Abstract
A key component in the economic structure of countries is organizations of popular and solidarity economy (EPS), which have been shown to contribute significantly to local welfare by fostering social cohesion and economic development. However, local development socioeconomic process variables such as the Index of Unsatisfied Basic Needs, level of dependency ratio and non-oil gross value added, exhibit a discernible spatiality, this study addresses this gap by investigating the relationship between the distribution of non-financial EPS organizations and local development variables in Ecuador at the cantonal level. The methodology employed in this study focused on exploratory spatial data analysis (ESDA) and geographically weighted regression (GWR), capturing variations as a function of geographic location. The results revealed that the distribution of non-financial EPS in Ecuador shows significant spatial autocorrelation at the cantonal level. The GWR model estimated with the three cantonal socioeconomic variables (adjusted R2= 0.7134, AICc = 434.34) suggests that in cantons with lower welfare index, there is less presence of EPS organizations. In conclusion, this study provides a detailed view of the spatial distribution of non-financial EPSs in Ecuador and their relationship with local development. Although the overall spatial autocorrelation is weak, the identification of local patterns suggests the need for specific approaches to address inequalities in local development. These findings underscore the importance of considering spatial dynamics in the design of economic and social development policies, providing a basis for future research in local economics and development.
| Original language | English |
|---|---|
| Pages (from-to) | 2343-2350 |
| Number of pages | 8 |
| Journal | International Journal of Sustainable Development and Planning |
| Volume | 20 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 2025 |
Bibliographical note
Publisher Copyright:©2025 The authors.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
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SDG 8 Decent Work and Economic Growth
Keywords
- geographically weighted regression (GWR)
- local development
- popular and solidarity economy (EPS)
- spatial analysis
- spatial autocorrelation
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