Abstract
Since her very beginning, the European Union (EU) has been subject to various challenges, even called crises by common parlance. In this chapter, we argue that the EU shows remarkable stability and significantly strong resilience to conflicts. Testing this statement, we deal with time series analysis, that is, Vector Auto Regression Modelling is used to test this statement for the purpose of impulse response function interpretation. It is shown that the onset of much debated challenges the EU faced has even statistically significantly increased the overall positive opinion of Europeans on the EU, thereby strengthening her legitimacy and political significance. Findings prove significant characteristics of strong resilience to shocks. Implications for the leverage of the European Neighbourhood Policy (ENP) point to low leverage of ENP due to a biased use of instruments. To improve the leverage of ENP, its benchmark indicators have to meet concerns about the dynamics of hybrid democracies.
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Notes
- 1.
The author cannot resist temptation to add a few words on Brexit. Work on this chapter was finished by 10 February 2019, the process of a possible Brexit in full swing. I have been pleased by watching negotiations over the past two years. The interplay of intergovernmental (EU) and domestic (British) politics shaped a process to the effect of a high likelihood for a Nash-equilibrium, most probably with a second referendum and, given the learning curves of British voters a yes-we’ll-stay outcome.
- 2.
The difference in magnitude between the time series and this empirical snapshot is due to the fact that for the former, GDP in constant dollar prices of 2010 were used to avoid distortion due to inflation.
References
Bar-El, R. (2008). Dictators, Development, and the Virtue of Political Instability. Public Choice, 138(1–2), 29–44.
Bolle, M., & Fläschner, O. (2014). The European Union: Stability Despite Challenges. Baltic Journal of European Studies, 4(2), 20–33.
Bolle, M., & Kanthak, L. (2013). The Eurozone Crisis: An Opportunity or a Setback for Europe? In E. Cihelkova (Ed.), Changes in Governance in the Context of the Global Crisis. Berlin: Free University Berlin.
Bueno de Mesquita, B., Smith, A., Siverson, R., & Morrow, J. D. (2003). The Logic of Political Survival. Cambridge, MA: MIT Press.
Enders, W., & Sandler, T. (1993). The Effectiveness of Antiterrorism Policies: A Vector-Auto-Regression-Intervention Analysis. American Political Science Review, 87(4), 829–844.
European Commission. (2013). Effects of the Economic and Financial Crisis on the European Public Opinion. Retrieved July 19, 2016, from https://www.google.de/?gws_rd=ssl#q=european+commission+40+years+eurobarometer+crisis.
European Commission. (2016). Public Opinion. Retrieved July 19, 2016, from http://ec.europa.eu/COMMFrontOffice/PublicOpinion.
European Parliament. (2013). European Parliament Eurobarometer (EB 79.5) ‘One Year to Go Until the 2014 European Elections’ Institutional Part, Analytical Overview. Retrieved July 19, 2016, from http://www.europarl.europa.eu/pdf/eurobarometre/2013/election/synth_finale_en.pdf.
European Parliament. (2015a). Major Changes in European Public Opinion Towards the EU Since 1973. Desk Research, 2015 Edition.
European Parliament. (2015b). Analytical Overview Part II. Retrieved July 19, 2016, from http://www.europarl.europa.eu/pdf/eurobarometre/2015/2015parlemeter/eb84_1_synthese_analytique_partie_II_en.pdf.
Freeman, J. R., Williams, J. T., & Lin, T. (1989). Vector Autoregression and the Study of Politics. American Journal of Political Science, 33, 842–877.
Hamilton, J. D. (1994). Time Series Analysis. New Jersey: Princeton University Press.
Hawkins, D., Lake, D., Nielson, D., & Tierney, M. (2011). Delegation Under Anarchy: States, International Organizations, and Principal-Agent Theory. In D. Hawkins, D. Lake, D. Nielson, & M. Tierney (Eds.), Delegation and Agency in International Organizations (1st ed.). Cambridge: Cambridge University Press.
Luhmann, N. (1995). Social Systems. Stanford: Stanford University Press.
Pollack, M. (1997). Delegation, Agency, and Agenda Setting in the European Community. International Organization, 51(1), 99–134.
Putnam, R. (1988). Diplomacy and Domestic Politics: The Logic of Two-Level Games. International Organization, 42(03), 427.
Salhi, A. (2010). Challenge or Change? The Political Economy of Regime Stability in Autocratic Political Systems. 1. Aufl. Baden-Baden: Nomos.
Schimmelfennig, F. (2016). Good Governance and Differentiated Integration: Graded Membership in the European Union. European Journal of Political Research, 55(4), 789–810.
Stock, J. H., & Watson, M. W. (2001). Vector Auto-Regressions. The Journal of Economic Perspectives, 15(4), 101–115.
The Political Terror Scale. (2016). The Political Terror Scale. Retrieved July 19, 2016, from http://www.politicalterrorscale.org.
Wessels, W. (2016). Summit. Retrieved from www.summit.uni-köln.de.
World Bank. (2016). GDP Per Capita (Constant 2010 US$). Retrieved July 19, 2016, from http://data.worldbank.org/indicator/NY.GDP.PCAP.KD.
Acknowledgements
This chapter has been written at the occasion of the EURINT 2016 conference. It has been edited again in January 2019. Even with Brexit still looming there has been no need to change the major message. The author would like to thank O. Flaeschner for his most excellent way of dealing with empirics on VAR. Splendid work on editing by Dr. Chen and J. Penlington is highly appreciated too. As junior research fellows at the JMC/FU Berlin, Ms Zhang Cheng and Ms Shahrzad Shahmohammadi (MA) have been immensely supportive to this research project.
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Appendices
Annex 7.1: VAR Models
As the data are compiled in time series format, this chapter draws on time series econometrics. The impact of the above listed crises will be evaluated based on the responses they cause in the two time series. This is accomplished by calculating the impulse response functions for 12 vector auto-regression models (VAR) of which each corresponds to one crisis and one of the two above-mentioned variables. The crises are represented by dummy variables, which take the value 1 in the year of the onset of the crisis and 0 otherwise. The impulse response functions allow tracing the dynamic impact of the crisis impulse on the respective time series.
The vector auto-regressions methodology has found broad application in macroeconomics. Even though this methodology has found some application in the field of politics (e.g. Enders and Sandler 1993), it is comparatively uncommon yet immensely useful (compare, Freeman et al. 1989). In contrast to mere eyeballing, this method of time series econometrics allows to distinguish between statistically significant and statistically insignificant responses, factoring in the standard deviation in the time series in question.
The following formula represents a basic, bivariate VAR model in any of the data points of a time series that is potentially affected by the values of itself or the other time series from up to six time periods ago.
Hence, for instance, the time series of the social stability index or the net public approval time series will be explained on the basis of their own weighed lags as well as the weighed crisis dummies, that is:
The impulse responses were calculated on the basis of recursive VARs in differences, in which the dummy time series is ordered before the respective response time series. A model in differences uses the first differences of the time series in question to account for non-stationarity exhibited in the time series. For a detailed mathematical treatment of VARs and impulse response functions, refer to Stock and Watson (2001) and Hamilton (1994).
Annex 7.2: Data
1.1 Public Approval
The presented values for public approval and public disapproval are taken from the since 1973 published Eurobarometer. Public approval is defined as the ratio of people answering the following question ‘Generally speaking, do you think that (OUR COUNTRY)’s membership of the European Union is…?’ with ‘A good thing’. On the contrary, public disapproval is defined by the ratio of those who answer the same question with ‘A bad thing’. The values until 2011 could be taken from the public opinion webpage of the European Commission (2016), subsequent values were taken from two publications from the European Parliament (2013, 2015a, b). Until 2011, the Eurobarometer reports half-yearly values and yearly values thereafter. The time series was collapsed to yearly data points. Missing data points were dealt with by means of averaging of the data point before and after it. On the basis of these values for public approval and disapproval, the net approval rates were calculated by subtracting the disapproval time series from the approval time series. The resulting time series is shown hereafter.
The presented values for public approval and public disapproval are taken from the Eurobarometer (published since 1973). Public approval is defined as the ratio of people answering the following question ‘Generally speaking, do you think that (OUR COUNTRY)’s membership of the European Union is…?’ with ‘A good thing’. On the contrary, public disapproval is defined by the ratio of those who answer the same question with ‘A bad thing’. The values until 2011 could be taken from the public opinion webpage of the European Commission (2016), subsequent values were taken from two publications from the European Parliament (2013, 2015a, b). Until 2011, the Eurobarometer reports half-yearly values and yearly values thereafter. The time series was collapsed to yearly data points. Missing data points were dealt with by means of averaging of the data point before and after it. On the basis of these values for public approval and disapproval, the net approval rates were calculated by subtracting the disapproval time series from the approval time series. The resulting time series is shown hereafter.
1.2 Construction of the Social Stability Index
The Social Stability Index (SSI = ln (GDP per capita/political terror scale) used in the time series on which the models are based is constructed on the basis of the real GDP capita value measured in constant US dollars of 2010 as reported by the World Bank (2016). For every year, the real GDP per capita and political terror scale values are the averages of the values for the countries with EU membership, that is, 6 in the beginning and 28 in the end. Every enlargement is accounted for by including the respective countries into the arithmetic mean. The political terror scale links up to the values reported by the US state department and available as download from the webpage of the project The Political Terror Scale (2016).
Annex 7.3: Charts
Annex 7.4: Data—Descriptive Statistics
Variable | Min. Year | Max. Year | Min. | Max. | Average | Std. Dev. |
---|---|---|---|---|---|---|
Social stability index | 1983 | 2000 | 9.719225 | 10.3164 | 10.00716 | 0.1317246 |
Political terror scale | multiple | 2007 | 1 | 1.62963 | 1.19919 | 0.1986472 |
GDP/capita (2010 $) | 1976 | 2008 | 18076.04 | 34601.46 | 26841.73 | 5592.251 |
Public approval | 2011 | 1991 | 0.4658 | 0.68495 | 0.5489961 | 0.0538752 |
Public disapproval | 1991 | 2011 | 0.0704 | 0.185 | 0.1312479 | 0.0276633 |
Net approval | 2011 | 1991 | 0.2808 | 0.6146 | 0.4177526 | 0.0790581 |
Annex 7.5: Sketch of Stability conditions for a Negotiation Model
A stability analysis of such a process hints at formal mathematical implications for resilience. With
denoting variables and parameters of a potential model, interaction between nations can be described by a set of differential equations:
and
where t denotes (points in) time.
Once the state of the system deviates from its equilibrium due to a symmetric shock, governmental negotiations start (Eq. 7.1). On the grounds of institutional settings and vis-a-vis tit-for-tat rules of the game political reforms (Eq. 7.2) will be implemented.
Local stability around the steady state (Jacobi-Matrix) would reveal Eigenvalues between 1 and 0 which denote Grenzstabilität, a stable cycle. Chaos seems possible face to global (large) deviations because of non-linearities involved.
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Bolle, M. (2019). Resilience of the EU and Leverage of the European Neighbourhood Policy: Good News and Bad News. In: Rouet, G., Pascariu, G.C. (eds) Resilience and the EU’s Eastern Neighbourhood Countries. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-25606-7_7
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