In most countries, including Malaysia, paying attention to progress closer than just at the national level becomes important to level up the country as a whole.
With that in mind, the McKinsey Global Institute has deployed nighttime luminosity and other cutting-edge techniques to build a database that allows us to examine the world at a granularity 230 times greater than the view we get from country-level data sets. We move from 178 countries, averaging 40 million people, 700,000 sq km and US$700 billion (RM3.2 trillion) of gross domestic product in 2019, to more than 40,000 microregions, averaging 180,000 people, 3,000 sq km and US$3 billion of GDP.
The first thing that is immediately apparent in this zoomed-in view is that within-country differences are often far more pronounced than between-country variations. For example, the story of China’s phenomenal economic growth over the past 20 years is well known. Yet fast growth wasn’t universal — per capita growth rates between big cities like Yulin and Anshan differed by as much as 10 times. Elsewhere in Asia, Mapusa, in the Indian state of Goa, India, had the same GDP per capita, about US$33,000, as Porto, Portugal’s second-largest city, while Portugal’s national GDP per capita is more than five times that of India.
Our pixelated view makes it clear that the world has experienced extensive progress in life expectancy and GDP per capita. By 2019, 3.5 billion people, or close to half of the world’s population, lived in microregions where GDP per capita was above US$8,300 and life expectancy was greater than 72.5 years. But roll back to 2000, and only 21% of people enjoyed these conditions.
In 2000, such living standards were found in just 12 microregions along China’s coastline that were home to 71 million people. By 2019, 86% of China’s population living in 170 microregions had those levels of income and life expectancy. For instance, most microregions in the Shaanxi and Jiangxi provinces were among them, with life expectancies increasing between six and 10 years and GDP per capita growing between 7% and 12%.
Similar remarkable progress was found across Asia as countries there developed regional trade ties that stimulated economic growth. At the beginning of that period, just 16 million people living in 18 microregions in four countries — Brunei, India, Malaysia and Vietnam — had GDP per capita and life expectancy in the top 30%, but two decades later, 322 million people living in 1,000 Asian microregions had achieved those levels of prosperity and well-being. For instance, no one living in Thailand had such longevity and wealth at the beginning of the period, but 63% of its population did by 2019.
In India, almost half the population, or about 490 million people, lived in microregions with living standards in the bottom 30% globally, or US$2,400 in GDP per capita and 65.6 years in life expectancy in 2000. Most of those microregions were found in the north and east of the country. For example, GDP per capita in Uttar Pradesh, India’s most populous state, was less than US$1,500. Odisha, on the Bay of Bengal, was recovering from a major cyclone, and its GDP per capita stood at US$2,000. Lifespans in both places were barely above 60 years.
Over the same period, some Indian states achieved the life expectancy and income levels of the top 30% of the world’s population in 2000. GDP per capita in Kerala, for example, almost tripled to US$10,000.
Granular data can inform business decisions
As companies grapple with economic and political upheaval, granular data about the world can help them reduce risks and increase resilience. The macroeconomic forces emerging in the wake of the Covid-19 pandemic have shifted priorities for business, and geopolitical tensions are forcing a rethink of global supply chains and shifting consumption patterns. At the same time, companies face demands to demonstrate their contribution to addressing weighty societal issues like climate change and inclusivity.
Corporate planning, decision making and strategy can all be enhanced by the insights granular data offers. As large companies seek to redesign supply chains, for instance, they can help enhance the geographic diversity of existing suppliers by using microregional data to find optimal locations for new factories and plants.
Granular data can help identify the location of talent and skills critical to a business and highlight local and regional assets that go unnoticed at the country level. Just consider what’s happened in Kaysone Phomvihane, once known as Savannakhet, in Laos. Serving the French as an administrative and commercial centre and later the American air force, the microregion’s prosperity grew over the past 20 years. During that period, several multinational programmes more tightly integrated Laos into the regional transportation and trade hub, including a bridge linking Kaysone Phomvihane to the industrial city of Mukdahan, Thailand. GDP per capita increased at a rate of about 10% annually in Kaysone Phomvihane, pushing it from US$1,500 in 2000 to US$10,000 in 2019, while life expectancy increased from 54.1 years to 65.8 years.
As Kaysone Phomvihane illustrates, variation within countries can be substantial, and the common country lens we usually apply obscures microregional richness. In fact, we find that a country’s GDP per capita growth rate only explains about 20% of the variation in growth rates among its microregions.
There is no question that the Covid-19 pandemic stalled and even reversed some of the gains made over the two decades that preceded it in Asia and around the world. Restoring the momentum of progress will be important and understanding how development unfolded at a granular level can help us learn from those places that were successful as well as ensure that resources are deployed where they’re most needed.
This article originally appeared in The Edge.