A new metric of consensus for Likert scales

TL;DR

This paper introduces a new consensus metric for Likert scales that quantifies agreement levels and demonstrates its usefulness in improving unemployment rate forecasts across European countries.

Contribution

The study proposes a novel consensus metric for Likert scales and applies it to economic forecasting, showing its effectiveness in predicting unemployment rates.

Findings

Consensus metric effectively measures agreement levels.

Including consensus improves unemployment forecast accuracy.

Consensus-based metrics are useful for tracking economic variables.

Abstract

In this study we present a metric of consensus for Likert scales. The measure gives the level of agreement as the percentage of consensus among respondents. The proposed framework allows to design a positional indicator that gives the degree of agreement for each item independently of the number of reply options. In order to assess the performance of the proposed metric of consensus, in an iterated one-period ahead forecasting experiment we test whether the inclusion of the degree of agreement in expectations regarding the evolution of unemployment improves out-of-sample forecast accuracy in eight European countries. We find evidence that the degree of agreement among consumers contains useful information to predict unemployment rates. These results show the usefulness of consensus-based metrics to track the evolution of economic variables.