Total factor logistics productivity: A spatial approach to the Italian regions

The trilemma of innovation, logistics performance, and environmental quality in 25 topmost logistics countries: A quantile regression evidence

“…One possible reason is that large cities are more likely to be internationally connected than small municipalities. Using a spatial approach to the Italian regions, Barilla et al (2020) investigated the main variables affecting the Total Factor Productvity (TFP) in the transport and logistics sectors. They collected data covering the 2007–2015 period, estimating a regional production function.…”

China’s Industrial TFPs at the Prefectural Level and the Law of Their Spatial–Temporal Evolution

“…Recently, some research pointed out that the industrial TFPs calculated by the Solow residual method and its four expansions were not scientifically accurate because the spillover effects of the explained variable, the explanatory variable, and the random disturbance term are usually ignored in the estimation of the empirical production function [34,35]. Thus, spatial econometrists began to embed spatial econometric models into the calculation methods of TFP.…”

“…Tientao et al (2016) used the spatial Durbin model to capture technology spillover and calculate the TFP [34]. Barilla et al (2020) embedded the general nesting spatial model (GNSM) into the Solow residual method to calculate total factor logistics productivity [35]. However, this kind of embedded analysis is still in the stage of exploration and attempt, and its method logic is not entirely mature.…”

“…In the analysis of Tientao et al (2016), the embedded model was not a general formula of the spatial econometric model but the spatial Durbin model (SDM) [34]. In the analysis of Barilla et al (2020), although the general formula of the spatial econometric model had been taken into account, only four kinds of estimated results from the models of NSM, SAR, SEM, and SAC were reported [35]. Actually, as the general formula of the spatial econometric model, the GNSM could be degraded to seven other spatial econometric models, including NSM, SXL, SAR, SEM, SDM, SDEM, and SAC [36,37].…”

“…In this paper, GNSM will also be embedded into the Solow residual method to estimate the empirical production function and calculate China's industrial TFP at the prefectural level. In contrast with the analysis of Barilla et al (2020) [35], our study will provide scientific solutions for calculating industrial TFPs under the framework of both the Solow residual method and the spatial econometrics, and also choose an optimal model from all the estimated results of the potential models to determine the shares of the input factors. Moreover, the spatiotemporal difference and evolution law of the calculated industrial TFPs of China's prefectural cities will also be analyzed using Dagum's Gini coefficient and kernel density estimation.…”