Boosting Manufacturing Productivity Through R&D: International Comparisons with Special Focus on Italy

Abstract

Using data for twelve manufacturing industries of five developed countries over the period 1980–2002, we perform a dynamic panel estimation of the long-run elasticity of TFP with respect to R&D capital. The highest elasticity is found for the US (0.39), followed by Germany (0.29–32); intermediate values are achieved by France (0.19–0.21) and Spain (0.19), while Italy records the lowest R&D impact (0.08–0.12). The latter finding, supported by an in depth analysis based on a longer time-span and more accurate data, can be ascribed to the declining R&D efforts undertaken, during the Nineties, by Italian manufacturing industries.

Appendices

Appendix 1 Data sources

ISTAT, Statistiche sulla ricerca scientifica: Business total Research and Development expenditure by industry (billions of liras in current prices). Reference period: 1967–1987.

OECD ANBERD, R&D expenditure in industry (ISIC. Rev.2) Vol. 2002 Release 1 (millions of national currency at current prices). Reference period: 1973–1980.

OECD, Structural Analysis (STAN) database, 2005: Industry Value Added (at current and constant prices), Gross Fixed Capital Formation (at current and constant prices), Total Employment (employees and self-employed, in thousands), Full-Time Equivalent Employment (available for some countries only), Labour Compensation (at current prices), Net and Gross Fixed Capital (at constant prices). Reference period: 1980–2002.

OECD, Science and Technology Database, 2006: Business total Research and Development expenditure by industry (millions of national currency in current prices). Reference period: 1981–86 and 1987–2002.

Appendix 2 R&D capital stock for selected industries

Fig. 2

Chemicals and pharmaceuticals

Fig. 3

Electrical and optical equipment. * = All the series are built by applying a harmonised deflator obtained from US hedonic prices adjusted for rate of inflation’s differentials

Fig. 4

Transport equipment

Appendix 3 Supplementary analysis of the Italian case

In this section the raw measure of TFP employed in previous estimates is adjusted for the degree of utilisation and the quality changes of labour and capital. This is extension is performed exclusively for the Italian case due to the possibility of collecting the required industry data for this country only.

With respect to labour input, instead of employed people, we use the number of full time equivalent (FTE) employed persons, which is a better (though not the best¹⁵) proxy for the effective utilisation of labour. Moreover, we adjust such a measure for quality changes by using the white/blue collars’ ratio at industry level, taken from the surveys on Italian manufacturing firms carried out by Mediocredito Centrale-Capitalia.¹⁶ This correction is aimed at avoiding the risk that the long-run coefficient of R&D might pick up the increase in the workforce skills occurred during the period considered. Our quality-adjusted measure of labour is computed by augmenting the number of FTE employed persons ($\tilde{L}$) with the share of white collars on total employment (w):

$$ {\bar{\tilde{L}}_{i,t}} = {\tilde{L}_{i,t}}*\left( {1 + {w_{i,t}}} \right) $$

This correction implies the assumption that white collars have a marginal productivity two times higher than that of blue collars. Although, this hypothesis seems quite strong at a first sight, it must be stressed that the estimate of the long-run R&D elasticity does not significantly change when a different productivity ratio is applied (1.5 or 3 instead of 2). It should be added that this kind of correction is more suitable for the Italian case than that implemented, for instance, by Griffith et al. (2004). Under the hypothesis of perfectly competitive labour markets, they obtain a quality-adjusted measure of labour input by weighting each group of workers with the corresponding wage bill:

$$ {\bar{\tilde{L}}_{i,t}} = {W_{i,t}}^S{B_{i,t}}^{1 - S} $$

where W and B are respectively the number of white- and blue-collar FTE workers, S and 1-S their wage bills. A more elaborated procedure is that used by Brandolini and Cipolline (2001) who computed a quality-adjusted measure of labour input by distinguishing Italian workers in five educational levels, weighted by their respective average wages. For the whole Italian economy, the inclusion of such a measure reduced substantially the contribution of TFP to the growth of value added over 1977–2000.

However, at least until the early Nineties, the high bargaining power of the Italian trade unions did not allow that the wages of white collars were too far from those of blue collars; as a consequence, since the changes in relative wages were modest and not particularly affected by those in relative productivity, the correction proposed by the above authors may produce misleading results.¹⁷

Moving to capital input, as in Griffith et al. (2004), our measure of capital adjusted for its degree of utilisation is

$$ {\tilde{K}_{i,t}} = {K_{i,t}}*\left( {1 + \frac{{{Y_{i,t}} - {{\hat{Y}}_{i,t}}}}{{{{\hat{Y}}_{i,t}}}}} \right) $$

where Y _i,t is the industry valued added at constant prices and ${\hat{Y}_{i,t}}$ is the fitted value which arises from regressing, for each separate industry, the real value added on a constant and a time trend.

Following Jorgenson et al (1987), an indicator of capital quality (q) has been obtained from the newly available industry series on investment types released by ISTAT (see Appendix 1), computed as the ratio between the capital stock evaluated respectively at rental (R) and market prices (p):

$$ {q_{i,t}} = \frac{{\sum\limits_{j = 1}^9 {{R_{ij,t}}} {K_{ij,t}}}}{{\sum\limits_{j = 1}^9 {{p_{ij,t}}} {K_{ij,t}}}},\,\,\,\,\,{R_{ij,t}} = {p_{ij,t}}({r_{i,t}} + {\delta_{ij}} - {\pi_{ij,t}}),\,\,\,\,\,{\pi_{ij,t}} = \frac{{{p_{ij,t}} - {p_{ij,t - 1}}}}{{{p_{ij,t - 1}}}} $$

where, in this case, the suffixes i and j denote respectively industries and types of asset,¹⁸ r is the nominal rate of return, δ denotes the asset specific depreciation rate (obtained as described in Section 3), and π is the rate of change of the investment prices.

The nominal rate of return is estimated internally from ISTAT industry accounts by recurring to the following (ex-post) formula:

$$ {r_{i,t}} = \frac{{GO{S_{i,t}} - \sum\limits_{j = 1}^9 {\left( {{\delta_{ij}} - {\pi_{ij,t}}} \right)} {p_{ij,t}}{K_{ij,t}}}}{{\sum\limits_{j = 1}^9 {{p_{ij,t}}} {K_{ij,t}}}} $$

which is based on the Gross Operating Surplus (GOS) reduced by the remuneration of self-employed workers.

To be stressed is that the difference between R and p reflects the substitution towards assets with relatively high marginal products as they are characterized by a relatively high physical deterioration and economic obsolescence. This adjustment should avoid that θ captures the qualitative growth of tangible capital, fuelled by the rising adoption by manufacturing firms of technologically advanced capital as computers, communication equipment and software. Accordingly, our measure of quality-adjusted capital is:

$$ {\bar{\tilde{K}}_{i,t}} = {\tilde{K}_{i,t}}*\left( {1 + {q_{i,t}}} \right) $$

Summing up, the adjusted measure of TFP (in logs) is given by:

$$ \ln T\bar{\tilde{F}}{P_{it}} = \ln {Y_{it}} - {s_{Li}}\ln {\bar{\tilde{L}}_{i,t}} - (1 - {s_{Li}})\ln {\bar{\tilde{K}}_{i,t}} $$

(cf. Eq. 2, Section 3).

By using the adjusted measure of TFP, Table 8 shows that the estimated elasticities of R&D capital do not substantially change from those reported on Tables 5, 6 and 7 (Section 5). For the period 1970–2002, the results are almost identical and point to a low impact of R&D. By dropping out the last ten years, the log-run effects of R&D on the adjusted TFP are slightly lower than those arising with a measure of unadjusted TFP. In any case, they are significantly higher than that found for the entire period under examination, suggesting that the low R&D impact is a reflection of the declining R&D efforts recorded during the last decade.

Table 8 Italy 1970–2002: Panel DOLS estimates of the R&D impact on adjusted TFP (12 mfg. industries)