Olle Krönby & Nils Brandsma Economic Inequality & Voter Turnout 1 Economic Inequality and Voting Participation "Well, because poor people don’t vote. I mean, that’s just a fact," - Bernie Sanders. 2016 Authors: Nils Brandsma & Olle Krönby Supervisor: Stig Blomskog Södertörns högskola | Institution of Political Economics Bachelor Thesis: 15 hp Economics | Fall 2016
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Economic Inequality and Voting Participation1073981/FULLTEXT01.pdf · economic inequality depresses voter turnout for all income groups in this study. (Solt 2008) Benny Geys (2006)
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Olle Krönby & Nils Brandsma Economic Inequality & Voter Turnout
1
Economic Inequality and Voting Participation
"Well, because poor people don’t vote. I mean, that’s just a fact," - Bernie Sanders. 2016
Authors: Nils Brandsma & Olle Krönby
Supervisor: Stig Blomskog
Södertörns högskola | Institution of Political Economics
Bachelor Thesis: 15 hp
Economics | Fall 2016
Olle Krönby & Nils Brandsma Economic Inequality & Voter Turnout
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Abstract The following paper assesses a statistical relationship between Economic Inequality and
Voting Participation among a sizable amount of nations across the world representing all
continents. With an deductive approach, three theoretical standpoints of interest are
presented: one that describes a negative, another inconclusive, and one with a positive
relationship between the variables of interest. Through panel data analysis the study finds
support in favour of a negative relationship in that as economic inequality rises, voting
participation in parliamentary elections decreases.
Olle Krönby & Nils Brandsma Economic Inequality & Voter Turnout
VAP=Voting age population, α=Intercept, β=Coefficient, υit=Fixed individuality term, Ti=Time, ε=error term
The models that will be analyzed are the random effect models, which are deemed more
appropriate through the Hausman test (appendix 1). In theory, we might suspect an omitted
variable bias when looking at the relation between Freedom House scores and GINI, or
perhaps education and GINI, but a few examples we know of might also offset that
assumption. The most immediate example is the case of the United States, where freedom
levels, GDP per capita and education levels are all quite high, and yet they suffer from a
higher level of inequality than other similar states located in Scandinavia.
Sources: Human Development Reports, The World Bank.
The above graph shows similarities between the Scandinavian countries and the US in
GDP/capita and Education levels, yet the difference in inequality is higher.
The Republic of Korea has also succeeded in creating economic growth, enjoying similarly
high levels of education, GDP per capita and freedom scores but may be considered lacking
Olle Krönby & Nils Brandsma Economic Inequality & Voter Turnout
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in the equality department (Denney, Steven 2014). Rather than following the assumption that
inequality is reduced by market powers as a result of economic growth, high levels of civil
and political rights and having a highly educated population, we could attribute the
differences to political and social culture. States in Scandinavia has achieved lower inequality
through the means of active government participation, such as progressive taxes, and Japan is
an example of how social culture has worked to prevent high inequality levels (The
Economist 2015). Then again, these are just a handful of states across our dataset, and we
should be careful before assuming that this is all true for the rest of the world. One option
would be to try to capture these individualities within a fixed effect regression, but finding
the necessary data across the world for political and social culture around economic
inequality will prove to be most difficult. The variable that comes closest to capturing these
differences may be our variable for freedom house scores, but since it’s entirely possible to
have political rights and civil liberties without the government pursuing active measures to
redistribute income it is far from capturing all of these effects.
The intuition in this case, is unable to provide us with clear answers, and because of this fact
we have chosen to let the Hausman test decide for us which model should be used. For the
data we are using in this study, the Hausman test does not observe significant bias among the
independent and control variables and therefore the analysis will be performed with the
random effects regression method. However, since the intuition is divided on the issue, we
have chosen to include the results of the fixed effect models in the appendix.
We have also chosen to analyze the population and GDP per capita variables log-transformed
with the natural base e. This is because these variables in their normal state matter differently
across the countries observed. An increase with a thousand people in India has different
implications than an increase by the same amount in Finland. However, if they increase their
population by 1% then that has more similar implications for both, because their institutions
are relatively adapted to their current population. Growth in GDP also makes more sense to
analyze in terms of percent increase or decrease, for a few reasons. The main reason is similar
to the one stated about population, that we are analyzing economies that has different sizes,
and one units increase in GDP matters differently for different economies, whereas a
percentage increase accounts for size in a more adequate manner. The second reason is
because when discussing GDP in general, we usually discuss it in terms of percentage change
and not with absolute numbers.
Olle Krönby & Nils Brandsma Economic Inequality & Voter Turnout
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4.1 Variables
Voter Turnout
This variable is gathered from the International Institute for democracy and electoral
assistance (IDEA) and consists of officially published voting results, measured by percentage
of votes from registered voters.
Voting age population turnout (VAP)
This second turnout variable measures the turnout of the entire population above the age of
voting. The variable above measures the turnout based on the registered voters. Our research
attempts to measure voter apathy and disenfranchisement based on income inequality, and
not registering to vote should be regarded as non-participation as much as not turning up to
the voting booths. There is a reason we chose to have both voting age population and regular
voter participation, which is because this measure also has its downsides. One problem is that
this measure does not take into account that people might have legal barriers to registering
their vote, and that there are potential problems with the actual data since it is merely an
estimate by IDEA. Measured in percentage points.
GINI-index
The variable GINI refers to the statistical measure of dispersion of income in a nation. This
means that the variable measures income inequality. The GINI coefficient is usually a
number between zero and one, where one is the maximum inequality. Our data uses the
equivalent conversion of numbers between 0 and 100, so instead of a value of 0,47 the
number would be 47. Using the GINI index as our measure for inequality puts us in the
position of having to defend the measure system. The GINI has been criticized for a number
of reasons, and alternative measures have been proposed. The Luxemburg income study is an
alternative index for measuring inequality, which is used by cited professor Frederick Solt,
who we criticized for using too few countries and mostly western ones at that. The problem
however, is not his ambitions, but the data he was using. The Luxemburg income study is a
great database for wealth and income of countries, done through extensive use of different
surveys to produce trustworthy data. The thoroughness of the LIS data is a good thing for
validity, but this data takes time to produce, and since we aim to provide data on more
countries than the LIS can provide we have unfortunately not been able to use this database.
Olle Krönby & Nils Brandsma Economic Inequality & Voter Turnout
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It should be noted that the LIS bases it’s inequality measure on the GINI coefficient still.
Another alternative is using the Palma index, which calculates inequality a different way than
the GINI, and according to some it provides more telling results. There is a problem here in
that it is a relatively new index, and is ill suited for pre-2015 studies. Using the Palma would
exclude almost all of our available data on turnout results; hence we have chosen to perform
this study with the GINI index. (Cobham 2013)
Gross Domestic Product per capita with Purchasing Power Parity
The variable for GDP per capita with respect to Purchasing Power Parity is measuring the
general wealth of the countries examined. Using the addition of per capita and purchasing
power parity is because this more accurately describes the available resources. We are
interested in the wealth of individuals in the state, and their political behavior, not in the
wealth of a country.
Population
Size of population, gathered from IDEA. There are a few different reasons to include a
measure of population, but the main one is that we want to control for it because it might
have an impact in the sense that a higher population may lead to a lower turnout since each
individual vote matters less, as suggested by Geys (2006 642). There are a few other
population type variables that relates to socio-economic factor we could use, such as the level
of urbanization, rate of population growth or population density that could control for similar
things. The argument for a simple population size is that it this study aims to understand what
makes people vote or not, not what they intend to vote for. Urbanization and population
density might be variables more affecting a left-right decision rather than a vote-abstain
decision, whereas the individual votes carries equal weight in most states. Of course, there
are differences across our countries as well, the most accessible example being the difference
in voting power between American states (FairVote 2016). Population density would also be
an interesting variable, as higher density should in theory lower the cost of gathering
information about options, and therefore increasing turnout. (Geys 2006 642-644).
Education
The variable education refers to mean years of education for men and women aged 25 and
above. The data is collected from the World Bank, and is more incomplete than the rest of the
variables observed, except for GINI. We have chosen to run the variable last in each
regression, to ensure our first four models have the maximum amount of observation.
Olle Krönby & Nils Brandsma Economic Inequality & Voter Turnout
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Freedom house
Data from international organization Freedom House and their reports on civil and political
liberties. Score ranges between 1 and 5 where the former is the most free and the latter the
least free.
5. Analysis
In this chapter we will firstly present our regression analyses starting with the bivariate model
and then advancing to several multivariate models. After the presentation of the regressions
we will analyse and describe the result to distinguish whether or not we can answer our
research question.
5.1 Summary of Variables N: observations counting each country+year as one n: Number of groups (countries) T: time variable, elections. Period of analysis is 2000-2013 Description 1: Panel data: presidential
Panel variable N n T Min T Max T Median T
Country + year 160 62 13 1 4 3
Variable N n Mean Min Max Std.deviation
Voter Turnout (%)
158 62 64.05 22.36 95.7 14.12
Voting age population turnout (%)
159 62 60.21 18.93 97.85 14.36
GINI 67 36 36.27 8.1 61 9.88
GDP/capita PPP 156 61 10450 530 51433 11644
Education 110 59 7.4 1.3 12.3 3.2
Population 160 62 24,7 (million) 19092 313 (million) 52.6 (million)
Freedom House 160 62 2.6 1 5 1.2
Olle Krönby & Nils Brandsma Economic Inequality & Voter Turnout
The 107% VAP turnout makes our methodological choices more clear, since the estimate is a
more accurate description of reality for countries that require registration before voting. This
might increase the cost of voting, depending on the process for registering to vote. The voter
turnout variable is also only based on amount of voters among those who registered, which in
some cases can be very different from what is actually the number of people eligible to vote.
This is however, still an estimate, and it has more errors than the official statistics. Therefore,
we have chosen both variables for analysis. The second thing that is important to point out is the lack of observations for GINI, which
decreases the amount of observations we eventually end up with. The findings of Geys and
Cancela (2006, 2016) report, that most inequality over turnout studies fail can most likely be * This result requires an explanation. The turnout was not 107.56% in the election, as that is impossible. IDEA explains this phenomenon with several explanations relating to actual estimation of figures but also the process of registration. IDEA firstly points out that the voting age population figures are based on estimates, which might differ from the true values (as with all estimates). Another issue springs from the fact that data used for the variable may be gathered from different sources, one for VAP, and another for registration. With the problem of estimates in mind, the different sources for data may have different estimates, which lead to discrepancies between the two measures. Secondly, the lists presented by governments or organizations may be flawed in the number of registered voters. Examples of this can be individuals listed two or more times, or that no longer eligible voters are not removed from the voting lists. (IDEA 2016)
Olle Krönby & Nils Brandsma Economic Inequality & Voter Turnout
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traced back to this. The studies of economic inequality has been historically cursed with
obstructions such as lack of data and questionable measuring systems, as Piketty mentions in
the first chapter of The Capital in the 21st Century (Piketty, Thomas 2013). Further back in
time, even less observations for GINI is available, which in the case of adding more length to
the dataset would have caused even more missing observations and on these grounds we have
chosen to not expand the time period of analysis.
To some extent this is also true for the variable regarding education, but as the latter plays the
role of a control variable is easier to work around. As you will notice, it is included after
every other control variable in the regressions to keep the first four models observations at
the highest possible level. Since education is mentioned in all our mentioned previous
studies, as well as being a central part of the theories we have still chosen to include this
variable in our regressions.
The population data ranges between relatively small countries, such as S:t Kitts & Newis, S:t
Vincent and the Grenadines and The Federated States of Micronesia and larger ones such as
India. This might lead to an overestimation in how much population matters for turnout, since
the difference in population can be several million people in some cases. This is also a reason
why we have chosen to log-transform our population and GDP per capita variables.
Olle Krönby & Nils Brandsma Economic Inequality & Voter Turnout
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Model 1: Presidential voter turnout
Model 1 2 3 4 5
Dependent variable: Voter Turnout
Constant (Standard error)
65.56*** (7.17)
93.56*** (20.53)
97.67*** (29.58)
91.42*** (35.51).
122.14*** (43.68)
GINI -.03 (.18)
-.14 (.20)
-.16 (.16)
-.16 (.21)
-.21 (.21)
ln(GDP/capita, PPP)
-2.62 (1.83)
-2.83 (1.88)
-2.32 (2.48)
-.79 3.43
ln(Population) -.10 (1.34)
-.09 (1.34)
-2.38 (1.62)
Freedom House .66 (2.25)
1.21 (2.48)
Education -1.10 (1.09)
Country + year (Countries)
66 35
66 35
66 35
66 35
43 28
R2 within between overall
0.22 0.0005 0.01
0.22 0.0005 0.01
0.23 0.0005 0.01
0.21 0.002 0.02
0.2 0.14 0.11
Significance level codes: * - 10% level, ** - 5% level, *** - 1% level of significance. For our first model, there is little to be said since almost nothing is statistically significant.
Most likely, there are too few observations and the data is sporadic at best.
Olle Krönby & Nils Brandsma Economic Inequality & Voter Turnout
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Model 2: Presidential voting age population turnout
Model 1 2 3 4 5
Dependent variable: Voting Age Population Turnout
Constant (Standard error)
68.26*** (6.21)
79.00*** (17.94)
120.73*** (24.77)
100.67*** (29.21).
93.09** (41.73)
GINI -.15 (.16)
-.18 (.17)
-.15 (.21)
-.17 (.16)
-.17 (.19)
ln(GDP/capita, PPP)
-1.03 (1.63)
-1.14 (1.57)
.49 (2.05)
2.62 (3.31)
ln(Population) -2.63** (1.11)
-2.61** (1.09)
-3.03** (1.53)
Freedom House 2.14 (1.84)
3.03 (2.35)
Education -.90 (1.05)
Country + year (Countries)
67 36
67 36
67 36
67 36
44 29
R2 within between overall
0.04 0.001 0.03
0.09 0.0002 0.03
0.17 0.12 0.1
0.08 0.17 0.13
0.06 0.21 0.16
Significance level codes: * - 10% level, ** - 5% level, *** - 1% level of significance. In model two, the only significant relationship observed is from the Population variable.
Since nothing else but the constant is significant, there is little to be said other than that
having a large population tends to depress turnout according to our data.
Olle Krönby & Nils Brandsma Economic Inequality & Voter Turnout
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Model 3: Parliamentary voter turnout
Model 1 2 3 4 5
Dependent variable: Voter Turnout
Constant (Standard error)
82.96*** (6.15)
103.47*** (15.39)
132.43*** (20.21)
163.34*** (22.74).
175.81*** (27.52)
GINI -.54*** (.16)
-.64*** (.17)
-.61*** (.17)
-.57*** (.16)
-.51** (.20)
ln(GDP/capita, PPP)
-1.85 (1.28)
-1.40 (1.24)
-3.96** (1.55)
-4.61** (2.34)
ln(Population) -2.15** (.94)
-2.14** (.90)
-2.56** (1.02)
Freedom House -3.82** (1.40)
-4.84** (1.67)
Education -.02 (.75)
Country + year (Countries)
130 60
130 60
130 60
130 60
98 52
R2 within between overall
0.06 0.09 0.14
0.14 0.06 0.11
0.13 0.15 0.16
0.15 0.23 0.19
0.15 0.29 0.22
Significance level codes: * - 10% level, ** - 5% level, *** - 1% level of significance. For our first model that analyzes the parliamentary election we see more of significant
relationships. First of all, in this model our variable for inequality is significant and shows a
negative correlation, and for every one unit increase on the GINI scale, voter turnout in the
official statistics is depressed by roughly 0,5-0,6 percentage units. GDP per capita,
Population and Freedom House scores all display a negative significant relation with voter
turnout, which will be subject to further analysis in the results chapter. Our explanatory
power measured in R-squared is 22%, which in the case of voter turnout is not necessarily
bad. Turnout is dependent on a lot of variables, some very specific to the country or time of
election. Some countries has election day as a public holiday to make sure voting does not
mean losing income from work, other do not. There is simply no model big enough to
account for all these specific laws and practices that affect turnout. In these circumstances,
22% explanatory power should not be considered a bad result.
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Model 4 : Parliamentary voting age population turnout
Model 1 2 3 4 5
Dependent variable: Voting Age Population Turnout
Constant (Standard error)
81.84*** (6.08)
73.23*** (15.33)
117.36*** (19.73)
141.37*** (23.04).
153.26*** (26.75)
GINI -.59*** (.16)
-.56*** (.17)
-.55*** (.16)
-.53*** (.16)
-.37** (.18)
ln(GDP/capita, PPP)
.79 (1.29)
1.20 (1.13)
-.81 (1.60)
-2.65 (2.29)
ln(Population) -3.05** (.91)
-3.02*** (.88)
-3.31*** (.97)
Freedom House -2.83** (1.44)
-3.64** (1.62)
Education .76 (.73)
Country + year (Countries)
130 61
130 61
130 61
130 61
98 53
R2 within between overall
0.02 0.15 0.17
0.01 0.17 0.18
0.02 0.32 0.25
0.04 0.36 0.25
0.05 0.35 0.24
Significance level codes: * - 10% level, ** - 5% level, *** - 1% level. Similarly to the model for voting turnout in parliamentary systems, the above model for the
voting age population turnout displays similar characteristics. Perhaps surprisingly, inequality
matters less in all five models in the VAP model than for the model examining official
statistics. This may indicate a few different things. First of all, it could mean that registration
does not increase the the cost of voting. However, since this is in contrast to studies made on
the subject, this is most likely not the case (Rosenstone & Wolfinger 1978). It could also be a
result of turnout being lower overall, and the control variables account for less of a
difference. When measuring voting age population turnout, GDP per capita is no longer
statistically significant, in any of the models. Having a high population still has a negative
effect on voter turnout, and having less political and civil freedoms measured by the freedom
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house index also show a negative correlation. The R-squared values tell us that we can in this
model approximately account for 24% of the effect on turnout.
6. Results
The models 1 & 2 on presidential elections show insignificant results most likely because of
lack in the data. Hence this thesis will continue to focus on models 3 & 4 for parliamentary
elections where we find significant and effectful results. The following chapter will present
the result of the analysis with regard to the earlier stated research question and theory. To
recap, the research question that will be answered in this thesis is as follows:
To what extent does economic inequality affect the civil participation of voters in national
elections?
In models 3 & 4 we find that a one-unit change in the GINI index results in an approximate
decrease in voter turnout by 0.4 to 0.6 percentage points. As seen in the models, the
explanatory power of the regressions increases with the adding of more control variables
except for education. The insignificant result of education can be explained by the drop in
observations when the variable is added; therefore the quality and R2 value of the whole
model decreases. The variable for population size seems to be negatively related to turnout,
which indicates that the idea presented by Geys (2006), that with a higher population the
relative power of the vote decreases, may be true. It could however also be a result of the
inclusion of micro states, as discussed earlier.
In the theory chapter we presented three different views on economic inequality and the
effect on turnout. Firstly, there was Conflict Theory, which argues that in more unequal
societies individuals with lower income will have a higher utility of voting, and thus the
theory predicts turnout to be higher in more unequal nations (Meltzer & Richards 1981).
Secondly, we discussed Relative Power Theory by Goodin & Dryzek (1980), which predicts
that in a more economically unequal society, people will lose their incentive to vote due to
lower relative power and therefore a higher GINI score would decrease turnout. Thirdly, we
had the Resource Theory by Verba, Schlozman & Brady (1995) who argue that political
participation depends on three resources: civic skills, money and free time. The theory finds
three forms of political participation but for the purpose of this study we focus on actual
voting, which is argued not to be affected by income.
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Out of these three theories, only Relative Power Theory by Goodin & Dryzek (1980) is able
to relate to the findings in the models of this paper. Since the variable for GINI (a higher
GINI indicates a more economically unequal society/nation) has a negative relationship with
turnout and voting age population turnout, this paper supports this theory. But this is not to be
mistaken for a causal relationship since turnout is affected by multiple more variables than
the ones included in this paper. Conflict Theory is the only theory fully rejected, because the
relationship seen in every model contradicts the theory of higher inequality leading to higher
voting participation. As for Resource Theory, the variable included in our paper that would
give an indication of this theory being supported is education as a part of its resources that
affect political interest. But the variable for education is not statistically significant and
therefore inconclusive. Unanswered is the question of who deters from voting, whether it is
the richer or poorer population of a nation. But as GINI increases, the resources of a country
are concentrated to a smaller portion of its citizens and thus the number of richer individuals
is smaller. As a result, when the GINI-score is high, the population deterring from voting
should be the ones with less income.
There is a possible point of concern given that we only find significant results for the
parliamentary models, namely that the presidential election may have a larger force of voter
attraction in systems that employ voting for both parliament and president. This could lead to
an overestimation of the effect on voter turnout. However, in theory this election should be as
important to people as the elections for president, and a result showing lower turnout could
still be a result of low access to information or political interest, possibly because of voter
disenfranchisement and economic inequality.
7. Conclusions
Our findings reflect mainly those of Solt (2008) in that inequality is negatively related to
voter turnout. Since this study separates itself from the previous work in the sense that it
includes countries previously excluded from inequality versus political participation, so the
difference in results from earlier work is of interest. Since the Solt study is the most similar in
terms of results to ours the difference here is interesting.
We can conclude that the theory that seems to be most related to our findings is the relative
power theory. We have in this study not made any regressions for different income
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percentiles, where we measure the difference among different income groups for each
country. Neither have we controlled for measures of free time and civic skills, other than
education. Were such data available for our sample, we would have gladly ventured in that
direction, to more clearly distinguish which of relative power and resource theory is more
applicable.
One point of criticism towards the paper, which to some extent can be applied to many other
turnout-studies, is the aspect of a low explanatory capacity. This can be seen throughout all
models of this paper where R2 is closer to zero rather than one. The support that we find in
favour of Solts’ arguing and the relative power theory of lower turnout in more economically
unequal societies is of importance and statistically significant, however the results are only
partially explanatory to voter turnout and there are many more variables that affect turnout
than those included in the models of this paper. But essentially this papers research question
is not what affects voter turnout, it is rather how economic inequality pushes turnout in a
certain direction of increasing or decreasing. Thus, the paper has the capacity of answering
the research question even though its given low values for R2. The result of achieving a
higher value for R in this study would consist of adding more variables to the models, by
doing so the models would have a better explanatory power but would not benefit the
capacity of answering our research question.
For a final conclusion, this study has observed that for parliamentary elections, economic
inequality has a negative effect of participation in democracies.
8. Topics for future research There is still much to be found in the future regarding the effect of inequality over turnout.
First of all, the future looks promising in terms of providing trustworthy and accurate data on
inequality, both with datasets such as the Luxemburg Income Study, and new measuring
systems such as the Palma index.
Future research is encouraged with the same research question and intuition as this but with a
shifted focus of size and geography. Of interest would be similar studies with more detail but
with smaller focus of specific continents or groups of nations, perhaps not with the focus on
western societies to illuminate the difference. The wide focus of this paper allows for a more
Olle Krönby & Nils Brandsma Economic Inequality & Voter Turnout
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representative study of the whole world compared to earlier research, but in light of
differentiation in demography, culture, political institutions et cetera, the results may differ
among parts of the world.
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References Pew Research Center (2016) “U.S. voter turnout trails most developed countries” Gathered