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Logistic Mixed Modelling of Determinants of International Migration from the Southern Ethiopia: Small Area Estimation Approach

Received: 29 March 2017     Accepted: 15 April 2017     Published: 27 May 2017
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Abstract

The main objective of this study is to investigate socio-demographic and economic characteristics of a household on international migration and to estimate small area proportions at district and enumeration area level. Migration status refers to whether a household has at least one member who ever migrated abroad or not. A total of 2288 data are collected from sixteen randomly sampled districts in Hadiya and Kembata-Tembaro zonal areas, Southern Ethiopia. Several versions of the binary logistic mixed models, as special cases of the generalized linear mixed model, are analyzed and compared. The findings of the study reveal that about 39.4% of the households have at least one international migrant, and the rest 60.6% have no such migrants. Based on analysis of the generalized linear model and stepwise variable selection, four predictors are found to be significantly related to household migration status at 5% significance level. These are age, occupation, and educational level of household head and family size. Then twelve mixed models are analyzed and compared. The best fitting model to the data is found to be the logistic mixed regression model consisting of the six predictors with age nested within districts as random effects. Area or district specific random effect has variance of 1.6180. The district level random variation founded on final model with six predictor variables about the presence of migrant in the households such as the variation between districts is 33% and variation within the district is 67%. From analysis of the final model, it is found that the likelihood of a household of having international migrant increases with head's age and family size. An increase of family size by one person increases the log odds of having migrant by 0.131 indicating that large family size is one of the determinants for migration in the study area. The migration prevalence varies among the zones, the districts and the enumeration areas. Household characteristics: age, educational level and occupation of head, and family size are determinants of international migration. Community based intervention is needed so as to monitor and regulate the international migration for the benefits of the society.

Published in American Journal of Theoretical and Applied Statistics (Volume 6, Issue 3)
DOI 10.11648/j.ajtas.20170603.16
Page(s) 170-182
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), 2017. Published by Science Publishing Group

Keywords

GLM, GLMM, Migration, Mixed Logistic, Small Area Estimation

References
[1] Chi, G., and Voss, P. (2005). Migration Decision Making: A Hierarchical Regression Approach. Journal of Regional Analysis and Policy, Vol 35, PP 11-22.
[2] Abrham, T., Sandra, A., and Aynadis, Y., (2014).Assessment on the Socio Economic Situation and Needs of Ethiopian Returnees from Kingdom of Saudi Arabia (KSA).
[3] United Nations, Department of Economic and Social Affairs, Population Division, UNDESA (2016). International Migration Report 2015: Highlights. (ST/ESA/SER.A/375).
[4] Rango, M., and Laczko, F. (2014). Global Migration Trends: An Overview. International Organization for Migration, Saving Migrants Lives, Geneva.
[5] Teshome, D., Bailey. A, and Teller, Ch. (2013). Irregular Migration: Causes and Consequences of Young Adult Migration from Southern Ethiopia to Republic of South Africa. Paper Presented at the XXVII IUSSP International Population Conference 26-31 August, 2013 Busan, South Korea.
[6] Molefe, W.B. (2011). Sample Design for Small Area Estimation. Doctor of Philosophy Thesis, Center for Statistical and Survey Methodology, University of Wollongong, http://ro.uow.edu.au/theses/3495.
[7] Longford, N. T. (2006). Sample Size Calculation for Small Area Estimation. Survey Methodology, Vol 32 No 1, PP 87-96.
[8] Levy, P. S., and Lemeshow, S. (2008). Sampling of Populations: Methods and Applications. 4th Edition.
[9] Cochran, W. G. (1977). Sampling Techniques. 3rd Edition. Harvard University, New York.
[10] Naing L., Winn T., and Rusli B. N. (2006). Practical Issues in Calculating the Sample Size for Prevalence Studies. Medical Statistics, Vol 1, PP 9-14.
[11] Rao, J. N. K. (2003). Small Area Estimation. Wiley, Hoboken, NJ.
[12] Lohr, S. L. (2010). Sampling: Design and Analysis. 2nd Edition.
[13] Pfeffermann, D. (2013). New Important Developments in Small Area Estimation. Statistical Science, Vol 28, No 1, PP 40–68. DOI: 10.1214/12-STS395.
[14] Setiawan, A., and Tarumi, T. (2004). Small Geographic Area Estimation in WinBUGS with Two Approaches Prediction. Journal of the Faculty of Environmental Science and Technology, Vo1 9, No 1, PP 9-17.
[15] McCullagh, P., and Nelder, J. A. (1989). Generalized Linear Models, 2nd Edition. Chapman and Hall.
[16] Jiang, J. (2007). Linear and Generalized Linear Mixed Models and Their Applications. Springer Series in Statistics.
[17] Faraway, J. J. (2006). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Chapman & Hall.
[18] McCulloch C. E, and Searle S. R. (2001). Generalized, Linear and Mixed Models, 2nd Edition. Wiley Publishing.
[19] Zhao, Y., Staudenmayer, J., Coull, B. A., Wand, M. P. (2006). General Design Bayesian Generalized Linear Mixed Models. Statistical Science, Vol 21, No 1, PP 35–51.
[20] Fay, R. E., and Herriot, R. A. (1979). Estimates of Income for Small Places: An Application of James-Stein Procedures to Census Data. Journal of the American Statistical Association, Vol 74, PP 269-277.
[21] Hilbe, J. (2009). Logistic Regression Models. Jet Propulsion Laboratory, California Institute of Technology and Arizona State University, U. S. A.
[22] Berridge, D. M., and Crouchley, R. (2011). Multivariate Generalized Linear Mixed Models Using R. Lancaster University, CRC Press, Taylor and Francis Group.
[23] Browne W. J., Subramanian, S. V., Jones, K., and Goldstein, H. (2005). Variance Partitioning in Multilevel Logistic Models that Exhibit over Dispersion. Journal of Royal Statistical Society, Vol 168, PP 599–613.
[24] Hosmer, D. W., Jr., and Lemeshow, S. (2000). Applied Logistic Regression. 2nd Edition. New York: John Wiley & Sons.
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  • APA Style

    Tsedeke Lambore Gemecho, Ayele Taye Goshu. (2017). Logistic Mixed Modelling of Determinants of International Migration from the Southern Ethiopia: Small Area Estimation Approach. American Journal of Theoretical and Applied Statistics, 6(3), 170-182. https://doi.org/10.11648/j.ajtas.20170603.16

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    ACS Style

    Tsedeke Lambore Gemecho; Ayele Taye Goshu. Logistic Mixed Modelling of Determinants of International Migration from the Southern Ethiopia: Small Area Estimation Approach. Am. J. Theor. Appl. Stat. 2017, 6(3), 170-182. doi: 10.11648/j.ajtas.20170603.16

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    AMA Style

    Tsedeke Lambore Gemecho, Ayele Taye Goshu. Logistic Mixed Modelling of Determinants of International Migration from the Southern Ethiopia: Small Area Estimation Approach. Am J Theor Appl Stat. 2017;6(3):170-182. doi: 10.11648/j.ajtas.20170603.16

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  • @article{10.11648/j.ajtas.20170603.16,
      author = {Tsedeke Lambore Gemecho and Ayele Taye Goshu},
      title = {Logistic Mixed Modelling of Determinants of International Migration from the Southern Ethiopia: Small Area Estimation Approach},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {6},
      number = {3},
      pages = {170-182},
      doi = {10.11648/j.ajtas.20170603.16},
      url = {https://doi.org/10.11648/j.ajtas.20170603.16},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20170603.16},
      abstract = {The main objective of this study is to investigate socio-demographic and economic characteristics of a household on international migration and to estimate small area proportions at district and enumeration area level. Migration status refers to whether a household has at least one member who ever migrated abroad or not. A total of 2288 data are collected from sixteen randomly sampled districts in Hadiya and Kembata-Tembaro zonal areas, Southern Ethiopia. Several versions of the binary logistic mixed models, as special cases of the generalized linear mixed model, are analyzed and compared. The findings of the study reveal that about 39.4% of the households have at least one international migrant, and the rest 60.6% have no such migrants. Based on analysis of the generalized linear model and stepwise variable selection, four predictors are found to be significantly related to household migration status at 5% significance level. These are age, occupation, and educational level of household head and family size. Then twelve mixed models are analyzed and compared. The best fitting model to the data is found to be the logistic mixed regression model consisting of the six predictors with age nested within districts as random effects. Area or district specific random effect has variance of 1.6180. The district level random variation founded on final model with six predictor variables about the presence of migrant in the households such as the variation between districts is 33% and variation within the district is 67%. From analysis of the final model, it is found that the likelihood of a household of having international migrant increases with head's age and family size. An increase of family size by one person increases the log odds of having migrant by 0.131 indicating that large family size is one of the determinants for migration in the study area. The migration prevalence varies among the zones, the districts and the enumeration areas. Household characteristics: age, educational level and occupation of head, and family size are determinants of international migration. Community based intervention is needed so as to monitor and regulate the international migration for the benefits of the society.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - Logistic Mixed Modelling of Determinants of International Migration from the Southern Ethiopia: Small Area Estimation Approach
    AU  - Tsedeke Lambore Gemecho
    AU  - Ayele Taye Goshu
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    DO  - 10.11648/j.ajtas.20170603.16
    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  - 170
    EP  - 182
    PB  - Science Publishing Group
    SN  - 2326-9006
    UR  - https://doi.org/10.11648/j.ajtas.20170603.16
    AB  - The main objective of this study is to investigate socio-demographic and economic characteristics of a household on international migration and to estimate small area proportions at district and enumeration area level. Migration status refers to whether a household has at least one member who ever migrated abroad or not. A total of 2288 data are collected from sixteen randomly sampled districts in Hadiya and Kembata-Tembaro zonal areas, Southern Ethiopia. Several versions of the binary logistic mixed models, as special cases of the generalized linear mixed model, are analyzed and compared. The findings of the study reveal that about 39.4% of the households have at least one international migrant, and the rest 60.6% have no such migrants. Based on analysis of the generalized linear model and stepwise variable selection, four predictors are found to be significantly related to household migration status at 5% significance level. These are age, occupation, and educational level of household head and family size. Then twelve mixed models are analyzed and compared. The best fitting model to the data is found to be the logistic mixed regression model consisting of the six predictors with age nested within districts as random effects. Area or district specific random effect has variance of 1.6180. The district level random variation founded on final model with six predictor variables about the presence of migrant in the households such as the variation between districts is 33% and variation within the district is 67%. From analysis of the final model, it is found that the likelihood of a household of having international migrant increases with head's age and family size. An increase of family size by one person increases the log odds of having migrant by 0.131 indicating that large family size is one of the determinants for migration in the study area. The migration prevalence varies among the zones, the districts and the enumeration areas. Household characteristics: age, educational level and occupation of head, and family size are determinants of international migration. Community based intervention is needed so as to monitor and regulate the international migration for the benefits of the society.
    VL  - 6
    IS  - 3
    ER  - 

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Author Information
  • School of Mathematical and Statistical Sciences, Hawassa University, Hawassa, Ethiopia

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

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