Leveraging Non-Traditional Data For The Covid-19 Socioeconomic Recovery Strategy

Article by Deepali Khanna: “To this end, it is opportune to ask the following questions: Can we harness the power of data routinely collected by companies—including transportation providers, mobile network operators, social media networks and others—for the public good? Can we bridge the data gap to give governments access to data, insights and tools that can inform national and local response and recovery strategies?

There is increasing recognition that traditional and non-traditional data should be seen as complementary resources. Non-traditional data can bring significant benefits in bridging existing data gaps but must still be calibrated against benchmarks based on established traditional data sources. These traditional datasets are widely seen as reliable as they are subject to established stringent international and national standards. However, they are often limited in frequency and granularity, especially in low- and middle-income countries, given the cost and time required to collect such data. For example, official economic indicators such as GDP, household consumption and consumer confidence may be available only up to national or regional level with quarterly updates…

In the Philippines, UNDP, with support from The Rockefeller Foundation and the government of Japan, recently setup the Pintig Lab: a multidisciplinary network of data scientists, economists, epidemiologists, mathematicians and political scientists, tasked with supporting data-driven crisis response and development strategies. In early 2021, the Lab conducted a study which explored how household spending on consumer-packaged goods, or fast-moving consumer goods (FMCGs), can been used to assess the socioeconomic impact of Covid-19 and identify heterogeneities in the pace of recovery across households in the Philippines. The Philippine National Economic Development Agency is now in the process of incorporating this data for their GDP forecasting, as additional input to their predictive models for consumption. Further, this data can be combined with other non-traditional datasets such as credit card or mobile wallet transactions, and machine learning techniques for higher-frequency GDP nowcasting, to allow for more nimble and responsive economic policies that can both absorb and anticipate the shocks of crisis….(More)”.