Towards High-Resolution Sex-Disaggregated Dynamic Mapping. Bosco, C.; Watson, S.; Game, A.; Brooks, C.; de Rigo, D.; Qader, S.; Greenhalgh, J.; Nilsen, K.; Ninneman, A.; and Wood, R. .
Towards High-Resolution Sex-Disaggregated Dynamic Mapping [pdf]Paper  abstract   bibtex   
While the social status and position of women and men, girls and boys in Nepal - as elsewhere - is cut through by geography, social class, race, ethnicity, and age (life-stage), historically women and girls have been disproportionately subject to gender-based disadvantages, both legally enshrined and institutionalised as social norms and expectations (Matinga et al., 2019). [\n] In recent years, the Government of Nepal has sought to address major sites of gender-based disadvantage, introducing a series of legal and regulatory provisions to strengthen women’s position in society and advance gender equality. The 2015 Constitution mandated that women occupy a third of parliamentary seats, and introduced a raft of new rights previously withheld from women. Newly available rights include: rights to inheritance (lineage), to reproductive and maternal health provision, and equal rights in property and family matters (Government of Nepal, 2015). There followed a series of measures to address gender-based inequalities in educational attainment and in legally recognised use-rights over land (at a time when under 20% of women had land registered in their name (IOM, 2016). [\n] Despite these recent moves to diminish gender-based inequalities, women and girls in Nepal - as elsewhere - continue to be disproportionately subject to gender-based disadvantages, both legally enshrined and institutionalised as social norms and expectations (Care, 2015). [\n] Against this backdrop, this study investigated the potential for novel digital data sources to support gender-equitable development across Nepal. [\n] The study was organised around two work packages. In the first, we combined nationally representative, geo-located survey data with satellite imagery and mobile phone data, to model and map spatial variations and gender-based inequalities for three, key development indicators (literacy, agriculture-based-occupations, and births in health facilities) across Nepal. [\n] The results obtained for work package one demonstrate the power of modern and robust statistical methods to exploit geolocated survey data in new and innovative ways, so permitting the geographical scale of survey estimates to be greatly refined. We discuss the data requirements underpinning good model performance, contrasting, for example, the weaker results obtained for male literacy rates with results for the best-performing indicators. [\n] Notwithstanding the potential for results to be improved through the inclusion of additional information, we suggest that the showcased techniques can (potentially) be applied to a wide variety of development indicators. We outline the practical relevance of the study outputs for the design, implementation, and monitoring of gender-equitable development in Nepal. [\n] The second work package sought to leverage de-identified mobile phone data to produce robust, frequently updatable, information on gendered mobility and migration patterns, trajectories, and dynamics within Nepal. This entailed the development of methods to predict gender for a ‘population’ of mobile phone subscribers. As part of this workstream, we administered a primary survey to validate gender for a representative sample of subscribers. [\n] To our knowledge, this study is the first time that a rigorous assessment of SIM-card (Subscriber Identification Module-card) sharing has been undertaken and incorporated into model architectures for demographic prediction. The study findings indicate that it is common for individuals to use one another’s SIM-cards, despite (overall) high rates of individual mobile phone ownership in Nepal. Our results suggest that the ‘single-SIM/single subscriber’ assumption (which has, to date, underpinned demographic prediction models) is untenable in the study setting. [\n] The uncertainty introduced by widespread SIM sharing in this setting is higher than traditionally allowed for by ‘classic methods’. The extent to which the pattern observed for Nepal holds in different settings is an empirical question. Ultimately, it may be necessary to reassess the performance of ‘classic methods’ to predict demographics from CDR data in light of previously undetected sources of uncertainty. This will depend on further research to assess the extent of (unacceptable) uncertainty posed by SIM use and sharing in different settings. [\n] Seeking to compensate for the uncertainty introduced by reported widespread SIM-sharing, we applied state-of-the-art semantic array programming - a robust, modular modelling approach - to model women’s and men’s mobility and migration patterns. [\n] While the model results are encouraging, indicating that analysis of individual CDR data can enhance our understanding of the spatial variation and temporal dynamics of sex and genderbased inequalities, more work is needed to unravel the implications of SIM sharing for gender (and more broadly, demographic) prediction models. We make a number of recommendations in this regard.
@report{boscoHighresolutionSexdisaggregatedDynamic2019,
  title = {Towards High-Resolution Sex-Disaggregated Dynamic Mapping},
  author = {Bosco, Claudio and Watson, Samantha and Game, Alina and Brooks, Chris and de Rigo, Daniele and Qader, Sarchil and Greenhalgh, Joshua and Nilsen, Kristine and Ninneman, Amy and Wood, Richard},
  date = {2019},
  pages = {85},
  institution = {{Flowminder Foundation}},
  location = {{Stockholm, Sweden}},
  url = {https://web.archive.org/web/20191216/https://data2x.org/wp-content/uploads/2019/12/TowardsHighResSexDisaggMapping_Flowminder.pdf},
  abstract = {While the social status and position of women and men, girls and boys in Nepal - as elsewhere - is cut through by geography, social class, race, ethnicity, and age (life-stage), historically women and girls have been disproportionately subject to gender-based disadvantages, both legally enshrined and institutionalised as social norms and expectations (Matinga et al., 2019).

[\textbackslash n] In recent years, the Government of Nepal has sought to address major sites of gender-based disadvantage, introducing a series of legal and regulatory provisions to strengthen women’s position in society and advance gender equality. The 2015 Constitution mandated that women occupy a third of parliamentary seats, and introduced a raft of new rights previously withheld from women. Newly available rights include: rights to inheritance (lineage), to reproductive and maternal health provision, and equal rights in property and family matters (Government of Nepal, 2015). There followed a series of measures to address gender-based inequalities in educational attainment and in legally recognised use-rights over land (at a time when under 20\% of women had land registered in their name (IOM, 2016).

[\textbackslash n] Despite these recent moves to diminish gender-based inequalities, women and girls in Nepal - as elsewhere - continue to be disproportionately subject to gender-based disadvantages, both legally enshrined and institutionalised as social norms and expectations (Care, 2015).

[\textbackslash n] Against this backdrop, this study investigated the potential for novel digital data sources to support gender-equitable development across Nepal.

[\textbackslash n] The study was organised around two work packages. In the first, we combined nationally representative, geo-located survey data with satellite imagery and mobile phone data, to model and map spatial variations and gender-based inequalities for three, key development indicators (literacy, agriculture-based-occupations, and births in health facilities) across Nepal.

[\textbackslash n] The results obtained for work package one demonstrate the power of modern and robust statistical methods to exploit geolocated survey data in new and innovative ways, so permitting the geographical scale of survey estimates to be greatly refined. We discuss the data requirements underpinning good model performance, contrasting, for example, the weaker results obtained for male literacy rates with results for the best-performing indicators.

[\textbackslash n] Notwithstanding the potential for results to be improved through the inclusion of additional information, we suggest that the showcased techniques can (potentially) be applied to a wide variety of development indicators. We outline the practical relevance of the study outputs for the design, implementation, and monitoring of gender-equitable development in Nepal.

[\textbackslash n] The second work package sought to leverage de-identified mobile phone data to produce robust, frequently updatable, information on gendered mobility and migration patterns, trajectories, and dynamics within Nepal. This entailed the development of methods to predict gender for a ‘population’ of mobile phone subscribers. As part of this workstream, we administered a primary survey to validate gender for a representative sample of subscribers.

[\textbackslash n] To our knowledge, this study is the first time that a rigorous assessment of SIM-card (Subscriber Identification Module-card) sharing has been undertaken and incorporated into model architectures for demographic prediction. The study findings indicate that it is common for individuals to use one another’s SIM-cards, despite (overall) high rates of individual mobile phone ownership in Nepal. Our results suggest that the ‘single-SIM/single subscriber’ assumption (which has, to date, underpinned demographic prediction models) is untenable in the study setting.

[\textbackslash n] The uncertainty introduced by widespread SIM sharing in this setting is higher than traditionally allowed for by ‘classic methods’. The extent to which the pattern observed for Nepal holds in different settings is an empirical question. Ultimately, it may be necessary to reassess the performance of ‘classic methods’ to predict demographics from CDR data in light of previously undetected sources of uncertainty. This will depend on further research to assess the extent of (unacceptable) uncertainty posed by SIM use and sharing in different settings.

[\textbackslash n] Seeking to compensate for the uncertainty introduced by reported widespread SIM-sharing, we applied state-of-the-art semantic array programming - a robust, modular modelling approach - to model women’s and men’s mobility and migration patterns.

[\textbackslash n] While the model results are encouraging, indicating that analysis of individual CDR data can enhance our understanding of the spatial variation and temporal dynamics of sex and genderbased inequalities, more work is needed to unravel the implications of SIM sharing for gender (and more broadly, demographic) prediction models. We make a number of recommendations in this regard.},
  keywords = {~INRMM-MiD:z-7PMPYTFE,bias-correction,data-collection-bias,data-transformation-modelling,data-uncertainty,gender-disaggregation,inequality,modelling-uncertainty,nepal,semantic-array-programming,spatial-disaggregation,statistics,survey,technology-mediated-communication},
  langid = {american},
  options = {useprefix=true}
}
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