An Electron Microprobe Evaluation of Diagenetic Alteration in

Journal of International Money and Finance 31 (2012) 593–605

Contents lists available at SciVerse ScienceDirect

Journal of International Money and Finance journal homepage: www.elsevier.com/locate/jimf

Trust no more? The impact of the crisis on citizens’ trust in central banks Sébastien Wälti* Swiss National Bank, Börsenstrasse 15, 8022 Zurich, Switzerland

a b s t r a c t JEL Classifications: E02 E31 E58 E63 P16 Keywords: Central bank Trust Crisis Sovereign bond yields Banking crisis

Citizens’ trust in economic institutions has generally declined since the onset of the crisis. In particular, Eurobarometer surveys show that trust in the European Central Bank (ECB) has fallen significantly during the crisis. This paper studies the determinants of public trust in the ECB over the lifetime of the euro. Net trust in the ECB has decreased significantly in those countries which have experienced increasing sovereign bond yields and financial market turbulence. The finding that country-specific variables affect citizens’ trust in the ECB may seem counterintuitive. However, it is consistent with strong evidence in the political science literature showing that domestic considerations play a significant role when citizens get an opportunity to express their opinion on EU matters. Ó 2011 Elsevier Ltd. All rights reserved.

There is no doubt that in order to be successful over the longer term, the ECB will have to win and maintain the trust and support of the European public. Otmar Issing (2000) 1. Introduction The persistence of economic institutions in a democratic society requires that its citizens trust them. The relationship between citizens and economic institutions can be understood within a principalagent model (Ehrmann et al., 2010; Kaltenthaler et al., 2010). In such a model, citizens are the principal and economic institutions the agent. When the principal loses confidence in the ability of the

* Tel.: þ41 44 6313943. E-mail address: [email protected]. 0261-5606/$ – see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.jimonfin.2011.11.012

594

S. Wälti / Journal of International Money and Finance 31 (2012) 593–605

agent to carry out its mandate, the principal is likely to vote for change. Therefore, economic institutions must earn the trust of citizens, they must maintain it, and they must do so on the basis of observable factors. Trust in economic institutions would be particularly important at times of crisis, when uncertainty increases markedly. Public opinion surveys show that citizens’ trust in economic institutions has actually declined since the onset of the crisis (Roth, 2009; Gros and Roth, 2009). For example, a Financial Times/Harris Poll was conducted in June 2009 across five European countries (United Kingdom, France, Italy, Spain and Germany) and the United States (Harris Interactive, 2009). This poll asked respondents the following question: “Do you feel the European Central Bank/Bank of England/Federal Reserve has responded appropriately to the challenges of the economic downturn and its consequences?” A majority answered no in each of these countries. Further evidence on trust in central banks can be obtained from Standard Eurobarometer surveys. Among other things, these surveys have asked citizens in the European Union whether they tend to trust European institutions or not. Fig. 1 depicts the crosscountry average of net trust of European citizens in the European Central Bank (ECB) from Spring 1999 until Fall 2010. Net trust is defined as the difference between the share of respondents in Standard Eurobarometer surveys who say they trust the ECB, minus the share of respondents who do not trust. Net trust stood at around 40 percent before the crisis and has since then fallen markedly. The purpose of this paper is to investigate empirically the determinants of public trust in the European Central Bank, in particular since the onset of the crisis. The crisis has manifested itself differently in different euro area countries. Thus, is the decline in trust related to large movements in inflation and unemployment, the typical arguments of social loss functions in macroeconomics, since the beginning of the crisis? Furthermore, is the fall in trust also related to other country-specific developments during the crisis, for example rising sovereign bond yields or financial market distress? This paper contributes two innovations to the literature and includes the most recent observations coming from the Standard Eurobarometer survey carried out in Fall 2010. The first innovation of this paper is to relate the fall in net trust to the large divergence of sovereign bond yields across euro area countries. Fischer and Hahn (2008) focus only on the first five years of the euro and thus, they do not include the crisis in their analysis. Gros and Roth (2010a) focus narrowly on output growth to identify the impact of the crisis on net trust. Yet, the crisis has manifested itself in different forms in different countries. Even though output growth may capture some part of banking sector distress and rising bond yields, we control for different forms of the crisis directly by including a large set of explanatory variables in our regression analysis. Ehrmann et al. (2010) also consider banking sector distress but do not take into account sovereign bond yields.1 The second innovation of this paper relates to the modeling of explanatory variables. Since Standard Eurobarometer surveys are carried out at irregular intervals, the construction of explanatory variables is somewhat complicated. While Fischer and Hahn (2008) make use of annual data, Gros and Roth (2010a) adjust the survey fieldwork dates in order to be able to use quarterly data. Instead, all explanatory variables in this paper are based on data available at the monthly frequency. Indeed, crises exhibit high-frequency dynamics, meaning that monthly data are most adequate to understand the link between net trust and macroeconomic developments. Finally, the sample period in this paper extends to the latest Standard Eurobarometer survey conducted in November 2010. Both Ehrmann et al. (2010) and Gros and Roth (2010a) use data only up to the Fall 2009 survey. Gros and Roth (2010a) conclude that “the change of net confidence seems to have come to a halt in October–November 2009”. However, as Gros and Roth (2010b) and Fig. 1 in this paper show, net trust fell further by a significant amount during Spring 2010. Those observations which were gathered in 2010 are therefore important in providing time variation in net trust. My results show that both rising sovereign bond yields and financial sector turbulence have reduced citizens’ trust in the ECB. In particular, a one-percentage point increase in sovereign bond yields reduces net trust by about seven percentage points, other things equal. The finding that country-specific variables affect citizens’ reported level of trust in the ECB may seem counterintuitive. However, it is

1 Ehrmann et al. (2010) also differs from this paper on methodological grounds. Ehrmann et al. (2010) perform an individuallevel analysis, while I focus on macroeconomic aggregates like in Gros and Roth (2010a).


595

0.6 0.5 0.4 0.3 0.2 0.1 0.0

99

00

01

02

03

04

05

06

07

08

09

10

Fig. 1. Average net trust across the euro area (1999–2010).

consistent with widespread evidence in the political science literature showing that domestic considerations play a significant role when citizens get an opportunity to express their opinion on EU matters. Section 2 describes the empirical approach, including the regression specification, the measurement of net trust and explanatory variables. Since standard Eurobarometer surveys are conducted twice a year at irregular intervals, the construction of the dataset deserves an extensive discussion. Section 3 presents baseline results, an interpretation and robustness checks. The final section provides short concluding remarks. 2. Empirical approach This section describes the panel regression specification, the measurement of net trust in the European Central Bank, as well as the construction of explanatory variables. 2.1. Regression specification Standard Eurobarometer surveys are carried out twice a year across the European Union. The availability of data across countries over time makes the use of panel data techniques very appealing. The dependent variable is the level of net trust in country i at survey t. The regression specification includes the typical arguments of social loss functions in macroeconomics, namely inflation and unemployment.2 The apparent association between the fall in net trust and the crisis could be explained by movements of inflation and unemployment during the crisis. Yet, other macroeconomic variables have experienced sometimes abrupt and large changes during the crisis. Thus, we add to the specification a number of explanatory variables capturing various aspects of the crisis to see whether the crisis has had an effect on trust beyond its impact on inflation and unemployment.

Trust i;t ¼ ai þ lt þ bpi;t þ gUi;t þ dXi;t þ εi;t

2

See, for example, Drazen (2000), chapter 4, for a discussion of social loss functions in macroeconomics.

(1)

596


where ai is a country fixed effect, lt is a time fixed effect capturing variation in net trust common to all countries at a given survey time, pi,t is inflation in country i at survey t, Ui,t stands for unemployment in country i at survey t, and Xi,t comprises other macroeconomic variables such as sovereign bond yields, financial market distress, and output growth. The country fixed effect will pick up unobserved time-invariant country heterogeneity. Fischer and Hahn (2008) postulate that net trust could vary across countries according to national differences in mentality, history and national economic institutions. The country fixed effect will also pick up possible time-invariant framing effects linked to Standard Eurobarometer surveys. The inclusion of time fixed effects deserves some discussion. Time fixed effects are typically included in panel regressions to capture unobserved aggregate shocks. By definition, such aggregate shocks affect all countries in the panel at a given survey time. If there are unobserved aggregate shocks in the data and time fixed effects are not included, omitted variable bias may be a serious concern. In this case, explanatory variables with a similar time pattern across countries may pick up the effect of these aggregate shocks. Thus, a statistically significant coefficient may be obtained not because an explanatory variable has itself a significant effect on the dependent variable, but because of aggregate shocks which have not been properly controlled for. Of course, even though time fixed effects may kill some omitted variable bias bathwater, they may also remove much of useful information in the baby, the variable of interest (Angrist and Pischke, 2009). Bearing this possibility in mind, however, all estimations in this paper include time fixed effects. The crisis has had a truly global scale and we should therefore consider seriously the possibility of unobserved aggregate shocks in the data.

2.2. Measurement of net trust The measure of net trust is constructed on the basis of the Standard Eurobarometer surveys. These surveys were established in 1973 and consist of approximately 1000 interviews for each member state at each survey (Germany has about 2000, Luxembourg about 500, and the United Kingdom about 1300, including 300 for Northern Ireland). Interviews are conducted twice a year, in spring and fall. Standard Eurobarometer surveys comprise a list of questions which repeat at every wave of the survey.3 The question about trust in European institutions is the following: “And, for each of them, please tell me if you tend to trust it or tend not to trust it?” The list of institutions includes the European Central Bank. In line with the existing literature, net trust is measured as the number of respondents in a country during a given survey which answer they trust the ECB, minus the number of respondents who do not trust, divided by the sum of those who trust and those who do not trust.4 Thus, those who answer that they do not know are not taken into account. The implicit assumption is that those who answer yes or no know something about the ECB. This assumption is supported by results in Kaltenthaler et al. (2010). In Eurobarometer surveys, people are also asked whether they have heard about the ECB or not. Kaltenthaler et al. (2010) find that those who claim to have heard about the ECB are significantly more likely to answer yes or no to the trust question than to say they do not know. The sample consists of the twelve member states (EA12) who adopted the euro early on: Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal and Spain. Data are retrieved from the Eurobarometer 51 (Spring 1999) to the Eurobarometer 74 (Fall 2010). Twenty-four waves of the survey for twelve countries gives us 284 observations (in fact, 288 minus four observations; Greece became a member in January 2001, so the first four waves for Greece are not taken into account).

3 Ehrmann et al. (2010) and Gros and Roth (2010a) also include information on net trust from the Special Eurobarometer survey 71.1 conducted in January–February 2009. In contrast, I exclude observations coming from that Special Eurobarometer survey because of framing effects. In that survey, the list of questions differed from that in Standard Eurobarometer surveys. By focusing exclusively on Standard Eurobarometer surveys, where the same questions are asked at each survey, framing effects are controlled for by including country fixed effects in all estimations. 4 Fischer and Hahn (2008) use a slightly different measure of net trust, dividing by the total number of respondents (including those who answer that they do not know). Regardless of which definition of net trust is chosen, the time pattern of net trust remains very similar. The correlation coefficient between both series exceeds 0.92 for all individual EA12 countries. I have used Fischer and Hahn’s definition as a robustness check and results remain unchanged.


597

Fig. 2. Construction of explanatory variables.

The average level of net trust across EA12 countries stood at around 40 percent until the crisis started. Since then, it has fallen markedly. This fall extends to all countries in the sample (see Table A5 and Figure A1 in the Appendix for evidence on individual countries). Yet, the magnitude of the fall is not uniform across countries. Some countries experience small decreases in net trust, like Austria or Finland, while others have seen larger falls, like Greece, Spain or Ireland. 2.3. Explanatory variables Standard Eurobarometer surveys are always carried out twice a year but the fieldwork does not always happen in the same months. The irregular occurrence of the fieldwork complicates the construction of explanatory variables. Thus, we must make an assumption about those observable factors which citizens consider when answering the Eurobarometer questions. In this paper, I shall assume that citizens consider the average of the values of explanatory variables between the month preceding the fieldwork back to the first month of the previous fieldwork. Fig. 2 depicts the information set of citizens when they answer questions for the fieldwork at survey t. When going back in time starting from the month before the fieldwork, why would one stop at the first month of the previous fieldwork? The main intuition is best exposed using an equation. Suppose that net trust depends only on inflation and unemployment. Thus,

Trust i;t ¼ a þ bpi;t þ gUi;t þ εi;t where country and time fixed effects and other explanatory variables are dropped for convenience. Again, the variable pi,t is the average inflation rate in country i between the month before the fieldwork t and the first month of the previous fieldwork t1. Similarly, the variable Ui,t is the average rate of unemployment in country i between the month before the fieldwork t and the first month of the previous fieldwork t1. Taking first differences, we get

DTrust i;t ¼ bDpi;t þ gDUi;t þ εi;t εi;t1

This equation makes it clear that any change in the level of trust between two surveys is explained by changes in the explanatory variables between these two surveys. Any information which was already available before is already reflected in the level of trust obtained at the fieldwork t1. This way of constructing explanatory variables improves on the existing literature. Fischer and Hahn (2008) average net trust values within each year and use one-year lags of explanatory variables. The use of lagged annual data is problematic because anything that occurs between the beginning of a year and the fieldwork during that year is assumed not to be taken into account by citizens. Gros and Roth (2010a) assume the irregular occurrence of survey fieldwork away and assume instead that all surveys are carried out in April and in October. This assumption permits the use of quarterly data, which are transformed into semester data to fit the adjusted cycle of surveys. This approach features two drawbacks. First, the necessary adjustment of survey fieldwork dates raises the risk of measurement error. Second, monthly data should be preferred because crises exhibit high-frequency dynamics. The European crisis is no exception. Indeed, Ehrmann et al. (2010) also use monthly data but the information set of citizens differs slightly from my approach. While I match exactly the number of months of data that citizens take into account to the occurrence of surveys, Ehrmann et al. (2010) assume that citizens always look back at the previous six months (regardless of the occurrence of surveys). In the

598


end, my approach shows that there is neither a need to adjust survey fieldwork dates, nor a need for the information set of citizens to be based on a fixed number of months prior to survey fieldwork. Data on the Harmonized Consumer Price Index (HCPI), unemployment and bond yields have been gathered from the online Eurostat database. Inflation is the year-on-year growth rate of the HCPI. Since high inflation and deflation are both “bads”, the rate of inflation is transformed by subtracting 2 (the assumed level of inflation consistent with price stability) and taking the absolute value. In terms of a social loss function, the social loss would increase whenever inflation departs from its level consistent with price stability. Data on total industrial production, industrial production excluding construction, and construction alone have been gathered from the OECD Main Economic Indicators. These three variables were transformed into growth rates by taking percentage changes. Data on construction activity were only available at the quarterly frequency for Greece, Ireland and Italy. Monthly observations for these countries were obtained by linear interpolation. Finally, data on broad stock market indices, banking sector stock indices and financial sector stock indices were obtained from Datastream. Returns were calculated as percentage changes. Sharply negative returns on either of the three indices signal financial market distress. 3. Results Table 1 presents my baseline estimation results. All regression specifications feature both country and time fixed effects as well as cluster-robust standard errors. The null hypothesis that all coefficient estimates are jointly equal to zero is always rejected. Similarly, the null hypothesis that all country fixed effects are equal is always rejected. The R-squared (within) statistic shows that about half of the total variation in net trust is explained by the explanatory variables. 3.1. Baseline results The divergence of sovereign bond yields across EA12 countries during the crisis has had a very strong impact on net trust. More precisely, net trust has decreased substantially in those countries where sovereign bond yields have risen significantly. A one-percentage point increase in sovereign bond yields reduces net trust by about seven percentage points, other things equal. This result is not surprising given the particular pattern of sovereign bond yields across EA12 countries over time. From 1999 (2001 for Greece) until the onset of the crisis, sovereign bond yields were almost perfectly correlated. Since the onset of the crisis, however, these bond yields have started to diverge markedly. The introduction of time fixed effects amounts to purging the data from the cross-country average at every survey. Since bond yields were very close to the cross-country average before the crisis, and moved away from the cross-country average during the crisis (when net trust started to fall), it is no surprise to obtain a strong relationship between bond yields and net trust when time fixed effects are included. Financial market distress also brings about a reduction in net trust in the ECB. This finding confirms the results of Ehrmann et al. (2010). Interestingly, coefficient estimates for banking sector and financial sector returns are statistically significant, while the coefficient estimate for stock market returns is not statistically different from zero. Together with the inclusion of time fixed effects, this finding means that we are really capturing financial market distress and not some common, unobserved shock which would affect all assets across the board. The coefficient estimates for other macroeconomic variables are generally not statistically significant, except for inflation in some but not all specifications. As discussed above, the lack of strong statistical significance of inflation and unemployment does not necessarily imply that these two variables have no effect on net trust. It is possible that their similar pattern across countries during the crisis are picked up by the time fixed effects. In fact, both these variables have a significant, negative impact on net trust across all specifications once time fixed effects are excluded. This being said, the estimated coefficient may just pick up unobserved aggregate shocks. In the end, my results do not allow to distinguish between these two possibilities. But we should not conclude strictly that the standard arguments of social loss functions in macroeconomics do not matter for net trust.

Table 1 Baseline resultsa,b

Inflation

(II)

(III)

(IV)

(V)

(VI)

(VII)

(VIII)

(IX)

(X)

1.028 (0.869) 0.179 (0.877) 7.711*** (1.076)

1.514* (0.751) 0.771 (0.916)

1.652** (0.681) 0.622 (0.882)

1.760** (0.761) 0.841 (0.929)

1.751** (0.768) 0.822 (0.973)

1.762** (0.761) 0.849 (0.959)

1.766** (0.798) 0.365 (0.928)

0.920 (0.863) 0.182 (0.871) 7.174*** (1.039) 0.076* (0.037)

1.035 (0.838) 0.107 (0.840) 6.892*** (0.999)

Bond yield Banking index return Financial index return Stock index return Industrial production incl. construction Industrial production excl. construction Construction Observations Countries Country fixed effects Time fixed effects R -squared (within) F statistic

0.118** (0.039)

0.105** (0.036)

0.160** (0.055) 0.005 (0.090) 0.053 (0.292) 0.040 (0.318)

284 12 Yes

284 12 Yes

284 12 Yes

284 12 Yes

284 12 Yes

284 12 Yes

284 12 Yes

0.154 (0.108) 268 12 Yes

Yes 0.478 12.71***

Yes 0.536 40.36***

Yes 0.494 41.57***

Yes 0.502 43.22***

Yes 0.478 16.11***

Yes 0.478 12.46***

Yes 0.478 13.57***

Yes 0.492 12.69***

284 12 Yes

284 12 Yes

Yes 0.542 40.77***

Yes 0.546 32.47***


Unemployment

(I) 1.761** (0.755) 0.838 (0.954)

*Significant at 10% level; **significant at 5% level; ***significant at 1% level. a Cluster-robust standard errors in parentheses. b The dependent variable is net trust in the European Central Bank multiplied by 100. All estimations include country and time fixed effects.

599

600


3.2. Interpretation The finding that country-specific variables such as sovereign bond yields or financial market distress affect citizens’ reported level of trust in the ECB may seem counterintuitive. However, it is consistent with widespread evidence in the political science literature showing that domestic considerations play a significant role when citizens get an opportunity to express their opinion on EU matters. The political science literature has assessed whether voters cast their votes in EU referenda based on European-wide issues, or whether domestic political considerations also play a role. EU referenda present an opportunity for voters to punish the government, or the political establishment in general, by voting against their recommendations. There is ample empirical evidence that voters take their decisions in EU referenda at least partly on the basis of domestic considerations. Franklin et al. (1995) found that national referenda on the Maastricht Treaty were predominantly influenced by domestic factors. Garry et al. (2005) also found a role for domestic politics in the outcome of the two Irish referenda on the Nice Treaty. Taggart (2006) discusses how domestic politics must be taken into account to understand the rejection in France and the Netherlands of the referendum on the European Constitutional Treaty. My finding is thus consistent with the following idea. When citizens get an opportunity to express their opinion about European institutions during survey fieldworks, their discontent with domestic macroeconomic management, which will likely prevail during crisis times, may partly reflect on their reported trust in European institutions. If this interpretation is correct, the finding that country-specific developments in euro area countries affects net trust in the ECB should extend to other European institutions also involved in crisis management. Table A1 presents estimation results for the European Commission, while Table A2 shows estimation results for the European Council. All regression specifications are the same as for baseline results, except for the dependent variable. Estimation results show that sovereign bond yields have a strong, negative impact on net trust in both the European Commission and the European Council. 3.3. Endogeneity In this subsection, I provide a robustness check for a possible endogeneity problem. This problem is best described using an example. Suppose that two surveys are carried out in April and in November. In other words, changes in net trust will be observed in April and in November. It is possible that trust actually changes in June and that this affects, say, inflation over the months before the November fieldwork. This amounts to a possible problem of reverse causality. However, there should be no reason why inflation (or any other explanatory variable) before April would be affected by changes in trust between April and November. I have estimated all specification regressions instrumenting all explanatory variables by their lagged values. Estimation results in Table A3 show that this IV strategy confirms my baseline results. 3.4. Structural break The purpose of this paper is to study the impact of the crisis, as captured in the time pattern of selected macroeconomic variables, on net trust in the ECB. There is another related question. Has the crisis changed the way in which macroeconomic variables affect net trust in the ECB? In other words, have coefficient estimates changed during the crisis period? This is a question about a possible structural break. I assess the stability of coefficient estimates through recursive estimation. Recursive estimation is appropriate because it does not require making an arbitrary assumption about the beginning of the crisis. Moreover, Eurobarometer surveys are carried out only twice a year and thus, a crisis sub-sample would be small. The sample is initially restricted to the pre-crisis time period Spring 1999–Fall 2006. I then re-estimate these specifications by successively increasing the sample size, by one survey at a time, and check whether coefficient estimates change over time. Results in Table A4 show that the coefficient estimates for sovereign bond yields and financial sector turbulence remain roughly stable as the sample size increases into the crisis period. However, their


601

statistical significance increases markedly in contrast to the pre-crisis period. Thus, it seems that the crisis as captured in the time pattern of macroeconomic variables has definitely affected net trust. But there does not seem to be a structural break in the sense that the elasticity of net trust to these macroeconomic variables changed over time. This is most evident in the case of financial sector returns. Ehrmann et al. (2010) also conclude against a structural break. 4. Concluding remarks This paper studies the determinants of public trust in the European Central Bank, in particular during the crisis. Survey results indicate that the level of net trust of citizens in the ECB has declined markedly since the onset of the crisis. The baseline econometric specification relates net trust to a range of macroeconomic variables capturing the many dimensions of the crisis, such as output downturns, rising sovereign bond yields and financial market distress. Rising sovereign bond yields and financial market distress during the crisis have reduced citizens’ net trust in the ECB. In particular, a one-percentage point increase in sovereign bond yields reduces net trust by about seven percentage points, other things equal. The finding that purely country-specific developments, for which the ECB has no mandate, reduce net rust is consistent with widespread evidence in the political science literature showing that domestic considerations play a significant role when citizens get an opportunity to express their opinion on EU matters. Hence, citizens’ discontent with domestic macroeconomic management, which will likely prevail during crisis times, seems to partly reflect on their reported trust in European institutions. Acknowledgements The views expressed in this paper are those of the author and do not necessarily represent those of the Swiss National Bank. I am grateful for helpful discussions and suggestions to two anonymous referees, Agnès Bénassy-Quéré, Nikolay Hristov, Signe Krogstrup, Simone Meier, Fabien Rondeau, and participants at the 2011 Meeting of the European Public Choice Society, the Eighth Annual NBP-SNB Joint Seminar, and the 2011 International Conference on Macroeconomic Analysis and International Finance. Appendix Table A1 Net trust in the European Commission.a,b

Inflation Unemployment Bond yield Banking index return Financial index return Stock index return Industrial production incl. construction Industrial production excl. construction Construction

(I)

(II)

(III)

(IV)

(V)

(VI)

(VII)

(VIII)

(IX)

1.599 (0.905) 1.394 (0.830)

0.670 (0.967) 0.558 (0.800) 9.784*** (1.251)

1.476 (0.827) 1.361 (0.841)

1.532* (0.850) 1.260 (0.858)

1.579 (0.908) 1.432 (0.807)

1.566 (0.935) 1.339 (0.851)

1.599 (0.909) 1.394 (0.835)

2.217** (0.885) 0.890 (0.886)

1.356 (0.885) 0.298 (0.957) 9.213*** (1.202)

0.223** (0.099)

0.095 (0.129)

0.059 (0.060) 0.099 (0.077) 0.072 (0.069) 0.184 (0.325) 0.000 (0.250)

(continued on next page)

602


Table A1 (continued )

Observations Countries Country fixed effects Time fixed effects R -squared (within) F statistic

(I)

(II)

(III)

(IV)

(V)

(VI)

(VII)

(VIII)

(IX)

284 12 Yes

284 12 Yes

284 12 Yes

284 12 Yes

284 12 Yes

284 12 Yes

284 12 Yes

268 12 Yes

268 12 Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

0.453

0.548

0.457

0.463

0.459

0.455

0.453

0.481

0.564

10.01***

15.58***

10.50***

5.03***

25.41***

16.31***

16.08***

9.18***

16.45***

*Significant at 10% level; ***significant at 5% level; *significant at 1% level. a Cluster-robust standard errors in parentheses. b The dependent variable is net trust in the European Commission multiplied by 100. All estimations include country and time fixed effects. Table A2 Net trust in the European Council.a,b

Inflation Unemployment

(I)

(II)

(III)

(IV)

(V)

(VI)

(VII)

(VIII)

(IX)

1.378 (0.978) 1.794** (0.686)

0.442 (1.020) 1.057* (0.583) 8.607*** (0.956)

1.231 (0.915) 1.758** (0.687)

1.262 (0.913) 1.646** (0.675)

1.337 (0.978) 1.839** (0.667)

1.354 (1.013) 1.717** (0.692)

1.380 (0.975) 1.784** (0.679)

1.735 (1.088) 1.308* (0.672)

0.850 (0.947) 0.768 (0.688) 8.217*** (1.029)

0.088 (0.110) 256 12 Yes Yes 0.502 34.86***

Bond yield Banking index return Financial index return Stock index return Industrial production incl. construction Industrial production excl. construction Construction

(0.050) 0.060 0.112 (0.069) 0.083 (0.059) 0.243 (0.296)

0.035 (0.282)

Observations Countries Country fixed effects Time fixed effects R -squared (within) F statistic

272 12 Yes

272 12 Yes

272 12 Yes

272 12 Yes

272 12 Yes

272 12 Yes

272 12 Yes

0.202** (0.082) 256 12 Yes

Yes 0.388 12.20***

Yes 0.487 26.79***

Yes 0.394 15.37***

Yes 0.404 7.37***

Yes 0.399 18.86***

Yes 0.393 3.96**

Yes 0.388 3.08**

Yes 0.416 11.30***

*Significant at 10% level; **significant at 5% level; ***significant at 1% level. a Cluster-robust standard errors in parentheses. b The dependent variable is net trust in the European Council multiplied by 100. All estimations include country and time fixed effects. Table A3 IV estimations.a,b (I) 3.129* (1.852) Unemployment 0.341 (0.454) Bond yield

Inflation

(II)

(III)

(IV)

(V)

(VI)

(VII)

(VIII)

(IX)

3.307* (1.750) 0.183 (0.454) 7.575*** (1.832)

3.711** (1.778) 0.242 (0.456)

3.828** (1.783) 0.066 (0.471)

2.981 (1.859) 0.322 (0.457)

3.538* (2.087) 0.248 (0.513)

2.888 (2.081) 0.378 (0.485)

2.774 (2.111) 0.244 (0.516)

3.518* (1.982) 0.841 (0.512) 5.768*** (1.996)


603

Table A3 (continued ) (I)

(II)

(III)

Banking index return Financial index return Stock index return Industrial production incl. construction Industrial production excl. construction Construction Observations Countries Country fixed effects Time fixed effects R-squared (within) Wald statistic

(IV)

(V)

(VI)

(VII)

(VIII)

(IX)

0.124 (0.098) 0.191** (0.098)

0.226** (0.111) 0.048 (0.074) 0.306 (0.701)

0.158 (0.654)

273 12 Yes

273 12 Yes

273 12 Yes

273 12 Yes

273 12 Yes

273 12 Yes

273 12 Yes

0.295** (0.121) 256 12 Yes

0.209* (0.120) 256 12 Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

0.478

0.532

0.488

0.497

0.475

0.474

0.478

0.488

0.537

3618.24*** 4037.43*** 3678.25*** 3742.01*** 3585.01*** 3575.44*** 3606.39*** 3129.59*** 3454.81***

* Significant at 10% level; **significant at 5% level; ***significant at 1% level. a Conventional standard errors in parentheses. IV estimations have been performed with the routine -xtivreg in Stata10. b The dependent variable is net trust in the European Central Bank multiplied by 100. All estimations include country and time fixed effects. Table A4 Recursive estimation.a,b Sample

Sample stops in

Obs.

Inflation

Unemployment

Bond yields

Financial index return

R-squared (within)

EB51-EB66 EB51-EB67 EB51-EB68 EB51-EB69 EB51-EB70 EB51-EB71 EB51-EB72 EB51-EB73 EB51-EB74

Sep–Oct 2006 Apr–May 2007 Sep–Nov 2007 Mar–May 2008 Oct–Nov 2008 Jun–Jul 2009 Oct–Nov 2009 May 2010 Nov 2010

188 200 212 224 236 248 260 272 284

0.115 0.153 0.126 0.061 0.016 0.065 0.434 1.274 1.035

1.459 1.194 1.199 1.118 0.846 0.545 0.360 0.105 0.107

8.024 8.890* 7.942 8.589* 10.814* 12.483** 11.719** 11.306*** 6.892***

0.111* 0.114* 0.102* 0.093 0.107* 0.119** 0.121** 0.111** 0.105**

0.257 0.241 0.214 0.196 0.201 0.292 0.320 0.462 0.546

*Significant at 10% level; **significant at 5% level; ***significant at 1% level. a Cluster-robust standard errors in parentheses. b The dependent variable is net trust in the European Central Bank multiplied by 100. All estimations include country and time fixed effects. Table A5 Net trust in EA12 countries, EB51-EB74 (Spring 1999–Fall 2010). Survey Fieldwork

AUT

BLG

FIN

EB51 EB52 EB53 EB54 EB55

0.2433 0.3178 0.1281 0.1749 0.2000

0.2513 0.2649 0.3420 0.2268 0.3096

0.3874 0.2690 0.2564 0.2646 0.2799

Mar–Apr 1999 Oct–Nov 1999 Apr–May 2000 Nov–Dec 2000 Apr–May 2001

FRA 0.1576 0.1710 0.2304 0.1337 0.1942

GER 0.3276 0.2801 0.1709 0.1881 0.3079

GRE 0.1509 0.1301 0.4836 0.1724 0.2061

IRE

ITA

LUX

NDL

POR

0.6474 0.5569 0.5967 0.5743 0.6651

0.5499 0.5354 0.4224 0.4254 0.4029

0.6659 0.4198 0.5141 0.4864 0.5696

0.6826 0.7786 0.6914 0.5633 0.6485

0.4260 0.6227 0.5406 0.4561 0.5108

SPA 0.1921 0.4210 0.3837 0.3343 0.2543

(continued on next page)

604


Table A5 (continued ) Survey Fieldwork

AUT

BLG

FIN

FRA

EB56 EB57 EB58 EB59 EB60 EB61 EB62 EB63 EB64 EB65 EB66 EB67 EB68 EB69 EB70 EB71 EB72 EB73 EB74

0.3649 0.2911 0.4500 0.2800 0.2776 0.1583 0.3358 0.2920 0.2594 0.2759 0.2877 0.2575 0.3190 0.2512 0.3037 0.2927 0.3258 0.1648 0.0753

0.4144 0.3297 0.5372 0.3712 0.2415 0.3131 0.5424 0.5094 0.3754 0.4235 0.4572 0.5091 0.4450 0.4553 0.3658 0.2549 0.2360 0.1648 0.2527

0.4204 0.4444 0.3964 0.5392 0.3825 0.5491 0.4646 0.5606 0.2773 0.4525 0.3603 0.4979 0.5072 0.5729 0.5987 0.5430 0.5955 0.4505 0.3696

0.2846 0.3787 0.3315 0.1271 0.4523 0.3217 0.1485 0.4822 0.2752 0.2289 0.4516 0.3817 0.0700 0.3377 0.5187 0.1007 0.3427 0.4629 0.1942 0.3293 0.4381 0.0092 0.2429 0.2855 0.0361 0.2981 0.1741 0.0526 0.3030 0.1670 0.0139 0.3883 0.2449 0.0556 0.4751 0.1196 0.2010 0.4734 0.0535 0.1338 0.3825 0.0120 0.0874 0.2563 0.0373 0.0050 0.1976 0.1575 0.0526 0.2000 0.0737 0.1688 0.0227 0.1875 0.0667 0.1236 0.3830

Oct–Nov 2001 Mar–May 2002 Oct–Nov 2002 Mar–Apr 2003 Oct–Nov 2003 Feb–Mar 2004 Oct–Nov 2004 May–Jun 2005 Oct–Nov 2005 Mar–May 2006 Sep–Oct 2006 Apr–May 2007 Sep–Nov 2007 Mar–May 2008 Oct–Nov 2008 Jun–Jul 2009 Oct–Nov 2009 May 2010 Nov 2010

GER

GRE

IRE

ITA

LUX

NDL

POR

0.7103 0.6738 0.6888 0.6137 0.6434 0.6337 0.6044 0.4527 0.5700 0.5682 0.5579 0.5966 0.5523 0.6480 0.3762 0.2798 0.3067 0.0864 0.1392

0.5854 0.6463 0.5873 0.5166 0.4290 0.3009 0.4069 0.4531 0.2339 0.4973 0.1300 0.3953 0.2135 0.2964 0.1703 0.2010 0.2000 0.1233 0.1429

0.6176 0.6250 0.5933 0.5662 0.5331 0.5753 0.5620 0.6497 0.6465 0.5500 0.5639 0.5328 0.5578 0.5526 0.4408 0.4915 0.4634 0.3735 0.4588

0.6633 0.6128 0.5618 0.6049 0.4440 0.5506 0.6829 0.5563 0.5991 0.6426 0.6877 0.6897 0.7713 0.7828 0.7472 0.5276 0.4824 0.2727 0.4186

0.4520 0.3559 0.5840 0.2371 0.4483 0.4379 0.5515 0.1857 0.4654 0.2687 0.4483 0.2977 0.5763 0.3377 0.5752 0.0987 0.5709 0.1065 0.4543 0.1975 0.4743 0.2421 0.4282 0.3583 0.3390 0.2476 0.4881 0.5601 0.3557 0.2798 0.3000 0.0717 0.4286 0.1220 0.0864 0.0244 0.1500 0.0633

SPA

Figure A1. Net trust in EA12 countries, EB51–EB74 (Spring 1999–Fall 2010).

References Angrist, J., Pischke, J., 2009. Mostly Harmless Econometrics. Princeton University Press, Princeton. Drazen, A., 2000. Political Economy in Macroeconomics. Princeton University Press, Princeton. Ehrmann, M., Soudan, M., Stracca, L., 2010. Explaining the Fall in Citizens’ Trust in the ECB during the Global Financial Crisis. European Central Bank. Manuscript. Fischer, J., Hahn, V., 2008. Determinants of Trust in the European Central Bank SSE/EFI Working Paper 695. Franklin, M., van der Eijk, C., Marsh, M., 1995. Referendum outcomes and trust in government: public support for Europe in the wake of Maastricht. West European Politics 18 (3), 101–117. Garry, J., Marsh, M., Sinnott, R., 2005. Second-order versus issue-voting effects in EU referendums: evidence from the Irish Nice Treaty referendums. European Union Politics 6 (2), 201–221.


605

Gros, D., Roth, F., 2009. The Crisis and Citizens’ Trust in Central Banks. VoxEU.org (10 September). Gros, D., Roth, F., 2010a. The Financial Crisis and Citizens’ Trust in the European Central Bank (Manuscript). Gros, D., Roth, F., 2010b. Die EZB verliert das Vertrauen der Bevölkerung. Frankfurter Allgemeine Zeitung (5 December). Harris Interactive, 2009. Monthly Opinions of Adults from Five European Countries and the United States. http://www. harrisinteractive.com/news/datatables/HI_FT_HarrisPoll_July2009.pdf July. Issing, O., 2000. Should We Have Faith in Central Banks? St. Edmund’s College Millennium Lecture, Cambridge. Kaltenthaler, K., Anderson, C., Miller, W., 2010. Accountability and independent central banks: Europeans and distrust of the European Central Bank. Journal of Common Market Studies 48 (5), 1261–1281. Roth, F., 2009. The effect of the financial crisis on systemic trust. Intereconomics 44 (4), 203–208. Taggart, P., 2006. Questions of Europe – the domestic politics of the 2005 French and Dutch referendums and their challenge for the study of European integration. Journal of Common Market Studies 44, 7–25.

An Electron Microprobe Evaluation of Diagenetic Alteration in

Recommend Documents