Scalable shift‐share analysis: Novel framework and application to France

Ruault, Jean-François; Schaeffer, Yves

doi:10.1111/pirs.12558

Cited by 6 publications

(4 citation statements)

References 36 publications

(75 reference statements)

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“…The importance of this component is related to some previous findings in the literature that incorporate the sectoral structure in the shift-share analysis and capture the intersectoral linkages within and between different geographical areas (see Márquez et al, 2009;Montanía & Dall'erba, 2022). Moreover, similar to the Esteban-Marquillas proposal (1972), this effect also contemplates the role of specialization in sector i as a source of regional growth (Ruault & Schaeffer, 2020). In Esteban-Marquilla's approach, this sectoral specialization is measured by the "homothetic employment," which is computed as the share of sector i in the national economy.…”

Section: Regionmentioning

confidence: 77%

“…However, this correction is also its main source of criticism, since it implies the inclusion of a residual effect called the “allocation component,” whose economic meaning has been eluded and often omitted in applied research (Artige & Van Neuss, 2014). Despite the limitations of both the traditional and Esteban‐Marquilla's SSA, they have been widely employed to study regional changes and propose policy guidelines (see, for example, Arcelus, 1984; Haynes & Machunda, 1987; Qiangsheng et al., 1997; Ruault & Schaeffer, 2020).…”

Section: A Brief Review Of the Shift‐share Literaturementioning

confidence: 99%

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Toward a more comprehensive shift‐share analysis: An illustration using regional data

Montanía,

Márquez,

Fernández‐Núñez

et al. 2023

Growth and Change

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Shift‐share analysis has been extensively used to investigate the different drivers of changes in socioeconomic variables in both spatial and non‐spatial contexts. This paper presents a comprehensive shift‐share formulation that, by considering all the possible interactions between the geographical and sectoral variables that interplay in a non‐spatial context, accounts for the intrinsic conditions of the regions that could affect the regional changes when spatial influences are not detected. The proposed technique is illustrated by analyzing the growth of industrial gross value added in the Spanish provinces between 2015 and 2019. Our results show the relevance of the intrinsic effects: the advantages (disadvantages) of the industrial sector within each province and the regional dynamism are important factors to explain the industrial changes in the Spanish regions. These findings provide evidence for the use of the comprehensive shift‐share as a tool that contribute to the regional analysis by identifying the characteristics of vulnerable regions that require further attention to avoid or lessen the effects of low economic growth.

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Section: Regionmentioning

confidence: 77%

Section: A Brief Review Of the Shift‐share Literaturementioning

confidence: 99%

Toward a more comprehensive shift‐share analysis: An illustration using regional data

Montanía,

Márquez,

Fernández‐Núñez

et al. 2023

Growth and Change

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“…The shift-share analysis (SSM) technique, which is descriptive rather than explanatory, is used to analyze the components of the HDI sector's employment growth: national share and industry mix and local shift components (Jackson and Haynes, 2020). Consistent with several other authors (Matlaba et al, 2012;Ruault and Schaeffer, 2020), we used exploratory spatial data analysis (ESDA) to display the spatial correlation of industry-mix and competitive effects, as proposed by Anselin (2010), Anselin (2019). The structural dissimilarity analysis performed in ArcGis PRO functions as an arbitrary clustering tool to correct SSM errors (Gallea, 2019).…”

Section: Introductionmentioning

confidence: 90%

Mapping European high-digital intensive sectors—regional growth accelerator for the circular economy

Pîrciog

Grigorescu

Lincaru

et al. 2023

Front. Environ. Sci.

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Globalization and the Fourth Industrial Revolution or Industry 4.0 act as shocks on regional labor markets and regional economies. The presence of a digital economy has high spillover effects on regional development, job creation, economic resilience, and sustainability; furthermore, it valuates eco-innovation and the clean economy. We believe that the process of digital transformation has a robust impact on the green and clean aspects of the entire economy. The consistency of high digital-intensive (HDI) sectors can be evaluated through high digital-intensive employment, human resources, and technological infrastructure, as these are the main pillars of digital transformation. The shift-share analysis method (SSM) is used in this study on employment growth during 2008–2018 for the EU27, the United Kingdom, and Norway, combined with a second stage of exploratory spatial data analysis (ESDA). The findings on national growth, industrial mix, and competitiveness are presented in GIS mapping system considering the Local Indicators of Spatial Association (LISA) technique at the NUTS2 level. This approach allows us to determine the clustering level of high digital-intensive employment and sectors, resilience based on connectivity and eco-innovation, and the regional potential of digital transformation. Policymakers and political or governmental decision-makers could consider the results of the present study as the starting point for developing and implementing their policies for a sustainable green regional economy and determine the emerging area patches that need to be stimulated.

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“…Artige observed a difference between the regional and national aggregate employment growth rates and separated the industry mix effect from the competitive effect [13]. Ruault generalized the shift-share analysis to encompass the spatial concentration of industry and industry emergence and catastrophic growth events based on the model of the Artige [14].…”

Section: Introductionmentioning

confidence: 99%

Rediscovering the Shift-Share EM2 Model: A Decomposition Framework of Unbalanced Employment Growth at the Industrial Level

Yang

2023

Sustainability

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The unbalanced growth of employment among regions is ultimately attributed to the difference in industrial structure and industrial competitiveness. At the regional level, both shift-share EM1 and EM2 models can realize the purpose of the shift-share model, that is, thoroughly separate the effects of the industrial structure and industrial competitiveness on employment growth deviation. While the expectation is to further achieve a complete separation on employment growth deviation at the industrial level, the EM1 model is not competent, and this is precisely the role of the EM2 model—to separate the employment growth deviation caused by the differences in industrial structure and industrial competitiveness at the industrial level. Therefore, the shift-share EM2 model can realize the separation of the structural component and competitive component at the industrial level. In the process of industrialization, the manufacturing industry presents a significant trend of unbalanced growth in both spatial and industrial dimensions. The setting of the share component in the EM2 model covers the balanced growth of employment in both spatial and industrial dimensions. The allocation component is reinterpreted as the magnification or inhibition effect of industrial competitiveness on the initial industrial structure. In order to exclude the influence of the regional employment scale difference, the concept and calculation method of the regional deviation degree is put forward. Finally, the spatial pattern of unbalanced employment growth in China’s manufacturing industry from 1999 to 2019 was analyzed using the shift-share EM2 model at the regional level. The results show that the spatial imbalance trend of employment growth in China’s manufacturing industry is significant, the regional deviation scale of employment growth forms a north–south difference pattern, and the regional deviation degree forms a southeast to northwest difference pattern. Taking Sichuan Province as an example, the application of the shift-share EM2 model at the industrial level is demonstrated.

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Scalable shift‐share analysis: Novel framework and application to France

Cited by 6 publications

References 36 publications

Toward a more comprehensive shift‐share analysis: An illustration using regional data

Toward a more comprehensive shift‐share analysis: An illustration using regional data

Mapping European high-digital intensive sectors—regional growth accelerator for the circular economy

Rediscovering the Shift-Share EM2 Model: A Decomposition Framework of Unbalanced Employment Growth at the Industrial Level

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