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Crisis and Citizens’ Trust in the European Central Bank: Panel Data Evidence for the Euro Area, 1999–2012

Part of the Contributions to Economics book series (CE)

Abstract

Throughout the crisis, citizens’ trust in the European Central Bank has significantly declined throughout the Euro Area (EA-12). Although a decline in the core countries of the EA-12 has been distinct, a more pronounced decline has been taking place in the peripheral countries of the EA-12. Taking panel data and using a fixed effects DFGLS estimation for an EA-12 country sample over the time period 1999–2012 with a total of 305 observations, this paper detects a negative and significant relationship between unemployment and trust in times of crisis. The robustness analysis of the paper confirms that this decrease in trust is strongly driven by the significant increase in unemployment rates in the four peripheral countries Spain, Ireland, Greece, and Portugal.

Keywords

  • Crisis
  • Trust
  • Unemployment
  • European Central Bank

Originally published: in: Felix Roth, Daniel Gros, and Felicitas Nowak-Lehmann D. Crisis and Citizens’ Trust in the European Central Bank—Panel data evidence for the Euro Area, 1990–2012. Journal of European Integration, Vol. 36, No. 3, 2014, pp. 303–320.

Felix Roth wants to thank the Verein für Socialpolitik for scheduling the special session ‘Trusting Banks in a Financial Crisis’ at its annual conference on 8 September 2010 in Kiel, which gave him the opportunity to present a preliminary version of the paper. He would like to thank the seminar participants of that session for their valuable comments. In addition, the authors would like to thank the participants of the 14th Göttinger Workshop ‘Internationale Wirtschaftsbeziehungen’ at the University of Göttingen in February 2012, the fourth IFABS Conference in Valencia in June 2012, and the seventh Annual International Symposium on Economic Theory, Policy and Applications in Athens in July 2012. The authors are grateful to Lars Jonung, the guest editor team from the Mannheim Centre for European Social Research Jale Tosun, Anne Wetzel and Galina Zapryanova, as well as two anonymous referees for their valuable comments. Two preliminary working paper versions of this paper were published as CEPS Working Document 334 on 26 July 2010 and as CEGE Discussion Paper 124 at the University of Göttingen in May 2011.

1 Introduction

The bankruptcy of Lehman Brothers in September 2008 triggered global crises of both trust (Guiso, 2010; Sapienza & Zingales, 2012) and confidence (Tonkiss, 2009) and acted as the starting point of a financial and economic crisis for most advanced economies worldwide, including the advanced economies in the Euro Area (EA) (European Economic Advisory Group, 2010). Within the EA, the financial and economic crisis culminated in a sovereign debt crisis from 2010 onwards (De Grauwe, 2010). The breeding ground of the financial crisis was mostly created by a lack of regulation within the institutional framework of the financial system in the United States as well as in Europe (Acharya et al., 2009; De Grauwe, 2009; Financial Crisis Inquiry Commission, 2011; Stiglitz, 2009). Since central banks are commonly identified as the major guardians of the financial system (Healy, 2001, p. 22), the financial and economic crisis will most likely have negatively affected citizens’ trust in central banks. Indeed, it has been shown that citizens’ trust in national central banks (Gros & Roth, 2009; Wälti, 2012) and in the European Central Bank (ECB) (Ehrmann et al., 2013; Jones, 2009; Roth, 2009a; Wälti, 2012) reached all-time lows in January/February 2009 and May 2010. Based on these findings, it seems worthwhile to analyse the precise channels that caused and transmitted this loss of citizens’ trust in central banks.

In this context this contribution focuses on the EA and citizens’ trust in the ECB over a 13-year time period (from 1999 to 2012). It is structured in the following manner. It first embeds the concept of citizens’ trust in the ECB within the overall concept of systemic trust and elaborates what might be the consequences of an enduring loss of citizens’ trust in the ECB. In the next step, the paper tries to identify those factors that most likely led to the loss of citizens’ trust in the ECB. Based upon these theoretical assumptions, the paper elaborates on the measurement of the data, the model specification and the research design. A description of the trend in citizens’ trust is then followed by a discussion about methodological issues, a presentation of the econometric results and a discussion of our results in the context of previous empirical findings—as well as the underlying theoretical assumptions. The conclusions summarize the main findings.

2 Theoretical Links

2.1 The Consequences of an Enduring Loss of Citizens’ Trust in the ECB

Trust can be conceptualized as one of three forms: thick, interpersonal, and systemic or institutional trust (Khodyakov, 2007; Roth, 2009b). As this paper will analyse citizens’ trust in the ECB, it will take the concept of systemic trust as its starting point. A prominent (and for our paper suitable) elaboration of systemic trust is given in the sociological discipline by Luhmann (2000) and Giddens (1996). Both authors stress the importance of systemic trust in today’s modern complex societies (Giddens, 1996, p. 165; Luhmann, 2000, p. 26). For Luhmann, systemic trust is necessary to reduce the complexity of modern societies in order to stabilize their very foundations (Luhmann, 2000, p. 72). Giddens characterizes systemic trust as necessary to secure the functioning of modern societies and warns that decreasing levels of systemic trust have in some cases the potential to break apart institutional arrangements (Giddens, 1996, p. 166). Concerning the latter argument, political scientists such as Kaltenthaler et al. (2010) focus on trust in (policymaking) institutions. Alongside Kosfeld et al. (2005, p. 673), Kaltenthaler, Anderson and Miller (2010, p. 1262) argue that a certain level of citizens’ trust in a policymaking institution is crucial for the legitimacy of that institution.

How do these arguments apply to the concept of trust in the ECB and what are the consequences of an enduring loss of citizens’ trust in the ECB? As the ECB is a (policymaking) institution, it can be argued that a certain level of citizens’ trust would be crucial to maintain its legitimacy. In addition, as the ECB is an independent institution that is not democratically elected (as highlighted in Article 130 TFEU of the Treaty of Lisbon (2010)), the legitimacy argument applies to an even greater extent than to other policymaking institutions. In this respect, a high level of citizens’ trust in the ECB can be characterized as a proxy for a high approval rating among citizens, which ultimately secures the independence of the ECB. It follows from the above argumentation that a loss of trust will leave the ECB vulnerable to political influence, as citizens will most likely pressure politicians to minimize the ECB’s independence (Kaltenthaler et al., 2010, p. 1261). This reasoning is shared by ECB policymakers. Via publicly available communications (ECB, 2010), an interview with the then president Wim Duisenberg (Wenkel, 2008) and other interviews with experts (Kaltenthaler et al., 2010, p. 1267), ECB policymakers confirm that they depend on citizens’ trust in the ECB to resist pressures from politicians and to secure their independence.

As we have argued that a loss of trust in the ECB will endanger the ECB’s independence, we still have to clarify why this granted independence is important for the ECB. Concerning the importance of the independence of central banks, a general and a crisis-embedded argument can be mentioned. In the context of the general argument, a detailed literature survey by Eijfinger and de Haan (1996) evaluating the pre-existing theoretical and empirical literature concluded that the independence of central banks is associated with lower inflation rates. And lower inflation rates entail fewer costs to long-term economic growth (Eijfinger & de Haan, 1996, p. 54). In the context of the crisis-embedded argument, the ECB’s decision to become the lender of last resort in the government bond market (De Grauwe, 2013, p. 520) was pivotal in stabilizing the Eurozone in times of crisis (De Grauwe & Ji, 2013a, 2013b, p. 2). However, as the broadening of the ECB’s mandate has provoked strong opposition (De Grauwe, 2013, p. 522; Fratzscher, 2013; Giavazzi et al., 2013), it seems reasonable to argue that the ECB’s granted independence has played a significant role in its continuing effort to stabilize the Eurozone in times of crisis.

2.2 Possible Drivers of Citizens’ Trust in the ECB

Although citizens’ perceptions might influence their systemic trust (Banducci et al., 2009, p. 572), this paper focuses on the impact of three macro-economic variables: 1) unemployment, 2) inflation, and 3) growth of GDP per capita when trying to identify those factors that led to an erosion of citizens’ trust in the ECB. This undertaking seems to be reasonable as it is soundly rooted in economic theory when considering the literature on popularity functions (Bellucci & Lewis-Beck, 2011, pp. 192–94; Nannestad & Paldam, 1994, pp. 215–16) and the existing literature on trust in the ECB (Fischer & Hahn, 2008). Nevertheless, as the most recent economic literature linking institutional trust to business cycles stresses the important role of unemployment in explaining systemic trust (Stevenson & Wolfers, 2011) and given that the unemployment rate has increased significantly, particularly in the periphery countries of the EA, throughout the crisis (and not the inflation rate—which has been muted by the ECB), this contribution primarily focuses on the unemployment coefficient in depicting its econometric results.

3 Measurement of Data, Model Specification, and Research Design

3.1 Measurement of Data

Measures of trust in the ECB were based upon the biannual standard Eurobarometer (EB) surveys from spring 1999 (EB51) to autumn 2012 (EB78).Footnote 1 Respondents were asked the following question: ‘I would like to ask you a question about how much trust you have in certain institutions. For each of the following European bodies, please tell me if you tend to trust it or not to trust it’. Respondents were then presented with a range of institutions. Possible answers included the following three categories: ‘Tend to trust it’, ‘Tend not to trust it’ and ‘Don’t know’. Applying a concept introduced by Gärtner (1997, pp. 488–89), we utilize a ‘net trust’ measure, which is obtained by subtracting the percentage of those who trust from those who do not trust.Footnote 2 In order to make our trust data match with our macroeconomic data, a procedure proposed by Wälti (2012, p. 597) is applied.Footnote 3 Monthly data on unemployment, inflation (change of HICP) and sovereign bond yield rates were retrieved from Eurostat. The values for unemployment were adjusted seasonally. Quarterly data on GDP and population size were taken from Eurostat’s data.Footnote 4,Footnote 5 The quarterly data were interpolated to gain monthly observations in order to utilize the monthly matching approach.Footnote 6

3.2 Model Specification

Within our baseline model, an unbalanced panel, net trust in the ECB is estimated as a function of unemployment, inflation, growth of GDP per capita, and other important control variables. As this contribution is interested in explaining the ‘within variation’ throughout the crisis period, a fixed-effects estimation approach is utilized. The baseline model for our estimation, which holds in the long term when all adjustments have come to an end, reads as follows:

$$ \mathrm{Trust}\_{\mathrm{ECB}}_{it}={\alpha}_i+\beta {\mathrm{Unemployment}}_{it}+\chi {\mathrm{Inflation}}_{it}+\delta {\mathrm{Growth}}_{it}+{\phi Z}_{it}+{w}_{it} $$
(6.1)

where i characterizes each country and t represents each time period. Trust_ECBit is the net trust amount in the ECB for country i during period t. Unemploymentit, Inflationit, Growthit and Zit are accordingly unemployment, inflation, growth of GDP per capita and important control variables such as indicators of financial stress, e.g. sovereign bond yields. αi depicts a country-specific constant term and wit is the error term. As we utilize a Feasible Generalized Least Square (FGLS) estimation approach, time dummies are not included within our baseline estimation as they are mutually exclusive with FGLS.

3.3 Research Design

Our baseline econometric analysis will estimate Eq. (6.1) with the aid of an EA-12 country sample (Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, and Spain) over the 13-year time period from 1999 to 2012.Footnote 7 With 29 time periods (t = 29) and 12 countries (n = 12) and thus with a ratio of t/n of 2.4, estimation of Eq. (6.1) will be performed via time series econometrics. As we identify the events associated with the bankruptcy of Lehman Brothers in September 2008 as the start of the crisis, a pre-crisis period (3–4/1999–3-5/2008) will be differentiated from a crisis period (10–11/2008–11/2012) within the descriptive and econometric analysis. In addition, throughout the analysis, a core country sample, the EA-8 (Austria, Belgium, Finland, France, Germany, Italy, Luxembourg, and the Netherlands) will be differentiated from a periphery country sample, the EA-4 (Greece, Ireland, Portugal, and Spain).

4 Descriptive Statistics

Table 6.1 shows the levels of net trust in the ECB before the crisis (3–5/2008) and in the fourth year of the crisis (11/2012) and the values for the changes in net trust (11/2012–3-5/2008) for all EA-12 countries, as well as an EA-12, EA-4 and EA-8 country sample.Footnote 8 Table 6.1 clarifies that in the EA-12 net trust in the ECB has declined significantly throughout the crisis by no less than 45% points. Whereas a majority of citizens still trusted the ECB before the crisis (+29%), in the fourth year of the crisis a majority distrusted the ECB (−16%). However, with 34% points (from 27% to −7%), the decline is less pronounced in the EA-8 compared to a decline of 84% points (from +34% to −50%) in the EA-4. This significant difference in the aggregate trends can be explained by analysing the values for the individual countries. Whereas the four periphery countries Spain, Ireland, Greece, and Portugal have faced a significant decline in trust in the ECB with values of −98%, −67%, −65%, and −52% points, respectively, core countries such as Austria and France faced only a moderate decline by −17% and −14% points, respectively. Overall, comparing the decline in trust in the ECB to other European institutions, such as the European Commission and European Parliament (EP) over the same time frame, the decline in trust in the ECB is the more significant in all EA-12 countries (see here Roth et al., 2013, pp. 8–9).Footnote 9

Table 6.1 Net trust levels and changes in net trust in the EA-12, EA-4, EA-8, and individual EA-12 countries (2008–2012)

Given that Table 6.1 only depicts a before–after comparison for two points in time (3–5/2008 and 11/2012), Fig. 6.1 compares the 13-year time trends (from 1999 to 2012) for the EA-12 country sample with those from the EA-4 and EA-8 (for the time trends of all individual EA-12 countries (see Fig. 6.A1 in the Appendix). Four interesting findings emerge. First, trust significantly declined throughout the crisis period (10–11/2008 to 11/2012) in the EA-12 in comparison to the pre-crisis period (3–4/1999–3-5/2008, with mean levels declining by 24% points from 25% to 1%) and departed from its long-term trend (with standard deviations tripling). Second, the decline was more pronounced in the EA-4 with a drop in mean levels of 34% points in comparison to 22% points in the EA-8 and standard deviations quadrupling in the EA-4, but only doubling in the EA-8. Third, whereas the EA-4 and EA-8 trends are highly correlated throughout the pre-crisis period and even in the direct aftermath of the financial crisis until 5/2010, from 5/2010 [the start of the sovereign debt crisis onwards (De Grauwe, 2010)], the decline in the EA-4 continued steadily, reaching a level of −50% in 11/2012, in comparison to a level of −7% for the EA-8. Fourth, taking aside the short time period from 1–2/2009 until 6–7/2009, a majority of citizens mistrusted the ECB in the EA-12 and EA-8 from 11/2011 onwards. In the EA-4, a majority of citizens already mistrusted the ECB from 11–12/2010 onwards. However, whereas the majority of mistrust in the EA-8 is still narrow, with a net value of −7% in 11/2012 (in Austria, Finland, the Netherlands and Luxembourg a majority of citizens actually still trusted the ECB—see Table 6.1), already a large majority mistrusted the ECB in 11/2012 in the EA-4 with a net value of −50% (in Greece and Spain, in total values, 81% and 75% of citizens, respectively, mistrusted the ECB in contrast to only 17% of citizens in both countries who still trusted the ECB).

Fig. 6.1
figure 1

Net trust in the ECB in per cent, EA12, EA4, and EA8, 1999–2012

Notes: As the figure depicts net values, all values below 0 indicate that a majority of respondents mistrust the ECB. For the aggregation of the EA-12, EA-4, and EA-8, population weights were applied. The dotted line represents the incidence of the bankruptcy of Lehman Brothers in September 2008.

Sources: Standard EBs 51–78 and Special EB 71.1.

5 Econometric Analysis

5.1 Discussion of the Estimation Procedure

We estimated Eq. (6.1) by means of dynamic ordinary least squares (DOLS), a method that permits us to fully control for endogeneity of the regressors (Stock & Watson, 1993; Wooldridge, 2009).Footnote 10 In order to correct for autocorrelation,Footnote 11 we apply a FGLS procedure.Footnote 12 Both applications lead to the following Eq. (6.2)Footnote 13:

$$ {\displaystyle \begin{array}{l}\mathrm{Trust}\_{\mathrm{ECB}}_{it}^{\ast }={\alpha}_i+{\beta}_1{\mathrm{Unemployment}}_{it}^{\ast }+{\chi}_1{\mathrm{Inflation}}_{it}^{\ast }+{\delta}_1{\mathrm{Growth}}_{it}^{\ast }+{\phi}_1{Z}_{it}^{\ast }+\\ {}\sum \limits_{p=-1}^{p=+1}{\beta}_{2p}\Delta {\mathrm{Unemployment}}_{it-p}^{\ast }+\sum \limits_{p=-1}^{p=+1}{\chi}_{2p}\Delta {\mathrm{Inflation}}_{it-p}^{\ast }+\sum \limits_{p=-1}^{p=+1}{\delta}_{2p}\Delta {\mathrm{Growth}}_{it-p}^{\ast }+\\ {}\sum \limits_{p=-1}^{p=+1}{\phi}_{2p}\Delta {Z}_{it-p}^{\ast }+{u}_{it}\end{array}} $$
(6.2)

with αi being the country fixed effect and Δ indicating that the variables are in first differences. Unemployment, inflation and growth turn exogenous and the coefficients β1, χ1, δ1 and ϕ1 ensue a t-distribution. This property permits us to derive statistical inferences on the impact of unemployment, inflation and growth.Footnote 14 The asterisk (*) indicates that the variables have been transformed (purged from autoregressive processes) and that the error term uit fulfils the requirements of the classical linear regression model (i.e. it is free from autocorrelation).

5.2 Econometric Results

Estimating Eq. (6.2), regression 1 in Table 6.2 reports the results for the full sample (FS) (from 3–4/1999 to 11/2012) for the association between unemployment and trust in the ECB. Trust in the ECB is negatively and significantly (99% level) associated with unemployment (−4.9). Given that we would expect a structural break caused by the crisis,Footnote 15 regressions 2 and 3 in Table 6.2 report the results for a pre-crisis (BC) sample (from 3–4/1999 to 3–5/2008) and a crisis (C) sample (from 10–11/2008 to 11/2012). In the pre-crisis period, one detects no significant relationship between trust and unemployment in regression 2. In the crisis sample (regression 3), trust in the ECB is strongly negatively (−5.5) and highly significantly (99% level) related to unemployment. With a coefficient of this size, one can conclude that in times of crisis a 1% point increase in unemployment is related to a decrease of 5.5 in net trust in the ECB. Furthermore, it becomes evident that the significant association for unemployment in the FS is strongly driven by the crisis period.

Table 6.2 Unemployment and net trust in the ECB, fixed-effects DFGLS estimations, EA12

5.3 Sensitivity of Results

As the highly significant (99% level) and strong relationship (−5.5) between unemployment and trust in times of crisis would have important policy implications (due to the fact that unemployment rates have increased significantly in the periphery countries), Table 6.3 conducts a sensitivity analysis on this relationship. Row 1 in Table 6.3 depicts the coefficient of unemployment from regression 3 in Table 6.2. Rows 2–5 exclude Spain, Ireland, Greece, and Portugal. After the consecutive exclusion of Spain and Ireland (row 3), the relationship decreases in size (−2.9) and significance (below the 90% level). Once all four countries are excluded, the overall size of the coefficient remains at −2.9, but with a standard error of 3.14 loses in significance (row 5). This indicates that the strong negative (−5.5) and highly significant (99% level) relationship between unemployment and trust is largely driven by the EA-4 countries. In Spain, Greece, Ireland and Portugal a significant increase in unemployment rates throughout the crisis (16.6%, 17.5%, 10% and 7.5% points from 3–5/ 2008 to 11/2012) is associated with a decline in trust of 98%, 65%, 67%, and 52% points (see results in Fig. 6.A2 and Table 6.1, respectively).Footnote 16

Table 6.3 Sensitivity analysis between unemployment and trust in times of crisis

Rows 6–10 analyse the robustness of the unemployment coefficient when altering the time periods utilized. Since the beginning of the financial and economic crisis can be located as early as 2007 (Stiglitz, 2012, p. 1), row 6 analyses a crisis sample starting from 9–11/2007. The unemployment coefficient slightly increases in size (−6.2). Excluding one period at a time and commencing with the observation in 11/2012 in rows 7–10, the coefficient remains robust throughout the crisis although steadily declines in size. We can be sure that our econometric analysis has not omitted any important variables, having found that our time series are cointegrated. However, to take up concerns over missing variables, row 11 includes the additional variable sovereign bond yields as most recent empirical results have stressed their importance for trust in the ECB (Wälti, 2012). After the inclusion of sovereign bond yields, the coefficient of unemployment (−5.4) remains robust.Footnote 17 In row 12, we keep the additional variable sovereign bond yields and shorten the time frame from 10–11/2008 to 5/2011. The coefficient of unemployment still remains highly significant (99% level) but declines in size to −3.9. However, by analysing a time frame from 10–11/2008 to 11–12/2010 in row 13, the relationship between unemployment and trust loses significance (90% level) and strength (−3.4). Hence, it appears reasonable to conclude that the highly significant and negative relationship between unemployment and trust in the ECB is driven by the time period from 5/2011 onwards (the second year of the sovereign debt crisis).

Rows 14 and 15 perform two additional robustness tests. By excluding the Special EB71.1 in row 14, the results remain robust (−5.2). The inclusion of time-fixed effects instead of utilizing the FGLS approach in row 15 produces a slightly smaller coefficient (−4.3) but yields a poor Durbin–Watson statistic.

6 Discussion of Results

6.1 Discussion of Results Compared to Previous Empirical Findings

Besides a cross-sectional empirical study (Kaltenthaler et al., 2010), a macro-economic panel analysis (Fischer & Hahn, 2008)—both of which focus exclusively on the pre-crisis period, and a publication and working papers that conduct micro-based analyses (Bursian & Furth, 2011; Ehrmann et al., 2013; Farfaque et al., 2012), the only macro-based empirical evidence for the crisis period that can be directly compared to our results are the findings by Wälti (2012). With these findings, our empirical analysis comes to an ambivalent conclusion. On the one hand, it confirms the conclusion by Wälti (2012) that in the aftermath of the financial crisis from 10–11/2008 until 11–12/2010 unemployment was only weakly related to trust in the ECB.Footnote 18 On the other hand, we contradict this finding once analysing a longer crisis time period. Utilizing a crisis time period from 10–11/2008 to 11/2012, we find a strong negative relationship between unemployment and trust from 5/2011 onwards. This relationship is strongly driven by the four periphery countries Spain, Ireland, Greece and Portugal, in which a significant increase in unemployment rates is related to a significant decline in trust in the ECB.Footnote 19

In this respect, it should be noted that the significant increase in unemployment rates in the EA-4 has not only affected trust in the ECB but also trust in the EC, EP and national institutions (see also Ehrmann et al., 2013; Roth et al., 2013).Footnote 20

6.2 Discussion of Results in Light of the Underlying Theoretical Assumptions

Drawing upon the theoretical links, the empirical evidence showing that a majority of citizens in the EA-12 started to mistrust the ECB from 11/2011 onwards (in the EA-4 from 11–12/2010 onwards) should be worrying for the decision-makers of the ECB because it endangers the legitimacy of the ECB and thus ultimately its independence (Kaltenthaler et al., 2010, p. 1262).Footnote 21 Given the low approval rating, it becomes more likely that the ECB will become vulnerable to political influence (Torres, 2013) and that citizens will start to pressure politicians to minimize its independence (Kaltenthaler et al., 2010, p. 1267). Following the general argument, as the independence of central banks is associated with lower inflation rates (Eijfinger & de Haan, 1996) and as lower inflation rates are associated with long-term economic growth (Eijfinger & de Haan, 1996, p. 54), the loss of the ECB’s independence would most likely harm long-term economic growth. Following the crisis-embedded argument, the ECB’s independence permitted it to broaden its mandate to assure financial stability even against strong opposition (De Grauwe, 2013, p. 522; Fratzscher, 2013; Giavazzi et al., 2013). And as this broadened mandate continues to stabilize the Eurozone in times of crisis (De Grauwe & Ji, 2013a, 2013b, p. 2), a loss of the ECB’s independence would most likely endanger the stability of the Eurozone.

One might now still want to reflect upon the question of whether the significant decline in trust in the ECB poses an obstacle or an opportunity for further EU/EA integration (Tosun et al., 2014). The answer to this question remains ambivalent. On the one hand, the loss of trust in the ECB across all EA-12 countries endangers the legitimacy of the ECB, an institution that has become one of the central actors in securing the stability of the Eurozone. On the other hand, some of the policy measures advocated within the EA, amongst others reducing the high unemployment rates in the EA-4, can most likely only be resolved by collective action and will thus trigger a process of deeper political integration within the EA. As such, the current crisis could be identified as a clear opportunity for further deepening of the EU/EA integration process. The empirical evidence that a majority of EA-12 citizens supports the euro in times of crisis (Roth et al., 2012; for the Greek case, see also Clements et al., 2014) should be viewed as an ideal prerequisite for the implementation of a deeper political integration process within the EA.Footnote 22

7 Conclusions

This contribution has examined the trends and determinants of net trust in the ECB, focusing on unemployment and particularly on the crisis period from 10–11/2008 to 11/2012. Five findings deserve attention.

First, throughout the crisis net trust in the ECB has declined significantly in the EA-12 and has departed from its long-term trends. However, whereas this decline in trust has been distinct in the EA-8, the decline in the EA-4 has been even more pronounced.

Second, from 11/2011 onwards, a majority of citizens started to mistrust the ECB in the EA-12 and EA-8. In the EA-4, this trend already started from 11–12/2010 with large majorities mistrusting the ECB in 11/2012.

Third, with a majority of citizens mistrusting the ECB from 11/2011 onwards, the ECB’s legitimacy might be endangered. With its legitimacy potentially endangered, it will prove more difficult for the policymakers of the ECB to resist pressures from politicians to minimize their independence. Concerning the general argument, a loss of the ECB’s independence would endanger price stability and therefore harm long-term economic growth. Concerning the crisis-embedded argument, a loss of the ECB’s independence would endanger the ECB’s new mandate to assure financial stability and stabilize the Eurozone in times of crisis.

Fourth, taking panel data and using a fixed effects DFGLS estimation for an EA-12 country sample over the time period 1999–2012, this paper detects a strong and significant negative relationship between unemployment and trust in times of crisis. This relationship remains robust to a range of alterations and is strongly driven by the significant increase in unemployment rates in the EA-4 and from 5/2011 onwards.

Fifth, a reduction of the high unemployment rates in the EA-4 seems to be necessary in order to restore trust in the ECB in those countries. And this issue may determine the future of Eurozone integration and systemic trust.

Notes

  1. 1.

    Standard EB surveys are administered to about 1,000 respondents per EU country. The interviews are performed face-to-face in the home of the respondents. For each standard EB survey, new and independent samples are derived. To guarantee the polling of a representative sample of the population, the sampling design is multistage and random. The raw data are available on CD-ROM from Gesis ZA Data Service for Standard EBs 51–62 (Gesis, 2005a, 2005b) and were received on request from Gesis ZA Data Service for Standard EBs 63–69 (Gesis, 2009). Data for the Standard EBs 70–78 and Special EB 71.1 were taken from the European Commission’s (EC) tables of results (2009a; , 2009b; , 2009c, 2010a, 2010b, 2011a, 2011b, 2012a, 2012b). Following Jones (2009) and Ehrmann et al. (2013), the observations from the Special EB 71.1 in 1–2/2009 were taken into consideration. For a detailed reasoning, see Roth et al. (2013, p. 4). The elimination of data from EB71.1 does not modify the econometric results in any significant way (see results in row 14 in Table 6.3).

  2. 2.

    A net trust measure seemed adequate as the ‘Don’t Know’ answers varied over a wide range from 0% in Greece in EB 71 to 44.6% in Portugal in EB 51 with an overall mean value of 20.5%. However, it should be pointed out that net trust and trust measures correlate as high as 0.92. For an equation showing how to calculate net trust, see Roth et al. (2013, p. 4).

  3. 3.

    Although the monthly matching methodology by Wälti (2012, p. 597) correlates as high as 0.99 for the variables unemployment and inflation and 0.95 for the variable growth of GDP per capita, when comparing it to a semester-matching methodology, the monthly methodological approach seems to be preferable in order to prevent any potential overlap between the explanatory macro-economic variables and the EB data. The exact months of polling for the EBs surveys are displayed in the legend of the x-axis in Fig. 6.1.

  4. 4.

    GDP data were chain-linked, the reference year being 2005, and seasonally adjusted. Data on GDP were missing for Greece from the second quarter of 2011 onwards.

  5. 5.

    Due to inconsistent data on population size and breaks in some country time series within the official Eurostat data, values had to be exchanged by means of interpolation whenever required.

  6. 6.

    Possible measurement errors from the performed interpolation seem improbable, as the monthly constructed variables correlate with the semester data as high as 0.95 for growth of GDP per capita.

  7. 7.

    For Greece, time trend data from 2001 onwards were taken. The five countries Slovakia, Slovenia, Malta, Cyprus, and Estonia were not analysed as their accession occurred only recently and thus time trend data would not have been available from 1999 onwards.

  8. 8.

    For reasons of validity, population-weighted trust trends are utilized for the EA-12, EA-4 and EA-8 country sample. However, population-weighted and non-population weighted aggregates are highly correlated.

  9. 9.

    With the exception of Greece’s decline in trust in the EC, trust in the ECB has decreased more significantly than trust in the EC and EP in all EA-12 countries from 3–5/2008 to 11/2012. In comparison to the EC and EP, the decrease in trust in the ECB is significantly higher (one standard deviation above the mean) in particular in the three core countries Germany, Netherlands and Finland. In those three countries, the additional trust decline varies from 29% to 38% points of net trust with respect to the EC and 29%–32% points of net trust with respect to the EP, with Germany showing the largest additional decline of 38% and 32% points, respectively.

  10. 10.

    A prerequisite for using the DOLS approach is that the variables entering the model are non-stationary and that all the series are in a long-run relationship (cointegrated). In our case, all series are integrated of order 1, i.e. they are I(1) (and thus non-stationary, non-stationarity of inflation and growth of GDP per capita is due to non-stationarity (non-constancy) of the variance of these series) and they are cointegrated. Results for the panel unit root tests and Kao’s residual cointegration test can be obtained from the authors on request.

  11. 11.

    We found first-order autocorrelation to be present.

  12. 12.

    FGLS is not compatible with time-fixed effects but picks up shocks and their influence over short to medium term periods. In addition, the potential inclusion of time dummies would not alter our results in any significant manner (see results in row 15 in Table 6.3), and it could be shown that time-fixed effects do not tackle the problem of autocorrelation of the error term.

  13. 13.

    For a detailed explanation of all steps leading from Eq. (6.1) to Eq. (6.2) within a similar model specification, please see Roth et al. (2013, pp. 12–4).

  14. 14.

    The coefficients β2p, χ2p, δ2p and ϕ2p are linked to the endogenous part of the explanatory variables and do not result in a t-distribution. Since we are not interested in the influence of these ‘differenced variables’ on trust, they will not be depicted.

  15. 15.

    In addition to the theoretical validity of differentiating a pre-crisis from a crisis period, empirically, a Chow-test showed a structural break between the pre-crisis period (3–4/1999–3-5/2008) and the crisis period (10–11/2008–11/2012). Results can be obtained from the authors on request.

  16. 16.

    The insignificant relationship between unemployment and trust in the EA-8 is largely driven by the German case in which an actual decrease in the unemployment rate of 2.8% points (from 3–5/2008 to 11/2012) is associated with a significant decline in net trust in the ECB of 48% points (see here also Fig. 6.A2). Once excluding the German case from the EA-8 country sample, the relationship between unemployment and trust regains significance (90% level) and the coefficient regains strength (−7.1).

  17. 17.

    This is logical as in the case of Spain trust decreased significantly during the second year of the sovereign debt crisis, while its sovereign bond yields remained relatively stable.

  18. 18.

    Whereas our econometric analysis actually still finds a weak (90% level) relationship, Wälti’s (2012) findings point towards an insignificant relationship.

  19. 19.

    This is in contrast to the German case where an actual reduction of the unemployment rate is associated with a significant decline in trust in the ECB. A plausible hypothesis for the German case might be that the broadening of the ECB’s mandate to assure financial stability throughout the crisis has led to a decline in trust in the ECB.

  20. 20.

    As the decline of trust in the ECB might be interpreted as part of a general crisis of trust in European institutions, it becomes debatable whether other trust variables, such as citizens’ trust in the EC and the EP, should be included in the model specification. We excluded these variables for two reasons. First, as trust in the EC and the EP is equally determined by inflation, growth and unemployment (Roth et al., 2013), it is econometrically incorrect to include these trust variables in the regression, because doing so would lead not only to double counting but also to endogeneity. Second, the Durbin–Watson statistic (being around 2) did not give us reason to worry about omitted variables.

  21. 21.

    It should be mentioned, however, that in 1–2/2009 net trust temporarily reached a value of −1%. In this instance, however, net trust recovered to a value of +14 only five months later in 6–7/ 2009.

  22. 22.

    In contrast to the support for the euro, the support for the European Union actually declined more strongly (Braun & Tausendpfund, 2014).

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Appendix

Appendix

Fig. 6.A1
figure 2

Net trust in the ECB in EA12 countries, 1999–2012

Notes: For Greece, the time trend is displayed from 2001 onwards. As the figure depicts net values, all values below 0 indicate that a majority of respondents’ mistrust. The dotted line represents the incidence of the bankruptcy of Lehman Brothers in September 2008.

Sources: Standard EBs 51–78 and Special EB 71.1.

Fig. 6.A2
figure 3

Scatterplot between delta unemployment and delta trust in the ECB, 2012–2008

Notes: Delta Unemployment and Delta trust comprise the changes in the unemployment rate and in Trust from 3–5/2008 to 11/2012.

Sources: Standard EB 69 and 78 and Eurostat data.

Table 6.A1 Summary statistics EA12

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Roth, F., Gros, D., Nowak-Lehmann D., F. (2022). Crisis and Citizens’ Trust in the European Central Bank: Panel Data Evidence for the Euro Area, 1999–2012. In: Public Support for the Euro. Contributions to Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-86024-0_6

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