Spatial Econometric Model of Poverty in Java Island

Mulugeta Aklilu Zewdie; M. Nur Aidi; Bagus Sartono

doi:doi:10.11648/j.ajtas.20150406.11

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Spatial Econometric Model of Poverty in Java Island

Mulugeta Aklilu Zewdie, M. Nur Aidi, Bagus Sartono

Published in American Journal of Theoretical and Applied Statistics (Volume 4, Issue 6)

Received: 26 August 2015 Accepted: 15 September 2015 Published: 26 September 2015

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Abstract

This paper gives the concept of spatial econometric model and applies it to analyze the spatial dimensions of poverty and its determinants using data from Java Island 2010 census survey, for 105 districts of Java Island. Dependent variable used in this research is percentage of poverty rate at particular district and predictors are some selected variables that are correlated to poverty. Weighted matrix is obtained by using queen contiguity criteria and four statistical models are applied to the data, Ordinary Least Square regression model, Spatial Error Model, Spatial Lag Model and Spatial Durbin Model. It is shown that the OLS estimates of the poverty function suffer from spatial effects that indicated the OLS model are miss specified since Moran Index test also confirmed the existence of spatial autocorrelation. LM and Robust LM are used for testing the existence of spatial effect. The Likelihood Ratio common factor test and AIC are used for model selection criteria. Gauss Markov Assumptions are done and the Spatial Lag model proved to be better than other model for a given data and the result shows that Education and Working hours has significant impact on poverty.

Published in	American Journal of Theoretical and Applied Statistics (Volume 4, Issue 6)
DOI	10.11648/j.ajtas.20150406.11
Page(s)	420-425
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Poverty, Spatial Effects, Econometrics, Spatial Error Model, Special Lag Model, Spatial Durbin Model, LM, Robust LM, LRcom, Gauss-Markov &AIC

References

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[3]	Anselin L. 2010. Lagrange multiplier diagnostics for spatial dependence and heterogeneity, Geographical Analysis Willy on line library.
[4]	Andrew Mckay and David Lawson 2003. Assessing the Extent and Nature of Chronic Poverty in Low Income Countries: Issues and Evidence, University of Nottingham, UK.
[5]	Andy M. & Emilie P. 2011. How strong is the evidence for the existence of poverty traps? A multi country assessment, Working Paper series.
[6]	Ann H. 2007. Globalization and Poverty, NBER Books, National Bureau of Economic Research.
[7]	Coudouel A., Jesko H. and Quentin W. 2002. Poverty Measurement and Analysis, in the PRSP Sourcebook, World Bank, Washington D. C.
[8]	Giuseppe A. 2006. Spatial Econometrics Statistical Foundations and Applications to Regional Convergence, Italy.
[9]	Hentschel J., Lanjouw P. and Poggi J. 2000. Combining census and survey data to trace the spatial dimensions of poverty: a case study of Ecuador. World Bank Econ.
[10]	Jay lee and David W. 2001. Statistical analysis with arc view GIS, John Wiley, New York.
[11]	Lesage James P. 1999. The Theory and Practice of Spatial Econometrics, Department of Economics University of Toledo.
[12]	Lesage J. and Pace K. 2009.Introduction to Spatial Econometrics, Boca Raton: CRC Press.
[13]	Mur J. and Angulo A. 2006. The Spatial Durbin Model and the Common Factor Tests, Spatial Economic Analysis.
[14]	National Development Planning Agency (BAPPENAS) 2010. Report on the Achievement of the Millennium Development Goals Indonesia.
[15]	Paraguas F. & Anton A. 2005. Spatial Econometrics Modeling of Poverty paper presented on the 8th WSEAS International Conference on applied mathematics, Tenerife, Spain.
[16]	Pebley R. and Sastry N. 2003. Neighborhoods, Poverty and Children’s Well-being, University of California, Los Angeles.
[17]	Rawlings J., Sastry G. Pentula, David A. 1998. Applied Regression Analysis A Research Tool Second Edition. Raleigh, North Carolina USA.
[18]	Sohair F. 2013. Application of Spatial Regression Models to Income Poverty Ratios in Middle Delta Contiguous Counties in Egypt. Tanta University, Tanta, Egypt
[19]	Székely M., N. Lustig, M. Cumpa, J. Antonio M. 2000. Do We Know How Much Poverty There Is? Inter-American Development Bank, Felipe Herrera Library.

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APA Style

Mulugeta Aklilu Zewdie, M. Nur Aidi, Bagus Sartono. (2015). Spatial Econometric Model of Poverty in Java Island. American Journal of Theoretical and Applied Statistics, 4(6), 420-425. https://doi.org/10.11648/j.ajtas.20150406.11

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ACS Style

Mulugeta Aklilu Zewdie; M. Nur Aidi; Bagus Sartono. Spatial Econometric Model of Poverty in Java Island. Am. J. Theor. Appl. Stat. 2015, 4(6), 420-425. doi: 10.11648/j.ajtas.20150406.11

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AMA Style

Mulugeta Aklilu Zewdie, M. Nur Aidi, Bagus Sartono. Spatial Econometric Model of Poverty in Java Island. Am J Theor Appl Stat. 2015;4(6):420-425. doi: 10.11648/j.ajtas.20150406.11

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@article{10.11648/j.ajtas.20150406.11,
  author = {Mulugeta Aklilu Zewdie and M. Nur Aidi and Bagus Sartono},
  title = {Spatial Econometric Model of Poverty in Java Island},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {4},
  number = {6},
  pages = {420-425},
  doi = {10.11648/j.ajtas.20150406.11},
  url = {https://doi.org/10.11648/j.ajtas.20150406.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150406.11},
  abstract = {This paper gives the concept of spatial econometric model and applies it to analyze the spatial dimensions of poverty and its determinants using data from Java Island 2010 census survey, for 105 districts of Java Island. Dependent variable used in this research is percentage of poverty rate at particular district and predictors are some selected variables that are correlated to poverty. Weighted matrix is obtained by using queen contiguity criteria and four statistical models are applied to the data, Ordinary Least Square regression model, Spatial Error Model, Spatial Lag Model and Spatial Durbin Model. It is shown that the OLS estimates of the poverty function suffer from spatial effects that indicated the OLS model are miss specified since Moran Index test also confirmed the existence of spatial autocorrelation. LM and Robust LM are used for testing the existence of spatial effect. The Likelihood Ratio common factor test and AIC are used for model selection criteria. Gauss Markov Assumptions are done and the Spatial Lag model proved to be better than other model for a given data and the result shows that Education and Working hours has significant impact on poverty.},
 year = {2015}
}

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TY  - JOUR
T1  - Spatial Econometric Model of Poverty in Java Island
AU  - Mulugeta Aklilu Zewdie
AU  - M. Nur Aidi
AU  - Bagus Sartono
Y1  - 2015/09/26
PY  - 2015
N1  - https://doi.org/10.11648/j.ajtas.20150406.11
DO  - 10.11648/j.ajtas.20150406.11
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 420
EP  - 425
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20150406.11
AB  - This paper gives the concept of spatial econometric model and applies it to analyze the spatial dimensions of poverty and its determinants using data from Java Island 2010 census survey, for 105 districts of Java Island. Dependent variable used in this research is percentage of poverty rate at particular district and predictors are some selected variables that are correlated to poverty. Weighted matrix is obtained by using queen contiguity criteria and four statistical models are applied to the data, Ordinary Least Square regression model, Spatial Error Model, Spatial Lag Model and Spatial Durbin Model. It is shown that the OLS estimates of the poverty function suffer from spatial effects that indicated the OLS model are miss specified since Moran Index test also confirmed the existence of spatial autocorrelation. LM and Robust LM are used for testing the existence of spatial effect. The Likelihood Ratio common factor test and AIC are used for model selection criteria. Gauss Markov Assumptions are done and the Spatial Lag model proved to be better than other model for a given data and the result shows that Education and Working hours has significant impact on poverty.
VL  - 4
IS  - 6
ER  -

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Author Information

Mulugeta Aklilu Zewdie

Department of Statistics, Faculty of Natural and Computational Science, Mekelle University, Mekelle, Ethiopia
M. Nur Aidi

Department of Statistics, Faculty of Mathematics and Science, Bogor Agricultural University, Bogor, Indonesia
Bagus Sartono

Department of Statistics, Faculty of Mathematics and Science, Bogor Agricultural University, Bogor, Indonesia

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Plain Text BibTeX RIS

APA Style

Mulugeta Aklilu Zewdie, M. Nur Aidi, Bagus Sartono. (2015). Spatial Econometric Model of Poverty in Java Island. American Journal of Theoretical and Applied Statistics, 4(6), 420-425. https://doi.org/10.11648/j.ajtas.20150406.11

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ACS Style

Mulugeta Aklilu Zewdie; M. Nur Aidi; Bagus Sartono. Spatial Econometric Model of Poverty in Java Island. Am. J. Theor. Appl. Stat. 2015, 4(6), 420-425. doi: 10.11648/j.ajtas.20150406.11

Copy | Download

AMA Style

Mulugeta Aklilu Zewdie, M. Nur Aidi, Bagus Sartono. Spatial Econometric Model of Poverty in Java Island. Am J Theor Appl Stat. 2015;4(6):420-425. doi: 10.11648/j.ajtas.20150406.11

Copy | Download

@article{10.11648/j.ajtas.20150406.11,
  author = {Mulugeta Aklilu Zewdie and M. Nur Aidi and Bagus Sartono},
  title = {Spatial Econometric Model of Poverty in Java Island},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {4},
  number = {6},
  pages = {420-425},
  doi = {10.11648/j.ajtas.20150406.11},
  url = {https://doi.org/10.11648/j.ajtas.20150406.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150406.11},
  abstract = {This paper gives the concept of spatial econometric model and applies it to analyze the spatial dimensions of poverty and its determinants using data from Java Island 2010 census survey, for 105 districts of Java Island. Dependent variable used in this research is percentage of poverty rate at particular district and predictors are some selected variables that are correlated to poverty. Weighted matrix is obtained by using queen contiguity criteria and four statistical models are applied to the data, Ordinary Least Square regression model, Spatial Error Model, Spatial Lag Model and Spatial Durbin Model. It is shown that the OLS estimates of the poverty function suffer from spatial effects that indicated the OLS model are miss specified since Moran Index test also confirmed the existence of spatial autocorrelation. LM and Robust LM are used for testing the existence of spatial effect. The Likelihood Ratio common factor test and AIC are used for model selection criteria. Gauss Markov Assumptions are done and the Spatial Lag model proved to be better than other model for a given data and the result shows that Education and Working hours has significant impact on poverty.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Spatial Econometric Model of Poverty in Java Island
AU  - Mulugeta Aklilu Zewdie
AU  - M. Nur Aidi
AU  - Bagus Sartono
Y1  - 2015/09/26
PY  - 2015
N1  - https://doi.org/10.11648/j.ajtas.20150406.11
DO  - 10.11648/j.ajtas.20150406.11
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 420
EP  - 425
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20150406.11
AB  - This paper gives the concept of spatial econometric model and applies it to analyze the spatial dimensions of poverty and its determinants using data from Java Island 2010 census survey, for 105 districts of Java Island. Dependent variable used in this research is percentage of poverty rate at particular district and predictors are some selected variables that are correlated to poverty. Weighted matrix is obtained by using queen contiguity criteria and four statistical models are applied to the data, Ordinary Least Square regression model, Spatial Error Model, Spatial Lag Model and Spatial Durbin Model. It is shown that the OLS estimates of the poverty function suffer from spatial effects that indicated the OLS model are miss specified since Moran Index test also confirmed the existence of spatial autocorrelation. LM and Robust LM are used for testing the existence of spatial effect. The Likelihood Ratio common factor test and AIC are used for model selection criteria. Gauss Markov Assumptions are done and the Spatial Lag model proved to be better than other model for a given data and the result shows that Education and Working hours has significant impact on poverty.
VL  - 4
IS  - 6
ER  -

Copy | Download