Measuring development with inequality: How (should) aggregate indicators of development account for inequality?

Syrovátka, Miroslav; Schlossárek, Martin

doi:10.1016/j.ecolecon.2019.04.032

Cited by 11 publications

(3 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…. 13 ) computed for all 34 towns located in the study area and aggregated into a single index using the additive method (Gan et al, 2017;Nardo et al, 2005;OECD, 2008;Saisana and Tarantola, 2002;Syrovátka and Schlossarek, 2019). The additive aggregation method, and especially the weighted arithmetic mean, is the widest used method (Gan et al, 2017;Pollesch and Dale, 2015) as it sums up the normalized values of sub-indicators.…”

Section: Methodsmentioning

confidence: 99%

Measuring the Extent and Patterns of Urban Shrinkage for Small Towns Using R

2022

View full text Add to dashboard Cite

Urban shrinking is a phenomenon as common as urban expansion nowadays and it affects urban settlements of all sizes, especially from developed and industrialized countries in Europe, America and Asia. The paper aims to assess the patterns of shrinkage for small and medium sized towns in Oltenia region (Romania), considering demographic, economic and social indicators with a methodological approach which considers the use of different functions and applications of R packages. Thirteen selected indicators are analysed to perform the multivariate analysis on Principal Component Analysis using the prcomp() function and the ggplot2 package to visualize the patterns of urban shrinkage. Two composite indicators were additionally created to measure the extent of urban shrinkage: CSI (Composite Shrinking Index) and RDC (Regional Demographic Change) for two-time intervals. Based on the CSI, three major categories of shrinking were observed: persistent shrinkage, mild shrinking or slow evolution toward shrinking, where the vast majority of towns are found (including mining towns, where there still is a delayed restructuring of state-owned enterprises, and towns characterised by the agrarization of local economies), and stagnant/stabilized shrinkage.

show abstract

Section: Methodsmentioning

confidence: 99%

Measuring the Extent and Patterns of Urban Shrinkage for Small Towns Using R

2022

View full text Add to dashboard Cite

show abstract

“…To quantify the differences in social aspects between countries, studies such as Jitmaneeroj (2017) and Syrovátka and Schlossarek (2019), have compared the utility of several social indexes. Moreover, many studies analyses the relationship between indexes of social development and economic indicators (Asandului et al, 2016;Bren et al, 2019).…”

Section: Socio-economic Drivers Of Migrationmentioning

confidence: 99%

An exploratory analysis of the interactions between the determinants of migratory flows

Mihai

Novo-Corti

2022

Papers in Regional Science

View full text Add to dashboard Cite

More than often, studies explaining migration causes centre on economic explanations and minimize other factors' explanatory power. This research aims at creating a comprehensive model of migration determinants taking into account four dimensions: economic, social, cultural, and digital. A path model consisting of these dimensions was created and estimated through partial least squares structural equation modelling (PLS-SEM). The PLS-path model was applied to Romanian migration flows to 21 EU member states during 2007-2017. The findings indicate that social and technological developments have significant impacts on migration flows and that digital distance has a full mediation effect on the relationship between cultural distance and migration flows.

show abstract

“…In considering current differences in gendered poverty UN Women (2015a) (Syrovátka and Schlossarek, 2017). The exclusive use of LAC countries for establishing poverty trends in the Progress Report is explained as due to LAC being the only region where analysis of the poorest households by gender composition has been undertaken over time (UNW, 2015a:45).…”

Section: -16mentioning

confidence: 99%

Gender and poverty: what we know, don’t know, and need to know for Agenda 2030

Bradshaw

Chant

Linneker

2017

Gender, Place & Culture

View full text Add to dashboard Cite

Drawing on historical debates on gender, poverty, and the 'feminisation of poverty' this paper reflects on current evidence, methods and analysis of gendered poverty. It focuses on initiatives by UN Women, including the Progress of the World's Women 2015-16, which represents one of the most concerted attempts by an international agency to reflect on what we know about the contemporary state of women's poverty in various parts of the developing and transitional world. Our analysis of the data compiled by UN Women raises questions about what might account for the overrepresentation of women among the poor in official accounts of poverty, and how this is plausibly changing (or not) over time. The paper highlights that analysis of what is measured and how needs to be understood in relation to who is the focus of measurement. The lack of available data which is fit for purpose questions the extent to which gender poverty differences are 'real' or statistical. There is a continued reliance on comparing female with male headed households, and the move by UN Women to adopt the notion of Female Only Households reflects available data driving conceptual understandings of women's poverty, rather than conceptual advances driving the search for better data. Wider UN processes highlight that while sensitivity to differences among women and their subjectivities are paramount in understanding the multiple processes accounting for gender bias in poverty burdens, they are still accorded little priority. It is recognised that to monitor advances in Agenda 2030 will require more and better statistics. Our review suggests we know little about how poverty is experienced by women and men and that we are still far from having a set of tools able to adequately measure and monitor gendered poverty.

show abstract

Measuring development with inequality: How (should) aggregate indicators of development account for inequality?

Cited by 11 publications

References 41 publications

Measuring the Extent and Patterns of Urban Shrinkage for Small Towns Using R

Measuring the Extent and Patterns of Urban Shrinkage for Small Towns Using R

An exploratory analysis of the interactions between the determinants of migratory flows

Gender and poverty: what we know, don’t know, and need to know for Agenda 2030

Contact Info

Product

Resources

About