De Nicolò, Silvia ; 
Ferrante, Maria Rosaria ; 
Pacei, Silvia
 
(2022)
Small area estimation of inequality measures using mixtures of betas.
    Bologna:
    Dipartimento di Scienze Statistiche "Paolo Fortunati", Alma Mater Studiorum Università di Bologna,
    p. 28.
     
     DOI 
10.6092/unibo/amsacta/7073.
    
    In: Quaderni di Dipartimento. Serie Ricerche
    	 (2).
    
    
     ISSN 1973-9346. 
  
  
 
  
  	
  	
	
  
  
  
  
  
  
  
    
  
    
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      Abstract
      Economic inequalities referring to specific regions are crucial in deepening spatial heterogeneity. Income surveys are generally planned to produce reliable estimates at countries or macro region levels, thus we implement a small area model for a set of inequality measures (Gini, Relative Theil and Atkinson indexes) to obtain microregion estimates. Considering that inequality estimators are unit-interval defined with skewed and heavy-tailed distributions, we propose a Bayesian hierarchical model at area level involving a Beta mixture. An application on EU-SILC data is carried out and a design-based simulation is performed. Our model outperforms in terms of bias, coverage and error the standard Beta regression model. Moreover, we extend the analysis of inequality estimators by deriving their approximate variance functions.
     
    
      Abstract
      Economic inequalities referring to specific regions are crucial in deepening spatial heterogeneity. Income surveys are generally planned to produce reliable estimates at countries or macro region levels, thus we implement a small area model for a set of inequality measures (Gini, Relative Theil and Atkinson indexes) to obtain microregion estimates. Considering that inequality estimators are unit-interval defined with skewed and heavy-tailed distributions, we propose a Bayesian hierarchical model at area level involving a Beta mixture. An application on EU-SILC data is carried out and a design-based simulation is performed. Our model outperforms in terms of bias, coverage and error the standard Beta regression model. Moreover, we extend the analysis of inequality estimators by deriving their approximate variance functions.
     
  
  
    
    
      Tipologia del documento
      Monografia
(Working paper)
      
      
      
      
        
          Autori
          
          
        
      
        
      
        
      
        
      
        
          Settori scientifico-disciplinari
          
          
        
      
        
          ISSN
          1973-9346
          
        
      
        
      
        
          DOI
          
          
        
      
        
      
        
      
        
      
        
          Data di deposito
          07 Nov 2022 07:43
          
        
      
        
          Ultima modifica
          07 Nov 2022 07:43
          
        
      
        
      
      
      URI
      
      
     
   
  
    Altri metadati
    
      Tipologia del documento
      Monografia
(Working paper)
      
      
      
      
        
          Autori
          
          
        
      
        
      
        
      
        
      
        
          Settori scientifico-disciplinari
          
          
        
      
        
          ISSN
          1973-9346
          
        
      
        
      
        
          DOI
          
          
        
      
        
      
        
      
        
      
        
          Data di deposito
          07 Nov 2022 07:43
          
        
      
        
          Ultima modifica
          07 Nov 2022 07:43
          
        
      
        
      
      
      URI
      
      
     
   
  
  
  
  
  
  
  
  
  
  
  
  
    
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