Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/316764 
Erscheinungsjahr: 
2025
Schriftenreihe/Nr.: 
IZA Discussion Papers No. 17809
Verlag: 
Institute of Labor Economics (IZA), Bonn
Zusammenfassung: 
Evidence on how energy poverty persistence and vulnerability to key factors are distributed across different population groups remains scarce. This paper seeks to bridge this gap by analyzing the dynamics and determinants of energy poverty within population clusters. The significance of the paper is highlighted in the integration of a two-stage Generalized Method of Moments (GMM) estimation procedure with K-means cluster analysis. K-means clustering is a fundamental tool within AI to understand and find patterns and structure in data without labeled outputs. Two key findings emerge. First, the degree of energy poverty state dependence varies substantially across clusters, with some segments of the population deeply entrenched and facing significant barriers to escape. Second, variables critical for identifying at-risk groups, such as income and energy prices, exhibit different impacts across clusters. These findings highlight the need for targeted policy interventions tailored to the specific vulnerabilities of distinct population segments.
Schlagwörter: 
energy poverty
state dependence
K-means clustering
Generalized Method of Moments
JEL: 
Q40
I32
C38
C33
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
1.37 MB





Publikationen in EconStor sind urheberrechtlich geschützt.