Google: Central Banks' New Economic Indicator
Margo Sugarman spent months last year searching on Google for double ovens, low-noise mixers, and other appliances to complete her dream kitchen. Not only did those queries guide the Tel Mond (Israel) resident to the best deals for her 70,000 shekel ($17,680) renovation, they also helped the Bank of Israel, which looks to searches such as Sugarman’s to assess the state of the nation’s $243 billion economy.
Israel’s central bank is at the forefront of the hunt for new economic indicators. It analyzes keyword counts on Google for everything from aerobics classes to refrigerators to gauge consumer demand before releasing government statistics. The bank is not alone. The Federal Reserve and the central banks of Britain, Italy, Spain, Turkey, and Chile have followed Israel’s lead with their own studies to see if search volumes on Google correlate with broader economic trends.
At stake is the ability of the central banks to act more nimbly. Greater foresight could make the difference between a slowdown and a recession, a recovery and an inflation-stoked bubble, says Erik Brynjolfsson, a member of the Federal Reserve Bank of Boston’s Academic Advisory Council. “When central bankers were looking at traditional data, they were essentially looking out the rearview mirror,” says Brynjolfsson, a professor at the Massachusetts Institute of Technology. The December 2009 study he co-authored on predicting U.S. home sales using Google search data was cited in studies by three central banks. “If the Fed had had access to this information, they would have been able to make better forecasts of what was happening to the housing market and known more quickly the depth of the problem,” he says.
The development of Google as an economic tool started with a hunch at Google’s Mountain View (Calif.) headquarters. After developing a new website reporting how often users searched for certain keywords, Hal Varian, Google’s chief economist, says he wondered whether these data could foreshadow what traditional economic reports would show later. So he ran the numbers. The result was a 23-page paper he co-wrote in April 2009, demonstrating how data reported on the Google Trends service improved forecasts of auto and home sales and retail spending in the U.S.
Tanya Suhoy, a senior economist at the Bank of Israel, released a paper three months later that found newly available Google-mined data helped predict slowdowns and slumps in Israel. Economists from six central banks, including the Federal Reserve, began to ask similar questions: Did more people browsing for cars predict an increase in auto sales? Was a jump in research on unemployment benefits a hint that people were losing jobs?
One challenge for policymakers is setting rates based on data often gathered weeks before. The Department of Commerce typically publishes its monthly retail report two weeks into the following month. Google makes its updated data available one to three days after searches.
Even the biggest proponents of Google-as-indicator cite the need to proceed cautiously. The figures go back only to 2004. By limiting its sample to Internet users, the search volumes may not reflect purchases by those who tend to live more offline: the elderly and the poor. “Potentially, using Google could be interesting, but at the moment its forecasts of macroeconomic variables aren’t reliable,” says Lucrezia Reichlin, a professor at London Business School. “Google is sexy and something may come of it, but more research is needed.”
Despite those limitations, the Bank of England has started tracking Google searches. The popularity of search terms such as “JSA,” short for the job seekers’ allowance, helped predict unemployment data, according to a June 2011 study authored by BOE researchers Nick McLaren and Rachana Shanbhogue. Search counts “are likely to become an increasingly useful source of information about economic behavior,” they wrote. At the Bank of Spain, Concha Artola and Enrique Galán analyzed travel-related queries in the U.K. in a paper released in March. Their conclusion: Searches predicted the inflow of British tourists into Spain with a lead of almost one month.
At the New York Fed, Rebecca Hellerstein and Menno Middeldorp compared the popularity of the phrase “mortgage refinance” with an index tracking the number of refinancing applications filed. They found that the forecasting model including the Google data predicted the index more accurately than the model excluding the Google data. “This is all in its infancy, but it’s fascinating,” San Francisco Fed President John Williams told reporters in March. It’s “an enormous amount of information” that will better help “us understand in very real time what’s going on.”