An expanded knowledge production function

This paper uses a theoretical motivation for an Expanded Knowledge Production Function(EKPF) that encompasses both path dependence and spatial spillovers to search for evidences inBrazil using a Dynamic Spatial Panel Data approach. The purpose is to identify the determinantsof knowledge production in the 2005-2015 period as well as its temporal evolution, usinginnovation patents as proxies. Regarding its spatial distribution, we identified a North-Southdisparity for the knowledge production in Brazil, with Southeast and South producing alarge part of the country’s patents. Based on the EKPF, we confirmed the importance ofpath dependence and knowledge spillovers to explain the Brazilian innovation. In addition,population density, which generates Jacobian externalities and economies of agglomeration, isan important structural feature in the short run while the number of researchers in universitiesand an increased economic scale are essential to knowledge production in the long run.

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University & FDI knowledge spillovers and large & medium-sized industrial enterprises technology innovation — Knowledge production function model based on panel data

MSIE 2011 ◽

10.1109/msie.2011.5707449 ◽

2011 ◽

Author(s):

Wan Kunyang ◽

Yu Shujuan

Keyword(s):

Panel Data ◽

Production Function ◽

Knowledge Production ◽

Technology Innovation ◽

Knowledge Spillovers ◽

Knowledge Production Function ◽

Function Model ◽

Industrial Enterprises ◽

Model Based

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Revisiting the Spatial Autoregressive Exponential Model for Counts and Other Nonnegative Variables, with Application to the Knowledge Production Function

Sustainability ◽

10.3390/su13052843 ◽

2021 ◽

Vol 13 (5) ◽

pp. 2843

Author(s):

Isabel Proença ◽

Ludgero Glórias

Keyword(s):

Production Function ◽

Knowledge Production ◽

Exponential Model ◽

Model Specification ◽

Knowledge Production Function ◽

Socioeconomic Environment ◽

Explanatory Variables ◽

Spatial Spillovers ◽

Spatial Autoregressive ◽

Pseudo Maximum Likelihood

This paper proposes a two-step pseudo-maximum likelihood estimator of a spatial autoregressive exponential model for counts and other nonnegative variables; it is particularly useful for dealing with zeros. It considers a model specification allowing us to easily determine the direct and indirect partial effects of explanatory variables (spatial spillovers and externalities). A simulation study shows that this method generally behaves better in terms of bias and root mean square error than existing procedures. An empirical example estimating a knowledge production function for the NUTS II European regions is analyzed. Results show that there is spatial dependence between regions on the creation of innovation, where regions less able to transform R&D expenses into innovation benefit from knowledge spatial spillovers through indirect effects. It is also concluded that the socioeconomic environment is important and that, unlike public R&D institutions, private companies are efficient at knowledge production.

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