IMPACT OF POLITICAL INSTABILITY ON ECONOMIC GROWTH, POVERTY

IMPACT OF POLITICAL INSTABILITY ON
ECONOMIC GROWTH, POVERTY AND
INCOME INEQUALITY
Iram Shehzadi1, Hafiz Muhammad Abubakar Siddique2 and M.
Tariq Majeed3
Abstract
We study the impact of political instability on economic
growth, income distribution and poverty. The
estimates are obtained by applying Heteroscedasticity consistent
OLS on a cross-section of 103 coun-
tries from 1984-2011. We take into account alternative
dimensions of political instability: formal,
informal and military coups D’Etat. Formal and informal
political instability has statistically signifi-
cant and positive impact on poverty and inequality. Similarly
we find that direct effect of Coups
D’Etat on both poverty and income inequality is insignificant,
while its indirect effect (through
economic growth) is significant. On the whole our study
indicates political instability is detrimental
to the process of economic growth, worsen income distribution
and increases poverty.
Keywords: Economic Growth, Income Inequality, Poverty,
Political Instability, Heteroscedasticity
Consistent OLS.
JEL Classification: I390

Introduction
An emerging concept in literature relates economic growth to
the instability in political
regime. Political economists disagree about the definition and
measurement of political instability.
For the purpose of defining political instability we distinguish
between its formal and informal dimen-
sions. Formal political instability arises due to elections and
constitutional changes (Campos & Kara-
nasos, 2008). On the other hand, informal political instability
originates through protests, assassina-
tions, riots, strikes and violations (Campos & Karanasos, 2008).
These formal and informal measures
have been combined to define political instability. The first
definition is labeled as “Social Political
Instability”. This is the simplest definition and it covers only
informal measures of political
1 Govt. Degree College for Women, Gujranwala, Pakistan.
Email: [email protected]
2 Federal Urdu University, Islamabad, Pakistan. Email:
[email protected]
3 School of Economics, Quaid-i-Azam University, Islamabad,
Pakistan. Email: [email protected]
instability. The second measures “Government Changes” covers
a broader definition of political
instability and is based on informal political instability,
economic and institutional measures.
Whatever are the definitional and measurement problems,
higher level of political instability
is undesirable. It shortens the time span of policy makers to
lead and implement optimal macroeco-
nomic policies. Absence of coherent and consistent policies
greatly reduces the government ability to

response to shocks appropriately; this uncertainty cause
macroeconomic misbalances such as less
economic growth, inflation, poverty, and inequality etc. There
exist an ample amount of literature
documenting the relationship of political instability with
growth, saving rate, investment, land
inequality, crime rate, debt, capital and inflation (Venieris &
Gupta, 1986; Ozler & Tabellini, 1991;
Edwards & Tabellini, 1991; Alesina et al., 1996; Devereux &
Wen, 1998; Syed & Ahmad, 2013). Less
attention has been paid to empirically examine the relationship
of political instability with income
inequality and poverty.
Uncertainty associated with unstable political regimes may
have adverse effects on the
wellbeing of poor segment of society. Political instability can
affect poverty in a number of ways.
First, uncertainty regarding government policies reduces
accumulation of human and physical capital
leading to a decline in investment. This low level of domestic
and foreign investment depresses faster
economic growth, which in turn increases poverty (Dollar &
Kraay, 2002). Second, political instabili-
ty is also expected to affect income inequality and poverty
through its impact on growth. Any frequent
switch of government, political violence, strikes and/or
revolutions may hinder the effectiveness of
pro-poor policy programs. For example, it is possible that the
new government, whether obtained
through constitutional or non-constitutional changes, is such
that it promotes pro-rich policies. The
alleged government then serves its own political allies and do
not promote pro-poor policies; thereby
causing more inequality and poverty. Given the close linkages
between political instability, growth,

income inequality and poverty no efforts has been made to
collectively examine these relationships.
This study makes several contributions. First, we improve on
the existing literature by exam-
ining the relationship of political instability with income
distribution and poverty. To this end, we use
direct and indirect channels linking political instability to
poverty and income inequality. Second, we
contribute to the existing literature by using alternative
measures of political instability. We use three
broad set of measures; (i) formal political instability measures:
an index of constitutional and
non-constitutional measures of political instability, (ii) informal
political instability: an index of
measures of mass violation, and (iii) coups D'Etat.
This study answers the following key questions:
• Does political instability reduce economic growth?
• Does political instability increase income inequality and
poverty?
• How close are the links from political instability to
economic growth and then to poverty?
• How close are the links from political instability to
economic growth and then to income inequality?
The rest of this paper is arranged as follows. In section 2,
background and related work
clarify how this research relates to the existing work. Section 3
formulate empirical models and
explain the methodology. Section 4 defines data sources and
provides some basic summary statistics.
In section 5, we interpret and discuss empirical results. The last
section 6 concludes.
Literature Review

There is a large amount of studies exploring the relationship
between economic growth and
political instability. Alesina et al. (1996) using data for 113
countries find that countries with higher
degree of political instability grow slower than others. Their
findings reveal that political instability is
persistent in character; the occurrence of a government collapse
raises the probability of future
government collapses. Gurgul and Lach (2013) while studying
impact of government changes in 10
CEE transitional economies also support the negative
relationship of political instability with
economic growth.
Some studies have explored channels through which political
instability affects growth
(Barro, 1991; Devereux & Wen, 1998; Aisen & Veiga, 2013).
Aisen and Veiga (2013) investigate
relationship between political instability and GDP growth for
169 countries from 1960 to 2004. They
find that higher degree of political instability is associated with
lower growth rates. This damaging
effect of political instability on GDP growth is transferred
through the negative effects of political
instability on total factor productivity, human capital and
physical capital.
Similarly, Barro (1991) finds an inverse relationship between
political unrest and growth rate
of GDP for a large sample of countries. He finds that political
instability reduces growth and invest-
ment through its adverse effects on property rights. Moreover,
Devereux and Wen (1998) developed a
linear endogenous growth model where economic growth and
government spending are linked to

political instability. They find evidence from cross country
regressions that political instability reduc-
es growth and increases share of government in GDP.
In the same vein, Jong-A-Pin (2009) re-examines the effect of
alternative dimensions of
political instability on economic growth. He finds that
instability of political regime depresses
economic growth. He also assumed the reverse causality
between these dimensions. Lower economic
growth increases political instability, while, higher growth
fosters stability within government.
Some studies lift up doubt on the negative relationship of
political instability with economic
growth. Ali (2001) explores the relationship between a variety
of political instability measures, policy
uncertainty and economic growth. His findings show that policy
instability has a more significant
effect on economic growth than political instability. Similarly,
Campos and Nugent (2002) using two
different measures of political instability find that the negative
impact of political instability on
growth is only contemporaneous. In the long run, they did not
found any evidence for the negative
relationship between political instability and economic growth.
Literature describing relationship between economic growth
and political instability is quite
established. On the other end, literature regarding impact of
political instability on income inequality
and poverty is yet to be developed. The link, however, has been
developed from inequality and pover-
ty to political instability. Studies have found that income
inequality and poverty is an important cause

of political instability. Alesina and Perotti (1996) examine the
impact of income distribution on
investment through the channel of political instability. They
find that income inequality cause
socio-political unrest and reduce investment, and ultimately
hamper growth. Muller and Seligson
(1987) and Wang et al. (1993) using alternative techniques and
robustness checks also confirm that
income inequality has positive association with political
instability.
Londregan and Poole (1990) find that poverty and lower
economic growth increases the
chances of coups DEtat- a measure of forced government
changes (Alesina & Perotti, 1994).
Likewise, Alcantar-Toledo and Venieris (2014) suggest that
political instability, fiscal policy and
income inequality are the major factors hindering economic
growth. More importantly they confirm
that lower growth and socio political instability are main causes
of poverty traps. Thus, the
overwhelming amount of literature suggests that political
instability is inversely related with econom-
ic growth and positively with income inequality and poverty.
Empirical Model Specification And Methodology
Political instability and economic growth
The empirical model for growth is derived from Aisen and
Viega (2013):
Where, Growth is dependent variable and is calculated as
growth rate of GDP per capita. IGDPPC

(log), is the value of GDP per capita in 1984; it is expected to
have a negative coefficient to confirm
convergence effect.
Political Instability, Poverty and Income Inequality
Based on the poverty and inequality literature (Kuznets, 1955;
Chong & Calderon, 2000;
Dollar & Kraay, 2002; Gupta et al., 2002) we use following
determinants of poverty and income
inequality:
Pov is explained variable and represent number of people
living in moderate poverty (less
than $2/day). Gini is also dependent variable and is calculated
from Lorenz curve; it represents distri-
bution of income. Ipov and IGini are values of poverty and
inequality in 1984; a positive coefficient
is expected.
The explanatory variable PI in all equations is main variable
and it represent political
instability index. We use three broad dimensions of political
instability: (i) formal political instability
measures: an index of constitutional and non-constitutional
measures of political instability, (ii) infor-
mal political instability: an index of measures of mass violation,
and (iii) Coups D'Etat. All measures
are expected to have negative association with growth, and
positive with poverty and income inequali-
ty. Inf , Ethnicity , Pop and Corruption are other covariates of
growth, poverty and inequality. All
these determinants are expected to have inverse relationship
with growth, and positive with inequality
and poverty.

Econometric Methodology
Since we are using cross country data the problem of
heteroscedasticity is likely to occur. In
the presence of heteroscedasticity OLS still yields unbiased
estimates but they are no longer efficient.
A variety of methods is available to correct heteroscedasticity.
The simplest method involves trans-
forming functional form of dependent variable or entire model
(Carroll & Ruppert, 1988). This
method is discouraged because it is difficult to know that which
functional form is optimal. The
second method is Weighted Least Square (WLS). This method
corrects heteroscedasticity by weight-
ing each observation by sum of square residuals (Greene, 2003;
Gujarati, 2012). Although WLS
produce efficient estimates it is applicable only when the
magnitude and form of heteroscedasticity is
known.
An alternative and most appropriate procedure is to use test of
hetero-consistent standard
error on OLS estimates. In this method the original model is
estimated using OLS and then white’s
test is applied to obtain hetero-consistent standard errors
(White, 1980). In this study we are using
hetero-consistent standard error OLS (HCOLS) because it
allows one to avoid heteroscedasticity
without using weights and it is also applicable even if nothing
is known about the form of heterosce-
dasticity.
Data Description, Sources And Definition Of Variables
The relationship of political instability with growth, poverty

and income inequality is evalu-
ated using three alternative categories of political instability.
The first dimension named “formal
political instability” (FPI) is defined as propensity of
government collapse by either constitutional
and/or non-constitutional means. The FPI index is composed of
four celebrated measures: legislative
elections, major constitutional changes, major cabinet changes
and effective executive changes. The
second index “informal political instability” (IPI) is based on
five measures of mass violation: assassi-
nations, strikes, purges, riots and revolutions. The last measure
“coups D’Etat” reflects the forced
transfer of power which in now commonplace in many
countries. Data on all the political instability
measures is taken from Cross National Time Series Data
Archives (CNTS report, 2012). Table 1
portrays the pair wise correlation matrix among different
measures of political instability. Table shows
a fairly low correlation among ten measures of political
instability. Only assassinations and revolu-
tions, and legislative elections and coups d’Etat have partial
correlation greater than 0.50. This little
correlation among alternatives measures of political instability
shows that each measure has some
information and properties that are not captured by others. Each
set of these measures forms an
important dimension of political instability which is different
from the other. Table 2 is the summary
statistics of all important variables used in this study.
Assassinations, a measure of informal political
instability, has the highest average value (& standard deviation)
of 4.14 (0.52).
Data on economic and institutional variables is from World
Development Indicators online database

(2014), PovcalNet online database (2014), International Country
Risk Guide database (2013) and
Alesina et al. (2003). The data set include 103 countries from
both developed and developing regions
of world. The cross section is made by taking average of the
data between period 1984 and 2011.
Table 3 displays the correlation matrix of political instability
measures with growth, poverty and
income inequality. Each measure of political instability has
standard negative relationship with
growth and positive with poverty head count ratio and income
inequality.
Table 1
Correlation matrix of political instability variables
Table 2
Descriptive statistics of important variables
Table 3
Correlation matrix of political instability with growth, poverty
and income inequality
Table 5 depicts results for the HCOLS estimation of political
instability on poverty head
count ratio. Column 1 show that informal political instability
has positive and significant coefficient
indicating that higher level of political instability worsen
poverty rates. An occurrence of additional
strikes, riots, and revolutions hinder the effectiveness of
Government policies, threaten foreign invest-
ment and create unemployment which worsen poverty rates.
Column 2 and 4 show that formal politi-
cal instability and coups D’Etat has positive coefficients, but

they are not significant statistically. Our
results also indicate that a constitutional change, an important
formal political instability measure, has
significant positive impact on poverty. While direct effect of
formal political instability and coups
D’Etat on poverty is insignificant there indirect effect is
significant. On the other hand, indirect impact
of informal political instability is insignificant. The indirect
effect of political instability measures is
channeled through economic growth and is estimated using
simultaneous equation approach where all
other determinants of growth are excluded.
Table 5
Impact of political instability on poverty
(Table Continued...)
Table 6
Indirect impact of political instability on poverty
Statistical results for the impact of political instability on
income inequality are given in
Table 7 and 8. It is clear from the Tables that direct and
indirect impact of formal and informal politi-
cal instability on income inequality is statistically significant.
Direct impact of coups D’Etat is insig-
nificant while its indirect impact on inequality is significant.
Our results confirm that all dimensions
of political instability are detriment to income inequality;
higher level of political instability increases
income inequality.
We also found support for Kuznets hypothesis which states that

initial level of development
in GDP will cause inequality to increase, while at alter stages
growth in GDP reduces income inequali-
ty. Other detriments of income inequality, such as, ethnic
tension and inflation have expected positive
signs.
Table 7
Impact of political instability on income inequality
Table 8
Indirect impact of political instability on income inequality
Conclusion
The objective of this study has been to examine the relationship
of political instability with
economic growth, poverty and income inequality. We have used
three dimensions of political instabil-
ity namely, formal political instability, informal political
instability and coups D’Etat. Our findings for
the impact of different dimensions of political instability on
growth are in conformity with most of
literature, suggesting that higher degree of political unrest
reduces the economic growth.
In analyzing the link of political instability with poverty and
income inequality we have used both
direct and indirect means. Formal and informal political
instability has statistically significant and
positive impact on poverty; higher level of political instability
increases poverty. Similarly we find
that direct effect of Coups D’Etat on both poverty and income
inequality is insignificant, while its

indirect effect (through economic growth) is significant. On the
whole our study indicates that all
dimensions of political instability have damaging repercussion
on poverty and income inequality.
Our results suggest that governments in highly politically
instable countries need to address the root
causes of political instability and try to make a stable political
system and policies. Only then, coun-
tries can attain higher and sustained economic growth and lower
poverty and inequality rates.
References
Aisen, A., & Veiga, F. J. (2013). How does political instability
affect economic growth?. European
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Alcántar-Toledo, J., & Venieris, Y. P. (2014). Fiscal policy,
growth, income distribution and sociopo
litical instability. European Journal of Political Economy, 34,
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literature. The World Bank Economic Review, 8(3), 351-371.
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Wacziarg, R. (2003). Fractionalization.
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economic growth: An empirical investigation.
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Campos, N. F., & Karanasos, M. G. (2008). Growth, volatility
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Chong, A., & Calderon, C. (2000). Institutional quality and
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Edwards, S., & Tabellini, G. (1991). Political instability,
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Gurgul, H., & Lach, Ł. (2013). Political instability and
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instability and its impact on economic
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Appendix
Data and Variables Description
Interpretation of Empirical Results
Table 4 presents heteroscadisticity consistent OLS estimation
results for growth model using
averaged data for 103 countries from 1984-2011. To make
analysis more comparable to existing
studies we have grouped political instability index into three

dimensions. Column 1 of the Table 4
shows that coups D’Etat, a measure of forced government, has
negative and statistical significant
coefficient. The estimated parameter indicates that occurrence
of an additional coups D’Etat per year
will reduce economic growth by 0.04 points. Results for other
measures are reported in columns 2 and
5. Our results allow us to distinguish between impacts of
different dimensions of political instability
on growth. From column 2 we find that formal political
instability has statistically significant impact
on growth while impact of informal political instability is
insignificant. Cabinet changes and legisla-
tive elections, most widely used measures of formal political
instability, also support our hypothesis
regarding adverse impact of political instability on growth.
Results regarding the other determinants of growth are also
according to our expectations.
Initial GDPPC have negative and significant sign confirming
conditional convergence hypothesis as
suggested by new classical growth models. Finally, higher
inflation, population growth, and ethnic
tensions slow down growth. Corruption, although not
significant, but has expected positive sign.
Table 4
Impact of political instability on economic growth
PAKISTAN BUSINESS REVIEW 825
Volume 20 Issue 4, Jan, 2019Research

IMPACT OF POLITICAL INSTABILITY ON
ECONOMIC GROWTH, POVERTY AND
INCOME INEQUALITY
Iram Shehzadi1, Hafiz Muhammad Abubakar Siddique2 and M.
Tariq Majeed3
Abstract
We study the impact of political instability on economic
growth, income distribution and poverty. The
estimates are obtained by applying Heteroscedasticity consistent
OLS on a cross-section of 103 coun-
tries from 1984-2011. We take into account alternative
dimensions of political instability: formal,
informal and military coups D’Etat. Formal and informal
political instability has statistically signifi-
cant and positive impact on poverty and inequality. Similarly
we find that direct effect of Coups
D’Etat on both poverty and income inequality is insignificant,
while its indirect effect (through
economic growth) is significant. On the whole our study
indicates political instability is detrimental
to the process of economic growth, worsen income distribution
and increases poverty.
Keywords: Economic Growth, Income Inequality, Poverty,
Political Instability, Heteroscedasticity
Consistent OLS.
JEL Classification: I390
Introduction
An emerging concept in literature relates economic growth to

the instability in political
regime. Political economists disagree about the definition and
measurement of political instability.
For the purpose of defining political instability we distinguish
between its formal and informal dimen-
sions. Formal political instability arises due to elections and
constitutional changes (Campos & Kara-
nasos, 2008). On the other hand, informal political instability
originates through protests, assassina-
tions, riots, strikes and violations (Campos & Karanasos, 2008).
These formal and informal measures
have been combined to define political instability. The firs t
definition is labeled as “Social Political
Instability”. This is the simplest definition and it covers only
informal measures of political
1 Govt. Degree College for Women, Gujranwala, Pakistan.
Email: [email protected]
2 Federal Urdu University, Islamabad, Pakistan. Email:
[email protected]
3 School of Economics, Quaid-i-Azam University, Islamabad,
Pakistan. Email: [email protected]
instability. The second measures “Government Changes” covers
a broader definition of political
instability and is based on informal political instability,
economic and institutional measures.
Whatever are the definitional and measurement problems,
higher level of political instability
is undesirable. It shortens the time span of policy makers to
lead and implement optimal macroeco-
nomic policies. Absence of coherent and consistent policies
greatly reduces the government ability to
response to shocks appropriately; this uncertainty cause
macroeconomic misbalances such as less
economic growth, inflation, poverty, and inequality etc. There

exist an ample amount of literature
documenting the relationship of political instability with
growth, saving rate, investment, land
inequality, crime rate, debt, capital and inflation (Venieris &
Gupta, 1986; Ozler & Tabellini, 1991;
Edwards & Tabellini, 1991; Alesina et al., 1996; Devereux &
Wen, 1998; Syed & Ahmad, 2013). Less
attention has been paid to empirically examine the relationship
of political instability with income
inequality and poverty.
Uncertainty associated with unstable political regimes may
have adverse effects on the
wellbeing of poor segment of society. Political instability can
affect poverty in a number of ways.
First, uncertainty regarding government policies reduces
accumulation of human and physical capital
leading to a decline in investment. This low level of domestic
and foreign investment depresses faster
economic growth, which in turn increases poverty (Dollar &
Kraay, 2002). Second, political instabili-
ty is also expected to affect income inequality and poverty
through its impact on growth. Any frequent
switch of government, political violence, strikes and/or
revolutions may hinder the effectiveness of
pro-poor policy programs. For example, it is possible that the
new government, whether obtained
through constitutional or non-constitutional changes, is such
that it promotes pro-rich policies. The
alleged government then serves its own political allies and do
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Relationship between income inequality and
residential segregation of socioeconomic groups
Tiit Tammaru, Szymon Marcin´czak, Raivo Aunap, Maarten van
Ham &
Heleen Janssen
To cite this article: Tiit Tammaru, Szymon Marcin´czak, Raivo
Aunap, Maarten van Ham &
Heleen Janssen (2020) Relationship between income inequality
and residential segregation of
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10.1080/00343404.2018.1540035
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Relationship between income inequality and residential
segregation of socioeconomic groups
Tiit Tammarua , Szymon Marcińczakb , Raivo Aunapc, Maarten
van Hamd and
Heleen Janssene
ABSTRACT
This paper provides new insights into the relationships between

income inequality and residential segregation between
socioeconomic groups by undertaking a comparative study of
European urban regions. In Europe, income inequalities
are the lowest in North Europe and the highest in South Europe.
In many East European countries, a switch from low
inequality to high inequality has taken place. The main findings
show that changes in the levels of residential
segregation between socioeconomic groups correlate to changes
in the levels of income inequality found
approximately 10 years earlier, that is, with a time lag.
KEYWORDS
income inequality; socioeconomic segregation; comparative
urban studies; South Europe; North Europe; East Europe
JEL Z13
HISTORY Received 26 August 2017; in revised form 5 October
2018
INTRODUCTION
Residential segregation between socioeconomic groups in
European urban regions has grown in the last decades
(Fujita & Maloutas, 2016; Kazepov, 2005; Musterd &
Ostendorf, 1998; Tammaru, Marcińczak, van Ham, &
Musterd, 2016). By residential segregation between socio-
economic groups, we understand an uneven distribution of
different occupational or income groups across residential
neighbourhoods of an urban region. Income inequality,
the uneven distribution of income between people and
households, is often considered to be the most critical cat-
alyst for residential segregation between socioeconomic
groups (Musterd & Ostendorf, 1998; Quillian &
Lagrange, 2016). The residential choices of the top
socioeconomic groups who earn the highest incomes

often drive the rise of segregation in urban regions, as
they have the most freedom to realize their housing and
neighbourhood preferences that are either different or not
achievable for other income groups (Harvey, 1985; Hul-
chansky, 2010; Maloutas, 2016). Contrary to the top socio-
economic groups, the bottom socioeconomic groups with
the lowest incomes usually have less choice of where to
live. In most European urban regions there is a substantial
overlap between income inequality and inequality along
ethnic lines since many immigrant groups are overrepre-
sented in low-skilled jobs and associated low incomes.
The marginalization and spatial concentration of lower
income people, often with an ethnic minority background,
signals deeply entrenched structural inequality on the
© 2019 The Author(s). Published by Informa UK Limited,
trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the
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License (http://creativecommons.org/licenses/by-nc-
nd/4.0/), which permits non-commercial re-use, distribution,
and reproduction in any medium, provided the original work is
properly cited, and is not altered, transformed, or built
upon in any way.
CONTACT
a(Corresponding author) [email protected]
Faculty of Science and Technology, Institute of Ecology and
Earth Sciences, Department of Geography, University of Tartu,
Tartu, Estonia; and
Faculty of Architecture and the Built Environment, OTB –
Research for the Built Environment, Delft University of
Technology, Delft, the Netherlands.
b [email protected]

Tartu, Estonia; and
Institute of Urban Geography and Tourism Studies, Faculty of
Geography, University of Lodz, Lodz, Poland.
c [email protected]
Tartu, Estonia.
d [email protected]
Technology, Delft, the Netherlands;
and School of Geography & Sustainable Development,
University of St Andrews, St Andrews, UK.
e [email protected]
Technology, Delft, the Netherlands.
REGIONAL STUDIES
2020, VOL. 54, NO. 4, 450–461
https://doi.org/10.1080/00343404.2018.1540035
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labour and housing markets (Malmberg, Andresson, &
Östh, 2013).
A recent European comparative study shows that simi-
lar national levels of income inequality correspond with
very different residential segregation levels between the
top and bottom socioeconomic groups (for the definition
of socioeconomic groups, see below) in European urban
regions. The relationship between income inequality and
socioeconomic segregation is complex and previous studies
(e.g., Musterd, Marcińczak, van Ham, & Tammaru, 2017;
Tammaru et al., 2016) and we could not document a one-
to-one relationship between the two since this relationship
hinges on many factors. Since the 1980s, globalization,
restructuring of labour markets and the liberalization of
the economy have led to rising income inequality across
the globe (Piketty, 2013; World Inequality Report,
2018). Previous studies have suggested that it takes time
before a rise in income inequality leads to higher levels of
socioeconomic segregation, and therefore it is needed to
take into account time lags between changes in the two
phenomena (Marcińczak et al., 2015; Musterd et al.,
2017; Wessel, 2016).
The aim of this paper is to obtain more insight into the
relationship between income inequality and socioeconomic
segregation. Although a large volume of studies exists on
both income inequalities and residential segregation, their
connection is poorly studied, especially in a comparative
framework. This paper builds on Tammaru et al. (2016)
who compared levels of socioeconomic segregation in 13
European urban regions in 2000 and 2010 and found an
increase in segregation in all but one studied city. In this
paper, we will explicitly study the link between income

inequality and socioeconomic segregation by taking into
account time lags between changes in income inequality
and changes in segregation between the top and bottom
socioeconomic groups. In order to do this, we use a longer
time frame measuring income inequality since 1980 and
socioeconomic segregation from 1990. We focus the ana-
lyses on the urban regions located in North, South and
East Europe. The North European countries represent
the lowest levels of income inequality in Europe; the
South European countries represent the highest levels of
income inequality in Europe; and many East European
counties, including Estonia and Hungary, have switched
from the most equal to most unequal countries in Europe
(Statistical Office of the European Communities (EURO-
STAT), 2018).
We seek answers to three central research questions:
. What are the differences in socioeconomic segregation
in North, South and East Europe?
. Is there a relationship between the change of socioeco-
nomic segregation and change in income inequality 10
years earlier?
. Are there variations in the relationship between income
inequality and socioeconomic segregation in North,
South and East Europe with different income inequality
contexts?
We start the study with the analysis of changes in the
Gini index of the countries included since 1980. We
then analyze the levels of residential segregation between
the top and bottom socioeconomic groups measured by
the dissimilarity index at the 1990, 2000 and 2010 census
rounds. Finally, we will explore the relationship between

the Gini index and the dissimilarity index to obtain more
insight into the relationship between income inequality
and residential segregation between the top and bottom
socioeconomic groups.
The empirical evidence comes from the urban regions
of Helsinki (Finland), Oslo (Norway), Stockholm (Swe-
den) in the North of Europe; from Athens (Greece),
Madrid (Spain) and Milan (Italy) in the South of Europe;
and from Tallinn (Estonia) and Budapest (Hungary) from
the East of Europe. Although the data for the empirical
study is[ simple, on the one hand, arranging a spatially
detailed and comparable data set for a broad set of urban
regions from different parts of Europe was a significant
challenge and a possible explanation for the fact that very
few comparative studies exist so far.
MECHANISMS THAT RELATED INCOME
INEQUALITY AND SOCIOECONOMIC
SEGREGATION
Fundamentally, the most critical cause of residential segre-
gation between socioeconomic groups is income inequality
(Nightingale, 2012; Préteceille, 2016). The income
inequality started to grow globally during the 1980s
(World Inequality Report, 2018; Piketty, 2013; Sachs,
2012) together with rapid globalization, economic liberal -
ization, marketization and deindustrialization that, com-
bined, shape today’s social relations and spatial structures
(Marcuse & Van Kempen, 2000; Tammaru et al., 2016).
The levels of income inequality were already high in
South Europe in 1980, with the Gini index ranging
between 30 and 35 in Greece, Italy and Spain in 1980
(Figure 1). The Gini indices were the lowest, around 20,
in North Europe and in the formerly socialist countries
in East Europe in 1980. In international comparison, the

Nordic countries were relatively equal societies and
wealthy, while the formerly socialist countries in the East
of Europe were relatively equal but poor (Kornai, 1992).
Figure 1 shows the trajectories of the Gini index change
since 1980. In North European countries, the Gini indices
have steadily but slowly increased between 1980 and 2015
and now hover around 25. In South European countries,
the Gini indices decreased in the 1980s but climbed back
to the levels of 1980 thereafter. In other words, the differ -
ences in income inequalities between North and South
Europe have decreased during the last few decades, but
income inequality is still considerably higher in the South
compared with North Europe. In East European countries,
the Gini index increased rapidly in the 1990s to the levels
of South Europe. Since then the income inequality in Esto-
nia has remained at the South European levels, but have
decreased in Hungary. Across the board and irrespective
of the initial levels of income inequality, the most rapid
segregation of socioeconomic groups 451
REGIONAL STUDIES
changes in the Gini indices took place in the 1990s, allow -
ing it to be analyzed whether this was followed by a rise in
residential segregation between top and bottom socioeco-
nomic groups a decade later in the 2000s.
In short, the case study countries provide an interesting
mix of income inequality contexts in Europe for analyzing
how income inequality may be related to socioeconomic
segregation. Although residential segregation between

socioeconomic groups is fundamentally related to income
inequality, there are several underlying spatial mechanisms
that can connect the two: (1) changes in household numbers
that affect the distribution of top and bottom socioeconomic
groups over the neighbourhoods (population shrinkage or
growth, natural change, immigration); (2) residential mobi-
lity within the urban regions (people changing residential
neighbourhood because their incomes increase or decrease);
and (3) the geography of housing and its differentiation,
attracting, forcing or constraining the residential mobility
of households. The interplay between these factors is com-
plicated, and it takes time, therefore, before a change in
the level of income inequality results in changes to the
level of residential segregation between the top and bottom
socioeconomic groups. Next, the paper will discuss the three
mechanisms in greater detail.
Differential change in household numbers
across neighbourhoods
Changes in household numbers affect the distribution of
top and bottom socioeconomic groups over the neighbour-
hoods. The extent of immigration is the most important
contributory factor here. The transition from a Fordist to
a post-Fordist economy and social mobility through edu-
cation brought along the professionalization of, usually,
the native workforce not only in global cities (Sassen,
1991) but also in other major cities (Costa & de Valk,
2018; Marcuse & van Kempen, 2002). The employer
demand for low-skilled workers remained as the (low-
paid) service sector developed and the number of foreign
immigrants – getting overrepresented in those low-
skilled/low-paid jobs – started to grow in both North and
South Europe in the 1990s (Castles, de Haas, & Miller,
2013). Although the ethnic dimensions of income inequal-
ity have been present in Western Europe with the arrival of

guest workers since the 1950s, the professionalization of
the native workforce and the residualization of low -skilled
jobs to immigrants reinforced the ethnic component of
economic inequality (Sassen, 1991), especially in those
countries that experienced the second wave of international
migrants that started in the 1990s (Castles et al., 2013).
As the incomes of immigrants are, on average, lower
compared with natives (EUROSTAT, 2018), their neigh-
bourhood choice is restricted by various constraints such as
their lower purchasing power on the housing market, limited
number and clustered location of affordable housing in cer -
tain parts of the urban region (Arbaci & Malheiros, 2010;
Hulchansky,2010;Malheiros,2002).Thesocialmixpolicies
that are in place in many European countries have not always
been able to stop the growth of levels of residential segre-
gation for various reasons, including ineffective implemen-
tation of the policies, failures in policy design or because of
conflicting policy aims (Andersson, Bråmå, & Holmqvist,
2010). In this light, Andersson and Kährik (2016) refer to
‘eth-class’ segregation, a process of double sorting of non-
Western immigrants to low-paid jobs and less prestigious
neighbourhoods with affordable housing, and natives to
high-paid jobs and more prestigious neighbourhoods.
Since residential segregation of ethnic groups is driven
by income, by preferences to reside together with co-eth-
nic, and discrimination, segregation in urban regions with
a high share of immigrants can grow more rapidly than
income inequality itself (Préteceille, 2016). In short,
changes in the population composition of neighbourhoods
as a result of foreign immigration and immigrant sorting
into low-paid jobs and affordable housing, in particular,
are among the primary causes of increased residential seg-
regation between socioeconomic groups (Arbaci, 2007;

Figure 1. Per capita Gini index in the case study countries,
1980–2015.
452 Tiit Tammaru et al.
REGIONAL STUDIES
Cassiers & Kesteloot, 2012; Musterd et al., 2017). How-
ever, other factors may be important as well, for example,
the differential fertility of different ethnic and income
groups (Finney & Simpson, 2009).
Differential residential mobility of
socioeconomic groups
Levels of segregation may also change as a result of the
differential residential mobility of top and bottom socioeco-
nomic groups between urban neighbourhoods. In parallel to
growing income inequalities, the share of the top socioeco-
nomic groups has increased in many European cities (Butler,
Hamnett, & Ramsden, 2008; Hamnett, 1994). Since money
buys choice on the housing market (Hulchansky, 2010), the
residential mobility of high-income earners affects both the
level and the geography of segregation. The increasing
demand for higher end housing has led to stark increases
of land and housing prices in desirable areas (Préteceille,
2016), and spill over effects to formerly low-income neigh-
bourhoods, pushing up house prices there (Leal & Sorando,
2016). These areas are often adjacent to the already existing
high-income neighbourhoods (Préteceille, 2007). However,
the most important macro-geographical change in the distri-
bution of socioeconomic groups pertains to the movement of
high-income households to the central areas of the city or
gentrification of the inner-city neighbourhoods, and the
movement of low-income households to the urban margins

or suburbanization of poverty (Hochstenbach & Musterd,
2018).
Such changes in the residential relocation of the top and
bottom socioeconomic groups will bring along mixing of
different income groups in urban neighbourhoods (Mar-
cińczak, Musterd, van Ham, & Tammaru, 2016; Musterd
& van Gent, 2016). The segregation paradox refers to this
inverse relationship between income inequality and resi-
dential segregation: increasing income inequality may
bring along lowering levels of residential segregation
between socioeconomic groups (Sýkora, 2009). However,
ultimately such differential residential mobility patterns of
top and bottom socioeconomic groups will lead to higher
levels of segregation, for example, when high-income
groups colonize the inner city (Leal & Sorando, 2016),
and lower income groups retreat to urban fringe, often to
the modernist housing estates built in the 1960s–80s
(Kavanagh, Lee, & Pryce, 2016; Lelévrier & Melic,
2018; Musterd et al., 2017).
Uneven geography of housing
The geography of housing is an essential factor that
attracts, forces or constrains people with different income
levels to undertake residential change. Both the extent
and speed with which selective residential mobility of top
and bottom socioeconomic groups leads to changes in the
levels of residential segregation hinges, first, on how
uneven is the geography of housing, or how the urban
neighbourhoods are planned in terms of their housing
mix (Fujita & Maloutas, 2016; Préteceille, 2016). The
more spatially clustered the low-cost housing is, the more
likely it is that low-income households with little choice
in terms of housing sort into those neighbourhoods, contri-
buting to the rise of the level of residential segregation

(Marcińczak et al., 2016). In many North, South and
East European urban regions, affordable housing can
often be found in the modernist housing estates from the
1960s–70s (Hess et al., 2018). Sweden became world
famous with its so-called ‘Million Programme’: the con-
struction of modernist houses took place on large suburban
plots of land, filling them with homogenous housing
(Andersson & Bråmå, 2018). Today, the attraction of
many of the modernist housing estates has decreased as
new and more attractive housing for higher income groups
has become available (Andersson & Bråmå, 2018; Wassen-
berg, 2013).
However, the fortunes of the neighbourhoods may also
change with time. According to rent-gap theory (Smith,
1987), the movement of high-income groups to the inner
city may be related to the interests of the investor seeking
higher profits. Namely, the expected higher profits for
attracting high-income earners to low-income neighbour-
hoods could be attractive for investors, leading to the reno-
vation and new housing construction in such areas. The
housing allocation matters, too. If social housing is spatially
clustered but available to all income groups, segregatio n
levels change slower. However, what has happened in
many European urban regions is a process called the resi -
dualization of social housing: market elements have been
introduced to better quality social housing, often to balance
city budgets (Urban, 2018). The contraction of the social
housing segment, in turn, brings along the need to grant
access to such housing mainly to the low-income house-
holds, driving up levels of segregation, especially when
the residualized part of the social housing is spatial ly con-
centrated to certain parts of the city (Hochstenbach,
2017; Hoekstra, 2017).
To conclude, although the most important cause for the

increase in residential segregation between socioeconomic
groups is an increase in income inequality, there is no
one-to-one relationship between the two. The spatial
mechanisms that link the two relate to changes in the
population in urban neighbourhoods, the differential resi -
dential mobility of socioeconomic groups and the nature
and change of the urban housing stock. These mechanisms
take time to show up in changed levels of segregation.
Therefore, we will test the hypothesis that income inequal -
ity is related to socioeconomic segregation with a time lag
in the empirical parts of the paper. By examining the lagged
relationship between income inequality and socioeconomic
segregation, we hope to document better the relationship
between the two compared with previous studies (e.g.,
Musterd et al., 2017; Tammaru et al., 2016) that measure
them at the same time.
DATA AND METHODS
For the analyses of segregation, we used population data for
Athens, Budapest, Madrid, Milan, Tallinn, Helsinki, Oslo
and Stockholm from the years 1989/1990/1991, 2000/
2001 and 2010/2011, or the last three census rounds.
segregation of socioeconomic groups 453
REGIONAL STUDIES
The data on the levels of segregation were systematically
collected and provided by researchers from each urban
region under study – partly within the book project ‘Socio-
economic Segregation in European Capital Cities’ (Tam-
maru et al., 2016) and partly specifically for the current

paper. (We greatly acknowledge the support of all the
country teams, without whom this paper would not be
possible.) Despite some minor inconsistencies across time
and between countries, census years provide the most
reliable information on socioeconomic segregation across
Europe. Data for Athens, Budapest, Madrid, Milan and
Tallinn are based on censuses. Data on Helsinki, Oslo
and Stockholm are based on registers. All case study
areas are defined as urban regions since residential segre-
gation processes evolve at the level of regional housing mar -
kets (cf. Tammaru et al., 2016). The census data used do
not include data on income, and the register data used do
not include information on occupations.
Although both censuses and registers contain infor-
mation about education, education is only weakly related
to income, while there is a strong correlation between occu-
pation and income (Tammaru et al., 2016). Hence, we
measure socioeconomic status using occupational groups
in census-based countries and data on income in register-
based countries. A note of caution relates to the use of
these different variables. An increase in income inequality
could in itself increase levels of residential segregation
when measured by income. This should not affect segre-
gation measured by using occupational categories, because
in this case although professionals earn more and unskilled
workers earn less, if all continue to live in the same neigh-
bourhoods, the dissimilarity index does not change. There-
fore, rising levels of income inequality might lead to
different outcomes in cities where residential segregation
is measured by income compared with cities where occu-
pational status is used.
Top and bottom socioeconomic groups are defined as
follows. In register-based countries (Finland, Norway,

Sweden) we use income quintiles and show levels of segre-
gation between people belonging to the first and fifth
Table 1. Spatial units used in the study.
City Spatial characteristics used in the studya
Athens About 3.1 million inhabitants lived in the Athens Urban
Region in 2011, comprising 58 municipalities on the
continental part of the Attiki region. The neighbourhood
definition is based on 2.835 urban analysis units (URANU),
which are either individual census tracts or groups of census
tracts with an average of 1.200 residents. Census tracts
are defined by the Greek Statistical Authority (ELSTAT), w hile
the regrouping in URANUs was produced by the project
‘Dynamic Management and Mapping of Social Data’ conducted
by the National Centre for Social Research (EKKE)
Budapest About 1.7 million people lived in Budapest in 2011.
Budapest is divided into 1600 discrete territorial units on the
basis of functional and morphological attributes with an average
of about 1000 inhabitants
Helsinki About 1.5 million inhabitants lived in the Helsinki
Metropolitan Region in 2011. The definition of neighbourhoods
is
based on zip code areas. There are 303 zip code areas in
Helsinki with an average population size of 4865 people
Madrid About 6.4 million inhabitants lived in the Madrid Urban
Region in 2011. The neighbourhood definition is based on

groupings of census tracts since the Census of Population and
Housing of 2011 is not representative at a more
disaggregated territorial level. Usually, the Spanish Statistical
Offices provides data for neighbourhoods with an
average size of 20,000. However, it is possible to obtain more
detailed data depending on the exact data needed for
research. The data asked for the comparative segregation study
needed data by International Standard Classification
of Occupations (ISCO) which allowed tract groupings with an
average of 12,252 residents to be created
Milan About 1.2 million people lived in the city of Milan and
3.0 million in the province of Milan in 2011. With regard to the
spatial units, census tracts, census areas (ACE), districts
(circoscrizioni), administrative districts (zone di
decentramento) are available. The average number of people is
219 in census tracts and 14,778 in ACE areas. The
findings of trends yield similar results; neighbourhoods units
used in the final analysis were census tracts
Oslo About 1.2 million lived in the Oslo Region in 2011. The
neighbourhood definition is based on census tracts with an
average population of 594 inhabitants
Stockholm About 1.2 million people lived in the Stockholm
built-up area in 2011. The neighbourhood definition is based on

small area market statistics (SAMS) areas. The study includes
655 neighbourhoods with the average size of 2100
people
Tallinn About 0.5 million inhabitants live in Tallinn urban
region. The neighbourhood definition is based on census tracts
with an average population of 494 inhabitants
Note: aWe define cities as a continuous built-up area that forms
a common housing market. In other words, the analysis is not
confined to administrative city
boundaries. However, within this broad definition of a common
housing market area, authors of different city reports in
Socioeconomic Segregation in
European Capital Cities: East Meets West (Tammaru,
Marcińczak, van Ham, & Musterd, 2016) adapted it to their
specific context. We rely on this local expert
knowledge in the concrete definition of the city regions.
Sources: The neighbourhood definitions were made for
comparative research published in Tammaru et al. (2016). For
the more detailed descriptions of the
city definitions and spatial units, see Andersson and Kährik
(2016), Kovács and Szabó (2016), Leal and Sorando (2016),
Maloutas (2016), Petsimeris and
Rimoldi (2016), Tammaru et al. (2016), Wessel (2016), and
Kauppinen and van Ham …
“If They Focus on Giving Us a Chance in Life We Can Actually
Do
Something in This World”: Poverty, Inequality, and Youths’
Critical Consciousness

Amanda L. Roy
University of Illinois – Chicago
C. Cybele Raver, Michael D. Masucci,
and Meriah DeJoseph
New York University
Critical consciousness (CC) has emerged as a framework for
understanding how low-income and
racial/ethnic minority youth recognize, interpret, and work to
change the experiences and systems of
oppression that they face in their daily lives. Despite this,
relatively little is known about how youths’
experiences with economic hardship and structural oppression
shape how they “read their world” and
motivate participation in critical action behaviors. We explore
this issue using a mixed-methods design
and present our findings in two studies. In Study 1 we examine
the types of issues that a sample of
low-income and predominantly racial/ethnic minority youth
(ages 13–17) living in the Chicago area
discuss when asked to reflect on issues that are important to
them. The most commonly mentioned
themes were community violence (59%), prejudice and
intolerance (31%), world issues (25%), and
economic disparities (18%). In Study 2 we examine youths’
quantitative reports of engaging in critical
action behavior; more than 65% had participated in at least one
activity targeting social change in the
previous 6 months. We then examined relationships between
youths’ experiences with poverty within
their households and neighborhoods, neighborhood income
inequality, and exposure to violence and
youths’ likelihood of participating in critical action behaviors.

Greater exposure to violence and
neighborhood income inequality were related to an increased
likelihood of engaging in critical action
behaviors. This work highlights the diverse ways that low -
income and racial/ethnic minority youth reflect
on societal inequality and their commitment to effecting change
through sociopolitical participation.
Keywords: critical consciousness, mixed methods, adolescence,
poverty, sociopolitical participation
Developmental scientists and youth advocates have issued calls
for greater recognition of young people (and particularly those
who have been societally marginalized) as sociopolitically
active
participants in their own futures and the futures of their commu-
nities (e.g., Cammarota, 2011; Ginwright, & James, 2002;
Gutiér-
rez, 2008; Kirshner, 2007; Watts & Flanagan, 2007). In keeping
with this perspective, critical consciousness (CC) has emerged
as
a framework for understanding how youth “learn to critically
analyze their social conditions and act to change them,” particu-
larly when those conditions involve persistent and
institutionalized
discrimination or marginalization (Watts, Diemer, & Voight,
2011,
p. 44). As the social conditions and experiences of young
people
are multifaceted and contextual, so too is the development of
critical consciousness likely to be. The development of critical
consciousness occurs in response to individual experiences and
within the specific contexts that individuals are embedded (Di -
emer, McWhirter, Ozer, & Rapa, 2015; Freire, 2000; Gutiérrez,
2008; Sánchez Carmen et al., 2015). Therefore, youths’

reflections
on the social issues that are important to them are likely to vary
and, in part, be determined by differential experiences with
mar-
ginalization and oppression.
Critical consciousness has been conceptualized as having three
components: critical reflection (recognition and rejection of
soci-
etal inequities), political efficacy (one’s ability to effect
change),
and critical action (actions taken to change society e.g.,
community
organizing; Watts et al., 2011). Prior research on critical action
behaviors among racial/ethnic minority and lower income youth
has been mixed; some work has found rates of participation to
be
lower than those found in higher income, predominantly white
samples (APSA Task Force on Inequality & American Democ-
racy, 2004; Hart & Atkins, 2002; Stepick & Stepick, 2002),
Amanda L. Roy, Department of Psychology, University of
Illinois –
Chicago; C. Cybele Raver, Michael D. Masucci, and Meriah
DeJoseph,
Steinhardt School of Culture, Education, and Human
Development, New
York University.
Research reported in this publication was supported by the
National
Center for Advancing Translational Sciences, National
Institutes of Health,
under Grant KL2TR002002. The research reported here was also
supported
by the Institute of Education Sciences, U.S. Department of

Education,
through Grant R305B080019 and by Award R01HD046160 from
the
Eunice Kennedy Shriver National Institute of Child Health and
Human
Development. The opinions expressed are those of the authors
and do not
represent the views of the Institute or the U.S. Department of
Education,
the Eunice Kennedy Shriver National Institute of Child Health
and Human
Development, or the National Institutes of Health.
Correspondence concerning this article should be addressed to
Amanda
L. Roy, Department of Psychology, University of Illinois –
Chicago, 1007
West Harrison Street, 1046D BSB, Chicago, IL 60607. E-mail:
[email protected]
uic.edu
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Developmental Psychology
© 2019 American Psychological Association 2019, Vol. 55, No.
3, 550 –561
0012-1649/19/$12.00 http://dx.doi.org/10.1037/dev0000586

550
http://dx.doi.org/10.1037/dev0000586
whereas others describe the varied ways that lower income,
racial/
ethnic minority youth participate in their schools and
communities
(Ginwright, 2007; Kirshner, 2007; Rubin, 2007) despite differ-
ences in the types of opportunities available to them (Atkins &
Hart, 2003; Flanagan & Levine, 2010; Kahne & Middaugh,
2008).
In addition, recent research has found community-level income
inequality to be positively, rather than negatively, related to
higher
levels of critical action, particularly among lower income youth
(Godfrey & Cherng, 2016). Therefore, it may be that exposure
to
different types of economic hardship and structural oppression
(i.e., number of years experiencing “deep poverty,” perceptions
of
greater financial hardship, greater neighborhood income
inequality
and poverty, and higher exposure to violence) may differentially
shape youths’ identification of issues that matter to them and
their
likelihood of engaging in critical action behaviors.
The current article uses a mixed-methods design to first (a)
describe the issues that are important to a sample of low -income
and predominantly racial/ethnic minority youth living in the
Chi-

cago area and then (b) predict the likelihood of their
participation
in critical action behaviors based on their experiences with
differ-
ent types of economic hardship and structural oppression. By
asking youth to reflect on the issues that are important to them,
we
are able to descriptively explore the themes that youth
spontane-
ously generate and consider the degree to which those themes
embody critical reflection, specifically in terms of the types of
social justice issues that youth identify. In addition, we explore
whether youths’ experiences with economic hardship and struc-
tural oppression are related to their participation in critical
action
behaviors. Specifically, we examine the degree to which youths’
experiences with poverty within their households (# of years
experiencing “deep poverty” and perceptions of financial hard-
ship), neighborhood income inequality (Gini index of youths’
residential census tract), neighborhood poverty (% poor within
youths’ residential census tracts), and youths’ reports of
exposure
to violence within their families and communities are predictive
of
students’ likelihood to take action. Many of the youth in our
study
live in neighborhoods with some of Chicago’s highest levels of
poverty and crime; accordingly, we examine the ways that
youths’
daily lived interactions with these aspects of economic hardship
and structural oppression shape how they “read their world” and
subsequently act to change it.
Critical Consciousness Among Youth
Brazilian educator Paulo Freire (1973, 2000) defined critical

consciousness as “learning to perceive social, political, and eco-
nomic contradictions, and to take action against the oppressive
elements of reality” (Freire, 2000, p. 35). Freire developed CC
as
a pedagogical method to raise Brazilian peasants’ ability to
“read
the world” or recognize the social conditions that foster
inequality
and marginalization, such as the unequal distribution of
resources
and access to opportunities (Diemer, Kauffman, Koenig,
Trahan,
& Hsieh, 2006). Since its inception, CC has been embraced by
scholars in multiple fields as a strategy for marginalized youth
to
resist oppression by helping them both understand and then
work
to change unjust social conditions through constructive social
action (Cammarota, 2011; Ginwright, & James, 2002; Gutiérrez,
2008; Kirshner, 2007; Morrell, 2002; Watts, Griffith, & Abdul-
Adil, 1999; Watts et al., 2011). Although individuals
experiencing
privilege in some aspects of their lives may also think critically
about inequality and advocate for social change through critical
action, the framework of CC was developed specific to the
expe-
riences of an oppressed population and subsequent scholarship
has
primarily applied this framework with similarly oppressed or
mar-
ginalized populations (Cammarota, 2004; Diemer et al., 2015;
Gutiérrez, 2008; Morrell, 2002). Moreover, one study found the
positive benefits of CC on career development to be most
evident
among youth who experience racial/ethnic and socioeconomic

oppression (Diemer et al., 2010).
In part driven by its interdisciplinary nature, critical conscious-
ness has been defined in several different ways. As highlighted
earlier, research coming out of the field of psychology has con-
ceptualized critical consciousness as consisting of three compo-
nents: critical reflection, political efficacy, and critical action
(Watts et al., 2011; Watts & Hipolito-Delgado, 2015). Critical
reflection involves the recognition and rejection of societal
ineq-
uities based on characteristics/experiences such as
race/ethnicity,
gender, and economic standing that constrain well-being and
agency. In addition, critical reflection is characterized by an
ability
to make more structural (e.g., we have an unequal social system
that constrains opportunities) rather than individual (e.g., some
people work harder) attributions about inequality (Watts et al.,
2011). Political efficacy refers to an individual’s perceived
ability
to effect social change via individual behavior and/or activism.
Finally, critical action refers to the actual behaviors that
individ-
uals engage in to effect societal change. This can include a wide
range of behaviors such as those represented in more traditional
measures of civic engagement such as voting, to more proximal
behaviors such as posting on social media about a social or
political issue (Watts et al., 2011).
Some theoretical and empirical work have posited that critical
reflection and critical action are closely intertwined, arguing
that
an individual’s ability to recognize societal inequality is an im-
portant precursor to engaging in behavior to fight against it (Di-
emer & Rapa, 2016; Watts et al., 2011). Others, including Freire
himself (Freire, 1973, 2000), have conceptualized CC as a

trans-
actional process in which thought, action, and reflection occur
in
no specific order and without strict boundaries (Sánchez
Carmen et
al., 2015). This perspective acknowledges that social context
and
lived experience continuously shape individuals’ understanding
of
oppression and their motivation and opportunity to engage in
critical action.
Context and Critical Consciousness
What are the contextual factors that shape youths’ understand-
ing of inequality and oppression and motivate youth to engage
in
critical action behaviors? Prior work has argued that CC
develop-
ment occurs when marginalized youth are given the opportunity
to
and support for reflecting on and challenging social
inequalities,
which in turn can motivate desire to effect social change
through
engaging in critical action (Atkins & Hart, 2003; Balcazar, Tan-
don, Kaplan, & Izzo, 2001; Diemer et al., 2006; Diemer & Li,
2011; Flanagan & Levine, 2010; Kahne & Middaugh, 2008;
Watts et al., 1999). Support for CC development can come from
multiple sources in youths’ lives including parents, peers,
teachers, and community members. Empirical work with quan-
titative data has found sociopolitical support from both parents
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551POVERTY AND YOUTHS’ CRITICAL CONSCIOUSNESS
and peers to predict youths’ critical action (Diemer & Li, 2011),
whereas an open classroom climate was positively related to
youths’ critical action, but not critical reflection (Godfrey &
Gray-
man, 2014). In addition, qualitative examinations of youths’ so-
ciopolitical participation have consistently demonstrated the di-
verse ways that lower income, racial/ethnic minority youth both
survive and resist the violence, inequality, and oppression that
they
face in their daily lives (Cammarota, 2011; Ginwright, & James,
2002; Gutiérrez, 2008; Kirshner, 2007; Morrell, 2002). Recent
examples include youths’ involvement in the Dreamers
movement
(Forenza, Rogers, & Lardier, 2017) and Black Lives Matter (El -
Amin et al., 2017).
Although scholars have recognized the importance of oppor tu-
nity and support for fostering youths’ critical consciousness,
less is
understood about how marginalized youth recognize and make
sense of oppression within the context of their lived experience.
As
critical reflection describes how marginalized people “read their
world,” it makes sense that critical consciousness develops

within
the specific contexts that shape and constrain individual lives
(Diemer et al., 2015; Freire, 2000). Lower income, racial/ethnic
minority youth are embedded in intersecting systems of
oppression
that foster inequities across multiple domains including class,
race,
and gender (Sánchez Carmen et al., 2015). Therefore, critical
consciousness is likely not only to vary across people, but also
as
a function of the specific types of marginalization that people
experience (Diemer et al., 2015). For example, a youth who has
grown up in extreme poverty may be more frequently exposed,
as
well as more attuned, to socioeconomic disparities, whereas a
youth who experiences violence in her daily life may be more
aware of the unequal distribution of supports for neighborhood
safety within a city (Bennett et al., 2007; Boslaugh, Luke,
Brown-
son, Naleid, & Kreuter, 2004). For example, in a recent
multilevel,
health-related survey of adults living in St. Louis, African
Amer-
ican residents’ ratings of neighborhood safety were much more
closely tied to higher versus lower levels of neighborhood
segre-
gation than were white residents’ ratings (Boslaugh et al.,
2004).
Similarly, Diemer and colleagues (2006) found individuals’
expe-
riences to be reflected in the written vignettes of low -income,
youth of color such that discussions of sexism and gender
inequity
were much less sophisticated among males compared to females.
To extend this emerging body of research, we examine Chicago
students’ critical thinking about the social issues that matter to

them.
Youths’ individual experiences with inequality and oppression
may also differentially affect their likelihood of engaging in
crit-
ical action. Some previous research has suggested that lower
socioeconomic status (SES) and racial/ethnic minority youth are
less likely to be civically engaged than their higher income,
white
peers (APSA Task Force on Inequality & American Democracy,
2004; Flanagan & Levine, 2010; Hart & Atkins, 2002). These
disparities are thought to be, in part, determined by disparities
in
access to opportunities for participation (Atkins & Hart, 2003;
Flanagan & Levine, 2010; Kahne & Middaugh, 2008). At the
same
time, quantitative research has shown other contextual factors to
also matter for youths’ civic participation. Connection to one’s
neighborhood has been shown to be positively related to youths’
rates of community involvement (Wray-Lake, Rote, Gupta, God-
frey, & Sirin, 2015) and likelihood of voting, volunteering, and
helping others in the community (Duke, Skay, Pettingell, &
Borowsky, 2009). In addition, county-level income inequality
has
been linked to higher rates of civic engagement particularly
among
low-SES and racial/ethnic minority youth (Godfrey & Cherng,
2016). Therefore, it may be that exposure to different types of
contextual hardship and oppression may differentially influence
youths’ opportunities and motivation for critical action.
The Present Studies
The present studies use a concurrent transformative mixed-
methods design (Creswell, Plano Clark, Gutmann, & Hanson,

2003) to further understanding of youth CC among a sample of
low-income and predominantly racial/ethnic minority youth
living
in the Chicago area. We describe our design as concurrent trans-
formative because both qualitative and quantitative data were
collected during the same assessment and our research goals are
grounded in the theoretical framework of CC (Creswell et al.,
2003). Structured as two studies, we first use youths’ responses
to
an open-ended prompt to explore the types of issues, with
special
attention paid to social justice issues, which are important to
them.
In Study 2, we use quantitative measures of youths’ experiences
with poverty within their households (# of years experiencing
“deep poverty,” perceptions of financial hardship),
neighborhood
income inequality (Gini index of youths’ residential census
tract),
neighborhood poverty (% residents in poverty) and youths’
reports
of exposure to violence in their families and communities to
predict the likelihood of their participation in critical action be -
haviors. Chicago represents a particularly salient context for ex-
ploring these questions given its extremely high rates of racial/
ethnic segregation and unequal distribution of poverty and
violence (Quillian, 2012).
In Study 1, we expect that the issues youth describe as being
important to them will reflect their own experiences with
poverty
and community violence. We anticipate that discussions of eco-
nomic hardship, violence, and prejudice/discrimination will be
some of the most common issues raised. We also expect that the
students in our sample will report at least moderate levels of
critical action to change their worlds for the better though this

question has been relatively unexplored in past research. In
Study
2, we anticipate that youths’ experiences with poverty within
their
households, neighborhood income inequality (Gini index of
youths’ residential census tract), neighborhood poverty, and re-
ports of exposure to violence will be related to likelihood of
engaging in critical action behaviors. Keeping with findings
from
previous research, we predict that youth who have greater expo-
sure to poverty (both household and neighborhood), report
greater
financial hardship, and have higher levels of exposure to
violence
will be less likely to engage in critical action, whereas youth
exposed to higher levels of neighborhood income inequality will
be more likely.
Study 1
Method
Sample. We capitalize on longitudinal data (collected at five
waves between 2004 and 2016) from a sample of predominantly
African American and Latino adolescents living largely in high-
poverty, Chicago neighborhoods. Youth were originally
recruited
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552 ROY, RAVER, MASUCCI, AND DEJOSEPH
into the Chicago School Readiness Project (CSRP) as part of a
socioemotional intervention trial implemented in Chicago Head
Start preschool programs in two cohorts between 2004 and 2006
(Raver et al., 2009, 2011). Children and families were assessed
when children were in preschool (Wave 1, N � 602),
kindergarten
(Wave 2, N � 398), third (Wave 3, N � 505), fifth (Wave 4, N
�
491), sixth/seventh (Wave 5, N � 353), and ninth/tenth (Wave
6,
N � 469) grades. In Waves 1– 4, data collection spanned a two-
year period so that the two cohorts of youth were assessed when
they were in the same grade; in Waves 5– 6 data collection took
place at one point in time when the two cohorts of youth were in
different grades. Data collection was conducted by a contracted
survey research firm that has worked with the project for Waves
4 – 6 of data collection. Our high rates of sample retention
(78% at
Wave 6) are in part attributable to the close contact the research
team maintains with families throughout the year and the
targeted
hiring of assessors who live and work in the same communities
as
participant families. Although all youth lived in Chicago at
base-
line, some moved outside of the city limits over the course of
their
lives. At Wave 6, 77% of the sample lived within the city limits.
Of the 23% who had moved out of the city, the majority

remained
within the greater Chicago area (within �50 mile radius of Chi-
cago). This research has been approved by New York
University’s
Institutional Review Board as a part of the Neuroscience and
Education Lab (IRB#: FY2016196).
Data used in Study 1 were collected during the Wave 6 assess-
ment. As a part of the study design, only a random subsample of
232 youth were asked to respond to the open-ended reflection
question as part of a short “purpose for learning” intervention
(Yeager et al., 2014) that was delivered as part of the
computerized
assessment. Randomization into treatment and control
conditions
was determined when the computerized assessment was
launched;
youth responded to the open-ended prompt before receiving the
intervention content. Of the 469 youth who completed the Wave
6
assessment, 217 (46%) provided a response to the open-ended
reflection (See Table 1). The majority of youth who completed
the
open-ended reflection and make up the study sample for Study 1
are female (55%) and identified as being African American
(67%)
or Latino (24%). A small percentage of youth are biracial (5%),
white (4%) or described themselves as “other” (1%). On
average,
youth were 15 years old (SD � .79) at the Wave 6 assessment.
Averaging across all waves of data, the average income-to-
needs
ratio (INR) for the sample was 0.89 (SD � 0.67), indicating that
the majority of youth lived in households whose income and
family size placed them below the national poverty line (defined

as
having an income-to-needs ratio equal to or less than 1) for the
majority of their lives.
Measure: Open-ended reflection. Youth were asked to read
the following statement and given the opportunity to write a
short
response.
Sometimes the world isn’t fair. And almost everyone at some
time
sees this and thinks the world could be better in one way or
another.
Some people want there to be less prejudice, some want less
violence
or aggression, and others want to reduce poverty, pollution, or
diseases. People want their neighborhoods to be better. Other
people
want different kinds of changes. Think about all the issues that
matter
to you personally. In the box below, write a few sentences about
problems that matter to you and why you think they are big
problems.
Analytic strategy. An iterative, collaborative process was
used to thematically code the open-ended responses (N � 217;
Hill
et al., 2005; Hill, Thompson, & Williams, 1997). First, we
devel-
oped codes for recurrent themes and categories in a multistep
process. Working in collaboration with the first author (a
female,
white faculty member), two undergraduate research assistants
(both female, racial/ethnic minority students) generated themes
based on their read of 50 responses selected at random from the
full 217. Rather than relying on researcher-generated categories,

the coders were instructed to look for commonalities that
occurred
across the responses when generating their codes. Gaps and dis -
crepancies in the coding frame were discussed and resolved
among
the three person team. After finalizing the coding frame, the
first
author and a third research assistant (female, white, post-BA)
applied the codes to the full set of responses (N � 217) with
each
response receiving up to three codes. Cohen’s Kappa, a measure
of
the amount of agreement between raters on the application of
Table 1
Descriptive Statistics for Studies 1 and 2
Characteristic
Study 1 (N � 217) Study 2 (N � 461)
% Mean (SD) Range % Mean (SD) Range
Female 55% 54%
Race/ethnicity
African American 67% 68%
Latino/a 24% 25%
Biracial 5% 4%
White 4% 3%
Other 1% �1%
Age 15.33 (0.79) 13.30–16.96 15.32 (0.81) 13.18–17.02
Income-to-needs ratio 0.89 (0.67) 0.00–3.79 0.87 (0.63) 0.00–
3.79
Waves in deep poverty 1.88 (1.59) 0–6 1.84 (1.64) 0–6

Financial hardship 0.19 (0.25) 0–1 0.19 (0.26) 0–1
NB income inequality 0.45 (0.06) 0.33–0.60 0.45 (0.06) 0.29–
0.65
NB poverty 29.61 (10.00) 3.50–71.79 29.69 (10.17) 3.50–71.79
Exposure to violence 0.28 (0.25) 0–1 0.27 (0.25) 0–1
Critical action behaviors 1.10 (1.11) 0–5 1.15 (1.14) 0–5
Note. NB � neighborhood.
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553POVERTY AND YOUTHS’ CRITICAL CONSCIOUSNESS
subtheme codes after adjusting for chance, was determined to be
.77, which is within the acceptable guidelines for interrater reli -
ability (Landis & Koch, 1977).
After thematic codes were applied, the two primary coders
conducted a follow-up analysis by applying a second set of
codes
to indicate whether youths responses referenced their own expe-
riences (e.g., “Living in the South Side of Chicago can be
rough.
There’s violence all the time, especially when it gets nicer
out.”)

versus a broader societal issue (e.g., “I say equality because I
think
everyone should be equal not matter what their gender or skin
color is.”) and whether youth expressed a desire to address the
issue (e.g., “I think that if we do good things for the world . . .
we
can change the world”) versus not mentioning a desire to fix the
problem. Interrater reliabilities were .76 and .70, respectively,
which is within the acceptable range.
Results
In developing and analyzing the open-ended reflections among
the subsample of CSRP-enrolled youth who were prompted (N
�
217), six higher-order themes were identified, five of which had
specific subthemes embedded within them (see Table 2). The six
higher-order themes that youth discussed included community
violence (59% of the 217 open-ended reflections included
discus-
sions of violence), prejudice and intolerance (31%), world
issues
(25%), economic disparities and/or a lack of opportunities to
get
ahead (18%), individual or interpersonal challenges (9%), and
issues related to mental health and well-being (3%). In addition,
an
‘other’ code (5%) was included for responses that could not be
categorized using the other thematic codes.
The majority of the youth described concerns that highlighted
their awareness of social justice issues, conceptualized here as
issues related to the unequal distribution of resources or unfair
treatment of others based on specific traits. Three (community
violence, prejudice and intolerance, economic disparities) of the
six themes directly refer to experiences of inequality and

oppres-
sion. In fact, 82% of the youth in our sample described issues
that
were coded as at least one of these three themes. For example,
in
one response which was coded as referencing both community
violence and prejudice/intolerance a respondent wrote:
The problems in the world that upset me are all the police
brutality
and the innocent killing [sic] of teen black males. These …
5
Title of the Paper in Full Goes Here
Student Name
Course Name and Number
Instructor’s Name
Date Submitted
1
Introduction: develop an introduction paragraph of at least 150
words that clearly explains the Poverty and income inequality
topic,and the importance of further research, and ethical
implications.

Thesis Statement: Type your thesis statement here. Please note
that the thesis statement will be included as the last sentence in
the introduction paragraph when writing your final paper.
Annotation 1:
Reference: Include a complete reference for the source. Format
your reference according to APA style for a journal article or
other scholarly source.
Annotation: In your own words, explain how this source
contributes to answering your research question. Your
annotation should be one to two paragraphs long (150 words or
more) and fully address purpose, content, evidence, and relation
to other sources you found on this topic following this order:
1. In the first sentence, explain the purpose (or the main point)
of the source. Then, describe the content and elements of the
source.
2. After explaining the overall structure of the source,
summarize the evidence that the author uses to support his or
her claims. Does the author use numbers, statistics, historical
documents, or draw from work created by other intellectuals?
3. Next, explain how the source relates to other sources you
have found on this topic throughout the course. Point out how
it contradicts or supports these sources.
4. Finally, briefly describe how the source answers to your
research question.
Annotation 2:

source.
research question.
Annotation 3:
source.
research question.
Annotation 4:

source.
4. Finally, briefly describe how the source answ ers to your
research question.
Annotation 5:
source.

research question.

IMPACT OF POLITICAL INSTABILITY ON ECONOMIC GROWTH, POVERTY

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