The Importance of Data Analysis in Monitoring Global Poverty
While taking action is an important part of fighting global poverty, it is also critical that international organizations correctly assess the situation through different methods of data collection and analysis. Monitoring global poverty is crucial for ending it.
Since the World Bank’s first census in 1975, attempts to monitor global poverty levels have widened in both scope and methodology. The invention of PovcalNet in the 1980s enabled researchers to access the poverty distributions of 191 countries online. However, the diversification of research methods entailed as much inconsistency as convenience, as data collected by different teams seemed to suggest entirely different results.
Since one organization cannot survey all the households of the world, analysts often collect survey results from the governments of different countries. This introduces inconsistencies into investigation methods, including differing methods of selecting and interviewing sample populations.
When measuring qualitative measures such as household participation, patterns of consumption and perception towards poverty, long-term participatory observation can be more appropriate than surveys, as the wording of questions can manipulate the results.
After data is collected, it is classified and represented into charts or graphs, where more complications can occur. There exist many statistical methodologies, including parametric, non-parametric and lognormal, and countries differ on how to define poverty in various environments.
To standardize data collection and facilitate monitoring global poverty, the World Bank has been urging nations to adopt the National Strategies for the Development of Statistics (NSDS), emphasized at the Marrakech Action Plan for Statistics in 2004.
NSDS requires not only economic support, but political cohesion between departments and local communities in each country. The NSDS Knowledge Base will compile research techniques and provide 100 indicators to the progress of Sustainable Development Goals so that results from different countries can be comparable.
Difficulties of standardization often derive from insufficient infrastructure, such as the failure to register all citizens on census, and requires a long-term investment. In such cases, innovative measures can improve cost-benefit efficiency.
The UN’s Data for Development report from 2015 suggests using satellite imagery and mobile-phone-based data collection. Instead of designing a separate survey, data from social media and mobile call traffic can be repurposed as an indirect indicator. In East Africa, for example, mobile technology is expected to cut up to 60 percent of the cost of traditional paper surveys.
– Haena Chu