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

Photo: Flickr

Big Data Fight Against Poverty
Big Data, as its name would suggest, refers to large sets of unstructured and structured data generated at high speeds from digital and traditional data sources around the globe. The big data movement has gained momentum over the years, particularly in the business sphere, but experts have also realized that insights derived from big data have implications for the fight against poverty.

In the agricultural space for example, The Forum for Agricultural Research in Africa has found that farmers in Africa barely produce what they need to get by.

Food Policy experts have found that helping these farmers in Africa and other parts of the world produce more food is key to lifting millions out of poverty. One of the key ways of attaining this goal, according to the experts, is by providing farmers, scientists and entrepreneurs in the agricultural sector with adequate access to data and information generated at agricultural research centres worldwide.

Ft Magazine highlights how big data is being used to mitigate the harmful effects that accompany natural disasters. It explains that in the aftermath of the devastating Haiti earthquake of 2010, researchers at the Karolinska Institute of Columbia University successfully managed to track the locations of 600,000 displaced people using data mining techniques.

The article in Ft Magazine also illustrates how analyzing data from social media sites like Twitter and Facebook can provide early warning systems for both human and natural disasters. For example, increased references to food or ethnic strife on these sites can serve as indicators of possible famine or civil unrest.

Mark Van Rijmenam, the founder of Datafloq, further empathizes the use of Big data in the disaster response field, saying “Big data offers, for example, the possibility to predict food shortages by combining variables such as drought, weather conditions, migrations, market prices, seasonal variation and previous productions.”

In the area of public health and sanitation, Van Rijmenam talks about harnessing data from call detail records to map variations in the population of low-income dwellers in order to direct efforts at building water pipes and latrine facilities for the slum dwellers. This effort will see improved sanitation in such areas, bringing about better health.

A pilot program by the World Bank in Tanzania called SMS for life has generated major improvements in the distribution of malarial medical stock. By getting clinical workers to send an SMS with their stock count every week, the program has enabled senior coordinating staff to re-stock clinics more accurately.

SMS for life has managed to reduce the number of rural health facilities in Tanzania without medical stock from 78 percent to 26 percent.

World Bank blogger Alla Morrison has likened the transformative potential of big data to the transformative effect that electricity had on industry in the 19th century. She argues that big data is a game changer for business, and notes the unprecedented productivity gains in the second industrial revolution due to businesses in all sectors taking advantage of the new electrical resource.

Likewise, as humanity forges ahead, it is important that organizations, governments and individuals take advantage of big data to address the seemingly intractable challenge of poverty.

June Samo

Sources: Enterra Solutions 1, Enterra Solutions 2, FT Magazine, IEEE, SAS, Smart Data Collective, UN GLOBAL PULSE, World Bank 1, World Bank 2
Photo: Flickr