On the robustness of R&D

Abstract

Alternative models of productivity predict a range of its determinants besides that of research and development (R&D). We investigate the robustness of R&D vis-à-vis a dozen productivity determinants in a panel of 16 Organisation for Economic Co-operation and Development countries through panel cointegration, bootstrap simulations and extensive sensitivity tests. Domestic knowledge stocks, international knowledge diffusion and human capital remain robust across all measures. The cross-country differences in accumulated knowledge stocks and human capital appear to explain productivity differences across countries.

Data Appendix

Data on multifactor productivity (P ^m) are obtained from the OECD (2008). German data are available from 1991 only; we extrapolated pre-1991 data using the growth rate of the TFP series obtained from Timmer et al. (2003). However, dropping Germany from the sample does not change our results (see Sect. 6). Two further measures of TFP are used for robustness. The first one is our own measure of TFP (P ^a) calculated as: log P ^a = log GDP − 0.3 log K − 0.7 log L, where K is total net physical capital stock and L is total employment level. The second measure of TFP (P ^ec) is published by the EC. Domestic business-sector R&D capital stocks (S ^b) are calculated from business-sector R&D expenditure ($E_{b}^{RD}$), using the perpetual inventory method. Initial stock, $S_{0}^{b}$, is calculated as:

$$S_{0}^{b} = \frac{{E_{b,0}^{RD} }}{g + \delta }$$

(7)

where δ denotes the depreciation rate, g is the average annual growth rate of $E_{b}^{RD}$ over the sample, and $E_{b,o}^{RD}$ is the initial value of $E_{b}^{RD}$. This method of computing capital stocks requires making assumptions about the average life of capital stocks and depreciation rates, which do not always capture the complexity of different types of capital assets and the different depreciation rates affecting them. The issues of taxes on capital assets and the price of capital further complicate the matter. However, this method is widely used in the literature on the grounds of cost and convenience, and we do the same. All R&D capital stocks are computed using 15, 10 and 5 % depreciation rates. Public-sector R&D expenditure ($E_{p}^{RD}$) is the total R&D expenditure of the government and higher education sectors. Public-sector R&D capital stocks (S ^p) are generated from public-sector R&D expenditure ($E_{p}^{RD}$), applying the same approach as in Eq. (7). Due to the lack of R&D deflators, R&D expenditure data are converted to constant prices by the GDP deflators. Initial capital stocks, $S_{o}^{b}$ and $S_{0}^{p}$, are generated for the earliest year for which R&D expenditure data are available (their availability ranges from the late 1960s to early 1980s).

We compute four different measures of foreign R&D capital stocks using bilateral imports, bilateral R&D collaborations and stocks of bilateral inward and outward FDI as weights, following the approach suggested by Lichtenberg and van Pottelsberghe (1998). In this framework, the import ratio-weighted foreign R&D capital stock ($S^{fm}$) is:

$$S_{i,t}^{fm} = \sum\limits_{j = 1}^{N - i} {(M_{ij,t} /Y_{j,t} )*S_{j,t}^{b} }$$

(8)

where, Y _j denotes the GDP of country j and M _ij is the imports of country i from country j; throughout, ‘t’ denotes time subscript. We use bilateral capital import ratios which include chemicals and related products (SITC 5), manufactured goods classified chiefly by material (SITC 6), machinery and transport equipment (SITC 7), and miscellaneous manufactured articles (SITC 8). Agro-industries and raw materials (SITC 0–4) are excluded. The bilateral R&D collaboration-weighted foreign knowledge stock (S ^fc) is:

$$S_{i,t}^{fc} = \sum\limits_{j = 1}^{N - i} {(PC_{ij,t} /TP_{i,t} )*S_{j,t}^{b} }$$

(9)

where TP _i is country i’s total patent applications and PC _ij is its joint patent applications with countries J, both filed at the EPO. Data on patent applications are obtained from the EPO. We compute 15X23 matrixes of bilateral patent cooperation coefficients for each sample country. Likewise, foreign R&D capital stocks based on inward (S ^fI) and outward (S ^fO) FDI stocks are computed as:

$$S_{i,t}^{fI} = \sum\limits_{j = 1}^{N - i} {(FDI_{ij,t} /K_{j,t} )*S_{j,t}^{b} }$$

(10)

$$S_{i,t}^{fO} = \sum\limits_{j = 1}^{N - i} {(FDO_{ij,t} /K_{j,t} )*S_{j,t}^{b} }$$

(11)

where K _j is country J’s capital stock, generated from non-resident fixed capital formation using the perpetual inventory method at an 8.0 % depreciation rate. FDI _ij is country i’s FDI stock originating in country j; FDO _ij is country J’s FDI stocks originating in country i. Data are expressed in constant 2,000 prices using the GDP deflator. The relevant weights for all foreign knowledge stocks are computed using 3-year moving averages to avoid yearly fluctuations. Human capital (H) is proxied by the average years of schooling of the 25–64 age group. Bassanini and Scarpetta (2002) kindly provided data for the period up to 2000; we extrapolate the last four observations. We acknowledge that this is only a rough measure of human capital, but we do not have any suitable alternative measures. Data on ICT investment consist of non-resident investment in hardware, communications equipment and software. They are expressed as a percentage of GDP. High-technology exports (X ^h) and imports (M ^h) are expressed as a percentage of total exports and imports, respectively. We follow the OECD’s (2007) definitions of high-technology items of trade, which include: pharmaceuticals (ISIC.2423); office, accounting and computing machinery (ISIC.30); radio, TV and communications equipment (ISIC.32); medical, precision and optical instruments (ISIC.33); and aircraft and spacecraft (ISIC.353). Service sector (SER) is measured as the value added of the service sector relative to GDP. The service sector consists of ISIC Rev.3 industries from 50 to 90. The proxy for the business cycle is the rate of employment (E).

Stocks of public infrastructure (Z) is generated from government’s fixed capital formation (I ^gov) using the perpetual inventory method (Eq. 7). I ^gov is converted to constant 2000 PPP US dollars using the fixed capital formation deflator. Measures of Z based on 3, 5 and 8 % depreciation rates are generated. Data on stocks of inward (F ^I) and outward (F ^O) FDI are published by the United Nations Conference on Trade and Development (UNCTAD) in current US dollars. They are converted to constant PPP dollars using GDP deflator and PPP exchange rates. Banking sector development is proxied by the ratio of private-sector credit by deposit money banks and other financial institutions to GDP (P ^K). Two measures of capital market development are the stock market capitalization-to-GDP ratio (S ^MC) and the stock market total value traded-to-GDP ratio (S ^MV). They are well-known measures of financial sector development (see Beck and Levine 2002; Luintel et al. 2008).

Data	Sources
Multifactor productivity and ICT	Multifactor productivity database (OECD)
Total factor productivity, capital stocks	AMECO database (European Commission)
R&D expenditure	Research and development database (OECD)
Human capital	Bassanini and Scarpetta (2002)
High-technology exports and imports, and total exports and imports	STAN bilateral database (OECD)
Stocks of total inward and outward foreign direct investment	UNCTAD’s foreign investment database
Private-sector credit by deposit money banks and other financial institutions to GDP, stock market capitalization to GDP and stock market total value traded to GDP	World Bank
Service sector value added	STAN indicators database (OECD)
GDP, GDP deflator, total employment level, employment rate, PPP exchange rate, government fixed capital formation and its deflator, non-resident fixed capital formation and its deflator, investment	Analytical database (OECD)
Bilateral imports	International trade by commodities statistics database (OECD)
Patent applications at the European Patent Office	Patent database (OECD)