Introduction

Unfinished Business in the Epidemiology

the question whether expectations are well anchored i.e. whether inflation expectations are close to the target rate. Most of the empirical literature on anchoring, however, uses data for professional forecasters, assuming implicitly that these are fully matched by the general public (seeDräger and Lamla(2013a) for an exception). But if expectations are formed in line with the hypotheses of sticky information or the epidemiology model, it might well be the case that the predictions of households deviate from the expectations of experts.¹ Finally, in addition to adjusting their forecasts only gradually, households are often found to disagree about the future (Armantier et al., 2012). And if agents disagree persistently with respect to future outcomes of economic variables, this might call for an adapted communication strategy of central banks (Sims,2009).

In this chapter, we test the sticky information expectation hypothesis in some detail. More precisely, we focus on the epidemiology model proposed byCarroll(2003) since it provides a direct way to test the “ease-of-information hypothesis” by incorporating a prominent role of the news media.² In line with most of the literature, we refer to inflation expectations and use survey data for household expectations and proxy the best available forecast with survey expectations of professional forecasters. More precisely, we use U.S. data from the Michigan Survey and the Survey of Professional Forecasters (SPF) from January 1980 - November 2011.

We focus on three dimensions of the epidemiology model. First, we analyze whether the expectation formation process changes over time, i.e. whether households build different forecasts in times of high or low inflation, or in times of economic crisis. Moreover, related toLamla and Sarferaz(2012), we test whether the degree of updating varies over time in line with the amount of news coverage on inflation. Second, we use both aggregate and micro survey data in our analysis thereby studying whether the results depend on the aggrega-tion level of the data employed. As it has been stressed byDovern et al.(2013), theories of expectation formation are mostly formulated on the individual level and are subsequently applied to aggregate survey data using for example the cross-sectional mean forecast. How-ever, aggregating individual survey responses might be problematic if it masks important heterogeneity on the micro level. This is particularly relevant with respect to measuring the strength of the news media effect. Empirical research in communication studies has found much stronger media effects on the aggregate level compared to the micro level (Krause and Gehrau, 2007). A possible explanation for this “paradox of agenda setting” might be that only a part of the population follows the news media and subsequently circulates the infor-mation to non-users. In order to test this explanation, we separate the full sample of survey respondents into households who have heard news on economic issues, on inflation, and on good or bad news on inflation. We then test if these groups are more receptive to news

me-1In this context,Coibion and Gorodnichenko(2012) have recently emphasized that it is important to allow for different expectation formation processes of different agents.

2The baseline equation of the sticky information model and the epidemiology model is essentially the same.

For differences with respect to the rational inattention variant, which are beyond the scope of this paper, see for exampleDräger and Lamla(2013b) andDovern et al.(2013).

dia coverage compared to others, and if their forecast error is lower. Finally, we test whether the news media effect is non-linear. Research in psychology suggests that individuals only pay attention to news if the stimulus passes a certain threshold.³ On the other hand, there can be a satiation level: If the media treat a certain topic extensively over some period, read-ers loose interest and are thus less willing to react to new incoming information. We test for the possible non-linearity of news media effects by fitting Smooth Transition Autoregressive Models. For the best of our knowledge, this is first time that non-linear news effects on the inflation expectation of households are investigated in the literature.

Our empirical analysis yields the following results. Overall, we find that the epidemiology model is supported by the data. Households partly use the best available forecast and their own past forecast when forming beliefs about future inflation. In addition, households ad-just more to experts in times of low and stable inflation and during economic crisis. More news coverage of inflation generally lowers the gap between households’ and professional forecasters’ predictions, however, the effect is not stable over time. In times of falling infla-tion, the news media lower the expectation gap, whereas in times of economic crisis, more articles on inflation increase the gap. Comparing the results using macro and micro data, we find that the speed of updating is lower if we use micro level data. While this result is in contrast to other findings (Dräger and Lamla,2013b), it might stem from the fact that we also consider households who only take part in the survey once, and that we allow for time variation. In contrast to the degree of updating, the media effect is found to be larger on the micro level. Looking at households with different news perceptions, we find that those who claim to have heard news on inflation commit larger forecast errors than other house-holds while at the same time being more receptive to media reports. Finally, our analysis suggests that the media effect is non-linear. An increasing number of news reports increases the impact from expert expectations. For all households, the adjustment takes place only gradually, whereas those who have heard news about inflation are much quicker in reacting to rising amounts of news coverage.

Our paper is closely related to two recent studies of the micro data of the Michigan Survey.

Dräger and Lamla(2013b) show that the updating frequency of households is much higher if the analysis is conducted with micro data. Coverage of inflation in the news media is found to have no effect on the updating frequency and the precision of forecasts. By contrast, if participants claim to have heard news on inflation, they are more likely to adjust their expectations resulting, however, in a larger forecast error. Pfajfar and Santoro(2013) show that a rising amount of news coverage increases the gap between households’ and experts expectations which is in marked contrast to the prediction of the epidemiology model. Both of these studies use the short rotating panel dimension of the Michigan survey. In each

3This point relates the to so called “Weber-Fechner law” stating that the effect of a stimulus is not constant but depends on the initial level of the stimulus. See for an application to the perceptions of inflationThaler(1980) andBatchelor(1986).

month, about 40% of all participants are reinterviewed a second time six months after the first interview. While using the rotating panel is appealing due to the fact that it allows studying whether and how the same individual changes her expectations over time, it also has some disadvantages. Individuals might pay (more) attention to the news simply because they participate in a survey. Since they know that they will interviewed a second time, they try their best to look good when being faced with the interviewer.⁴ Moreover, the individual updating period is fixed by assumption. The second interview will take place six months after the first one, and if individuals have changed their forecasts several times in between the survey rounds, this will not appear in the responses. Finally, it remains unknown whether participants in the second interview will be reminded of the forecasts they have made in the first interview. Therefore, expectation updating might arise simply because participants do not remember their previous forecast.

Besides of the rotating panel dimension, both Dräger and Lamla (2013b) and Pfajfar and Santoro(2013) use the fraction of households who have heard news on inflation interchange-ably with the amount of news coverage in the media. Whereas self-reported news might be preferable to the number of newspaper articles because it measures the actual informa-tion set of households more closely, it also suffers from severe overreporting (Prior, 2009).

Therefore, we take a slightly different perspective and test whether households who claim to have heard news about inflation are also affected more by the news media compared to other households.

We start our analysis with a brief exposition of the epidemiology model and a discussion of its particular features that we are going to analyze in detail (Section2.2). In Section (2.3), we describe the data set and provide summary statistics of the micro data of households’

inflation expectations which already provides important insights about the expectation for-mation. The empirical analysis is divided into three parts. We start with estimating the epidemiology model without news media in Section (2.4), before including news coverage in a linear framework in Section (2.5) and allowing for non-linear effects in Section (2.6).

Section (2.7) summarizes the results.