Barbuti_Di-Palma_Dubini_Thibault-APPLICATIONS-PROJECT

[Words length: 4464]
The Influence of the Global Financial
Crisis on Individuals' Trust
in their National Political Systems
Evidence from EU Countries
Applications for Economics, Management and Finance (30050)
Applied Research Project and Report
In submitting this assignment:
1. We declare that this written assignment is our own work and does not include
a. material from published sources used without proper acknowledgment or
b. material copied from the work of other students.
2. We declare that this assignment has not been submitted for assessment in
any other course at any university.
3. We have a photocopy and electronic version of this assignment in our
possession.
Francesco Barbuti (1741701)
Nicola Di Palma (1715085)
Nicolò Dubini (1747609)
Quentin Thibault (1710607)

1| T h e I n f l u e n c e o f t h e G l o b a l F i n a n c i a l C r i s i s o n I n d i v i d u a l s ’
T r u s t i n t h e i r N a t i o n a l P o l i t i c a l S y s t e m s
Summary
This paper aims at assessing the impact of the Global Financial Crisis (GFC) on
individuals’ trust in their national political systems, taking into account data about 18
European Countries. The outcome variable is a factor made ad-hoc for our research,
in order to comprehensively depict individuals’ political trust. The crisis will instead be
measured by means of different indicators, providing both macro and individual-level
perspectives. The main findings of the paper indicate that (i) there is in fact a
significant relationship between the GFC and national political trust, that (ii)
individual-level variables exert greater influence on the explained factor than macro
variables and that (iii) different patterns can be identified in different countries.
Introduction
Our interest in uncovering the linkage between the 2008 Global Financial Crisis
(GFC) and individuals’ trust in their national political systems stems from the great
extent to which the GFC itself has had repercussions on people’s everyday life.
As the IMF reports, financial systems “provide a framework for carrying out economic
transactions and monetary policy, and help to efficiently channel savings into
investment, thereby supporting economic growth”. They retain enormous risks and,
for this reason, even a relatively small instability can have considerable economic
and social effects. At the same time, social discomfort can easily result in political
dissatisfaction, since the social sphere is strictly related to the political one.
We aim to further investigate whether or not the global crisis has corroded the above
mentioned citizens’ trust and, if so, to what extent. Two different years, 2006 and
2010, are considered in order to accurately underline the potential differences
between the pre-crisis scenario on the one hand and the aftermath period on the
other. With regards to the chosen variables, we opt for creating an ad-hoc factor for
political trust, instead of relying on a single indicator, in order to better grasp different
perspectives all related to the topic. In particular, we merge variables regarding
citizens’ attitudes towards Parliament, politicians, political parties, legal system and
national government. Instead, the crisis is measured by economical indicators.

Background
Since 2007, the big mortgage crisis in the U.S. started a dangerous domino effect in
the financial markets that would lead, in 2008, to the beginning of "the largest
financial shock since the Great Depression” (2008 World Economic Outlook -
International Monetary Fund).
In the U.S., social resentment towards political institutions started early on in the
financial crisis, even before the bankruptcy of investment bank Lehman Brothers on
September 15, 2008. In March 2008, J.P. Morgan Chase acquired Bear Stearns,
another global investment bank, on the verge of failure. After that, on September 6,
2008, two Government-Sponsored Enterprises specializing in mortgage
securitization, Fannie Mae and Freddie Mac, were subject to federal takeover after
having suffered from heavy losses. After these two bailouts, concern was raised in
the American population, regarding the dangers of moral hazard that such moves
would implicate. This concern led to the famous aphorism by former chairman of the
Federal Reserve Bank, Ben Bernanke, “There are no atheists in foxholes and no
ideologues in financial crises", referring to policy-makers who ex ante profess a free-
market ideology and sensitivity to the dangers of moral hazard from financial bailouts,
but who seem to forget about that ideology when faced with a financial crisis. After
these events, from 2008 onwards, EU national governments began in fact to deal
with the idea that the crisis had reached also the “Old Continent”: there was evidence
that the economic turmoil and social discomfort was already spread in all European
countries.
A potential list of the casualties would be endless: from the entire investment banking
system, to the biggest mortgage and insurance companies, to many of the largest
commercial banks, every economic agent, in its own way and extent, has been
negatively touched by the Global Financial Crisis (GFC).
An economic depression affects several areas of interest: from 2008 to 2009 GDP
fell 3.2% in high income economies while growing at a rate of only 1.2% in
developing countries and global trade declined by 12% worldwide (see World Bank
database). Household consumption kept declining. Moreover, millions of families lost
their homes for foreclosure and unemployment reached the highest levels in 15
years. We assume that such macroeconomic shocks do have an impact on people’s
attitude towards institutions and politics, especially because they are generally
perceived as failures of the system. While most of the scholars focused on the
relationship between the crisis and political support, in this paper we focus on the
effects of the GFC on individuals’ trust in their respective political institutions.
According to Roth, trust plays “a crucial part in the stability and maintenance of the
social, political and economic system”, commonly because “when trust breaks down,
the social system is threatened with unrest, the democratic legitimacy of the political
system is endangered and the legitimacy of the market-based economy is called into
question” (The Effects of the Financial Crisis on Systemic Trust - Roth, Nowak-
Lehmann and Otter, 2009).
However, before doing any further analysis, we need to define the concept of
trust. Trust “is a disposition to engage in social exchanges that involve uncertainty

and vulnerability, but are also potentially rewarding” (Bicchieri, Duffy and Tolle,
1994). Hence, trust is based on taking a leap of faith, regardless of any possible fear
or doubt, mainly because of a certain level of uncertainty. This concept, normally
belonging to the interpersonal relationships field, is easily transferable to the
economical and political sphere.
According to Jennifer L. Hochschild (2010), political trust can be divided with respect
to four different recipients: the parliament, the legal system, the politicians, and the
political parties. Moreover, such recipients are in fact highly correlated: “in countries
where respondents lost faith in their parliament, they also lost faith in other aspects of
the political system; more generally, a loss of faith in one arena is usually associated
with a loss of faith in the other three arenas” (How Did the 2008 Economic Crisis
Affect Social and Political Solidarity in Europe? - Hochschild, June 2010).
It is not surprising that people are very confident with governments capable of
fostering economic growth, for instance by means of the creation of new job
opportunities, the offer of high-level social services, or the imposition of fair taxes,
corresponding to the level of services offered. On the contrary, public trust can drop
in case of misgovernment, an untransparent conduct, a lack of interest towards
public welfare, or a tax burden set too high.
There is a general bias of trust in being positively correlated with the economic
performance: it is generally high when the economy performs well, while it is lower
when the economy performs poorly. Moreover, “since the late 1960s trust in national
institutions has been decreasing in all democratic countries , including a reduction of
confidence in government, parliament and political parties” (Blind, 2006, citing Dalton
and Wattenberg, 2000). According to the 2010 Eurobarometer survey, the economic
crisis was at that time still ongoing and 48% of the interviewed citizens strongly
believed that the worse was yet to come, with all its negative consequences on the
job market. However, this percentage is progressively dropping. At the same time,
the percentage of people believing that the crisis had by then reached its peak was
rising. People have different opinions about whether national institutions addressed
the crisis from the outset in the best possible way and whether they will do so in the
future. In fact, 39% of respondents believed that their respective national government
had correctly approached the crisis since its beginning, while 55% of them had not
the same opinion.
Finally, we want to verify whether the phenomenon known as “rally around the flag” is
actually observable in the EU countries taken into consideration. Such phenomenon
is in fact defined as the increase of short-run popular support of the President of the
United States during periods of international crisis or war, but we can easily extend it
to our case.

Data description
We based our research paper on two different databases, namely the European
Social Survey (ESS) database and The World Bank Financial Development and
Structure database. The ESS database is an academically-driven cross-national
survey that has been conducted every two years across Europe since 2001, aimed at
measuring the attitudes, beliefs and behaviour patterns of the populations in more
than thirty nations (ESS website). The World Bank Financial Development and
Structure database is a collection of data on 31 indicators that measure the size,
activity and efficiency of financial intermediaries and markets across countries and
over time (The World Bank website). In order to capture the effect of the GFC on our
variables, we considered two different ESS rounds, ESS3 (2006) and ESS5 (2010),
respectively two years prior and two years post the start of the crisis. Our sample is
composed of eighteen countries as we could not find respective data for all the EU
countries in both the databases.
Both databases are composed of secondary data, given that they have been
composed by another organization for an intent other than our research paper. In
particular, the European Social Survey database was built by a research team
headquartered at the City University of London. The World Bank has both collected
its own data and combined other institutions’ data in order to compose its database.
We mainly used the ESS database in order to assess European citizens’ trust
towards their governments and national institutions. Among the others, it also
provides variables describing individuals’ feelings about the state of the economy,
which have been useful to analyse how the crisis affected citizens’ perception of the
economic world around them. The World Bank database has been used as the
source of global macro data, in order to estimate the crisis’ financial effects on each
of the countries considered. As we were concerned with individuals at the country
level, we used only the Post-stratification weight to correct for over sampling.
Starting from this point, the following variables have been selected, as being
consistent with the purpose of our study:
Variable Description Measure
Country
cntry
This variable denotes which country each case belongs to. Nominal
Political Trust index
poltrst_idx
This is an index we created through factor analysis from 5
different variables, which were correlated between each
other and described individuals' trust towards several
political institutions.
Scale
How satisfied with
present state of
economy in country
stfeco
This variable is based on an anchored scale ranging from
0 (Extremely dissatisfied) to 10 (Extremely satisfied).
Ordinal
Inflation
infl
This variable describes the annual percentage change in
the cost of buying a basket of goods and services.
Scale
Total unemployment
unmpl
This variable refers to the percentage of total labor force
that is without a job and seeking for it.
Scale

GDP per capita
gdp
This variable defines the Gross Domestic Product divided
by midyear population. It is calculated in current US dollars
on a thousand scale.
Scale
Post-stratification
weight
pspwght
The post-stratification weight corrects for oversampling at
the country level.
Scale
Age of respondent
age
This variable assigns to every individual his age in
numbers, starting from 15-y-olds.
Scale
Yers of full-time
education completed
eduyrs
Years of full-time education completed by the respondent Scale
Gender
gndr
Gender of the respondent Nominal
Among the variables described above, we only computed the Political Trust Index,
leaving the others untouched as we found them in either the ESS or The World Bank
databases. We opted for taking into consideration three control variables, namely
"Age of respondent", "Years of full-time education completed" and "Gender". These
variables, whose purpose is to clarify the explained relationship between the others,
are kept constant. Moreover, we decided not to remove the outliers from the datasets
because they are clearly not caused by data errors or by any other misrepresentation
of data.
With regard to the factor, our aim was to create an index including different variables
related to individuals’ trust towards several national political institutions, in order to
later use it as the dependent variable in our linear multiple regression model. We
therefore decided to run a factor analysis, so to evaluate the possibility of grouping
together such variables. As far as the extraction method is concerned, we selected
the Principal Axis Factoring (PAF) one, given that data summarization was our main
objective and that we had no previous knowledge about the level of common
variance of the variables. We acknowledged we could have used another extraction
method allowing us to impose a priori the number of factors. However, we still opted
for using PAF in order to have confirmation that such number was in fact one. We
decided to initially consider 8 different variables in our factor analysis, namely “Trust
in country's parliament” (trstprl), “Trust in the legal system” (trstlgl), “Trust in
politicians” (trstplt), “Trust in political parties” (trstprt), “How satisfied with the national
government” (stfgov), “How close to party” (prtdgcl), “Voted last national election”
(vote), and “Taken part in lawful public demonstration last 12 months” (pbldmn).
Firstly, we decided to run a Missing Value Analysis, in order to assess whether there
was a high percentage of any missing values. As a result, we dropped one variable,
“How close to party” (prtdgcl), because it was characterized by a too high percentage
(about 59%). With regard to the other variables, we substituted their respective
missing values with a sample mean estimation.
Afterwards, we ran the factor analysis on the remaining variables, and we realized
that two variables, “Voted last national election” (vote) and “Taken part in lawful
public demonstration last 12 months” (pbldmn) were characterized by a correlation

with all the other variable lower than 0.05, so we opted to remove them too. We then
ran again the factor analysis on the finally remaining five variables.
Firstly, we tested correlations between variables through a correlation matrix. This
tool allowed us to analyse the different Measures of Sampling Adequacy (MSAs)
between variables, thus defining the level of potential contribution that each of them
would bring to the factor.
Since every MSA we obtained was higher than 0.7, we were confirmed that the factor
creation would have been adequate.
Then, we examined two very important measures, namely Kaiser-Meyer-Olkin
Measure of Sampling Adequacy and Bartlett’s Test of Sphericity, which determine if
the correlation matrix is factorable, i.e. if there is enough correlation between
variables to create a factor. In particular, the first one specifies the level of correlation
between the considered variables with respect to the entire factor model; the second
one highlights the difference between the correlation matrix and the identity matrix,
i.e. the matrix implicating no correlation between the variables.
We obtained an extremely satisfactory KMO MSA, given that it was higher than 0.7:
this outcome confirmed that there was enough correlation within our data.
Moreover, the Bartlett’s test supported that result, given that its p-value was lower
than 0.05, thus confirming that the correlation matrix was significant from a statistical
point of view.
Subsequently, we considered the determinant of the correlation matrix, which is an
indicator of the level of correlation between variables. A value equal to zero implies
perfect multicollinearity, which is a negative result with respect to a factor analysis,
given that it prevents any further statistical analysis based on covariation. Since we
obtained a determinant different from zero, our factor model was confirmed to be
feasible. Furthermore, a low determinant entails a good correlation: so, being our
value very close to zero (0.036), its goodness was validated.

Furthermore, each communality measures the percentage of the variance of its
respective variable that can be explained by the factor. A value lower than 0.2 is
problematic with respect to the creation of a factor.
Since almost all values we got were significantly high, and each of them was higher
after the extraction process, we were able to confirm the reliability of the ongoing
process.
We then considered the percentage of variance that could have been explained by
the factor under construction.
The resulting output clearly indicated that choosing only one factor would have been
the best choice, given that one alone was able to explain about 63.9% of the total
variance, thus representing an extremely good result. This was confirmed by the fact
that only one factor had an eigenvalue greater than one (3.515) and that there was a
huge gap between the latter and the following value (0.576).
Furthermore, we had an additional confirmation by means of a scree plot, which is a
graph indicating the optimal number of factors derived from the eigenvalues’
distribution. As a rule of thumb, the best choice depends on how many eigenvalues
are located to the left of the so-called “elbow”; in our case, only one factor satisfied
that condition.

Finally, we only had to verify how the five variables considered loaded into the
chosen factor. So, we looked at the factor matrix and we noticed that all of them
were highly loaded into the factor. As a consequence, all the five variables we
considered fitted the newly created factor properly.
Multivariate analysis
As previously mentioned, we conducted the same analysis in 2006 and 2010, in
order to then compare the results and infer the relative conclusions on the effect that
the financial crisis had on national political trust. If not otherwise specified, the output
below refer to 2010. However, the analysis is the very same for both years, in all the
parts of data screening, missing value analysis and multiple linear regression.
Before starting the regression analysis, we wanted to investigate whether some
variables were characterized by missing values. In order to do, so we ran a Missing
Value Analysis in SPSS. The following is the output we got:
As expected, the only variables presenting any missing values are stfeco, hincfel,
age and eduyrs. In fact, we had already run the MVA for the variables entering
poltrst_idx when doing the factor analysis (see "data description" chapter) and we
already knew that the variables from The World Bank database did not present any
missing values. We opted for replacing the missing values with the variables’ mean.
Even though we were aware of the possible problems that such choice could
generally cause, we considered that the value (2.5%) in not high enough to depress
the standard errors, change the correlations with the other variables or alter in any
significant way the distribution of cases.
Afterwards, we were able to run the multiple linear regression. As said before, we
considered as dependent variable the Political Trust Index, an indicator built for the
purpose of this specific research by means of factor analysis; we considered as
independent variables four different ones, namely “How satisfied with present state of
economy in country” (stfeco) “Inflation” (infl), “Unemployment” (unmpl) and “GDP per
capita” (gdp). All the five underlying variables of poltrst_idx, and stfeco and hincfel

covariates were gathered from ESS databases (rounds 3 and 5), while the other
three independent variables come from The World Bank database (2006 and 2010
rounds).
We finally ran the linear regression and obtained the following output:
As it is clear from the summary, our model is able to explain around 32% (31% in
2006) of the total variance of the output variable. This value is consistent with our
expectations, since citizens’ confidence towards their respective national political
systems is indeed influenced by non-economical variables, which were purposely not
object of our research. Therefore, we considered the value as highly satisfactory and
proceeded with the analysis.
As we can see from the ANOVA table, the model passes the F-test of overall
significance. This is a so-called "simultaneous test", meaning that it assesses all the
coefficients simultaneously, and it checks to see if all of the predictors are unrelated
to the response variable. In this case we could state that the overall model fits the
data properly. Also in 2006 the p-value was 0.000.

Finally, we were able to analyse the single coefficients of each covariate by looking
at the coefficients table above. We observed that all the p-values of the fixed
variables were at least below the 0.05 significance threshold, meaning that we could
reject the null hypothesis of the t-test and confirm that each covariate was in fact
statistically significant.
We then moved to the evaluation and interpretation of each coefficient for both years
2006 and 2010. In order to do so, we had to consider that the outcome variable, the
Political Trust Index, varies in a range of circa 5 points. We did not change the scale
in order to maintain a mean of zero and a standard deviation of 1.
Constant (-0.885 in 2006 and -1.615 in 2010): the constant value tells us that,
without accounting for the other variables, the level of political trust among the
individuals considered was lower in 2006 compared to 2010. However, in both
years the level was still not particularly high; this represents the portion of
political trust variance not explained by the covariates.
Feeling about household's income nowadays (-0.015 in 2006 and -0.067 in
2010): as expected, the sign of both the coefficients is negative, with 2010
displaying a higher absolute value than 2006. In fact, the higher the value of
the variable, the worse the individual feels about household's income.
How satisfied with present state of economy in country (0.213 in 2006 and
0.203 in 2010): the two coefficients' values are similar in the two years
considered. Indeed, this covariate is the one with the greatest influence on the
output. Together with the previous one, this is an individual-level variable. As
we will explain below, these are the kinds of variables that matter the most
when considering the impact on national political trust.
Inflation (-0.045 in 2006 and 0.097 in 2010): here the result is less intuitive and
needs careful interpretation. In 2006 the level of inflation was 37% higher than
in 2010. Therefore, our result seems to confirm Roth's view according to
which, "As inflation does not play a role in the crisis period, the full sample
result in which inflation matters is driven by the pre-crisis period". Therefore,
inflation seems to have a negative effect on political trust only in a good
economic scenario and not in times of crisis.
Unemployment (-0.020 in 2006 and 0.021 in 2010): this variable seems to
have a very weak effect on political trust. Both coefficients are below 0.03 in
absolute value.
GDP per capita (0.007 in 2006 and 0.018 in 2010): this value is entered as
input of the regression in thousands of USD. The coefficient is very close to 0
in 2006, but it's higher in 2010. Intuitively, this means that the level of GDP
influences individuals more when it is lower.
We then wanted to provide a snapshot of the political trust changes between 2006
and 2010 in the different countries considered.

-0,50
-0,40
-0,30
-0,20
-0,10
0,00
0,10
0,20
0,30
0,40
0,50
0,60
SI CY IE ES SK FI PT DK BE FR GB EE DE NL BG SE PL HU
PoliticalTrustIndex
Countries
Political Trust Changes
The bar chart above shows the changes in the Political Trust Index among the
eighteen countries considered, ranked from the most negative to the most positive
one. Our results are in line with the ones of both Roth and Braun in terms of the
differences between countries. We produced two boxplots to visually inspect the
distributions of individuals' trust within the countries where the level of the index
varied more than 0.20 in absolute value.
Negative changes in trust
between 2006 and 2010
Positive changes in trust
between 2006 and 2010

Conclusions
The results of our analysis allow us to state that the Financial Crisis actually had an impact
on individuals’ trust towards their respective national political systems. In particular, in line
with our expectations, we observed a clear difference between the effects of perceived
economic conditions on the one hand and actual financial performance of the country on the
other. The former had in fact the greatest impact on national political trust, as shown by the
coefficients of the two individual level covariates. With regards to the macro-level variables,
these were indeed statistically significant, but had a more restricted effect on the actual level
of our index, presumably due to their less direct relation with the latter.
In the final part of the analysis, we briefly focused on the individual countries, thus
acknowledging some differences in both the level and the direction of the change in trust
between populations of different nations. In terms of trust, some countries resented more
than others of the economic upheaval. Consistently with our model, these are among those
where the macro-level variables considered worsened the most during the crisis period.
However, contrary to what one would expect, as of 2010, many countries experienced an
increase in the level of trust, despite being totally involved in the crisis’ aftermath. These
findings are actually consistent with the formerly mentioned “rally around the flag” effect, also
noted by Roth in his research paper. Indeed, our research could be the starting pointing for
further analyses at a specific-country level.
As previously mentioned in the report, the phenomenon we explained is certainly influenced
by variables out of the socio-economical sphere, which were not the object of our model.
Instead, our overall research is able to explain in full the effects of the factors considered.

References
Data sources:
European Social Survey, third and fifth rounds
World Bank Financial Development and Structure, 2006 and 2010 rounds
Eurobarometer surveys, 2006 and 2010 surveys
Reference papers:
Roth, Nowak-Lehmann, Otter (June 2011): Has the financial crisis shattered
citizens’ trust in national and European governmental institutions? Evidence from
the EU member states, 1999-2010
https://www.ceps.eu/system/files/book/2011/02/WD%20343%20Roth%20et%20a
l%20on%20trust.pdf
Rebuilding trust after times of crisis - a practical guide (PwC):
https://www.pwc.nl/nl/assets/documents/pwc-rebuilding-trust.pdf
Kroknes (Master thesis, Spring 2013): Economic performance and political trust:
the impact of the financial crisis on European citizens
http://brage.bibsys.no/xmlui/bitstream/handle/11250/268620/643452_FULLTEXT
01.pdf?sequence=1&isAllowed=y
Braun, Tausendpfund (March 2013): The impact of the financial crisis on political
support in Europe
http://ecpr.eu/Filestore/PaperProposal/7e861d88-2d08-4988-abaa-
ac54d31cb79b.pdf
Hochschild J., (June 2010): How did the 2008 economic crisis affect social and
political solidarity in Europe?
http://scholar.harvard.edu/jlhochschild/publications/how-did-2008-economic-
crisis-affect-social-and-political-solidarity-europe
Text books:
Newbold, Carlson, Thorn (2007): Statistics for Business and Economics. 6th Ed.
Warner (2008): Applied Statistics: From Bivariate through Multivariate
Techniques.
Hair, Tatham, Anderson, Black, Babin (2006): Multivariate Data Analysis. 6th Ed.
Norušis (2006): SPSS 15.0, Statistical Procedures Companion.

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