With the digital makeover of the economy, which has allowed for transactions carried out by individuals and companies to be traced and recorded in real-time, traditional methods of collecting economic data, such as in-person surveys and interviews, run the risk of becoming outdated.

Digital records of such transactions—whether online or brick and mortar—at a product level and the growing ability to store and analyse such information offer an opportunity to better capture production and price statistics, says a new National Bureau of Economic Research (NBER) study authored by Gabriel Ehrlich of University of Michigan and others.

Such sales and price data, obtained directly and simultaneously from a single source, will also greatly refine measurement of economic activity and productivity at a product-level, according to the authors.

Advances in research and technology have also made it possible for digitized transactions data to be innovatively used to capture economic activity, the authors suggest. For instance, the “unified price index” technique developed by Redding and Weinstein (2018) makes it possible to capture the effect of changes in product appeal on their prices. However, the authors warn that more work is required to refine these techniques before they are incorporated by statistical agencies. Such agencies may not find it easy to access and process such kind of data, they argue.

Building key national indicators from product-level transactions data would also necessitate re-engineering of the statistical architecture in most countries to change how data is collected and accessed for official statistics. For instance, the simultaneous collection of price and quantity data requires combining the data collection activities that are currently spread over multiple arms of the statistical machinery. Such a shift, however, may be inevitable as non-response rates and costs of traditional surveys escalate over time, the authors argue.

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Africa’s nascent artificial intelligence sector just got its biggest boost from Google which is opening its first Africa AI research center in Accra, Ghana’s capital.

Though Accra has a vibrant tech industry, it would not have been the obvious location for many Africa tech watchers when compared with Nairobi or nearby Lagos where Google has already announced it would open its first Launchpad Space outside the US. Last month, Facebook also opened its first startup hub in Africa there.

Google had been laying the pipeline, both figuratively and physically, for future developments in Accra for a few years now. Back in 2015, the Mountain View, California tech giant started work on a fiber optic network, called Project Link, across the city to improve internet speeds.

Ghana also has good a font of young talent from its public universities and newer centers like the computer science program at the highly-regarded private institution, Ashesi University.

Even then, deciding on the location was a difficult decision, said Jeff Dean, team lead for Google Brain, under which its AI projects are housed. “In the end, we chose Accra because of a strong ecosystem of local universities and its proximity to a branch of the African Institute of Mathematical Sciences, and our experience in the country,” said Dean on Twitter in response to Quartz’s questions. “It is our hope that over time we can consider other research locations as well.”

The company says it’s trying to bring together top machine learning researchers and engineers at the new center dedicated to AI research and its applications. Google is making a big bet on AI for its future. In 2016 alone it invested $30 billion on AI and machine learning research.

AI is already having a major footprints across Africa. In Ethiopia, where Sophia (the robot that was granted Saudi citizenship in 2017), was partly developed, futurists and enthusiasts are betting on AI’s massive potential to drive development in place of the government’s own bet on manufacturing. Instead of large industrial parks for Chinese factories, they want increased investment for research and development into new age technologies like AI. “They think that advanced technologies are a luxury. It’s not a luxury, it is crucial,” says Getnet Assefa, founder of iCog, Ethiopia’s first AI lab.

In Nigeria, machine learning is being used by doctors for the early detection of birth asphyxia—the third highest cause of under-5 mortality in Africa. China, on its way to become the leader in artificial intelligence, is using Zimbabwe as the test ground to help its facial recognition systems identify faces with dark skin.

Despite these and many other projects, artificial intelligence is not necessarily a straightforward topic for governments across the continent. AI is already taking away some factory jobs even before there were enough to go round in what is being termed as ‘premature deindustrialisation’.

With high levels of unemployment in many countries, particularly with young Africans there are concerns with some African policy makers that these types of technological advances, which will see machines and robots learn to “think” like humans will only worsen unemployment. Yet such consideration might be seen as short-term thinking. Research from PricewaterhouseCoopers estimated “artificial intelligence technologies could increase global GDP by $15.7 trillion, a full 14%, by 2030 of which $1.2 trillion would be added for Africa.

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In October 2015, even as a battle against high inflation was raging, a committee of economists advising the Reserve Bank of India (RBI) governor on monetary policy made an interesting observation that was lost amid the usual noisy debates about interest rates: “Moreover, there has been comfort on the inflation front—wholesale prices are contracting, GDP (gross domestic product) consumption deflator has been low at around 3%, and with vendors engaged in e-commerce offering low prices, retail inflation may be lower than what the headline number suggests.” (Italics added).

Something similar happened in the latest monetary policy announced earlier this month, and it too was largely ignored. The RBI said it would be setting up a data sciences laboratory that would employ professionals with skills in computer science, data analytics, statistics, economics, econometrics and finance. The unit is expected to begin work in December. An RBI official told me that this would be an independent division whose work would be used for everything from inflation management to banking supervision to financial inclusion. “In the backdrop of ongoing explosion in information gathering, computing capability and analytical toolkits, policymaking benefits not only from data collected through regulatory returns and surveys but also from large volumes of structured and unstructured real-time information sourced from consumer interactions in the digital world,” the Indian central bank said in a release.

Such a unit could help monetary policymakers get a better sense of how prices are moving in real time. Consider the example mentioned at the beginning of this column—on how prices actually paid by consumers depend on the discounts being offered by online retailers that their offline competitors then have to match. Such work is already being done elsewhere. For example, the Billion Prices Project by the Sloan School of Management at the Massachusetts Institute of Technology tracks the prices of some five million items sold every day by online retailers in 70 countries.

A central bank that has inflation control as its primary task could then get an understanding of how prices are moving in real time—though the inflation forecast would continue to be the intermediate policy target.

Or consider another example. The financial system is now completely computerized, so large volumes of data are generated on a daily basis. A dynamic database of bank lending activity could help a regulator spot outlier activity that may lead to trouble later. This assumes that data across the banking sector is aggregated. Trends in retail lending can also be spotted by plugging into the databases of consumer credit rating agencies. There is a flip side as well, given the growing criticism that machine-learning algorithms can reinforce human prejudices, so it is possible that the RBI can use such critical analysis to detect financial exclusion based on religion, caste, gender or location.

Many other central banks are already experimenting with Big Data analytics. In a December 2017 Bloomberg article, Jeanette Rodrigues and Alessandro Speciale cited the examples of central banks from Japan, China, Singapore, Indonesia, Thailand, the US, Europe and the UK on how they are embracing Big Data analytics. The two journalists also gave the example of India, though at that point, all that was on the table was a technology unit to look at cybersecurity. The recent announcement means that Big Data is finally entering the portals of the Indian central bank.

This column had noted in August 2017 that the Union finance ministry had already begun to experiment with Big Data. The Economic Survey of that year had done three things.

First, it had used data from the goods and services tax network (GSTN) to analyse trends in interstate commerce.

Second, numbers from the Indian Railways ticketing system had been used to map out internal migration.

Third, the finance ministry had used satellite data to argue that India is perhaps more urbanized than the traditional sources of data tell us.

Indian policymakers usually use data based on periodic sample surveys. Policy is often crafted in a fog. A lot of the initial estimates are very different from the final numbers. Measures of economic activity such as the index of industrial production (IIP) are, as one former RBI governor said, bewildering. There is an urgent need to overhaul a creaking statistical system that was once one of the best in the world. However, there is also a need to move to more contemporary styles of data analysis that can provide important realtime numbers about economic activity, prices and confidence.

In a recent meeting with journalists, the new head of the US Federal Reserve System, Jerome Powell, said that “it almost has to be true” that the spread of online retailers (as well as the effects of global supply chains) is keeping inflation under control. Economists in that country are still scratching their puzzled heads about why inflation has not gone up despite the US having one of the tightest labour markets in this century, unlike what the famous Phillips curve has taught them to expect.

So put this down as one of the most under-appreciated developments of the past few months—the embrace of Big Data analytics by the Indian economic policy establishment.

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