Constructing Sociodemographic Indicators for National Statistical Institutes by Using Mobile Phone Data: Estimating Literacy Rates in Senegal

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractModern 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.

From Start to Finish: A Framework for the Production of Small Area Official Statistics

“…considered hierarchical Bayes approaches to fitting area level models for estimating non-linear indicators. Schmid et al (2017) presented a first attempt to use sources of 'big data', in particular mobile phone data, as covariate information in area level models. We believe that researchers should invest more effort on developing methodologies and software that can be used when population microdata for the covariates are not available or are available for only a sample from the target population.…”

Better coverage, better outcomes? Mapping mobile network data to official statistics using satellite imagery and radio propagation modelling

“…By exploiting both mobility and (social) network characteristics of mobile phone metadata, [37][38][39][40][41][42] and [43,44] use mobile phone metadata to model disease spreading and integration, respectively. Mobile usage patterns have been explored to provide fine granular insights on socio-demographic indicators such as multi-dimensional poverty [2,3], literacy [1,45] and economic vulnerability [46,47]. While most of these studies have mapped mobile phone metadata and groundtruth data using point-to-polygon allocation or voronoi tessellation, very few studies have applied more elaborate approximation schemes.…”

“…In the case of call detail records (CDRs), the geographic reference is provided by the antenna location, often stored as a point coordinate of the physical location of the corresponding base transmitter station (BTS). Due to its simplicity, some scientific literature treat antennas as point coordinates [ 1 ]. However, the interactions captured by the antenna do not happen entirely at this exact coordinate, but within the coverage area of the antenna—the cell.…”

“…In the application, I revisit the simulation study of Schmid et al [ 1 ] published in 2017 in the Journal of the Royal Statistical Society Series A on fine-granular unemployment estimates from mobile phone metadata in Senegal in order to investigate the effects of different mapping schemes on the unemployment outcomes. Therefore, I re-run the original simulation with the difference that I implement multiple mapping schemes to derive area-level covariates from CDRs.…”

Estimating Literacy Levels at a Detailed Regional Level: an Application Using Dutch Data

“…To the best of our knowledge, SAE techniques in the context of literacy skills have only been applied sparsely, and take a quite different approach than the one we present here. Schmid et al (2017) use self-assessed literacy from the Demographic and Health Survey in combination with mobile phone data to estimate literacy in Senegal, as a way to use alternative data sources instead of requiring statistics on socio-demographic indicators. Gibson and Hewson (2012) use UK census data and SAE modeling to obtain synthetic estimates of literacy levels in detailed geographical areas.…”