Innovation and corporate growth in the evolution of the drug industry

Bottazzi, Giulio; Dosi, Giovanni; Lippi, Marco; Pammolli, Fabio; Riccaboni, Massimo

doi:10.1016/s0167-7187(01)00068-6

Cited by 270 publications

(164 citation statements)

References 15 publications

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“…However, the hazard function for the Laplace distribution is eventually constant (see Table 1) and Theorem 2d implies that, for such a noise distribution, the proportion of high skill agents does not increase over time, as a result of repeated selection, but reaches a plateau. This result may be relevant to explaining the puzzling weak association between productivity and growth (Bottazzi et al, 2001(Bottazzi et al, , 2010: repeated selection based on growth will not lead to an increasing proportion of highly productive firms.…”

Section: Stock-market Returns Fit a T-distribution (Blattberg And Gonmentioning

confidence: 86%

“…Our results also have interesting implications for firm growth rates. Evidence suggests that firm growth rates follow a Laplace distribution (Bottazzi et al, 2001;Bottazzi & Secchi, 2003) 3 with a low degree of autocorrelation (Coad, 2007). The Laplace distribution is heavy-tailed distribution which is not 'long-tailed'.…”

Section: Stock-market Returns Fit a T-distribution (Blattberg And Gonmentioning

confidence: 99%

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When More Selection Is Worse

2017

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We demonstrate a paradox of selection: the average level of skill among the survivors of selection may initially increase but eventually decrease. This result occurs in a simple model in which performance is not frequency dependent, there are no delayed effects, and skill is unrelated to risktaking. The performance of an agent in any given period equals a skill component plus a noise term. We show that the average skill of survivors eventually decreases when the noise terms in consecutive periods are dependent and drawn from a distribution with a 'long' tail -a sub-class of heavy-tailed distribution. This result occurs because only agents with extremely high level of performance survive many periods and extreme performance is not diagnostic of high skill when the noise term is drawn from a long-tailed distribution.

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Section: Stock-market Returns Fit a T-distribution (Blattberg And Gonmentioning

confidence: 86%

Section: Stock-market Returns Fit a T-distribution (Blattberg And Gonmentioning

confidence: 99%

When More Selection Is Worse

2017

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“…Another example is the autocorrelation between growth rates at different points in time (Bottazzi et al 2001), although the empirical evidence in the latter case is not as conclusive as for the quantities mentioned previously.…”

Section: Size and Growth-rate Distributionsmentioning

confidence: 87%

“…If such information was available, many interesting questions could be studied. For instance, one could consider fused and divided organizations as one overarching entity, like the concept of a "superfirm" in Bottazzi et al (2001Bottazzi et al ( , 2002; Bottazzi and Secchi (2006b) and see which impact this has on growth statistics. Classical economic studies of firms look at such events, e.g.…”

Section: Discussionmentioning

confidence: 99%

“…Heavy-tailed growth patterns have been reported for example in pharmaceutical (Bottazzi et al 2001;De Fabritiis et al 2003;Fu et al 2005;Matia et al 2004), furniture, shoes and printing (Bottazzi andSecchi 2003a, 2006a), textiles (Bottazzi and Secchi 2003b), metals and chemicals (Bottazzi and Secchi 2003a) or food (Bottazzi and Secchi 2003a). Apart from these studies, little is known about the statistics of other types of organizations, for example publicly-driven ones.…”

Section: Sector and Industry Growthmentioning

confidence: 99%

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Sector, industry and inter-organizational movement statistics in the Stockholm Region: informing organizational growth models

Mondani

2018

Qual Quant

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Organizational growth processes exhibit interesting statistical regularities, chiefly the heavy-tailed pattern of the size and growth-rate (i.e. yearly change in size) distributions. In spite of its ubiquity, empirical studies of growth are often limited to private activities and specific sectors, and generative models on the other hand are built on simplified assumptions and only aim at reproducing stylized facts. In this study, we use a unique Swedish longitudinal database on employment in the Stockholm Region, to analyze the interplay between organizational growth statistics by ownership sector, industrial activity and interorganizational employee movements during a period of 14 years. We fit distributions for organizational size and growth rates. We find that the body of the aggregate growth-rate distribution is dominated by public sector growth, while the private sector dominates the tails. Industries with mostly public organizations tend to have a lognormal size distribution, while privately-owned industries are better fitted by a truncated power law. Growthrate distributions are fitted to an exponential power (Subbotin) distribution. We decompose the change in size into incoming and outgoing employee movements, and find that the distribution of aggregated movements is well approximated by a lognormal distribution. Most organizations that do not grow have however in-and outgoing movements, but these mostly cancel each other out.

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2013

Growth Curve Modeling

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Innovation and corporate growth in the evolution of the drug industry

Cited by 270 publications

References 15 publications

When More Selection Is Worse

When More Selection Is Worse

Sector, industry and inter-organizational movement statistics in the Stockholm Region: informing organizational growth models

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