Meeting Development Challenges with Trusted Data

Policymakers responding to COVID-19 need reliable data upon which to base their decisions.
Policymakers responding to COVID-19 need reliable data upon which to base their decisions.

By Yasuyuki Sawada, Elaine S. Tan

World Statistics Day reminds us that to ensure social and economic growth, support for data-backed policymaking is needed.

The economic transformation of developing countries in Asia over the past few generations has been well documented. From a share of 4% of global GDP in 1960 to almost 35% in 2019, the region’s economic growth has led to a dramatic drop in the number of people living below $1.90 a day, from 1.5 billion in 1990 to 263 million in 2015. 

The COVID-19 pandemic is likely to reverse part of these achievements. We forecast that, for the first time in almost 60 years, developing countries in Asia will face negative growth in 2020, leading to an additional 78 million people living below $1.90 a day.

Quick government action can contain and mitigate the consequences of the pandemic, and other crises in the future. However, accurate data is indispensable for informed decisions and evidence-based policymaking. To design and implement good programs, governments require trusted, high quality and timely data to understand unfolding events and their impact, as well as policy options and their potential outcomes.

For instance, a successful policy supporting vulnerable households and enterprises needs data on which of these are eligible, where they are located, and through which channels support may be provided. Governments can then use such information to target more precisely the areas of need, ensuring that socio-economic development leaves no one behind.

The potential of data in policymaking is realized only when statisticians and analysts are equipped with the right skills, and trusted statistical products and tools are created to translate records into insights and evidence.

Quick government action can contain and mitigate the consequences of the pandemic, and other crises in the future.

Key priorities for the region include national accounts and price statistics. It is also critical to support the use of digital technologies for data collection (such as computer-assisted interviewing in surveys) and dissemination (via the international data exchange system that uses Statistical Data and Metadata eXchange standards).

Another important tool is the multi-regional input-output tables, which were developed in 2014. These tables integrate domestic economic structures into a regional framework, and have been employed widely by various organizations to examine structural transformation of economies, global value chains and regional integration, and other development policies.

Estimates of the impacts of the COVID-19 pandemic on youth unemployment, trade credit and supply chain shocks have also been generated using the tables. These insights show that the shocks from lockdowns due to COVID-19 have reverberated far and wide.

The use of Big Data, in particular remote sensing data, is another important element. The national statistics offices of Viet Nam, the Lao People’s Democratic Republic, Thailand and the Philippines, have worked with ADB to develop a methodology to estimate rice paddy areas and production through the use of satellite imagery.

Another innovative use of data is daytime satellite imagery and nightlights to generate granular poverty maps of Thailand and the Philippines. Such maps have practical uses in mitigating the impact of the COVID-19 pandemic, and can be a data source to target vulnerable households in food distribution programs.

Digitization and data will increasingly be significant drivers of economic growth in the region. To ensure inclusive and sustainable growth, support for good policymaking through trusted knowledge products and data resources is needed.

This entails institutionalizing the use of advanced statistics, as well as incorporating new sources, technologies and techniques into data work. It also means training a new generation of statisticians and data analysts who are well equipped to work with non-traditional data.