Introduction

Google Search Requests, the News Media

surveys often face a small sample problem, both across time and respondents. Third, sur-vey respondents might lack an incentive to state their best possible expectations due to the absence of financial consequences and peer pressure. In addition,Kahneman(2011) has re-cently claimed that economic decisions are taken by the use of two mental “systems”: while filling in a survey puts the respondent into a situation that activates his cognitive reasoning, consumption decisions by contrast might primarily be governed by intuition. In this regard, if the same individuals participate repeatedly in the same survey, learning effects might re-sult in much better predictions compared to individuals that do not take part in the survey.

Finally, many countries still lack surveys that ask respondents to express their expectations in terms of a precise number or within predefined ranges. Instead, qualitative answers are provided making it necessary to apply data transformations that depend on various, often restrictive assumptions (Nardo,2003).

In this chapter, we propose the use of a supplementary measure for inflation expectations and explore its usefulness: the number of Google search requests for inflation. People in-creasingly turn to the internet if they feel the need for more information on a certain topic.

And among the various search engines, Google currently has a market share of about 66%

in the U.S., hence representing the majority of all search queries.² While Google search data have successfully been used in forecasting (see our survey below), we propose its appli-cation in the context of inflation expectations since it does not suffer from the disadvan-tages of survey data. Internet search intensity does not depend on framing effects stemming form question wording, nor do the raw series have to be quantified. The data can easily be downloaded without charge, and since the number of downloads is virtually unlimited, the small sample problem is avoided. Moreover, the number of searches comes as a by-product of users’ internet activities, hence search intensity is not affected by the particular circum-stances of a survey or a telephone interview. If individuals use the Google web page in order to find information on a certain topic, they do so because they already feel the need to get in-formed, either because they are reluctant to seem uninformed in daily talks, or because they have a specific economic transaction in mind which makes it necessary to possess the latest news on inflation. This alleviates the “cheap talk” problem encountered in survey data. Fi-nally, since Google search data is available on a weekly basis, this means that internet search requests could serve as a supplement to the existing survey data which is often compiled on a monthly basis and only released with some time lag. This is of particular interest for monetary policy that seeks to monitor price developments as timely as possible. It is against this background that the Bank of England states: “The Bank will continue to monitor these data (...). As further developments are made in this area (...), these data are likely to be-come an increasingly useful source of information about economic behavior” (McLaren and Shanbhogue,2011).

2Web-statistics should be read carefully due to varying methods of calculation. However, the two leading web analyzerscomScore(2012) andExperian Hitwise(2012) both report a market share of 66%.

In this chapter, we thus aim at exploring whether Google search requests can deliver new insights to the process of inflation expectation formation. For that purpose, we analyze U.S.

data from January 2005 to May 2011 on households’ and professional forecasters’ expecta-tions measured via survey data, newspaper articles and television reports on inflation, and Google search requests for inflation. In line withCarroll (2003)’s epidemiology model, we think of households adjusting both their demand for information and their expectations to the opinions of experts via the news media.

It is important to note at this stage that we do not consider Google search requests as an alternative measure for inflation expectations, but rather as a supplementary variable that can shed more light on the direction of future price expectations. Individuals can search for inflation in the web because they have heard about inflation in the news media and want to get more information on the topic which might subsequently result in an update of their expectations. Alternatively, according to the expectancy confirmation hypothesis (Traut-Mattausch et al., 2004, 2007), individuals might already expect higher prices in the future and seek to confirm their initial beliefs. While we aim at exploring these links in our empirical analysis, we broadly consider Google search request as a measure of attention (Da et al.,2011) and of the demand for information.

The contribution of this chapter is twofold: First, we analyze the news content of web searches on inflation. More precisely, we want to know whether search intensity evolves in a systematic way that can be attributed to real economic data. Note that Google searches might simply mirror the news coverage of inflation in the media, hence there might be no additional gain of using web searches in addition to the number of newspaper articles. To test whether Google searches are different, we compare the reaction of Google searches, TV reports and newspaper articles to changes in prices, variables describing the monetary policy and lagged values of households’ and professional forecasters’ expectations. In a sec-ond part, we take into account the various feedback effects among the news media, Google search requests and the inflation expectations of households and professional forecasters by estimating Vector Autoregressive models.

Our results show that users’ demand for information can indeed be linked to economic fundamentals: Google search requests can be explained by price changes much better than media reports. Google users distinguish between headline and core inflation and they react asymmetrically: the demand for information increases if core inflation falls. Furthermore, internet users understand the difference between relative and overall price changes: they search less for inflation if the relative price variability increases. In periods of historically high inflation rates, the number of search requests is significantly larger. Also, and in con-trast to media coverage, stock prices do not affect internet searches for inflation, but rising oil prices are found to reduce users’ demand for information on inflation. Moreover, in-ternet users pay attention to central bank behavior: unscheduled conference calls as well as issued statements increase search intensity. In addition, we find a positive effect from

house-holds’ inflation expectations in the previous period on search requests: Google users seek for additional information if they belief prices to rise in the future. Higher inflation forecasts of experts only marginally increase Google search requests, but if professional forecasters disagree a lot on future prices, the resulting uncertainty leads to a large increase in Google users’ demand for information.

With regard to the results of the VAR models, we find that television news coverage is driv-ing newspaper coverage, in addition to a feedback effect. Builddriv-ing on this result, we show that Google search requests for inflation are mainly determined by TV reports and only to a lesser degree by newspaper articles. Again, we find considerable feedback effects, suggest-ing that journalists consider the interests of their readers when decidsuggest-ing on the newspaper’s agenda. Finally, taking into account households’ and professional forecasters’ inflation ex-pectations, we show that households’ forecasts are driven by TV reports, newspaper articles, and Google searches, while the feedback effect from expectations on web searches is rather small and estimated less precisely. Furthermore, the impulse response from shocks on web searches to expectations is estimated more efficiently for weekly data, which indicates that the demand for new information has a rather short-run impact on peoples’ expectations.

About 20% of the forecast error variance decomposition of households’ inflation expecta-tions can be explained by Google search requests.

We start the chapter with a brief description of studies that use Google search requests in economics, with a special focus on how web query data can fit into the expectation forma-tion process (Secforma-tion4.2). We then explain our estimation approach in Section (4.3) before describing the compilation of the media and Google data in Section (4.4). Subsequently, Sec-tion (4.5) presents the results and SecSec-tion (4.6) concludes and discusses various direcSec-tions for further research.