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Traditional economic reports, on the other hand, look at events that have taken place. Unemployment figures tell us how many people are claiming benefits rather than how many people have been put at risk of redundancy. Average house prices are based on completed transactions, not how many people are currently looking to buy. So while we can be fairly confident of these reports, they don’t provide us with particularly current insights.

This trade-off between confidence and currency was, in the past, largely academic as analysing current data was almost impossible. But in the age of the real-time web, this might be about to change: maybe patterns in search behaviour can give us a glimpse of future patterns in the economy.

We first became interested in this topic back in spring 2009, so we analysed search patterns for two sets of keywords as the UK economy went into recession. We looked for relationships between these search patterns and related economic indicators, and listed some tentative predictions based on what we observed.

House prices

In April 2009, we looked at volumes for 23 keywords that homebuyers might use, including buying a home, cheap mortgage and mortgage providers. UK search volumes for these keywords were then compared to house prices.

House prices charted against search volumes for 23 related keywords, from January 2004 to April 2009. Sources: Nationwide, Google Insights

Searches typically decline as autumn ends before rebounding in January. But in 2008, the January rebound was lacklustre and the decline came in spring – much earlier than usual. This was in line with house prices, which peaked in late 2007 and dropped severely from spring 2008.

In the first few months of 2009, however, search volumes enjoyed a far stronger January rebound than in the previous year – so we hypothesised that house prices would bottom out or even start to rise again in the middle of 2009. Let’s look at how accurate that hypothesis turned out to be.

House prices charted against search volumes for 23 related keywords, from January 2004 to December 2009 Sources: Nationwide, Google Insights

Sure enough, the search volume resurgence was accompanied by house price growth throughout 2009. But you’ll notice that search volumes soon tapered off, with a particularly steep fall after August. Our revised hypothesis, then, is that house prices will initially plateau and then drop again. We’ll revisit the statistics in spring 2010 to see how things turn out.

Financial difficulties

The second set of keywords we analysed was related to impending financial difficulties such as joblessness, debt and insolvency. They included signing on, mortgage arrears and debt problems, and were compared to the UK jobless rate.

UK unemployment rate charted against search volumes for 24 related keywords, from January 2004 to April 2009 Sources: Office for National Statistics, Google Insights

These search volumes dip at the end of each year before rising in January – and the rise in early 2008 was more pronounced than in previous years. The jobless rate started climbing three months later, suggesting that in this case search patterns might anticipate economic statistics. We observed that search volumes had dropped significantly in the first few months of 2009, so our hypothesis was that the jobless rate would stabilise but not drop between April and July. The chart below shows what actually happened.

UK unemployment rate charted against search volumes for 24 related keywords, from January 2004 to April 2009 Sources: Office for National Statistics, Google Insights

The unemployment rate has indeed stabilised, wavering between 7.7% and 7.8% since early June, suggesting that our original hypothesis was valid. And search volumes have kept on dropping throughout 2009. If search trends do anticipate economic reports in this case, we should see the unemployment rate drop steadily between now and spring 2010. Again, we’ll revisit these figures in April to see if this happens.

Conclusion

Our hypotheses from April 2009 were largely borne out as the year progressed: the drop in house prices was reversed and unemployment rates stabilised. So maybe there is some truth to the notion that search patterns can shed some light on forthcoming economic change.

But these hypotheses were in tune with the economic mood of the time. Many commentators were talking about green shoots and a V-shaped recession – there was a feeling that recovery was just around the corner. Today, we remain in what has become the longest-running recession in recorded history and there is considerable uncertainty about what 2010 will bring.

Our new hypotheses are less likely to be tainted by current economic consensus, precisely because no real consensus seems to exist right now. For this reason, the idea of search predicting the future will be seriously tested as the year unfolds. Don’t forget to come back in April 2010 to see the results for yourself.

The study involved 66 graduate students with either normal or corrected vision being given a short story to read online. A preliminary reading test was carried out on participants so the study could predetermine their reading speed. Different text layouts were used, such as multiple column, full justification and so on. Study participants were tested for both reading speed and reading comprehension.

• Reading speed: Multiple-column layouts impaired reading speed when text was left-justified. However, left-justified text was read more quickly in a single column layout than full-justified text. The highest reading speed was 269.33 words per minute for two-column, full-justified text.
• Reading comprehension: No significant variation was found across the different text formats.
• Fast versus slow readers: Faster readers benefited most from the 2-column, fully-justified layout. Slow readers benefited from 1-column, left-justified text.

The study was perhaps limited by the fact that the participants, as undergraduates, were heavier readers of online text than the average member of the population. I’d be interested to see if any similar studies have been carried out with a larger sample size, broader age range and a more representative mix of internet ‘natives’ versus internet ‘newbies’. Does anyone know of any? If I find some I’ll post them here.