Economic Sentiment Deepens European Monetary Integration


The question of abandoning national currencies and replacing them with the common currency awaits seven more EU member states (Bulgaria, Croatia, the Czech Republic, Hungary, Poland, Romania, and Sweden). The main euro-related concern in these countries is the net economic effect of the stated currency conversion. According to the Optimum Currency Area (OCA) theory, the efficiency of a common monetary policy immensely depends on euro area (EA) business cycle synchronisation.

Our paper focuses on several aspects of European monetary integration that have insofar been quite neglected in the literature. Even if the current composition of the EA is an OCA, how well do the future euro adopters fit into the OCA concept? Second, is it possible that the general business cycle synchronization is a mere by-product of synchronicity among the economic sentiment cycles? In that context, we provide evidence that non-EA economies are quite strongly synchronized with the EA core, and we find that the synchronization of economic sentiment cycles could be an important contributor to the validity of OCA in the EA context.

In this research field, two specific strands of literature have emerged. The first deals with the business cycle synchronization of European economies, while the second focuses on the synchronization of economic sentiment cycles among these countries. Our goal is to combine both approaches.

The EA is built upon the concept of OCA, aiming to be a geographical area that maximizes economic benefits by means of a common currency. The similarity of shocks and the policy responses to these shocks is a fundamental OCA prerequisite that encompasses most of the other OCA features. If the strength and duration of economic shocks among countries that form a monetary union highly resemble each other, those countries enjoy low costs of common monetary policy, and form an optimal monetary area. Therefore, the OCA theory implies that an economy joining the EA should give up its monetary sovereignty and the possibility of using autonomous stabilization policies. In that sense, a strong level of uniformity between the business cycles of the joining country and the EA itself is a necessary condition for the efficiency of the economic and monetary union.

To investigate this issue, we assess cycle synchronization of 17 European economies. Specifically, we examine eight recent additions to the EA (countries that joined the EA after its first enlargement in 1999, i.e., Cyprus, Greece, Estonia, Latvia, Lithuania, Malta, the Slovak Republic, and Slovenia), as well as nine economies that have not yet adopted the common currency: Bulgaria, Croatia, Czechia, Denmark, Hungary, Poland, Romania, Sweden, and the United Kingdom (UK).

We assess two distinct datasets: survey-based (depicting economic sentiment) and national accounts (macroeconomic variables depicting the state of an economy). The first dataset refers to the Economic Sentiment Indicator (ESI), an economy-wide indicator of the European Commission, measuring managers’ and consumers’ attitudes about the prevailing economic climate, as well as their expectations of future trajectories of relevant economic variables. ESI is a monthly indicator of the prevailing business and consumer sentiment, and it mimics the movement of GDP along with a variety of macroeconomic variables (Claveria et al., 2007; Sorić, 2018; Lehmann, 2020). In that sense, it is expected that cycles of economic sentiment share a common pattern with business cycles in general.

The second dataset refers to GDP, obtained from Eurostat. We use the Hodrick and Prescott (1997) filter to extract the cyclical components of both analysed time series (GDP and ESI) for each of the 17 assessed countries.

To illustrate the dataset, Figure 1 shows that the two cycles for core EA countries are highly similar to each other, with the ESI cycle being slightly more volatile. Both cycles vividly reflect the four major economic shocks in the examined period: the dot-com bubble recession in the early 2000s, the global financial crisis, the European sovereign debt crisis, and the turmoil caused by the COVID-19 pandemic.


Figure 1. GDP and ESI cycle for EA11.

Note: Dashed line corresponds to the ESI cycle (measured on the right vertical axis), full line corresponds to GDP cycle (left vertical axis). Vertical line refers to the introduction of euro in 1999.


We explore three different measures of cycle coherence. First, the most widely utilized synchronization measure is the moving window (Pearson’s) correlation coefficient. Second, cycles can differ in economic phases (recession vs. expansion), so we also assess a measure of (phase) synchronicity. Finally, we calculate a measure of cycle similarity focusing on the amplitude of the cycles. A detailed formulation of these three coherence measures is available in our paper. The three indicators are calculated for GDP cycles for each of the 17 assessed economies vis-à-vis the EA11, and the exact same procedure is repeated for their ESI cycles.

To understand the importance of synchronicity between business cycles, let us scrutinize a hypothetical scenario of a new country joining the EA. This country’s business cycle must be synchronized with the EA in order for the common monetary policy to be effective. Should the joining country be in a state of recession when the ECB increases key interest rates, such a contractionary monetary policy would have a detrimental effect on the joining economy.

To examine whether the three-cycle coherence indicators are significantly different in economic downturns and upturns, we identify the turning points of the GDP cycle for each of the 17 non-EA economies, and calculate the three examined coherence indicators with respect to EA11, separately for recessions and expansions.

Our results have shown that a mismatch in phase synchronicity between euro-adopting countries and the EA core is highly unlikely. Additionally, the coherence of examined cycles magnifies during recessions in most of our specifications. Two out of three coherence measures indicate a significantly stronger relationship in recessions in the case of GDP. On the other hand, all three indicators suggest that ESI cycle is more closely related to the aggregate EA11 sentiment cycle in recessionary periods than in expansions, although the difference is statistically significant only for phase synchronicity. To sum up, our findings reveal that synchronization is even stronger in recessions, which can be seen as an argument favouring EA enlargement.

Our second concern is that the previously conducted research does not reveal the causality direction between the synchronization of business cycles and economic sentiment cycles (Hohnisch and Westerhoff, 2008, p. 258). Therefore, Toda–Yamamoto variations of the Granger causality test are applied to scrutinize the causality direction between the synchronization of two corresponding cycles.

We find much more evidence of business cycles being driven by sentiment cycles than the other way around. Our results strongly indicate that sentiment synchronization precedes the overall business cycle synchronization, indicating the relevance of animal spirits in driving the economy as a whole.

These conclusions should inspire researchers and policymakers to widen their focus and include economic sentiment indicators in their analysis of business cycle synchronization in the OCA context. Since co-movements of sentiment cycles drive the synchronization of business cycles, the issue of primary focus should not only be the consequence, but the cause as well.


The blog draws on the JCMS article Economic Sentiment and Aggregate Activity: A Tale of Two European Cycles




Petar Sorić is an Associate Professor at the Faculty of Economics and Business Zagreb (EFZG). His research interests include time series analysis and behavioural macroeconomics. His academic publications have earned several awards, out of which the Croatian National Science Award of in 2013 stands out.





Ivana Lolić is a postdoctoral fellow at the Department of Statistics at the Faculty of Economics and Business (University of Zagreb). She has MSc in Mathematical Statistics and PhD in Economics. Her primary research area is behavioral economics, econometrics, and machine learning.





Marija Logarušić is a Research Assistant and PhD student at the Faculty of Economics and Business, University of Zagreb. Her research interests are in the areas of behavioral economics and econometrics.