1,721,594 research outputs found
Intangible Capital and Productivity at the Firm Level: a Panel Data Assessment
Full text linkThe econometric literature on measuring returns on intangible capital is vast, but we still know little about the effects on productivity of different types of intellectual capital (R&D and patents) and customer capital (trademarks and advertising).
The aim of this paper is to estimate the marginal productivity of different types of intangibles by relying on the theoretical framework of the production function, which we apply to a large panel of Italian companies. To this end, the European accounting system makes it possible to compare the impact on productivity of intangibles measured from expenditures (as usual in Anglo-American studies) with the impact of intangible assets reported by companies in their balance sheets (a measure which is available in the Italian context, for example, but less common in the literature).
Our results contribute two main findings to the literature. First, among the intangible components, the highest marginal productivity is that of intellectual capital, customer capital and intangible assets. Second, the use of accounting information on intangible investments is crucial to find high effects of intangible assets on productivity, while intangibles measured from expenses seem to play a more limited role. Preliminary results obtained from sub-samples mimicking the presence of spillovers deliver higher effects of intellectual capital on productivity, suggesting that intangibles’ social value is larger than the part we can estimate with individual firm data
Intangible capital and productivity: an exploration on a panel of Italian manufacturing firms
No full textThe aim of this paper is to investigate the relationship between intangible capital and productivity
performance for an unbalanced panel data sample of about 14,000 Italian manufacturing firms over the 18
years 1982-1999. We take advantage of detailed (and rarely available) accounting information on bookvalues
and investment flows for different components of intangible capital in an attempt to assess its
productivity, both relatively to that of tangible capital and in absolute terms. Contrary to most studies in the
existing empirical literature, we do not only rely on an extended Cobb-Douglas production function in which
intangible capital and tangible capital enter multiplicatively, which implies that the output elasticities of
these two types of capital are constant and that their elasticity of substitution is one. We confront this
specification to another Cobb-Douglas formulation in terms of an additive expression “total capital” (i.e., a
weighed sum of its tangible and intangible components), which implies a constant marginal productivity of
intangible capital relative to tangible capital (but varying output elasticities and an infinite elasticity of
substitution). This formulation, which is also often considered, leads to estimates that can be difficult to
reconcile with the estimates obtained with the more usual fully multiplicative Cobb-Douglas formulation (in
part because depending more crucially on the measurement of intangible capital and tangible capital they are usually less robust – see for example Mairesse and Sassenou, 1991 ). We thus also experiment with the
“simplest” encompassing formulation, in which “total capital” is expressed as a CES function of its tangible
and intangible components (implying also a constant elasticity of substitution, but possibly different from
being one or infinite).
Besides confronting different functional forms, we systematically investigate the empirical evidence
in the time-series dimension of our data, which allows to control simply for permanent forms of unobserved
firm’s heterogeneity at the cost of usually fragile estimates, and not only in the cross-sectional dimension,
where they are typically much more robust (and frequently more plausible as well). We do so mainly by
considering the traditional standard pooled total, within-firm and one-year and five-year difference estimates,
but also various GMM type estimators in an attempt to control for potential errors in variables and
simultaneity specification biases. We also further explore the robustness of our main results by comparing
them when using alternative measures of intangible and tangible capital as well as different samples or subsamples.
Although, as could be expected, all our results cannot be reconciled in full details, they convey
overall a rather consistent and reasonable picture. In particular, the time series type evidence is not strongly
at variance with the cross-sectional findings, and provides economically and statistically significant estimates
of the order of magnitude of the elasticity of intangible capital and the range of its marginal productivity
relative to that of tangible capital.
Going one step further, we also investigate between potential productivity differences between the
different components of intangibles as they are recorded in the firms’ detailed balanced sheets or
“capitalised” by us on the basis of the corresponding investment flows (recorded in firms’ current accounts),
and between what we can call intellectual capital (i.e. R&D expenditures; patenting and related costs;...) and
customer capital (i.e., advertising expenditures; trademarks and related costs;...). Although our results have
still to be investigated as thoroughly as when only considering intangible capital as a whole, they seem
promising.
On the whole, in spite of the great difficulties of measurement and contrary to the scepticism shared
by many firm analysts and economists, our findings clearly indicate that firms’ accounting data on intangible
capital stocks has real informative content. This should comfort the position of who do consider that
intangible expenditures should be and can be realistically treated as investments in practise (not only in
theory) and who advocate for the development and progress in measuring and reporting such expenditures
and in capitalising them in firms accounts--- and of course in national accounts as well
Exploring the Relationship between Intangible Capital and Productivity in Italian Manufacturing Firms
No full textThe aim of this paper is to investigate the relationship between output, employment, and
physical and intangible capital for an unbalanced panel of Italian manufacturing companies
during the 1982-1999 period. In Italy the literature on this issue is less developed, if
compared, as an example, to that for the US or for France. Hence, attention is devoted to test
whether our results are analogous to those presented in the existing empirical literature. A
second interest aspect, linked to the particular country analysed, is the Italian accounting
normative on intangibles. Differently from the Anglo-Saxon Generally Accepted Accounting
Principles (GAAP) and from International Accounting Standards (IAS), intangibles costs may
be capitalized as an asset, rather than directly expensed: an example is advanced R&D,
capitalised, versus basic R&D, expensed. Therefore, we try to disentangle the informative
content of intangibles assets compared to the one of intangible capital stock reconstructed
from direct expenses. Finally, the data-set we use reports detailed accounting information on
different intangible components. This allows us to explore the contribution to productivity of
intellectual capital (R&D and patents) and of customer capital (marks and advertising). The
relationship between firms’ productivity and intangibles appears both comparable with that of
other countries and robust to the use of alternative panel estimators
Exploring the Relationship between Intangible Capital and Productivity in Italian Manufacturing Firms
No full textWe investigate the relationship between intangible capital and productivity performance for an unbalanced panel data sample of about 14,000 Italian manufacturing firms over the 18 years 1982-1999. We take advantage of detailed (and rarely available) accounting information on balance sheets, current accounts and investment flows for different components of intangible. In fact, we can compare: (a) total intangibles; (b) what we call the “intangibles capitalised by us”, i.e. the intangible stock computed by capitalising the expenditures recorded in firms’ current accounts (this is the definition usually employed by the empirical literature of the issue); (c) the intangible capital as it is recorded in the firms’ detailed balanced sheets (a new definition of intangibles, in line with the Italian Generally Accepted Accounting Principles, GAAP); (d) what we call “intellectual capital” (i.e. R&D expenditures; patenting and related costs); (e) what we call “customer capital” (i.e., advertising expenditures; trademarks and related costs). The theoretical framework is flexible. We experiment with a CES in total capital production function, as well as with the two nested Cobb-Douglas production functions (the multiplicative and the additive). Besides, we systematically investigate a variety of specification of the production functions, from the least constrained - non constant returns to scale - to the most constrained one - total factor productivity. Finally, we also assess the robustness of results by comparing different panel estimating techniques, alternative measures of intangible and tangible capitals, and different samples. Our results confirm that firms’ accounting data on intangible capital stocks has real informative content
EVIDENCE FROM CROSS-COUNTRY ECONOMETRIC REGRESSIONS ON THE IMPACT OF R&D ON PRODUCTIVITY GROWTH: A first update of Guellec and Van Pottelsberghe (2001) on “R&D and Productivity Growth: Panel Data Analysis of 16 OECD countries”
No full textPh.D. research output in STEM: the role of gender and race in supervision
Full text linkWe study whether student-advisor gender and race couples matter for publication productivity of Ph.D. students in South Africa. We consider the sample of all Ph.D.s in STEM graduating between 2000 and 2014, after the recent systematic introduction of doctoral programs in this country. We investigate the joint effects of gender and race for the whole sample and looking separately at the sub-samples of (1) whitewhite; (2) black-black; and (3) black-white student-advisor couples. We find early career productivity differences: while female students publish on average 10% to 20% fewer articles than males, this is true mainly for female students working with a male advisor, not for those working with a female one. These disparities are similar, though more pronounced, when looking at the joint effects of gender and race for the white-white and black-black student-advisor pairs. We also explore whether publication productivity differences change significantly for students with a high, medium, or low “productivity-profile”, and find that they are U-shaped. Female students with a high (or low) “productivity-profile” studying with female advisors are as productive than male students with a high (or low) “productivity-profile” studying with male advisor
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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