modernization theory: how to measure and … · modernization theory: how to measure and...

Modernization Theory: How to Measureand Operationalize it When Gauging Variationin Women’s Representation?

Daniel Stockemer • Aksel Sundstrom

Accepted: 2 December 2014� Springer Science+Business Media Dordrecht 2014

Abstract Modernization theory, one of the most influential theories in the social sci-

ences, 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 ter-

tiary 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, Canadae-mail: [email protected]

A. SundstromDepartment of Political Science, University of Gothenburg, Sprangkullsgatan 19,Box 100, 405 30 Goteborg, Swedene-mail: [email protected]

123

Soc Indic ResDOI 10.1007/s11205-014-0844-y

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 mod-

ernization 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 develop-

ment, 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 hap-

pened 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 moderni-

zation. 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.

D. Stockemer, A. Sundstrom

123

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, In-

glehart 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 con-

verts 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’’ (In-

glehart 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 post-

industrial 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

Modernization Theory for Women’s Representation

123

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 service-

oriented economy. However, they do not test this link directly. Second, aggregating survey

data almost always runs the risk of ecological fallacies when making inferences. Rela-

tionships 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 Devel-

opment 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 develop-

mental 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 Swit-

zerland 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 per-

centage 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 WorldValues Survey does not provide regional identifiers and the European Social Survey—which actually hasregional identifiers—does not pose gender-relevant questions (see European Social Survey 2012).


123

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 per-

centage 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 classifi-

cation); 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 EmploymentInstitute (2013). The data on Iceland is taken from Statistics Iceland (2012).


123

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 Republicof 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 asstatistical and electoral authorities. In some countries such data is not collected by government agencies. Forthree countries—France the Republic of Ireland and Switzerland—we therefore relied on renownedscholarly experts that have compiled such figures in their own research (see ‘‘Appendix 2’’).


123

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 � Percentage of citizens working in industryð Þþ b2 � Percentage of citizens working in the service sec torð Þ þ b3 � Population densityð Þþ b4 � Women0s labor force participationð Þ þ b5� b34 � Country dummiesð Þ 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 womenin national parliaments, we have added ‘‘Appendix 3’’. In ‘‘Appendix 3’’, we compare the average share ofwomen in local councils per country with the share of elected women in national parliaments. Across the 30countries of our analysis, we find that the two figures are highly correlated (p = 0.67).


123

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 dif-

fusion 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 repre-

sentation 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

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 theproportion of locally elected female councilors aggregated to a mean of each region. Figures for Albania, theRepublic of Ireland and Iceland are more fine-grained than illustrated here


123

modernization theory portrays, such as urbanization, increased female labor force partic-

ipation, 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 Cor-

relation 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.

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’’ fordetails). The variable is the proportion of locally elected female councilors aggregated to a mean of eachregion. The boxplots are ordered along the mean value of the regions in each country. Moreover, theboxplots report the 25th and 75th percentiles of the distribution through the lower of upper hinges of eachbox. While the whiskers refer to 1.5 of the interquartile range, the single dots are the extreme outliers in thisdistribution

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


123

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 simulta-

neous phenomena of modernization. Because they capture the same dimension of mod-

ernization, 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

010

2030

4050

7 8 9 10 11log per capita GDP

Share locally elected women Fitted values

Fig. 3 Scatterplot displaying the relationship between log per capita GDP and the regional average share ofwomen municipal councilors. The regression equation for this figure is -40.31 ? 6.71x

010

2030

4050

10 20 30 40 50 60Employed in industry (%)


Fig. 4 Scatterplot displaying the relationship between the industrial sector as share of employed personsand the regional average share of women municipal councilors. The regression equation for this figure is27.20-0.111x


123

percentage of service sector employees of the total economy explains 36 % of cross-

regional 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 %

010

2030

4050

20 40 60 80 100Employed in service (%)


Fig. 5 Scatterplot displaying the relationship between the service sector as share of employed persons andthe regional average share of women municipal councilors. The regression equation for this figure is-2.07 ? 0.427x

-20

020

4060

.2 .4 .6 .8 1

Female labor force participation rate


Fig. 6 Scatterplot displaying the relationship between the female labor force participation rate and theregional average share of women municipal councilors. The regression equation for this figure is-19.01 ? 2.58x


123

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

010

2030

4050

0 2000 4000 6000 8000

Population Density


Fig. 7 Scatterplot displaying the relationship between the population density and the regional averageshare 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 asshare of employedpersons

Service sector asshare of employedpersons

Female laborforceparticipation

LogGDPpercapita

Populationdensity

Industrial sector asshare of employedpersons

1

Service sector asshare of employedpersons

-0.475* 1

Female labor forceparticipation

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)


123

that can only be captured by multiple indicators. Relatedly, our results should inform the

comparative literature measuring the link between various modernization- and develop-

mental 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 participa-

tion in the workforce and the percentage of employees in the service sector are con-

siderable 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

Table 3 Determinants of the average share of locally elected women per region

Unstandardizedcoefficient

SE Sig.

Industry sector as share of employed persons 0.080 0.037 0.031

Service sector as share of employed persons 0.124 0.043 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 dummyvariable for Albania is used as a reference category. The estimates of the dummy variables are not reportedhere since they are not of theoretical interest for this article. The table reports the results from the modelwith all countries included. The model also has a very high R2. This high predictive power stems from thefact 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


123

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 ofmunicipalities/localauthorities

Electedin year

Albania City and municipal councils (keshilli 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 (opcinsko vijece) 429 2009

CzechRepublic

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 (kepviselo-testulet) 3,175 2010

Iceland Municipal council (sveitars-/bæjars-/borgarstjorn) 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 judetean) 41 2012

Slovakia Local council (obecne zastupitelstvo in municipalitiesand mestske zastupitelstvo in cities)

2,792 2010

Slovenia Municipal council (obcinski svet) 211 2010

Spain Local council (concejal) 8,117 2011

Sweden Municipal assembly (kommunfullmaktige) 290 2010


123

Appendix 2

See Table 5.

Table 4 continued

Country Name of municipal councils Number ofmunicipalities/localauthorities

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 reportsdata across several years for these countries. In Austria, local elections are held at different occasions in theBundeslander. In Finland, Aland has a special electoral cycle. This is also the case for Scotland in the UK.For a more thorough description, see Sundstrom (2013), Sundstrom and Wangnerud (2014)

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 Bundeslander 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

CzechRepublic

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 fur Statistikund 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 Direccao Geral deAdministracao Interna

Romania Respective regional authorities’ websites


123

Appendix 3

See Fig. 8.

References

Bell, D. (1974). The coming of post-industrial society. New York: Basic Books.Black, C. E. (1965). The dynamics of modernization. London: Harper Collins Books.Burns, N., Schlozman, K. L., & Verba, S. (2001). The private roots of public action. Cambridge: Harvard

University Press.Dahlerup, D. (1988). From a small to a large minority: Women in Scandinavian politics. Scandinavian

Political Studies, 11(4), 275–297.Dahlerup, D. (Ed.). (2006). Women, quotas and politics. London: Routledge.Duverger, M. (1955). The political role of women. Paris: UNESCO.

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, theConvention of Scottish Local Authorities, the Local Government Staff Commission inBelfast

For a more thorough description of the sources used, see Sundstrom (2013), Sundstrom and Wangnerud(2014)

Fig. 8 Women in local councils and national parliaments in 30 countries. Comments: The variable onlocally elected women is an average figure for each country, estimated from the dataset in this article. Thefigures on women in parliament refer to the latest figures from the Inter-Parliamentary Union (2013)


123

Egner, B., Sweeting, D., & Klok, P. J. (2013). Local councilors in comparative perspective. In B. Egner, D.Sweeting, & P. J. Klok (Eds.), Local councilors in Europe. Urban and regional research international(Vol. 14). Berlin: Springer.

Employment Institute. (2013). Regions of the European Union. Bratislava, Slovakia: Institut Zamestnanosti.http://www.iz.sk/en/projects/eu-regions

European Social Survey. (2012). ESS-5 2010 Documentation report. Edition 3.0. Bergen, Norway: EuropeanSocial Survey Data Archive, Norwegian Social Science Data Services.

Hill, D. B. (1981). Political culture and female political representation. Journal of Politics, 43(1), 159–168.Hughes, M. (2009). Armed conflict, international linkages, and women’s parliamentary representation in

developing nations. Social Problems, 56(1), 174–204.Inglehart, R. F. (1990). Culture shift in advanced industrial society. Princeton: Princeton University Press.Inglehart, R. F. (1997). Modernization and postmodernization: Cultural, economic, and political change in

43 societies. Princeton: Princeton University Press.Inglehart, R. F., & Baker, W. E. (2000). Modernization, cultural change, and the persistence of traditional

values. American Sociological Review, 65(1), 19–51.Inglehart, R. F., & Norris, P. (2000). The true clash of civilizations. Foreign Policy, 135, 62–70.Inglehart, R. F., & Norris, P. (2003). Rising tide: Gender equality and cultural change around the world.

Cambridge: Cambridge University Press.Inglehart, R. F., & Welzel, C. (2005). Exploring the unknown: Predicting the responses of publics not yet

surveyed. International Review of Sociology, 15(1), 173–201.Inter-Parliamentary Union. (2013). Women in National parliaments: Statistical archive. http://www.ipu.org/

wmn-e/classif-arc.htmIversen, T., & Rosenbluth, F. (2008). Work and power: The connection between female labor force par-

ticipation and political representation. Annual Review of Political Science, 11, 479–495.Kittilson, M. C. (2006). Challenging parties, changing parliaments: Women and elected office in contem-

porary Western Europe. Columbus: Ohio State University Press.Krook, M. L. (2009). Quotas for women in politics: Gender and candidate selection reform worldwide.

Oxford: Oxford University Press.Lipset, S. M. (1959). Some social requisites of democracy: Economic development and political legitimacy.

American Political Science Review, 53, 69–105.Matland, R. E. (1998). Women’s representation in national legislatures: Developed and developing coun-

tries. Legislative Studies Quarterly, 23(1), 109–125.Norris, P. (1985). Women’s legislative participation in Western Europe. West European Politics, 8(4), 90–101.Norris, P. (1993). Comparing legislative recruitment. In L. Joni & P. Norris (Eds.), Gender and party

politics. London: Sage Publications.Norris, P., & Inglehart, R. F. (2001). Cultural obstacles to equal representation. Journal of Democracy,

12(3), 126–140.Rosen, J. (2013). The effects of political institutions on women’s political representation: A comparative

analysis of 168 countries from 1992 to 2010. Political Research Quarterly, 66(2), 306–321.Rosenbluth, F., Salmond, R., & Thies, Michael. F. (2006). Welfare works: Explaining female legislative

representation. Politics and Gender, 2(2), 165–192.Ross, M. L. (2008). Oil, islam and women. American Political Science Review, 102(1), 107–123.Ruedin, D. (2012). The representation of women in national parliaments: A cross-national comparison.

European Sociological Review, 28(1), 96–109.Rule, W. (1987). Electoral systems, contextual factors, and women’s opportunity for election to parliament

in twenty-three democracies. Western Political Quarterly, 40(2), 477–498.Statistics Iceland. (2012). Statistics of Iceland III, 105. In: Statistical Yearbook of Iceland 2012. Reykjavik,

Iceland: Statistics Iceland. http://www.statice.is/lisalib/getfile.aspx?itemid=14331.Stockemer, D. (2009). Women’s parliamentary representation: Are women more highly represented in

(consolidated) democracies than in non-democracies?’. Contemporary Politics, 15(3), 429–443.Stockemer, D., & Byrne, M. (2012). Women’s representation around the world: The importance of women’s

participation in the workforce. Parliamentary Affairs, 65(4), 802–821.Sugarman, D. B., & Straus, M. A. (1988). Indicators of gender equality for American states and regions.

Social Indicators Research, 20(3), 229–270.Sundstrom, A. (2013). Women’s local representation within 30 European countries: A comparative dataset

on regional figures. QoG working paper 2013:18. University of Gothenburg: The Quality of Gov-ernment Institute.

Sundstrom, A., & Wangnerud, L. (2014). Corruption as an obstacle to women’s political representation:Evidence from local councils in 18 European countries. Party Politics. doi:10.1177/1354068814549339.


123

http://www.iz.sk/en/projects/eu-regions

http://www.ipu.org/wmn-e/classif-arc.htm

http://www.ipu.org/wmn-e/classif-arc.htm

http://www.statice.is/lisalib/getfile.aspx?itemid=14331

http://dx.doi.org/10.1177/1354068814549339

http://dx.doi.org/10.1177/1354068814549339

Tripp, A. M., & Kang, A. (2008). The global impact of quotas: On the fast track to increased femalelegislative representation. Comparative Political Studies, 41, 338–361.

White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heter-oskedasticity. Econometrica, 48, 817–830.

World Values Survey. (2009). World Values Survey 1981–2008, Official Aggregate v.20090901. WorldValues Survey Association (www.worldvaluessurvey.org). Aggregate File Producer: ASEP/JDS,Madrid.


123

http://www.worldvaluessurvey.org

modernization theory: how to measure and … · modernization theory: how to measure and...

Documents