Regional Business‐Cycle Synchronization, Sector Specialization and EU Accession

Abstract

We examine the effects of Eastern and Northern enlargement of the EU on regional business-cycle synchronization and sector specialization. Difference-in-difference estimates show that cyclical synchronicity decreased and differences in sector structure increased in acceding region-pairs after Eastern enlargement. For Northern enlargement, results are more ambiguous. Moreover, in both enlargement episodes, region-pairs with highly synchronous business cycles before accession experienced weaker cyclical and structural convergence than region-pairs with less synchronous cycles. Likewise, region-pairs with more similar sector structures before accession experienced stronger divergence (or weaker convergence) of structural similarity and business-cycle synchronicity after the enlargement. We argue that these results call for developing more differentiated hypotheses on EU enlargement's effects on business-cycle synchronization and sector specialization.

Mexican states' business cycles co‐movement over the period 2000–2014. A panel data model estimation

“…On the one hand, the econometric estimates of the final model specification imply that variables advocated by the gravitational model contribute in explaining the variability of the business cycles co‐movement in Mexican states. In particular, one measure of “mass”, the log of the product of GDP per capita ( GDP ) of states i and j , has positive and significant effects regardless of the de‐trending method the correlations are based on, which is a standard result in the literature in terms of this variable (Bierbaumer‐Polly et al, ) or the aggregate GDP (Artis & Okubo, , ; Barrios & De Lucio, ). The other mass measure, the log of the product of population density ( PD ), has significant effects only when the CF filter is used to extract the cycle indicator.…”

“…On the basis of these arguments, several papers have analyzed the case of the U.S. states, which is commonly used as a benchmark to contrast the experience of other countries (Aguiar-Conraria, Brinca, Gudjónsson, & Soares, 2017;Beine & Coulombe, 2005). Other papers have examined the experience of regions in countries within the context of the EU (Barrios & de Lucio, 2003;Bierbaumer-Polly, Huber, & Rozmahel, 2016;Correia & Gouveia, 2013). Some others in turn, have also examined the experience of specific countries (Artis & Okubo, 2010, 2011.…”

Business Cycle Synchronization in the EMU: Core vs. Periphery

“…In the following, we introduce a correlation index developed by Cerqueira and Martins (2009) that gains increasing popularity in business cycle research (Gächter and Riedl, 2014;Bierbaumer-Polly et al, 2016). As opposed to normal correlation coefficients, the correlation index of Cerqueira and Martins (2009) provides a measure of bilateral correlation for higher -in our case quarterly -frequencies and captures time-variability in bilateral business cycle correlations since it distinguishes between specific episodes of higher and lower synchronization, respectively.…”

A Feasible Unemployment‐Based Shock Absorber for the Euro Area

“…Data availability restricts our simulations to ten countries, while the EA now includes 19 economies. The growing heterogeneity (Bierbaumer‐Polly et al ., ; Di Giorgio, ) increases the need of a fiscal stabilizer, but it could also amplify the fears of cross‐country long‐term redistribution. The analysis presented here shows that a carefully designed risk‐sharing mechanism can provide non‐negligible stabilization with little or no cross‐country redistribution.…”