Meta-analysis of the business cycle correlation between the euro area and the CEECs

Abstract

We review the literature on business cycle correlation between the euro area and the Central and Eastern European countries (CEECs), a topic that has gained attention as the newest EU members approach monetary union. Our meta-analysis of 35 identified publications suggests that some CEECs already have comparably high correlation with the euro area business cycle. We find that estimation methodologies can have a significant effect on correlation coefficients. While CEEC central bankers tend to be more conservative in their estimates than academics or eurosystem researchers, we find no evidence of a geographical bias in the studies. Journal of Comparative Economics 34 (3) (2006) 518–537.

Introduction

Soon after the European Union's Eastern Enlargement in May 2004, several new member states joined the Exchange Rate Mechanism II (ERM II). At present, seven new states participate in the ERM II; the remaining new member countries along with potential EU members Bulgaria and Romania are all expected to join in the coming years.¹ Thus, after completing the mandatory two-years in ERM II, Slovenia will introduce the euro in 2007 while the other countries may follow soon after. In this paper, we consider the growing literature on business cycle correlation between the countries of the Central and Eastern Europe (CEECs), namely Bulgaria, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, and Slovenia, and the euro area. Optimum currency area (OCA) theory states that a high degree of business cycle synchronization should be an important criterion for participation in a monetary union. This criterion is applied to issues related to euro adoption and exchange rate regimes in the new member states of the EU as well as to other countries having intensive trade and economic relations with the EU, including prospects for eventual EU membership.

On one hand, the CEECs are relatively small compared to the euro area and are expected to be affected strongly by the business cycle of their most important trading partner, i.e., the euro area. On the other hand, these countries are likely to benefit disproportionately from EU integration and the introduction of the euro, reflecting the relative economic size of the regions. Given these contradictory expectations, authors have applied a range of methodologies and sample periods in measuring recent business cycles. Economic analysis of CEECs is characterized by significant data problems. In general, reliable time series are available only from the beginning of the 1990s; for some countries, data availability is even more limited. Data comparisons from multiple sources often show significant differences and frequent data revisions may make replications of analyses difficult. As a result, the robustness of results reported in any particular study is questionable.

Such data problems are not unfamiliar to researchers in other fields in the natural and social sciences. Meta-analysis of existing studies is a potentially useful way to gain more robust results, as Lipsey and Wilson (2001) discuss. Meta-analyses basically summarize published results on particular topics, provide an aggregate overview of a subject, and allow an analysis of the factors that may influence the results, e.g., data definition, time period, or author characteristics. The use of meta-analysis has become a popular economics research tool, e.g. Stanley (2001) and Stanley and Jarrell (2005), most notably in monetary economics with papers by De Grauwe and Costa Storti (2005), Rose and Stanley (2005), and Knell and Stix (2005). Thus, meta-analysis provides the means to extend analysis beyond standard literature surveys.

The paper is structured as follows. The next section reviews the optimum currency area theory from the perspective of the new member states. Section 3 presents a meta-analysis of 35 publications having more than 450 point estimates of business cycle correlations between the CEECs and the euro area. Section 4 considers the position of the new member states within the EU using meta-regression analysis. Section 5 concludes with a summary and some policy implications.