The spatial features of recent crises in a developing country: analysing regional economic resilience for the Brazilian case

Tupy, Igor Santos; Silva, Fernanda Faria; Amaral, Pedro; Cavalcante, Anderson

doi:10.1080/00343404.2020.1851025

Cited by 13 publications

(13 citation statements)

References 41 publications

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“…Anselin (2016) later proposed this same estimation method and described the adequate instruments. López et al (2020), Tupy et al (2021), andPáez et al (2021) applied this methodology to estimate SUR-SLM models. The list of proposed instruments for SUR-SLM and SUR-SDM, along with a full description of the method, is available in López et al (2020) where an extensive Monte Carlo evaluates this method and compares the results with the ML estimation.…”

Section: The 3sls Estimation Methods Based On Instrumental Variablesmentioning

confidence: 99%

“…Simultaneously, an important number of empirical papers have implemented these methodological developments. Papers by Rey and Montouri (1999), Egger and Pfaffermayr (2004), Le Gallo and Dall'erba (2006), Lundberg (2006), Fingleton (2007), Moscone, Tosetti, and Knapp (2007), Le Gallo and Chasco (2008), Lauridsen, Bech, López, and Maté (2010), Kakamu et al (2012), Cotteleer and Van Kooten (2012), He, Páez, Liu, and Jiang (2015), Izón, Hand, Mccollum, Thacher, and Berrens (2016), López, Martínez-Ortiz, and Cegarra-Navarro (2017), Franklin and Rey (2016), Tupy, Silva, Amaral, and Cavalcante (2021), and Páez, López, Menezes, Cavalcanti, and Pitta (2021) are well-known examples in this line.…”

Section: Introductionmentioning

confidence: 99%

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spsur: An R Package for Dealing with Spatial Seemingly Unrelated Regression Models

Mı́nguez¹,

López²,

Mur³

2022

J. Stat. Soft.

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Spatial seemingly unrelated regression (spatial SUR) models are a useful multiequational econometric specification to simultaneously incorporate spatial effects and correlated error terms across equations. The purpose of the spsur R package is to supply a complete set of functions to test for spatial structures in the residual of a SUR model; to estimate the most popular specifications by applying different methods and test for linear restrictions on the parameters. The package also facilitates the estimation of socalled spatial impacts, conveniently adapted to a SUR framework. The package includes functions to simulate datasets with the features decided by the user, which may be useful in teaching activities or in more general research projects. The article concludes with a real data application showing the potential that spsur has to examine the relation of individual mobility over geographic areas and the incidence of COVID-19 in Spain during the first lockdown.

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Section: The 3sls Estimation Methods Based On Instrumental Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

spsur: An R Package for Dealing with Spatial Seemingly Unrelated Regression Models

Mı́nguez¹,

López²,

Mur³

2022

J. Stat. Soft.

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“…O grupo de regiões consideradas resistentes, isto é, que não experienciaram recessão no período, são justamente as menores regiões, apresentando baixa densidade populacional, baixo PIB per capita, e, também, baixas taxas de industrialização. Trabalhos como o Tupy et al (2020) já mostraram que, de fato, a crise de 2015-16 apresentou uma ampla dispersão no território, afetando a grande maioria das regiões. Nesta pesquisa , verifica-se que as regiões resistentes não só compõem o menor grupo entre os quatro, como também apresenta as regiões com menor participação na economia nacional.…”

Section: Uma Análise Das Trajetórias Regionais No Pós-criseunclassified

“…O índice de Martin, Sunley e Tyler (2016) tem sido amplamente utilizado em estudos sobre resiliência (Tupy et al, 2020). Nesse sentido, sua construção pode ser analisada a partir da ótica da decomposição de Shift-Share.…”

Section: Dinâmicas Setoriais E Resiliência Regionalunclassified

Resiliência Econômica E Dinâmica Regional-Setorial No Brasil Pós-Crise/Regional Resilience and Sector Dinamics in Post-Crisis Brazil: An Exploratory Analysis for the 2014-2019 Period

Eberhardt

Tupy

2022

IGEPEC

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Essa pesquisa busca explorar a resiliência economia regional brasileira para analisar como a crise de 2015-16 impactou sua base econômica. Dessa forma, podemos identificar quais estruturas produtivas conseguem proteger a região de uma crise. Para tanto, foi utilizado o ferramental do Shift-Share para isolar os efeitos nacional, estrutural e locacional. Os resultados indicam que regiões mais dependentes do comércio internacional conseguiram ter mais dinamismo em seus setores de atividade e, assim, tiveram desempenho melhor em relação à economia nacional.

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“…With the growing interest in explaining the causes and consequences of crises on regions, regional studies have progressively used the concept of resilience (Davies 2011 ; Bathelt et al 2013 ; Eraydın 2013 , 2016 ; Dijkstra et al 2015 ; Crescenzi et al 2016 ; Cuadrado-Roura et al 2016 ; Martin et al 2016 ; Doğruel et al 2018 ; Brinks and Ibert 2020 ; Gong et al 2020 ; Houston 2020 ; Kapitsinis 2020 ; Klimanov et al 2020 ; Goschin and Constantin 2021 ; Tupy et al 2020 ; Borsati et al 2022 ; Kim et al 2022 ; Li et al 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Economic impact of the COVID-19 outbreak in Turkey: analysis of vulnerability and resilience of regions and diversely affected economic sectors

2022

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The COVID-19 outbreak has deeply affected the global economy of most countries and Turkey is no exception. However, the impacts of the outbreak differ on a regional basis, and both scientists and policymakers have neglected this regional differentiation. In an attempt to redress this situation, our study aimed to reveal the regional disparities related to the economic impacts of the outbreak and the dynamics that created this differentiation in Turkey. Our statistical analyses were carried out based on two different periods. The first period covered the first trimester when the outbreak began, and several shutdowns were implemented. The second period covered the second trimester when society began to open up again. The first trimester was termed the shock period, and the second trimester the recovery period. We developed a resilient–vulnerability index based on selected variables such as employment, energy consumption, exports, and the number of companies established and closed using a location quotient (LQ) analysis. While our index offers a picture of resilient and vulnerable regions, we also used this index as a dependent variable in our study. In the second stage, we focused on what kinds of dynamics gave rise to the resilience or vulnerability of a region. Our findings revealed that regions are economically affected by the outbreak at different levels. The regression analysis results showed that the innovation capacity and export levels of the regions predict regional resilience negatively, while firm size predicts positively. The recovery of regions also differed regionally. Our analyses show that Turkish regions with relatively larger economies recover more slowly, while regions with smaller economies recover more quickly.

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The spatial features of recent crises in a developing country: analysing regional economic resilience for the Brazilian case

Cited by 13 publications

References 41 publications

spsur: An R Package for Dealing with Spatial Seemingly Unrelated Regression Models

spsur: An R Package for Dealing with Spatial Seemingly Unrelated Regression Models

Resiliência Econômica E Dinâmica Regional-Setorial No Brasil Pós-Crise/Regional Resilience and Sector Dinamics in Post-Crisis Brazil: An Exploratory Analysis for the 2014-2019 Period

Economic impact of the COVID-19 outbreak in Turkey: analysis of vulnerability and resilience of regions and diversely affected economic sectors

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