Measuring Regional Spillovers in Long- and Short-Run Models of Total Factor Productivity, Trade, and FDI

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThis article applies the novel concept of global panel cointegration to analyze the role played by trade and foreign direct investment (FDI) activity in driving regional total factor productivity (TFP). Using West German state-level data for the period 1976-2008, the approach allows us to identify the magnitude of direct trade and FDI effects as well as spatial spillovers from these variables. The author finds that the inclusion of spatial lags significantly improves the fit of the empirical model and allows us to strongly reject the null of no cointegration among the variables in the full spatial specification. For the long-run cointegration equation, the empirical results hint at export-and FDI-led growth. Additionally, outward FDI activity shows to have positive spatial spillover effects among German regions, while the spatial patterns of import and inward FDI activity indicate substitution effects of interregional input-output linkages in favor of international ones over the sample period 1976-2008. In the short run, TFP growth is predominantly affected by changes in exports, inward and outward FDI stocks, where the latter variable also provokes positive spillovers. The author uses alternative spatial weighting matrices based on geographical information as well as interregional goods transport flows to check the 1 Rheinisch-Westfðlisches Institut für Wirtschaftsforschung (RWI), Essen, Germany Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz-bzw. Nationallizenz frei zugänglich. This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively. robustness of the obtained spillover effects in the long and short run. Here, the results turn out to be similar for the different empirical specifications employed throughout the analysis. Finally, summing over the four variables to get a direct and indirect net effect of internationalization activity, the author finds that the direct effect is always positive, while the indirect net effect is positive in the short run but slightly negative in the long-run equation.

The Impact of Model Choice on Estimates of Regional TFP

“…In contrast to the previous model, the pooled panel approach (Marrocu, Paci, and Usai 2013; Mitze 2014) exploits the panel structure of the data set rather than estimating elasticities for each cross section separately. More precisely, equation (6) is used to estimate factor elasticities.…”

“…Disparities of productivity performances across regions are central to many questions addressed by researchers in regional science as well as by policy institutions concerned with regional economic development such as the European Union’s cohesion policy: are less developed regions catching up to productivity levels of more prosperous regions (Di Liberto and Usai 2013; Vogel 2015)? What drives regional disparities in productivity levels and growth and which determinants cause these differences (Dettori, Marrocu, and Paci 2012; Marrocu and Paci 2013; Mitze 2014)? Before starting with the investigation of these issues, researchers have to choose a productivity measure which then forms the basis for the actual analysis.…”

“…Before starting with the investigation of these issues, researchers have to choose a productivity measure which then forms the basis for the actual analysis. Many empirical works on the regional level prefer total factor productivity (henceforth TFP) over alternative measures, as TFP takes into account cross-regional differences in factor endowments of capital and labor (to name a few: Antonelli, Patrucco, and Quatraro 2011; Brixy 2014; Capello and Lenzi 2015; Marrocu and Paci 2013; Mitze 2014). However, as TFP is not a directly observable quantity and there is no recognized standard method to estimate TFP at the current state, researchers have to choose from a set of available TFP estimation approaches.…”

“…The reason to restrict the analysis to a rather homogeneous set of estimation methods is that if within such a homogeneous set of methods the results show substantial differences, this can be expected largely for more heterogeneous models. For this purpose, we investigate approaches relying on the Cobb–Douglas production function including two inputs, which, to the best of our knowledge, is the most frequently used method of regional TFP estimation (just to name a few applications, see Dettori, Marrocu, and Paci 2012; LeSage and Fischer 2012; Marrocu and Paci 2013; Mitze 2014; Vogel 2015). More specifically, five different TFP models are examined, and regional TFP levels and TFP growth rates obtained from these models are compared.…”

“…The first model is the classic model in TFP estimation, that is, the neoclassical accounting approach (applied on a regional level, among others, by Byrne, Fazio, and Piacentino 2009; Derbyshire, Gardiner, and Waights 2013; LeSage and Fischer 2012; Scoppa 2007). Furthermore, the cross-section approach (used by Berlemann and Wesselhöft 2012; Capello and Lenzi 2015) and the pooled panel approach (Marrocu, Paci, and Usai 2013; Mitze 2014) are investigated. As a fourth model, the fixed-effects model (Dettori, Marrocu, and Paci 2012; Ladu 2012; Marrocu and Paci 2012, 2013) is analyzed.…”