Download Modernization Theory: How to Measure and Operationalize it When

Soc Indic Res
DOI 10.1007/s11205-014-0844-y
Modernization Theory: How to Measure
and Operationalize it When Gauging Variation
in Women’s Representation?
Daniel Stockemer • Aksel Sundström
Accepted: 2 December 2014
Ó Springer Science+Business Media Dordrecht 2014
Abstract Modernization theory, one of the most influential theories in the social sciences, holds that as the composition of the economy develops, from an agrarian to a
postindustrial society, communities will develop post-materialist values, which should lead
to a higher representation of women in elected positions. However, while this reasoning is
intuitive, there is no consensus on how to operationalize and measure this process. Existing
studies use different types of national level proxy measures such as aggregated survey data
on public attitudes on gender equality and broad development indicators such as per capita
GDP or population density. In this article, we not only highlight that existing strategies are
suboptimal as they run the risk of creating ecological inference fallacies for the former type
of indicators and measurement error for the second type of factors, but also offer some finer
grained operationalization of modernization theory at the regional level. In more detail, we
illustrate that modernization is a multifaceted concept, which is primarily characterized by
urbanization, women’s increased labor force participation and a strengthening of the tertiary sector. Using an original dataset on 285 European regions we illustrate that any of
these three characteristics of modernization has an independent impact on women’s
representation.
Keywords Modernization theory Measurement Regional level Women’s
representation Local councils
D. Stockemer (&)
School of Political Studies, University of Ottawa, 120 University, Social Sciences Building,
Room 7076, Ottawa, ON K1N 6N5, Canada
e-mail: [email protected]
A. Sundström
Department of Political Science, University of Gothenburg, Sprängkullsgatan 19,
Box 100, 405 30 Göteborg, Sweden
e-mail: [email protected]
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D. Stockemer, A. Sundström
1 Introduction
Modernization theory is one of the most influential theories in the social sciences. The
theory postulates that economic development brings large changes in values from material
survival values to post-materialist quality of life concerns (e.g. Bell 1974). More precisely,
the theory claims that as the composition of the economy develops from agrarian to
industrial and then to postindustrial, citizens will increasingly embrace cosmopolitan and
post-materialist values such as environmental protection, self-expression and gender
equality (Inglehart 1990, 1997). However, while these stipulations are intuitive it is more
difficult to test these claims, and there is debate on how to operationalize modernization
theory. What is the appropriate level of analysis? Have the processes of modernization
spread uniformly within one country? Should modernization be measured by survey data or
by structural indicators? If the use of structural indicators is preferable, which ones should
be used (e.g. per capita GDP, the percent of individuals employed in agriculture, industry
and service, the degree of urbanization, or women’s participation in the labor force)?
We will answer these questions by evaluating the link between modernization theory
and women’s representation in parliament. The modernization argument has previously
been tested in two ways. Some studies (e.g. Norris 1985; Norris and Inglehart 2001;
Inglehart and Norris 2003) use aggregated national survey data on public attitudes towards
gender equality (e.g. data from the World Value Survey) to measure the impact of modernization on the share of women deputies in parliament. Others (e.g. Hughes 2009; Rosen
2013) employ national development indicators, such as the level of economic development, to measure the influence of modernization on women’s representation in parliament.
In the analytical part of this article, we will show that both types of measurement are
suboptimal. The use of the first types of measures, aggregated survey data, provides an
indirect test of modernization theory, at best. It measures the influence of individuals’
values on women’s representation without linking it to economic and social features, which
are central to modernization theory. In addition, aggregated survey data hides within unit
variation and runs the risk of ecological inference fallacy.1
The second type of indicators, national development indicators, by definition assume
that the changes in societies, from an agrarian to a post-materialist economy, have happened roughly at the same pace within one country. As a result, these ‘‘national measures’’
are based on the stipulation that there is no or little within-country variation in modernization. Aware of these pitfalls of previous work (e.g. Inglehart and Norris 2003; Rosen
2013), we switch the unit of analysis from the national to the regional level and suggest
four possible measurements of modernization: (1) the regional per capita GDP, (2) the
percentage of citizens employed in agriculture, industry and the service sector, (3)
women’s participation in the labor force and (4) population density. We test if these four
concepts can be used interchangeably or whether specific indicators should be employed to
measure the influence of modernization on women’s representation. Our statistical analysis
of an original dataset, which includes measures of the aggregated share of local women
councilors per region, regional measurements of our four modernization proxy variables, as
well as country dummies across 285 regions in 30 European countries, provides interesting
findings: First our results indicate that three of the four measures (i.e. the strengthening of
service sector jobs, women’s participation in the labor force and population density) are
1
Ecological inference fallacy refers to the problem of deducing conclusions about smaller units (i.e.
regions) on the basis of larger units such as countries.
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distinct from each other; these proxies are only moderately correlated. Second we show
that these variables all explain variation in the regional share of locally elected women.
This article proceeds as follows: In the next section, we situate this article within the
relevant literature. We then describe our empirical strategy, data and methodology. After
presenting the results we conclude by discussing the implications of our findings and by
outlining possible directions for future studies.
2 Theory
Modernization theory has been a constant in scholarly works for the past 150–200 years.
Early works (e.g. such as Marx and Weber) discussed the influence of industrialization on
wealth, urbanization and income inequalities. In the post World War II period, scholars
(e.g. Lipset 1959; Black 1965) started to examine the link between industrialization and
democracy. Then, starting in the 1970s, a third wave of modernization scholars (e.g. Bell
1974) has dealt with the change of the industrial society to a post-industrial economy and
discussed the effects of this change on the family structure and social values. At the core of
this more recent modernization scholarship are the writings on the massive shift towards
postmaterialist values in the developed world (Inglehart 1990, 1997). Most notably, Inglehart has written extensively on the changes economic development has triggered in
Western societies over the past 30 or 40 years. According to Inglehart and Welzel (2005),
post industrialization brings changes in mass education and work-life relations and converts public attitudes towards the family, authorities and life priorities.
Changing views on appropriate gender roles and transformations in gender relations are
at the core of the third wave of modernization theorizing. According to Inglehart (1990,
347), modernization changed women’s roles in society in two subsequent steps. First, the
transformation from agrarian to postindustrial societies triggered a ‘‘gradual erosion of
traditional gender roles that formerly severely inhibited political action by women’’ (Inglehart 1990, 337). Among others, it allowed women to gain full citizenship rights such as
the right to vote. Second, a still ongoing postindustrial phase has brought a change toward
increased equality as women have shifted to higher-status economic roles in management
and earned influence in civic life (Inglehart and Norris 2000, 68). According to Inglehart
and Baker (2000, 28), this value shift manifests itself by the fact that postindustrial
societies now accept women as much as they accept men in leading societal positions. In
the book Rising Tide, Inglehart and Norris (2003) develop this reasoning further. They
claim that ‘‘there are clearly established contrasts between countries at different levels of
societal modernization, with agrarian nations being the most traditional in emphasizing
sharply divided sex roles, industrial societies in the early stages of transition, and postindustrial societies the most egalitarian in their beliefs about the roles of women and men’’
(Inglehart and Norris 2003, 159).
While the hypothesis derived from modernization theory is straightforward—the more
developed and service-oriented the economy of a geographical unit is, the higher its share
of women in elected bodies should be—scholarship has so far struggled to adequately
operationalize and test modernization’s influence on women’s representation. So far, the
dominant approach has been to gauge modernization by survey responses aggregated to the
country level using questions such as whether men or women make better political leaders
or whether jobs, if scarce, should go to a man or a woman. We see three limitations in these
indicators: First, the operationalization of modernization through nationally aggregated
survey data is an indirect measure; it gauges attitudes, but a priori does not link them to
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any type of society. Existing studies (e.g. Inglehart and Norris 2003) assume that countries
by virtue of having postmaterialist values are modernized or have a predominately serviceoriented economy. However, they do not test this link directly. Second, aggregating survey
data almost always runs the risk of ecological fallacies when making inferences. Relationships at the micro-level might not necessarily resonate one-to-one at the macro-level.
Third and relatedly, the aggregation of national survey data is based on the assumption that
there are no or only few intra-country differences in values: an assumption that is hard to
make without justification.2
A second approach to measure national modernization is through structural indicators,
such as the Human Development Index (HDI), developed by the United Nations Development Program (UNDP) (Hughes 2009; Rosen 2013). For example, the HDI distinguishes
three types of countries; low-, medium-, and high-human development countries, using the
threshold values 0.5 and 0.8. While the UNDP’s operationalization provides developmental benchmarks, the HDI suffers from two major drawbacks when used in testing the
influence of modernization on women’s representation. First, the categories are too broad
to make meaningful differentiations. For example, the index considers any country with an
HDI of 0.8 or higher as highly developed, or in our terminology, postindustrial. Implicitly,
this indicates that Poland and Switzerland have the same level of industrialization, an
assumption that is too simplistic to hold. For example, \5 % of the population in Switzerland engages in agriculture and around 70 % engages in the service sector, while
Poland’s workforce distribution is 15 % in agriculture and around 50 % in service. In
addition, and even more importantly, the UNDP’s broad characterization of countries hides
important variation within countries. This variation within nations in various development
indicators is larger than the variation between countries for two-thirds of the countries
covered by this analysis. Moreover, this within variation is also substantively large. For
example, in many countries—including Portugal, Romania and Turkey—there is variation
of more than 20 % points between regions in the indicators per capita GDP or the percentage of the population that is employed in agriculture, industry or service, respectively.
In short, we think that the processes of modernization have not evolved equally throughout
Western countries. In European countries there are urban centers where the service sector
makes up 80 or 90 % of all jobs. At the same time, there are also rural regions where the
agrarian sector is still a dominating force of the economy. Inglehart and Norris (2003, 20)
acknowledge in their own work that a focus on countries hides this variation: ‘‘some
important trade-offs are involved in this approach, notably the loss of contextual depth’’. In
our study we want to provide this conceptual depth and measure modernization theory in a
more nuanced way. In more detail we operationalize key aspects of the theory at the regional
level and suggest four proxy variables, which all capture different facets of modernization and
which theoretically and empirically should all have an impact on women’s representation: (1)
the regional per capita GDP, (2) the percentage of citizens employed in agriculture, industry
and the service sector, (3) women’s participation in the labor force and (4) population density.
2.1 Measuring Modernization
First, we measure modernization in its classical way by regional development (i.e. the
regional GDP per capita). According to Burns et al. (2001) and Rosen (2013), development
2
No regional attitudinal measure exists currently that includes measures of gender equality. The World
Values Survey does not provide regional identifiers and the European Social Survey—which actually has
regional identifiers—does not pose gender-relevant questions (see European Social Survey 2012).
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should foster the election of women in two ways. First there is supply of competent female
candidates to be chosen for elected office. For example, in highly educated geographical
units, women are professionally active in two pools from which candidates and elected
representatives are chosen, lawyers and teachers (Hill 1981). In addition, with higher
development there should also be higher demand for the inclusion of women in positions of
power. In particular women that take on important professional and associational positions
also strive for political representation (Burns et al. 2001).3
Second, to emphasize the degree to which the shift in production from fields and
factories to the service sector has taken place in a given region, we distinguish the percentage of individuals employed in the three types of professions: agriculture, industry and
the service sector (Inglehart and Norris 2003, 22). While we are not aware of any study that
uses this measure, we deem this operationalization compatible with modernization theory,
because it measures the three types of societies that provide the base for Inglehart’s thesis.
Our indicator is also more nuanced than broad characterizations (i.e. the UNDP classification); it allows us to measure the degree to which the orientation of the economy towards
agriculture, industry and service has an impact on women’s representation. In the analysis,
we measure the percentage of individuals employed in industry and service, respectively,
with the percentage of agriculturally employed citizens serving as the reference category.4
Third, we measure modernization by the population density in a region (Sugarman and
Straus 1988), hypothesizing that the processes of modernization such as women’s entry
into the paid workforce, reduced fertility rates and the intergenerational shift in values
(from essential ‘‘survival’’ values to post-material ‘‘quality of life’’ values) should be more
pronounced in cities than in the countryside (see Inglehart 1990, 1997). City dwellers have
access to foreign influences, live in a multicultural environment and are exposed to modern
forms of living such as gay and lesbian households. In contrast, individuals in rural areas
are frequently attached to the traditional family as the nucleus of their life and value
traditions and religion. As a result of these different lifestyles citizens in cities should
embrace gender equality to higher degrees than individuals in the countryside, which
should lead to some fairer representation of the sexes in elected positions. There are some
empirical studies (e.g. Duverger 1955, 84; Norris 1993, 743), which support the idea that
urban areas are more supportive of women’s issues and female candidates than rural areas.
We operationalize the regional population density by the number of people (in thousands)
per square kilometer.5
Fourth, we operationalize modernization by a regional measure of women’s labor force
participation. One of the most visible aspects of the modernization process consists of
women’s entry into the paid workforce. More women in paid employment, especially in
high-end jobs, should help loosen traditional gender roles (Iversen and Rosenbluth 2008,
408). In addition, ‘‘moving into the paid labor force has a consciousness raising effect on
women’s political participation and propensity to articulate political demands’’ (Matland
1998, 118). In this sense, high rates of women’s workforce participation should increase
both the desire of professionally active women to become politically involved and enter the
pool of qualified candidates for parties to pick from. We measure women’s workforce
3
The GDP figures come foremost from the Eurostat regional database and the Employment Institute (2013).
The data on Iceland is taken from Statistics Iceland (2012).
4
These figures come foremost from the Eurostat regional database and the Employment Institute (2013).
The data on Iceland is taken from Statistics Iceland (2012).
5
The data on population density comes foremost from the Eurostat regional database and the Employment
Institute (2013). The data on Iceland is taken from Statistics Iceland (2012).
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participation by how much, compared to men, women contribute to a country’s GDP. The
indicator gauges the proportion of women to men, who are active in the labor force. A
value of 1 signifies that men and women contribute equally to the economy. A value of 0.5
suggests that women contribute half as much as men to a region’s economy, whereas a
value of two signifies that women contribute twice as much as men to the regional
economy (Stockemer and Byrne 2012).6 While this indicator might be suboptimal or
‘‘gendered’’ as it neither includes women’s participation in the informal sector of the
economy nor women’s contributions at home, it is the only measure for which we could
retrieve data for. Our operationalization (i.e. the share women contribute to the economy in
a geographical unit) is also widely used in the literature (e.g. see Iversen and Rosenbluth
2008; Ross 2008; Stockemer 2009).
2.2 Dependent Variable
In this study, the dependent variable is the share of elected women in local councils,
aggregated to the regional mean in 30 European countries, including Turkey. We collected
original data on the gender composition of elected bodies in the lowest administrative tiers
for these countries, from the most recent elections in which data was available (for the type
of local councils and a list of countries included in the analysis, see ‘‘Appendix 1’’). Local
councils are deliberative assemblies constituted by councilors elected by direct universal
suffrage. Although the responsibilities of these local parliaments differ and their political
context has a large variance, they are all ‘‘a crucial element in local representative
democracy, linking ordinary citizens to local decision makers’’ (Egner et al. 2013, 12). Our
choice to aggregate women’s representation in local councils rather than in regional
assemblies is also informed by the fact that these assemblies are more homogeneous in
their functions and responsibilities than the corresponding regional assemblies, whose
power differs tremendously in the countries under investigation in this article.
The regions of the countries in this dataset are based on the system of the Nomenclature
of Territorial Units for Statistics (NUTS) where possible.7 In countries (e.g. Albania)
where the NUTS system does not exist we use the first tier below the national level (the
source for this data is described in ‘‘Appendix 2’’).8 In total we analyze the share of women
in the local councils of 285 regions across 30 countries from the most recent elections in
which data is available.
3 Methodology
The new comparative dataset, which we constructed, provides the ideal conditions to test
the influence from the different indicators related to modernization theory on women’s
representation. There are wide variations between regions in the data. The share of locally
elected women ranges from the region Mellersta Norrland in Sweden, where 46.48 % of
6
These figures are primarily taken from the Eurostat regional database and the Employment Institute
(2013). The data on Iceland is taken from Statistics Iceland (2012).
7
For some countries with a single, or very few, NUTS regions— such as the Baltic countries, the Republic
of Ireland and Iceland—the regions are technically even more disaggregated than their NUTS structure.
8
Regarding the sources for this data, the aim was to find official figures from government bodies, such as
statistical and electoral authorities. In some countries such data is not collected by government agencies. For
three countries—France the Republic of Ireland and Switzerland—we therefore relied on renowned
scholarly experts that have compiled such figures in their own research (see ‘‘Appendix 2’’).
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the local councilors are women, to the Turkish region of Hatay with 1.86 % female
councilors. Within the same country, women’s representation normally differs by 15–20 %
points between regions (see Figs. 1, 2).9 All four regional modernization indicators show
similar regional dispersions. For example, the population density within one country
normally ranges from hundred or less inhabitants per square kilometer to several hundreds,
if not thousands, of inhabitants per square kilometer. The other three factors differ by more
than 100 % between regions and by a minimum of 20 % between regions within the same
country (see Table 1).
To measure, if at all, which of the modernization indicators impact the regional levels of
women’s representation, we engage in a three-step process. First, we construct scatterplots
measuring the bivariate relationships between the four modernization indicators and
women’s representation. We find that they are all related to women’s representation levels,
and therefore measure in the second step if these indicators are distinct or whether we can
use them interchangeably. Third, we create a multivariate regression model including three
of the four modernization proxy variables (i.e. the percentage of citizens employed in
agriculture, industry and the service sector, women’s participation in the labor force and
population density), which we found to be rather distinct in our correlation analysis. As in
the statistical tests before, our dependent variable is the share of locally elected women
aggregated to the regional level. Trying to gauge modernization theory’s influence on
women’s representation in the most conservative way, we also include 29 country dummy
variables (with Albania serving as the reference category). These country dummies control
for national level factors such as the institutional context and the laws of the country—for
instance, the type of electoral system and the existence of legislative gender quotas—and
therefore allow for a ‘‘purer’’ representation of our three modernization proxy variables’
influence on women’s representation (Ruedin 2012, 97). Moreover, because the variance
across observations is not homoscedastic (i.e. the variance differs quite considerably across
regions), we specify our equation as an ordinary least squares model (OLS) with Huber
White Standard Errors (White 1980). The equation for the model is the following:
Average share of locally elected women per region ¼ b0j
þ b1 ðPercentage of citizens working in industryÞ
þ b2 ðPercentage of citizens working in the service sec tor Þ þ b3 ðPopulation densityÞ
þ b4 ðWomen0 s labor force participationÞ þ b5 b34 ðCountry dummiesÞ þ e
4 Results
First, the bivariate scatter plots indicate that, except for the percentage of the workforce
employed in industry, all other proxy variables of modernization theory are significantly
related to the dependent variable—the share of locally elected women aggregated to the
regional level. As predicted by modernization theory, regions with a high per capita GDP,
regions with a high percentage of service sector jobs, densely populated regions and
regions with a strong female presence in the workforce are frontrunners in women’s
9
To put our local figures in perspective with the more commonly used figures on the percentage of women
in national parliaments, we have added ‘‘Appendix 3’’. In ‘‘Appendix 3’’, we compare the average share of
women in local councils per country with the share of elected women in national parliaments. Across the 30
countries of our analysis, we find that the two figures are highly correlated (p = 0.67).
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Fig. 1 The average share of female local councilors in the regions of 30 countries (percentage). Comments:
The figures refer to the most recent elections available (see ‘‘Appendix 1’’ for details). The variable is the
proportion of locally elected female councilors aggregated to a mean of each region. Figures for Albania, the
Republic of Ireland and Iceland are more fine-grained than illustrated here
representation (see Figs. 3, 4, 5, 6, 7 in the Appendix). Figure 4 specifically indicates that
women’s representation does not benefit from industrialization, but rather from the diffusion of service sector jobs. This finding is supported by previous research. In many West
European regions and countries the service sector has become the dominant mode of
production since the 1970s, which was when women’s representation started to increase
significantly in Western countries (see Norris 1985; Rule 1987; Dahlerup 1988). Figure 7
illustrates that the positive relationship between population density and women’s representation at the local level is driven by relatively few regions with a high population
density, such as the Brussels region in Belgium. However, it also highlights that the four
regions with a population density of 4,000 and above have all over 30 % of women local
councillors supporting the notion that urban centers are prone to push gender equality, at
least when it comes to the descriptive representation of women.
Having shown that the four indicators—the log per capita GDP, the percentage of
citizens employed in the service sector, women’s participation in the labor force and
population density—follow the predictions of modernization theory, the next important
question is whether these four factors are distinct from each other or whether they represent
the same concept. In other words, we are interested in whether the changes in society that
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Fig. 2 Boxplot of the average regional share of female local councilors in 30 European countries
(percentage). Comments: The figures refer to the most recent elections available (see ‘‘Appendix 1’’ for
details). The variable is the proportion of locally elected female councilors aggregated to a mean of each
region. The boxplots are ordered along the mean value of the regions in each country. Moreover, the
boxplots report the 25th and 75th percentiles of the distribution through the lower of upper hinges of each
box. While the whiskers refer to 1.5 of the interquartile range, the single dots are the extreme outliers in this
distribution
Table 1 Summary statistics of the dependent variable and the independent variables
Mean
Std.
Min
Max
Percent of elected women
24.36
10.78
1.87
46.49
Agricultural sector share of employed persons
11.54
13.46
0
66.3
Industrial sector as share of employed persons
25.72
8.02
8.02
56.9
Service sector as share of employed persons
61.85
15.08
12.2
88.9
Female labor force participation
0.85
0.15
0.24
1.06
Log GDP per capita
9.63
0.88
7.27
11.21
Population density
264
673.74
1.2
6,767.32
modernization theory portrays, such as urbanization, increased female labor force participation, growth of wealth and the strengthening of service sector jobs, happen at the same
pace or not. If these societal transformations happen consecutively, it would be enough to
include anyone of the four proxy variables for modernization theory in any model on
women’s representation. However, if modernization is a more asymmetrical process, then
we need to measure it by several variables. To determine the degree to which the four
measurable concepts of modernization are distinct from each other or represent the same
concept, we run a correlation matrix (Table 2).
The correlation matrix indicates that while nearly all modernization indicators are
correlated (i.e. p \ 0.01), the correlations are weak to moderate (i.e. the Pearson’s Correlation Coefficient (r) is between 0.2 and 0.5). This is, we find that while female labor
force participation, population density and the composition of the economy are linked,
each of these proxy variables also captures a distinct facet of modernization theory.
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0
10
20
30
40
50
D. Stockemer, A. Sundström
7
8
10
9
11
log per capita GDP
Share locally elected women
Fitted values
0
10
20
30
40
50
Fig. 3 Scatterplot displaying the relationship between log per capita GDP and the regional average share of
women municipal councilors. The regression equation for this figure is -40.31 ? 6.71x
10
20
30
40
50
60
Employed in industry (%)
Share locally elected women
Fitted values
Fig. 4 Scatterplot displaying the relationship between the industrial sector as share of employed persons
and the regional average share of women municipal councilors. The regression equation for this figure is
27.20-0.111x
Consequently, modernization theory should be measured by these multiple indicators.
There is only one strong correlation between the log GDP per capita and the percentage of
individuals who are employed in the service sector (i.e. r = 0.84), indicating that increases
in regions’ GDP per capita and a strengthening of the service sector are nearly simultaneous phenomena of modernization. Because they capture the same dimension of modernization, researchers should use one or the other indicator when measuring
modernization. However, while closely linked, we argue that the percentage of service
sector employees in the overall workforce is the ‘‘better indicator’’. We find that if we
regress each of the two indicators separately on women’s representation the variable
measuring the percentage of service sector employees explains more of the variance in
women’s representation than the level of material affluence per capita. In more detail, the
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0
10
20
30
40
50
Modernization Theory for Women’s Representation
20
60
40
80
100
Employed in service (%)
Share locally elected women
Fitted values
-20
0
20
40
60
Fig. 5 Scatterplot displaying the relationship between the service sector as share of employed persons and
the regional average share of women municipal councilors. The regression equation for this figure is
-2.07 ? 0.427x
.2
.4
.6
.8
1
Female labor force participation rate
Share locally elected women
Fitted values
Fig. 6 Scatterplot displaying the relationship between the female labor force participation rate and the
regional average share of women municipal councilors. The regression equation for this figure is
-19.01 ? 2.58x
percentage of service sector employees of the total economy explains 36 % of crossregional variation in women’s representation, whereas the log GDP per capita only
explains 30 % of the variance.
Based on the findings from our correlation analysis, we exclude the log GDP per capita and
include the other four original modernization proxy variables in our multivariate regression
model. The results from this model confirm the findings from the bivariate analysis (see
Table 3): all four regression coefficients are statistically significant and are positively related
to women’s representation. However, the coefficient of the variable that gauge the percentage
of the population employed in industry is substantively small. The model predicts that per
10 % points more citizens employed in industry, women’s representation increases by 0.8 %
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0
10
20
30
40
50
D. Stockemer, A. Sundström
0
2000
4000
6000
8000
Population Density
Share locally elected women
Fitted values
Fig. 7 Scatterplot displaying the relationship between the population density and the regional average
share of women municipal councilors. The regression equation for this figure is 23.72 ? 0.002x
Table 2 Correlation matrix of the five proxy variables of modernization theory
Industrial sector as
share of employed
persons
Service sector as
share of employed
persons
Female labor
force
participation
Log
GDP
per
capita
Population
density
1
Industrial sector as
share of employed
persons
Service sector as
share of employed
persons
-0.475*
1
Female labor force
participation
0.006
0.450*
1
Log GDP per capita
-0.351*
0.841*
0.464*
1
Population density
-0.210*
0.277*
0.040
0.229
1
* p \ 0.01 (two tailed)
points. As predicted by the correlation analysis, the other regression coefficients have
somewhat of a stronger influence. For example, a difference in the population density of
1,000 triggers a two-point difference in women’s representation. These findings are also
robust for alternative specifications. For instance, if we run the analysis excluding one or
several countries with the most variation in the modernization indicators such as Turkey and
Albania we get nearly identical results as in the full sample. The results contain several
interesting findings. For one, a high percentage of service sector jobs, urbanization and a
high presence of women in the labor force create the ideal conditions for high women’s
representation. Moreover, the fact that each of these three proxy measures of modernization
has an independent influence on women’s representation indicates that the processes of
modernization do not happen simultaneously, but rather at a different pace in various parts
of a country. For theory, this implies that modernization is a multifaceted concept, a concept
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Modernization Theory for Women’s Representation
Table 3 Determinants of the average share of locally elected women per region
Unstandardized
coefficient
SE
Sig.
Industry sector as share of employed persons
0.080
0.037
Service sector as share of employed persons
0.124
0.043
0.031
0.005
Female labor force participation
8.886
2.849
0.001
Population density
0.002
0.001
0.004
Constant
11.200
10.666
0.295
R2
0.91
Adjusted R2
0.90
Root MSE
3.43
N
285
Results of the multivariate regression analysis. The model includes 29 country dummies, where the dummy
variable for Albania is used as a reference category. The estimates of the dummy variables are not reported
here since they are not of theoretical interest for this article. The table reports the results from the model
with all countries included. The model also has a very high R2. This high predictive power stems from the
fact that we included country dummies into the model. These country dummies control for all between—
country variation in the data and therefore increase the explanatory power of the model
that can only be captured by multiple indicators. Relatedly, our results should inform the
comparative literature measuring the link between various modernization- and developmental indicators on women’s representation (e.g. Norris 1985; Rule 1987; Matland 1998;
Kittilson 2006; Tripp and Kang 2008; Krook 2009). In the future, studies measuring
modernization should take into consideration that this rather complex economic and societal
process cannot be measured by one indicator alone. Rather, modernization has spread at
various paces throughout regions and countries. Only regional level studies can capture
these nuances.
5 Conclusions
In this article, we have made two contributions. First, we have shown that modernization is
a multifaceted concept that cannot be measured by one indicator. Rather, modernization
processes occur through various means at different paces within and between countries. In
particular, urbanization, the growth of the service sector and increases in female economic
participation diffuse at various rates within territories. This implies that if we want capture
the processes of modernization we have to gauge them through multiple indicators. Our
example, featuring women’s representation as the dependent variable, attests to this
finding. In our multivariate regression model we find that all of the three modernization
proxies have an independent influence on the share of female local councilors aggregated
at the regional level.
Second and relatedly, our analysis confirms that modernization should be measured
at the sub-national level. While differences in population density, women’s participation in the workforce and the percentage of employees in the service sector are considerable between countries, there are even larger variations within countries. This
applies to Europe and even more so many modernizing countries, including Brazil,
China and India, where within-country differences in modernization should be even
larger than in the relatively homogenous European countries. This suggests that if
123
D. Stockemer, A. Sundström
comparative research wants to measure the influence of modernization on women’s
representation and other phenomena, scholars have much to gain by switching the unit
of analysis from the national to the sub-national level. While this would require strong
data collection efforts, it would allow researchers to have more conceptual clarity and a
better representation of modernization’s underlying phenomena. In this sense, we hope
that our study has incited others to replicate our study using sub-national data for
different countries and continents.
Appendix 1
See Table 4.
Table 4 Local councils in 30 countries
Country
Name of municipal councils
Number of
municipalities/local
authorities
Elected
in year
Albania
City and municipal councils (këshilli bashkiak)
373
2011
Austria
Municipal council (gemeinderat)
2,357
2009–12
Belgium
Municipal council (conseil communal/gemeenteraad)
589
2012
Bulgaria
Municipal council (obchtinski savet)
264
2011
Croatia
Municipal council (općinsko vijeće)
429
2009
Czech
Republic
Municipal council (zastupitelstvo obce)
6,250
2010
Denmark
Municipal council (kommunalbestyrelse)
98
2009
Estonia
Municipal council (volikogu)
206
2009
Finland
Municipal council (kunnanvaltuusto)
336
2007–08
France
Department councils (conseil general)
101
2008
Germany
Local council (gemeinderat)
*14,000
2011
Greece
Municipal council (dimotiko simvoulio)
325
2010
Hungary
Municipal body of representatives (képviselõ-testület)
3,175
2010
Iceland
Municipal council (sveitars-/bæjars-/borgarstjórn)
76
2010
Ireland
City council/county council
34
2009
Italy
Local council (consiglio comunale)
8,094
2011
Latvia
Municipal council (dome)
110
2013
Lithuania
Local council (savivaldyb_es taryba)
60
2011
Netherlands
Local council (gemeenteraad)
418
2009
Norway
Local council (kommunestyret)
430
2011
Poland
Municipal council (rada gminy)
2,479
2010
Portugal
Parish assembly (assembleia de freguesia)
4,259
2009
Romania
County council (consiliul judeţean)
41
2012
Slovakia
Local council (obecné zastupiteľstvo in municipalities
and mestské zastupiteľstvo in cities)
2,792
2010
Slovenia
Municipal council (obcinski svet)
211
2010
Spain
Local council (concejal)
8,117
2011
Sweden
Municipal assembly (kommunfullmäktige)
290
2010
123
Modernization Theory for Women’s Representation
Table 4 continued
Country
Name of municipal councils
Number of
municipalities/local
authorities
Elected in year
Switzerland
Local council (kommunalen Exekutiven)
2,551
2009
Turkey
Municipal council (belediye meclisi)
2,959
2009
UK
Local authority councils
466
2010–12
In some countries local elections are not held simultaneously across all regions. Therefore the table reports
data across several years for these countries. In Austria, local elections are held at different occasions in the
Bundesländer. In Finland, Åland has a special electoral cycle. This is also the case for Scotland in the UK.
For a more thorough description, see Sundström (2013), Sundström and Wängnerud (2014)
Appendix 2
See Table 5.
Table 5 Sources from which data on women’s representation was collected
Country
Sources
Albania
The Central election commission of Albania
Austria
The Verbindungsstelle der Bundesländer and additional regional authorities
Belgium
The Information Center of the Brussels Region, the Agentschap voor Binnenlands Bestuur,
and the Union des Villes et Communes de Wallonie
Bulgaria
The Central Election Commission of Bulgaria
Croatia
Croatian Bureau of Statistics
Czech
Republic
The Information Services Unit of the Headquarters of the Czech Statistical Office
Denmark
The Danish statistical yearbook 2011
Estonia
Elections Department of the Chancellery of the Riigikogu (Parliament)
Finland
Statistics Finland
France
Dr. Aurelia Troupel, Montpellier 1 University
Germany
Statistisches Bundesamt, Statistischer Informationsservice, and Landesbetrieb für Statistik
und Kommunikationstechnologie Niedersachsen
Greece
The Hellenic Ministry of Interior
Hungary
The Election Information Service at the National Election Office of Hungary
Iceland
Statistics Iceland
Ireland
Dr. Adrian Kavanagh and Dr. Claire McGing, National University of Ireland, Maytooth
Italy
The Ministry of Interior, Italy
Latvia
Central Election Commission of Latvia
Lithuania
The Central Electoral Commission of the Republic of Lithuania
Netherlands
The Dutch Institute for Public Administration
Norway
Statistics Norway
Poland
The National Electoral Commission of Poland
Portugal
The Directorate of Legal Services and Electoral Studies of the Direcção Geral de
Administração Interna
Romania
Respective regional authorities’ websites
123
D. Stockemer, A. Sundström
Table 5 continued
Country
Sources
Slovakia
The International Relations Department, Association of Towns and Communities of Slovakia
Slovenia
Statistical Office of the Republic of Slovenia
Spain
The Ministry of Interior, Spain
Sweden
The unit for Democracy Statistics of Statistics Sweden
Switzerland
Dr. Andreas Ladner, University of Lausanne
Turkey
Turkish Statistical Institute
UK
The UK Local Government Association, the Welsch Local Government Association, the
Convention of Scottish Local Authorities, the Local Government Staff Commission in
Belfast
For a more thorough description of the sources used, see Sundström (2013), Sundström and Wängnerud
(2014)
Appendix 3
See Fig. 8.
Fig. 8 Women in local councils and national parliaments in 30 countries. Comments: The variable on
locally elected women is an average figure for each country, estimated from the dataset in this article. The
figures on women in parliament refer to the latest figures from the Inter-Parliamentary Union (2013)
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