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عنوان فارسی مقاله:

اثرات رشد بلند مدت سیاست تحقیق و توسعه


عنوان انگلیسی مقاله:

The long-run growth effects of R&D policy


سال انتشار : 2017



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بخشی از مقاله انگلیسی:


3. Empirical analysis 

Our empirical analysis is aimed at evaluating whether, and to what extent, R&D policy influences the long-run rate of productivity growth. In essence, we estimate a growth equation which includes the main determinants identified by the two strands of Schumpeterian growth theory, as described above. Our empirical specification is general enough to nest, under certain parameter conditions, either fully-endogenous growth or semi-endogenous growth and, hence, we are able to fully discriminate between these two classes of models.The empirical model is estimated within a dynamic panel data setting on twenty US manufacturing industries over the 1975–2000 period. In what follows, we describe the econometric methodology and the data. 3.1. Estimation procedure and econometric issues Following the extensive literature on tax policy and economic growth, we estimate an equation where the rate of productivity growth, gy, is a function of the innovation policy variable, (Lee and Gordon, 2005; Gemmell et al., 2011; Arnold et al., 2011). We augmentthis specification with the set of variables identified by the two strands of Schumpeterian growth theory as drivers of longrun growth, i.e., the rate of population growth, n, and the rate of knowledge growth, gA (or R&D intensity, s): gy,it = 0i + zit−1 + it, (4) where i denotes industries, t the year of observation, 0i are industry fixed effects, zit = {it, gF,it, gA,it} is a matrix of regressors, is a vector of the corresponding long-run parameters. it is an error term described below. Explanatory variables are one-year lagged with respect to gy to mitigate reverse causality problems. Although the limited time span in this paper (25 years) might not be suf- ficient to understand whether the growth effect of R&D policy is permanent or transitory, the time interval should be nevertheless sufficient to understand whether this effect is or not persistent (Gemmell et al., 2011). To infer , we estimate the growth equation by means of a novel technique of regression, the CS-DL approach (Chudik et al., 2016). This procedure considers a dynamic version of Eq. (4), expressed as an Auto-Regressive Distributed Lags (ARDL) model, and reformulates it in a way to avoid the bias in the long-run coefficients arising from the inconsistency in the parameter of the lagged dependent variable (Nickel effect). The main advantage ofthe CS-DL regression is that it yields more precise long-run estimates than ARDL when the time dimension of the data is not sufficiently long (less than 50 time observations). 



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کلمات کلیدی:

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