Variable Label Variable
OECD_AERO_EXP_SHARE Export market share: aerospace industry
OECD_BIOTECH_PATENT_APPL_PCT Number of patents in the biotechnology sector—applications filed under the PCT (priority year)
OECD_ELEC_BALANCE Trade balance: electronic industry (millions of current dollars)
OECD_ELEC_EXP_SHARE Export market share: electronic industry
OECD_GERD_$ Gross domestic expenditure on R&D (millions of current PPP dollars)
OECD_INSTR_BALANCE Trade balance: instruments industry (millions of current dollars)
OECD_INSTR_EXP_SHARE Export market share: instruments industry
OECD_OC_BALANCE Trade balance: office machinery and computer industry (millions of current dollars)
OECD_OC_EXP_SHARE Export market share: office machinery and computer industry
OECD_PATENT_APPL_ICT Number of patents in the ICT sector—applications filed under the PCT (priority year)
OECD_PATENT_APPL_PCT Number of patent applications filed under the PCT (priority year)
OECD_PHARMA_BALANCE Trade balance: pharmaceutical industry (millions of current dollars)
OECD_PHARMA_EXP_SHARE Export market share: pharmaceutical industry
OECD_R&D_HR_FTE R&D personnel—full-time equivalent
OECD_RES_FTE Researchers—full-time equivalent
OECD_RES_HC Researchers—head count
OECD_TECH_BOP_PAYMENTS_$ Technology balance of payments: payments (millions of current dollars)
OECD_TECH_BOP_RECEIPTS_$ Technology balance of payments: receipts (millions of current dollars)
OECD_TRIADIC_PATENT_FAMILIES Number of Triadic Patent Families (priority year)
UN_GERD_$ Gross domestic expenditure on R&D (millions of current PPP dollars)
UN_OSS_FTE Other supporting staff—full-time equivalent
UN_OSS_HC Other supporting staff—head count
UN_R&D_HR_FTE R&D personnel—full-time equivalent
UN_R&D_HR_HC R&D personnel—head count
UN_RES_FTE Researchers—full-time equivalent
UN_RES_HC Researchers—head count
UN_TECH_FTE Technicians—full-time equivalent
UN_TECH_HC Technicians—head count

NOTES: AERO = aerospace industry; APPL = application; BOP = balance of payments; ELEC = electronic industry; EMPL = employment; EU = European Union; EURO = Eurostat; EXP = export market share; FTE = full-time equivalent; GERD = gross domestic expenditure on research and development; HC = head count; HR = human resources; HRST = human resources in science and technology; HTECH = high technology; ICT = information and communication technology; INSTR = instruments industry; KIS = knowledge-intensive services; NONEU = non-European Union; OC = office machinery and computer; OSS = other supporting staff; PCT = Patent Cooperation Treaty; PHARMA = pharmaceutical industry; PPP = purchasing power parity; R&D = research and development; RD = R&D; RES = researchers; SE = science and engineering; TECH = technicians; TRD = trade; UN = United Nations; UNESCO = United Nations Educational, Scientific and Cultural Organization.

Statistics manipulated the available data in Table 1 of NSF 11-300 and converted percentage figures into levels; the results are shown in Table F-8. Survey results from Canada’s 2003 Survey of Innovation, which are available in CANSIM, are problematic to interpret as it is often difficult to understand what the denominator is. In some data tables, it is clear that the denominator is innovative firms, while for other tables the user must guess. One can calculate the total number of innovative firms receiving tax credits or total number of innovative firms reporting customers as an important source of innovation information if information on total innovative firms is available. Hence, staff of the Committee on National Statistics could not convert percentage figures into levels in the case of innovation data from CANSIM. It would be useful if more information on the surveyed population, such as total population, sample size, and response rate, were readily available. This information needs to be published by industry classification, as is evident from Tables F-8 and F-9.19


Many Nations Analysis

Figures F-4 to F-6 show a cluster heat map, a hierarchical cluster tree, and the multidimensional scaling of a Pearson correlation matrix, respectively. The input matrix consists of the main S&T variables from the OECD, UNESCO, and Eurostat databases. The red and orange squares along the diagonal of the heat map in Figure F-4 show that those variables are very closely related to each other, and either they could be merged, or the most well-behaved and consistent variables among them could be selected. Figures F-5 and F-8 show clusters of variables. Broadly speaking, human resource variables form one category and trade variables another. Figures F-6 and F-9 show sets of variables that are either similar or dissimilar. In these two figures, the dimensions have no interpretation, and one is looking for clusters of variables that would indicate they belong together. Strong correlation patterns are observed in the variables on researchers, technicians, and other supporting staff. These variables are closely grouped together. Moreover, within these variables, those produced by the same organization


19For further information, see Lonmo (2005, Table 1).

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