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.

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

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.

An Input-output Analysis of China’s Innovative Resources based on Knowledge Production Function

Under the background of the gradual disappearance of China’s population dividend and the increasing restriction of resources and environment, economic development urgently needs to change from “factor driven” to “innovation driven”. Research on the impact of innovation resources on economic growth is the key to the implementation of innovation driven strategy. According to the knowledge production function, innovation resources are divided into innovation material resources, innovation knowledge resources and innovation human resources. On this basis, the correlation and weight between the input and output of all kinds of innovation resources are investigated to explore the ideas and development direction of optimizing the allocation of innovation resources in China, and to provide practical basis for the formulation of relevant innovation incentive system.