Specialization, R&amp;D and productivity growth: evidence from EU regions

Piras, Gianfranco; Postiglione, Paolo; Aroca, Patrício

doi:10.1007/s00168-010-0424-2

Cited by 27 publications

(17 citation statements)

References 45 publications

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“…Works such as Anselin and Rey (1991) provided then the statistical criteria to select the more appropriate model. 3 The need for more structural or theory-driven empirical growth models with spatial effects led to important contributions such as Niebuhr (2001), Ying (2003), , Dall'erba and Le Gallo (2008), Piras et al (2012). These studies are characterized by the inclusion of both the spatial lag of the dependent variable and control variables X within a conditional β-convergence framework.…”

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confidence: 99%

Regional Growth with Spatial Dependence: A Case Study on Early Italian Industrialization

Ciccarelli

Fachin

2016

SSRN Journal

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Abstract. This paper estimates a conditional β-convergence model of labour productivity growth in Italy's manufacturing industry during 1871-1911, accounting for spatial dependence. The empirical evidence is based on a recent set of data at provincial (NUTS 3) level on manufacturing value added at 1911 prices, and a new set of data on human and social capital, political participation, and infrastructures. By focusing on a country and a time when the agglomeration forces and spillover effects advocated by the new economic geography were only starting to operate, we can investigate a particularly interesting case study. Our results suggest that human capital, a cooperative culture, and initial productivity in neighbouring provinces can explain much of the geographical variability of productivity growth in manufacturing in nineteenth-century Italy.JEL classification: R11, R12, O47, N13, N93

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confidence: 99%

Regional Growth with Spatial Dependence: A Case Study on Early Italian Industrialization

Ciccarelli

Fachin

2016

SSRN Journal

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“…The studies using time series or panel data analysis considering variables of R&D investment find positive and statistically significant effect on economic growth (Del Monte and Papagni (2003), Botazzi and Peri (2003), Akçomak and Ter Weel (2009), Piras et al, (2011), Park (1995), Freire-Serén (1999), Bassanini and Scarpetta (2001), 3 See the works in this context: (Del Monte and Papagni (2003), Botazzi and Peri (2003), Akçomak and Ter Weel (2009), Piras et al, (2011)). 4 See the in this study conducted on Southeast Asian countries: Yanyun and Mingqian (2004).…”

Section: Empirical Literaturementioning

confidence: 97%

The Effects of Research and Development (R&D) Investments on Sustainable Economic Growth: Evidence from OECD Countries (1996-2015)

Yazgan

Yalçınkaya

2018

Review of Economic Perspectives

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This study is devoted to the empirical analysis by second generation panel data analysis of the effects of the R&D investment variables in different qualifications in OECD countries grouped as OECD-20 and OECD-9 based on the income levels of the economic growth for the period of 1996-2015. Within this context, the purpose of this study is to evaluate whether or not the economic growth performances of OECD-20 and OECD-9 countries have a sustainable structure that endogenizes the technological advancements and occurs by the increments in average factor productivity. At the end of the paper it is determined that all the R&D variables in different qualifications of the OECD-20 group have a higher income level in sample period and have positive and statistically significant effects on the economic growth. On the other hand, only the private sector, universities and the total R&D investments have positive and statistically significant effects on the economic growth of the OECD-9 group which has comparatively lower income level. However, it is specified that the size of the positive and statistically significant effects of the R&D investment variables in different qualifications is more than two times bigger in the OECD-20 group as opposed to the OECD-9 group. These results reveal that the economic performances of OECD-20 countries in the investigated period have a more substantial relation with the qualified and sustainable structure that endogenizes the technologic advancements and occurs by the increments in average factor productivity. All of this shows that the R&D investments also are substantially sufficient to change the long-term economic growth performances and income levels of the countries in OECD-20 and OECD-9 groups.

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“…The non‐spatial model used in our application takes, for region i , the following form (see Fingleton ; Piras, Postiglione, and Aroca for further details)

p_{i} = b_{0} + b_{1} q_{i} + b_{2} l_{i} + b_{3} G_{i} + b_{4} u_{i} + ε_{i}

where l i measures the peripherality in terms of geographical distance as transportation time by road of a given region from Luxembourg, which is considered the central location of Europe (Dall'erba, Percoco, and Piras ), G i is the technological gap in terms of labor productivity between each region of the sample and the leading region, and u i is a variable that captures urbanization and is measured in terms of population density (Ciccone and Hall ). Equation assumes increasing human capital with decreasing peripherality ( l i ).…”

Section: Verdoorn's Law: a Re‐visitationmentioning

confidence: 99%

“…Recently, attempts have been made to provide theoretical foundations for a spatial augmented version of the original Verdoorn's specification. The main contributions were dedicated to the inclusion of spatial dependence in the economic model (Fingleton , Piras, Postiglione, and Aroca ). Studies that have extended Verdoorn's law by taking into account spatial dependence have highlighted strong evidence for the hypothesis of increasing returns and for the presence of spatial spillovers: the growth of labor productivity of one region strongly depends on the behavior of its neighboring sites (see, among others, Dall'erba, Percoco, and Piras ).…”

Section: Introductionmentioning

confidence: 99%

Spatial Clusters in EU Productivity Growth

Postiglione¹,

Andreano²,

Benedetti³

2016

Growth and Change

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In the last year, a central issue in regional economic growth debate has been represented by the empirical analysis of Verdoorn's law related to the long‐term dynamic relationship between the rate of growth in output and the productivity growth due to increasing returns. Several papers have tested Verdoorn's law on European countries as well as many other world economies. Recently, attempts have been made to provide foundations for a spatial version of the original law specification. The main contributions were dedicated to the inclusion of spatial dependence in the economic model. Surprisingly, in the literature on Verdoom's law the analysis of the spatial heterogeneity is not often considered. The aim of this paper is the regional analysis of the spatial dependence and heterogeneity in Verdoorn's law, identifying spatial regimes that can be interpreted as clusters of productivity growth in European regions at NUTS 2 level. To pursue this objective, an optimization algorithm for the identification of groups is used. This constitutes a modified version of Simulated Annealing.

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Specialization, R&D and productivity growth: evidence from EU regions

Cited by 27 publications

References 45 publications

Regional Growth with Spatial Dependence: A Case Study on Early Italian Industrialization

Regional Growth with Spatial Dependence: A Case Study on Early Italian Industrialization

The Effects of Research and Development (R&D) Investments on Sustainable Economic Growth: Evidence from OECD Countries (1996-2015)

Spatial Clusters in EU Productivity Growth

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