Nowcasting the economic impact of COVID-19: Leveraging big data to predict economic recovery from COVID-19 of Asia and the Pacific

Start of implementation: 2021

Technology type: Artificial intelligence, big data

Technology service provider: Ddata

ITD’s Partner ADB Department: Economic Research and Development Impact Department (ERDI)

In line with ADB’s Operational Priority: Strengthening governance and institutional capacity

o   Strengthened public management and financial stability

o   Enhanced governance and institutional capacity for service delivery

o   Strengthened country systems and standards

The COVID-19 pandemic has become one of the biggest global health crises, but its impact goes beyond health. Experts project that the poorest countries will feel the impact of the pandemic for years, while ADB warned in 2020 that the global economy could suffer up to $8.8 trillion in losses because of the pandemic. Asia and the Pacific account for about 30% of the overall decline in global output, which is equivalent to economic losses worth $2.5 trillion.

Policymakers and businesses need timely data to base decisions that would support economic recovery. Official statistics are released on a monthly, quarterly,or yearly basis. However, some developing countries experience a significant lag in releasing this data; it can take any time between a few weeks to several months for these to be released.

This initiative saw the development of a minimum viable product (MVP) that could demonstrate how multiple and varied datasets could be used for nowcasting to determine COVID-19’s impact on GDP growth rate in select countries in Asia and the Pacific. Nowcasting is done to obtain early estimates of key economic indicators, which are usually released at low frequencies and with significant delays, by using data related to the identified variables but are collected more frequently and released in a timely manner.

The MVP was developed by Ddata, which was selected in ADB’s “Nowcasting the Economic Impact of COVID-19” challenge, besting 65 other teams. Ddata used a combination of big data and machine learning to develop its nowcasting model that links and aggregates thousands of datasets to predict the movement of a target indicator, in this case, GDP growth rates. Raw data came from multiple sources, had different types, and had varying release dates to enable the model to reflect holistic economic measurements, eliminate noise, and increase the model’s accuracy in making predictions.

Some of the data types and sources used in the study are as follows: (i) governmental—includes accounts, production, sectors finance, interest rates, investment trade, e.g., CEIC data, Penn World Tables; (ii) academic—covers poverty, competitiveness, governance, and complexity, e.g., World Bank Development, World Economic Forum Global Competitiveness Index, International Monetary Fund World Economic Outlook; (iii) financial stocks—covers stock prices, e.g., Armenia Securities Exchange, South Pacific Stock Exchange, Philippines Stock Exchange; (iv) financial index—covers global indexes, commodity prices, trade index, exchange rates, global bond volatility, and equity, e.g., NASDAQ Global, Yale S&P, MarketWatch; (v) sentiment—covers news sentiment and investor confidence, e.g., FnSentS Web News Sentiment, Yale Confidence, AAll Investor Sentiment; and (vi) sensor—covers information on weather, COVID-19, and traffic, e.g., Open Weather data, CEIC.

ADB and Ddata agreed to narrow the focus of this initiative to the quarterly GDP growth rates of five countries in the Asia and Pacific region (Armenia, Fiji, Myanmar, Pakistan, and the Philippines). These countries had different geographical locations and varied in terms of the types of data they release and the frequency with which these official statistics arereleased, making them good representative cases for the Asia and Pacific region.

Ddata presented its final output to ADB on 29 July 2021. The model accurately nowcasted GDP growth over time for 6–12 months across all countries using multiple data sources.30 The accuracy was particularly good for high-data countries such as the Philippines. Meanwhile, the model was able to identify data-point variations for low-data countries such as Pakistan and Myanmar. The nowcast results were released weekly, thus ensuring timeliness.

The model was in line with or even better than similar models in terms of analysis errors. Ddata also developed cloud-based automated pipelines that allowed the creation of release-based updates.

The nowcasting model has progressed to the beta stage of testing by the end of the MVP development.

Ddata intended to gather more data to further improve its model, which is scalable, by incentivizing people to add data to the peer-to-peer distributed ledger technology-based data-sharing platform they created to crowdsource alternative datasets.