We tried a number of variants as sensitivity tests for the GIV results. We first fitted trainingintangiblecapital using estimation based on a pooled sample of all countries. This estimation suggests a strong assumption that there is no cross-country heterogeneity in trainingcapital ’s associations with instrumental variables. It is highly unlikely from our Chi2 tests using the SUEST. Moreover, the F test of IVs shows a weak fit even with much larger sample size than country by country estimation (F tests 2.42, Prob > F =0.0465) which is lower than the reference F test level (around 10) in Staiger and Stock (1997). In second stage regression, the fitted trainingcapital alone from the first stage regressions of all countries is insignificantly associated with labour productivity, while the training*ICT interaction terms are still positive and significant. We also tried more specifications with country*year, country*industry dummies in the first stage regressions for the pooled sample of all countries to test country-specific business cycle or country*industry fixed effects. All specifications with country*year or country*industry dummies gave much higher (and unrealistic) 2 nd stage coefficients maybe due to loss of too many degrees of freedom. Therefore, we think our main results are not overstating the case.
Although there is general agreement regarding the idea that entrepreneurship contributes to economic growth, how such a contribution occurs, and how important it is, continue to be open questions in entrepreneurship research. One of the methodological approaches to entrepreneurship and growth is that proposed by Audretsch and Keilbach (2004a). This consists of considering entrepreneurship to be a productive input that, together with labour and capital, contributes to the output of the economy, but with one important difference: entrepreneurship is a public good from which everyone in the economy can benefit without hampering the effectiveness of the use of the input by others. This approach has been applied in different institutional contexts such as Germany (Audretsch and Keilbach, 2004a,b,c & 2005; Audretsch et al., 2008; Audretsch et al., 2006; Mueller, 2006 & 2007), European regions (Bönte et al., 2008), Brazil (Cravo et al., 2010), the USA (Stough et al., 2008; Chang, 2011; Hafer, 2013) and the world (Laborda et al., 2011), among others. These studies provide evidence that regional entrepreneurship capital is positively related with regional production. The most commonly used indicators of entrepreneurship capital have been based on the number of firms (incumbent or new, in absolute or relative terms, or their respective growth rates over time).
Depreciation rates for ICT tangible capital are as in the EUKLEMS, which in turn follows Jorgenson et al. (2005). Depreciation is assumed to be geometric at rates for vehicles, buildings, plant and computer equipment of 0.25, 0.025, 0.13, and 0.40 respectively. As for intangible assets, they are assumed to be the same for all industries. We discuss depreciation in the context of intangible assets in more detail below, but the asset-specific depreciation rates for intangibles are as follows: 33% for software, 60% for advertising and market research, 40% for training and organisational investments, 20% for R&D (broadly defined, thus including Design, Mineral Exploration, Financial Innovation, Artistic Originals and non-scientific R&D). Given that the EU KLEMS database does not provide data on capital tax rates by country, industry and year, and that Timmer et al. (2010) point out that evidence for major European countries shows that their inclusion has only a very minor effect on growth rates of capital services and TFP, we did not introduce a tax adjustment.
The paper re-examines the “stylized facts” of the balanced growth in developed economies, looking specifically at capitalproductivity variable. The economic data is obtained from European Commission AMECO database, spanning 1961-2014 period. For a sample of 22 OECD economies, the paper applies univariate LM unit root tests with one or two structural breaks, and estimates error-correction and linear trend models with breaks. It is shown that diverse statistical patterns were present across economies and overall mixed evidence is provided as to the stability of capitalproductivity and balanced growth in general. Specifically, both upward and downward trends in capitalproductivity were present, while in several economies mean reversion and random walk patterns were observed. The data and results were largely in line with major theoretical explanations pertaining to capitalproductivity. With regard to determinants of the capitalproductivity movements, the structure of capital stock and the prices of capital goods were likely most salient.
On the other hand, the bank capital channel works out only if the following assumptions are satisfied (Francis and Osborne, 2009b:1): if banks do not have a sufficient capital buffer through which they could insulate themselves from the movements in the credit supply when regulatory changes occur; if capital enlargement is a costly process; if economic agents are highly dependable upon bank loan financing. If the phenomena of demand-driven and supply-driven credit rationing are taken into consideration, the macroeconomic effects of “adequate” capitalization might be an argument for a reasonable or, on the other hand, a more stringent criticism of the (supra)national prudential authorities. Thus, the importance of principles and practice of the occurrence of business and economic cycles is temporarily downgraded, while primacy in the explanations of movements in the aggregate credit and investment level is given to the effects of the capital requirements or to the supply side of the process. It is in this sense that the phrase capital crunch is used to indicate the cause of pro-cyclicality or, to be more precise, the contraction in credit activities, particularly in the case of more weakly capitalised banks and of loan categories assigned with high risk weights (for example, loans to small and medium-sized firms, which are anyway highly dependent on bank financing). However, the capital requirements are not the only relevant factor of the credit activity level and structure, and recent researches with inconsistent conclusions brought this issue sharply into focus. Gambacorta and Mistrulli (2004) empirically confirm that the bank capital might cause shocks in aggregate lending; Brissimis and Delis (2009) prove that bank specificities cannot be the main reasons for the aforementioned conclusions; while Berrospide and Edge (00) verify the modest impact of bank capital on lending. A solution to reconcile these contradictions is modelled by Miyake and Nakamura (007), who ascribe to capital regulation long-term stabilising effects that address the macroeconomic consequences of negative shocks to productivity. On the other hand, in the short term it can have a pro-cyclical effect, because of which the tightening of capital regulation needs timing precisely; this is the key practical implication of their research. Generally, there are two groups of conclusions present in this type of research: () the implementation of capital requirements did not induce the credit shock, () the implementation of capital requirements combined with another supply-side and with demand-side determinants of the credit level contributed to the development of the credit shock.
Although there is general agreement regarding the idea that entrepreneurship contributes to economic growth, how such a contribution occurs, and how important it is, continue to be open questions in entrepreneurship research. One of the methodological approaches to entrepreneurship and growth is that proposed by Audretsch and Keilbach (2004a). This consists of considering entrepreneurship to be a productive input that, together with labour and capital, contributes to the output of the economy, but with one important difference: entrepreneurship is a public good from which everyone in the economy can benefit without hampering the effectiveness of the use of the input by others. This approach has been applied in different institutional contexts such as Germany (Audretsch and Keilbach, 2004a, 2004b, 2004c, 2005; Audretsch et al., 2008; Audretsch et al., 2006; Mueller, 2006, 2007), European regions (Bönte et al., 2008), Brazil (Cravo et al., 2010), the USA (Stough et al., 2008; Chang, 2011; Hafer, 2013) and the world (Laborda et al., 2011), among others. These studies provide evidence that regional entrepreneurship capital is positively related with regional production. The most commonly used indicators of entrepreneurship capital have been based on the number of firms (incumbent or new, in absolute or relative terms, or their respective growth rates over time).
Human capital has long been seen as important in determining economic growth (Lucas, 1988). Countries may adopt and utilise technologies differently, depending on their skill endowments (Lewis, 2005; Acemoglu, 1998). Much research effort has been devoted to the issue of whether technical change is skill-biased and on the impact of information and communications technology (ICT) on the demand for skilled labour (e.g. Bartel and Lichtenberg (1987), Autor, Katz and Krueger, 1998, Machin and van Reenen, 1998). In a similar vein research has highlighted that organisational changes and other forms of intangible investment such as workforce training are necessary to gain significant productivity benefits from using ICT (Bertschek and Kaiser, 2004; Bresnahan, Brynjolfsson and Hitt 2002; Brynjolfsson, Hitt and Yang, 2002, Black and Lynch, 2001). Helpman and Rangel (1999) argue that technological changes may lead to an initial slow down because the diffusion process requires more education or training. Thus the overall skills of the workforce have to be higher for a successful diffusion, for which firms will have to replace the unskilled workers with the more skilled ones or with ones with higher educational qualifications. The literature on technology and organisational capital suggests that an important element of organisational change is retraining of the workforce.
micro and macro levels, have found evidence that firms invest sizable resources for purposes other than the accumulation of physical and human capital. 3 These investments could be con- sidered investments in intangiblecapital. But we need a more precise definition of intangible investments. In this regard I closely follow CHS. Their definition of investment is based on the idea that “any use of resources that reduces current consumption in order to increase it in the future qualifies as an investment”. They distinguish between tangible and intangible investments. In the tangible category they include the usual investments in structures, tools and machinery. For the intangibles, they identify three main categories of investment. The first category is computerized investment and consists mainly of computer software. The second category is innovative property, which is divided into two subcategories. The first subcategory is scientific R&D and consists of National Science Foundation’s industrial R&D series. The second subcategory is non-scientific R&D, which includes revenues of non-scientific commercial R&D industry, spending for new product development by financial services and insurance firms and cost of development of new product by the entertainment industry. The third category is economic competencies. This is also divided into two subcategories. The first subcategory is brand equity and consists of a fraction of the advertisement expenditure. The second subcate- gory is firm specific resources and includes a fraction of the cost of employer-provided worker training and management time devoted to enhancing the productivity of the firm. 4
The first main hypothesis argues that the intellectual capital strategies one of the functional strategies in modern organizations which seek to achieve excellence and high quality achievements. This argument is approved by Edvinsson and Malone who used the descriptive scales and measures to highlight the importance of the intellectual capital in modern organizations’ strategies by exploring the opinions and attitudes of the targeted samples. Despite the weakness in the questionnaire which was the tool of the study, the models related to the intellectual capital and the intellectual property were more accurate and easier to use in generating better results to justify the increase in the organizations’ financial return. The researchers applied a set of scales to convert knowledge into intellectual property and the intellectual capital model which measures every component of the intellectual capital components. They also applied a model to convert intangible assets into intangiblecapital and the methodological approach to evaluate intellectual assets. These models calculated some intangible cognitive assets such as: (patents, trademarks and copyrights). Nonetheless, these scales only calculate some components of the intangible knowledge assets, such as (patents, trademark, and copyrights), but do not calculate other factors such as trade secrets that are protected by the power of law in order to prevent converting them into public knowledge.
Abstract: The aim of this paper is to identify the economic sectors in the Greek economy which are dynamic in terms of their technological characteristics expressing its economic perspectives. To do this, the paper uses the Clustering Analysis methodology for grouping the various sectors of economic activity in Greece. The twenty-one sectors of economic activity are thus assembled into clusters presenting similar technology characteristics and the empirical results are discussed. The technical analysis is based on Growth Accounting methodology to estimate technological change, as well as labor and capitalproductivity in the various sectors of the Greek economy over the period 1988-1998. The results show that the various sectors of economic activity tend to form three (3) distinct clusters experiencing similar technological and growth characteristics. Meanwhile, the technological level, as measured through annual growth in Total Factor Productivity, has remained practically unchanged. Finally, technological change accounts for about 40% of economic growth, which is slightly lower compared with the relative performance of other O.E.C.D. countries.
The new society is characterized by major changes in the development of all activities in it, changes that are to comprise both existent and new activities required by the newly-forming process, because nowadays there is such a great value distorsion, with knowledge as the most important production factor, representing the bases of power exertion, generating productivity growth and ensuring business competitiveness.
Creativity is not just innate talent from birth or the Affairs of the field experience but also can be learned and taught. Someone who has an entrepreneurial talents can develop her talent through education because some are people who know the potential (traits) and learn to develop its potential to capture the opportunities as well as to organize his efforts in realizing his ideals and thus to become the entrepreneurial success is not enough to just have the talent but must also have knowledge of all aspects of the business which is practiced and to acquire the knowledge required education or training. This is the fundamental relationship of human capital and entrepreneurial creativity. This is in line with the results of research from Niels Bosma and Mirjam van Praag (2004)
Tacit knowledge: Knowledge which cannot be traced in documents and publication and are not formally available. Tacit knowledge is acquired through job training, joint activities, and special group effort. It can also be termed as personalized knowledge and perspective specific knowledge which is difficult to capture and articulate. The difficult tasks for intrinsic figuring out are how to identify, generate, share and manage it. It is mainly traced and expressed through a process of interface, deliberate, and trial and error encountered in practice'.
The third assumption, which is the key assumption in the paper, is that the other part of technological progress is innovation devoted to increasing the productivity of physical investment in producing new physical capital. Hence, in the model, the advances of new physical capital embody current technological progress. This assumption is crucial to simultaneously generate a positive covariation between R&D investment and future stock returns, and a negative covariation between physical investment and future stock returns. The assumption of embodiment captures the fact that successful innovations increase the productivity of equipment and machines and reduce the costs of production process (Levin and Reiss 1988, Cohen and Klepper 1996). For instance, in petroleum re…ning, biochemical industry, etc., more than two thirds of the total R&D expenditure is dedicated to innovations in reducing production costs. Likewise, a number of other industries, including petrochemicals, food and beverage manufacturing, semiconductor plants,