Estimating Poverty Using Cell Phone Data
The dramatic expansion of mobile phone use in developing countries has given rise to a rich and largely untapped source of information about the characteristics of communities and regions. Call Detail Records (CDRs) obtained from cellular phones provide highly granular real-time data that can be used to assess socio-economic behavior including consumption, mobility, and social patterns. This paper examines the results of a CDR analysis focused on five administrative departments in the south west region of Guatemala, which used mobile phone data to predict observed poverty rates. Its findings indicate that CDR-based research methods have the potential to replicate the poverty estimates obtained from traditional forms of data collection, like household surveys or censuses, at a fraction of the cost. In particular, CDRs were more helpful in predicting urban and total poverty in Guatemala more accurately than rural poverty. Moreover, although the poverty estimates produced by CDR analysis do not perfectly match those generated by surveys and censuses, the results show that more comprehensive data could greatly enhance their predictive power. CDR analysis has especially promising applications in Guatemala and other developing countries, which suffer from high rates of poverty and inequality, and where limited fiscal and budgetary resources complicate the task of data collection and underscore the importance of precisely targeting public expenditures to achieve their maximum antipoverty impact.
| Main Authors: | , , , , |
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| Format: | Working Paper biblioteca |
| Language: | English en_US |
| Published: |
World Bank, Washington, DC
2017-02
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| Subjects: | cell phone data, poverty estimation, big data, poverty measurement, call detail records, CDR, data collection, household surveys, social assistance targeting, poverty reduction, |
| Online Access: | http://documents.worldbank.org/curated/en/122541487082260120/Estimating-poverty-using-cell-phone-data-evidence-from-Guatemala https://hdl.handle.net/10986/26136 |
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dig-okr-10986261362024-12-18T05:50:07Z Estimating Poverty Using Cell Phone Data Evidence from Guatemala Hong, Lingzi Hernandez, Marco Frias-Martinez, Vanessa Whitby, Andrew Frias-Martinez, Enrique cell phone data poverty estimation big data poverty measurement call detail records CDR data collection household surveys social assistance targeting poverty reduction The dramatic expansion of mobile phone use in developing countries has given rise to a rich and largely untapped source of information about the characteristics of communities and regions. Call Detail Records (CDRs) obtained from cellular phones provide highly granular real-time data that can be used to assess socio-economic behavior including consumption, mobility, and social patterns. This paper examines the results of a CDR analysis focused on five administrative departments in the south west region of Guatemala, which used mobile phone data to predict observed poverty rates. Its findings indicate that CDR-based research methods have the potential to replicate the poverty estimates obtained from traditional forms of data collection, like household surveys or censuses, at a fraction of the cost. In particular, CDRs were more helpful in predicting urban and total poverty in Guatemala more accurately than rural poverty. Moreover, although the poverty estimates produced by CDR analysis do not perfectly match those generated by surveys and censuses, the results show that more comprehensive data could greatly enhance their predictive power. CDR analysis has especially promising applications in Guatemala and other developing countries, which suffer from high rates of poverty and inequality, and where limited fiscal and budgetary resources complicate the task of data collection and underscore the importance of precisely targeting public expenditures to achieve their maximum antipoverty impact. 2017-02-22T22:06:54Z 2017-02-22T22:06:54Z 2017-02 Working Paper Document de travail Documento de trabajo http://documents.worldbank.org/curated/en/122541487082260120/Estimating-poverty-using-cell-phone-data-evidence-from-Guatemala https://hdl.handle.net/10986/26136 English en_US Policy Research Working Paper;No. 7969 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank application/pdf text/plain World Bank, Washington, DC |
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| component |
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| databasecode |
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| region |
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| libraryname |
Biblioteca del Banco Mundial |
| language |
English en_US |
| topic |
cell phone data poverty estimation big data poverty measurement call detail records CDR data collection household surveys social assistance targeting poverty reduction cell phone data poverty estimation big data poverty measurement call detail records CDR data collection household surveys social assistance targeting poverty reduction |
| spellingShingle |
cell phone data poverty estimation big data poverty measurement call detail records CDR data collection household surveys social assistance targeting poverty reduction cell phone data poverty estimation big data poverty measurement call detail records CDR data collection household surveys social assistance targeting poverty reduction Hong, Lingzi Hernandez, Marco Frias-Martinez, Vanessa Whitby, Andrew Frias-Martinez, Enrique Estimating Poverty Using Cell Phone Data |
| description |
The dramatic expansion of mobile phone
use in developing countries has given rise to a rich and
largely untapped source of information about the
characteristics of communities and regions. Call Detail
Records (CDRs) obtained from cellular phones provide highly
granular real-time data that can be used to assess
socio-economic behavior including consumption, mobility, and
social patterns. This paper examines the results of a CDR
analysis focused on five administrative departments in the
south west region of Guatemala, which used mobile phone data
to predict observed poverty rates. Its findings indicate
that CDR-based research methods have the potential to
replicate the poverty estimates obtained from traditional
forms of data collection, like household surveys or
censuses, at a fraction of the cost. In particular, CDRs
were more helpful in predicting urban and total poverty in
Guatemala more accurately than rural poverty. Moreover,
although the poverty estimates produced by CDR analysis do
not perfectly match those generated by surveys and censuses,
the results show that more comprehensive data could greatly
enhance their predictive power. CDR analysis has especially
promising applications in Guatemala and other developing
countries, which suffer from high rates of poverty and
inequality, and where limited fiscal and budgetary resources
complicate the task of data collection and underscore the
importance of precisely targeting public expenditures to
achieve their maximum antipoverty impact. |
| format |
Working Paper |
| topic_facet |
cell phone data poverty estimation big data poverty measurement call detail records CDR data collection household surveys social assistance targeting poverty reduction |
| author |
Hong, Lingzi Hernandez, Marco Frias-Martinez, Vanessa Whitby, Andrew Frias-Martinez, Enrique |
| author_facet |
Hong, Lingzi Hernandez, Marco Frias-Martinez, Vanessa Whitby, Andrew Frias-Martinez, Enrique |
| author_sort |
Hong, Lingzi |
| title |
Estimating Poverty Using Cell Phone Data |
| title_short |
Estimating Poverty Using Cell Phone Data |
| title_full |
Estimating Poverty Using Cell Phone Data |
| title_fullStr |
Estimating Poverty Using Cell Phone Data |
| title_full_unstemmed |
Estimating Poverty Using Cell Phone Data |
| title_sort |
estimating poverty using cell phone data |
| publisher |
World Bank, Washington, DC |
| publishDate |
2017-02 |
| url |
http://documents.worldbank.org/curated/en/122541487082260120/Estimating-poverty-using-cell-phone-data-evidence-from-Guatemala https://hdl.handle.net/10986/26136 |
| work_keys_str_mv |
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| _version_ |
1819034817904246784 |