Characteristics of the information content in business sentiment surveys

Purpose – The purpose of this manuscript is to quantify exactly how much information and/or predictive content is contained in business sentiment surveys. Design/methodology/approach – This paper uses techniques drawn from information theory econometrics, and more specifically the theory of information entropy, to characterize the predictive content of business sentiment surveys. The authors apply these techniques to publicly available information obtained from various editions of the Federal Reserve Bank of New York's Empire State Manufacturing Survey , one of the most popular business sentiment surveys conducted in the USA. Parametric and non‐parametric statistical analyses are used to examine differences in the quantity of predictive content across various questions in the survey. Findings – The results suggest that business sentiment surveys contain a reasonably high degree of informative content. However, the amount of informative content varies considerably from question to question in the survey. Questions that are more general in nature and ask about current perceptions (as opposed to future expectations) contain more informative content. Originality/value – Business sentiment surveys are a practical, low‐cost method to assess the current and expected future state of local and regional economies. However, the value of these surveys is questionable if they do not contain much information. This research finds that such surveys do contain a large amount of information, and are worth administering. However, specific types of survey items convey more information that others, which also suggests that business sentiments surveys can be further revised to maximize the amount of content gained from respondents.