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Financial Linkages and Sectoral Business Cycle Synchronization: Evidence from Europe

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Abstract

We analyze whether financial integration leads to converging or diverging business cycles using a dynamic spatial model. Our model allows for contemporaneous spillovers of shocks to GDP growth between countries that are financially integrated and delivers a scalar measure of the spillover intensity at each point in time. For a financial network of ten European countries from 1996 to 2017, we find that the spillover effects are positive on average and much larger during periods of financial stress, pointing towards stronger business cycle synchronization. Dismantling GDP growth into value added growth of ten major industries, we observe that spillover intensities vary significantly. The findings are robust to a variety of alternative model specifications.

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Notes

  1. (Static) spatial models have recently become popular in the empirical finance literature, see, e.g., Tonzer (2015), Herskovic et al. (2020), and Denbee et al. (2021).

  2. Acemoglu et al. (2012), for example, emphasizes the relevance of the network structure for spillover effects between sectors.

  3. International co-movement through firms in one country and their cross-border links is analyzed by, for example, Di Giovanni et al. (2018) and Kleinert et al. (2015). The role of trade among countries for business cycle synchronization is assessed by Abbott et al. (2008) or Arkolakis and Ramanarayanan (2009). In our model specification, we control for trade linkages, which might be a relevant driver of business cycle synchronization, while our main focus is on the role of financial linkages.

  4. While financial integration can take different forms, Hoffmann et al. (2019) find that banking integration dominates equity market integration in the euro area with the latter being still limited in size.

  5. While inter-office positions might change faster between inter-related offices compared to other cross-border positions, the series is still sufficiently stable such that no concerns due to volatility emerge.

  6. Belgium, Denmark, Finland, France, Germany, Ireland, Netherlands, Sweden, Switzerland, UK. In terms of financial assets (in Euros, unconsolidated) of the economy obtained from Eurostat, our countries cover on average over the sample period, more than 78% of the European countries’ financial assets (EU28 and Switzerland).

  7. The continuous reporting of cross-border positions is needed to construct the weight matrix as explained in the next section. In robustness tests, we add non-European countries when analyzing GDP growth.

  8. Agriculture, forestry, and fishing. Arts, entertainment, recreation, and other services. Construction. Financial and insurance activities. Industry (except construction). Information and communication. Professional, scientific, and tech activities. Public administration, deference, education, human health and social work. Real estate activities. Wholesale and retail trade, transport, accommodation, and food.

  9. For example, the EU Klems Database or the OECD provides information on value added by industry as well but only at a yearly frequency. Similarly, the United Nations Statistical Yearbook would only offer data on a yearly frequency and with a less detailed breakdown by industry.

  10. Economic theory suggests that economic growth is determined by production factors such as technology, labor input, as well as capital and government expenditure (Barro and Lee 1994; Moral-Benito 2012; Jorgenson 1988; Sala-i-Martin et al. 2004; Solow 1962; Zeira 1998). More recent studies focus on the role of financial or trade integration for economic development (Guiso et al. 2004; Kose et al. 2006; Schularick and Steger 2010). We test for the robustness of results when excluding controls in Sect. 4.

  11. In Sect. 4, we test the sensitivity of our main result to issues related to reverse causality, which might arise when relating GDP growth to controls such as labor productivity or gross fixed capital formation.

  12. To ensure weak exogeneity, the trade data matrices are lagged by four quarters. Furthermore, each matrix is normalized by its largest eigenvalue.

  13. In our estimations, the dependent variable observations are transformed to have mean zero and unit standard deviation, to account for country heterogeneity, which justifies the absence of fixed effects and heterogeneous variances.

  14. For convenience, we refer to the weight matrix including the lagged financial linkages as \(W_{t}\) throughout the paper.

  15. See www.gasmodel.com for a more complete compilation of papers.

  16. It is to note that the reduced form of the spatial lag model with spatial errors is nonlinear and can not be estimated using least squares, because it is not possible to linearly disentangle \(\rho _t\), \(\beta\), and \(\delta\). However, building on arguments from White (1996) it has been shown in Blasques et al. (2016) that under some regularity conditions the maximum likelihood estimator of the static coefficients in the score-driven spatial model, including \(\omega , A, B, \beta , \delta , \sigma ^2,\) and \(\nu\), (the first three of which, together with the data, provide the ingredients for \(\rho _t\)) is uniquely identified, consistent, and asymptotically normally distributed.

  17. The results are robust towards employing an alternative model specification using OLS regressions with covariates, no spatial term, country and year fixed effects. These results are available upon request.

  18. For simplicity, we abstract from the regressors and the spatial error dependence.

  19. Please note that this analysis is a sector-by-sector analysis without considering cross-sectoral spillovers.

  20. The full tables covering all models per industrial sector can be obtained upon request.

  21. This sector comprises mostly legal, management or engineering activities, see: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=File:F1_Sectoral_analysis_of_Professional,_scientific_and_technical_activities_(NACE_Section_M),_EU-28,_2016.png.

  22. The role of shock spillovers between interconnected sectors depending on the network structure is discussed by Acemoglu et al. (2012).

  23. The presence of multinational firms as such can result in international co-movements (Cravino and Levchenko (2017); Di Giovanni et al. (2018); Kleinert et al. (2015)). Such co-movements can be fueled in case multinational banks follow their multinational customers (Buch (2000)), in this context it is also important that we do not net out inter-office positions between banks.

  24. E.g., for the United States, Laeven and Valencia (2013) show high values of external dependence for machinery, other industries or professional goods. Kroszner et al. (2007) find that sectors relying more on external finance via banks are hit more by banking crises and consequently tighter financial constraints (see also, Braun and Larrain (2005); Chava and Purnanandam (2011)).

  25. Unfortunately, we can not extend our sample period backwards due to changes in the reporting standards of the BIS.

  26. E.g., Kalemli-Ozcan et al. (2013a) find different spillover patterns from 2007 onwards, which guides the choice of sample periods to compute average in/direct effects.

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Acknowledgement

We thank two anonymous referees and Emine Boz, Michael Barkholz, Franziska Bremus, Michela Rancan, and Katheryn Russ for very helpful comments and discussions. Funding from the European Social Fund (ESF) of the European Commission is gratefully acknowledged by Lena Tonzer. Julia Schaumburg thanks the Dutch Science Foundation (NWO, grants VENI451-15-022 and VI.Vidi.191.169) for financial support. On behalf of all authors, the corresponding author states that there is no conflict of interest. All errors are solely our own responsibility.

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  1. Halle Institute for Economic Research (IWH), Kleine Maerkerstraße 8, 06108, Halle (Saale), Germany

    Hannes Böhm & Lena Tonzer

  2. Vrije Universiteit Amsterdam and Tinbergen Institute, Amsterdam, The Netherlands

    Julia Schaumburg & Lena Tonzer

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  1. Hannes Böhm
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Correspondence to Lena Tonzer.

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Böhm, H., Schaumburg, J. & Tonzer, L. Financial Linkages and Sectoral Business Cycle Synchronization: Evidence from Europe. IMF Econ Rev 70, 698–734 (2022). https://doi.org/10.1057/s41308-022-00173-9

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