative agreements.
Within this literature, the studies of knowledge creation and sharing are most related to the current project.
Hagedoorn reviews the corpus of work on the organization and outcomes of R&D partnerships. That body of work
establishes which countries and sectors tend to utilize partnership relationships for cost-cutting, strategic or
learning purposes. Trust between partners is the primary method identified in these studies for dealing with theconfidentiality or sensitivity of shared knowledge. Longevity of the partnership is not important; flexibility toconfigure and reconfigure relationships among companies is strategically more profitable. Yet, for the purposes of
the current study, the focus on business partnerships misses some of the relational elements that are present
when a government agency is one of the alliance partners.
There are studies of government-business partnerships as well. For example, Brinkerhoff (2002a,b) focuses on
government-nonprofit partnerships. In the former study, Brinkerhoff focuses on assessment not only related toperformance of the partnerships but also their design and implementation. In the latter study, she develops an“inter-organizational relationship matrix” that suggests a taxonomic approach to constructing and implementing
the relationship. One dimension measures “mutuality,” where organizations either place high or low on a scalethat measures interdependence of each organization’s mission, objectives and responsibilities, as well as the level
to which partners coordinate decision-making processes. The other dimension measures “organizational identity.”
Here, organizations that have strong, independent identities place high on the scale. Using this framework, a“partnership” has organizations that are interdependent particularly when decisions are made and one
organization is not identifiably dominant in terms of mission or expertise. If mutual decision-making were not acharacteristic of the relationship, then the Brinkerhoff’s framework would suggest either a contracting or
extension relationship. If one of the organizations had a dominant identity vis-à-vis the others, then the paradigm
would suggest that the work be done internally or by an extension of the organization. This last case characterizes
a relationship that might facilitate partnering with the IRS on the use of tax microdata for studies related toinnovation.
However, there is a need for more explicit rules of engagement that allow collaboration between researchers anda government agency, particularly when highly sensitive information is accessed and analyzed. When partnerships
involve the sharing and creation of knowledge and other intangible assets, with the added intricacies that comewith federal statutes regulating the collection and use of sensitive data, then the organizational structure isinherently complex. Little research has been done in this area and few organizational templates exist for14
university-government knowledge-creation (or data development or data usage) partnerships. Figure 6 provides
some insight into the type of organizational structure that might make sense.

Infrastructure:
Research Inputs:

Agency Mission & Statutes;
Technology; Culture

IRS Data;
Researchers’ Models& Tools

New Research Outputs:
Data Manipulation
Techniques; Statistics; Articles

Process Criteria:
Accessibility & Transparency; Integrity;
Privacy, Confidentiality & IP Rights;
Adaptability; Sustainability; Evaluation

FIGURE 6
In the case of data extraction and analysis, this schematic suggests that the university-government partnershipmust have the following features:
Agency Mission: The partnership must serve the agency mission. Researchers must be able to demonstrate that
their access is necessary to help the tax administration system. This should not be an insuperable challenge,
particularly given that [throughout] IRS must process data for tax administration purposes in such a way thatstatistical usage is also optimized. Researchers will need to recognize that economic research may in and of itself
not assist the IRS directly with its central mission, ensuring that the appropriate amount of tax is paid by everyone.
However, because data quality is so critical for effectively and efficiently administering the tax system—including
statistical systems for administration—the connection should not be that difficult to make. The perceptions
12
problem for researchers should not be underestimated with this administrative perspective, and any project
would need to monitor perceptions, particularly given the compliance structure of the current federal tax system.
One approach would be to focus on how the data system can be improved for both administrative and statisticalresearch purposes—honestly, they are not that different in terms of the systems and data quality that are needed.
If researchers are daunted by this potential, they should not be, but if they insist on resisting a role that serves
both purposes, they should understand their exclusion from both access and input.
Accessibility: Researchers must have ready access to the data for the reasons outlined in the initial sections of
this paper. Yet, access and research projects must comply with agency mission and statutes that govern datacollection, storage and sharing, avoiding the potential perception that access is granted for academic fishingexpeditions or only to serve the researcher in his/her professional aggrandizement.

12

See Greenia, Jensen and Lane (2001).

15
Clearly, current access modalities are very far from ideal. Yet advances in the computer sciences could be used toaddress access issues in a more scientific manner. Indeed, there is no basis from a computer security point of viewwhy researchers could not access confidential data remotely from their offices, especially when physical security isalso addressed. For example, IRS agents must have access, including remote access, to confidential tax data for
their field examination activities, including in their hotel rooms and at clients’ business sites. Protecting databases
against intruders has a long history in computer science (Dobkin et al., 1979). Computer scientists themselves areinterested in protecting the confidentiality of the data on which they do research (for example, the Abilene
Observatory supports the collection and dissemination of network data, such as IP addresses). Cyberinfrastructureadvances have the potential to greatly expand the set of access modalities, particularly with respect to remote
access. The Cybertrust initiative at NSF has created a research community that focuses on developing network
computers that are more predictable and less vulnerable to attack and abuse, that are developed, configured,
operated, and evaluated by a well-trained workforce, and that educate the public in the secure and ethicaloperation of such computers. The Department of Defense has developed different levels of web-based accessranging from unclassified (nipr-net) to secret (sipr-net) to top-secret (jwics-net) using off the shelf technology.
Similarly, the PORTIA project focuses on both the technical challenges of handling sensitive data and the policy andlegal issues facing data subjects, data owners and data users. Indeed, recent developments at European statisticalagencies, such as Statistics Sweden, Statistics Netherlands and the UK Office of National Statistics, as well as the
NORC data enclave, have demonstrated that remote access is not only feasible, but is low cost and as secure as onsite access procedure.
Transparency: The data consortium must minimize the burden on agency staff by developing a high quality
metadata documentation system, whereby information about code, variable structures, historical anomalies andprevious research is linked in a user friendly format. Education and training of the data consortium members(users) will be implemented by a third party (neither the agency staff nor users).
Integrity: A peer review process must be put in place to ensure the integrity of data use, particularly with respect
to purposes and procedures that researchers and analysts propose. The reviewers will also determine priorities forusing the data. Reviewers must be able to garner and assess community and user input on data development anddistribution.
Privacy, Confidentiality and Intellectual Property Rights: The data consortium must utilize anorganizational infrastructure that ensures that researchers and analysts have the ability to access, analyze, andvisualize the data without compromising privacy and confidentiality of the respondents. It must also be clear who
has the intellectual property rights for publications and patents that are produced in the data consortium.
Adaptability: The data consortium must be able to adapt to technological changes and changes in datataxonomies. This ensures the ongoing quality and longitudinal consistency of the data.
Sustainability: It must be a partnership that creates a database and access to the database ensuring thatanalytical work can be built on and replicated. An incentive structure must be created that encourages new
discoveries on what can be done with the data and punishes mal activities. It is worth noting that the importanceof avoiding even the perception of a “mal” activity cannot be over-emphasized with respect to tax data. One such
incident could destroy the entire arrangement for many years to come.

16

6. SUMMARY AND NEXT STEPSThis paper provides an outline of the potential value of access to tax data that addresses a current national
imperative. It has identified the key issues that need to be addressed before such access could occur, and begins
to identify an organizational structure that could be developed to advance the joint interests of both the tax
agency and the research community.
There are several steps that need to be taken before this approach can become reality.
apparent.
1.
2.
3.
4.
5.

Several are readily

The research policy community could work with the appropriate federal agencies, to determine whether
the proposed approach can provide a scientific basis to guide science and innovation policy.
The research policy community could work with the appropriate federal agencies to identify the resourceand scientific infrastructure necessary to facilitate the approach.
The research policy community could work with the appropriate federal agencies to identify theorganizational infrastructure that is most likely to achieve the goals of the America COMPETES Act.
The research policy community could work with the appropriate federal agencies to identify the accessand confidentiality requirements that will ensure that the minimal data access required by law is attained.
The research policy community could work with the appropriate federal agencies to identify the resource
requirements necessary to bring the proposed approach to the implementation stage.

17

REFERENCES
Alster, Norm (1986), “Power Partners: Winning Through Strategic Alliances,” Electronic Business, March 1, pp. 10108.
Bertodo, R..G. (1990), “The Strategic Alliance: Automotive Paradigm for the 1990s,” International Journal ofTechnology Management, vol. 5, no. 4, pp. 375-88.
Bleeke, Joel and David Ernst (1993), Collaborating to Compete, John Wiley & Sons.
Borys, Bryan and David B. Jemison (1989), “Hybrid Arrangements as Strategic Alliances: Theoretical Issues in
Organizational Combinations,” Academy of Management Review, vol. 14, no. 2, 234-49.
Contractor and Lorange, “Why Should Firms Cooperate?” in Cooperative Strategies in International Business,
Lexington, 1988.
Doyle, Pat, Julia Lane, Jules Theeuwes and Laura Zayatz Confidentiality, Disclosure and Data Access: Theory andPractical Applications for Statistical Agencies, North Holland, 2001
Gold, Andrew, Arvind Malhotra and Albert Segars (2001), “Knowledge Management: An Organizational
Capabilities Perspective” Journal of Management Information systems, Vol. 18, no. 1, pp. 185-214.
Greenia, Nick, J. Bradford Jensen and Julia Lane “Business Perceptions of Confidentiality,” in Confidentiality,
Disclosure, and Data Access: Theory and Practical Applications for Statistical Agencies, ed. Pat Doyle, Julia I. Lane,
Jules J. M. Theeuwes, and Laura V. Zayatz, 2001, New York: Elsevier Science North Holland Press
Hamel, Gary (1991), “Competition for Competence and Inter-Partner Learning Within International Strategic
Alliances,” Strategic Management Journal, vol. 12, pp. 83-103.
Hamel, Gary, Yves L. Doz and C.K. Prahalad (1989), “Collaborate with Your Competitors—and Win,” Harvard
Business Review, January-February, pp. 133-39.
Hagedoorn, John (2002), “Inter-firm R&D Partnerships: An Overview of Major Trends and Patterns Since 1960,”
Research Policy, Vol. 31, pp. 477-92.
Brinkerhoff, Jennifer M. (2002a), “Assessing and Improving Partnership Relationships and Outcomes: A Proposed
Framework,” Evaluation and Program Planning, Vol. 25, pp. 215-31.
Brinkerhoff, Jennifer M. (2002b), “Government-nonprofit Partnership:
Administration Development, Vol. 22, pp. 19-30.

A Defining Framework,” Public

Kogut, Bruce (1988), “Joint Ventures: Theoretical and Empirical Perspectives,” Strategic Management Journal, vol.
9, pp. 319-32.
Lane, Julia (2007) Optimizing the Use of Microdata: An Overview of the Issues Journal of Official Statistics, pp.
299—317

18
Lane, Julia (2003). “Uses of Microdata: Keynote Speech,” in Statistical Confidentiality and Access to Microdata:
Proceedings of the Seminar Session of the 2003 Conference of European Statisticians. Geneva, pp. 11- 20.
Madsen, Peter (2003) “The Ethics of Confidentiality: The Tension Between Confidentiality and the Integrity ofData Analysis in Social Science Research”, mimeo, Carnegie Mellon University
National Science Foundation, DataNet. http://www.nsf.gov/pubs/2007/nsf07601/nsf07601.pdf
Nichols, Nancy A. (1993), “From Complacency to Competitiveness: An Interview with Vitro’s Ernesto Martens,”
Harvard Business Review, September-October, pp. 163-71.
Olleros, Francisco-Javier and Roderick J. MacDonald (1988), “Strategic Alliances: Managing Complementarily to
Capitalize on Emerging Technologies,” Technovation, vol. 7, pp. 155-76.
Roberts, Edward B. (1980), “New Ventures for Corporate Growth,” Harvard Business Review, July-August, pp. 134142.
Roberts, Edward B. and Charles A. Berry (1985), “Entering New Businesses: Selecting Strategies for Success,” Sloan
Management Review, Spring, pp. 3-17.

19

APPENDICES

20

21

22

23

Form1065

24

25

26

27

Form 1065sk1

28

29

Form1040