Research and Development Data Needs Proceedings of a Workshop (2005) / Chapter Skim
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Research and Development Data Needs: Proceedings of a Workshop
Research and Development Data Needs: Proceedings of a Workshop
Pages 1-45
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From page 1... ...
My coauthor Sumiye Okubo and I recently wrote a paper about the contribution of R&D to economic growth. We integrated R&D Into the system of National Accounts by putting it on the product side and the Income side, m good national Income accounting practice.
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From page 2... ...
If the decision is made to capitalize R&D others will seek our advice about how to do it. At the meeting in the Netherlands participants will consider how R&D might be capitalized in the System of National Accounts and the relationship between SNA and the Frascati Manual, the OECD's guide to the collection of R&D data.
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From page 3... ...
Micro data. In my dreams I want to be able to go to NSF and use data that cannot be put m a publication because of confidentiality concerns, much m the same way that you can go to Census Research Centers and access the micro data under strictly controlled conditions.
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I suspect we are going to hear these repeatedly today. One is the problem of common definitions of R&D, particularly with respect to borderline activities, such as software development, where some of the activity is R&D, and some of it is not.
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I want to say a word about the relationship of R&D and national accounts. In my view there is a need for learnmg on both sides.
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In fact, as part of the President's Management Agenda we have asked R&D agencies to justify the relevance, quality, and performance of their programs m makmg new proposals or contmumg existmg programs. Although I am talkmg about R&D data, I should also refer to the federal science and technology budget which came about in part as a result of a National Research Council recommendation that we needed a better estimate of the mvestment m new knowledge creation as distinct from the development of new end products or prototypes, for example, military equipment.
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Finally, m preparmg the budget's R&D chapter this year we included a number of charts that derived from other data sources. We used NSF data to look at some of the international comparisons.
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Because GDP is the key policy variable m the minds of members of Congress, the R&D policy analyst has to start there and work backward. The problem with this model is that when you work back to the technology Investment component, and technology is characterized as a homogeneous entity, it is very difficult to make a clear case for many of the federal roles m supporting technology.
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From page 9... ...
Next is something I call infratechnology, which is a collection of infrastructural technologies present m any high technology industry and that are essential to the actual conduct of R&D and eventually the control of the production. For an agency like NIST, we are obliged to collect our own data to develop and rationalize programs that affect either the generic technology box or the infratechnology box.
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From page 10... ...
Again going back to the national level, these NSF data show that over the past 10 years development, which is where most industrial funding goes, increased over 70 percent. Basic research increased almost as much, but that is misleading because most of the Increase is the result of growth in federal funding for health science.
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From page 11... ...
This year we have received approval to mandate the entire survey form, so for the first time m 50-odd years, all of the questions must be answered by industrial firms. We have added some questions about collaborative alliances to the survey form and questions about activities involving new technologies -- biotechnology, software development, nanotechnology, and new materials.
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David Trinkle mentioned the federal science and technology budget. We have expanded the NSF survey at least for the Department of Defense to break out the development components.
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Finally, a topic that will be addressed in a later session is cross-national R&D. The Na60nal Science Foumda60n, Census Bureau, and the Bureau of Economic Analyses are about to sign a memorandum of understanding to link at the micro data level the stadshcs from the industry R&D survey with data that BEA collects on foreign direct investments in the United States, and the U.S.
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It is high risk, long-term research of the kind that you might expect a university to be doing. But it requires major facilities such as accelerators, light sources, and reactors that are currently running a billion dollars and more to build.
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Over the weekend, I reviewed some of the allocations by field, but instead of looking at the budget and deciding where the money went, I looked at the NSF data and asked myself if I could figure out which programs the figures represented. I was quite successful m doing that, which suggested to me that the program offices have generally been doing the classification correctly.
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This is what the academic community is looking for from a taxonomy. They are trying to classify doctoral programs to obtain reputational rankings of similar programs and to associate a wide range of descriptive data about students, faculty, and research resources with each program.
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Because if you call something interdisciplinary, what do you call it? We are trying to deal with it by saying people can allocate their time across different doctoral programs, and the total has to add up to 100 percent, and then we can find out people who are supervising dissertations in more than one field.
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It is largely irrelevant how good the research is, if it does not have a complete value chain it will have no value. We manage that by something called the "time-to-market process," which is asynchronous with the budget cycle.
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So a way to envision this is that the enterprise data, which is the basic reporting and classification unit for the NSF survey, is a weighted average of the lines of business for that company, whether two lines 19
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And that makes a difference if you try to do what Bronwyn Hall and I did in a report for NSF, which was to look at the two micro data sets and try to make sense out of the data that are reported m one place versus another Now I want to comment on the study referred to by John Jankowski done by the special services branch of the Census Bureau. As he noted, it does not recommend that companies be asked to provide estimates at the sub-company level because the terminology that is used to describe those sub-units varies a lot, because companies say they are not able to describe units in industry terms, and finally because details on basic research, applied research, and development are not available for such-units.
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Handling multinational companies is problematic because a company will report data differently on the RD- I, merely because of an Eternal legal reorganization of the company structure. Revisit that Census Bureau conclusion that line of business reporting should not be done.
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From page 22... ...
I think we could draw the frame for that from the RD-1 as we know who the big R&D performers are. Another development at the Census Bureau that I think may help us quite a bit in the future is the advent of employer/employee matched data sets.
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Now from our informal interviews with some of these companies, we asked them well, if you are doing R&D, what type of R&D are you doing, what do you consider R&D? And they tell us that building new systems requiring a significant amount of software development, they consider R&D.
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The second category of recommendations involves modifications to the mstrument itself Here what you might have are some questions on technology areas, such as biotechnology, nanotechnology, and areas of software development such as systems integration. This will be important guidance for firms m the manufacturing as well as m the service sector because both are undertaking these types of activities.
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RON JARMIN: I would characterize the European efforts m terms of services R&D m the same fashion. They are influential m the OECD's development of the Frascati Manual, the guidelines countries aspire to for collecting R&D statistics.
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The CORE database which Al Link is responsible for, and the NCRA-RJV database, which Nick Vonortas is m charge of But then we have again a long list of proprietary databases that have been used to study the different aspects of collaboration -- the Securities Data Company, ISI, Recombinant Capital, and so on. We also have data on university partnerships from the Association of University Technology Managers.
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From page 27... ...
Finally, it would also be useful to facilitate linkages between existmg data sets on collaboration and linkages with data sets on economic performance such as the files that exist at the U.S. Census Bureau.
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Consider the numbers m Science and Engineering Indicators 2000. According to the Merit database, which is compiled from press accounts, there were 574 alliances strategic alliances.
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It captures strategic alliances with some kind of an explicit technology content, whether the conduct of research or the exchange of results, globally and across all sectors. The third type of data gathering focuses on particular areas.
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From page 30... ...
CIIARLES DUKE: Alliances are made to complete value chains, and value chains are offering specific things or a particular product or a particular service. Xerox has a huge variety of these alliances and generally speaking they last a year or two at most.
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From page 31... ...
The only way to make sense out of it is to understand what these firms are trying to do, what their product line is, whether they have holes in their value chains, and whether they are trying to fill a hole m their value chain by making an alliance PARTICIPANT: Perhaps the notion of trying to determine the end point of an alliance is unrealistic because they don't actually end, they just take different forms, they evolve, and you can see this m work we've been looking at in the engrneenng research center, which gets funding from NSF for a period of time and then the money goes away, but the centers don't go away, and the vibrations don't go away. They tend to change dimension, they change significance, the free riders disappear because they no longer can benefit.
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Although there are sometimes confidentiality problems at the state level, m most cases you can get aggregate state level data but rarely but rarely any real detail. NSF has some detail for the ten largest states, as I understand it, but for most states only one number.
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I also want to comment on the memorandum of understanding concernmg sharing of Census data, BEA data, and NSF data. I think this is a great advance.
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In terms of location, as you would expect home based augmenting facilities are built close to universities where the spillovers that you are seeking are highest. Home base exploiting facilities are generally close to centers of demand or close to existing manufacturing facilities.
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Second, the relative share of home base augmenting facilities is increasing. This is a game largely being played amongst industrialized countries, with the exception of China where you see some home base exploiting facilities, and India and Southeast Asia, where you also see some home base exploitmg activities.
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parent activity in the United States and on foreign affiliates' activity abroad. For foreign direct rnvestrnent m the United States we collect data on the U.S.
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From page 37... ...
This could be done under two legal authorities, a 1990 act that allows BEA to link its data on foreign direct investment m the United States to establishment data collected m the economic censuses and a new 2002 statute on data sharing We are going to attempt the linkage first by doing a computer match of employer identification numbers that are reported to both BEA and Census. If we are not successful on the computer match then we will go to other infommation such as names and addresses.
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From page 38... ...
We expect to be able to improve both our sample frame and the Census Bureau sample frame. We expect to find cases where companies that are reporting to us are not reporting to the Census Bureau even though they should be reporting, and vice versa.
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If we think these organizational changes are productivity enhancing, which is my bias from studying other kinds of organization, we may be seemg some real improvements m R&D And if mformation technology is driving research as it has been driving other parts of the business enterprise, we are seemg enormous productivity benefits. But the work so far is all m manufacturing firms so we need to study the service sector.
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From page 40... ...
These arrangements are very specific to the value chains of the product lines involved, even within a firm. NED IIOWENSTINE: The RD- 1 has a question about total foreign spending for R&D without distinguishing whether it is R&D by an affiliated company or not.
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If they had better measures they would have less difficulty justifying their strategies. CONCLUDING OBSERVATIONS BRONWYN HALL: Before we conclude I want to recognize Al Johnson from Corning and from the Industrial Research Institute.
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And we have talked a lot about value chain and understanding how R&D fits into the value chain, but not umlil a recent session did I hear a discussion of size. Let me tell you a story.
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From page 43... ...
It gets commercialized as we move along the value chain, and then things change. Perhaps the labor force m the firm has to be upgraded to deal with the new activity or downgraded or replaced, who knows, but there will be social and economic outcomes.
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JOHN JANKOWSKI: I don't want to address that question of whether Xerox should or should not be including that m its RD-1 response, but we do give guidance to specifically exclude social science R&D from the industrial research totals. We had a serious concern that companies would start including market research m their R&D totals and we felt pretty confident that we did not want that to be reported on this survey.
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From page 45... ...
PARTICIPANT: I recently retired from the National Center for Health Statistics, a federal statistical agency. What they have been doing and what the Census Bureau has been doing is setting up what they call research data centers, and these allow qualified researchers access to data that are not released for confidentiality reasons.
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