Technology spillover and TFP growth: A spatial Durbin model

Tientao, Aligui; Legros, Diègo; Pichery, Marie-Claude

doi:10.1016/j.inteco.2015.04.004

Cited by 64 publications

(32 citation statements)

References 35 publications

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“…Distances from technological frontier (Colino, Benito-Osorio, & Rueda-Armengot, 2014;)our proxy is the TFP level at current PPPs (USA ¼ 1); Human capital (Tientao, Legros, & Pichery, 2016;Tvaronavi cien_ e, Tarkhanova, & Durglishvili, 2018;Vandenbussche, Aghion, & Meghir, 2006)in our research expressed as mean years of schooling; Institutions (Balcerzak & Pietrzak, 2016;Coe et al, 2009; Janto n-Drozdowska & Majewska, 2015)in our study, the institutional environment is measured by selected governance indices of the World Bank: government effectiveness and political stability; Trade openness (Danquah et al, 2014;Filippetti et al, 2017;Fracasso et al, 2015) measured with the use of export of goods and services as percentage of GDP, import of goods and services as percentage of GDP; Knowledge stock (Coe & Helpman, 1995;Fracasso & Vittucci Marzetti, 2015;Madsen, 2008;Tientao et al, 2016) measured with the use of patent applications; Knowledge investmentsmeasured with the research and development expenditures as percentage of GDP; Capital stockquantified directly as a capital stock at current PPPs.…”

Section: Research Methodology and Datamentioning

confidence: 99%

TFP spillover effects via trade and FDI channels

Pietrucha

Żelazny

2019

Economic Research-Ekonomska Istraživanja

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As a consequence of globalisation, economic growth and productivity have become more sensitive to developments beyond national frontiers. This paper explores trade (import and export) and foreign direct investment (FDI) as channels of international total factor productivity (TFP) spillovers. FDI and trade are potentially important sources of productivity growth; however, empirical literature is inconclusive as to the nature and extent of spillovers and does not simultaneously cover all three channels. Our main goal is to examine the transmission of TFP spillover effects jointly through trade and FDI channels in 41 countries (members of the EU and OECD) during the period 1995-2014. We use dynamic panel estimation strategies. The main findings are: 1) Each of the spillover channels of TFP is significant for TFP when studied separately; 2) Joint examination of all channels allows for concluding that the export channel is the dominant one.

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Section: Research Methodology and Datamentioning

confidence: 99%

TFP spillover effects via trade and FDI channels

Pietrucha

Żelazny

2019

Economic Research-Ekonomska Istraživanja

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“…Presumably, Indus is negatively correlated with TFP. In addition, factors like Density and Innov are also common variables in the literature [33,34]. In this paper, Density is measured by the number of people per unit area, while Innov is calculated by the number of patents per capita.…”

Section: Control Variablesmentioning

confidence: 99%

Did Haze Pollution Harm the Quality of Economic Development?—An Empirical Study Based on China’s PM2.5 Concentrations

Zhao

Yuan

2020

Sustainability

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Based on the PM2.5 haze data of China's provinces between 2004-2016, this paper systematically explores the impact of haze pollution on the quality of China's economic development, as well as its transmission mechanisms. This is achieved by measuring the quality of economic development with total factor productivity. Furthermore, this paper innovatively uses precipitation as an instrumental variable for mitigating the endogeneity of the haze pollution variable, by which the impact of haze pollution on the quality of China's economic development is estimated within the framework of two-stage least squares. It is found that: the haze pollution has degraded the quality of China's economic development significantly; the labor supply loss, counter urbanization and human capital disruption are the three major transmission channels through which haze pollution affects the quality of China's economic development; strengthening government's environmental management is effective in mitigating the adverse impact of haze pollution on the economic development quality; and that China's unique fiscal decentralization system has exacerbated the negative economic effect of haze pollution. The policy implications of this paper are as follows: Improvement of economic development quality is a prerequisite for the transition of economic development mode; and the governmental management of haze is conducive to enhancing the quality of atmospheric environment and economic development, and to promoting the high-quality development of the Chinese economy.

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“…More importantly, the space Durbin model also considers the impact of interpretation variables on traffic crashes in the adjacent zones. The SDM model can be rewritten as [23] (…”

Section: Spatial Weightmentioning

confidence: 99%

Macrolevel Traffic Crash Analysis: A Spatial Econometric Model Approach

Wang

Chen

Huang

et al. 2019

Mathematical Problems in Engineering

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This study presents a spatial approach for the macrolevel traffic crashes analysis based on point-of-interest (POI) data and other related data from an open source. The spatial autoregression is explored by Moran's I Index with three spatial weight features (i.e., (a) Rook, (b) Queen, and (c) Euclidean distance). The traditional Ordinary Least Square (OLS) model, the Spatial Lag Model (SLM), the Spatial Error Model (SEM), and the Spatial Durbin Model (SDM) were developed to describe the spatial correlations among 2,114 Traffic Analysis Zones (TAZs) of Tianjin, one of the four municipalities in China. Results of the models indicated that the SDM with the Rook spatial weight feature is found to be the optimal spatial model to characterize the relationship of various variables and crashes. The results show that population density, consumption density, intersection density, and road density have significantly positive influence on traffic crashes, whereas company density, hotel density, and residential density have significant but negative effects in the local TAZ. The spillover effects coefficient of population density and road density are positive, indicating that the increase of these variables in the surrounding TAZs will lead to the increase of crashes in the target zone. The impacts of company density and hotel density are just the opposite. In general, the research findings can help transportation planners and managers better understand the general characteristics of traffic crashes and improve the situation of traffic security.

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Technology spillover and TFP growth: A spatial Durbin model

Cited by 64 publications

References 35 publications

TFP spillover effects via trade and FDI channels

TFP spillover effects via trade and FDI channels

Did Haze Pollution Harm the Quality of Economic Development?—An Empirical Study Based on China’s PM2.5 Concentrations

Macrolevel Traffic Crash Analysis: A Spatial Econometric Model Approach

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