چکیده:
Poverty can have spatial dimensions. While general figures
might show the spatial pattern of poverty, there is a need for
more detailed examination of this pattern. Relying on simple
figures of spatial patterns and ranking regions based on them
might be misleading. This paper tries to apply spatial analysis
methodsm, namely spatial autoregressive models to find out
the impacts of space on the human poverty index (HPI).
The results suggest that there is a significant spatial
relationship between the HP{ in the Iranian provinces. This
means that the location of the provinces has some
contributions to the differences of HPI in the country.
However, the results show that space can partially explain the
variytions in poverty.
خلاصه ماشینی:
This study tried to open another dimension in the analysis of poverty using spatial econometrics methods and data on the human poverty index.
Nevertheless, a deep understanding of the impacts of spatial factor on poverty wiJl need more sophisticated models and more appropriate data.
"Geographically wighted regression: a method for exploring spatial nonstationarity", Geographical Analysis, VoL28, pp.
"Bayesian Regression and the expansion Method", Geographical Analysis, Vol. 24, pp.
"Bayesian Estimation of Spatial Autoregressive Models", l nternatienal Regional Science Review, 1997, Vol. 20, number 1 & 2, pp.
"Testing Criteria for Determining Leading Regions in Wage Transmission Models", Journal ofRegional Science, 1990, Volume 30, no.
"Using Spatial Contiguity as Bayesian Prior Information in Regional Forecasting Models", International Regional Science Review, Vol. 18, no.
'Localised autocorrelation diagnostic statistic ( LADS)for spatial models: conceptualisation, utilisation and computation ·, Regional Science and Urban Economics 22, 333-346.