Technical Efficiency and knowledge dissemination

As broadly described in Gallié and Poux (2010), in the last two decades, R&D cooperation has attracted a considerable amount of attention. Many empirical studies, in economics or in management, have investigated the motives for and potential benefits of cooperation as compared to internal R&D. Cooperation enables firms to internalize knowledge spillovers, facilitates knowledge transfers between them (in particular between firms and universities), helps them gain access to complementary knowledge and technologies, generates scale economies of research, enables firms to speed the commercialization of new products or technologies, to avoid duplicative R&D efforts, to share costs and risk, to gain access to foreign or new markets. Since R&D collaboration, cooperation was most often captured as a homogenous object (i.e.

R&D cooperation vs. internal R&D)⁹.

At a given moment of time, when technology and production environment are essentially the same, producers may exhibit different productivity levels due to

8 This topic has been broadly examined in: Kokkinou A. (2011a) Innovation Policy, Competitiveness, and Growth: Towards Convergence or Divergence? in Patricia Ordonez de Pablos, W.B. Lee and Jingyuan Zhao (editors) Regional Innovation Systems and Sustainable Development: Emerging Technologies, Information Science Reference, Hershey, New York, pp. 187 – 201.

9 This topic has been broadly examined in: Kokkinou A. (2009b) Economic Growth, Innovation and Collaborative Research and Development Activities, στο ICBE 2009, 4^th edition.

differences in their production efficiency¹⁰. Within economic growth process, therefore, efficiency of productivity of resources becomes a critical element in economic growth, through utilizing the available, yet scarce, resources more productively.

Within this framework, productivity represents the estimation of how well a producer uses the available resources to produce outputs from inputs. However, the productivity theory literature has emphasized factors such as productive efficiency, mainly through technological spillovers, increasing returns, learning by doing, und unobserved inputs (e.g. human capital quality), whereas the empirical industrial organization literature has emphasized the degree of openness of countries to imports and industry structure (Koop, 2001)¹¹.

Innovation and technology is an important source of industry competitiveness through facilitating cooperation. In particular, they can improve collective processes of learning and the creation, transfer and diffusion of knowledge, critical for innovation.

Such cooperation and the networks that are formed help to translate knowledge into economic opportunity, while at the same time building the relationships between organizations which can act as a catalyst for innovation.

Following the main findings from literature survey, there are two complimentary sets of conditions need to be satisfied for industries to sustain productivity and efficiency in competitive environment. The first is that they must have suitable levels of both physical infrastructure and human capital. The second is that, in the new knowledge-based economy, they must have the capacity to innovate and to use both existing and new technologies effectively. Industrial and innovation policy is aimed at strengthening the competitiveness of producers by promoting competition, ensuring access to markets and establishing an environment which is conducive to R&D. As

10 Variation in productivity, either across producers or through time is thus a residual which Abramovitz (1956) characterized as ‘a residual of our ignorance’.

11 This topic has been broadly examined in: Kokkinou A. (2009a) Strategy for Entrepreneurship and Innovation Activities in the knowledge Economy in Women Participation and Innovation Activities:

Knowledge Based Economy, Women’s Press, New Delhi, India.

recognized, lack of innovative capacity stems not only from deficiencies in the research base and low levels of R&D expenditure but also from weaknesses in the links between research centers and businesses, and slow take-up of information and communication technologies. Knowledge and access to it has become the driving force of productivity, much more than natural resources or the ability to exploit abundant low-cost labor, have become the major determinants of economic competitiveness since it is through these that industries can increase their productive efficiency. Innovation, therefore, holds the key to maintaining and strengthening efficiency which in turn inessential for achieving sustained economic development.

These environmental factors are spatially confined externalities with different scales of influence. Some factors, such as the legal and cultural framework or large research institutes, operate mainly at national level, generating national systems of innovation (Lundvall, 1992), other factors, such as skilled labour supply and networks linking firms and support institutions have a more limited territorial span, and are the basis of regional systems of innovation (Braczyk et al., 1998).

As far as empirical modelling is regarded, estimation of production functions using OLS methods correspond closely with the neoclassical approach. Here, all producers use the best purpose technology, and any deviation in their output, positive or negative, is attributed solely to idiosyncratic productivity shocks. This leads to the interpretation of the Solow residual as a measure of TFP (Solow, 1957). By contrast, neo-Schumpeterian theory has generated a rich variety of empirical studies that attempt to identify both the evolution of the frontier and the catching up capacity of different countries and regions. These studies treat investment in physical capital as an exogenous process and thus, rather than looking at the dynamics of capital accumulation, they are centred on comparative analyses of TFP levels. Neo-Schumpeterian empirical studies can be divided into two main approaches, according to the econometric techniques used: The first approach is inspired by the work of Färe et al. (1994), who applied Data Envelopment Analysis (DEA) to a sample of OECD countries over a 10-year period. Kumar and Russell (2002) develop a related methodology, where the evolution of labour productivity is decomposed into physical capital accumulation (movement along the frontier) and increase of TFP; rise in TFP

results from a combination of technical progress (upward movement of the frontier) and catching up (movement towards the frontier).

The second approach is Stochastic Frontier Analysis (SFA), which decomposes the residuals of an estimated production function into an efficiency component, corresponding to a negative valued random effect having a skewed distribution, and an idiosyncratic zero mean zero skewness random error. SFA is relatively robust to random noise arising from measurement errors and erratic variations in the level of TFP, and can accommodate idiosyncratic productivity shocks. Further, by explicitly modeling departures from the frontier as a combination of inefficiency and idiosyncratic shocks, SFA offers unique and useful interpretation combining the neo- Schumpeterian and neoclassical approaches.

Because of these advantages, as in Bhattacharjee et al. (2009), SFA has emerged as the most popular methodology to study TFP at the firm level, either for crosssection comparison of efficiencies (Green and Mayes 1991), or analysis of efficiency dynamics using panel data (Tsionas, 2006), or for analyzing spatial influences on the efficiency of firms in specific industries (Coelli et al., 1999). SFA has also been applied to study TFP at the macroeconomic level, although less frequently. For example, Kneller and Stevens (2006), using panel data on manufacturing industries of OECD countries, analyzed the skewed component of the error term, representing the distance to the technological frontier, as a function of the levels of investment in R&D and human capital, which in turn are related to the absorptive capacity of the economic system. Neo-Schumpeterian theory applied to SFA implies a negative skewness in the distribution of TFP (Carree, 2002), while standard OLS assumes a symmetrical distribution. Therefore, the empirical observation in several studies that the cross-sectional distribution of TFP is positively skewed (Green and Mayes, 1991, Fritsch and Stephan, 2004) casts serious doubts about the validity of the theoretical approaches adopted and the consistency of the estimation methods¹².

12 These seemingly contradictory results have been explained as arising either from weakness of the frontier methodology, mainly concerning the lack of robustness with respect to violation of normality and measurement of skewness (Simar and Wilson, 2005), or from a notion of superefficiency (Green and Mayes,1991).

Bhattacharjee et al. (2009) explore the idea that the productivity enhancing positive component captures innovative activity raising certain industries above common productivity standards at specific times. In addition, there may be an omitted variables problem (Temple, 1999) where common shocks, like the global business cycle or new technology developed by the leaders, can drive spillovers across countries or regions.

In so far as technology transfer depends on technology gap with the leaders (Lucas, 2000 and Hultberg et al., 2004) which is in turn driven by technological progress in leading regions, technology transfer can be characterized by time-specific common factors. Moreover, according to Bhattacharjee et al. (2009), more explicit modeling of innovation, particularly investment in R&D, human capital, international technological spillovers and spatial diffusion are also to be considered.

Even though the vast majority of empirical approaches limit cross-country heterogeneity in production technology to the specification of total factor productivity (TFP), Eberhardt and Teal (2011) present two general empirical frameworks for cross-country growth and productivity analysis and demonstrate that they encompass the various approaches in the growth empirics literature of the past two decades.

Solow (1956, 1970) makes clear that the stylized facts for which this model was developed were not interpreted as universal properties for every country in the world.

In contrast, the current literature imposes very strong homogeneity assumptions on the cross-country growth process as each country is assumed to have an identical aggregate production function. (Durlauf et al., 2001). Eberhardt and Teal (2011) argue that there are a number of important reasons why the standard cross-country growth regression framework needs to be reconsidered. Intuitively, the heterogeneity in production technology could be taken to mean that countries can choose an

‘appropriate’ production technology from a menu of feasible options. Further, the cross-country heterogeneity in TFP relates to differences both in the underlying processes that make up TFP and in the impact of those processes on output.

Following Mankiw et al. (1992) most empirical studies put this down to the failure to account for forms of intangible capital (human capital, social capital) in the regression model. This belief has led to a growth empirics literature that for the most part

neglects technology-parameter heterogeneity across countries and simplifies dynamics. The mainstream literature favours ever more sophisticated statistical devices (Sala-i-Martin et al., 2004; Moral-Benito, 2009) and ‘general-to-specific’

automatic model selection algorithms (Hendry and Krolzig, 2004; Ciccone and Jarocinski, 2008) – to pick out the ‘relevant’ variables in an augmented Solow regression model with time-averaged variables, so-called ‘Barro regressions’. At the last count no fewer than 145 variables have been investigated in their impact on growth (Durlauf et al., 2005) and most were found to matter in at least some studies.

A number of papers, however, question this paradigm and have integrated considerations of parameter heterogeneity into their cross-country empirics, also considering the time-series properties of the data, an issue largely ignored in the standard cross-country growth regression framework. Their regression results and diagnostic tests for variable non-stationarity and parameter heterogeneity confirm their importance in the empirical analysis (Pedroni, 2007; Canning and Pedroni, 2008).

Martin and Mitra (2002) estimate industrial production functions for agriculture and manufacturing using Crego et al. (1998) data for 1967 to 1992. Martin and Mitra (2002) allow for differential TFP levels and growth rates across countries, modelled via country-specific intercepts and linear trend terms in a pooled panel estimation using annual data for around 50 countries. TFP growth is captured by the country trends and thus assumed to be constant over time and heterogeneous across countries (and industries). Martin and Mitra (2002) results indicate considerable variation in TFP growth rates between industries and across countries, with TFP growth rates in agriculture commonly in excess of those in manufacturing.

Martin and Mitra (2002) address the issue of heterogeneity in TFP levels and growth rates in a static pooled fixed effects model, which imposes common technology parameters across countries. However, the estimation equations for agriculture and manufacturing are static and no investigation of error correlation is undertaken to justify this choice.

Keeping production technology constant across countries may be seen as a less restrictive assumption when investigating more homogeneous sets of economies, such as the group of OECD countries. Arnold et al. (2007) empirically compare two rival growth models, the human-capital-augmented Solow model with two industries, using annual panel data from 21 OECD countries over the 1971–2004 period. Their empirical specification allows for flexibility in the short-run dynamics across countries, while imposing common long-run production technology. The latter is consistent with the idea that the OECD countries have access to common technologies and have intensive intra-industry trade and foreign direct investment (Arnold et al., 2007). Using annual data for 20 Italian regions from 1970 to 2003, Pedroni (2007) and Canning and Pedroni (2008) estimate their empirical model by industry, comparing results for the heterogeneous parameter group mean. However, given the relatively recent emergence of cross-section correlation issues in macro panels only a small number of empirical papers combine cross-section correlation in macro panel data with heterogeneous production technology, including work by Bhattacharjee et al. (2009) and Fleisher et al. (2010) on production in Danish regions and Chinese provinces, respectively, as well as work by Cavalcanti et al. (2009) investigating the

‘natural resource curse’ in a panel of 53 countries. Moreover, Eberhardt and Teal (2009a, b) analysed cross-country macro data for the manufacturing (48 countries, 1970–2002; UNIDO, 2004) and agricultural (128 countries, 1961–2002; FAO, 2007) industries, respectively.