Innovations, off-shoring and specialization

Transcription

1 University of Perugia From the SelectedWorks of Francesco Venturini 2010 Innovations, off-shoring and specialization Ioannis Bournakis Michela Vecchi Francesco Venturini Available at:

2 INNOVATIONS, OFF-SHORING AND SPECIALIZATION Ioannis Bournakis Middlesex University Business School Michela Vecchi* Middlesex University Business School & National Institute of Economic and Social Research Francesco Venturini University of Perugia Abstract This paper investigates the role of innovations in affecting specialisation by allowing countries to rely not only on their traditional factor endowments but also on their abilities to increase their stock of knowledge (R&D) and technologically advanced (ICT) capital, and on their abilities to access resources in other countries (off-shoring). By looking at measures of specialisation based on industry shares of GDP or total employment, we present evidence for 18 manufacturing and service industries, using data for the US, Japan and the largest EU countries for the period Our results suggest that accumulating ICT or R&D at the industry level enhances both relative employment and value added and that, in some cases, national endowments of such inputs may reinforce this effect. On the other side, intraindustry purchases of off-shored inputs (so called narrow off-shoring) are more beneficial for value added than for occupation shares; this contrasts with evidence obtained for the broad off-shoring of material and service inputs, whose occupational effects translate one-to-one into changes in industry shares of GDP. VERY PRELIMINARY VERSION * Corresponding author. M.Vecchi@mdx.ac.uk. 1

3 1 Introduction Countries have always experienced changes in their patterns of specialisation over time and understanding the causes of such changes has been the objective of a long stream of research, starting from the XVIII century with Ricardo s theory of comparative advantage. The traditional theory focuses on the importance of internal factors, such as endowments and productivity, in driving countries specialisation and trade flows. Countries will specialise in those sectors where they enjoy a productivity advantage (Ricardo); or they will specialise in the production of those goods that intensively use factors that are abundant in the particular country (Heckscher Ohlin). Next to models of specialisation, a parallel literature has analysed the determinants of deindustrialisation, i.e. the decline in the share of manufacturing employment. Similarly to changes in value added shares, productivity and factor endowments are among the main factors affecting deindustrialisation. However, employment shares do not always move in the same direction as value added shares. Increases in productivity have a positive effect on value added and therefore tend to increase the shares of a particular sector. The effect on employment shares can be more ambiguous. As summarised in Rowthorn and Ramaswami (1999) the faster growth of productivity in one sector decreases the prices of final goods thereby stimulating demand and increasing employment. However, increases in productivity mean that more can be produced with fewer workers and hence employment shares will decrease. The net effect is difficult to assess. Theoretically, Pissarides and Ngai (2007) show that movements in employment shares depend on the elasticity of substitution between final goods. If the elasticity is less than one, employment will shift from high productivity to low productivity growth sectors. If the elasticity is greater than one employment shares will increase in the high productivity sectors and decrease in the low productivity industries. The latter situation is likely to characterise high tech sectors where new waves of products easily substitute existing ones, like the shift from mobile phones to smart phones; in such industries, employment and value added shares are expected to move in the same direction. Empirically, a test of this hypothesis can be carried out by using disaggregated data that allow to analyse changes in individual industries in both value added and employment shares. The first objective of this paper is to address this point, while controlling for national endowments of heterogeneous labour and capital assets. Traditionally, the analysis of the impact of factor endowments has distinguished between low and skilled labour (Harrigan 1997, Harrigan and Zakrajsek 2000, Nickell et al. 2

4 2008) but has relied on fairly aggregate measures of capital. This approach fails to account for the possible role played by intangible and innovative assets, such as ICT and R&D capital, on specialisation and deindustrialisation. Recently, Bournakis and Vecchi (2010) have addressed this issue and their results show that accounting for capital heterogeneity improves the understanding of the forces driving specialisation. In this paper we aim to extend the scope of that approach to a more detailed industry classification, and to the analysis of trends characterising both value added and employment shares. Although models that stress the role of internal factors in affecting specialisation and deindustrialisation are the benchmark approaches in the literature, the evolution of globalisation has induced new trends in international production that shift the pattern of specialisation across countries. In the era of globalisation the phenomenon of off-shoring is regarded as a common operation that allows countries to exploit international differences in factor prices. The evidence on the effects of globalisation on specialisation and structural change has still not reached a consensus. For example, Saeger (1997) shows that the imports of manufactured goods from developing countries have significantly affected the shares of the manufacturing sector in OECD countries. Rowthorn and Rowasmany (1999), on the other hand, show that the impact of North-South trade on specialisation and de-industrialisation is not particularly large and often lacks statistical significance. These contributions, although valuable, have two main limitations. Firstly, they do not provide a clear theoretical link between off-shoring and specialisation/deindustrialisation. Secondly, they neglect the interplay between off-shoring and innovation 1. A further objective of our paper is to provide a theoretical framework for the impact of off-shoring on specialisation and structural change, which operates via the link between off-shoring and productivity, while controlling for the effect of innovations Existing empirical evidence has assessed the impact of off-shoring on productivity and on the skill composition of labour demand. For example, the Mc Kinsey Institute (2003) finds that for every $1 spent in outsourcing, a gain of $1.10 is generated in the US economy. However, the effect might be different for individual industries as some will see an increase in their shares via the increase in productivity; others will experience a decrease in their shares because their output is now produced elsewhere. As for the impact on employment shares, in the latter case we expect it to be negative; however, for the expanding industries, 1 Evidence on the role of trade in fostering innovation and the efficient use of intangibles is very extensive (Venturini 2010). 3

5 there might be a positive effect, as discussed above. Hence, off-shoring will lead to a redistribution of resources across the economy. Our contribution, by analysis productivity and off-shoring within the same framework, will provide further insights into the determinants of specialisation and structural change at the industry level. Our analysis uses industry level data for eight OECD countries, Denmark, Finland, Germany, Italy, Japan, Netherlands, UK and US, from 1990 to These countries cover an interesting range of specialisation patterns and off-shoring practices, as well as being characterised by different endowments of ICT and intangible capital, and different institutional frameworks. Our study will use different off-shoring indicators, including broad indicators of either material or service off-shoring, as well as narrow measures of off-shoring practices. The industry set will include both manufacturing and service industries to provide a comprehensive analysis of changes in countries industry structure. The importance of including service industries in the analysis is justified by the increasing importance of the Service sector in all developed countries (Abramovsky et al. 2004, Inklaar et al. 2008) as well as by the increasing importance of service off-shoring (OECD 2006, ch. 3). For example, Abramovsky and Griffith (2006) emphasise that one of the most substantial changes in economic activity over recent years has been the substantial growth in the outsourcing of business services. Therefore, understanding of the forces behind the expansion of the service sector is another of the main objectives of our work. The work is structured as follows. Section 2 surveys the main contributions on specialisation and structural change and how these have been affected by globalisation. Section 3 develops the analytical framework and draws the empirical strategy followed in our e work. Next, in section 4, we describe our data and present summary statistics. Section 5 presents the main results and discusses their implications. Finally, Section 6 concludes the paper. 4

6 2. Background The analysis of the impact of factor endowments and productivity on specialisation is longstanding and it has produced fairly consistent results across different contributions. Productivity improvement, generally captured by a relative measure of Total Factor Productivity (TFP), contributes to the increase in manufacturing production and hence it is a potential factor affecting countries international competitiveness (Harrigan 1997). The analysis so far has mainly concentrated on a small number of manufacturing and service industries (Nickell et al. 2002). A recent paper by Cadot et al. (2007) is a notable exception as it is based on a sample of 28 manufacturing industries in several OECD countries. The analysis looks at the impact of productivity, factor endowments, and tariffs on specialisation. The results generally confirm the predictions of the classical theory, i.e. both productivity and factor endowments are important in determining countries specialisation and international competitiveness. Tariffs, on the other hand, do not appear to play significant effects in most industries, and this has important policy implications. In fact, the message that the paper puts across is that Governments should direct their policies towards the accumulation of the right assets, both physical and human, and to encourage the adoption of new technologies. The introduction of an index of TFP in the analysis is a very useful tool as it accounts for all those factors that can increase productivity, including, for example, technological innovations, managerial practices and human capital. In an attempt to discriminate between the impact of innovative and intangible assets and other factors, Bournakis and Vecchi (2010) include measures of R&D capital and ICT capital in their analysis of specialisation patterns in Europe. Their results emphasise the role of ICT in driving resources away from manufacturing and into services; while increasing endowments of R&D capital promote increases in the shares of the manufacturing sectors. While some progress has been made on the impact of internal factors on specialization, the evidence is rather poor with regard to the role of international off-shoring. As briefly mentioned in the introduction, existing studies provide profoundly divergent opinions on the impact of off-shoring on specialization. The analysis has mainly relied on the estimations of models which include a measure for North-South trade (usually the import of manufacturing inputs from developing countries), next to a measure of productivity and resource endowment, in explaining movements in value added/employment shares in the manufacturing sector (Saeger 1997, Rowtorn and Ramaswami 1999). The evidence is usually 5

7 based on aggregate data, referring mainly to the whole of the manufacturing sector. However, when using data at such level of aggregation, the heterogeneity across different industries is completely neglected and no cross industry dynamic adjustments are accounted for. On the other hand, the literature on the impact of off-shoring on employment shares or wage bill for skilled and unskilled workers is quite extensive. For example, for the United States, Feenstra and Hanson (1996, 1999) estimate that the observed increase in the nonproduction worker share of the wage bill can be explained by off-shoring. Hijzen et al. (2010) show that international outsourcing had a strong negative impact on the demand for unskilled labour in the UK 2. While part of the literature emphasises the negative effects of globalisation because it potentially deteriorates the employment prospects in developed countries (Wood 1995), more recent contributions highlight its beneficial effects. For example, Bloom et al (2009) argue that, by freeing resources usually devoted to the production of low tech products, globalisation has allowed developed countries to focus more intensively on high tech production. Their results document the positive impact of manufacturing imports from China on technical change in European firms. Subcontracting of material activities to foreign producers is likely to enhance the productivity of home factors. Off-shoring is a firm s decision, which is likely to incorporate general equilibrium effects. At the micro level, a rationally behaving firm chooses to outsource some activities foreseeing the possibility of increasing profit margin due to lower cost of production. From this point of view, off-shoring is expected to have a positive impact on productivity. In the short run, this leads to a reduction in production volumes and inputs utilization, primarily low-skilled workers. Over a longer horizon, however, we can observe a stabilization in resource reallocation across sectors as well as a rise in value added or employment, due to increases in labour productivity associated with off-shoring; this may occur especially in high-tech industries where knowledge-based activities (R&D and ICT investment) continuously fuel the capacity of firms to compete on the international market. In recent years, off-shoring has spread from the manufacturing to the service sector, starting particularly from the beginning of the 1990s (Crino, 2008). This has been a consequence of the adoption and diffusion of ICT that have made the tradability of services feasible. On the other hand, when international outsourcing of service activities is pronounced it is difficult to ascertain the intensity of deindustrialization as input reallocation 2 See Crino (2008) for a survey. 6

8 occurs in favor of foreign sectors and we do not see changes in employment or output shares. From this standpoint, adopting a detailed industry breakdown in studying the inner mechanisms of structural change and specialization seems particularly important. 3. Analytical framework Our starting point for analysing the sources of specialisation is the standard theory of international trade. We assume that countries are endowed with production factors used to produce final goods. Production exhibits constant returns to scale and, at an industry level, firms operate under perfect competition in both product and factor markets. Our theoretical framework follows Dixit and Norman (1980) and recent extensions by Harrigan (1997) and Redding et al. (2006). The model assumes the following revenue function of country c: Q = F( P, V ) (1) c, t c, t c, t where F (.) is the economy c s revenue function including prices of final goods, P c,t, and factor endowments, V c,t. Subscript t indexes time. Given that c s revenue function is continuous and twice differentiable, the economy s vector of profit maximizing net output is given by: q c, t f (.) = (2) p Harrigan (1997) augments equation (1) with a technological parameter θ, which represents technological differences at the industry level. This parameter introduces technology in a Hicks-neutral manner implying that, with the same amount of inputs, industry i in country c at year t is θ times more productive than a reference point 3. Following this approach, we rewrite the revenue function (1) as follows: f ( θ p, v ) (3) c, t c, t c, t This productivity formulation implies that the effect of technology on output acts similarly to industry-specific prices. Next we follow Woodland (1982) and Kohli (1991) and we approximate our revenue function with a second order translog function: 3 In the construction of the technological parameter, we show that the reference point is an arithmetic mean of all observations included in the sample. 7

9 1 ln r( Θ p, V ) = a + a lnθ P+ a lnθ P lnθ P 00 0 i i i i, k i i k k i 2 i k 1 + b lnv + b lnv V + c lnθ P lnv 0 j j j, µ j µ i, j i i j j 2 j µ i j (4) where the summations over i and k run from 1 to K industries (i.e. i k) and the summations over j and µ run from 1 to M factor endowments. Symmetry in cross-effects require that a i, k a and b j, µ = b µ, j for all i,k,j and µ. Furthermore, linear homogeneity of the revenue = k, i function implies that: a 0i = 1, b 0i = 1 i i, a i, k = 0, b j, µ = 0, b i, j = 0. k j j Differentiating (4) with respect to industry price P i, we obtain the following benchmark equation of specialisation that determines the share of industry i s value added to GDP as a function of prices, technology and nation-wide factor endowments (time and country subscripts have been removed for expositional convenience): (5) s = a + c lnv + a ln P + a lnθ i, c, t 0 i i, j c i, k k i, k k j k i Assuming that differences in relative prices can be replaced by a set of time dummies, we arrive at the specification of the model estimated in Harrigan (1997), where value added is a function of relative productivity and factor endowments: (6) where is a well behaved error term. Equation (6) represents our benchmark model. National endowments of labour and capital also account for different types of skills within the working age population, as in Harrigan (1997) and Nickell et al. (2008). A first extension of equation (6) will focus on different types of capital assets and will include endowments of non-ict and ICT capital as well as endowments of intangible assets (R&D capital), following previous work by Bournakis and Vecchi (2010). A further development of our analysis is to introduce the assumption that technological parameter θ is a function of the off-shoring activities undertaken by industry i, as well as its innovative and intangible inputs (ICT, human capital and R&D). Off-shoring the 8

10 can affect productivity by allowing a more efficient use of resources, increasing the varieties of services available to a firm/industry, and promoting exchanges of know-how across countries (Crino 2009). The impact of various forms of intangible assets on productivity has been documented widely both on a theoretical and empirical ground (Griffith et al. 2003a and 2003b, Vandenbusshe et al. 2006). We can thus write technology as: θ,, = g,, ( G, Χ ) (7) i c t i c t where G stands for a measure of off-shoring activities in industry i and X denotes a vectors other technology determinants. One can represent equation (7) in logarithm form as: n n i, c, t = i ln Gi, c, t + i xi, c, t + i, c, t lnθ β γ ω (8) The summation term on the right hand-side indicates the elasticity of technology with respect to the n industry level factors that affect productivity and ω is a stochastic measurement error. Combining equations (6) with equation (8) we can re-write the value added share equation as follows: (9) Off-shoring affects industry level productivity as captured byβ and these changes in productivity have general equilibrium effects through the coefficients a i, k. Simplifying further equation (9), we can obtain our empirical specification where the value added share in industry i is a function of factor endowments, productivity and off-shoring activity as follows: (10) i where δ = ai, kβi and λ a, n n = i k γ i. Parameters δ and λ represent the indirect effects of i i off-shoring and other productivity determinants on specialisation. To analyse the impact of factor endowments, productivity and off-shoring on structural change, following Rowtorn and Ramaswami (1999) we also reformulate both our benchmark model (equation 6) and equation (10) using employment shares as the dependent variable. 9

11 4. Data and descriptive analysis 4.1 Data sources The empirical analysis is based on a sample of 18 industries (12 Manufacturing industries and 6 Service industries) for US, Japan and six EU countries (Denmark, Finland, Germany, Italy, Netherlands and UK). To perform our analysis we use a set of industry level as well as national level data. Most of the industry level data on output, employment shares and productivity are derived from the EUKLEMS data base. Data on labour endowments, classified according to three groups of educational levels, come from the Barro-Lee (2001) data set. R&D data is derived from various versions of OECD ANBERD and Science and Technology database. By national endowment of R&D, we mean total business R&D (BERD). Our measure of Hicks-neutral technology is Total Factor Productivity index, A. The construction of this index follows the methodology suggested by Caves et al. (1982), van Ark and Pilat (1993) and Harrigan (1999). The derivation of this index is based on the assumption that value added is produced by two heterogeneous inputs, labour (L), and capital (K). The methodology adopted in this analysis accounts for differences in quantity and quality of the inputs in the different countries. The current measure of TFP is based on the standard neoclassical assumptions of perfect competition and constant returns to scale. TFP in each country is expressed relative to a hypothetical frontier or reference country. The latter is the average level of TFP in the eight countries in each industry. Assuming a Cobb-Douglas production technology, for each industry i of country c at year t, the production function is as follows: (11) We define the production function of the reference country as: (12) The bar over a variable indicates the geometric average of all observations in an individual industry i for year t. Therefore, the logarithmic expression of RTFP (relative total factor productivity) is given by: 10

12 (13) The labour share α is measured as the ratio of labour compensation to value added. The weighted variable is the labour share s arithmetic mean of all observations in industry i at year t. The EU-KLEMS database from the Groningen Growth and Development Centre (GGDC) is the main data provider for the construction of TFP. To obtain a meaningful measure of RTFP, we convert value added, labour and capital compensation and investment in capital assets into international US Dollars using the GDP purchasing power parity (PPP) exchange rate reported by the World Bank Development Indicators - International Comparison Project (ICP). 4 Finally, we express all values in 1995 constant prices using the industry price deflators of the EU-KLEMS data base. Labour input in equation (12) accounts for heterogeneous labour by aggregating three types of workers identified according to their educational attainment (low skill, intermediate skill, and high skill) 5, weighted by the share of each type of in total labour compensation. Similarly the construction of the capital stock is obtained by aggregating ICT and non-ict assets, weighted by the share of each asset in total capital compensation 6. Our indicators of international outsourcing (off-shoring) intensity at an industry level are built following the common practice originally proposed by by Feenstra and Hanson (1999, 2003): (14) where III it are imported intermediate inputs, NE it total purchases of non-energy inputs (materials and services) by industry i at time t on both the domestic and foreign markets. When a full set of Input-Output matrices is available, III it can be extracted from the import matrix, NE it from the use matrix. When IO matrices are not available on a yearly base, III it can be estimated as follows under a proportionality hypothesis (assuming only one tradable good): 4 There are limitations with the use of a GDP PPP-exchange rate conversion method if one takes into account that prices differ across sectors in the economy. Provided that PPP-exchange rates for a disaggregate industry level are not available for a long time series, we believe that the method used is the best alternative. 5 The division of labour according to the level of educational attainment can cause some problems as the educational system has been subject to changes over time. The method used from EU-KLEMS ensures that this division is consistent over time for each country. See also O Mahony and Timmer (2009). 6 For more details on the construction of the Relative TFP index, see Bournakis and Vecchi (2010). 11

13 . (15) III c,t are total (economy-wide) imports of the tradable good (available on a regular base), which are multiplied by the share of industry i on total (economy-wide) imports in a benchmark year b. τ c,b is given by the ratio between III c,i,b and III c,b which are taken from the available IO matrix at the benchmark years, b=1995, 2000 and For missing intermediate years, τ is linearly interpolated, while values for the pre-1995 period are backwardly extrapolated from the levels of 1995 by applying the changes of rate of the period Non-energy expenses for intermediate inputs, NE c,i,t are taken from EU KLEMS database (Crino, 2008), and exclude fuels and mining products. In our empirical analysis, we distinctly use a measure disentangling imports of materials from private services (respectively denoted by M and S), so that the expression (13) can be re-worded as follows: (16a) (16b) The main shortcoming of the broad indicators illustrated in the previous section is that they include all sector purchases of intermediates. A finer indicator can be obtained by considering only within-industry transactions, i.e. intermediate imports of domestic (manufacturing or service) industry j from foreign (manufacturing or service) industry i. As the most relevant efficiency gains of off-shoring derive from production tasks outsourced abroad to businesses within the same industry of the relocating firm, we also use a narrow index collecting purchases of intermediate inputs from the same foreign industry. We therefore construct two additional narrow indicators defined as follows. (16c) As a consequence, in the specification based on both broad material/services measures of offshoring (MOS/SOS) and the narrow one (NOS), the former are net of the latter value, and thus have to be regarded as differential off-shoring indicators (, see ). The importance of including either measure is to separate the effect on specialisation of international 12

14 outsourcing from structural change (de-industrialisation); the latter leads the share of services on GDP to rise over time. Data on total imports distinguished by goods type come from Bilateral Trade Database (various releases); for trade by services categories we refer to OECD EBOPS database which, whenever necessary, has been integrated with UNCTAD series. All variables are expressed in current prices; national currencies have been converted into US dollars exploiting OECD bilateral exchange rates. Note that in robustness check we also normalize imported intermediate inputs on value added in place of non-energy inputs expenditure. 4.2 Descriptive statistics Table 1a shows industry shares of total value added (GDP); we present the percentages at the initial year on the left-hand side, the average annual rate of change observed over the period ( ) on the right-hand one. Most of these countries underwent significant structural changes in the 1980s (Brakman et al. 2010), and a large fraction of market economy was represented by service industries at the beginning of the Nineties. The largest sector in 1990 was wholesale and retail trade, with a GDP share ranging from 10.1% in Germany to 14% in Italy; the second largest sector in most countries is business services, which has also experienced the largest rate of growth over the period. In manufacturing, electrical equipment was the leading sector in Japan, Germany and US; food, beverage and tobacco in Denmark, Netherlands and UK, while wood in Finland and textile in Italy. Looking at country variation of industry shares, a larger heterogeneity emerge in manufacturing. On the one hand, if we look at high-tech productions, the shares of transport equipment range from 3.5% in Germany to 0.8 in Denmark, while electrical equipment ranges from 4.8 of Japan to 1.6 of Denmark. On the other hand, among low-tech sectors, the extension of textile in Italy is four times larger than in the Netherlands. As a result of the deepening of deindustrialization, the share of service sector grew rapidly in all the OECD area. Business services expanded at a faster rate in all countries but Japan where the growth of financial intermediation was the most noticeable. In manufacturing, most sectors lost ground with respect to service industries. The most dynamic performance is exhibited by Electrical equipment in Finland (+6.23%) and Chemicals in Denmark (+1.96%). 13

15 Table 1a: Industry share on GDP ( ) VALUE ADDED SHARES ON GDP (1990) RATE OF CHANGE DNK FIN GER ITA JPN NLD UK US DNK FIN GER ITA JPN NLD UK US 15t16 FOOD, BEVERAGES t19 TEXTILE, LEATHER WOOD AND CORK t22 PULP, PAPER, PRINTING CHEMICALS RUBBER AND PLASTICS OTHER NON-METALLIC MINERALS 27t28 BASIC METALS, FABRICATED METAL PRODUCTS MACHINERY, NEC t33 ELECTRICAL AND OPTICAL EQ t35 TRANSPORT EQ t37 MANUFACTURING NEC G WHOLESALE, RETAIL TRADE H HOTELS AND RESTAURANTS 60t63 TRANSPORT AND STORAGE POST,COMMUNICATIONS J FINANCIAL INTERMEDIATION t74 BUSINESS SERVICES

16 Table 1b: Industry share on total employment ( ) EMPLOYMENT SHARES(1990) RATE OF CHANGE DNK FIN GER ITA JPN NLD UK US DNK FIN GER ITA JPN NLD UK US 15t16 FOOD, BEVERAGES t19 TEXTILE, LEATHER WOOD AND CORK t22 PULP, PAPER, PRINTING CHEMICALS RUBBER AND PLASTICS OTHER NON-METALLIC MINERALS t28 BASIC METALS, FABRICATED METAL PRODUCTS MACHINERY, NEC t33 ELECTRICAL AND OPTICAL EQ t35 TRANSPORT EQ t37 MANUFACTURING NEC G WHOLESALE, RETAIL TRADE H HOTELS AND RESTAURANTS t63 TRANSPORT AND STORAGE POST,COMMUNICATIONS J FINANCIAL INTERMEDIATION t74 BUSINESS SERVICES

17 Similarly to Table 1a, Table 1b presents industry shares of total employment, in levels for the initial year of our sample and in rates of growth (average changeover the period ). Employment shares are quite similar across countries, especially in the manufacturing sector. Particularly large is the size of the textile industry in Italy (4.26%) if compared with the country with the smallest share (the Netherlands with a share of 0.49%). The textile industry is in fact the third largest sector in Italy, after post and telecommunications and transport and storage. In all countries we notice that the majority of service industries are characterized by the largest employment shares within each country as well as by positive rates of growth in the period under consideration. The average rate of growth of the manufacturing shares, on the other hand, testify the decline of most industries with only a couple of exceptions, such as the chemical and the electrical equipment industries in Denmark and Finland. If one compares industry share of GDP and total employment, there emerges a large gap in most service sectors (excluding wholesale) where, due to measurement issues of sectoral output, labour productivity is downsized and this depresses GDP shares with respect to employment. In manufacturing, large disparities can be identified in textile of Italy and Japan, where employment shares remarkably exceed value added shares. The reverse holds in such capital intensive industries as chemicals or transport equipment. It should be observed that, probably due the revival in labour productivity, high-tech industries exhibited a faster expansion of output share in the last fifteen years. To analyse the cross-country and cross-industries differences in relative TFP, Table 2 presents the TFP gap at the beginning and at the end of our sample period (1990 and 2005). The TFP gap is computed as follows: (16) where RTFP f,c,t is the country with the highest TFP relative to the average in sector i at time t (frontier country). Consistently with Harrigan (1999), the United States is the frontier country in most industries throughout our sample, particularly in the last year where it turns out to be the leader in 12 out of 18 industries. It is surprising that the leadership position is mainly among manufacturing industries; in this branch of the economy, the relative technological performance of US deteriorates only in Wood where is overtaken by Finland. On the other hand, among service industries, the US lost ground in Post and Telecommunication in 2005 to the Netherlands, and in Business Activities where Germany became the frontier country. In comparative terms, the distance of Italian industries from the technology frontier increases in 16

18 all production fields but Business Services. Note that, as a result of market deregulation policies undertaken in last years, the majority of EU countries improved their relative technological performance; Finland is the only EU member state where the distance reduced more in manufacturing than service industries. Table 3 shows both the nation-wide and industry factor endowments. Industry-level variables are weighted by their share on total value added in The left-hand side panel of the table reports the average value of the variables at the beginning of the period, while the left-hand side panel shows the average annual rate of change over the period At an economy-wide level, the US stand out for the highest initial level of ICT capital input per worker (3.6 billions of 1995 dollars), followed by Italy and Germany; on the other tail of the distribution, we find Denmark and Finland. Most laggard countries were characterized by a faster ICT capital deepening between 1990 and 2005 and thus caught up the adoption levels of forward economies. 7 When we look at non-ict capital-labour ratio, there is a large crosscountry differential which is likely to as reflect the different structure of the examined economies. The national endowment of knowledge capital confirms the prominence of US in innovation activities (4.6 billion dollars per worker). At an economy-wide level, Finland exhibits the most rapid increase in R&D between 1990 and 2005 (9.5% per year). The US also dominate in terms of share of population with medium and higher education (89%), half of which is represented by people with tertiary education. At the bottom of the ranking, we find Italy (40.5 and 9%) which partly filled the gap with the other OECD member states over time. The bottom panel of Table 3 reports the industry-level (un-weighted) average values of the variables. Note that when industry-specific factor endowments are higher than the economy-wide one means that this type of input is more intensively adopted in smaller sectors (as for ICT and R&D capital); the reverse holds when the factor endowment of an average industry level falls below the national mean (non-ict capital). When looking at offshoring indicators it can be observed that in 1990 the Netherlands were the most intensively engaged in international outsourcing of production tasks that takes place between the same types of sectors: on average, Dutch industries off-shored to foreign counterparts about 14.9% of total intermediate inputs (narrow off-shoring). 7 For the G7 countries of the sample, our results are consistent with Jorgenson (2005). 17

19 Table 2: Analysis of productivity frontier, 1990 and t16 17t t t t33 34t35 36t37 G H 60t63 64 J 71t74 Country Year Food, Beverages & Tobacco Textile Wood Pulp, Paper, Printing Chemicals Rubber & Plastics Non Metallic Mineral Basic Metals Machinery NEC Electrical equip. Transport Equip. Manuf. NEC Wholesal e & Retail Hotels Transport & Storage Post & Telecoms Financial Intermediation Business Activities DNK FIN GER ITA JPN NLD UK US Figures represent the exponential of the TFP gap. A value of 100 identifies the country/sector leader. 18

20 Table 3. Factor endowments LEVELS 1990 RATE OF CHANGE DNK FIN GER ITA JPN NLD UK US DNK FIN GER ITA JPN NLD UK US ECONOMY-WIDE FACTOR ENDOWMENTS ICT capital per worker (billions USD 1995) Non-ICT capital per worker (bill. USD 1995) R&D capital per worker (bill. USD 1995) Population share with secondary/tertiary education (%) Workers (millions) INDUSTRY FACTOR ENDOWMENTS ICT capital per worker (billions USD 1995) Non-ICT capital per worker (bill. USD 1995) R&D capital per worker (bill. USD 1995) Imported material intermediate inputs over total input expenditure (%) Imported service intermediate inputs over total input expenditure (%) Own-industry imported intermediate inputs over total input expenditure (%) Industry-specific factor endowments are weighted with employment shares at

21 The higher propensity of the Netherlands to off-shore intermediate is confirmed even when we consider transactions of material or service intermediates (i.e. phases of production moved abroad to other types of industries; broad off-shoring). It should be emphasized that the narrow type of international outsourcing sizably increased in all OECD countries, whereas the picture on material or service (differential) off-shoring is mixed. Table 4 shed lights on cross-industry differentials on factor endowments. Consistently with Table 3, we present un-weighted mean levels at 1990, and their rates of change over our sample period. Service sectors are particularly intensive of ICT capital, primarily Post and telecommunications and financial intermediation (with respectively 37.9 and 7.8 billion dollar per worker in 1990). Among manufacturing only chemicals and electrical equipment denote comparable values. 8 The latter sectors as well as transport equipment are the most involved in knowledge generating activities; their endowment of R&D capital amounts to 44, 34 and 30 billion dollars per employee. Among the tertiary sectors, post and communications confirm their highly innovative profile, being characterized by an endowment of knowledge capital of 15 billon dollars. In the early 1990s, textile was the sector with the highest value of the narrow offshoring index, followed by chemicals (respectively 24 and 19% of total intermediate costs). Focusing on the broad differential measure of off-shoring, it emerges that the rubber and plastic industry was the most engaged sector in material outsourcing (29%), while business services strongly relied on the off-shoring of their non-material tasks (22%). As a last step of this descriptive section, we look at the dynamics of the industry-level determinants of specialisation patterns. Some key points are in order. First, ICT capital deepening has increased more than non-ict assets in all the OECD area, with homogeneous trends across sectors. Second, probably due to the low levels of the early 1990s, R&D capital input per worker expanded more in services than manufacturing. The intensity of material off-shoring (broad indicator) fell in most industries; this trend was most pronounced in transport equipment (-3.4% per year). The most relevant exception is wholesale for which the share of material inputs purchased grew by an annual rate of 3.4%; this may reflect the deep restructuring occurred in this sector and the building of a large international network in the distributive chain in recent years. 8 The industry profile on traditional capital intensity of production is described by the second column of the table. 20

22 Table 4 : Summary statistics at an industry level ICT capital per worker Non-ICT capital per worker LEVELS 1990 RATE OF CHANGE R&D capital per worker Broad Material Off- Shoring (%) Broad Service Off- Shoring (%) Narrow Offshoring (%) ICT capital per worker Non-ICT capital per worker R&D capital per worker Broad Material Off- Shoring (%) Broad Service Off- Shoring (%) Narrow Offshoring (%) 15t16 FOOD, BEVERAGES t19 TEXTILE, LEATHER WOOD AND CORK t22 PULP, PAPER, PRINTING CHEMICALS RUBBER AND PLASTICS OTHER NON-METALLIC MINERALS t28 BASIC METALS, FABRICATED METAL PRODUCTS MACHINERY, NEC t33 ELECTRICAL AND OPTICAL EQ t35 TRANSPORT EQ t37 MANUFACTURING NEC G WHOLESALE, RETAIL TRADE H HOTELS AND RESTAURANTS t63 TRANSPORT AND STORAGE NA NA 64 POST,COMMUNICATIONS NA NA J FINANCIAL INTERMEDIATION t74 BUSINESS SERVICES NA not available. 21

23 Consistent with the mains trends described in the literature, both the broad and the narrow measure of off-shoring are characterised by an increasing trend in most service industries. This increase is taking place at a faster rate compared to the manufacturing sector. 5. Econometric results 5.1 The role of productivity and factor endowments. We start by showing industry-by-industry results for our benchmark model (equation 6) which follows Harrigan (1997). We compare results based on Ordinary Least Squares (OLS) vìs-a-vis with findings yielded by a Seemingly Unrelated Regression estimator (SUR). The results are presented in Table 5. The SUR estimator is more efficient than OLS because it controls for the contemporaneous dependence among industries by accounting for contemporaneous cross-equation error correlation. For each specification, we present results obtained using value added shares and employment shares as dependent variables (Table 5a and 5b respectively). It should be noted that the coefficients of the regressors expressed in logs have to be interpreted as semi-elasticities. Table 5, Panel a shows that, in most sectors, the impact of relative TFP on value added shares is positive and statistically significant, as predicted by the theory. Coefficient estimates are particularly high for electrical equipment, wholesale and retail and financial intermediation, where a 10% increase in relative TFP generates an increase in VA shares of respectively 0.2, 0.3 and 0.17%. For most sectors, our results are consistent with Harrigan (1997) with the exception of the chemical industry where we e find a lower effect of productivity. This may be due to the more recent time period covered in our analysis, during which this sector was characterized by either a lower fertility of innovation or a slower technological progress with respect to the 1970s and 1980s (Chakrabarti 1990). Endowment of total capital has a negative impact on the VA shares of most manufacturing industries; this occurs as these sectors are shrinking compared to the tertiary industries which, as we showed, are less capital intensive. Conversely, total capital is positive for wholesale and financial services. Endowment of skilled workers substantially increases the value added shares of service sectors while, in manufacturing, we only identify a positive effect in machinery. On the other hand, the impact of unskilled workers is either non statistically significant or negative in services. This group of workers appears to have a stronger effect in manufacturing industries. Our estimates for the impact of skilled/unskilled workers on specialisation patterns closely mirror the relative intensity of blue/white collars of the secondary and tertiary sectors. 22

24 The results obtained using the Seeming Unrelated Regression estimator broadly confirm the OLS ones. A relevant difference can be found in the role of skill labour endowment which now becomes significant for electrical equipment suggesting that a 10% increase in skilled workers leads to a 0.05% rise in the industry share on GDP. The results for the employment share equations are presented in Table 5b. Similarly to table 5a, the SUR estimates are consistent with OLS so we will mainly refer to the latter in the following discussion. Our results show that relative productivity mainly contributes to decrease employment shares in most industries. This is consistent with the view that TFP improvements positively affect output per worker and, at least in the short-run, crowd out employment levels. However, we also observe that in three high-tech manufacturing industries (machinery, electrical equipment and transport equip.) productivity improvements have positive effects on both value added and employment shares. This is likely to be the consequence of a fast expansion of the final demand for high-tech products, which are characterised by a high substitution effect between old and new vintages (Hummel 1999). Hence, our results are consistent with the theoretical prediction of Pissarides and Ngai (2007), whereby increases in productivity will increase employment shares in those sectors characterised by high degree of substitution among final products. Relative TFP, on the other hand, negatively affects the employment shares of all service industries. Turning the attention to factor endowments, increasing availability of capital assets at the national level has a negative and significant impact in most manufacturing sectors, suggesting the presence of capital-labour substitution; with the exclusion of the chemical industry this effect appears to be non-linear as it is increasing in the level of capital intensity of the sectors (see Table 4 above). On the other hand, our results suggest the presence of capital-labour complementarity in services where larger capital endowments are associated with higher employment shares. The endowment of skilled labour positively affects the employment shares in several manufacturing industries and the effect is particularly strong in basic metals, machinery and electrical equipment. This finding further supports our previous conclusions regarding the effect of RTFP: because of the complementarity between technology and skilled labour the latter has a positive impact on the employment share of high tech industries. 23