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traditional methods of collecting economic data, such as in-person surveys and interviews, run the risk of becoming outdated.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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SEC Chief Economist and Director of the Division of Economic and Risk Analysis S.P. Kothari highlightedpolicy challenges of big data.

Speaking at the National Bureau of Economic Research (NBER) conference on big data and high-performance computing, Mr. Kothari stated that the SEC faces three main big-data policy challenges: security, technology and communications.

First, he said that the “volume, velocity, and variety of big data” create security risks. Mr. Kothari urged the SEC to be “mindful of the data it collects and its sensitive nature.” Second, he said that technology poses an “arms race” among firms, with the risk that those who have better technology will profit significantly at the expense of others. Third, he said that issues concerning big data are complex and “increasing require specialized training to understand.” He warned that the SEC focuses on retail investors, but there are numerous stakeholders including “pension funds, municipal bond issuers, brokerage firms, hedge funds, and Congress.” In communicating to each group, he said, “one size does NOT fit all.”

Mr. Kothari also described the potential of big data with respect to identifying and stopping bad actors. He pointed to the Consolidated Audit Trail, which, when completed by the self regulatory organizations, “will provide a single, comprehensive database enabling regulators to track more efficiently and thoroughly all trading activity in equities and options throughout the U.S. markets.”

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