The impact of R&D on factor-augmenting technical change – an empirical assessment at the sector level

Abstract

The aim of the paper is to quantify endogenous factor-augmenting technical change driven by R&D investments in a panel of 11 OECD countries over 198711 OECD countries over -2007. This paper contributes to the scant empirical evidence on the speed, sources and direction of technical change for various sectors and production factors. Assuming costminimization behavior, a CES framework is used to derive a system of equations that is estimated by a GMM system estimator. The estimated factor-augmenting technology parameters show that in most sectors, technical change was labor-augmenting and laborsaving. Statistically significant effects of manufacturing and services R&D were found on factor-augmenting technical change (with the highest R&D elasticities found in the high-tech manufacturing and transport, storage and communication sectors). Whereas 'in-house' R&D stimulates total factor productivity, R&D spilled over to other sectors has a capital-augmenting effect accompanied by a higher use of labor. The results of this study provide a starting point for incorporating endogenous factor-augmenting technical change in impact assessment models aimed at broad policy analysis including economic growth, food security or climate change. ARTICLE HISTORY

External finance and agricultural productivity growth

“…Our methodological contribution consists of proposing a theoretical model that explains how external finance affects productivity through the indirect impact on capital augmenting‐technological improvements, without imposing the direction of the impact of external finance on productivity a priori. This method has been previously applied in agricultural, environmental and R&D economic modeling, and widely used for computable general equilibrium models (Carraro & De Cian, 2013; Dudu & Smeets Kristkova, 2017; Khafagy & Vigani, 2022; Smeets Kristkova et al, 2017; Van der Werf, 2008).…”

“…We assume that the production of a representative farm is a two‐levels three‐inputs nested CES production function with factor‐specific technology parameters and CRS (Carraro & De Cian, 2013; Khafagy & Vigani, 2022; Smeets Kristkova et al, 2017; Van der Werf, 2008).…”

“…It is difficult to find external instrumental variables in the FADN that explain the growth rates of prices and are not correlated with growth rates of quantities at the same time, as both variables are highly correlated by default. Several studies have used the lagged values of the endogenous variables as instruments, such as Acemoglu et al (2008), Murtin (2013), Smeets Kristkova et al (2017), Jetter and Parmeter (2018), and Khafagy and Vigani (2022), however, using lagged endogenous explanatory variables as instruments is not without limitations (Bellemare et al, 2017; Reed, 2015). Reed (2015) suggests that using lagged explanatory variables is an effective strategy when they are correlated with the simultaneously determined explanatory variable, and are not included in the main estimating equation.…”

“…We chose lagged internal instruments of growth rates of prices that are correlated with the endogenous variables, and we tested the validity of the instruments using Hansen's J χ 2 for testing the overidentifying restrictions. We used only the first‐order lag to maintain a sufficient number of observations for all regions included in the examined time period, and there is no economic reason to assume that higher‐order lags would explain the values of endogenous variables better than the first‐order lag, especially that the endogenous variables here are first‐differenced (Smeets Kristkova et al, 2017, p. 395). We also tested for Bellemare et al's (2017) validity conditions for the whole dataset using fixed‐effect panel regression on each equation of the system of Equation ().…”

Effect of innovation capacity, production capacity and vertical specialization on innovation performance in China's electronic manufacturing: Analysis from the supply and demand sides

“…Another contribution of this paper is that we build an innovation performance decomposition model. Most models regarding innovation performance in the literature have used econometric models[ 28 , 32 , 33 ]. Notably, econometric models cannot identify on what level the changes of determinants affect innovation performance.…”