The Impact of Model Choice on Estimates of Regional TFP

Schatzer, Thomas; Siller, Matthias; Walde, Janette; Tappeiner, Gottfried

doi:10.1177/0160017618754311

Cited by 14 publications

(10 citation statements)

References 40 publications

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“…In the relevant literature, a wide range of different approaches is currently used to determine TFP as a crucial measure of productivity. As Schatzer et al (2019) demonstrate, the model selection has an essential impact on estimation results for both TFP levels and TFP growth rates. Recently, to estimate TFP, a production frontier approach is frequently adopted as, according to O'Donnell (2012), it allows to avoid the possible bias resulting from the assumption, common in the classical literature on economic growth, that all economic units operate efficiently.…”

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 99%

The relationship between TFP and innovation performance: evidence from EU regions

Kijek

Matras-Bolibok

2019

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Research background: Total factor productivity (TFP) determines how efficiently and intensely the available inputs are used and combined in production process. Improvement of TFP performance requires identification of its determinants, thus enabling policy actions to focus on them. Since the ability to create and absorb innovation is considered as a crucial factor of economic development, the investigation of the impact of the level of regional innovative performance on TFP distribution across EU regions is an important research problem. Purpose of the article: The aim of the paper is twofold. Firstly, we attempt to develop a theoretical framework for the analysis of the impact of innovation on TFP. Secondly, we assess TFP levels for regions in the EU and investigate whether innovations account for the observed regional disparities in TFP. Methods: The research sample consists of 202 European Union (EU) regions at NUTS 1 and NUTS 2 level from 22 countries. The regional data on GDP, employment and gross fixed capital formation come from the Eurostat. The source of data on the level of innovativeness of European regions is the Regional Innovation Scoreboard. To calculate TFP, we use the multiplicatively-complete Färe-Primont index. In turn, to examine the impact of innovation on TFP, we employ the spatially-lagged X model. Findings & Value added: Our findings show a high degree of dispersion in TFP across EU regions. We find a positive impact of regional innovation performance on TFP. Although theoretical papers on economic development and regional economics suggest that improvements in TFP are key to regional economic performance, and that innovations are crucial to gain such productivity effects, there is a dearth of empirical studies on the link between innovation and TFP at the regional level. Therefore, our paper attempts to fill this gap by providing the evidence of positive effect of innovation externalities on TFP in European regions.

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Section: Literature Review and Hypotheses Developmentmentioning

confidence: 99%

The relationship between TFP and innovation performance: evidence from EU regions

Kijek

Matras-Bolibok

2019

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“…That is, a prescribed ̇ may exceed output. Another important uncertainty is the dynamics of TFP, which interpretations differ in theory (Lipsey and Carlaw, 2004) and in positive empirical studies leading to essentially different results (Schatzer et al, 2019). 14 While the efforts to increase TFP and switch from the use of limited resources to backstop technologies are important (Solow, 1974a;Easterly and Levine, 2001;Bretschger and Smulders, 2012;Sesmero and Fulginiti, 2016), there is evidence (Brander, 2010;Byrne et al, 2016) that the pace of innovation in the crucially important sectors is cause for concern.…”

Section: Leo Tolstoymentioning

confidence: 99%

Extraction path and sustainability

Бажанов

2022

Resources Policy

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This paper offers an approach to construct a family of extraction paths for nonrenewables that guarantee long-run sustainability of an imperfect economy. A path from this family leads to a monotonic growth of output with a decreasing rate of growth if a sustainability condition holds. Otherwise, the path leads either to a bounded decline or U-shaped path of output. In this sense, the paper extends neoclassical results and provides a bridge between neoclassical and degrowth theories because neoclassical tools are used to quantify degrowth scenarios. The offered path can be incentive-compatible for climate change problems because it reduces the extraction of polluting minerals consistently with the IPCC goals. That is, the climate-benefiting emission cuts by the parties of climate agreements may be guided by purely "egoistic" motives-to make own economies long-run sustainable.) is less than one. Then where is a magic trick?Recall that Hartwick (2003) stated that "This result is local in time" and the production function must be "with substitutability among inputs." 8 An example below illustrates general issues 9 reflected in these disclaimers. The example considers a non-optimal economy under (i) and (ii) with the data that are close to real economies. The point of interest is the effect of changes in while other parameters, including the resource-labor and capital-labor substitutabilities, are fixed. Calculations are provided in Appendix A.

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“…A wide range of different approaches is currently used in the literature to determine TFP. According to Schatzer et al (2019), the choice of a model has an essential impact on estimation results for both TFP levels and TFP growth rates. Having reviewed most of the available methodologies for productivity estimation Del Gatto et al (2011) suggest distinguishing between deterministic methodologies, whose output is a 'calculated' measure of TFP, and econometric ones, which yield 'estimated' productivity levels and/or growth rates as well as between frontier and non-frontier approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

Technological convergence across European regions

Kijek

Matras-Bolibok

2020

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Research background: Given the pivotal role of innovations and technological progress in shaping the economic development of regions and the crucial significance of spatial dimension of innovation processes at the regional level, the assessment of technological convergence in the regional scope becomes an essential research problem. Technological convergence could be identified on the basis of the analysis of total factor productivity (TFP). The significance of the technological convergence analysis results from the fact that income convergence can be both accelerated or impeded, depending on whether the initial differences in the level of technology (TFP) decrease or increase over time. Purpose of the article: The aim of the paper is twofold. Firstly, we attempt to develop a theoretical framework for the analysis of the technological convergence. Secondly, we investigate the technological convergence (on the basis of the TFP analysis) across European regions. Methods: During the first stage of the research, we employ the multiplicatively-complete Färe-Primont index to calculate TFP. The second stage of the study includes estimation of spatial panel models applied to assess the level of technological convergence across European regions. The research sample consists of 273 NUTS 2 European Union (EU) regions over the period 2010– 2016. Findings & Value added: The results of the study confirm a clear division of Europe into the Western European regions with high TFP values and the Eastern European regions with low TFP level. The research also shows that in the Eastern European regions the process of reducing the differences in the productivity levels is faster than in Western European regions. Since the issue of technological convergence is still not sufficiently explored in the relevant literature our paper attempts to fill a cognitive and methodological gap in the investigation of the technological convergence in the European regional space.

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The Impact of Model Choice on Estimates of Regional TFP

Cited by 14 publications

References 40 publications

The relationship between TFP and innovation performance: evidence from EU regions

The relationship between TFP and innovation performance: evidence from EU regions

Extraction path and sustainability

Technological convergence across European regions

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