When people lose their jobs, their behaviour changes. They might leave the house less, be awake at different hours, and phone fewer or different people. These individual changes accumulate into large patterns that can be seen in big data sets, like mobile phone and social media usage.

Two papers published last week in the Journal of the Royal Society Interface and PLOS One explain how our phone and social media usage can give clues about our employment status. These data sources are important, the authors write, because they can track and predict changes in the economy faster than traditional methods.

The methods that governments currently use to collect macroeconomic statistics are based on “a paradigm of data collection and analysis begun in the 1930s,” writes Jameson Toole and the other authors of the Royal Society Interface paper. “Most economic statistics are constructed from either survey data or administrative records.”

In the UK, the Office for National Statistics (ONS) uses multiple methods to gather statistics on employment and unemployment. It surveys businesses, households, and both private and public sector employees, and also gathers monthly details on people claiming unemployment benefits. Each method has its strengths and weaknesses: for instance, household surveys catch all age ranges and those who aren't eligible for unemployment benefits, while business surveys can capture employment by region and industry.

These multiple analyses are published on anything from a six-weekly to an annual basis. The statistics are time-consuming and expensive to collect and analyse, meaning that they often lag behind the reality of the economy, making up for the lag with a fine-grained level of detail and accuracy. Surely there's a better way?

Playing fast and loose

Detecting the echo of unemployment in big data is much faster. As a case study, Toole and his team looked at what happened to mobile phone data in the wake of a mass layoff at a factory in an undisclosed location in Europe. When a car-parts factory closed in December 2006, 1,100 people lost their jobs, in a town of approximately 15,000 people.

The researchers used data from a mobile phone provider to examine the anonymised phone records of people in the town. The area is served by three mobile phone towers that cover a largely unpopulated region of 220 km2, and no other towns. The researchers didn’t distinguish between the three towers, since factors like obstruction by buildings could play a role in routing calls, but considered all three towers to indicate calls made from the manufacturing plant or town.

First, they checked whether they could detect the closure of the plant in the phone data. They estimated the date based on the volume of calls through the three towers, and then compared their estimated date to the real date of the layoff. They matched.

Next, they looked for patterns in how people used their phones before and after the layoff. People who worked at the plant but didn’t live in the town would have seen a sharp dropoff in calls made near the plant after the layoff date, so the researchers focused on individuals whose data showed this pattern. They found that these people made fewer calls, to fewer unique contacts, and that their mobility reduced—the number of towers their calls routed through diminished. They also looked at “churn,” which is the amount of change in who the person called from month to month. The newly unemployed people had higher churn compared to controls, indicating that their social networks were less stable.

Making predictions

If it’s possible to identify behaviour suggesting unemployment in call logs, it should also be possible to see this on a much wider scale than a small factory town. To explore this, the researchers looked at 10 million mobile subscribers in a different undisclosed European country between 2006 and 2009. Instead of looking for mass layoffs, they looked at the data for the behavioural signals they had identified from the factory case study.

They used that data to guess what the unemployment rate was in a particular quarter, such as Q1 2007. They also used the data from that quarter to predict the next quarter’s rates, in this case Q2 2007. In this way, they could compare their estimates and predictions to official records for these time periods. Both the current estimate and the future prediction rates correlated strongly with official records, although the correlation was slightly weaker for future predictions.

Social media can tell us different things about how people behave following unemployment, but may also be useful. The authors of the PLOS One article used 19.6 million geo-located tweets from Spain in 2012 and 2013 to establish how much people moved around, how early people were active on the network each morning, and how grammatically correct their tweets were.

They found a correlation between regions with higher employment and tweets showing more mobility, earlier rising times, and correct grammar. As with the phone data, these social media fingerprints could be used to produce accurate estimates of unemployment in a given region.

Faster prediction could be extremely helpful in making economic decisions, says David Lazer, one of the authors of the Royal Society Interface paper. “There’s an enormous amount invested by public and private entities to measure the status of the economy in order to better inform policy interventions. If we could understand the changes in the state of the economy faster, we might be able to intervene faster.”

It’s an interesting application of existing methods to a new dataset, says Mirco Musolesi, a data scientist who wasn’t involved in this research. Because studies like this are “essentially very cheap,” he says, “these findings would be very interesting for policy-makers and researchers.”

Listing image by Alejandro Llorente, Manuel Garcia-Herranz, Manuel Cebrian, Esteban Moro (PLOS One)