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Constructing socio-demographic indicators for National Statistical Institutes using mobile phone data: Estimating literacy rates in Senegal

Author

Listed:
  • Schmid, Timo
  • Bruckschen, Fabian
  • Salvati, Nicola
  • Zbiranski, Till

Abstract

Modern systems of official statistics require the accurate and timely estimation of socio-demographic indicators for disaggregated geographical regions. Traditional data collection methods such as censuses or household surveys impose great financial and organizational burdens for National Statistical Institutes. The rise of new information and communication technologies offers promising sources to mitigate these shortcomings. In this paper we propose a unified approach for National Statistical Institutes based on small area estimation that allows for the estimation of socio-demographic indicators by using mobile phone data. In particular, the methodology is applied to mobile phone data from Senegal for deriving sub-national estimates of the share of illiterates disaggregated by gender. The estimates are used to identify hot spots of illiterates with a need for additional infrastructure or policy adjustments. Although the paper focuses on literacy as a particular socio-demographic indicator, the proposed approach is applicable to indicators from national statistics in general.

Suggested Citation

  • Schmid, Timo & Bruckschen, Fabian & Salvati, Nicola & Zbiranski, Till, 2016. "Constructing socio-demographic indicators for National Statistical Institutes using mobile phone data: Estimating literacy rates in Senegal," Discussion Papers 2016/9, Free University Berlin, School of Business & Economics.
    • Handle: RePEc:zbw:fubsbe:20169
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    References listed on IDEAS

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    1. Gauri Sankar Datta & J. N. K. Rao & David Daniel Smith, 2005. "On measuring the variability of small area estimators under a basic area level model," Biometrika, Biometrika Trust, vol. 92(1), pages 183-196, March.
    2. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, vol. 71(1), pages 355-364, January.
    3. Jean D. Opsomer & Mario Francisco-Fernández & Xiaoxi Li, 2012. "Model-Based Non-parametric Variance Estimation for Systematic Sampling," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 39(3), pages 528-542, September.
    4. Raghunathan, Trivellore E. & Xie, Dawei & Schenker, Nathaniel & Parsons, Van L. & Davis, William W. & Dodd, Kevin W. & Feuer, Eric J., 2007. "Combining Information From Two Surveys to Estimate County-Level Prevalence Rates of Cancer Risk Factors and Screening," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 474-486, June.
    5. Esther López-Vizcaíno & María José Lombardía & Domingo Morales, 2015. "Small area estimation of labour force indicators under a multinomial model with correlated time and area effects," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(3), pages 535-565, June.
    6. Lynn M. R. Ybarra & Sharon L. Lohr, 2008. "Small area estimation when auxiliary information is measured with error," Biometrika, Biometrika Trust, vol. 95(4), pages 919-931.
    7. González-Manteiga, W. & Lombardi­a, M.J. & Molina, I. & Morales, D. & Santamari­a, L., 2008. "Analytic and bootstrap approximations of prediction errors under a multivariate Fay-Herriot model," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5242-5252, August.
    8. Ray Chambers & Nicola Salvati & Nikos Tzavidis, 2016. "Semiparametric small area estimation for binary outcomes with application to unemployment estimation for local authorities in the UK," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 453-479, February.
    9. Marhuenda, Yolanda & Morales, Domingo & del Carmen Pardo, María, 2014. "Information criteria for Fay–Herriot model selection," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 268-280.
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    Keywords

    indicators; model-based estimation; official statistics; small area estimation;
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