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significant class of potential users of R&D data, i.e. the person interested in one very specific sub-item, such as a sub-field of science or a product field (holography or computer controls for machine tools). As already pointed out, the Manual is essentially designed to measure national R&D efforts and to categorise them in various ways. Except for special inventories of specific fields, few member countries have pushed sub-categorisation to such a detailed level, and it is unlikely that such detail would be obtainable at the OECD level.
49.Furthermore, it is difficult to establish norms for categories of interest to national governments when reviewing the types of research funded from public monies, when such research may have various policy connotations. Strategic research is one area that has received considerable attention. It is generally taken to mean research which a nation sees as a priority for developing its research base and ultimately its economy. What is and is not strategic varies among member countries. Nevertheless, in recognition of the policy importance of strategic research in certain countries, Chapter 4 of the Manual gives some attention to its identification.
1.10.R&D surveys, reliability of data and international comparability
50.While a certain amount of R&D data can be derived from published sources, there is no substitute for a special R&D survey. Most of the Manual has been drafted on the assumption that surveys of at least all the major national performers of R&D will be made. Nevertheless, it may be necessary for both respondents and surveying agencies to produce estimates; this question is discussed at length in Chapter 7.
51.It is hard to generalise about how far such estimates are necessary or how far they affect the reliability of the data, as the situation will vary from country to country. Nevertheless, it is generally the case that “subjective” estimation by respondents is probably greatest for the breakdown between basic research, applied research and experimental development, while the use of “rule of thumb” estimation by survey agencies is probably greatest for R&D in the higher education sector. As a consequence, these data should be treated with circumspection. Annex 2 and a special supplement to the 1980 edition of the Manual give further guidance on this topic (OECD, 1989b).
52.National surveys which provide R&D data that are reasonably accurate and relevant to national users’ needs may not be internationally comparable. This may simply be because national definitions or classifications deviate from international norms. Such cases are generally documented in footnotes. The situation is more complex when the national situation does not correspond to the international norms. This is often true for sector analysis; for administrative reasons, apparently similar institutions may be placed in
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different sectors in different countries. Moreover, national perceptions of these norms may be different, notably for type of research analysis and for the analysis of R&D personnel by occupation. Such differences are impossible to quantify.
1.11.Government budget appropriations or outlays for R&D (GBAORD)
53.GBAORD data are often available much earlier than the results of retrospective R&D surveys and are framed in categories of particular interest to policy makers.
54.This topic is discussed separately in Chapter 8. Although the general definitions in Chapter 2 apply to GBAORD, specifications in the following chapters, which are essentially designed for performer-based reporting, often do not.
55.This type of analysis essentially seeks to ascertain government intentions or objectives when committing money to R&D. R&D funding is thus defined by the funder (including public GUF) and may be both forecast (budget proposals or initial budget appropriations) or retrospective (final budget or outlay). Whereas R&D statistics proper are collected by means of especially designed surveys, government R&D funding data generally have to be derived, at some stage or another, from national budgets, which are based on their own standard methods and terminology. Although the links between survey and GBAORD data have improved in recent years, the analysis will always be a balance between what is desirable from the R&D point of view and what is available from the budget or related sources.
56.The aim of classifying GBAORD by socio-economic objective is to help governments to formulate science and technology policy. Consequently, the categories have to be broad, and the series are intended to reflect the amount of resources devoted to each primary purpose (defence, industrial development, etc.). Nevertheless, the fit is never perfect and always reflects the policy intentions of a given programme rather than its precise content. Because of this and because of methodological constraints on the way data are compiled, the strict level of international comparability is probably lower for GBAORD data than for most of the other series discussed in the Manual.
1.12.Topics of special interest
57.There is often a demand for R&D data for a specific priority area, which cuts across the standard institutional and functional classifications. Data to meet this demand often have to be built up from special extractions or tabulations. Annexes 4 and 5 deal with currently popular priority areas.
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58.Health R&D has become a policy concern in recent years, and various international studies have been made. Health R&D data are not directly available from any of the standard classifications described in the Manual. A pragmatic method of deriving estimates of health-related R&D from existing data sources is described in Annex 4. It is an aid to data compilation and interpretation and should not be regarded as an international recommendation.
59.The OECD is developing statistics and indicators on the information economy and information society. It is possible to calculate an aggregate for R&D in selected information and communication technology (ICT) sectors on the basis of the agreed list of industries belonging to the ICT sector, as described in Annex 4.
60.Following information technology, biotechnology is expected to be the next pervasive technology of great significance for future economic development. The OECD has started work to develop a statistical framework for biotechnology. Some ideas for questions on biotechnology in R&D surveys and the concept of a special survey on biotechnology are presented in Annex 4.
61.The regional distribution of R&D activities is of great policy interest not only within the EU but also in other OECD countries, especially those with federal constitutions. A recommendation to distribute some variables by region is included in Chapters 5 and 6, and Annex 5 explains some methodological aspects.
1.13.A final word to the user of R&D data
62.To conclude, four general points may be made about the use of both R&D statistics and R&D funding data:
–Such series are only a summary quantitative reflection of very complex patterns of activities and institutions. For this reason, it may be dangerous to use them “neat”. They should, as far as possible, be analysed in the light of relevant qualitative information. Particularly in the case of international comparisons, the size, aspirations, economic structure and institutional arrangements of the countries concerned should be taken into consideration.
–Users generally refer to R&D data with a question in mind: “Is our national university research effort declining?” “Does my firm spend a higher proportion of its funds on basic research than the average for my industry?”, etc. To answer such questions, it is necessary to identify the relevant basic data and then use them to construct an R&D indicator. Some basic data may be accurate enough to answer one question but not another. For example, GBAORD data are useful for answering general questions about trends in easily defined objectives: “Is there any sign that defence R&D is picking up
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again in the OECD area?” They are not suitable for specific questions about less easily defined objectives: “Does my country spend more or less in absolute terms on R&D for environmental protection than country X?”
–One particularly useful way of constructing such indicators for international comparisons is to compare R&D inputs with a corresponding economic series, for example, by taking GERD as a percentage of GDP. Such broad indicators are fairly accurate but may be biased if there are major differences in the economic structure of the countries compared. For example, the activities of big R&D-intensive multinationals may influence the GERD/GDP ratio in a particular country quite significantly. The classifications and norms used to collect R&D statistics are, as far as possible, compatible with those for general statistics, and, while it is much more difficult to make detailed comparisons between R&D and non-R&D series, establishing such “structural” R&D indicators can be particularly revealing.
–The problems of data quality and comparability noted above are characteristic of the whole range of data on dynamic socio-economic activities – such as employment or international trade – which are important to policy makers, managers, analysts and others. The philosophy underlying the evolution of R&D statistical standards in the Manual has been to identify and gradually resolve these problems by exploring various approaches and learning from member countries’ experience.
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ISBN 92-64-19903-9
Frascati Manual 2002
Proposed Standard Practice for Surveys on Research and Experimental Development
© OECD 2002
Chapter 2
Basic Definitions and Conventions
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