As global economies brace for a structural transformation unseen in a century, traditional statistics and macroeconomic research cry out for a methodology overhaul and new way of thinking that fit in with times of uncertainty, chaos, complexity and ambiguity.

The Chinese mainland and Hong Kong — the two economies that have what it takes to be the most affected by tremendous changes — are being urged to carve out a new path of data analysis to better reflect the story unfolding and make policy decisions more targeted.

The problem with data from major economies isn’t new, but it’s getting increasingly impossible to ignore. The latest case in point is that the number of US workers on payrolls for the year through March was revised downward by 818,000, according to the nation’s Labor Department on Wednesday — after an uncharacteristic delay of more than a half-hour.

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Although the revision is preliminary and will not be finalized until February, it marks the largest downward adjustment since the 902,000 reduction to employment in March 2009, when the economy was in the throes of its worst recession in decades.

Revisions to economic data are widespread and normal. Raw data often contains very strong seasonal patterns, and seasonally adjusting it can offer a clearer view of what is happening in an economic cycle.

“But revision on such a scale essentially upends our shared understanding of statistics,” said a Shanghai-based investment manager who wishes to remain anonymous. “It simply makes macroeconomic research more like theology.”

Adjustments to previously released numbers have become increasingly common in recent years in the US. Bruce Pang, Hong Kong-based chief economist and head of research of Greater China at JLL, said the COVID-19 pandemic has brought about some fundamental changes to the inner workings of statistics and the traditional way people interpret data. To be sure, the US is not alone.

“The pandemic makes data collection harder, distorts the definitions of basic concepts such as unemployment, and hinders efficiency of forecasting models that worked well in the old days,” Pang said. “In the midst of uncertainty, chaos, complexity and ambiguity, analysis and projections are based on data with greater-than-ever volatility. This justifies the revisions, at least to some degree.

“But indeed, whether data need to be adjusted to such an extent remains an open question,” he added.

Risks to economic data, especially ones from major economies, are particularly notable because of the attention received from policymakers and investors worldwide. As data work is resource intensive, for the same indicator of GDP, for instance, developed economies, including Hong Kong, tend to publish on a quarterly basis, while most developing economies, like the Chinese mainland, choose to report on a yearly basis. The latter can be more prone to distortion and bias.

Wang Xiaoyu, assistant professor in the Thrust of Financial Technology of the Hong Kong University of Science and Technology (Guangzhou), expresses serious doubts about the dynamic nature of data and the fundamental methodology of macroeconomic research.

“From the selection or creation of indicators, the construction of a sample set, to the process of making assumptions and analytical reasoning, each step can be subject to many complex factors and distortions,” Wang said. “Just like one making assumptions about no friction or air resistance, or negligible electrical resistance, or assuming the existence of a vacuum state in physics, it may be quite problematic to apply these data and arguments to economic practices.”

Given the bewildering global economic conditions, making assumptions can be problematic in theory right from the outset, Wang said.

“Perfect data never exist, especially at a time when global economies are struggling with a structural shift,” Wang said. “It particularly rings true for the Chinese mainland and Hong Kong, as both economies have what it takes to become the most affected by the sheer power of the structural change.”

Wang said she believes the takeaway lesson for China is that when the traditional statistical theory and methods prove to be behind the times — just like a train that has been running smoothly for years that is now getting off track — more timely efforts must be made to get them back on track.

As China shifts its focus from dizzying economic expansion to high-quality development, how the data being compiled could better monitor and reflect the story unfolding in certain parts of the world’s second-largest economy is open to debate.

“With the property sector generating more than 20 percent of China’s GDP, the sector itself is still crucial to our growth. True enough,” said a Hong Kong-based economist on condition of anonymity. “But if we continue to gauge the economic growth following the old routine, it’s easy to lose sight of what’s going on in other segments of the national economy.”

The economist highlighted the all-around improvement of various industries in Shenzhen, apparent from the number of Hong Kong residents heading north to shop, and success stories of nontraditional domestic tourist destinations such as Zibo, Shandong province, and Harbin, capital of Northeast China’s Heilongjiang province, which stands as a living embodiment of the quality improvement of government services.

“The existing statistical approach basically takes no account of these ongoing changes,” he said.

Similarly for Hong Kong, it is important to steer clear of the “path dependence” on statistical methods. “At the very least, the issue should be brought to the table,” Wang said.

Looking ahead, growth opportunities will become more segment-based and industry-focused. So should the statistics be. “In the end, we need our policymaking more sector-targeted and industry-oriented,” the economist said.

Nowadays, it is common for investors and researchers to takes into account as much outside data as possible, often from private sector data providers, to give them a better sense of what’s going on in the economy.

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But for industry players in China, the Shanghai-based investment manager calls for a shift in way of thinking.

He grew bored by the sameness of reports produced by China-based brokerage firms, which dare not even question the reliability of US-related data.

“The problematic revision is the elephant in the room. Before doing some concrete work on statistical methodology revamping, I think it’s equally important to abandon the extreme faith in authenticity of foreign data.”

Contact the writer at sophialuo@chinadailyhk.com