American Journal of Theoretical and Applied Statistics

| Peer-Reviewed |

Spatial Modelling of Disparity in Economic Activity and Unemployment in Southern and Oromia Regional States of Ethiopia

Received: 28 July 2015    Accepted: 08 August 2015    Published: 19 August 2015
Views:       Downloads:

Share This Article

Abstract

Growth of productivity is the precondition to improve the living standard of people and maintain competitiveness in the globalized economy. However, wide regional deferential in labor force implies inefficiency as whole and might affect both aggregate unemployment and national output. The basic goal of this study was to model disparity in economic activity and unemployment in Southern and Oromia Regional States of Ethiopia, by incorporating spatial effects. Population and Housing Census data for 381 districts were used. The exploratory spatial data analysis, OLS regression model, and spatial econometric models were employed. The exploratory spatial data analysis results revealed that both economic activity and unemployment rates in a given district were directly affected by those of its neighbors. Economic activity and unemployment rates for males and females also spatially depended on that of neighboring districts. Spatial autocorrelations between unemployment and economic activity rates is negative. In modeling aspect, relying on specification diagnostics and measures of fit; spatial lag model was found to be the best model for modelling both economic activity and unemployment rates. The modelling results revealed that both estimates of spatial autoregressive parameters indicated the existence of spatial spillover in economic activity and unemployment rates. Spatial lag model analysis also demonstrated that average number of persons per household, crude birth rate, female and male unemployment rate were significant factors of economic activity rates. The factors, percentage of urban population, economic inactivity rate, percentage of self-employed population, percentage of unpaid family employers, and average number of persons per household were found as being factors behind disparities in unemployment rates across regions districts. In conclusion, as expected the economic activity and unemployment variables had the nature of correlation over space. It is recommended that most effective policy mix is required for stabilizing and alleviating disparity in both economic activities and unemployment of the districts considered in the regions.

DOI 10.11648/j.ajtas.20150405.15
Published in American Journal of Theoretical and Applied Statistics (Volume 4, Issue 5, September 2015)
Page(s) 347-358
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), 2024. Published by Science Publishing Group

Keywords

Economic Activity, Autocorrelation, Spatial Dependence, Spatial Econometrics, Unemployment

References
[1] Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, Dordrecht. www.google.com/googlebooks: (Jan - November, 2011).
[2] Anselin, L. (1995). Local Indicators of Spatial Association –LISA. Geographical Analysis, Ohio University Press Submitted 6/94 Revised Version.
[3] Anselin, L. (1996). The Moran Scatter Plot as an ESDA Tool to Assess Local Instability in Spatial Association. In M. Fischer, H. Scholten, and D. Unwin (eds.), Spatial Analytical Perspectives on GIS. London: Taylor and Francis, 111–125.
[4] Anselin, L. (1999). Spatial Econometrics.Mimeo, Brunto Centre, School of Social Sciences, University of Dallas, Texas.
[5] Anselin, L. (2003). GeoDa 0.9 User's Guide. Spatial Analysis Laboratory Urbana Champaign, IL: University of Illinois.
[6] Anselin, L., Bera, A., Florax, R. J., and Yoon, M. (1996). Simple Diagnostic Tests for Spatial Dependence, Regional Science and Urban Economics, 26: 77-104.
[7] Anselin, L. and Bera, A. (1998). Spatial Dependence in Linear Regression Models with and Introduction to Spatial Econometrics. In Ullah, A. and Giles, D. E., editors, Handbook of Applied Economic Statistics, pp.237-289. Marcel Deker, New York.
[8] Artis, M., Lopez, B. E. and Delbarrio, T. (1999). The Regional Distribution of Spanish Unemployment Rate: Spatial Analysis. Regional Studies: The Journal of the Regional Studies Association, 39(3): 305-318.
[9] Arbia, G. and Quha, D. (2007). A Class of Spatial Econometric Methods in the Empirical Analysis of Clusters of Firms in the Space. Discussion Paper of regional science.
[10] Bailey, T. C. and Gatrell, A. C. (1995). Interactive Spatial Data Analysis. Essex: Addison Wesley Longman Limited.
[11] Berhanu Denu, Abraham Tekeste, and Vander, H. (2005). Characteristics and Determinants of Youth Unemployment, Underemployment and Inadequate Employment in Ethiopia. Employment Policies Unit, Employment Strategy Department, Ethiopia.
[12] Berument, H., Dogan, N., and Tansel, A. (2008). Macroeconomic Policy and Unemployment by Economic Activity: Evidence from Turkey. IZA Discussion Paper No. 3461.
[13] Beln Solomon and Kimmel, J. (2009). Testing the Inverseness of Fertility and Labor Supply: The Case of Ethiopia. Forschungsinstitut zur Zukunft der Arbeit Institute for the Study of Labor, IZA Discussion Paper 3949.
[14] Bräuninger, M. and Pannenberg, M. (2002). Unemployment and Productivity Growth: An Empirical Analysis with in an Augmented Solow Model. Economic modelling, 19: 105-120.
[15] Cressie, N. (1993). Statistics for Spatial Data. John Wiley & Sons: New York).
[16] Copus, A., Hall, C., Barnes, A., Dalton, G. and Cook, P. (2006). Study on Employment in Rural Areas: Final Deliverable, Consistency of Rural Development Contract No. 30-CE-0009640/00-32.
[17] Darper, N. R. and Smith, H. (1998). Applied Regression Analysis. Third edition, John Wiley and Sons, New York.
[18] Dominicis, L., Florax, R. J. and Henri, G. L. (2008). A Meta- Analysis on the Relationship between Incomes Inequality and Economic. Scottish Journal of political economy. Scottish Economic Society, 55(5): 654-682.
[19] Elhorst, J. P. (2000). The Mystery of Regional Unemployment Differentials: a Survey of Theoretical and Empirical Explanations. Research Report No. 00C06, SOM, University of Groningen, Netherlands.
[20] Elhorst, J.P. (2003). The mystery of Regional Unemployment Differentials: Theoretical and Empirical Explanations. Journal of Economic Surveys, 17: 709–748.
[21] Girma Earo and Vanden, M. (2006). Poverty and the Rural non Farm Economy in Oromia, Ethiopia: Contributed Paper Prepared for Presentation at the International Association of Agricultural Economist Conference, Gold Coast, Australia.
[22] Graaff de, T., Florax, R, J. G. M. and Nijkamp, P. (2001). A General Misspecification Test for Spatial Regression Models: Dependence, Heterogeneity, and Nonlinearity, Journal of Regional Science, Vol.41, No.2, pp.255-276
[23] Haining, R. (1990). Spatial Data Analysis in the Social and EnvironmentalSciences. Cambridge University Press: Cambridge.
[24] Haining, R. (2003). Spatial Data Analysis: Theory and Practice. University of Cambridge: Cambridge.
[25] Hussmanns, R., Mehran, F. and Verma, V. (1990). Surveys of Economically Active Population, Employment, Unemployment and Underemployment: An ILO manual on concepts and methods, Geneva.
[26] IMF (2009). Letter of Intent, Memorandum of Economic and Financial Policies, and Technical Memorandum of Understanding, Ethiopia.
[27] ILO (2006). Labor Force Supply Classifications. Labour Force Survey User Guide Volume 5: LFS Classifications.
[28] ILO (1982). Resolution Concerning Statistics of the Economically Active Population, Employment, Unemployment and Underemployment. Adopted by the Thirteenth International Conference of Labour Statisticians, 2000 edition.
[29] ILO (1998): Guidelines Concerning the Treatment in Employment and Unemployment Statistics of Persons on extended Absences from Work; in: Current International Recommendations on Labour Statistics. Sixteenth International Conference of Labor Statisticians, 2000 edition, Geneva.
[30] ILO (2000). Resolution Concerning Statistics of the Economically Active Population, Employment, Unemployment and Underemployment. Current International Recommendations on Labour Statistics, 2000 edition, pp.86-87.
[31] Johnoston, J. and Dinardo, J. (1997). Econometric Methods. MCGRAW-HILL International Editions, 4th edition.
[32] Kelejian, H. and Robinson, D. (1992). Spatial Autocorrelation: A new Computationally Simple Test with an Application to Percapita County Police Expenditures. Regional Science and Urban Economics,22: 317-333.
[33] Kosfeld, R. and Dreger, C. (2006). Thresholds for Employment and Unemployment: A Spatial Analysis of German Regional Labour Markets, 1992–2000.John Wiley and Sons, New York.
[34] Lahiri, S.N. (2003). Central Limit Theorems for Weighted Sum of Spatial Process under Class of Stochastic and Fixed Designs. The Indian journal of statistics, 65(2).
[35] Lesage, J. P. (1999). The Theory and Practice of Spatial Econometrics. Department of Economics, University of Toledo.
[36] Lee Gallo, J. and Kamarianakis, Y. (2004). ESDA and Spatial Econometrics Modelling for Study of Regional Productivity Differentials in Europe union (1975-2000). 7th AGILE Conference on Geographic Information Science.
[37] Lesage J. P. and. Pace, R. K. (2009). Spatial Econometric Models. In Fisher, M. M. and Getis, A., editors, Handbook of Spatial Analysis: Software Tools, Methods and Application, pp.355-374, WMX Design GmbH, Heidelberg, Germany.
[38] Maierhofer, E. and Fischer, M. (2001). The Tyranny of Regional Unemployment Rates Conceptual, Measurement and Data Quality Problems. Paper presented to the 41st Congress of the European Regional Science Association, Zagreb.
[39] Maria, D. E. (2011). Spatial Unemployment Differentials in Colombia. Discussion Paper 14, JELClassification: R23, C14, C23, C31.
[40] MOFED (2010). Trends and Prospects for Meeting MDGs by 2015. Ethiopia.
[41] Montgomery, D.C., E. A. Peck and G.G. Vining (2001). Introduction to Linear Regression Analysis. 3rd edition, John Wiley and Sons, New York.
[42] Niebuhr, A. (2003). Spatial Interaction and Regional Unemployment in Europe. European Journal of Spatial Development, EJSD-ISSN 1650-9544, 5:2-24.
[43] Nijkamp, P., Cracolici, M.F. and Cuffaro, M. (2007). Geographical Distribution of Unemployment: An Analysis of Provincial Differences in Italy. Tinbergen Institute Discussion Paper 065/3.
[44] Overman, H. and Puga, D. (2002). Unemployment Clusters Across Europe’s Regions and Countries. Economic Policy, 17(34): 117-147.
[45] Rey S.J. and Montouri, B.D. (1999). U.S. Regional Income Convergence: a Spatial Econometric Perspective. Regional Studies, 33(2): 145-156.
[46] Stehrer, R., and Foster, N. (2009). The Determinants of Regional Economic Growth by Quintile. Wiiw Working Paper no.54, the Vienna institute for international economic studies.
[47] Smirnov, O., Anselin, L., Syabri, I. (2002). Visualizing Multivariate Spatial Correlation with Dynamically Linked Windows. In: Proceedings of the SCISS Specialist Meeting “New Tools for Spatial Data Analysis”. Santa Barbara, California, USA.
[48] Taylor, J. (1996). Regional Problems and Policies: a European Perspective. Australasian Journal of Regional Studies, 2: 103–131.
[49] Topa, G. (2001). Social Interactions, Local Spillovers and Unemployment. The review of Economic Studies, 2(68): 261-295.
[50] Trendle, B. (2006). Unemployment Variation in Metropolitan Brisbane: The role of Geographic Location and Demographic Characteristics. Australasian Journal of Regional Studies, 2(12).
[51] UN, (2003). Labor Supply. Regional Stocktaking Review.
[52] Ward, N. and Brown D. L. (2009). Placing the Rural in Regional Development. Puplished by Regional Studies, 1237-1244.
Author Information
  • Department of Statistics, College of Natural Sciences, Arba Minch University, Arba Minch, Ethiopia

  • Department of Statistics, School of Mathematical and Statistical Sciences, Hawassa University, Hawassa, Ethiopia

Cite This Article
  • APA Style

    Berisha Mekayhu Gelebo, Ayele Taye Goshu. (2015). Spatial Modelling of Disparity in Economic Activity and Unemployment in Southern and Oromia Regional States of Ethiopia. American Journal of Theoretical and Applied Statistics, 4(5), 347-358. https://doi.org/10.11648/j.ajtas.20150405.15

    Copy | Download

    ACS Style

    Berisha Mekayhu Gelebo; Ayele Taye Goshu. Spatial Modelling of Disparity in Economic Activity and Unemployment in Southern and Oromia Regional States of Ethiopia. Am. J. Theor. Appl. Stat. 2015, 4(5), 347-358. doi: 10.11648/j.ajtas.20150405.15

    Copy | Download

    AMA Style

    Berisha Mekayhu Gelebo, Ayele Taye Goshu. Spatial Modelling of Disparity in Economic Activity and Unemployment in Southern and Oromia Regional States of Ethiopia. Am J Theor Appl Stat. 2015;4(5):347-358. doi: 10.11648/j.ajtas.20150405.15

    Copy | Download

  • @article{10.11648/j.ajtas.20150405.15,
      author = {Berisha Mekayhu Gelebo and Ayele Taye Goshu},
      title = {Spatial Modelling of Disparity in Economic Activity and Unemployment in Southern and Oromia Regional States of Ethiopia},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {4},
      number = {5},
      pages = {347-358},
      doi = {10.11648/j.ajtas.20150405.15},
      url = {https://doi.org/10.11648/j.ajtas.20150405.15},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajtas.20150405.15},
      abstract = {Growth of productivity is the precondition to improve the living standard of people and maintain competitiveness in the globalized economy. However, wide regional deferential in labor force implies inefficiency as whole and might affect both aggregate unemployment and national output. The basic goal of this study was to model disparity in economic activity and unemployment in Southern and Oromia Regional States of Ethiopia, by incorporating spatial effects. Population and Housing Census data for 381 districts were used. The exploratory spatial data analysis, OLS regression model, and spatial econometric models were employed. The exploratory spatial data analysis results revealed that both economic activity and unemployment rates in a given district were directly affected by those of its neighbors. Economic activity and unemployment rates for males and females also spatially depended on that of neighboring districts. Spatial autocorrelations between unemployment and economic activity rates is negative. In modeling aspect, relying on specification diagnostics and measures of fit; spatial lag model was found to be the best model for modelling both economic activity and unemployment rates. The modelling results revealed that both estimates of spatial autoregressive parameters indicated the existence of spatial spillover in economic activity and unemployment rates. Spatial lag model analysis also demonstrated that average number of persons per household, crude birth rate, female and male unemployment rate were significant factors of economic activity rates. The factors, percentage of urban population, economic inactivity rate, percentage of self-employed population, percentage of unpaid family employers, and average number of persons per household were found as being factors behind disparities in unemployment rates across regions districts. In conclusion, as expected the economic activity and unemployment variables had the nature of correlation over space. It is recommended that most effective policy mix is required for stabilizing and alleviating disparity in both economic activities and unemployment of the districts considered in the regions.},
     year = {2015}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Spatial Modelling of Disparity in Economic Activity and Unemployment in Southern and Oromia Regional States of Ethiopia
    AU  - Berisha Mekayhu Gelebo
    AU  - Ayele Taye Goshu
    Y1  - 2015/08/19
    PY  - 2015
    N1  - https://doi.org/10.11648/j.ajtas.20150405.15
    DO  - 10.11648/j.ajtas.20150405.15
    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  - 347
    EP  - 358
    PB  - Science Publishing Group
    SN  - 2326-9006
    UR  - https://doi.org/10.11648/j.ajtas.20150405.15
    AB  - Growth of productivity is the precondition to improve the living standard of people and maintain competitiveness in the globalized economy. However, wide regional deferential in labor force implies inefficiency as whole and might affect both aggregate unemployment and national output. The basic goal of this study was to model disparity in economic activity and unemployment in Southern and Oromia Regional States of Ethiopia, by incorporating spatial effects. Population and Housing Census data for 381 districts were used. The exploratory spatial data analysis, OLS regression model, and spatial econometric models were employed. The exploratory spatial data analysis results revealed that both economic activity and unemployment rates in a given district were directly affected by those of its neighbors. Economic activity and unemployment rates for males and females also spatially depended on that of neighboring districts. Spatial autocorrelations between unemployment and economic activity rates is negative. In modeling aspect, relying on specification diagnostics and measures of fit; spatial lag model was found to be the best model for modelling both economic activity and unemployment rates. The modelling results revealed that both estimates of spatial autoregressive parameters indicated the existence of spatial spillover in economic activity and unemployment rates. Spatial lag model analysis also demonstrated that average number of persons per household, crude birth rate, female and male unemployment rate were significant factors of economic activity rates. The factors, percentage of urban population, economic inactivity rate, percentage of self-employed population, percentage of unpaid family employers, and average number of persons per household were found as being factors behind disparities in unemployment rates across regions districts. In conclusion, as expected the economic activity and unemployment variables had the nature of correlation over space. It is recommended that most effective policy mix is required for stabilizing and alleviating disparity in both economic activities and unemployment of the districts considered in the regions.
    VL  - 4
    IS  - 5
    ER  - 

    Copy | Download

  • Sections